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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research. Refereed/Peer-reviewed

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.

Evolutionary game dynamics is a combination of game theory and dynamical systems. Using dynamical theory, we investigate chaotic behavior in asymmetric Rock-Paper-Scissors games under imitative dynamics with two different populations. Our theoretical analysis and numerical simulations demonstrate that the dynamical system can give rise to chaotic behavior in zero-sum and positive-sum asymmetric games. However, chaos cannot occur in a negative-sum asymmetric game. In particular, the numerical simulations show that there is a limit cycle in a negative-sum asymmetric game.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

This paper presents a thorough review of transmitarray devices particularly aiming antenna beamsteering, gathering some of the most relevant solutions published by the scientific community in the field. First, the background for realizing 1-D and 2-D antenna beamsteering with a transmitarray is introduced. Subsequently, several examples of unit-cells for transmitarray implementation and complete transmitarray designs presented in the literature are outlined. Each solution is analyzed in detail, identifying the nature of its layout, e.g., based on microstrip patches, frequency selective surfaces (FSS), or metamaterials (MMs), and the method employed to enable electronic reconfigurability, e.g., p-i-n diodes, varactor diodes, or microelectromechanical systems (MEMS). In addition, some models with the capability of controlling the wavefront polarization modes are also included herein since these are the base of hybrid transmitarrays, i.e., transmitarray with both electronic beamsteering and polarization control. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation and bandwidth, insertion loss, physical dimensions, and maximum beamsteering range, when available.

In this short paper, our main objective is to construct binary locally repairable codes (LRCs) with good properties. Two constructions of LRCs with short lengthes are proposed. The first one is that define a u-linearly independent set (u-LIS) of an LRC with disjoint repair groups (DRGs) and enlarge it into another one with bigger size to construct new LRCs. The second is puncturing check matrices of known codes to construct new LRCs. As an application, many new binary LRCs are constructed from distance optimal linear codes, which are also locality optimal according to the Cadambe-Mazumdar (C-M) bound.

For current steganalysis of the image content-adaptive steganography, there are multiple problems to be improved, such as high difficulty and low accuracy, when detecting images with various contents and textures. For this problem, an improved steganalytic method is proposed in this paper based on the pre-classification and feature selection. First, using the features extracted based on the dependency analysis of image adjacent data, the images with various content and texture complexities are pre-classified as multiple clusters by the K-means algorithms. Then, the performance of existing various steganalytic features are analyzed for different clusters of images, and the optimal features for each cluster are selected for final classification. The experimental results show that the detection accuracy could be improved by the proposed method, and the rationality and availability are also verified. At the same time, the analysis and experimental results in this paper also show that the images with rich content and complex texture should be paid more attention both in steganography and in steganalysis.

Power transformers are considered as one of the main parts of any power system. Because of their importance and high cost, it is important to check the state of the insulation system inside them from time to time. Dissolved gas analysis test as well as Furan compounds analysis has been widely used as recommended methods to assess the transformer insulation system state. This paper introduces an empirical form that can predict the degree of polymerization of the paper insulation system inside the transformer by measuring Furan compounds. This proposed empirical form is validated by comparing its results with other methods reported in the literature in two different case studies. Subsequently, the proposed formula is used to predict the lifetime and the insulation system state of a transformer that operates in Abo Sultan steam power plant in Egypt. The 2-FAL had been measured five times for this transformer in the period between 2013 and 2015. Depending on these five measurements, an equation that can predict 2-FAL level inside this transformer in relation to its age has been proposed. This proposed equation is validated by comparing the measured and estimated 2-FAL in the period between 2016 and 2019. According to the attained results, it was found that the insulation system inside the investigated transformer seems to face a serious problem and this transformer cannot remain in service after 2027. Finally, the paper introduced a general technique that can be used for any transformer in order to predict its remaining lifetime.

