Reviewerly/exHarmony huggingface dataset:
Deep Learning based Facial Recognition System for Attendance Maintenance

For attendance purposes, face recognition technology can be used to identify individuals within a company. In order to assess the effectiveness of any organisation, attendance records must be maintained and evaluated. Automating the conventional method of recording attendance is the aim of building an attendance monitoring system. The Yale face database has 95.76 percent of the Automated pictures. The Haar-cascade classifier and the LBPH (Local Binary Pattern Histogram) algorithm, both of which are implemented in Python and the OpenCV library, are used in our proposed model to identify the positive and negative features of the face. Numerous algorithms and techniques have been developed to enhance face recognition performance. The Tkinter GUI interface is utilised for user interface purposes. The everyday tasks of attendance marking and evaluation are handled by the attendance management system with less human interaction. For attendance purposes, face recognition technology can be used to identify individuals within a company. In order to assess the effectiveness of any organisation, attendance records must be maintained and evaluated. Automating the conventional method of recording attendance is the aim of building an attendance monitoring system. The everyday tasks of attendance marking and evaluation are handled with less human interaction thanks to the Automated Attendance Management System. The best outcomes from the algorithm, enhanced LBP, and PCA are then used to classify and recognise the facial images. The attendance of the identified student will then be noted and saved in an excel file. Students who sign in more than once will be alerted, and those who are not registered will be able to do so right away. When trained on two images per person, the average recognition accuracy for high-quality images is 100%, for low-quality images it is 94.11%, and for the Yale face database it is 95.76%. The Haar-cascade classifier and the LBPH (Local Binary Pattern Histogram) algorithm, both of which are implemented in Python and the OpenCV library, are used in our proposed model to identify the positive and negative features of the face. Numerous algorithms and techniques have been developed to enhance face recognition performance. The Tkinter GUI interface is utilised for user interface purposes.
Mode Collapse in Generative Adversarial Networks: An Overview

With the rise of a new framework known as Generative Adversarial Networks (GANs), generative models have gained considerable amount of attention in the area of unsupervised learning. GANs have been thoroughly studied since their emergence in 2014, leading to an enormous amount of new models and applications built on this said framework. Although despite their success, GANs suffer from some notorious problems during training, hindering further advances in the field. This paper seeks to highlight one of the most encountered problems in GAN training, namely the “Helvetica scenario” as stated by its authors or “mode collapse” as widely known. We will try to provide an overview of this said challenge, what is it, why it occurs, and some suggested workarounds to reduce its impact on training.
Load Balancing in Cloud Computing using Mutative BFO

On a per-user basis, cloud computing provides its end customers with increasingly scalable virtual resources over the Internet. Numerous users and businesses have already moved to the cloud for accessing the services that are offered by a variety of cloud service providers. To handle so many requests and clients, cloud providers must manage their servers. As a result, service providers must manage the load to guarantee that resources are being used effectively. The problem of load balancing is known to be NP-hard, and a number of researchers have suggested a variety of solutions using meta-heuristic algorithms. The paper provides a mutative bacterial Foraging optimization to reduce response time and improve resource utilization. In a heterogeneous cloud setup with dynamic workloads, the proposed algorithm performs better when compared to the other meta-heuristic algorithm.
Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Virtual Reality (VR) is now a well-established technology which offers realistic and immersive virtual worlds to the user usually by means of Head-Mounted Displays (HMDs).Actually, these devices can also be backed by a cloud server which can host the game server or even directly render the virtual world, as in the well-known cloud gaming paradigm.However, due to the drastically low latencies that technology requires, it is more convenient, when possible, to use servers that are as close to the users as possible.As a consequence, implementing the game or the render server in the Fog Computing layer is a concrete possibility.In this paper, we investigate, by using the Meta Quest 2 device, which are the QoE trade-offs, in terms of graphic quality and network performance, both in the case in which the HMD performs the 3d rendering locally by using a sample game written with Unreal Engine and in the case in which the 3d rendering is done in the Fog by means of the nVidia CloudXR framework and Oculus Air Link.From the results of our experiments, we found that remote rendering offers a stable frame rate against a higher quality image.Instead, local rendering sets the best possible graphics quality against the optimal frame rate.Additionally, we saw how remote rendering uses video compression in the case of decreasing bandwidth available to adjust graphics quality and FPS.The same does not hold for Motion-to-Photon latency, which increases with distance, reducing the general QoE.
An Improved A* Path Planning Algorithm of Autonomous Underwater Vehicles

