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Interdependent dimensions including categorical and continuous variables can be seen commonly as heterogeneous behavioral data in the real world. Mixed-type objects are more or less associated in terms of certain coupling relationships. The usual representation of such behavioral data is an information table with explicit behavior exterior dimensions (i.e. the original attributes to describe data heterogeneity), assuming the independence of dimensions and the independence of objects. However, both variables and objects are actually very often interdependent on one another either explicitly or implicitly in functional and semantic manners. Limited research has been done in analyzing such interactions among dimensions and those relationships among objects, leading to the learning results to be more local than global. This paper proposes the interdependence analysis to capture the functional multifarious relationships among attributes and among objects in heterogeneous data by addressing the coupling context and coupling weights in unsupervised learning. Such global couplings consider the interactions within discrete dimensions, within numerical attributes and across them, as well as the relationships within an individual object and between multiple objects, to form the attribute-based and object-based coupled data representation schemes based on feature conversion and neighborhood calculation. In addition, we interpret both the representation models via implicit behavior interior dimensions (i.e. the newly defined attributes to model data interdependence) to explain the intrinsic rationales for the superiority of our proposed methods. This work explicitly models the coupling of multiple attributes and the coupling of multiple objects for heterogeneous data sets, demonstrated by various data mining and machine learning applications, such as cluster structure analysis, data clustering evaluation, and data density comparison. Moreover, the sensitivity study is carried out to tune the neighborhood parameter and weight parameter, and the scalability analysis is explored to test the robustness of both models. Extensive experiments on a series of synthetic data sets and multiple UCI data sets show that our proposed framework can effectively capture the global couplings of both heterogeneous variables and mixed-type objects, and is superior to the traditional way as well as the state-of-the-art approaches, which is also verified by statistical analysis. [ABSTRACT FROM AUTHOR]

Wireless communications play an important role in ensuring ease of access to shared electronic health records (EHR) across health service providers which is essential and significant for prompt patients’ care, especially in cases of emergency medical conditions. With the need to support anytime, anywhere access to, potentially bandwidth hungry, medical records, electronic healthcare applications will continue to benefit from advanced wireless network technologies such as 5G and beyond. With sharing, it is crucial to provide patients with security and privacy guarantees, and allow them to certain control of access to their data. Existing solutions mostly assume that patients are available to authorize requests to access their EHR, which is impractical as the patient may be unconscious. This paper proposes a secure and privacy protecting protocol whereby the patient can pre-delegate the authorization for the access of his/her EHR. Our patient(user)-centric proposal combines Self-Sovereign Identity (SSI) concepts and model with Secure Multi-party Computation (SMPC) and Threshold Cryptography (TC) to enable secure identity and authorization verification. A block cipher encryption sharing approach is adopted for the threshold SMPC which extends the AES-GCM symmetric encryption model into a full-fledged cryptographic platform. Two mechanisms are implemented for the block cipher encryption, namely XOR and Cascade, and experiments are conducted to compare them. We conclude that the XOR mechanism can scale for larger thresholds, while Cascade performed better for a lower threshold (≤ 3). This paper also performs a threat analysis of the protocol and approach, and validates its correctness and complexity. We conclude that the approach can achieve the security and privacy protection of the patient’s personal EHR, as well as the autonomy of the patient to control the authorization for the access and sharing. [ABSTRACT FROM AUTHOR]

Bias in peer review entails systematic prejudice that prevents accurate and objective assessment of scientific studies. The disparity between referees' opinions on the same paper typically makes it difficult to judge the paper's quality. This article presents a comprehensive study of peer review biases with regard to 2 aspects of referees: the static profiles (factual authority and self-reported confidence) and the dynamic behavioral context (the temporal ordering of reviews by a single reviewer), exploiting anonymized, real-world review reports of 2 different international conferences in information systems / computer science. Our work extends conventional bias research by considering multiple biases occurring simultaneously. Our findings show that the referees' static profiles are more dominant in peer review bias when compared to their dynamic behavioral context. Of the static profiles, self-reported confidence improved both conference fitness and impact-based bias reductions, while factual authority could only contribute to conference fitness-based bias reduction. Our results also clearly show that the reliability of referees' judgments varies along their static profiles and is contingent on the temporal interval between 2 consecutive reviews. [ABSTRACT FROM AUTHOR]

