Reviewerly/exHarmony huggingface dataset:
Comparative analysis of deep learning models for detecting face mask

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.
GradCa: Generalizing to unseen domains via gradient calibration

In domain generalization (DG) problem, we hope to train a robust model from multiple source domains and generalize it to unseen domains. However, the trained model can not perform well in the unseen domain since the domain-invariant representation is hard to learn across multiple source domains. In this work, we propose a novel gradient optimization strategy named gradient calibration (GradCa) which optimizes the dominant gradients and conflicting gradients without learning extra parameters. The dominant gradients are suppressed for addressing to be inclined to the optimization directions of specific domains. Then two strategies (average and sign-mask) are designed for further alleviating the conflicting gradients. These types of gradients are effectively calibrated for improving the domain-invariant representation learning. Extension experiments on four benchmark datasets have shown the competitive results and the effectiveness of improving the model generalization with GradCa.
Rebalance Weights AdaBoost-SVM Model for Imbalanced Data

Classification of imbalanced data is a challenging task that has captured considerable interest in numerous scientific fields by virtue of the great practical value of minority accuracy. Some methods for improving generalization performance have been developed to address this classification situation. Here, we propose a cost-sensitive ensemble learning method using a support vector machine as a base learner of AdaBoost for classifying imbalanced data. Considering that the existing methods are not well studied in terms of how to precisely control the classification accuracy of the minority class, we developed a novel way to rebalance the weights of AdaBoost, and the weights influence the base learner training. This weighting strategy increases the sample weight of the misclassified minority while decreasing the sample weight of the misclassified majority until their distributions are even in each round. Furthermore, we included P-mean as one of the assessment markers and discussed why it is necessary. Experiments were conducted to compare the proposed and comparison 10 models on 18 datasets in terms of six different metrics. Through comprehensive experimental findings, the statistical study is performed to verify the efficacy and usability of the proposed model.
Evaluation of Visual Notations as a Basis for ICS Security Design Decisions

For making informed security decisions during the design of industrial control systems (ICS), engineers need to process large amounts of security-relevant information outside their area of expertise. This problem moves the presentation of the security-relevant information into focus: security-relevant engineering information must be presented to security decision-makers in a way that enables them to decide upon security measures to build a defensible system. Visual representations have the potential to effectively convey suchlike information, thus saving the engineers’ brain capacity for the security decision-making. However, research shows that this potential is only realized if the visualizations are carefully constructed for cognitive effectiveness. As a prerequisite for constructing a visual language for security engineering in the future, this paper explores two scientific questions: 1) what are the requirements for visualizing security-relevant engineering information in a way that enables engineers to make security decisions during ICS design? and 2) which existing visual languages meet (parts of) these requirements? The evaluation of existing visualizations reveals that there is a need for an improved, specialized visual language for security engineering that builds upon established engineering visualizations like piping and instrumentation diagrams and network maps, represents all security-relevant information as icons to achieve semantic transparency, and includes filtering mechanisms to reduce the complexity of each single diagram. The paper finishes with defining the main pillars of a future visual language that should allow ICS engineers to quickly capture security-relevant information and guide them through the process of selecting the right security measures to design a defensible ICS.
Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework

Federated learning is a privacy-preserving distributed learning paradigm where multiple devices collaboratively train a model, which is applicable to edge computing environments. However, the non-IID data distributed in multiple devices degrades the performance of the federated model due to severe weight divergence. This paper presents a clustered federated learning framework named cFedFN for visual classification tasks in order to reduce the degradation. Especially, this framework introduces the computation of feature norm vectors in the local training process and divides the devices into multiple groups by the similarities of the data distributions to reduce the weight divergences for better performance. As a result, this framework gains better performance on non-IID data without leakage of the private raw data. Experiments on various visual classification datasets demonstrate the superiority of this framework over the state-of-the-art clustered federated learning frameworks.
A Stabilizer Framework for the Contextual Subspace Variational Quantum Eigensolver and the Noncontextual Projection Ansatz

