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Recent years witnessed the emergence of visible light communication (VLC) over screen-to-camera channel, such as barcode and unobtrusive optical pattern, due to the widely adoption of screen and camera in plenty of electronic devices. The prevalence of wide viewing angle screen and high standard cameras also imposes great threat for visible light communication, and the information leakage over screen-to-camera channel has been rarely explored. In this paper, we propose a secret key distribution system leveraging the unique color shift property over screen-to-camera channel. To facilitate such design, two practical secret key distribution methods, key matching-based and nearest next hop-based, are developed to map the secret key into a unique optical pattern on screen, which can only be correctly decoded by the legitimate user situated at an accessible region. We also provide theoretical analysis on the security of both methods. The performance of the proposed system is implemented with off-the-shelf devices and validated under various experimental scenarios. The results demonstrate that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining low recovery accuracy for the attackers.

Dzongkha typing is time-consuming. A word in Dzongkha is formed by either a single syllable or multiple syllables. A single syllable ར (property) and multiple syllabic word ས་སག་སབས་སབསཔ (cloudy) require 6 and 22 keypresses respectively. Similarly, most of the syllables and words require several keypresses. To date, the study on syllable prediction has not been done. Moreover, the lack of text corpus poses a challenge. The purpose of this study was to develop the next syllables prediction system to reduce keystrokes and typing-time. The proposed system takes a single syllable and predicts the next top five probable syllables. The best suitable syllable is selected to form a word and subsequently, a word predicts the next plausible syllables. The corpus was curated with different genres collected from the Dzongkha Development Commission of Bhutan and Kuensel online. The dataset consisted of 31,199 sentences and 222,844 syllables. Using the n-gram method, 195,998 sequences were generated from the dataset and comprised of 2,929 unique syllables. The text sequences were converted into vectors using the word embedding and trained with the variants of Recurrent Neural Networks. The single-layer Long Short-Term Memory with 128 memory cells obtained the best training accuracy of 78.33%.

International audience; Checking the diagnosability of a time discrete event system usually consists in determining whether a single fault event can always be identified with certainty after a finite amount of time. The aim of this paper is to extend this type of analysis to more complex behaviors, called event patterns, and to propose an effective method to check diagnosability with the use of modelchecking techniques. To do so, we propose to convert the pattern diagnosability problem into checking a linear-time property over a specific time Petri net. Note to Practitioners-This paper is motivated by the problem of improving the monitoring and the supervision of systems like automated and robotised manufacturing systems. Based on a model of the system, the paper proposes a method to assert with certainty whether the available set of sensors will always provide enough information to ensure that a complex and unexpected behavior has not happened in the system. The proposed method uses a publicly available model-checking tool to perform this analysis.

Searchable Encryption (SE) enables a data owner to outsource encrypted data to an untrusted server while preserving keyword search functionality. Typically, the server learns whether or not a query has been performed more than once, which is called the search pattern. However, such kind of information leakage might be leveraged to break query privacy. To reduce such type of leakage and provide strong privacy guarantee, Wang et al. proposed a novel SE scheme based on the Paillier encryption scheme in INFOCOM'15. Unfortunately, their scheme cannot perform keyword search successfully, because the additive homomorphic property is not sufficient for their construction. In this paper, we first show that why their scheme fails to return the correct search result, and then propose a new SE scheme by adopting a special additive homomorphic encryption scheme to achieve the multiplicative homomorphic property efficiently. Furthermore, we enhance the security on the user side. Specifically, we use random polynomials with an appropriate degree to guarantee that the user cannot learn anything other than the desired search result. Finally, we present a formal security analysis and implement our scheme on a real-world database, which demonstrates that our construction can achieve the desired security properties with good performance.

Conformal prediction is an attractive framework for prediction that is distribution free. In this article, we study in details its homeostasis property under a general regression setup and also introduce the concepts of upper and lower predictive distributions and predictive curve to establish connections to left-, right- and two-tailed hypothesis testing problems as well as the developments in confidence distributions. The homeostasis property is very attractive, since it states that under some conditions the prediction results remain valid even if the model used for learning is completely wrong. We show explicitly why the property holds in a model-based setup and also explore the boundary when the property breaks down. Beside the typical assumption used in conformal prediction that the response and covariate pairs ( y , x ) of all subjects are iid distributed, we also study the classical regression setting in which the design is fixed with given (non-random) covariates x. The trade-offs among learning model accuracy, prediction valid and prediction efficiency are discussed, leading to an emphasis of more efforts on developing better learning models.

As a scientific programming language, Julia strives for performance but also provides high-level productivity features. To avoid performance pathologies, Julia users are expected to adhere to a coding discipline that enables so-called type stability. Informally, a function is type stable if the type of the output depends only on the types of the inputs, not their values. This paper provides a formal definition of type stability as well as a stronger property of type groundedness, shows that groundedness enables compiler optimizations, and proves the compiler correct. We also perform a corpus analysis to uncover how these type-related properties manifest in practice.

Detecting the evolutionary stages of social media events such as Twitter and Sina Weibo is beneficial for enterprises and governments to take necessary actions before emergent phenomena become uncontrollable. Prior work on event extraction from microblogs mostly focused on extracting event summary. However, the evolutionary stages of an event can provide more details about the evolution of the event, which are more critical to predict the future trend of the event. On the other hand, many events have a lifecycle-like evolutionary property, i.e., beginning, developing, climax, descending, and disappearing. Such a feature is helpful to detect the evolutionary stages of events. Thus, we propose a graph-based approach to represent and extract the evolutionary stages of events from microblogs based on this consideration. The contributions of this study are threefold. First, differing from existing methods that use a keyword set or a microblog set to represent an event, we propose a Keyword Popularity Information Graph (KPIG) to represent the keywords and the statistical information of events using a graph. With this mechanism, we can capture both the textual information and the statistical information of an event. Second, based on the KPIG graph, we present a graph-kernel-based approach to measure the similarity among events. Third, we conduct extensive experiments on a real dataset and compare our proposal with several competitor algorithms. The results show that our approach outperforms other competitors in terms of various metrics.

Community detection has emerged during the last decade as one of the most challenging problems in network science, which has been revisited with network representation learning recently and has attracted considerable attention. Many approaches have been proposed in recent years, including the latest methods based on graph convolutional network (GCN). Here, we propose a new network representation learning method based on GCN for community detection in attributed networks without prior label information. Inspired by the message pass mechanism of GCN and the local self-organizing property of community structure, we integrate a label sampling model and GCN into an unsupervised learning framework to uncover underlying community structures by fusing topology and attribute information. The label sampling model constructs a balanced training set by structural center location and neighbor node expansion to train the GCN. The experiments on various real-world networks give a comparison view to evaluate the proposed method. The experimental results demonstrate the proposed method performs more efficiently with a comparative performance over current state-of-the-art community detection algorithms.

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output , Instance Relations , Measure Functionality , and Measure Valence . We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.

Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the agent. In this paper, we present two improvements over the existing HER algorithm. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we reduce existing bias in HER by the removal of misleading samples. To test our algorithms, we built three challenging environments with sparse reward functions. Our empirical results in both environments show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm. A video showing experimental results is available at https://youtu.be/xjAiwJiSeLc.

Clustering is performed to partition samples into disjoint groups for facilitating the discovery of hidden patterns in the data. Many real-world applications involve various clustering methods , most of which only produce a single clustering. As a response to this issue, multiple clustering that aims to generate diverse and high-quality clustering, has emerged recently. This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive experimental results demonstrate that the ASNMFMC is superior to the existing multiple clustering methods and can explore diverse high-quality clustering.

Symmetry is an important property of human faces. Many researchers exploit symmetry to improve face recognition. In this paper, we explore the symmetry of faces under the framework of PCA (Principal Component Analysis). Unlike previous studies that manipulate facial symmetry by averaging the two halves, we argue that well-estimated symmetrical faces lie in a low-dimensional subspace. Inspired by this, a PCA-like model, referred to as Mirror PCA, is proposed to extract low-rank symmetrical features of faces, as well as their principal directions. Although the optimization problem is non-convex, a suboptimal solution could be found under existing optimization frameworks such as ADMM, PGD, etc. A number of experiments are conducted to verify the effectiveness and robustness of our method.

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer’s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.

We develop a high resolution face texture generation system which uses artist provided appearance controls as the conditions for a generative network. Artists are able to control various elements in the generated textures, such as the skin, eye, lip, and hair color. This is made possible by reparameterizing our dataset to the same UV mapping, allowing us to utilize image-to-image translation networks. Although our dataset is limited in size, only 126 samples in total, our system is still able to generate realistic face textures which strongly adhere to the input appearance attribute conditions because of our training augmentation methods. Once our system has generated the face texture, it is ready to be used in a modern game production environment. Thanks to our novel SuperResolution and material property recovery methods, our generated face textures are 4K resolution and have the associated material property maps required for raytraced rendering.