Delivering confidential messages in wireless network is gaining continuous attraction as a fundamental building block for the Internet of things (IoT). This paper overviews the confidential message transmission techniques in broadcast channel (BC) where a transmitter serves multiple users by delivering confidential messages to their intended users. Each confidential message must be prevented from extracting its information by any unintended users. First, we consider a basic network consisting of two users. In the two-user BC, the secret dirty paper coding (S-DPC) achieves the secrecy capacity region. However, since the DPC requires a high computational complexity for characterizing the capacity region, two sub-optimal transmission techniques with low complexities are explained. Second, we extend our discussion to the network including more than two users. For $K$ -user BC with confidential messages, the optimal transmission scheme and the secrecy capacity region have not yet been derived. Instead of presenting the optimal scheme, we explain two sub-optimal transmission techniques and compare their features and performances. In conclusion, we discuss the open problems in the confidential message transmission for networks with more than two users.

Recently, it has been shown that channel feedback helps to enhance the physical layer security (PLS) of communication systems. The current use of feedback focuses on generating secret keys protecting the transmitted messages, and this feedback scheme is proved to be optimal for some physically degraded channel models. In this paper, we revisit the model of the state-dependent wiretap channel with the noncausal state at the transmitter, which plays an important role in the PLS of the wireless communication systems. We propose a new feedback scheme for this model and show that the proposed new scheme performs better than the already existing secret key-based feedback scheme. The results of this paper are further explained via a Gaussian example.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

This paper examines the history of electrical engineering education, leveraging the concept of “expansive (dis)integration” to frame a number of key trends and challenges in the field. Our account is organized historically, starting with the origins and early development of electrical engineering education beginning in the late 1800s, and then tracing out the rise of new subfields and specialties during the inter-war and post-WWII periods. The development of computer engineering as a field is given special attention as a case study in disciplinary (dis)integration, while setting the stage for a discussion of broader trends associated with the rising influence of digital techniques and technologies across electrical engineering. The final sections of this paper report on some contemporary challenges and opportunities that may further transform the field in upcoming years and decades, with particular emphasis on issues of demographic diversity and perceptions of broader relevance and impact. The approach of this paper is largely historical, drawing on a wide variety of primary and secondary source materials. It is expected that this paper will be of interest to anyone who would like to know more about the historical development of electrical engineering education, including in relation to more contemporary currents in the field.

Random unitary beamforming (RUB) achieves multiuser diversity gain over multiple-input multiple-output broadcast channels with partial channel state information (CSI). RUB can asymptotically achieve the same growth of rate $M\log \log K$ as the optimal dirty paper coding or sub-optimal zero-forcing beamforming, which need full CSI at the transmitter. In this paper, we propose energy efficient RUB systems that select the optimal number of streams and transmission power to maximize energy efficiency (EE). In contrast to other beamforming schemes that use full CSI, optimizing RUB in terms of EE is a difficult task due to the CSI constraints. To solve this problem, we first select the EE-optimal number of streams and transmission power by using the statistical characteristics of RUB, which have been studied in previous works. Next, based on partial CSI feedback from users, we propose the method whereby the transmitter of RUB can adaptively control the transmission power. Simulation results demonstrate that our proposed systems can improve the EE performance of RUB and that the analytical results derived in this paper are accurate.

Citations play an important role in ranking of authors, journals, institutions, and organizations. Sometimes, citing documents cite a reference many times in their full-text, which is further used in many application scenarios, such as: 1) finding relationship between cited and citing papers; 2) identifying influential cited paper from set of references in citing paper; 3) identification of suitable citation functions; and 4) study of in-text citations in different logical sections of papers to conclude different findings. The accurate identification of in-text citations remained an open area of research. Recently, the complexities involving automatic identification of in-text citations have been reported with an accuracy rate of 58%. This is due to many issues as highlighted by the state-of-the-art research. This paper investigates such issues in further details: 1) by taking benefits from the previous research; 2) by analyzing different referencing formats; and 3) by experimenting on a comprehensive data set. Based on the investigation, this paper proposes a taxonomy and workable system, which utilizes a set of heuristics build from detailed study. The proposed model is then applied on unseen diversified data set taken from the Journal of Universal Computer Science and CiteSeer. The proposed model was able to achieve an average F-score of 0.97 as compared with the baseline 0.58.

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.

Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance Rds,on is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using Rds,on, to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analytical tool to implement a long-term mission profile that requires reliability assessment.

