Reviewerly/exHarmony huggingface dataset:
A Density-Center-Based Automatic Clustering Algorithm for IoT Data Analysis

With the rapid development of Internet of Things (IoT), much data has been produced, and new requirements have been posed for data mining. Clustering plays an essential role in discovering the underlying patterns of IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, and computer vision. Density clustering is crucial to find arbitrary-shaped clusters and noise points without knowing the number of clusters in advance. However, its efficiency and applicability are reduced sharply when there exists mutual interference among parameters. In this article, a new algorithm called density-center-based automatic clustering (DAC) is proposed. First, this work presents a nonparametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a data set. Finally, it selects appropriate density centers from a decision graph, which merge their surrounding points into the same groups. Experiments are conducted to show that DAC has higher accuracy than six classic and updated algorithms. Its effectiveness is shown via data from photovoltaic power and oil extraction systems. As an outstanding feature that its compared peers lack, it can determine parameters automatically. Thus this work greatly advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.
Facial mask detection system based on YOLOv4 algorithm

This paper mainly addresses the detection of facial mask wear under the new COVID-19. To meet this demand, this paper performs facial mask wear detection on specific targets through a model trained based on the YOLOv4 algorithm. It has the characteristics of fast detection and light weight, and the application of this system to daily mask wear detection requires high real-time system performance. YOLOv4 meets this requirement, so the system designed based on this model has practical significance. This paper further demonstrates that the facial mask detection system designed based on the YOLOv4 algorithm is capable of working in multiple scenes of daily life, successfully detecting whether the target is wearing a mask in many scenes such as routine, multi-person and occlusion environment.
Privacy-Preserving Multi-Robot Task Allocation via Secure Multi-Party Computation

Multi-robot task allocation is a practical way to identify synergies between robots. When all the robots within a system fall under the auspices and authority of a single organization, they can simply be compelled to share their information and participate in cooperative protocols. But when, for instance, they are rivals vying in the marketplace, their own private data may be copyrighted or sensitive, so that disclosing information may erode a competitive advantage. Yet, even limited cooperation, by offering some arbitrage of common resources (such as shared infrastructure), often reduces costs for all parties; indeed, competition and cooperation are not mutually exclusive. We examine the question of how to allocate robots to tasks optimally while ensuring that no task valuations, utilities, positions, or related data are released. We do this via an auction-based assignment algorithm implemented using secure multi-party computation operations, without requiring any trusted auctioneer. The approach offers precise and effective privacy guarantees that are stronger than present methods. We demonstrate the feasibility of the approach via tests in a case study inspired by autonomous driving. First, we tested the approach in a single-computer setup, using parties with virtual network interfaces, where we studied the effects of varying the number of parties and the associated parameters of the auction. Next, we tested the approach in a decentralized, physical test-bed using single board computers running over a WiFi LAN network. Finally, we conducted a small proof-of-concept experiment using two autonomous mobile robots performing a decentralized, private auction.
Contour-Based Wild Animal Instance Segmentation Using a Few-Shot Detector

Camera traps are widely used in wildlife research, conservation, and management, and abundant images are acquired every day. Efficient real-time instance segmentation networks can help ecologists label and study wild animals. However, existing deep convolutional neural networks require a large number of annotations and labels, which makes them unsuitable for small datasets. In this paper, we propose a two-stage method for the instance segmentation of wildlife, including object detection and contour approximation. In the object detection stage, we use FSOD (few-shot object detection) to recognize animal species and detect the initial bounding boxes of animals. In the case of a small wildlife dataset, this method may improve the generalization ability of the wild animal species recognition and even identify new species that only have a small number of training samples. In the second stage, deep snake is used as the contour approximation model for the instance segmentation of wild mammals. The initial bounding boxes generated in the first stage are input to deep snake to approximate the contours of the animal bodies. The model fuses the advantages of detecting new species and real-time instance segmentation. The experimental results show that the proposed method is more suitable for wild animal instance segmentation, in comparison with pixel-wise segmentation methods. In particular, the proposed method shows a better performance when facing challenging images.
A Modified Key Sifting Scheme With Artificial Neural Network Based Key Reconciliation Analysis in Quantum Cryptography