Zero-shot object detection (ZSD) aims to locate and recognize novel objects without additional training samples. Most existing methods usually map visual features to semantic space, resulting in a hubness problem, and learning an effective feature mapping between the two modalities remains a considerable challenge. In this work, we propose a novel end-to-end framework, Semantic-Visual Auto-Encoder (SVAE) network, to tackle the above issues. Distinct from previous works that utilize fully-connected layers to learn the feature mapping, we implement a 1-dimensional convolution with various shared filters to construct the auto-encoder, which maps semantic features to visual space to alleviate the hubness problem. Specifically, we design a novel loss function, Softplus Margin Focal Loss (SMFL), for object classification channel to align the projected semantic features in visual space and address the class imbalance problem. The SMFL improves the discrimination of projections on positive and negative categories and maintains the property of focal loss. Besides, to promote the localization performance for novel objects, we also provide semantic information for object localization channel and utilize a trainable matrix to align the semantic-visual mapping, considering noises in semantic representations. We conduct extensive experiments on four challenging benchmarks. The experimental results show the competitive performances compared with state-of-the-art approaches. Especially, we achieve 8.39%/6.58% mean average precision (mAP) improvements for ZSD/general-ZSD on Microsoft COCO benchmark.

People with colour vision deficiency (CVD) may have difficulty recognising or discriminating colours. To enhance their visual details, some methods have been developed to enhance colour contrast. Recently, researchers realized the importance of preserving naturalness in the processed images to avoid decreasing the quality of vision (QOV) and state-of-the-art image recolouring methods succeeded in realising both contrast enhancement and naturalness preservation by utilising optimisation model. However, the high computational cost of these methods prevents their wide application in daily lives. Moreover, these methods cannot automatically resolve the trade-off between contrast enhancement and naturalness preservation. This paper proposes Optimized Dichromacy Projection (ODP) as a novel high-speed image recolouring technique for dichromacy compensation. ODP achieves both contrast enhancement and naturalness preservation by computing an optimized projection from the colour gamut of normal vision to the colour gamut of dichromacy and can automatically adapt the weight of contrast enhancement and naturalness preservation to the property of individual images. Qualitative, quantitative, and subjective evaluation experiments show that the proposed method can generate images of qualities competitive to the state-of-the-art methods while drastically improving computation time.

Purpose Support structures are often needed in additive manufacturing (AM) to print overhangs. However, they are the extra materials that must be removed afterwards. When the supports have many contacts to the model or are even enclosed inside some concavities, removing them is very challenging and has a risk of damaging the part. Therefore, the purpose of this paper is to develop a new type of tree-support, named Escaping Tree-Support (ET-Sup), which tries to build all the supports onto the build plate to minimize the number of contact points. Design/methodology/approach The methodology is to first classify the support points into three categories: clear, obstructed and enclosed. A clear point has nothing between it and the build plate; an obstructed point is not clear, but there exists a path for it to reach the build plate; and an enclosed point has no way to reach the build plate. With this classification, the path for the obstructed points to come clear can be found through linking them to the clear points. All the operations are performed efficiently with the help of a ray representation. Findings The method is tested on different overhang features, including a lattice ball and a mushroom shape with a concave cap. All the supports generated for the examples can find their way to the build plate, which looks like they are escaping from the model. The computation time is around one second for these cases. Originality/value This is the first time truly realizing this “escaping” property in the generation of tree-like support structures. With this ET-Sup, it is expected that the AM industries can reduce the manufacturing lead time and save much labor work in post-processing.

Various verification techniques for temporal properties transform temporal verification to safety verification. For infinite-state systems, these transformations are inherently imprecise. That is, for some instances, the temporal property holds, but the resulting safety property does not. This paper introduces a mechanism for tackling this imprecision. This mechanism, which we call temporal prophecy, is inspired by prophecy variables. Temporal prophecy refines an infinite-state system using first-order linear temporal logic formulas, via a suitable tableau construction. For a specific liveness-to-safety transformation based on first-order logic, we show that using temporal prophecy strictly increases the precision. Furthermore, temporal prophecy leads to robustness of the proof method, which is manifested by a cut elimination theorem. We integrate our approach into the Ivy deductive verification system, and show that it can handle challenging temporal verification examples., 11 pages, presented at FMCAD 2018

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

A smart contract is a computer program that allows users to automate their actions on the blockchain platform. Given the significance of smart contracts in supporting important activities across industry sectors including supply chain, finance, legal, and medical services, there is a strong demand for verification and validation techniques. Yet, the vast majority of smart contracts lack any kind of formal specification, which is essential for establishing their correctness. In this survey, we investigate formal models and specifications of smart contracts presented in the literature and present a systematic overview to understand the common trends. We also discuss the current approaches used in verifying such property specifications and identify gaps with the hope to recognize promising directions for future work. Energy Market Authority (EMA) Ministry of Education (MOE) National Research Foundation (NRF) This research is partially supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (Award No. 2018-T1-002-069) and Tier 2 (Award No. MOE2018-T2-1-068), and by the National Research Foundation, Singapore, and the Energy Market Authority, under its Energy Programme (EP Award No. NRF2017EWT-EP003-023).

