Reviewerly/exHarmony huggingface dataset:
Run! This road has no ending! A systematic review of PLS-SEM application in strategic management research among developing nations

Partial least squares structural equation modelling (PLS-SEM) is emerging as a prominent methodological tool in strategic management research. Although it offers various advancements to stay relevant with growing research needs, the pace of PLS-SEM adoption may differ in different parts of the world. In this paper, we conducted a systematic review using the PRISMA framework and extracted from the top-ranking strategic management journals 120 articles published between 2011 and 2022 that presented a microscopic view on developing nations. Our findings reveal that despite the astounding methodological solutions offered by PLS-SEM, the studies from developing nations are still trailing behind developed nations in terms of fully exploiting the advancements of PLS-SEM to provide substantial insights to strategic management literature. This review identifies discrepancies in the current application of the method, discusses the most recent advancements and provides the best practices, standard guidelines and recommendations for the best use of PLS-SEM in strategic management research.
Autoencoder-Based Anomaly Detection in ECG Image Time Series Data: A Comparative Evaluation of Three Different Architectures

Anomaly detection is an essential component of machine learning that renders the outcomes neutral to any category or class. Due to the wide range of anomalies that might exist in time-series data, it plays a crucial role in time-series modelling. This research paper presents an image reconstruction-based approach for anomaly detection in electrocardiogram (ECG) time series data and image data using three different autoencoders, and concludes with a comparison analysis. Anomaly detection is crucial for accurate diagnosis and treatment of heart diseases, and the proposed approach utilizes three types of autoencoders to learn the normal patterns in image data and identify any deviations from these patterns as anomalies. The approach uses a dataset of normal images and is tested on a dataset containing both normal and anomalous images. To reconstruct unseen data like anomalies, models trained only on normal sets will not be able to do so. The results demonstrate that all three types of autoencoders can effectively reconstruct the images, but the Convolutional Autoencoder was able to reconstruct most of the anomaly images correctly, which may not be ideal for anomaly detection. On the other hand, the Deep Autoencoder and Stacked Autoencoder show the reconstruction of the anomalies similar to the normal images, hence, can be very effective for detecting anomalies in ECG image time-series data and similar problems. The proposed approach demonstrates the effectiveness of autoencoder-based anomaly detection for image data and provides a comparative analysis of different types of autoencoders for this task. This can potentially improve the accuracy and efficiency of anomaly detection in various image datasets.
Kullback-Leibler Divergence Based Method for Depression Diagnosis Using Video Data

Depression affects around four per cent of the total world population. The lack of trained professionals in low and middle-income countries has led to more than three-quarters of those affected receiving no treatment. This calls for the development of automated methods for depression detection. This work proposes a model to detect pertinent frames from video data using Kullback-Leibler Divergence and the concept of outliers. The distribution of microstructures of the frames, selected using the proposed method, is fed to the Support Vector Machine to classify those affected with depression from the controls. The proposed method is validated using the data obtained from the Department of Psychiatry, Center of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences and Dr Ram Manohar Lohia Hospital, New Delhi. The results are encouraging and pave the way for the use of this method in developing systems for the automated detection of the disease.
Gamification in Education: A Literature Review

The global changes taking place in society are also changing the approaches to the organization of the educational process, forcing teachers to find new educational tools capable of engaging students in active learning. The improvement of pedagogical practice is significantly influenced by gamification, which allows the use of game mechanics for this purpose. The present study is devoted to a systematic review of research on various issues of gamification of education. The authors conducted a literature review of sources selected from the web aggregator of scientific publications from 2018 to 2022 based on the keywords “education” and “gamification” found in titles, abstracts, and text. As a result of the clustering of articles conducted, the author’s six-item typology of the sample was proposed. The literature review revealed a large geography of the authors of the publications, including all continents of the world, as well as a positive attitude of all participants in the educational process to the use of gamification. The authors of the publications in the sample noted the increased motivation of students who became participants in the gamified educational process, but revealed the need to follow the individual characteristics of students’ learning behavior. The results of the study can be used to address the issue of applicability of gamepractices in the educational process at any level.
Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.
Low-Resource Neural Machine Translation: A Systematic Literature Review

In this study, a systematic literature review was conducted to examine the significant works in the literature on low-resource neural machine translation. Within the scope of the study, three research questions were identified to examine the low-resource neural machine translation literature. According to the inclusion and exclusion criteria, 45 studies were selected for review. After the relevant studies were identified, three research questions were aimed to be answered. The first research question is to identify the study directions and language pairs used in low-resource neural machine translation. The second research question aims to identify which deep learning methods are used in low-resource neural machine translation and which metrics are used to evaluate these methods. The third research question is to determine the bilingual and monolingual corpora used in the studies and the preferred development environments. In addition, the studies with the most commonly used language pairs were analyzed, and directions for future studies were made.
Reward Distribution in Proof-of-Stake Protocols: A Trade-Off Between Inclusion and Fairness

