Reviewerly/exHarmony huggingface dataset:
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Deep learning models have been widely used for anomaly detection in surveillance videos. Typical models are equipped with the capability to reconstruct normal videos and evaluate the reconstruction errors on anomalous videos to indicate the extent of abnormalities. However, existing approaches suffer from two disadvantages. Firstly, they can only encode the movements of each identity independently, without considering the interactions among identities which may also indicate anomalies. Secondly, they leverage inflexible models whose structures are fixed under different scenes, this configuration disables the understanding of scenes. In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different levels of graph representations. High-level graph representations encode the trajectories of people and the interactions among multiple identities while low-level graph representations encode the local body postures of each person. Furthermore, we propose to weightedly combine multiple branches that are better at different scenes. An improvement over single-level graph representations is achieved in this way. An understanding of scenes is achieved and serves anomaly detection. High-level graph representations are assigned higher weights to encode moving speed and directions of people in low-resolution videos while low-level graph representations are assigned higher weights to encode human skeletons in high-resolution videos. Experimental results show that the proposed HSTGCNN significantly outperforms current state-of-the-art models on four benchmark datasets (UCSD Pedestrian, ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable parameters.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks

This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by exchanging the locally trained models of mobile devices. By adopting SNNs as local models, FL can flexibly cope with the time-varying energy capacities of mobile devices. Combining FL and SNNs is however non-trivial, particularly under wireless connections with time-varying channel conditions. Furthermore, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, so are ill-suited to FL. Motivated by this, we propose a communication and energy efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models. By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations, while avoiding the inter-width interference during back propagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also can counteract non-IID data distributions and poor channel conditions, which is also corroborated by simulations.
Optimal Gain Sensing of Quantum-Limited Phase-Insensitive Amplifiers

Phase-insensitive optical amplifiers uniformly amplify each quadrature of an input field and are of both fundamental and technological importance. We find the quantum limit on the precision of estimating the gain of a quantum-limited phase-insensitive amplifier using a multimode probe that may also be entangled with an ancilla system. In stark contrast to the sensing of loss parameters, the average photon number N and number of input modes M of the probe are found to be equivalent and interchangeable resources for optimal gain sensing. All pure-state probes whose reduced state on the input modes to the amplifier is diagonal in the multimode number basis are proven to be quantum optimal under the same gain-independent measurement. We compare the best precision achievable using classical probes to the performance of an explicit photon-counting-based estimator on quantum probes and show that an advantage exists even for single-photon probes and inefficient photodetection. A closed-form expression for the energy-constrained Bures distance between two product amplifier channels is also derived.
FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data

FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) normal logic program (NLP) rule set for classification tasks. We present an improved FOLD-R algorithm, called FOLD-R++, that significantly increases the efficiency and scalability of FOLD-R by orders of magnitude. FOLD-R++ improves upon FOLD-R without compromising or losing information in the input training data during the encoding or feature selection phase. The FOLD-R++ algorithm is competitive in performance with the widely-used XGBoost algorithm, however, unlike XGBoost, the FOLD-R++ algorithm produces an explainable model. FOLD-R++ is also competitive in performance with the RIPPER system, however, on large datasets FOLD-R++ outperforms RIPPER. We also create a powerful tool-set by combining FOLD-R++ with s(CASP)—a goal-directed answer set programming (ASP) execution engine—to make predictions on new data samples using the normal logic program generated by FOLD-R++. The s(CASP) system also produces a justification for the prediction. Experiments presented in this paper show that our improved FOLD-R++ algorithm is a significant improvement over the original design and that the s(CASP) system can make predictions in an efficient manner as well.
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points

Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. Through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.
Parallel analysis of Ethereum blockchain transaction data using cluster computing

Abstract Ability to perform fast analysis on massive public blockchain transaction data is needed in various applications such as tracing fraudulent financial transactions. The blockchain data is continuously growing and is organized as a sequence of blocks containing transactions. This organization, however, cannot be used for parallel graph algorithms which need efficient distributed graph data structures. Using message passing libraries (MPI), we develop a scalable cluster-based system that constructs a distributed transaction graph in parallel and implement various transaction analysis algorithms. We report performance results from our system operating on roughly 5 years of 10.2 million block Ethereum Mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution, token transaction counting, connected components finding and our new parallel blacklisted address trace forest computation algorithm on a 16 node economical cluster set up on the Amazon cloud. Our system is able to construct a distributed graph of 766 million transactions in 218 s and compute the forest of blacklisted address traces in 32 s.
A Hybrid Method for Managing Agile Team in a Distributed Environment