An integral sliding mode control method for uncertain Takagi–Sugeno fuzzy systems is investigated in this paper. Considering the time-varying property of the fuzzy system control matrix , a dimensional-varying integral sliding mode controller is proposed. With a membership function piecewise linearization technique, the gain matrices of equivalent control law are derived. Then a dimension switching sliding model control scheme is designed to close the control loop. As a result, traditional restrictions on input matrix can be further relaxed. Finally, a numerical and a Diesel Engine Air-Path control examples are provided to certificate the merits and effectiveness of the proposed approach.

We present a new gripper and exploration approach that uses a finger with very low reflected inertia for probing and then grasping objects. The finger employs a transparent transmission, resulting in a light touch when contact occurs. The finger elements are stiff and mounted on precise Cartesian axes for accurate proprioceptive sensing. Experiments show that the finger can safely move faster into contacts than industrial parallel jaw grippers or even most force-controlled grippers with backdrivable transmissions. This property allows rapid proprioceptive probing of objects. Contact information is leveraged to execute grasping actions with a contact-first strategy and to reduce environment state uncertainty. We evaluate a particle filtering algorithm that inputs contact information from either proprioception, or a combination of tactile sensing and proprioception, to estimate object location. Both methods can estimate location within 2 mm; combined tactile sensing and proprioception requires fewer observations.

Ocular axial length (AL) is an important property of eyes used for determining their health prior to surgery. Estimation of AL is also crucial while making artificial lenses to replace impaired natural lenses. However, accurate measurement of AL requires a costly and bulky benchtop optical system. The complex structural features of eyes can be captured by fundus images, which can be easily captured nowadays with portable cameras. Here, we suggest a deep learning method for predicting AL based on fundus images with evidence of decision. This visual interpretation of predictions is achieved by post-processing, separated from the training process, to ensure that the architecture can be freely designed. Through the visualization technique, discriminative regions on input images can be localized to demonstrate specific areas of interest for predictions. In the experiments, we found a significant relationship between the fundus images and AL with achieving a coefficient of determination ( R2 ) of 0.67 and accuracy of 90%, within an error margin of $ \pm 1$ mm. Furthermore, visual evidence proves that the network uses consistent regions for predicting AL. The visual results of this study also point to a link between AL and biological structure of eyes, which paves the way for future research.

The reservoir computing (RC) has recently gained considerable attention in practice and many methods have been developed to study its internal mechanism. However, the specific role played by the reservoir nodes of RC in time series prediction is still to be defined. An interpretable RC model wherein its reservoir network is designated as the directed acyclic network (DAN) is proposed with focus on time series prediction in this paper. In virtue of asymmetric transitivity and hierarchical structure of DAN, we present a directed network embedding method to identify the latent memory property of each node in the DAN. Such memory property is utilized to characterize the roles played by the reservoir nodes on the prediction performance of the RC. Meanwhile, it can also be leveraged to identify the corresponding memory community of DAN. As a result, we demonstrate how the reservoir network structure takes effect on the prediction performance from the perspective of memory community. In addition, two novel hyperparameters with the deterministic meaning are introduced to quantify the influence of the model initialization on the reservoir input so as to facilitate further dissection of the interpretable RC. The experimental results indicate that tuning these hyperparameters, which is explicable in terms of the Taylor expansion of the activation function, serves as an essential step in achieving superior prediction performance. Finally, comparative experiments with some other RC models on various time series benchmarks are also conducted.

The distinguishing property of Reconfigurable Manufacturing Systems (RMS) is that they can rapidly and efficiently adapt to new production requirements, both in terms of their capacity and functionalities. For this type of systems to achieve the desired efficiency, it should be possible to easily and quickly setup and reconfigure all of their components. This includes fixturing jigs that are used to hold workpieces firmly in place to enable a robot to carry out the desired production processes. In this paper, we formulate a constrained nonlinear optimization problem that must be solved to determine an optimal layout of reconfigurable fixtures for a given set of workpieces. The optimization problem takes into account the kinematic limitations of the fixtures, which are built in shape of Sterwart platforms, and the characteristics of the workpieces that need to be fastened into the fixturing system. Experimental results are presented that demonstrate that the automatically computed fixturing system layouts satisfy different constraints typically imposed in production environments.