Proof-of-Stake (PoS) variants provide an energy-efficient alternative to Proof-of-Work (PoW).However, it is not clear whether practical PoS implementations are fair with respect to wealth, stake, and reward distribution.In this paper, we analyse the fairness of the four well-known PoS platforms Tezos, Polkadot, Cardano, and Casper through a data-driven approach.For this, we collect data on stakes and rewards for all the four platforms over several years.We then apply four measures to study the fairness along different dimensions.We calculate the Gini coefficient to explore the wealth inequality, the Nakamoto coefficient to investigate the degree of decentralization, and the expectational and robust fairness of the stake-reward proportions, i.e., if the validators receive their fair share of the rewards given their stake share.We can show that there are dynamics of high wealth inequalities and stake centralization in all the systems with Polkadot being the exception.With respect to stake-reward fairness, the platforms differ in the distributions of deviations from the fair share, i.e., in Cardano and Tezos, the differences have a lower magnitue as many small validators participate.However, by examining specific outcomes, we can show that platforms with a limited validator set such as Polkadot and Casper tend to be fairer regarding the reward payoffs, but it is not possible for smaller validators to participate.This mechanism sheds light on a trade-off that the platforms face: the more inclusive (open) the validation process is designed, the unfairer the reward distribution tends to be.Our results allow us to conclude that the way of how PoS is implemented matters greatly for its fairness.
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning PLM-based rankers requires a great amount of annotated training data. Annotating data involves a large manual effort and thus is expensive, especially in domain specific tasks. In this paper we investigate fine-tuning PLM-based rankers under limited training data and budget. We investigate two scenarios: fine-tuning a ranker from scratch, and domain adaptation starting with a ranker already fine-tuned on general data, and continuing fine-tuning on a target dataset.
A Comparative Study of Training Objectives for Clarification Facet Generation

Due to the ambiguity and vagueness of a user query, it is essential to identify the query facets for the clarification of user intents. Existing work on query facet generation has achieved compelling performance by sequentially predicting the next facet given previously generated facets based on pre-trained language generation models such as BART. Given a query, there are mainly two types of training objectives to guide the facet generation models. One is to generate the default sequence of ground-truth facets, and the other is to enumerate all the permutations of ground-truth facets and use the sequence that has the minimum loss for model updates. The second is permutation-invariant while the first is not. In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets. To this end, we propose another three training objectives of different aforementioned properties. For comprehensive comparisons, besides the commonly used evaluation that measures the matching with ground-truth facets, we also introduce two diversity metrics to measure the diversity of the generated facets. Based on an open-domain query facet dataset, i.e., MIMICS, we conduct extensive analyses and show the pros and cons of each method, which could shed light on model training for clarification facet generation. The code can be found at https://github.com/ShiyuNee/Facet-Generation.
Beyond the Last Layer: Deep Feature Loss Functions in Side-channel Analysis

This paper provides a novel perspective on improving the efficiency of side-channel analysis by applying two deep feature loss functions: Soft Nearest Neighbor (SoftNN) and Center loss. By leveraging these loss functions during the deep neural networks (DNNs) training phase, our study illuminates how profiling attacks can be more powerful. Deep feature loss functions incorporate the outputs from the DNN's intermediate layers into their computations, which reduces the distance between similar data points. As such, these techniques enhance the DNN's ability to generate more precise and meaningful representations, thereby improving its discriminative power. This paper presents empirical evidence illustrating the effectiveness of SoftNN and Center loss in strengthening DNN-based side-channel attacks. For instance, when using Center loss together with the focal loss ratio (FLR), it requires the least number of traces to break the ASCADf dataset. On the other hand, applying SoftNN with FLR successfully recovers the key for the ASCADr dataset with the least traces. The insights presented in this study can act as a baseline for more advanced investigations into the utility of such loss functions in deep learning-based side-channel analysis.
A Side-Channel Attack on a Masked Hardware Implementation of CRYSTALS-Kyber

NIST has recently selected CRYSTALS-Kyber as a new public key encryption and key establishment algorithm to be standardized. This makes it important to evaluate the resistance of CRYSTALS-Kyber implementations to side-channel attacks. Software implementations of CRYSTALS-Kyber have already been thoroughly analysed. The discovered vulnerabilities helped improve the subsequently released versions and promoted stronger countermeasures against side-channel attacks. In this paper, we present the first attack on a protected hardware implementation of CRYSTALS-Kyber. We demonstrate a practical message (shared key) recovery attack on the first-order masked FPGA implementation of Kyber-512 by Kamucheka et al. (2022) using power analysis based on the Hamming distance leakage model. The presented attack exploits a vulnerability located in the masked message decoding procedure which is called during the decryption step of the decapsulation. The message recovery is performed using a profiled deep learning-based method which extracts the message directly, without extracting each share explicitly. By repeating the same decapsulation process multiple times, it is possible to increase the success rate of full shared key recovery to 99%.
Random Postprocessing for Combinatorial Bayesian Optimization

Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in many steps to find the global optimum. Here, we numerically study the effect of a postprocessing method on Bayesian optimization that strictly prohibits duplicated samples in the dataset. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posteriori estimation. Our results provide a simple but general strategy to solve the slow convergence of Bayesian optimization for high-dimensional problems.
Numerical investigation and deep learning approach for fractal–fractional order dynamics of Hopfield neural network model

This paper investigates the dynamics of Hopfield neural networks involving fractal–fractional derivatives. The incorporation of fractal–fractional derivatives in the neural network framework brings forth novel modeling capabilities, capturing nonlocal dependencies, complex scaling behaviors, and memory effects. The aim of this study is to provide a comprehensive analysis of the dynamics of Hopfield neural networks with fractal–fractional derivatives, including the existence and uniqueness of solutions, stability properties, and numerical analysis techniques. Numerical analysis techniques, including the Adams–Bashforth method, are employed to accurately simulate the fractal–fractional Hopfield neural network system. Moreover, the obtained numerical data serves as validation for developing predictions using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural network methods. The findings contribute to the advancement of both fractional calculus and neural network theory, providing valuable insights for theoretical investigations and practical applications in complex systems analysis.
Software Bill of Materials in Java

Modern software applications are virtually never built entirely in-house. As a matter of fact, they reuse many third-party dependencies, which form the core of their software supply chain [1]. The large number of dependencies in an application has turned into a major challenge for both security and reliability. For example, to compromise a high-value application, malicious actors can choose to attack a less well-guarded dependency of the project [2]. Even when there is no malicious intent, bugs can propagate through the software supply chain and cause breakages in applications. Gathering accurate, upto- date information about all dependencies included in an application is, therefore, of vital importance.
Flexible continuous phase shifts method for binary defocusing technique with short pitch fringes

The microscopic fringe projection profilometry system using a digital micromirror device (DMD) can improve the height sensing sensitivity by reducing the fringe pitch. However, due to the discrete nature of the DMD, it is impossible to apply arbitrary and continuous phase shifts when the fringe pitch is reduced to a few pixels. The proposed method uses a plane-parallel plate to produce a displacement on the light passing through it and achieves continuous phase shifts by controlling the oblique angle of the plate. Experimental results demonstrate that the proposed method can introduce continuous phase shifts to quasi-sinusoidal fringes with a minimum pitch of 2 pixels and correctly demodulate the phase map using a 9-step phase-shifting algorithm. Simulation and experiments also show that the inherent phase shift error can be neglected when the relevant parameters of the plate are properly selected.
Health Recommendation System using Deep Learning-based Collaborative Filtering

The crucial aspect of the medical sector is healthcare in today's modern society. To analyze a massive quantity of medical information, a medical system is necessary to gain additional perspectives and facilitate prediction and diagnosis. This device should be intelligent enough to analyze a patient's state of health through social activities, individual health information, and behavior analysis. The Health Recommendation System (HRS) has become an essential mechanism for medical care. In this sense, efficient healthcare networks are critical for medical decision-making processes. The fundamental purpose is to maintain that sensitive information can be shared only at the right moment while guaranteeing the effectiveness of data, authenticity, security, and legal concerns. As some people use social media to recognize their medical problems, healthcare recommendation systems need to generate findings like diagnosis recommendations, medical insurance, medical passageway-based care strategies, and homeopathic remedies associated with a patient's health status. New studies aimed at the use of vast numbers of health information by integrating multidisciplinary data from various sources are addressed, which also decreases the burden and health care costs. This article presents a recommended intelligent HRS using the deep learning system of the Restricted Boltzmann Machine (RBM)-Coevolutionary Neural Network (CNN) that provides insights on how data mining techniques could be used to introduce an efficient and effective health recommendation systems engine and highlights the pharmaceutical industry's ability to translate from either a conventional scenario towards a more personalized. We developed our proposed system using TensorFlow and Python. We evaluate the suggested method's performance using distinct error quantities compared to alternative methods using the health care dataset. Furthermore, the suggested approach's accuracy, precision, recall, and F-measure were compared with the current methods.
Constructing comprehensive and discriminative representations with diverse attention for occluded person re-identification

Occluded person re-identification (Re-ID) is a challenging task that aims to match occluded person images to holistic ones across different camera views. Feature diversity is crucial for achieving high-performance Re-ID. Previous methods rely on additional annotations or hand-crafted rules to achieve feature diversity, which are inefficient or infeasible for occluded Re-ID. To address this, we propose the Diverse Attention Net (DANet) which utilizes attention mechanism to achieve diverse feature mining. Specifically, DANet incorporates a pair of complementary Diverse Parallel Attention Modules (DPAM), which, under the attention decorrelation constraint (ADC), help the model automatically capture diverse discriminative features in a global scope. Additionally, we propose an Efficient Transformer layer that can seamlessly integrate with the proposed DPAM and synergistically enhance the capability to handle occlusions. The resulting DANet construct a set of comprehensive representations that encode diverse discriminative features. Extensive experiments demonstrate DANet achieves state-of-the-art performance on both occluded and holistic ReID benchmarks.
Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices

Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture.