Reviewerly/exHarmony huggingface dataset:
FRMDB: Face Recognition Using Multiple Points of View

Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.
A Scalable Cross-Chain Access Control and Identity Authentication Scheme

Cross-chain is an emerging blockchain technology which builds the bridge across homogeneous and heterogeneous blockchains. However, due to the differentiation of different blockchains and the lack of access control and identity authentication of cross-chain operation subjects, existing cross-chain technologies are struggling to accomplish the identity transformation of cross-chain subjects between different chains, and also pose great challenges in terms of the traceability and supervision of dangerous transactions. To address the above issues, this paper proposes a scalable cross-chain access control and identity authentication scheme, which can authenticate the legitimacy of blockchains in the cross-chain system and ensure that all cross-chain operations are carried out by verified users. Furthermore, it will record all cross-chain operations with the help of Superchain in order to regulate and trace illegal transactions. Our scheme is scalable and, at the same time, has low invasiveness to blockchains in the cross-chain system. We implement the scheme and accordingly conduct the evaluations, which prove its security, efficiency, and scalability.
Training of Classification Models via Federated Learning and Homomorphic Encryption

With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive information such as family information, medical records, personal habits, or financial records that, if leaked, can generate problems. For this reason, this paper aims to introduce a protocol for training Multi-Layer Perceptron (MLP) neural networks via combining federated learning and homomorphic encryption, where the data are distributed in multiple clients, and the data privacy is preserved. This proposal was validated by running several simulations using a dataset for a multi-class classification problem, different MLP neural network architectures, and different numbers of participating clients. The results are shown for several metrics in the local and federated settings, and a comparative analysis is carried out. Additionally, the privacy guarantees of the proposal are formally analyzed under a set of defined assumptions, and the added value of the proposed protocol is identified compared with previous works in the same area of knowledge.
AER: Auto-Encoder with Regression for Time Series Anomaly Detection

Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. However, a scarcity of labeled data and ambiguous definitions of anomalies can complicate these efforts. Recent unsupervised machine learning methods have made remarkable progress in tackling this problem using either single-timestamp predictions or time series reconstructions. While traditionally considered separately, these methods are not mutually exclusive and can offer complementary perspectives on anomaly detection. This paper first highlights the successes and limitations of prediction-based and reconstruction-based methods with visualized time series signals and anomaly scores. We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method. Our model can produce bi-directional predictions while simultaneously reconstructing the original time series by optimizing a joint objective function. Furthermore, we propose several ways of combining the prediction and reconstruction errors through a series of ablation studies. Finally, we compare the performance of the AER architecture against two prediction-based methods and three reconstruction-based methods on 12 well-known univariate time series datasets from NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest averaged F1 score across all datasets (a 23.5% improvement compared to ARIMA) while retaining a runtime similar to its vanilla auto-encoder and regressor components. Our model is available in Orion <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , an opensource benchmarking tool for time series anomaly detection.
Super Hyper Dominating and Super Hyper Resolving on Neutrosophic Super Hyper Graphs and Their Directions in Game Theory and Neutrosophic Super Hyper Classes

In this research article, the notions of Super Hyper Dominating and Super Hyper Resolving are defined in the setting of neutrosophic Super Hyper Graphs. Some ideas are introduced on both notions of Super Hyper Dominating and Super Hyper Resolving, simultaneously and as the same with each other. Some neutrosophic Super Hyper Classes are defined based on the notion, Super Hyper Resolving. The terms of duality, totality, perfectness, connectedness, and stable, are added to basic framework and initial notions, Super Hyper Dominating and Super Hyper Resolving but the concentration is on the “perfectness” to figure out what’s going on when for all targeted Super Hyper Vertices, there’s only one Super Hyper Vertex in the intended set. There are some instances and some clarifications to make sense about what’s happened and what’s done in the starting definitions. The key point is about the minimum sets. There are some questions and some problems to be taken as some avenues to pursue this study and this research. A basic familiarity with Super Hyper Graph theory and neutrosophic Super Hyper Graph theory is proposed.
BLOCKCHAIN AND CRYPTOCURRENCY INNOVATION FOR A SUSTAINABLE FINANCIAL SYSTEM

FinTech ecosystem and business model development are extensively discussed in the literature. The adoption of innovative technologies and digital transformation has impacted the approach to carrying on the businesses and channels that offer financial highly reliable and instinctive services and products. This study primarily deals with the identification of data sets. Also, a systematic review protocol was created using the PRISMA method to describe the hypothesis and rational and planned techniques of the study. Blockchain technology has been employed in an assortment of sectors and used by several nations for improving ecological supportability. In fact, blockchain technology has been effective to increase green production, tracking and preserving environmental degradation and pollution-related data, and collecting and analyzing the low carbon or green data in real time for more timely decisions. There is a scarcity of literature on studying the impact of blockchain and cryptocurrency on adopting FinTech and its sustainability. Therefore, the aim of this study is to add to this body of literature an investigation on the impact of Blockchain and Cryptocurrency adoption on FinTech for the environment-friendly nature.
Learning Dual-Fused Modality-Aware Representations for RGBD Tracking

With the development of depth sensors in recent years, RGBD object tracking has received significant attention. Compared with the traditional RGB object tracking, the addition of the depth modality can effectively solve the target and background interference. However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored. On the other hand, some methods attempt to fuse the two modalities by treating them equally, resulting in the missing of modality-specific features. To tackle these limitations, we propose a novel Dual-fused Modality-aware Tracker (termed DMTracker) which aims to learn informative and discriminative representations of the target objects for robust RGBD tracking. The first fusion module focuses on extracting the shared information between modalities based on cross-modal attention. The second aims at integrating the RGB-specific and depth-specific information to enhance the fused features. By fusing both the modality-shared and modality-specific information in a modality-aware scheme, our DMTracker can learn discriminative representations in complex tracking scenes. Experiments show that our proposed tracker achieves very promising results on challenging RGBD benchmarks. Code is available at https://github.com/ShangGaoG/DMTracker .
Structural Relationship of Technology Adoption and Performance Factors in UAE Manufacturing Industry

The world is rapidly changing as a result of technology, which has played an important role in organisational development by improving operations and reducing obstacles. Businesses are constantly investing in technology in order to improve their performance and gain a competitive advantage over their competitors. Technological advancements assist businesses in automating their systems and management, providing them with the impetus to efficiently target customers through low-cost business solutions. As a result, this paper examined the relationship between technology adoption and the performance of business organisations involved in manufacturing.This study was conducted quantitatively, with data collected via questionnaire survey. The collected data was used to develop the model of structural relationship between the factors using PLS-SEM approach. Based on the validated PLS-SEM model, it was found that performance expectancy, effort expectancy, social influence, and facilitating condition all have a positive relationship with technology adoption. One of the most significant benefits of increased technological use in manufacturing firms is increased revenue through improved performance. The evaluation of the mediation effects of firm size and training on the relationship between technology adoption and manufacturing firm performance in the UAE revealed that the hypotheses' outcomes were positive, indicating that firm size and training do play a role in manufacturing performance. When using technology in manufacturing, the training and firm size have a significant impact on manufacturing performance.
Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM

The impact of the COVID-19 pandemic is very broad, especially in Indonesia. In early 2022, Indonesia entered the early stages of recovering conditions caused by the pandemic. The government has an option for the community to carry out a third dose of vaccination (booster). However, there are a number of pros and cons in the community regarding the booster vaccine. This study aims to conduct sentiment analysis related to public opinion on the COVID-19 booster vaccination in Indonesia with Naive Bayes model. The data source used comes from Twitter. The workflow of this research includes data crawling, labeling, preprocessing, dataset sharing, and model testing and comparison with other models, namely Decision Tree and SVM. The results of this study indicate that the largest AUC score falls to the SVM model (75.40%), but for more accurate precision falls to the Naive Bayes model (83.81%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.
Hybrid training of optical neural networks

Optical neural networks are often trained “in-silico” on digital simulators, but physical imperfections that cannot be modelled may lead to a “reality gap” between the simulator and the physical system. In this work we present hybrid training, where the weight matrix is trained by computing neuron values optically using the actual physical network.
360° Video for Research Communication and Dissemination: A Case Study and Guidelines

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Introduction:</b> 360° videos are increasingly popular channels for science communication and higher education; however, time-limited 360° videos that disseminate scientific research via platforms like YouTube remain underexamined. To address this problem, this experience report reviews the creation and evaluation of six 2D video interviews and six 360° video tours. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">About the case:</b> The European Commission's Joint Research Centre (JRC) and other public-facing organizations already publish 2D videos on social media channels and host 360° video content on their institutional websites. This case addresses the affordances and constraints of creating short 360° videos for publication on public-facing platforms. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Situating the case:</b> 360° video content has been incorporated into science communication and pedagogical practices in higher education. The authors review these developments and show the need for further research on time-limited 360° video. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods/approach:</b> Scientists researching energy-related technologies were invited to record 2D video interviews. Based on these interviews, six transcripts for 360° videos were drafted and recorded in the same lab settings. When the videos were published, European researchers and communication professionals were recruited to complete a short survey evaluating the videos’ relative merits. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results/discussion:</b> The survey results (n = 32) suggest a similar overall quality of the 2D video interviews and 360° video tours. Respondents ranked the interviewee or narrator as the best feature of both the 2D and 360° format, and 47% said that they would prefer to have a 360° video created about their research. Based on our experience, we provide guidelines related to the production and publication of short 360° videos. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> Further research and practice are required to understand which specific features of the 360° videos are most effective and whether this technology offers distinct advantages as a tool for dissemination. Further research and practice will establish more detailed approaches to 360° video.
"Technology for the Accurate Preparation of Silicon Capacitive Pressure Sensors with Biomedical Application"