Despite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

In this paper, exploratory and innovative research is done on the implementation technique of artistic color virtual reality for similarity image recovery. Based on similarity images, a nonlocal natural image before the regular term is proposed to deal with the single-image blind deblurring problem. This paper designs a new artistic color virtual reality realization technology based on similarity image restoration, which exploits the low-rank property between nonlocal similarity blocks in images and combines a strong convex term to enhance the convexity of the artistic color virtual reality model. We analyze virtual reality interaction design from the perspective of art color design, sort out the concept and content of design, analyze the elements, design principles, and evaluation criteria included in virtual reality interaction art color design, and explore the conceptual principles of virtual reality interaction art color design. A full understanding of the characteristics of the medium of the virtual reality interaction can help us to better use this medium as a tool to create works that aim to bring higher quality and experiential feeling with a perceptual communication method that is closer to natural interaction. Combining the power of technology, artistic colourful thinking, and a design approach paves the way forward. The study shows that virtual reality technology can effectively improve the status quo and promote the cultivation of professional practice ability in art color design, which is conducive to the cultivation of applied design talents.

Direct application of bio-oil from fast pyrolysis as a fuel has remained a challenge due to its undesirable attributes such as low heating value, high viscosity, high corrosiveness and storage instability. Solvent addition is a simple method for circumventing these disadvantages to allow further processing and storage. In this work, computer-aided molecular design tools were developed to design optimal solvents to upgrade bio-oil whilst having low environmental impact. Firstly, target solvent requirements were translated into measurable physical properties. As different property prediction models consist different levels of structural information, molecular signature descriptor was used as a common platform to formulate the design problem. Because of the differences in the required structural information of different property prediction models, signatures of different heights were needed in formulating the design problem. Due to the combinatorial nature of higher-order signatures, the complexity of a computer-aided molecular design problem increases with the height of signatures. Thus, a multi-stage framework was developed by developing consistency rules that restrict the number of higher-order signatures. Finally, phase stability analysis was conducted to evaluate the stability of the solvent-oil blend. As a result, optimal solvents that improve the solvent-oil blend properties while displaying low environmental impact were identified.

Traffic accidents forecasting represents a major priority for traffic governmental organisms around the world to ensure a decrease in life, property, and economic losses. The increasing amounts of ...

A parameter is a numerical or other measurable factor whose values characterize a system. Connectivity parameters have indispensable role in the analysis of connectivity of networks. Strength of a cycle in an unweighted graph is always one. But, in a fuzzy graph, strengths of cycles may vary even for a given pair of vertices. Cyclic reachability is a property that determines the overall connectedness of a network. This article introduces two connectivity parameters namely, cyclic connectivity index (CCI) and average CCI (ACCI) of fuzzy graphs, which can be used to represent the cyclic reachability. CCI of fuzzy graph theoretic structures, such as trees, blocks, $\theta$ -fuzzy graphs, and complete fuzzy graphs, are discussed. Vertices of a fuzzy graph are classified into three categories in terms of ACCI and their characterizations are obtained. Three algorithms are proposed. One of them is to help find CCI and ACCI of a given fuzzy graph. Another helps to identify the nature of vertices and the third to enhance the existing ACCI of a fuzzy graph. Also, future directions in the study of CCI and ACCI are proposed.

For the purpose of emulating realistic ray-based channels more exactly, two novel sum-of-cisoids (SOC) channel simulator parameters computation strategies, i.e., the bidirectional allocation (BA) and the simplified forward allocation (FA) methods, are proposed in this letter. Compared with the classical equal power method, the asymmetric computing property of these two methods further improves the spatial correlation emulation accuracy and significantly mitigates the intra-cluster correlation of sub-paths. As shown in the simulation results, both BA and FA methods enable the statistical spatial-temporal characteristics of emulated channel match extremely well with that of the target channel. More important, the proposed processes are deterministic, and will not introduce randomness in different realizations. Hence the proposed methods are always ergodic with arbitrary angle spreads.

This brief proposes an adaptive observer based on the super-twisting algorithm (STA) and its discrete-time realization with a predefined convergence time. In contrast to conventional adaptive STA that tries to adaptively reduces the gain sizes as much as possible in accordance with external disturbances, the proposed adaptive observer increases the gain sizes such that the convergence time is ensured to be within the predefined convergence-time period. The numerical chattering associated with these large gains is suppressed by employing the proposed discrete-time realization based on an implicit Euler discretization method. While keeping the property of predefined convergence time, the observation precision of the proposed discrete-time scheme is consistent with the STA, i.e., standard asymptotical second-order accuracy level. The superiority of the adaptive observer and its realization scheme is demonstrated through a circuit system example.