Quantum Cryptography emerged from the limitations of classical cryptography. It will play a vital role in information security after the availability of expected powerful quantum computers. Still many quantum primitives like quantum money, blind quantum computation, quantum copy protection, etc. are theoretical as they require a completely functional quantum computer for their implementation. But one prominent quantum cryptographic primitive, the Quantum Key Distribution (QKD) is possible with current technology. The QKD is a key establishment system having several stages namely raw key generation, key sifting, key reconciliation, and privacy amplification. In this paper, an efficient key sifting scheme has been developed. Successful simulation has shown that the proposed modified key sifting scheme requires less time to build the sifted key compared to the sifted key in conventional BB84 protocol in most cases. This paper also represents Tree Parity Machine (TPM) based key reconciliation analysis using different learning algorithms such as Hebbian, Anti-Hebbian, and Random-Walk. This reconciliation analysis helps to choose the optimum learning algorithm for Artificial Neural Network (ANN) based key reconciliation in future Quantum Key Distribution systems.
Investigating the efficiency of deep learning based security system in a real-time environment using YOLOv5

Surveillance Systems Application based on deep learning algorithms is speedily growing in a broad range of fields such as Facial Recognition, Real Time Attendance Systems etc. Identifying several appearances in a real time environment is very crucial due to its difficult and heterogenous environmental conditions and blocking effects. We used state-of-the-art YOLOv5 model for investigating the efficiency of surveillance system with very limited experimental analysis. We used Face Detection Dataset & Benchmark (FDDB) and Celebrity Face Recognition (CFR) Dataset for training from scratch and for testing over YOLOv5 and private dataset taken from run-time video stream. Experimentations showing that we got 93% accuracy on FDDB on the other hand 99% accuracy on the tailored dataset. Comparison has been made for the analysis showing that our algorithm has produced better outcomes with the predecessor editions of YOLOv5 like YOLOv4 and YOLOv3 respectively. The aforementioned models are also validated over the run-time streaming, and it has the ability to recognize many faces with maximal precision.
Fuzzy Pattern Tree Evolution Using Grammatical Evolution

A novel approach to induce Fuzzy Pattern Trees using Grammatical Evolution is presented in this paper. This new method, called Fuzzy Grammatical Evolution, is applied to a set of benchmark classification problems. Experimental results show that Fuzzy Grammatical Evolution attains similar and oftentimes better results when compared with state-of-the-art Fuzzy Pattern Tree composing methods, namely Fuzzy Pattern Trees evolved using Cartesian Genetic Programming, on a set of benchmark problems. We show that, although Cartesian Genetic Programming produces smaller trees, Fuzzy Grammatical Evolution produces better performing trees. Fuzzy Grammatical Evolution also benefits from a reduction in the number of necessary user-selectable parameters, while Cartesian Genetic Programming requires the selection of three crucial graph parameters before each experiment. To address the issue of bloat, an additional version of Fuzzy Grammatical Evolution using parsimony pressure was tested. The experimental results show that Fuzzy Grammatical Evolution with this extension routinely finds smaller trees than those using Cartesian Genetic Programming without any compromise in performance. To improve the performance of Fuzzy Grammatical Evolution, various ensemble methods were investigated. Boosting was seen to find the best individuals on half the benchmarks investigated.
Spatial audio in 360° videos

Immersive technologies are rapidly gaining traction across a variety of application domains. 360° video is one such technology, which can be captured with an omnidirectional multi-camera arrangement. With a Virtual Reality (VR) Head Mounted Display (HMD), users have the freedom to look in any direction they wish within the scene. While there is a plethora of work focused on modeling visual attention (VA) in VR, little research has considered the influence of the audio modality on VA in VR. It is well known that audio has an important role in VR experiences. Listeners can experience sound in all directions with high quality spatial audio. One such technique, Ambisonics or 3D audio, provides a full 360° sound soundscape.
A questionnaire-based and physiology-inspired quality of experience evaluation of an immersive multisensory wheelchair simulator

Immersive multimedia technologies such as virtual reality (VR) are now finding potential applications in domains outside of entertainment and gaming in areas such as health, education, and tourism, to name a few. This article presents a Quality of Experience (QoE) evaluation of an immersive haptic-based VR wheelchair simulator. The paper presents the results of an explicit and implicit (physiology-based) QoE evaluation of the Immersive Simulator in three different configurations: (a) desktop group (non-immersive); (b) headset 1 group (immersive with a high rate of motion acceleration); and (c) headset 2 group (immersive with a lower rate of motion acceleration). As part of the user evaluations, participants in each of the groups completed several questionnaires, including: an emotion questionnaire (SAM), cognitive task load (NASA-TLX), user expectations usability (SUS), and presence (IPQ). In addition, during the experience, physiological responses such as electrodermal activity (EDA) and heart rate variability (HRV) were recorded. The self-reported findings suggest that both headset groups had higher usability and presence levels than the desktop group. The two headset groups also had greater pleasant and exciting emotions than the desktop group. The NASA-TLX findings indicate that the headset 1 group presented the highest task cognitive load. The performance evaluation shows that both headset groups had a better results than the desktop group in terms of task completion.
The Impact of Agile Development Practices on Project Outcomes

Agile software development methods were introduced to minimize problems faced using traditional software development approaches. There are several Agile approaches used in developing software projects, these include Scrum, Extreme programming and Kanban. An Agile approach focuses on collaboration between customers and developers and encourages development teams to be self-organizing. To achieve this there are different Agile practices teams choose to use in their projects. Some teams only use one practice whilst others use a combination of practices. The most common practices used are stand-ups, user stories, Burndown chart/Burnup chart, pair programming, Epic and User stories. This paper reports on the analysis of the data collected from people involved in Agile software development teams and identifies that the combination of practices in Agile software development have an impact on the communication in the team, project requirements and project priorities, with more practices being adopted correlating with better project outcomes.
A field test of a federated learning/federated analytic blockchain network implementation in an HPC environment

The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.
Expressing rivalry online: antisemitic rhetoric among Dutch football supporters on Twitter

The aim of this study is to analyse online expressions of rivalry and hate speech in relation to antisemitic and philosemitic discourse(s) in Dutch professional men's football (soccer). We collected data from Twitter using the Twitter API and scraped Tweets relating to a match between the supposedly 'Jewish' club Ajax and its rival Feyenoord on 17 January 2021. A selection of the collected Tweets was analysed more in depth using narrative digital discourse analysis to interpret the Tweets. The research shows that antisemitic chants and slurs find their way towards the online domain, sometimes explicit and other times implicit. The analysis elaborates how these Tweets, while seemingly targeted towards the football rival, contribute to an exclusionary discourse in which being a 'Jew' is not wanted and contribute to the normalization and reproduction of antisemitism in football.
Evaluation of Motion Sickness Prediction Models for Autonomous Driving

The introduction of autonomous vehicles is expected to change the transportation system radically. One of the essential factors that affect the acceptance and choice of autonomous driving is passenger comfort. All people in the autonomous vehicle will be passengers and be able to perform non-driving tasks like reading etc. which increases the likelihood of motion sickness. This makes accurate estimation of motion sickness a necessity in the design stages of autonomous vehicles. The aim of this work is to review and apply two motion sickness prediction models (ISO-2631 and the 6D-SVC model) and evaluate their ability to capture individual motion sickness feelings using measured data and subjective assessment ratings from field tests. The comparison with the experimental results shows that the applied estimation models can be tuned to capture the individual motion sickness feelings. The results also show that habituation of motion sickness is an important property that needs to be taken into consideration and modelled.
Hierarchical template transformer for fine-grained sentiment controllable generation

Existing methods for text generation usually fed the overall sentiment polarity of a product as an input into the seq2seq model to generate a relatively fluent review. However, these methods cannot express more fine-grained sentiment polarity. Although some studies attempt to generate aspect-level sentiment controllable reviews, the personalized attribute of reviews would be ignored. In this paper, a hierarchical template-transformer model is proposed for personalized fine-grained sentiment controllable generation, which aims to generate aspect-level sentiment controllable reviews with personalized information. The hierarchical structure can effectively learn sentiment information and lexical information separately. The template transformer uses a part of speech (POS) template to guide the generation process and generate a smoother review. To verify our model, we used the existing model to obtain a corpus named FSCG-80 from Yelp, which contains 800K samples and conducted a series of experiments on this corpus. Experimental results show that our model can achieve up to 89.93% aspect-sentiment control accuracy and generate more fluent reviews.
Time Series Anomaly Detection Model Based on Multi-Features

For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7-0.8, which can meet the needs of practical application.
Improving Adversarial Robustness of CNNs via Maximum Margin

In recent years, adversarial examples have aroused widespread research interest and raised concerns about the safety of CNNs. We study adversarial machine learning inspired by a support vector machine (SVM), where the decision boundary with maximum margin is only determined by examples close to it. From the perspective of margin, the adversarial examples are the clean examples perturbed in the margin direction and adversarial training (AT) is equivalent to a data augmentation method that moves the input toward the decision boundary, the purpose also being to increase the margin. So we propose adversarial training with supported vector machine (AT-SVM) to improve the standard AT by inserting an SVM auxiliary classifier to learn a larger margin. In addition, we select examples close to the decision boundary through the SVM auxiliary classifier and train only on these more important examples. We prove that the SVM auxiliary classifier can constrain the high-layer feature map of the original network to make its margin larger, thereby improving the inter-class separability and intra-class compactness of the network. Experiments indicate that our proposed method can effectively improve the robustness against adversarial examples.
Facial Chirality: From Visual Self-Reflection to Robust Facial Feature Learning

As a fundamental vision task, facial expression recognition has made substantial progress recently. However, the recognition performance often degrades significantly in real-world scenarios due to the lack of robust facial features. In this paper, we propose an effective facial feature learning method that takes the advantage of facial chirality to discover the discriminative features for facial expression recognition. Most previous studies implicitly assume that human faces are symmetric. However, our work reveals that the facial asymmetric effect can be a crucial clue. Given a face image and its reflection without additional labels, we decouple the emotion-invariant facial features from the input image pair to better capture the emotion-related facial features. Moreover, as our model aligns emotion-related features of the image pair to enhance the recognition performance, the value of precise facial landmark alignment as a pre-processing step is reconsidered in this paper. Experiments demonstrate that the learned emotion-related features outperform the state of the art methods on several facial expression recognition benchmarks as well as real-world occlusion datasets, which manifests the effectiveness and robustness of the proposed model.
A Graph Neural Network and Pointer Network-Based Approach for QoS-Aware Service Composition

Quality-of-service (QoS)-aware service composition aims to aggregate multiple existing services to meet users&#x0027; complex functional and nonfunctional requirements that cannot be met by simple services. The accumulation of user tasks and service composition solutions makes it possible to mine empirical rules from those historical compositions to reduce the search space and thus improve the composition efficiency. Traditional empirical rule-based methods focus on mining with well-designed rules, ignoring the underlying correlations between tasks and between services. Meanwhile, infrequently used services are not valued by these methods, but these services may still be used in constructing optimal service solutions. In addition, many methods use reinforcement learning to compose services to efficiently construct service solutions, but they do not achieve the same effect as traditional metaheuristic methods. In view of the above shortcomings, considering the ability of graphs to express relationships, we first construct tasks and services as graphs and then use a graph neural network (GNN) to mine underlying correlations and predict the probability that each service will be used to construct the solution corresponding to the task. Next, based on these high-probability services, we utilize pointer network (PN)-based reinforcement learning to efficiently construct the initial service solution. The PN is often used to solve combinatorial optimization problems and is noninferior to metaheuristics for small-scale data. To increase the generalization ability of the network, we superimpose another layer on the PN. Finally, to take advantage of infrequently used services, we use the local whale optimization algorithm (WOA) to fine-tune the initial solution and obtain a high-quality solution. The experimental results show that our approach outperforms several existing methods in terms of both composition efficiency and solution quality.