This paper deals with investigating the closed-loop stability boundary of the highly flexible aircraft with structural flexibility and input saturation constraint. A method is presented to analyze the stable region based on the system features of the open-loop instability. In this paper, a dynamic model of highly flexible aircraft is made and the dynamic characteristics are analyzed and the long period mode and stability are investigated in terms of the trimmed model. Considering the saturation constraint of elevator deflection, the closed-loop stability boundary of the system under saturation constraint is discussed by combining the open-loop instability features of highly flexible aircraft. The analysis indicates that the boundary is related to the left eigenvector consistent with the amplitude constraint and the unstable poles of the control signals. Based on the long period instability of the aircraft, the formula of closed-loop stability boundary is analytically obtained. The convergence region of highly flexible aircraft is verified in terms of the LQR controller. The influence of structural flexibility and saturation constraint amplitude of elevator on the stability boundary of highly flexible aircraft is analyzed based on state constraints, which is also compared with the rigid aircraft. The simulation results show that the closed-loop stability of the system is restricted by the open-loop characteristic of the system and the control input saturation constraint.

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

The airway tree is one of the most important part in human respiratory system. Airway segmentation plays a crucial role in pulmonary disease diagnosis, localization and surgical navigation. We propose a novel method to improve airway segmentation in thoracic computed tomography(CT) using deep learning. In order to take into account the multi-scale changes of the airway and achieve accurate airway segmentation, we design an end-to-end Tiny Atrous Convolutional Network (TACNet) based on 3D convolution neural network. In view of the difficulty of classification due to the numerous branches of airway, two evaluation factors, namely, the angle of airway bifurcation and the buffer length of airway bifurcation are designed, which are used for airway classification by combining the centerline extraction. We train TACNet on the inspirator thoracic CT scans with ground truth, which are generated by clinicians and evaluate on our own clinical data sets and EXACT’09 data sets. Compared with state-of-the-art airway segmentation algorithms, proposed algorithm in this paper achieve very competitive results in 20 test datasets of the EXACT’09 challenge. The experimental results show that the algorithm proposed in this paper has high robustness and advantages regardless of airway segmentation or airway classification. In 20 test datasets of the EXACT’09 challenge, the average airway tree length detection rate is the best in the public literatures.

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

In the process of power transmission and distribution, non-technical losses are usually caused by users’ abnormal power consumption behavior. It will not only affect the dispatch and operation of the distribution network, bring hidden dangers to the security of the power grid, but also damage the operating costs of power companies and disrupt the operation of the power market. Aiming at users’ abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM). Firstly, according to the actual electricity theft behavior, six typical electricity theft modes are summarized, and 4 composite modes are obtained by combining them, so as to comprehensively test the detection performance of the model for various electricity theft behaviors. Secondly, a detection model based on PSO-Attention-LSTM is proposed, and the model is built using the TensorFlow framework. The model uses the attention mechanism to give different weights to the hidden state of LSTM, which reduces the loss of historical information, strengthens important information and suppresses useless information. Use PSO to solve the difficult problem of model parameter selection, and optimize the hyperparameters to improve the model performance. Finally, the data set of the University of Massachusetts was used for simulation and compared with convolutional neural network-long short term memory (CNN-LSTM), attention mechanism-based long short term memory (Attention-LSTM), LSTM, gated recurrent unit (GRU), support vector regression (SVR), random forest (RF) and linear regression (LR) to verify the effectiveness and accuracy of the method used in this article. In this paper, Matlab software is used to analyze and visualize the detection result data.

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

The aim of this paper is to design, analyze and optimize the “Multi-Stack Slotless Axial Flux Switching Permanent Magnet Machine”. In the design process, mathematical models are implemented, and the Finite Element Method (FEM) is performed to analyze the machine performances. The paper aims to minimize the occurrence of cogging torque and torque ripple in the multi-stack slotless stator AFPM machine. As a consequence, it reduces the vibrations in the machine and increases its life span. Multi-stack slotless stator AFPM machine with a right-angled trapezoid-shaped PM is proposed and comparison is done with conventional shape AFPM machine. In order to examine the performance of multi-stack slotless stator AFPM machine Finite Element Analysis (FEA) is used. To further enhance the characteristics of the designed machine with the proposed right-angled PM shape, optimization is done by considering inner and outer pole pitch as the design variables. In optimization process, krigging method assigned with Latin Hyper-cube Sampling and a genetic algorithm (GA) is performed due to suitability with non-linear data. Then, finite element analysis by JMAG-Designer is performed to verify the results. It is determined that optimized model has achieved 65% reduction in torque ripples as compared with the conventional design. Hence, this work attempts to optimize the performance of the AFPM machine.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer’s decision about which algorithm works best for their game.