The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These challenges are typically addressed using multi-column structures in existing methods. However, such an approach does not provide consistent improvement and transferability due to limited ability in capturing multi-scale features, sensitivity to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scalere-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT). Furthermore, in order to enlarge the diversity of densities, a randomly integrated loss is presented for training our single-branch method. Extensive experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property.

Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective -- selecting the smallest group of neurons for which the response remains equivalent to the original network -- has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an L2-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code is publicly available., Comment: Accepted at CVPR 2021 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data streams, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach, which we call MVCAPA, is shown to consistently estimate the number and location of the collective anomalies, a property that has not previously been shown for competing methods. MVCAPA can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation.

In this article, a novel sampled-data control method is proposed for direct current to direct current converter. According to its switching property, the direct current to direct current converter is described as a switched affine system. A novel error tracking switching law is designed based on the multi-Lyapunov functions method and sampled-data control strategy with variant sampling intervals. The sufficient condition concerning the state of the constructed switched affine system converging to a finite region is developed, which guarantees the outcome voltage can approach the desired value and achieve the voltage adjustment. Based on it, the condition under uncertain parameters is developed as well, which would be more desirable in applications. The effectiveness of the proposed method is verified by numerical simulations. The proposed method is also applicable to other types of power converters.

Several methods for dynamically adapting the local consistency property applied by a CP solver during search have been put forward in recent and older literature. We propose the classification of such methods in three categories depending on the level of granularity where decisions about which local consistency property to apply are taken: node, variable, and value oriented. We then present a detailed review of existing methods from each category, and evaluate them theoretically according to several criteria. Taking one recent representative method from each class, we then perform an experimental study. Results show that simple variable and value oriented methods are quite efficient when the older dom/ddeg heuristic is used for variable ordering, while a carefully tuned node oriented method does not seem to offer notable improvement compared to standard arc consistency propagation. In contrast, under the more realistic setting of dom/wdeg, the variable and value oriented methods cannot compete with standard propagation, while the node oriented method is very efficient. Finally, we obtain a new adaptive propagation method by integrating the variable and value oriented approaches and adding an amount of randomization The resulting method is simple, competitive, and almost parameter-free.

The connectivity of networks has been widely studied in many high-impact applications, ranging from immunization, critical infrastructure analysis, social network mining, to bioinformatic system studies. Regardless of the end application domains, connectivity minimization has always been a fundamental task to effectively control the functioning of the underlying system. The combinatorial nature of the connectivity minimization problem imposes an exponential computational complexity to find the optimal solution, which is intractable in large systems. To tackle the computational barrier, greedy algorithm is extensively used to ensure a near-optimal solution by exploiting the diminishing returns property of the problem. Despite the empirical success, the theoretical and algorithmic challenges of the problems still remain wide open. On the theoretical side, the intrinsic hardness and the approximability of the general connectivity minimization problem are still unknown except for a few special cases. On the algorithmic side, existing algorithms are hard to balance between the optimization quality and computational efficiency. In this article, we address the two challenges by (1) proving that the general connectivity minimization problem is NP-hard and is the best approximation ratio for any polynomial algorithms, and (2) proposing the algorithm CONTAIN and its variant CONTAIN + that can well balance optimization effectiveness and computational efficiency for eigen-function based connectivity minimization problems in large networks.

Neurobiologists recently found the brain can use sudden emerged channels to process information. Based on this finding, we put forward a question whether we can build a computation model that is able to integrate a sudden emerged new type of perceptual channel into itself in an online way. If such a computation model can be established, it will introduce a channel-free property to the computation model and meanwhile deepen our understanding about the extendibility of the brain. In this article, a biologically inspired neural network named artificial evolution (AE) network is proposed to handle the problem. When a new perceptual channel emerges, the neurons in the network can grow new connections to connect the emerged channel according to the Hebb rule. In this article, we design a sensory channel expansion experiment to test the AE network. The experimental results demonstrate that the AE network can handle the sudden emerged perceptual channels effectively.

Subspace clustering techniques divide the data set into various groups, where each group is represented by a subset of features known as subspace feature set, that are relevant to the objects in the group. The grouping is performed in such a way that similar objects are placed in the same group, whereas dissimilar objects are in different groups. Most of the previous subspace clustering methods have not considered an object to be a part of more than one cluster. However, in many real-life situations, an object may belong to more than one cluster. Moreover , subspace clustering algorithms developed in the past are based on single objective optimization framework which limits in optimizing only a particular shape or property of the clusters. To this end, we have developed an evolutionary-based overlapped subspace clustering method using multi-objective optimization framework. Various mutation operators have been used to explore the search space effectively. Multiple objectives that have been optimized simultaneously in this algorithm are ICC-index, MNR-index and PSM-index. The developed algorithm is evaluated with 7 real-life and 16 synthetic data sets. However, to check the efficiency of using multiple objectives, the proposed algorithm is also tested with 3 big data sets. An application of the proposed method is shown in bi-clustering the gene expression data. The results obtained using these 23 data sets and 3 big data sets are compared with many state-of-the-art algorithms. The comparative study illustrates the efficacy of the proposed algorithm over state-of-the-art algorithms.

Node clustering has attracted widespread concern due to the fact that graph structure helps to seek the geometric structure of data. Existing graph convolutional networks (GCNs) based node clustering methods usually contain two steps. First, learning latent nodes representations. Second, inferring the unknown labels. Despite the promising preliminary results, it cannot take full advantage of the information embedded in current pseudo clustering labels, resulting in suboptimal performance. In fact, it is the truth we do not know the true label of the node in clustering, but the pseudo clustering label of the node generated by each iteration is known. Under this condition, although the pseudo labels of some nodes are incorrect, the partial correct label information is useful and significant. Moreover, GCNs based node clustering aims to learn satisfactory low dimensional node representations which are usually used for clustering. However, existing GCNs based clustering methods fail to take into account distribution consistency between the raw data space and the latent space of nodes representations, resulting in insufficient representations. To address the above problems, we propose a novel GCN-based node clustering method. By introducing a self-supervision module, it can employ the good property of pseudo clustering labels to self-supervise the learning of node representations. Moreover, a latent distribution preserving term, which is measured by KL divergence, is employed to help the latent representations of the same sample to further have a consistent distribution in dimension space as well as in the original dimension space. We formulate the above two concerns into a unified optimization framework. Experimental results on several public datasets indicate our method is effective compared with the state-of-the-art node clustering methods.

Head pose estimation (HPE) under active infrared (IR) illumination has attracted much attention in the fields of computer vision and machine learning. However, IRHPE often suffers from the problems of low-quality IR images and ambiguous head pose. To tackle these issues, we propose a novel nonuniform Gaussian-label distribution learning network (NGDNet) for the HPE task. First, we reveal the essential properties from two different perspectives: 1) two head pose images change differently in pitch and yaw directions with the same angle increasing on the central pose; 2) the IR head pose variation first increases and then decreases in the pitch direction. Subsequently, the first property indicates the pose image label as a nonuniform label distribution (Gaussian function) with different long and short axes. The second property is leveraged to determine the distribution size in accordance with the similarities of adjacent hand poses. Lastly, the proposed NGDNet is verified on a new IRHPE dataset, which is built by our research group. Experimental results on several datasets demonstrate the effectiveness of the proposed model. Compared with conventional algorithms, our NGDNet model achieves state-of-the-art performance with 77.39% on IRHPE, 99.08% on CAS-PEAL-R1, and 87.41% on Pointing’04. Our code is publicly available at https://github.com/TingtingSL/NGDNet.

An ultrasonic positioning system (UPS) has demonstrated its high accuracy for years. However, few of the developed solutions have been deployed in practice to satisfy the localization demand of today’s smart devices, which lack ultrasonic sensors and were considered as being “deaf” to ultrasound. A recent finding demonstrates that ultrasound may be audible to the smart devices under certain conditions due to their microphone’s nonlinearity. Inspired by this insight, this work revisits the ultrasonic positioning technique and builds a practical UPS, called UPS+, for ultrasound-incapable smart devices. The core concept is to deploy two types of indoor beacon devices, which will advertise ultrasonic beacons at two different ultrasonic frequencies respectively. Their superimposed beacons are downconverted to a low-frequency by exploiting the nonlinearity effect at the receiver’s microphone. This underlying property functions as an implicit ultrasonic downconverter without inflicting harm to the hearing system of humans. We demonstrate UPS+, a fully functional UPS prototype, with centimeter-level localization accuracy using custom-made beacon hardware and well-designed algorithms.

Blur is a key property in the perception of COVID‐19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection regions, since different blur degrees cause the magnitudes change of TM on infection regions image, blur of infection regions can be captured by TM. With the above observation, a longitudinal objective quantitative evaluation method for COVID‐19 disease progression based on TM is proposed. COVID‐19 disease progression CT image database (COVID‐19 DPID) is built to employ radiologist subjective ratings and manual contouring, which can test and compare disease progression on the CT images acquired from the same patient over time. Then the images are preprocessed, including lung automatic segmentation, longitudinal registration, slice fusion, and a fused slice image with region of interest (ROI) is obtained. Next, the gradient of a fused ROI image is calculated to represent the shape. The gradient image of fused ROI is separated into same size blocks, a block energy is calculated as quadratic sum of non‐direct current moment values. Finally, the objective assessment score is obtained by TM energy‐normalized applying block variances. We have conducted experiment on COVID‐19 DPID and the experiment results indicate that our proposed metric supplies a satisfactory correlation with subjective evaluation scores, demonstrating effectiveness in the quantitative evaluation for COVID‐19 disease progression.