Quantum chemistry is a promising application for noisy intermediate-scale quantum (NISQ) devices. However, quantum computers have thus far not succeeded in providing solutions to problems of real scientific significance, with algorithmic advances being necessary to fully utilize even the modest NISQ machines available today. We discuss a method of ground state energy estimation predicated on a partitioning of the molecular Hamiltonian into two parts: one that is noncontextual and can be solved classically, supplemented by a contextual component that yields quantum corrections obtained via a Variational Quantum Eigensolver (VQE) routine. This approach has been termed Contextual Subspace VQE (CS-VQE); however, there are obstacles to overcome before it can be deployed on NISQ devices. The problem we address here is that of the ansatz, a parametrized quantum state over which we optimize during VQE; it is not initially clear how a splitting of the Hamiltonian should be reflected in the CS-VQE ansätze. We propose a "noncontextual projection" approach that is illuminated by a reformulation of CS-VQE in the stabilizer formalism. This defines an ansatz restriction from the full electronic structure problem to the contextual subspace and facilitates an implementation of CS-VQE that may be deployed on NISQ devices. We validate the noncontextual projection ansatz using a quantum simulator and demonstrate chemically precise ground state energy calculations for a suite of small molecules at a significant reduction in the required qubit count and circuit depth.
Security Reassessing in UAV-Assisted Cyber-Physical Systems Based on Federated Learning

Mobile-edge computing (MEC) is a popular method for increasing the quality of computing experience for consumers since it allows them to offload computing operations to MEC servers, which have significant processing capabilities. However, safety and information security are major challenges that must be addressed. We employ Federated Learning-based Cyber-Physical Systems (CPS) to address safety and information security in MEC systems, which offers data accuracy and un-sustainability. Security of UAV assisted CPS against cyber-attacks yet an another challenging problem. Because most cyber-attacks occur in unpredictable ways, it is difficult to define them in a structured manner. Instead of developing a unique cyber-attack model, we focus on exploring the dynamic behavior of the system to cyber-attacks throughout this article. Attacks that repeat themselves by interfering with system components or data are insignificant if they can be quickly identified by the system's control mechanism. Intelligent cyber attackers remain undetected by the tracking system by carefully designing cyber-attacks. Our primary goal is to examine the effectiveness of such cyberattacks from the aspect of the CPS and overcome them using Federated Learning (FL).
Simulated Annealing Based Integerization of Hidden Weights for Area-Efficient IoT Edge Intelligence

Neural network (NN) model accuracy has dramatically increased as a result of the usage of extreme parameterization. However, this comes with significant overheads, including increased area, energy consumption, higher memory, and latency. The deployment of these highly parameterized NN models is becoming a challenge in resource-constrained environments. We present a proof-of-concept methodology to address this problem. We proposed a novel approach based on Simulated Annealing (SA) for NN model optimization for designing efficient hardware for Internet-of-Things(IoT) edge intelligent devices. The SA algorithm is used to optimize a given NN model generated with a standard learning framework. It improves the pre-trained NN model by perturbing a specific amount of hidden layer neuron weights by rounding off some of the weights that are close to integers without compromising the model's accuracy. As a result of rounding, some weights may end up with zero value leading to model pruning. We validated the approach with three MLP classification benchmarks synthesized with Xilinx Vivado (v.2019.2). We assume all floating point weights are stored in IEEE 754 FP32 format. We performed three different SA runs with a constant temperature reduction rate ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> ) of 0.95 and a number of iterations at temperature (n) of 100, 1,000, and 10,000, respectively. In experiments I, II, and III, we achieve average flip-flop savings of 20.38% 22.93%, and 24.45% and LUT savings of 21.43%, 23.84%, and 25.51% respectively.
Helmet-Wearing Tracking Detection Based on StrongSORT