With the rapid increase in ocean exploration ac-tivities, more and more underwater vehicles for autonomous path planning ability have been designed and developed. There are many limitations on path planning using traditional A* algorithm, such as too many nodes, too many turning times and too small turning angles, and the designed path is not conducive to movement towards autonomous underwater vehicle (AUV). In order to overcome the aforementioned shortcomings, an improved A * algorithm employing path optimization is used to carry out smooth and optimization of the motion path of the AUV. Effectiveness and efficiency optimization of the improved algorithm are verified by simulation studies.
High Performance Ship Detection via Transformer and Feature Distillation

It has been successfully demonstrated that Transformer is superior in object detection via an encoder-decoder architecture, termed Detection Transformer (DETR). However, slow convergence speed and computational complexity degrade the performance when it is applied to ship detection. The primary reason is ship images are rarely available in large-scale public datasets for training a model. Besides, DETR requires high performance computing platforms to deploy and run, which is not friendly for ship detection application. Towards this end, we propose an Efficient Ship Detection Transformer termed ESDT, which comprises of three parts namely backbone, encoder and decoder. The backbone is implemented with the ResNet50 so that deep features can be extracted. Next, the extracted features are fed to the encoder implemented with multi-scale self-attention to capture the long-range dependency of features. Finally, the enhanced features are sent to the decoder for final ship detection. To speed up converge, we introduce a feature distillation mechanism to the ESDT for learning knowledge from the large pretrained DETR. Extensive experiments are performed on commonly-used ship detection dataset Seaships. Qualitative and quantitative results demonstrate the effectiveness and efficiency of our proposed method.
A quantum hamiltonian simulation benchmark

Abstract Hamiltonian simulation is one of the most important problems in quantum computation, and quantum singular value transformation (QSVT) is an efficient way to simulate a general class of Hamiltonians. However, the QSVT circuit typically involves multiple ancilla qubits and multi-qubit control gates. In order to simulate a certain class of n -qubit random Hamiltonians, we propose a drastically simplified quantum circuit that we refer to as the minimal QSVT circuit, which uses only one ancilla qubit and no multi-qubit controlled gates. We formulate a simple metric called the quantum unitary evolution score (QUES), which is a scalable quantum benchmark and can be verified without any need for classical computation. Under the globally depolarized noise model, we demonstrate that QUES is directly related to the circuit fidelity, and the potential classical hardness of an associated quantum circuit sampling problem. Under the same assumption, theoretical analysis suggests there exists an ‘optimal’ simulation time t opt ≈ 4.81, at which even a noisy quantum device may be sufficient to demonstrate the potential classical hardness.
ProFeat: Unsupervised image clustering via progressive feature refinement

Unsupervised image clustering is a chicken-and-egg problem that involves representation learning and clustering. To resolve the inter-dependency between them, many approaches that iteratively perform the two tasks have been proposed, but their accuracy is limited due to inaccurate intermediate representations and clusters. To overcome this, this paper proposes ProFeat, a novel iterative approach to unsupervised image clustering based on progressive feature refinement. To learn discriminative features for clustering while avoiding adversarial influence from inaccurate intermediate clusters, ProFeat rigorously divides representation learning and clustering by modeling a neural network for clustering as a composition of an embedding and a clustering function and introducing an auxiliary embedding function. ProFeat progressively refines representations using confident samples from intermediate clusters using an extended contrastive loss. This paper also proposes ensemble-based feature refinement for more robust clustering. Our experiments demonstrate that ProFeat achieves superior results compared to previous methods.
Collaborative Filtering system based on multi-level user clustering and aspect sentiment