To overcome the shortcomings and difficulties encountered by Agile teams in distributed environment, it is proposed to use a hybrid method based on the methods of eXtreme Programming and Scrumban. The proposed approach enabling to carry out quickly the stage of project initialization, reduces project risks associated with insufficient documentation, and offers events that promote effective communication both within the team and with clients.
A Deep Learning based Recognition System for Yemeni Sign Language

There are more than 466 million people with hearing disabilities in the world. Those people need to communicate with others, get learning and interact with activities around them. Sign language is the bridge to eliminate the gap between them and other people. Developing an automatic system to recognize sign language has a lot of challenges, especially for Yemeni sign language, as there are very few researches touching on this language. In this paper, we propose a new Convolution Neural Network based model for classifying the sign language of Yemen. The System was trained and tested using a dataset that includes 16,192 images gathered from 40 people with different distances and variations. The proposed model uses pre-processing methods to remove noises and reposition the images, etc. The results display that the proposed model achieved 94% accuracy.
Ensuring Academic Integrity of Online Examinations

The need for invigilation in any type of examination is a must to maintain academic integrity. Though e-learning systems have grown rapidly, proctors are yet to supervise a larger group of candidates through a live video stream during the examination. Often, online examinations require human invigilation either by live monitoring or inspection of recorded video to ensure academic integrity. This process is not feasible always since exams can be taken at any time and it involves high cost. Hence, this paper proposes an Online Proctoring System to automate invigilation processes by making use of inputs from a web browser, webcam video feed, and microphone audio feed, without using any external hardware or standalone application. Processing the webcam video is one of the main aspects when deciding the abnormal behavior of the candidate and features including head pose detection, eye gaze detection, liveness detection are used for predicting abnormal behaviors in this work. On the other hand, the audio stream is analyzed to detect activities such as asking for help from a person in the room. The evaluation results imply that combining several results and creating an ensemble platform produces better results.
Revisiting Textual Feature of Bug-Triage Approach

With the increase of software users, programmers use issue tracking systems to manage bug reports and researchers propose bug-triage approaches that assign bug reports to programmers. Programmers assign bug reports often according to their descriptions. Based on by this observation, prior approaches typically use classic natural language processing (NLP) to analyze bug reports. Although the technical choice is straightforward, the true effectiveness of this technical choice is largely unknown. Taking a state-of-the-art approach as an example, we explore the impact of textual features in bug triage. By enabling and disabling the textual features of this approach, we analyze their impacts on assigning thousands bug reports from six widely used open source projects. Our result shows that instead of improving it, textual features in fact reduce the effectiveness. In particular, after we turn off its textual features, the f-scores of the baseline approach are improved by 8%. After manual inspection, we find two reasons to explain our result: (1) classic NLP techniques are insufficient to analyze bug reports, because they are not pure natural language texts and contain other elements (e.g., code samples); and (2) some bug reports are poorly written. Our findings reveal a strong need and sufficient opportunities to explore more advanced techniques to handle these complicated elements in bug reports.
meta.shrinkage: An R Package for Meta-Analyses for Simultaneously Estimating Individual Means

Meta-analysis is an indispensable tool for synthesizing statistical results obtained from individual studies. Recently, non-Bayesian estimators for individual means were proposed by applying three methods: the James–Stein (JS) shrinkage estimator, isotonic regression estimator, and pretest (PT) estimator. In order to make these methods available to users, we develop a new R package meta.shrinkage. Our package can compute seven estimators (named JS, JS+, RML, RJS, RJS+, PT, and GPT). We introduce this R package along with the usage of the R functions and the “average-min-max” steps for the pool-adjacent violators algorithm. We conduct Monte Carlo simulations to validate the proposed R package to ensure that the package can work properly in a variety of scenarios. We also analyze a data example to show the ability of the R package.
Asymmetric Image Encryption Based on Twin Message Fusion

After the digital revolution, it has become imperative for us to protect personal information, one of the most important of this information is the image, the most widely used protection methods are encryption, and despite the research that included this topic, there are still a few problems, including encryption and decryption speed, image encryption size, and how keys can be shared by Safeway. This paper proposes a novel image asymmetric encryption scheme based on Magic Number Fragmentation and El-Gamal Encryption (MNF-G); to begin with, the plain- image is converted into blocks of bits; then, to decimal; finally, the plain-image well be encrypted these blocks by MNF-G algorithm. The experimental result shows that the proposed algorithm can successfully encrypt/decrypt images and it has a good encryption effect. Image encrypted developed by this scheme will be entirely different when compared to the original image file and will be suitable for the secured transmission over the internet. Thus, this model provides an additional level of security.
A Novel Metaheuristic: Fast Jaguar Algorithm

Metaheuristic algorithms play an extremely important role in the computational intelligence and optimization fields. The jaguar algorithm (JA) is a metaheuristic algorithm that has outstanding performances in both exploitation and exploration. This study proposed a novel metaheuristic algorithm, named the fast jaguar algorithm (FJA), which inherits JA’s advantage and significantly enhances its search ability. FJA uses the fast hunting mechanism to identify the trend in the local area effectively, and it only searches for the better side to reduce the computational cost. FJA well uses historical information through the adaptive exploit mechanism, which allows it to efficiently find the better tendency. Then, FJA utilizes the jump mechanism to discover the global optimal solution. FJA comprehensively considers more situations and then makes the right decision to find the optimal solution more precisely. The experiment results demonstrated the robust performance of FJA through function optimization and showed that the efficiency of FJA could outperform JA and other classical metaheuristics in terms of computational cost and stability.
Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults

The OTAGO exercise program is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise program has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.
Camera Setup and OpenPose Software without GPU for Calibration and Recording in Telerehabilitation Use

Home exercises are significant in the rehabilitation process of physiotherapy patients, which lack immediate feedback as to the proper movement and therefore might humper patient treatment. In this paper we are proposing an algorithm for fast tracking of human body movements performed during physiotherapeutic exercises using a simple webcam home setup and common domestically available CPU computing resources. We use OpenPose for detecting body vertices in key frames and a novel vertex tracking algorithm between video frames, which leverages encoded video Motion Vectors (MVs). We show excellent tracking accuracy between frames and x15 reduction in time, as compared to native OpenPose, which would require a Graphical Processing Unit (GPU) to perform in real time. We further provide a design and implementation of a precision camera system consisting of two cameras in the frontal and lateral direction, which were precisely positioned using a laser cross. This system will be also used to verify whether the webcam is able to record with sufficient quality to further image processing analysis. As part of this work, a camera system including supporting calibration and recording scripts was designed and implemented. The cameras triggers were synchronized by wire interconnection and set up by proposed script. In this work we synchronize two cameras and align their frames such that the OpenPose can be applied independently to each of the two channels to measure movements from two different body projections (3D).
Human pose estimation approaches for human activity recognition

Human pose estimation has tangible benefits in the growing trends in computer vision-based technology. Human-computer interactions can be enhanced by the applications of human pose estimation. This survey will include a set of papers for analysis of performance as well as the occlusion. In this paper, we discuss some of the methods used for human pose estimation to come up with a better method and to know the challenges with each method.
Hierarchical clustering for multiple nominal data streams with evolving behaviour

Abstract Over the decade, a number of attempts have been made towards data stream clustering, but most of the works fall under clustering by example approach. There are a number of applications where clustering by variable approach is required which involves clustering of multiple data streams as opposed to clustering data examples in a data stream. Furthermore, a few works have been presented for clustering multiple data streams and these are applicable to numeric data streams only. Hence, this research gap has motivated current research work. In the present work, a hierarchical clustering technique has been proposed to cluster multiple data streams where data are nominal. To address the concept changes in the data streams splitting and merging of the clusters in the hierarchical structure are performed. The decision to split or merge is based on the entropy measure, representing the cluster’s degree of disparity. The performance of the proposed technique has been analysed and compared to Agglomerative Nesting clustering technique on synthetic as well as a real-world dataset in terms of Dunn Index, Modified Hubert $$\varGamma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Γ</mml:mi> </mml:math> statistic, Cophenetic Correlation Coefficient, and Purity. The proposed technique outperforms Agglomerative Nesting clustering technique for concept evolving data streams. Furthermore, the effect of concept evolution on clustering structure and average entropy has been visualised for detailed analysis and understanding.
Ratio Sum Versus Sum Ratio for Linear Discriminant Analysis

Dimension reduction is a critical technology for high-dimensional data processing, where Linear Discriminant Analysis (LDA) and its variants are effective supervised methods. However, LDA prefers to feature with smaller variance, which causes feature with weak discriminative ability retained. In this paper, we propose a novel Ratio Sum for Linear Discriminant Analysis (RSLDA), which aims at maximizing discriminative ability of each feature in subspace. To be specific, it maximizes the sum of ratio of the between-class distance to the within-class distance in each dimension of subspace. Since the original RSLDA problem is difficult to obtain the closed solution, an equivalent problem is developed which can be solved by an alternative optimization algorithm. For solving the equivalent problem, it is transformed into two sub-problems, one of which can be solved directly, the other is changed into a convex optimization problem, where singular value decomposition is employed instead of matrix inversion. Consequently, performance of algorithm cannot be affected by the non-singularity of covariance matrix. Furthermore, Kernel RSLDA (KRSLDA) is presented to improve the robustness of RSLDA. Additionally, time complexity of RSLDA and KRSLDA are analyzed. Extensive experiments show that RSLDA and KRSLDA outperforms other comparison methods on toy datasets and multiple public datasets.