International audience; Spatial autocorrelation is inherent to remotely sensed data. Nearby pixels are more similar than distant ones. This property can help to improve the classification performance, by adding spatial or contextual features into the model. However, it can also lead to overestimation of generalisation capabilities, if the spatial dependence between training and test sets is ignored. In this paper, we review existing approaches that deal with spatial autocorrelation for image classification in remote sensing and demonstrate the importance of bias in accuracy metrics when spatial independence between the training and test sets is not respected. We compare three spatial and non-spatial cross-validation strategies at pixel and object levels and study how performances vary at different sample sizes. Experiments based on Sentinel-2 data for mapping two simple forest classes show that spatial leave-one-out cross-validation is the better strategy to provide unbiased estimates of predictive error. Its performance metrics are consistent with the real quality of the resulting map contrary to traditional non-spatial cross-validation that overestimates accuracy. This highlight the need to change practices in classification accuracy assessment. To encourage it we developped Museo ToolBox, an open-source python library that makes spatial cross-validation possible.

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.

Personal Health Record (PHR) is an online electronic application used by patients to store, retrieve and share their health information in a private and secure environment. While outsourcing the PHR into cloud environment, there exist issues in privacy while storing, searching and sharing of health information. To overcome these issues, an efficient retrieval of health records using Multi Keyword Searchable Attribute Based Encryption (MK-SABE) is proposed. To manage the increasing PHR data in the cloud, an Authorized File Level Deduplication technique is adopted. It eliminates redundant files, thereby reducing the communication overhead. Moreover, PHR data is encrypted before outsourcing and to perform searching over encrypted data, the proposed MK-SABE introduces Conjunctive Multi Keyword Searchable Attribute Based Encryption (CM-SABE). This maintains the searchable property after encryption for efficient retrieval of health files using range query. Further to ensure the trustworthiness while sharing the sensitive data, MK-SABE introduces the Location Based Encryption (LBE) and Dynamic Location Based ReEncryption (DLBRE) technique to provide additional security. From the experimental analysis, it is proved that the proposed MK-SABE reduces the storage complexity by 5%, keyword search time by 25% and improves the overall performance of PHR by 40% compared to the existing schemes.

The Crescent Method is a recently proposed decision method that can consider problems involving both risk and preferences. In this work, we elaborately discuss why and how to use this interesting method in decision making. We present its advantages in accurately merging both types of decisions. However, not all preferences are suitable to use with the Crescent Method and for melting with probability information. This study systematically proposes and analyzes those subclasses of preference vectors that are suitable for the Crescent Method. Unimodal preferences are shown to be suitable for the Crescent Method, but they are not closed under convex combination. Pure crescent preferences are shown to be suitable for the Crescent Method and to have the property of convexity. The interrelations and inclusions of certain different subclasses of preference vectors along with some examples are presented in detail.

Image dehazing has evolved into an attractive research field in the computer vision community in the past few decades. Previous traditional approaches attempt to design energy-based objective functions. However, they cannot accurately express the intrinsic characteristics of the images, posing weak adaptation ability for real-world complex scenarios. More recently, deep learning techniques for image dehazing have matured and become more reliable, showing outstanding performance. Nevertheless, these methods heavily depend on training data, restricting their application ranges. More importantly, both traditional and deep learning approaches all ignore a common issue, noises/artifacts always appear in the recovery process. To this end, a new Hadamard-Product (HP) model is proposed, which consists of a series of data-driven priors. Based on this model, we derive a Learnable Hadamard-Product-Propagation (LHPP) by cascading a series of principle-inspired guidance and recovery modules. In which, the principle-inspired guidance related to transmission is endowed the smoothness property, the other recovery module satisfies the distribution of natural images. The Hadamard-product-based propagations is generated in our developed learnable framework for the task of image dehazing. In this way, we can eliminate noises/artifacts in the recovery procedure to obtain the ideal outputs. Subsequently, since the generality of our HP model, we successfully extend our LHPP to settle low-light image enhancement and underwater image enhancement problems. A series of analytical experiments are performed to verify our effectiveness. Plenty of performance evaluations on three complex tasks fully reveal our superiority against multiple state-of-the-art methods.