Parafoil systems are strongly nonlinear due to the flexibility of the canopy and suspension lines, and this characteristic cannot be simulated accurately by existing dynamic models. Hence, model-independent control methods are necessary for parafoil systems. This paper introduces a model-free adaptive control method based on the iterative feedback tuning (IFT-MFAC) method for parafoil systems that is a data-driven control method in which only input/output (I/O) data are needed during construction. In this paper, the MFAC construction process and stability analysis are explained, and then the IFT method is used to tune the two stepping factors of the MFAC method. A six-degree-of-freedom (DOF) dynamic parafoil system model is built to assess the performance of the method in a simulation with disturbances added to imitate real flights. A series of simulations and hardware-in-loop (HIL) tests are designed to verify the performance of the IFT-MFAC controller, and it is compared with the PID control method, the active disturbance rejection control (ADRC) method, and the MFAC method.

Distribution network connected distributed energy resources (DER) are able to provide various flexibility services for distribution system operators (DSOs) and transmission system operators (TSOs). These local and system-wide flexibility services offered by DER can support the frequency ( $f$ ) and voltage ( $U$ ) management of a future power system with large amounts of weather-dependent renewable generation and electric vehicles. Depending on the magnitude of frequency deviation, other active network management-based frequency control services for TSOs could also be provided by DSOs in coordination with adaptive control of DER. This paper proposes utilisation of demand response based on frequency-dependent HV/MV transformer on-load tap-changer (OLTC) operation in case of larger frequency deviations. The main principle underlying the proposed scheme lies in the voltage dependency of the distribution network connected loads. In this paper, it is also proposed to, simultaneously with frequency-dependent OLTC control, utilise reverse reactive power -voltage (QU) - and adaptive active power -voltage (PU) -droops with distribution network connected DER units during these larger frequency deviations, in order to enable better frequency support service for TSOs from DSO networks. The effectivity and potential of the proposed schemes are shown through PSCAD simulations. In addition, this paper also presents a holistic and collaborative view of potential future frequency control services which are provided by DSO network-connected resources for TSOs at different frequency deviation levels.

Deep convolutional neural networks (DCNNs) have attracted extensive attention in synthetic aperture radar (SAR) image target detection. However, there is usually a lack of labeled data in SAR images which results in difficulties to improve the detection performance with DCNN based methods. Transfer learning borrows generic knowledge from other tasks for the target task and can be an effective solution to the small sample problem, but the features’ generality and specificity across various models are not discussed in target detection. In addition, conventional DCNN based models have too many parameters related to the prior information of the targets which increase the difficulty of transfer learning between different tasks. In this paper, we mainly conduct research in three folds to address these mentioned issues. First, the generality and specificity of features in different network models are analyzed in the field of SAR target detection. Second, a transitive transfer framework is developed for detection under small sample condition. Third, an anchor free rotatable detector with flexible structure is designed for SAR images detection to suit the transfer detection framework proposed in this paper and a reasonable explanation is given for its upper hand with small samples. Experimental results demonstrate that: 1) transitive transfer framework is valid in SAR target detection; 2) the anchor free detector in this framework is superior to the anchor based one; 3) the proposed detector achieves better performance under small sample condition by comparison with state-of-the-art methods.

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

The present article outlines the core technologies needed to realize the Hyperloop transportation system (HTS). Currently, the HTS vacuum tube train concept is viewed as the fastest way to cross the earth’s surface. However, the concept has not yet been demonstrated for subsonic or near-sonic speeds in large-scale implementations. Among the challenging technical areas are the tube’s depressurization, the capsule’s air resistance, and choked flows occurring around the capsule. There is also a need for effective levitation and propulsion solutions compatible with the velocities being proposed. Moreover, several conflicting objectives have been identified in the HTS design. It is highlighted that the tubes should be wide enough to lower the capsule’s drag forces but that a large-sized tube comes at the expense of higher operational energy costs and infrastructure investments. Another struggle is aiming at a low-cost passive track design and, and at the same time, a lightweight vehicle. One technical path is to turn the whole guideway into an electric propulsor, arriving at a ’lightweight capsule solution’ (LCS). Alternatively, the vehicle could be transformed into a ’modified airplane’ that stores massive amounts of onboard energy, yielding an energy-autonomous ’low infrastructure solution’ (LIS). In a case study, it is shown that even LIS technology is compatible with short-haul flights (1500km) but that it requires an energy reservoir of about 30% of the capsule’s overall mass. Results and discussions presented in this paper are supported by analytical predictions with parameters or input data either supported by referred simulations or experiments. In general, the paper aims to open up the discussion, provide a sufficient understanding of the Hyperloop field’s multidisciplinary aspects, and establish a foundation for further investigations. In the end, new research tasks have been defined, which have the potential to go beyond the state of current knowledge. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter’s dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of $\ell _{2,1} $ norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non- uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.

The simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and ${k}$ -means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.

Near-infrared spectroscopy (NIR) has become one of the most important methods for counterfeit drugs identification for its low cost, non-destructive, and on-site detection. However, it is often invalid for unknown samples beyond the scope of modeling samples, as well as it is not efficient in conditions of insufficient samples (insufficient number of samples within the class), unbalanced samples (large difference in the number of samples between classes), and sensitivity of identification results (different tolerance for different errors in application scenarios). To solve these problems, this paper proposes a general method for on-site identification of counterfeit drugs based on Siamese-network modeling with near-infrared spectroscopy, which especially constructs the train set and test set, learning the general knowledge of spectral differences to identify the different drugs by a costumed one-dimensional convolution neural network (1D-CNN), and finally answered the question of whether the on-site two spectra are pointed to the same drug. Based on experimental modeling samples of 1314 spectra, which are involved 9 drugs produced by 25 manufacturers, this paper has constructed and fully trained its model. Then, not known at the time of modeling, 4 drugs produced by 9 manufacturers are used for testing in the on-site application, and the accuracy rate amounts to 97.3%. For generalizing consideration, randomly divided into training and testing categories, the 32015 spectra of 135 drugs produced by 391 manufacturers in the spectral library are handled by the same processing. The generalization model is equally applicable, and the accuracy is above 97%. Compared with traditional binary classification identification methods such as SVM, PLS-DA, Auto-encoding, and one class (OC) threshold identification algorithms such as SVM-OC, SIMCA, conformity test (CT), the proposed method has the best identification ability for unknown samples in modeling.

The working life evaluation of metal oxide varistor (MOV) is always expected by MOV manufacturers and users. In Oct. 2019, the International Electrotechnical Commission (IEC) SC37B established a project team PT 61643–333 led by the author of this paper to draft a technical report about MOV life assessment. As an early researching achievement of PT 61643-333, this paper provides an evaluation method for the impulse life of MOV with the median rank regression (MRR) method based on the Weibull distribution, which provides an effective means for evaluating the impulse life of MOV. Firstly, the theory of Weibull distribution and impulse life characteristics of MOV are introduced. Then the evaluation method and procedure of MOV impulse life is derived from the combination of the MRR method and the impulse characteristics of MOV. Finally, an application example of the proposed evaluation method is given with the test results from real MOV samples. By establishing three basic impulse life characteristics equations, the life evaluation of MOV under arbitrary impulse current can be achieved. In addition, the fitting validity of six groups of samples is checked to prove the effectiveness of the proposed parameter model. And further sensitivity analysis of the variables has been done to examine the effect of changes in the values of the parameters on the propose model.

This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well as semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). This paper proposes a Semantic Attention Module (SAM) that introduces the semantic contextual clues from a segmentation subnet to guide a detection subnet. The SAM significantly boosts up the detection performance and computational cost by considerably decreasing the false alarm rate and it is completely independent of any other parameters. The experimental results show the effectiveness of each component of the network and demonstrate that the proposed MTSAN yields a better balance between accuracy and speed. Following the post-processing methods, the proposed module is tested and proved for its accuracy in the Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS). In addition, the proposed lightweight network is deployable on low-power embedded devices to meet the requirements of the real-time applications yielding 10FPS @ 512 X 256 on NVIDIA Jetson Xavier and 15FPS @ 512 X 256 on Texas Instrument’s TDA2x.

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.