A Collaborative Filtering (CF) method predicts an unknown overall rating of a target user towards an item based on the known overall ratings of the users that are similar to the target user. The similarity between two users is generally found based on their overall ratings toward items that both have reviewed. Two users may have similar overall ratings towards a given item, but different sentiments towards various aspects of the item. Understanding the effect of user sentiment towards specific aspects on ratings will sharpen estimates of user similarity as well as provide an rationale for making specific recommendations. We propose an Aspect-Sentiments based Multi-level Clustering of Users (ASMCU) approach that finds the multiple clusters of users similar to a specific user where similarity between users is based on various aspect sentiments. The proposed ASMCU CF approach can be used to predict both the overall ratings and the aspect-sentiments. The ASMCU based CF approach performed mostly better than and sometimes comparable to the eight well-established CF methods that rely only on the overall ratings or a particular aspect-sentiments. Note however that the ASMCU can also explicitly justify the recommendation in terms of aspect sentiments. We evaluated our approach using three datasets: One Hotel dataset and Two Beer datasets. The Hotel dataset involved six aspects and each Beer dataset has four aspects. Each dataset has one overall rating matrix and one sentiment tensor.
An <scp>IIoT</scp> based secure and sustainable smart supply chain system using sensor networks

Abstract Industrial Internet of Things (IIoT) is a very effective and efficient way to track products during their production, packaging, storage, and shipments. It can also help in monitoring their real‐time conditions such as location, speed of moments, and temperature. Presently most of the supply chain management systems are not effective due to their inefficient tracking of real‐time shipments, long delays in shipments and lost identification on a real‐time basis, and uneconomical storage of goods. To solve these problems, an IoT‐based secure and sustainable smart supply chain system using sensor networks is proposed that provides real‐time shipment tracking, storage, delay, and lost identification. Threshold‐based weighted probability formula is devised by considering the average distance and node spacing of sensor nodes, total network energy, residual, and maximum energy parameters to alleviate the hot spot problem. Additionally, the communication signals are transmitted in compression form with end‐to‐end encryption that reduces the use of bandwidth by decreasing the signal size. The percentage increment in the stability and the network lifespan of the proposed method is 35.19% and 7.23% &amp; 119.33% and 71.72% as compared to the T. Senthil et al method and S. Singh's method, respectively.
User-centered visual explorer of in-process comparison in spatiotemporal space

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user's exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback.
LAGH: Towards Asymmetrical Collaborative Bodily Play between 1st and 2nd Person Perspectives

This paper investigated to what extent social interactions and empathy of users could be induced when different control mechanisms were used in an asymmetric collaboration. We conducted a user study to explore the user experience under one decentralized and two centralized control conditions via using the proposed two-player asymmetric collaborative bodily play, LAGH, which supports perspective-taking through the integration with the first- and second-perspectives and shared objects. The two players have complementary views and controls to each other in an immersive environment. The results indicate that participant pairs were encouraged by the asymmetric collaboration interface to share their emotional and physiological perspectives with each other. When their control abilities were balanced, they were more motivated to perform information sharing and interact with each other, thereby enhancing closeness and stimulating empathy. Furthermore, users could improve the collaboration efficiency.
"Cyber security is a dark art": The CISO as Soothsayer

Commercial organisations continue to face a growing and evolving threat of data breaches and system compromises, making their cyber-security function critically important. Many organisations employ a Chief Information Security Officer (CISO) to lead such a function. We conducted in-depth, semi-structured interviews with 15 CISOs and six senior organisational leaders, between October 2019 and July 2020, as part of a wider exploration into the purpose of CISOs and cyber-security functions. In this paper, we employ broader security scholarship related to ontological security and sociological notions of identity work to provide an interpretative analysis of the CISO role in organisations. Research findings reveal that cyber security is an expert system that positions the CISO as an interpreter of something that is mystical, unknown and fearful to the uninitiated. They show how the fearful nature of cyber security contributes to it being considered an ontological threat by the organisation, while responding to that threat contributes to the organisation's overall identity. We further show how cyber security is analogous to a belief system and how one of the roles of the CISO is akin to that of a modern-day soothsayer for senior management; that this role is precarious and, at the same time, superior, leading to alienation within the organisation. Our study also highlights that the CISO identity of protector-from-threat, linked to the precarious position, motivates self-serving actions that we term 'cyber sophistry'. We conclude by outlining a series of implications for both organisations and CISOs.
Uninorm-like parametric activation functions for human-understandable neural models

We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpotent fuzzy logic and multi-criteria decision-making (MCDM). The learnable parameter has a semantic meaning indicating the level of compensation between input features. The neural network determines the parameters using gradient descent to find human-understandable relationships between input features. We demonstrate the utility and effectiveness of the model by successfully applying it to classification problems from the UCI Machine Learning Repository.
Research Data Management Commitment Drivers: An Analysis of Practices, Training, Policies, Infrastructure, and Motivation in Global Agricultural Science

Scientists largely acknowledge the value of research data management (RDM) to enable reproducibility and reuse. But, RDM practices are not sufficiently rewarded within the traditional academic reputation economy. Recent work showed that emerging RDM tools can offer new incentives and rewards. But, the design of such platforms and scientists' commitment to RDM is contingent on additional factors, including policies, training, and several types of personal motivation. To date, studies focused on investigating single or few of those RDM components within a given environment. In contrast, we conducted three studies within a global agricultural science organization, to provide a more complete account of RDM commitment drivers: one survey study (n = 23) and two qualitative explorations of regulatory frameworks (n = 17), as well as motivation, infrastructure, and training components (n = 13). Based on the sum of findings, we contribute to the triangulation of a recent RDM commitment evolution model. In particular, we find that strong support and suitable tools help develop RDM commitment, while policy conflicts, unclear data standards, and multi-platform sharing, lead to unexpected negotiation processes. We expect that these findings will help to better understand RDM commitment drivers, refine the RDM commitment evolution model, and benefit its application in science.
User Interface and User Experience Design Using Lean UX Method on Zakat Ummat Website

Zakat Ummat is a zakat management unit that utilizes the zakatummat.com website to distribute zakat funds, infaq, and alms from muzakki to amil. It is known that Zakat Ummah currently has problems with complicated transaction process layouts and is also known to be developing its website by adding some improvements. Based on the evaluation results using the Heuristic Evaluation method by 3 experts, the website usability value was obtained with a value of 66.07, with the conclusion that the website does not have a search feature and the UI and UX display are not optimal. This study aims to design a user interface recommendation in the form of a prototype design using the Lean UX development method and assess the results of the design using the Heuristic Evaluation (HE), Single Ease Question (SEQ), and System Usability Scale (SUS) methods. The evaluator carried out the prototype design test using the HE method with the usability value increasing from 66.07 to 81.52. While testing by users, the usability value of each function with the SEQ method obtained a value above 6 out of 7 rating scales, and the overall usability value obtained with the SUS method with a value of 73.91, where the value is in the Acceptable range or can be accepted by the user. In addition, the prototype design based on the Lean UX method was declared successful with valid values for each hypothesis made
Path Planning of Mobile Robot Based on Improved Genetic Algorithm

Abstract With the continuous development of science and technology, mobile robots are gradually applied to people’s routine life. As one of the key technologies of mobile robots, the performance of path planning algorithms is a significant factor affecting its efficiency. On the basis of using a traditional Genetic Algorithm for path planning, combined with specific application scenarios, this paper proposes a new path planning algorithm for mobile robots. The path smoothness and the number of path corners are added to the fitness function to evaluate the path, so as to select a more superior path. Simulation experiments in MATLAB show the validity and feasibility of the modified method. By comparison to the traditional GA and other classical path planning algorithms, the optimal path selected has fewer turning angles under the same path length, which has a better application effect and greatly improves the working efficiency and stability.
Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.