Object detection based on deep learning is one of the most important and fundamental tasks of computer vision. High-performance detection algorithms have been widely used in many practical fields. For the management of workers wearing helmets in construction scenarios, this paper proposes a framework model based on the YOLOv5 detection algorithm, combined with multi-object tracking algorithms, to monitor and track whether workers wear safety helmets in real-time video. The improved StrongSORT tracking algorithm of DeepSORT is selected to reduce the loss of the tracked object caused by the occlusion, trajectory blur, and motion scale of the object. The safety helmet dataset is trained with YOLOv5s, and the best result of training is used as the weight model in the StrongSORT tracking algorithm. The experimental results show that the mAP@0.5 of all classes in the YOLOv5s model can reach 95.1% in the validation dataset, mAP@0.5:0.95 is 62.1%, and the precision of wearing helmet is 95.7%. After the box regression loss function was changed from CIOU to Focal-EIOU, the mAP@0.5 increased to 95.4%, mAP@0.5:0.95 increased to 62.9%, and the precision of wearing helmet increased to 96.5%, which were increased by 0.3%, 0.8% and 0.8%, respectively. StrongSORT can update object trajectories in video frames at a speed of 0.05 s per frame. Based on the improved YOLOv5s combined with the StrongSORT tracking algorithm, the helmet-wearing tracking detection can achieve better performance.
Hybrid Particle Swarm Optimization with Deep Learning Driven Sarcasm Detection on Social Media

Sarcasm is a language phrase that transports the polar opposite of what is being said, usually something extremely disagreeable to mock or offend someone. Sarcasm was commonly employed on social networking sites daily. Since sarcasm might alter the significance of statement, the opinion analysis process is error-prone. Concerns regarding the integrity of analytics have developed as the utilization of automatic social media analytics apparatuses has extended. Based on the earlier study, sarcastic statements alone have considerably decreased the performance of automated sentiment analysis. This article develops a Hybrid Particle Swarm Optimization with Deep Learning Driven Sarcasm Detection (HPSO-DLSD) technique. The presented HPSO-DLSD technique mainly concentrates on the recognition of sarcasm on social media. In the presented HPSO-DLSD technique, the initial stage of data preprocessing is carried out. To detect and classify sarcasm, sparse stacked autoencoder (SAE) model is exploited and the detection performance can be boosted via the HPSO algorithm. The experimental result analysis of the HPSO-DLSD technique can be tested on benchmark dataset and the outcomes emphasized the enhancements of the HPSO-DLSD method over other current approaches.
APSL: Action-positive separation learning for unsupervised temporal action localization

Unsupervised temporal action localization in untrimmed videos is a challenging and open issue. Existing works focus on the “clustering + localization” framework for unsupervised temporal action localization. However, it heavily relies on features used for clustering and localization, e.g., features implying potential background information would degrade the localization performance. To address this problem, we propose a novel Action-positive Separation Learning (APSL) method. APSL follows a novel “feature separation + clustering + localization” iterative procedure. First, we introduce a novel feature separation learning (FSL) module. FSL employs separation learning to identify action and background features in a video, and then refines and removes potential action-negative and background-negative features (hard-to-locate) from the identified features employing contrastive learning, thus obtaining action-positive features (easy-to-locate). Next, in “clustering” step, we apply clustering to the separated action-positive features to obtain action pseudo-labels. In “localization” step, with action pseudo-labels and action-positive features, we employ a temporal action localization module to locate action instance regions, in turn, improving the performance of clustering and FSL. The three steps learn iteratively and reinforce each other during training. Comprehensive evaluations conducted on the THUMOS'14 and ActivityNet v1.2 datasets demonstrate that our method outperforms cutting-edge weakly supervised and unsupervised methods, obtaining state-of-the-art performance.
Efficient verification of Affleck-Kennedy-Lieb-Tasaki states

Affleck-Kennedy-Lieb-Tasaki (AKLT) states are an important class of many-body quantum states that are useful in quantum information processing, including measurement-based quantum computation in particular. Here we propose a general approach for constructing efficient verification protocols for AKLT states on arbitrary graphs with local spin measurements. Our verification protocols build on bond verification protocols and matching covers (including edge coloring) of the underlying graphs, which have a simple geometric and graphic picture. We also provide rigorous performance guarantee that is required for practical applications. With our approach, most AKLT states of wide interest, including those defined on one- and two-dimensional lattices, can be verified with a constant sample cost, which is independent of the system size and is dramatically more efficient than all previous approaches. As an illustration, we construct concrete verification protocols for AKLT states on various lattices and on arbitrary graphs up to five vertices.
Psycho-Emotional Burnout of the Personality in the Conditions of War

A common cause of health disorders in war is psychotraumatic situations that lead to psycho-emotional burnout of military personnel.&#x0D; The purpose of the article is to conduct an empirical study of the psycho-emotional burnout of military personnel. Tasks are set: to determine the emotional reactions and states of military personnel; to assess the level of manifestation of emotional burnout of military personnel.&#x0D; Research Methods: The study used valid methods, "Diagnosis of the level of emotional burnout", "Determination of mental "burnout", and «Burnout Syndrome» in the professions of the «man-man» system, for the non-parametric data correlation variables, the Spearman coefficient.&#x0D; Results: The study revealed the military personnel's inability to manage their emotions, which leads to depersonalization, personal alienation, and distance. The inadequate selective emotional response has 94.0%, which is an indisputable "sign of burnout". The peculiarities of emotion management of military personnel and their role in the genesis of emotional response in professional activity are revealed. It is established that the "economic manifestation of emotions" of military personnel can be manifested both in an excessively cheerful mood and aggression, which is frustrating behavior.&#x0D; Conclusion: In order to prevent psycho-emotional burnout, it is necessary to develop anti-stress skills, skills of controlling the manifestation of emotions based on critical analysis of the situation, and the ability not to throw out negative emotions on other people. To work with the military, it is necessary to substantiate the content of psychosocial counseling within the framework of a comprehensive system of psychological rehabilitation of servicemen.
THE APPLICATIONS OF MICROLEARNING IN HIGHER EDUCATION IN TEXTILES

Nowadays, the educational system is not able to satisfy the job market requirements in a sufficient rate. Because of this reason, the young people are not interested in spending the time to listen to lectures or to read bulky books. They prefer immediate use or application of the obtained knowledge and skills and require the options to learn anytime and anywhere, not only in the schools. It is therefore necessary for teachers and trainers to try to understand that young people are more active than their parents in general, and they choose their knowledge and skills, whose usefulness they can imagine or prove in a short time. This is possible to achieve using the online learning, which is widely used by different organizations (schools, universities and also companies). But online courses seem to imitate traditional ones (structure, resources, etc.), which does not bring the desired results. In view of these observations, the ability to learn and the learning speed are thus an important factor for increasing the quality of individual life, not only for students and adults. From this point of view, the lifelong education and training is a feature of the Information Society. Taking into account the fact that technologies are constantly evolving, learning never ceases, and students and employees should be familiar with new information, completing knowledge gaps and keeping learning materials. Micro-learning can fill these gaps as it arrives just in time and it is compliant with the specific needs the learners have. This article presents the possibility of using Moodle cloud to create micro-learning content.
Is hate speech detection the solution the world wants?

The machine learning (ML) research community has landed on automated hate speech detection as the vital tool in the mitigation of bad behavior online. However, it is not clear that this is a widely supported view outside of the ML world. Such a disconnect can have implications for whether automated detection tools are accepted or adopted. Here we lend insight into how other key stakeholders understand the challenge of addressing hate speech and the role automated detection plays in solving it. To do so, we develop and apply a structured approach to dissecting the discourses used by online platform companies, governments, and not-for-profit organizations when discussing hate speech. We find that, where hate speech mitigation is concerned, there is a profound disconnect between the computer science research community and other stakeholder groups—which puts progress on this important problem at serious risk. We identify urgent steps that need to be taken to incorporate computational researchers into a single, coherent, multistakeholder community that is working towards civil discourse online.
LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. Data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%.
Leveraging quantum computing for dynamic analyses of logical networks in systems biology

The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.