Florin Gaiseanu* Author Affiliations Science and Technology of Information Bucharest (Romania) and Barcelona, Spain Received: February 09, 2022 | Published: March 15, 2022 Corresponding author: Florin Gaiseanu, Research Professor, Science and Technology of Information Bucharest (Romania) and Barcelona, Spain DOI: 10.26717/BJSTR.2022.42.006777
A Visual Tracking Algorithm Combining Parallel Network and Dual Attention-Aware Mechanism

In order to solve the problems of semantic loss and inaccurate boundary detection in the process of object tracking, a visual tracking algorithm combining parallel structure and dual attentionattention mechanism is proposed in this paper.As backbone network, parallel structure is composed of Convolutional neural network and Attention Cooperative(CAC) processing module, which is used for feature extraction.Because this structure can capture the local and global information of the target at the same time, it can solve the problem of semantic information loss.Dual Attention-aware Network(DAN) is used for feature enhancement, which is composed of target-aware attention and boundary-aware attention, and the accuracy of boundary detection is enhanced by feature pyramid structure.Template online updating strategy is used to improve template quality, and an effective score prediction module-Template Elimination Mechanism(TEM) is designed in the CAC processing module to select high quality templates.This kind of object tracking algorithm which combines local and global information is called TrackCAC.The evaluation results on different datasets show that the algorithm can maintain high tracking precision and success in different scenarios.It shows good robustness and accuracy in the performance evaluation results on VOT datasets.
An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.
Internet of Things (IoT) as an Instrument to Improve Business and Marketing Strategies. A Literature Review

Creative destruction used by German sociologist Werner Sombart in 1913, and later known as Schumpeter's gale, could be considered the first conceptual stage of what happens nowadays in all industries "forced" to use the digital environment.First three industrial revolutions experienced by the humanity happened in over three centuries with tangible results that improved immensely the day-to-day life of people.The fourth one happened in decades, and it was characterized by the use of information and communication technology in various industries.As the literature review has revealed, Internet of Things (IoT) is part of Industry 4.0., linking industries and consumers in a smarter way through sensors and other devices, collecting valuable data and helping companies.This article proposes a navigation throughout the literature to demonstrate that IoT is a new and modern solution that has a big potential for development to help businesses to take smarter and swifter decisions from data gathered indirectly from consumers.Given that the papers analyzed are quite recent, we would say that this topic of IoT is only lightly investigated, and it opens many subjects of discussions, not only on the business and marketing side, but also economically, socially, and of course, technically.Various questions have arisen during the research: Is there a limitation in using this instrument in business and marketing strategies?Either technical or legal?The ethics of this process is under question and both companies and governments dedicated a lot of time to understand the implications of IoT in the future.
Enhancing Group Recommendation Using Attention Mechanisam

As indulging in group activities is prevalent in people’s day-to-day life, suggesting or recommending the information to a group of people has become a crucial task. Due to its importance in satisfying the group members, the recommendation for the group has captivated significant research efforts. The primary goal of group recommendation is aggregating the group members’ interests to infer the group decision. In this work, Group Recommendation using the attention mechanism model (GRAM) is built and optimized to address the issue of preference aggregation, which uses a neural attention network and neural collaborative filtering framework. The attention component uses to capture the effect of every member within the group. A neural collaborative filtering framework utilizes to learn the group-item interactions in the data. The GRAM model experiments on the CAMRa2011 data set. The model evaluation uses the various metrics, the Hit ratio and Normalized Discounted Cumulative Gain.
Occlusion-Aware Instance Segmentation Via BiLayer Network Architectures

Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previous instance segmentation methods, we model image formation as a composition of two overlapping layers, and propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ilayer <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolutional <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BCNet</i> ), where the top layer detects occluding objects (occluders) and the bottom layer infers partially occluded instances (occludees). The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression. We investigate the efficacy of bilayer structure using two popular convolutional network designs, namely, Fully Convolutional Network (FCN) and Graph Convolutional Network (GCN). Further, we formulate bilayer decoupling using the vision transformer (ViT), by representing instances in the image as separate learnable occluder and occludee queries. Large and consistent improvements using one/two-stage and query-based object detectors with various backbones and network layer choices validate the generalization ability of bilayer decoupling, as shown by extensive experiments on image instance segmentation benchmarks (COCO, KINS, COCOA) and video instance segmentation benchmarks (YTVIS, OVIS, BDD100 K MOTS), especially for heavy occlusion cases.