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Ground-moving objects in synthetic aperture radar (SAR) images appear defocused and azimuthally displaced using conventional SAR image formation algorithms. In this paper, a novel regression method based on convolutional neural networks (CNNs) for the estimation of radial velocity and slant range components of ground moving targets is proposed. Motion parameters estimation can be helpful for designing additional matched filters to focus and relocate moving targets. We have generated the training and the test data in such a way that each image is indeed a 2D data matrix of a moving target. In other words, each complex image contains the range-compressed signal of only one moving target with a specified pair of (range, radial velocity). To further decrease the estimation error, we employed transfer learning by fine-tuning the pretrained AlexNet architecture in a regression problem. To verify the effectiveness of the proposed method, simulations have been performed. The results demonstrate the effectiveness of the proposed method.

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

Background The COVID-19 pandemic has amplified the role of telehealth in health care delivery. Regional variation in internet access and telehealth use are well-documented, but the effect of neighborhood factors, including the pervasiveness of broadband internet, on older adults’ telehealth usage in the context of internet access is not known. Objective This study aimed to investigate how individual and neighborhood characteristics, including the pervasiveness of neighborhood broadband internet subscription, are associated with engagement in telehealth among older adults with internet access. Methods In this cross-sectional study, we included 5117 community-living older adults aged ≥65 years, who participated in the 2017 National Health and Aging Trends Study with census tract–level data for participants’ places of residence from the American Community Survey. Results Of an estimated 35.3 million community-living older adults, 21.1 million (59.7%) were internet users, and of this group, more than one-third (35.8%) engaged in telehealth. In a multivariable regression model adjusted for individual- and neighborhood-level factors, age, education, income, and the pervasiveness of neighborhood broadband internet subscription were associated with engagement in telehealth, while race, health, county metropolitan status, and neighborhood social deprivation were not. Among internet users, living in a neighborhood at the lowest (versus highest) tertile of broadband internet subscription was associated with being 40% less likely to engage in telehealth (adjusted odds ratio 0.61, 95% CI 0.42-0.87), all else equal. Conclusions Neighborhood broadband internet stands out as a mutable characteristic that is consequential to engagement in telehealth.

In this paper, we report on the economic and environmental issues associated with a pyrolysis plant for processing sugarcane residues. The analyses consider four scenarios: (i) raw sugarcane bagasse (SCB), (ii) raw sugarcane trash (SCT) and acid-leached (iii) sugarcane bagasse (L-SCB) and (iv) sugarcane trash (L-SCT). The systems were studied by integrating a comprehensive mathematical model and experimental data. The environmental impact assessment was performed using the Life Cycle Assessment (LCA) methodology, including agriculture and sugar industry stages into the system boundaries. The results demonstrate that the process economy heavily depends on the bio-oil composition, yield, feedstock cost, and leaching conditions. Bio-oil from L-SCT and L-SCB contained more than 30 wt%. levoglucosan, which made it a candidate to produce fermentable sugars or other valuable chemicals instead of being burnt in low-efficiency boilers. The unitary cost of bio-oil produced from L-SCT (0.89 USD/L) and L-SCB (0.75 USD/L) was below that obtained for raw biomass, even when the Total Investment Costs behaved differently. The environmental impacts, expressed in damage categories, associated with L-SCB and L-SCT, were nearly 86% higher than for SCB and SCT. The highest impact could be attributed to the citric acid (CA) production chain, biomass transport, and harvesting operations.

Objective. The purpose of the article is to analyse the existing methodology used to assess the efficiency of automated information security systems by studying open literature sources, international and industry standards of the Russian Federation on information security of automated systems, guidelines and orders of the Federal Service for Technical and Expert Control of Russia, as well as departmental orders, instructions and regulations on information security at informatization facilities of internal affairs bodies. The analysis results in identifying the advantages and disadvantages of the specified methodology, as well as the possibilities of its use when conducting a quantitative assessment of the efficiency of access control subsystems of information security systems at the informatization facilities of internal affairs bodies. Methods. To achieve this goal, the method for system analysis of approaches used to assess the efficiency of information security tools and systems has been applied. Results. The paper presents results of analysing the main approaches used to assess the efficiency of tools and systems for information security of automated systems. The paper determines the relationship between the efficiency indicator of access control subsystems of information security systems and the main disadvantage of their use in protected automated systems of internal affairs bodies. The paper substantiates main directions of improving the existing methodology, proposes methods and indicators for quantifying the efficiency of access control subsystems (including those modified on the basis of using new information and communication technologies) of information security systems in protected automated systems of internal affairs bodies. Conclusion. The results obtained can be used to quantify the security level of existing automated systems and those being developed at informatization facilities of internal affairs bodies.

Pulse-agile radar systems have demonstrated favorable performance in dynamic electromagnetic scenarios. However, the use of non-identical waveforms within a radar's coherent processing interval may lead to harmful distortion effects when pulse-Doppler processing is used. This paper presents an online learning framework to optimize detection performance while mitigating harmful sidelobe levels. The radar waveform selection process is formulated as a linear contextual bandit problem, within which waveform adaptations which exceed a tolerable level of expected distortion are eliminated. The constrained online learning approach is effective and computationally feasible, evidenced by simulations in a radar-communication coexistence scenario and in the presence of intentional adaptive jamming. This approach is applied to both stochastic and adversarial contextual bandit learning models and the detection performance in dynamic scenarios is evaluated., 6 pages, 4 figures, to be presented at IEEE Radar Conference, Atlanta GA, May 2021

Optoelectronic devices which play important roles in high-speed optical fiber networks can offer effective measurement methods for optoelectronic devices including optical modulators and photodetectors. Precise optical signal modulation is required for measurement applications. This paper focuses on high-speed and precise optical modulation devices and their application to device measurement. Optical modulators using electro-optic effect offers precise control of lightwaves for wideband signals. As examples, this paper describes frequency response measurement of photodetectors using high-precision amplitude modulation and wavelength domain measurement of optical filters using fast optical frequency sweep. Precise and high-speed modulation can be achieved by active trimming which compensates device structure imbalance due to fabrication error, where preciseness can be described by on-off extinction ratio. A Mach-Zehnder modulator with sub Mach-Zehnder interferometors can offer high extinction-ratio optical intensity modulation, which can be used for precise optoelectronic frequency response measurement. Precise modulation would be also useful for multi-level modulation schemes. To investigate impact of finite extinction ratio on optical modulation, duobinary modulation with small signal operation was demonstrated. For optical frequency domain analysis, single sideband modulation, which shifts optical frequency, can be used for generation of stimulus signals. Rapid measurement of optical filters was performed by using an optical sweeper consisting of an integrated Mach-Zehnder modulator for optical frequency control and an arbitrary waveform generator for generation of a source frequency chirp signal.

Useful real-life images meant for computer vision applications appear in complex forms with varied backgrounds. Convolutional neural networks with their invariance outperforms capsule networks (CapsNets) on such images. Recent research to improve the accuracy of CapsNets on complex images such as CIFAR10 involves the increase in depth of the networks. However, smaller architectures with efficient feature extractors can perform comparatively well with additional advantages of having less computational cost, reduced risk of overfitting, increased suitability for deployment on devices with smaller memories, and enhanced feasibility to perform distributed training across servers and in the cloud. This paper, therefore, proposes a shallow CapsNet based on dynamic routing with a custom squashing function and normalizer to gain all of these advantages. The model is evaluated on three publicly available datasets; fashion-MNIST, CIFAR-10, and tomato datasets. Experimental results show that the proposed model outperforms the original CapsNet by generating fewer parameters and performing comparably well in terms of accuracy with some state-of-the-art multi-lane and ensemble capsule network models.

First-person video summarization has emerged as an important research problem for computer vision and multimedia communities. In this paper, we show how different graph representations can be developed for accurately summarizing first-person (egocentric) videos in a computationally efficient manner. Each frame in a video is first represented as a weighted graph. A shot boundary detection method using graph based mutual information is developed. We next construct a weighted graph for each shot. A representative frame from each shot is selected using a graph centrality measure. A new way of characterizing egocentric video frames using a graph based center-surround model is shown next. Here, each representative frame is modeled as a union of a center region (graph) and a surround region (graph). By exploiting spectral measures of dissimilarity between the two (center and surround) graphs, optimal center and surround regions are determined. Optimal regions for all frames within a shot are kept the same as that of the representative frame. Center-surround differences in entropy and optical flow values along with PHOG (Pyramidal HOG) features are extracted from each frame. All frames in a video are finally represented by another weighted graph, termed as a Video Similarity Graph (VSG). The frames are clustered by applying a Minimum Spanning Tree (MST) based approach with a new measure for inadmissible edges. Frames closest to the centroid of each cluster are captured to build the summary. Experimental evaluation on two benchmark datasets indicate the advantage of the proposed formulation.

Deep learning has shown effectiveness in binary software vulnerability detection due to its outstanding feature extraction capability independent of human expert experience. However, detection approaches such as Instruction2vec still have the following defects: (1) the context between an instruction’s elements (opcode, registers, etc.) is not fully incorporated when embedding a single instruction into its vector representation; (2) the crucial regions that related to vulnerability are not highlighted when extracting features of the vulnerable code. In this paper, we propose a hierarchical attention network for binary software vulnerability detection (HAN-BSVD). Through HAN-BSVD, the contextual information is first enriched by the preprocessor with unifying jump address and normalizing instruction, and then preserved by the instruction embedding network that composed of Bi-GRU and word-attention module; the local features are captured and the crucial regions are highlighted by the feature extraction network that composed of Text-CNN and spatial-attention module. The proposed approach is evaluated on the Juliet Test Suite dataset and the ICLR19 dataset, detection result performs better than the other compared approaches. Extensive ablation studies are also conducted to further prove the effectiveness of each design choice.

Fault's diagnosis prior to its existence and the entire shut-down is critically important for the industry. Diagnosing the faults based on condition monitoring methods and artificial intelligence techniques is extremely potent. Detecting the bearing fault is a challenging task at the incipient level, which can lead to the malfunctioning of the machine. The usage of deep learning and machine learning algorithms produces a remarkable outcome in the domain of bearing fault diagnosis. In addition, the mentioned algorithms provide effective and reliable solutions for preventive maintenance. This paper summarizes the recently published works of artificial intelligence applications when it comes to bearing faults detection in rotary machines. Moreover, this work also presents several research gaps in this field.

Biclustering (co-clustering, two-mode clustering), as one of the classical unsupervised learning methods, has been applied in many different fields in recent years. Different types of biclustering methods have been developed such as probabilistic methods, two-way clustering methods, variance minimization methods, and so on. However, few regression-based methods have been proposed to the best of our knowledge. Such methods have been applied in traditional clustering, which can improve both the computational efficiency and the clustering accuracy. In this paper, we present a penalized regression-based method for localizing the biclusters (PRbiclust). By imposing Truncated LASSO Penalty (TLP) and group TLP terms to penalize the column vectors and the row vectors in the regression model, the structure of biclusters in the data matrix is recovered. The model is formulated as an optimization problem with nonconvex penalties, and a computationally efficient algorithm is proposed to solve it. Convergence of the algorithm is proved. To extract the biclusters from the recovered data matrix, we propose a graph-based localization method. An evaluation criterion is also proposed to measure the efficiency of bicluster localization when noise entries exist. We apply the proposed method to both simulated datasets with different setups and a real dataset. Experiments show that this method can well capture the bicluster structure, and performs better than the existing works.

The weather forecasting process is used to predict the future atmospheric condition of a specified location. The evolution of the big data era gives the chances to significantly increase the prediction accuracy of weather conditions. In this paper, a deep learning-based stacked sparse autoencoder (DSSAE) has been proposed for predicting the weather condition of a particular area. This model requires a pre-processing approach to obtain essential data from big weather data and increase the prediction model’s speed. For this, the principal component analysis (PCA) is utilized to reduce dimensionality and extraction of features with more significant variance. Also, it integrates the feature selection algorithm based on Binary Butterfly Optimization Algorithm (BBOA) along with a deep stack autoencoder to improve prediction accuracy. The proposed model is validated using the weather data taken from the division of weather underground for short term and long term weather prediction. The simulation consequences illustrate that the proposed model overtakes the existing models in terms of computation time, accuracy and error rate.

Along with the rapid development of network technologies, the server-side of an e-commerce website is becoming more and more untrustworthy. Thus, how to prevent the disclosure of users’ behavior privacy in online business activities has attracted people’s wide attention. Aiming at the protection of users’ commodity viewing privacy in a commercial website, this paper proposes to construct a group of dummy requests on a trusted client, then, which are submitted together with a user commodity viewing request to the untrusted server-side, so as to confuse and cover up the user preferences. First, we define a privacy model for a user commodity viewing service, in which we introduce a concept called entropy for commodity viewing probability to measure the confusion effect of dummy requests on user requests, and we introduce a concept called regional distance among commodity categories to measure the cover-up effect of dummy requests on users’ commodity viewing preferences. Second, we design an implementation algorithm to generate a group of ideal dummy requests that can meet the constraints formulated in the privacy model. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ commodity viewing privacy on the untrusted server-side, without compromising the availability of an e-commerce website. In this paper, we present a valuable research attempt to the protection of users’ behavior privacy in a commercial website, which is of positive significance for building a privacy-preserving e-commerce platform.

Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

Function-as-a-Service or FaaS is a popular delivery model of serverless computing where developers upload code to be executed in the cloud as short running stateless functions. Using smaller functions to decompose processing of larger tasks or workflows introduces the question of how to instrument application control flow to orchestrate an overall task or workflow. In this paper, we examine implications of using different methods to orchestrate the control flow of a serverless data processing pipeline composed as a set of independent FaaS functions. We performed experiments on the AWS Lambda FaaS platform and compared how four different patterns of control flow impact the cost and performance of the pipeline. We investigate control flow using client orchestration, microservice controllers, event-based triggers, and state-machines. Overall, we found that asynchronous methods led to lower orchestration costs, and that event-based orchestration incurred a performance penalty.

Microprocessors are widely used in space applications. ARM processors fabricated with FDSOI technologies are promising for future space programs. However, radiation-hardening approaches can greatly affect the resource overhead and performance. These hardening techniques need to be evaluated at system level to better understand their effectiveness, especially for modern CMOS technologies. In this paper, two ARM® Cortex® M0 cores, a reference core and a hardened core were implemented on the same die in a 28-nm FDSOI technology. The reference core was designed with components from the standard cell library, while the other one was implemented with hardened DICE flip-flops. Both of the cores shared the same on-chip TMR-protected SRAM. Heavy ion experiments showed that the error cross section of the core with DICE flip-flops was about 2 times smaller at LET of 40 MeV·cm2·mg−1. This suggests that the contribution from logic circuits in the microprocessor cores also need to be considered for better error reduction.

In this paper, we propose a method we call MEMEography for HCI research to understand people and their interactional contexts from the remixed internet memes they post in internet communities. While memes might not be the most obvious choice of a research subject, they allow us to investigate unfamiliar domains even when access to the field is beyond reach. We describe an initial approach of data selection, collection, prioritization and analysis. In addition, we demonstrate the kinds of insights we can gain through MEMEographies by analyzing a corpus of memes in the intensive care unit (ICU) context posted 2020 on Instagram. ICU memes open up insights into the environment, work practices, challenges, emotions and familiarized us with ICU practitioners’ language, even though access to an actual ICU was completely impossible during 2020.

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

This paper presents PlushPal, a web-based design tool for children to make plush toys interactive with machine learning (ML). With PlushPal, children attach micro:bit hardware to stuffed animals, design custom gestures for their toy, and build gesture-recognition ML models to trigger their own sounds. We describe how, in the context of storytelling, PlushPal introduces core concepts in ML including data sampling and model evaluation. We conducted online workshops and in-person play sessions with 11 children between 8-14 years old building interactive stuffed animals with PlushPal. In these play sessions, we observed how children imagined bringing their toys to life using ML, as well as how children’s data literacy changed as a result of experimenting with sensors, data sampling, and building their own ML models. Our work contributes a novel design space for children to express their ideas using gesture, as well as a description of observed debugging practices, building on efforts to support children using ML to enhance creative play.

Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.

The technical transformation and transfer of most services to digital platforms require that everyone has an electronic device connected to the Internet to assist them accomplish their tasks. Smartphones are one of the best options for everyone because of their small size and ease of transport, in addition to their high capabilities equivalent to a personal computer. It is necessary to identify these devices, usually by checking their IMEI (International Mobile Equipment Identity), to provide and manage several services like the cellular network service. On the other hand, criminals and counterfeiters can manipulate this identity to hide the device and prevent it from being tracked or to make high profits from selling substandard devices. Therefore, several recent proposals have emerged to create a strong fingerprint for use in device identification purposes. This paper reviews and discusses the existing methods to generate a device identity and defines their gaps. Also, it classifies the methods into four categories based on the used technique, namely PUF, machine learning, comparison approach, and sensor calibration. Additionally, it introduces the factors to consider when choosing the technique of device identification. It provides a list of possible attacks on each technology used in device identification methods.

This paper proposes to utilize the waste heat of a solar-driven Organic Rankine cycle (ORC) to support the regeneration process of the carbon capture by adsorption (CCA) subsystem. The waste heat is utilized in cascaded form, where the higher temperature component is supplied to the CCA device, and the lower temperature component is internally exchanged to increase the ORC efficiency. The proposed system is studied for the microgrid application, where a thermodynamic analysis is conducted to evaluate the electrical and exergy performance of the proposed system under 1 day of solar irradiance. The analysis is achieved by coupling the simulation models of each subsystem, where the influences of the ORC pressure ratio, turbine inlet temperature, regeneration temperature, and the CO2 input source are investigated. Results show that the proposed system is optimal when combined with CO2 capture of the animal farm; When a 50 m2 and 100 solar concentrations solar thermal plant is used, an optimal regeneration temperature of 75 °C existed to yield 4375.6 kg of CO2 per day and 0.3 MW electricity. The corresponding ORC and C O 2 capture exergy efficiencies are 0.25 and 0.012, respectively, which sum to be higher than the basic solar-ORC under the same conditions.

Visual odometry aims to estimate the relative pose between frames, which is a fundamental task for visual SLAM. In this paper, we present a novel line-based visual odometry (VO) algorithm that fully utilizes the characteristic of line to estimate the projected line of adjacent frame by minimizing the local gradient fitness evaluation. In contrast to the current feature-based or line-based visual odometry, we don ′ t need to explicitly match points or lines of two frames, which is non-trivial and inaccurate in challenging scenarios such as texture-less scenes. In our method, the projected line is calculated simultaneously with the local gradient fitting function of pose estimation based on the constraint that the orientation of the projected line should be perpendicular to the gradient orientation of pixels of its local regions. The proposed method is more robust and reliable than other line-based VO since it fully uses the pixel orientations in the local regions to estimate the projected line and relative pose. We evaluate our method on the real-world RGB-D dataset and synthetic benchmark dataset. Experimental results show that our method achieves the state-of-the-art algorithms in indoors scenes, especially in texture-less scenes.

In this paper, we address one specific video retrieval problem in terms of human face. Given one query in forms of either a frame or a sequence from a person, we search the database and return the most relevant face videos, i.e., ones have the same class label with the query. Such problem is very challenging due to the large intra-class variations and the high request on the efficiency of video representations in terms of both time and space. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes video faces into compact binary codes. Specifically, we devise an end-to-end convolutional neural network (CNN) framework that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high-dimensional representations of both frames and videos into Hamming space to generate binary codes. In such Hamming space, distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a novel bounded triplet hashing loss is elaborately designed, which takes all dissimilar pairs into consideration for each anchor point in a mini-batch, and the optimization of the loss function is smoother and more stable. Extensive experiments on challenging video face databases and general image/video datasets with comparison to the state-of-the-arts verify the effectiveness of our method in different kinds of retrieval scenarios.

In this paper, the dimension of artificial noise (AN) and corresponding transmit power in multiple-input multiple-output single-eavesdropper channels are investigated. It is widely known that AN is transmitted in the null space of the channel matrix of a legitimate link between a source and a destination. Here, the secrecy outage probabilities are derived for various dimensions of AN. Recognizing the fact that the secrecy outage performance depends on the dimension of the AN , an optimization problem is formulated to maximize the secrecy rate under a secrecy outage constraint. The optimal power splitting factor between the information and AN signals is then derived. Furthermore, the asymptotic behaviors of the optimal power splitting factor and the resulting maximum secrecy rate are examined. Through asymptotic analyses and numerical verification, it is formally shown that the full-dimension AN maximizes the secrecy rate under a secrecy outage constraint . Because the complexity of AN design increases as the dimension of the AN increases, we can achieve a tradeoff between secrecy performance and AN design complexity by determining the dimension of the AN. This study can provide a guideline of AN design specifically pertaining to the dimension of the AN.

Auto-scaling systems can dynamically scale the required resources for cloud-based services at runtime. This is an effective mechanism, enabling services to adapt to environmental changes. These systems establish the foundation for achieving elasticity in the modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud auto-scaling systems are one of the most complex and sophisticated created artifacts, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. To find an effective solution to this problem, an accurate prediction of the required amount of workload as well as the system metrics for future time periods are needed. Various solutions have already been proposed to tackle this problem. Many solutions make use of machine learning, statistical, and ensemble methods. In this paper, we view the auto-scaling problem as a sequence model and apply the convolutional neural networks to predict the future workload of cloud services. Also, by using neural networks, we obtain a mapping between the predicted workload as well as the real-time and future amounts of the required resources. We have also proposed a decision-making mechanism that takes into account different and sometimes conflicting user criteria resulting in the best-compromised decision. To this aim, we have used TOPSIS as a multi-criteria decision-making method for the decision-making component. In the evaluation section, we have examined the amount of prediction error, the amount of service level agreement violations, as well as the amount of resources' under-utilization. Evaluations demonstrate that the proposed approach for predicting the workload shows a 4 percent improvement over the existing approaches.

This paper considers a green closed-supply chain consisting of a manufacturer and a retailer. A Stackelberg game model of centralized decision-making and decentralized decision-making with manufacturer’s fairness concern was constructed based on the consideration of retailer’s sales effort. The decision-making of supply chain members under the above two situations and their reasons are analyzed in depth. According to the model, a green closed-loop supply chain with profit sharing contract coordination fairness is designed. Finally, the correctness of the model is verified by numerical simulation. We generate our findings from three aspects, as follows: when the manufacturer has fair concern behavior, it is not conducive to the environmental performance of green products, resulting in waste of resources, but also forcing retailers to reduce sales efforts and increase the retail price of products. Finally, the benefits of green closed-loop supply chain are seriously damaged. The profit-sharing contract could improve the relationship between members of the supply chain to achieve sustainable economic and environmental development.

A tangible user interface or TUI connects physical objects and digital interfaces. It is more interactive and interesting for users than a classic graphic user interface. This article presents a descriptive overview of TUI's real-world applications sorted into ten main application areas-teaching of traditional subjects, medicine and psychology, programming, database development, music and arts, modeling of 3D objects, modeling in architecture, literature and storytelling, adjustable TUI solutions, and commercial TUI smart toys. The paper focuses on TUI's technical solutions and a description of technical constructions that influences the applicability of TUIs in the real world. Based on the review, the technical concept was divided into two main approaches: the sensory technical concept and technology based on a computer vision algorithm. The sensory technical concept is processed to use wireless technology, sensors, and feedback possibilities in TUI applications. The image processing approach is processed to a marker and markerless approach for object recognition, the use of cameras, and the use of computer vision platforms for TUI applications. Web of Science 21 13 art. no. 4258

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.

The plethora of social media applications creates an ecosystem that can assist strategies for small business. However, the abundance of social media applications is attributed as a cause for confusion to business owners and executives making it difficult to conceive strategies for engaging effectively with social media. Following a practice theory, this paper uses an autoethnographic methodology to study how the social media ecosystem influences strategic practices of a small business. The study found that the social media ecosystem is crucial for enacting a strategy of a small business and that changes within the ecosystem influence the whole strategy. In addition, the study shows how context is interwoven with the social media ecosystem to affect the strategy of a small business. The study underscores the importance of strategizing practices for small business to develop agility that is necessary for developing and embedding digital transformation. Practically, the study highlights the significance and the need for developing countries to synchronize their soft infrastructure in order to help small businesses exploit the benefits of globalization during this era of social media.

Water bodies are a significant element of the Earth's landscape. The issue of water contamination is taking a chance with our lives. This paper addresses the issue and depicts the plan and testing of a proposed framework of a Smart Marine Saver using ARM7 LPC2148, the framework comprises of a central body, a solar panel, oil skimming and garbage gatherer or trash collector arrangement. This can play out the function of expelling marine oil spills incorporated with sensors - such as pH sensor, Ultrasonic sensors and MQ3 sensor. It can likewise play out the function of Scooping Trash or garbage from the water surface alongside extra applications to shield the boat from adverse environment condition automatically. The real-time location of the boat tracking facility using GPS locator is also installed in the system

In enterprise software markets, firms are increasingly using services-based business models built on open-source software (OSS) to compete with established, proprietary software firms. Because third-party firms can also strategically contribute to OSS and compete in the services market, the nature of competition between OSS constituents and proprietary software firms can be complex. Moreover, their incentives are likely influenced by the licensing schemes that govern OSS. We study a three-player game and examine how open-source licensing affects competition among an open-source originator, an open-source contributor, and a proprietor competing in an enterprise software market. In this regard, we examine (1) how quality investments and prices are endogenously determined in equilibrium, (2) how license restrictiveness impacts equilibrium investments and the quality of offerings, and (3) how license restrictiveness affects consumer surplus and social welfare. Although some in the open-source community often advocate restrictive licenses such as the GNU General Public License because it is not always in the best interest of the originator for the contributor to invest greater development effort, such licensing can actually be detrimental to both consumer surplus and social welfare when it exacerbates this incentive conflict. We find such an outcome in markets characterized by software providers with similar development capabilities yet cast in favor of the proprietor. In contrast, when these capabilities either become more dispersed or remain similar but tilt in favor of open source, a more restrictive license instead encourages greater effort from the OSS contributor, leads to higher OSS quality, and provides a larger societal benefit. This paper was accepted by Chris Forman, information systems.

With advancements in technology, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. Examples of these valuable data include healthcare and disease data such as privacy-preserving statistics on patients who suffered from diseases like the coronavirus disease 2019 (COVID-19). Analyzing these data can be for social good. For instance, data analytics on the healthcare and disease data often leads to the discovery of useful information and knowledge about the disease. Explainable artificial intelligence (XAI) further enhances the interpretability of the discovered knowledge. Consequently, the explainable data analytics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. In this paper, we present an explainable data analytics system for disease and healthcare informatics. Our system consists of two key components. The predictor component analyzes and mines historical disease and healthcare data for making predictions on future data. Although huge volumes of disease and healthcare data have been generated, volumes of available data may vary partially due to privacy concerns. So, the predictor makes predictions with different methods. It uses random forest With sufficient data and neural network-based few-shot learning (FSL) with limited data. The explainer component provides the general model reasoning and a meaningful explanation for specific predictions. As a database engineering application, we evaluate our system by applying it to real-life COVID-19 data. Evaluation results show the practicality of our system in explainable data analytics for disease and healthcare informatics.

Advancement in deep neural networks have made it possible to compose music that mimics music composition by humans. The capacity of deep learning architectures in learning musical style from arbitrary musical corpora have been explored in this paper. The paper proposes a method for generated from the estimated distribution. Musical chords have been extracted for various instruments to train a sequential model to generate the polyphonic music on some selected instruments. We demonstrate a simple method comprising a sequential LSTM models to generate polyphonic music. The results of the model evaluation show that generated music is pleasant to hear and is similar to music played by humans. This has great application in entertainment industry which enables music composers to generate variety of creative music.

This paper presents the design and development of a technological ecosystem which facilitates research at the intersection of Virtual Acoustics, Networking, and Telematic Music. Building upon existing robust software packages, we integrate and extend this work in the context of exploring the effect of telematically-shared dynamic virtual acoustic spaces and expressive movement trajectories of player sound source positions upon live play contexts. Through analysis of impulse responses generated by a distributable measurement kit, remote players are able to share these spaces with their collaborators. Supplementary tools developed as part of this research also extend established telematic software solutions, allowing for easier use of network based musical communication systems for both performers and technicians. A network topology of interconnected telematic solutions is described, allowing flexibility between various internet based audio platforms. Applications which challenge and contain these developments are discussed, including a large-scale musical ensemble, web based musical performance, and an ongoing study into collaborative telematic improvisation.

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.

Barge-type platforms with moonpools are a promising type of foundation for floating offshore wind turbines due to their good seakeeping performance. In this paper, the mean wave drift force on a barge-type vertical-axis floating wind turbine with multiple moonpools was investigated through physical model testing and numerical calculations using WAMIT. The focus was on the characteristics of mean drift load and its optimization potential. The present numerical results indicated that the application of moonpools was useful in reducing horizontal mean drift force at specific frequencies, and the reason was ascribed to the significant radiation effect of the resonant water oscillations in moonpools. The observed reduction effect on mean drift force was shown to be dependent on the viscous damping of moonpool resonance. The experimental results showed that the maximum response of the mean sway drift force was reduced by the gyroscopic effect of rotations of the vertical-axis wind turbine, and this reduction effect became stronger as the rotating speed of the wind turbine increased, but was weakened as wave amplitude increased. The comparisons between experimental data and potential flow predictions indicated that viscous effects should be taken into account to reasonably estimate the mean wave drift forces on barge-type floating wind turbines.

This paper proposes a new diagnostic method for sensor signals collected during semiconductor manufacturing. These signals provide important information for predicting the quality and yield of the finished product. Much of the data gathered during this process is time series data for fault detection and classification (FDC) in real time. This means that time series classification (TSC) must be performed during fabrication. With advances in semiconductor manufacturing, the distinction between normal and abnormal data has become increasingly significant as new challenges arise in their identification. One challenge is that an extremely high FDC performance is required, which directly impacts productivity and yield. However, general classification algorithms can have difficulty separating normal and abnormal data because of subtle differences. Another challenge is that the frequency of abnormal data is remarkably low. Hence, engineers can use only normal data to develop their models. This study presents a method that overcomes these problems and improves the FDC performance, it consists of two phases. Phase I has three steps: signal segmentation, feature extraction based on local outlier factors (LOF), and one-class classification (OCC) modeling using the isolation forest (iF) algorithm. Phase II, the test stage, consists of three steps: signal segmentation, feature extraction, and anomaly detection. The performance of the proposed method is superior to that of other baseline methods.

The quantum-resistant attribute is a new design criterion for cryptography algorithms in the era of quantum supremacy. Lattice-based cryptography is proved to be secure against quantum computing. CRYSTALS-Kyber is a lattice-based promising candidate in the post-quantum cryptography standardization process. This paper proposes a high-performance polynomial ring processor for the CRYSTALS-Kyber algorithm. The processor executes optimized polynomial ring arithmetic, which cuts off over 20%/50% on the times of modular multiplication/addition compared with the straightforward implementations. Besides, the forward and inverse Number Theoretic Transform (NTT) reuse the control logic with the help of an efficient configurable butterfly unit to minimize the area of the finite state machine. Further, the underlying dual-column sequential storage scheme breaks the bottleneck of memory accessing. To evaluate the performance, a fully pipelined architecture is implemented on a low-cost FPGA platform. Benefiting from these optimizations, the Kyber1024processor can perform NTT operation for a 4-dimensional polynomial vector in 17.1 μ s, and it achieves speedup by a factor of 2.1 compared with the state-of-the-art implementation.

The composition of worldwide energy consumption is undergoing tremendous changes due to the consumption of non-renewable fossil energy and emerging global warming issues. Renewable energy is now the focus of energy development to replace traditional fossil energy. Energy storage system (ESS) is playing a vital role in power system operations for smoothing the intermittency of renewable energy generation and enhancing the system stability. We divide ESS technologies into five categories, mainly covering their development history, performance characteristics, and advanced materials. Biomass storage and gas storage are also discussed, which are not considered in most reviews. After detailed research, the rapid development of each technology in recent years is introduced, and some representative research works are surveyed. We comprehensively summarized the advantages and disadvantages of various ESS technologies and presented several evaluation indicators for quantitative analysis. Hybrid ESS is also considered based on the complex market demand. Then, we investigate the applications of various ESS technologies as short-term, medium-term, and long-term storages in power systems, covering the power generation, transmission and distribution, and end-user. Finally, this paper reviews global developing trends, and identifies critical challenges and promising opportunities.

The world demands a smart and green future in every sector, which directly corresponds to increases in electrical energy demand one way or another. It is unfeasible to attain future energy demand with the present electrical infrastructure. That means more research and development is required. Future energy sources should be intermittent, and, in addition, the energy sector should be more inwards for distributed energy generation with demand side control. In such cases, the smartest and most autonomous system would be essential to deliver an adequate power supply with all electrical properties. A real-time monitoring and control system with a self-healing infrastructure is a forthcoming desideratum. By accepting these challenges, we have designed a smart street. The basic idea of the smart street is presented in this paper as a landing page, the paper is more focused on emphasizing information regarding the electrical energy flow algorithm for the household, street, and street battery storages. This algorithm is helpful for two-way energy flow and the automatic detection of islanding and the grid connection mode. It will be not only helpful for the users but to the utility as well.

Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).

[EN] Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term. Human-system integration introduces an important number of challenges and problems that have to be solved. On the one hand, humans should interact with systems even in those situations where their attentional, cognitive, and physical resources are limited in order to perform the interaction. On the other hand, systems must avoid overwhelming the user with unnecessary actions. Therefore, appropriate user-centered methods for AmI development should be used to help designers analyze and design human-in-the-loop interactions in AmI environments. This paper presents a user-centered design method that defines a process with a set of tools and techniques that supports the process steps in order to systematically design, prototype, and validate human-in-the-loop (HiL) solutions. The process starts with the definition of the HiL design, which defines how the system cooperates with the human. This HiL design is built using a conceptual framework that focuses on achieving human-system interactions that get human attention and avoid obtrusiveness. Then, we provide a software infrastructure to generate a prototype based on the HiL design and validate it by having end-users use a web simulator. The feedback data generated during the prototype user validation is gathered and used by a machine learning tool that infers the user's needs and preferences. Finally, these inferences are used to automatically enhance the human-in-the-loop designs and prototypes. We have validated the proposed method through a twofold perspective: an experiment to analyze the perception of interaction designers regarding their acceptance of the design method and another experiment to evaluate the usefulness of the "smart" prototyping technique. The results obtained point out the acceptability of the proposed method by designers and the useful adaptations provided by the "smart" prototyping technique to achieve a HiL design that adapts well to users' preferences and needs., This work has been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana under the projects TIN2017-84094-R and AICO/2019/009 and co-financed with ERDF.

COVID-19 pandemic caused a massive impact on healthcare, social life, and economies on a global scale. Apparently, technology has a vital role to enable ubiquitous and accessible digital health services in pandemic conditions as well as against “re-emergence” of COVID-19 disease in a post-pandemic era. Accordingly, 5G systems and 5G-enabled e-health solutions are paramount. This paper highlights methodologies to effectively utilize 5G for e-health use cases and its role to enable relevant digital services. It also provides a comprehensive discussion of the implementation issues, possible remedies and future research directions for 5G to alleviate the health challenges related to COVID-19. European Commission University College Dublin Academy of Finland Update citation details during checkdate report - AC

With image object detection techniques progressing, video object detection attracts a lot of attention more than ever before. However, the performance of detecting the object from a video suffers a lot as object occlusion can always lead to the object in the presence of appearance deterioration. To deal with this problem, the temporal correlation of video sequences is often used to reduce the effects of object occlusion in video object detection. In this paper, an optical-flow-feature fusion-based video object detection method is proposed with consideration of temporal coherence among video frames. To further reduce computational complexity, this paper also proposes a packet video processing method. Specifically, video frames are grouped first, and all frames in current group share the same optical flow feature map by feature fusion. Then the Target Image can be formed with object information enhanced by fusing the shared feature map with current frame. The proposed method gives rise to effective background information masking, so that the object detection network can focus more on the foreground object. This method effectively improves the object detection performance and the scene migration performance. Experimental results prove that the proposed method significantly improves the detection accuracy to 78.6% on ImageNet VID. In addition, VGG and ResNet are compared to further verify the effectiveness of the proposed method, and the results can be a persuasive evidence with the highest detection accuracy and acceptable time consumption.

Optimal load distribution and speed scaling in a heterogeneous data center that takes account of the tradeoff between the maintenance and operation costs and system performance are crucial aspects of cloud computing. In this paper, we aim to provide a novel multi-server control model with dynamic feedback to acquire dynamic states of the cloud system, and with queue waiting cost-awareness to optimize the queue wait time and load distribution in task assignment and server configuration management. Using the technology of speed scaling, each server in a data center is configured as an M/M/1 queue system with variable service rate, and the service rate is a function of the length of the task queue. We formulate two optimization problems, the optimal load distribution problem and the optimal controlling service rate problem, and provide algorithms to solve these problems, facilitating load distributions and service rate adjustments. We also present numerical simulations to validate our model. The results show our model to be efficient in multi-server dynamic configurations and task assignments according to the feedback information for the tradeoff between the system cost and performance.

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

Semiconductor-based avalanche photodiodes (APDs) have the advantages of lower power and simpler fabrication of arrays compared with photomultiplier tubes. It is critical for weak-light imaging that the APD is operated under back illumination and with high linear gain. However, linear gain is fairly low for conventional PIN APDs. This paper presents a back-illuminated APD, whose multiplication layer is designed to be a GaN/AlN periodically stacked structure. The GaN/AlN heterostructure is introduced to enhance the ratio of ionization coefficient between electrons and holes, which proves to be helpful for increasing the linear gain. Under back illumination, the prototype device demonstrates a record-high external quantum efficiency (reflecting linear gain) of ∼5 × 102 at the reverse bias of 66 V. The device response spectra were also studied to further confirm that only the photo-excited electrons can trigger the collision ionization in the multiplication layer.

Abstract Coordination is an important requirement for most Multiagent Systems. A setplay is a particular instance of a coordinated plan for multi-robot systems in collective sports. Setplays are usually designed by robotics specialists using some existing tools, like the SPlanner, or by hand-coding. This work presents recent improvements to the Strategy Planner (SPlanner) and its corresponding FCPortugal Setplays Framework (FSF) to provide sophisticated setplays. This toolkit is useful to design strategic plans for robotic soccer teams as a particular case of Multi-Agent Systems (MASs). The new enhancements enable more realistic setplays, including, but not limited to, the definition of better pass strategies and defensive setplays. The enhanced tool is used to populate a dataset with demonstrations made by soccer experts and used in a Learning from Demonstration (LfD) approach to allow robotic soccer teams to learn new setplays. A new demonstration mode in the RoboCup Soccer Simulation 3D (SSIM3D) viewer RoboViz was also introduced to integrate this tool with SPlanner. Domain experts can use this set of tools to capture a specific scene in a game in RoboViz and use it as an initial step for a new setplay recommendation in SPlanner. The resulting dataset is organized into fuzzy clusters to be used in a reinforcement learning strategy. This paper describes the whole process. Article Highlights This paper’s main contribution is generating a dataset of setplays to support learning from demonstration in robotic soccer. A set of new features were added to the Strategic Planner(SPlanner) to enable the design of more realistic setplays. The official RoboCup viewer (Roboviz) was integrated with SPlanner using a new demonstration mode.