Eigenvalue decomposition is widely used in dimensionality reduction for knowledge engineering, in particular principal component analysis and other similar spectral methods. Traditional eigenvalue decomposition algorithms for decomposing a matrix of size n × n are usually of complexity O(n3), due to a bottleneck in using Householder/Givens transforms to convert a general matrix to a tri-diagonal one. It is proposed in this article a new algorithm that takes only O(n2.584963 log 1/ɛ) computational complexity to achieve accuracy ɛ of eigenvalue decomposition for any ɛ > 0 . The basic idea of our algorithm is to convert a matrix into a diagonal form in multi-scale divide and conquer scheme, and the conversion is to iteratively and recursively apply two phases of operations called diagonal attractions and diagonal absorptions respectively. In a diagonal attraction, it attracts the off-diagonal entries to make the entries nearer to the diagonal larger in magnitude than those farther away from the diagonal. In a diagonal absorption, it absorbs the near-to-diagonal nonzero entries into the diagonal. In such a scheme, no Householder or Givens transforms are involved. Moreover, diagonal attractions and diagonal absorptions can be implemented with fast matrix multiplications. The scheme’s divide and conquer pattern also allows our algorithm to be easily mapped to modern computer hardware. Our algorithm also complements well the family of randomized eigenvalue/SVD algorithms using sampling techinques, which are of complexity O(nα polylog (1/ɛ)) with small α but very large overheads in the polylog. Their strength in the small exponent α of n in complexity was easily cancelled by the exploding overheads in the polylog. Now, with their low-accuracy estimate refined by our algorithm for high accuracy, their strength can be boosted significantly. [ABSTRACT FROM AUTHOR]

Entity alignment plays an essential role in the integration of knowledge graphs (KGs) as it seeks to identify entities that refer to the same real-world objects across different KGs. Recent research has primarily centred on embedding-based approaches. Among these approaches, there is a growing interest in graph neural networks (GNNs) due to their ability to capture complex relationships and incorporate node attributes within KGs. Despite the presence of several surveys in this area, they often lack comprehensive investigations specifically targeting GNN-based approaches. Moreover, they tend to evaluate overall performance without analysing the impact of individual components and methods. To bridge these gaps, this paper presents a framework for GNN-based entity alignment that captures the key characteristics of these approaches. We conduct a fine-grained analysis of individual components and assess their influences on alignment results. Our findings highlight specific module options that significantly affect the alignment outcomes. By carefully selecting suitable methods for combination, even basic GNN networks can achieve competitive alignment results. [ABSTRACT FROM AUTHOR]

With advances in networks, Artificial Intelligence (AI), and the Internet of Things, humanoid robots are rising in many areas, including elderly care, companion, education, and services in public sectors. Given their sensing and communication functionality, information leakage and unauthorized access will be of big concern. Very often, authentication techniques for service robots, especially those related to behavioral identification, have been developed, in which behavior models are created using raw data from sensors. However, behavioral-based authentication and re-authentication is still an open area for research, including cold start problems, accuracy, and uncertainty. This paper proposes a hierarchical implicit authentication system by joint built-in sensors and trust evaluation, coined sAuth, which exploits sensor data-based sliding window trust model to identify the service robot and its expected users. In order to mitigate the fluctuations of identification results in the real world environment, the trust evaluation is computed via combining the weighted intermediate identification probability of various small sliding windows. The performance of sAuth is evaluated under different scenarios where we show that (i) approximately 5–7% higher accuracy and 2–18% lower equal error rate can be achieved by our method compared to other works; and (ii) the hierarchical scheme with joint sensors and trust sliding windows improves the authentication accuracy significantly by comparing it with only sensor-based authentication. [ABSTRACT FROM AUTHOR]

Group behavior interactions, such as multirobot teamwork and group communications in social networks, are widely seen in both natural, social, and artificial behavior-related applications. Behavior interactions in a group are often associated with varying coupling relationships, for instance, conjunction or disjunction. Such coupling relationships challenge existing behavior representation methods, because they involve multiple behaviors from different actors, constraints on the interactions, and behavior evolution. In addition, the quality of behavior interactions are not checked through verification techniques. In this paper, we propose an ontology-based behavior modeling and checking system (OntoB for short) to explicitly represent and verify complex behavior relationships, aggregations, and constraints. The OntoB system provides both a visual behavior model and an abstract behavior tuple to capture behavioral elements, as well as building blocks. It formalizes various intra-coupled interactions (behaviors conducted by the same actor) via transition systems (TSs), and inter-coupled behavior aggregations (behaviors conducted by different actors) from temporal, inferential, and party-based perspectives. OntoB converts a behavior-oriented application into a TS and temporal logic formulas for further verification and refinement. We demonstrate and evaluate the effectiveness of the OntoB in modeling multirobot behaviors and their interactions in the Robocup soccer competition game. We show, that the OntoB system can effectively model complex behavior interactions, verify and refine the modeling of complex group behavior interactions in a sound manner. [ABSTRACT FROM AUTHOR]

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SE-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SE-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. (1) A salient subsequence chain (SSC) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. (2) A linear discriminant selection (LDS) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SE-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods. • Proposes a novel SE-shapelets method for semi-supervised time series clustering. • Defines a new SSC method that can avoid un-informative candidate shapelets. • Designs an effective LDS algorithm to discovery representative shapelets. [ABSTRACT FROM AUTHOR]

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein–protein interaction identification, including human–pathogen protein–protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human–bacterium protein–protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host–pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs. [ABSTRACT FROM AUTHOR]

Power load fluctuation is generally agreed to be a non-stationary stochastic process. The Fractional Brownian Motion (FBM) model is proposed to forecast a non-stationary time series with high accuracy. Computation of the Hurst exponent (H) for the power load data series using the Rescaled Range Analysis (R/S) in this study. This method is used to verify the Long-Range Dependent (LRD) characteristics of non-stationary power load data. For the real power load, however, H exponent takes on the self-similarity characteristics in a certain finite range of intervals, the global self-similarity is very rare to exist. The H exponent of the self-similarity usually has more than one value. We generalize multifractional H (t) to replace constant H. To improve the forecasting accuracy, the H (t) is optimized by the Quantum-Behaved Particle Swarm Optimization (QPSO). Once the optimal H (t) is obtained, then the optimal and parameters in the multi-Fractional Brownian Motion (mFBM) model can be deduced to forecast next power load data series with a higher accuracy. • Fractional Brownian Motion (FBM)as Stochastic model forecasts a power load series. • The parameters of the FBM are estimated by R/S and MSE methods. • The parameters of the FBM are optimized by PSO and QPSO. • The prediction errors of RNN,FBM,PSO + FBM,QPSO + FBM were analyzed by using box-plot and norm-plot. [ABSTRACT FROM AUTHOR]

A key goal of the Semantic Web is to shift social interaction patterns from a producer-centric paradigm to a consumer-centric one. Treating customers as the most valuable assets and making the business models work better for them are at the core of building successful consumer-centric business models. It follows that customizing business processes constitutes a major concern in the realm of a knowledge-pull-based human semantic Web. This paper conceptualizes the customization of service-based business processes leveraging the existing knowledge of Web services and business processes. We represent this conceptualization as a new Extensible Markup Language (XML) markup language Web Ontology Language-Business Process Customization (OWL-BPC), based on the de facto semantic markup language for Web-based information [Web Ontology Language (OWL)]. Furthermore, we report a framework, built on OWL-BPC, for customizing service-based business processes, which supports customization detection and enactment. Customization detection is enabled by a business-goal analysis, and customization enactment is enabled via event–condition–action rule inference. Our solution and framework have the following capabilities in dealing with inconsistencies and misalignments in business process interactions: 1) resolve semantic mismatch of process parameters; 2) handle behavioral mismatches which may or may not be compatible; and 3) process misaligned rendezvous requirements. Such capabilities are applicable to business processes with heterogeneous domain ontology. We present an architectural description of the implementation and a walk-through of an example of solving a customization problem as a validation of the proposed approach. [ABSTRACT FROM AUTHOR]

The approach to model autocorrelation functions of real-traffic traces in communication networks is presented based on optimal approximation in Hilbert space. The verifications are carried out with the real-traffic traces. [Copyright &y& Elsevier]

Facial Expression Recognition (FER) can be widely applied to various research areas, such as mental diseases diagnosis and human social/physiological interaction detection. With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. Although the laboratory-controlled FER systems achieve very high accuracy, around 97%, the technical transferring from the laboratory to real-world applications faces a great barrier of very low accuracy, approximately 50%. In this survey, we comprehensively discuss three significant challenges in the unconstrained real-world environments, such as illumination variation, head pose, and subject-dependence, which may not be resolved by only analysing images/videos in the FER system. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. We introduce three categories of sensors that may help improve the accuracy and reliability of an expression recognition system by tackling the challenges mentioned above in pure image/video processing. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for example in illumination variation and position shift situation. The last is target-focused sensors, such as infrared thermal sensors, which can facilitate the FER systems to filter useless visual contents and may help resist illumination variation. Also, we discuss the methods of fusing different inputs obtained from multimodal sensors in an emotion system. We comparatively review the most prominent multimodal emotional expression recognition approaches and point out their advantages and limitations. We briefly introduce the benchmark data sets related to FER systems for each category of sensors and extend our survey to the open challenges and issues. Meanwhile, we design a framework of an expression recognition system, which uses multimodal sensor data (provided by the three categories of sensors) to provide complete information about emotions to assist the pure face image/video analysis. We theoretically analyse the feasibility and achievability of our new expression recognition system, especially for the use in the wild environment, and point out the future directions to design an efficient, emotional expression recognition system. [ABSTRACT FROM AUTHOR]