Reviewerly/exHarmony huggingface dataset:
Prioritising Organisational Factors Impacting Cloud ERP Adoption and the Critical Issues Related to Security, Usability, and Vendors: A Systematic Literature Review

Cloud ERP is a type of enterprise resource planning (ERP) system that runs on the vendor's cloud platform instead of an on-premises network, enabling companies to connect through the Internet. The goal of this study was to rank and prioritise the factors driving cloud ERP adoption by organisations and to identify the critical issues in terms of security, usability, and vendors that impact adoption of cloud ERP systems. The assessment of critical success factors (CSFs) in on-premises ERP adoption and implementation has been well documented; however, no previous research has been carried out on CSFs in cloud ERP adoption. Therefore, the contribution of this research is to provide research and practice with the identification and analysis of 16 CSFs through a systematic literature review, where 73 publications on cloud ERP adoption were assessed from a range of different conferences and journals, using inclusion and exclusion criteria. Drawing from the literature, we found security, usability, and vendors were the top three most widely cited critical issues for the adoption of cloud-based ERP; hence, the second contribution of this study was an integrative model constructed with 12 drivers based on the security, usability, and vendor characteristics that may have greater influence as the top critical issues in the adoption of cloud ERP systems. We also identified critical gaps in current research, such as the inconclusiveness of findings related to security critical issues, usability critical issues, and vendor critical issues, by highlighting the most important drivers influencing those issues in cloud ERP adoption and the lack of discussion on the nature of the criticality of those CSFs. This research will aid in the development of new strategies or the revision of existing strategies and polices aimed at effectively integrating cloud ERP into cloud computing infrastructure. It will also allow cloud ERP suppliers to determine organisations' and business owners' expectations and implement appropriate tactics. A better understanding of the CSFs will narrow the field of failure and assist practitioners and managers in increasing their chances of success.
Contact tracing as a measure to combat COVID-19 and other infectious diseases

Background: Most of the mathematical modeling studies on COVID-19 transmission are based on continuous deterministic models that do not consider the characteristics of social networks.Methods: The effect of contact tracing on mitigating COVID-19, and other infectious diseases in general, is studied in a small-world network.This network has its advantages over the commonly used continuous deterministic mathematical models in that the characteristics of social networks can be properly incorporated.Results: Simulation results show that for the original strain of SARS-CoV-2, contact tracing can play an important role in reducing and delaying the peak daily new cases.New cases can be reduced by using symptom onset to isolate tracked individuals, but the benefit can be greatly enhanced by testing asymptomatic and presymptomatic individuals on the sixth to eighth day of infection.For the delta variant, or other variants of much higher infectivity, contact tracing alone cannot significantly lower the number of daily new cases but is able to delay the peaks greatly, thus affording more time to explore and implement pharmaceutical interventions.Conclusions: Contact tracing can be a very powerful tool to combat COVID-19 caused by the original strain or any variant of SARS-CoV-2.In order to make contact tracing effective, every effort is needed to expand the pool of contact tracing and provide all necessary support to the self-quarantined.
A Comparative Analysis of Video Shot Boundary Detection using Different Approaches

Video shot boundary detection is the crucial step in the field of video processing. This makes the task of content-based video retrieval, indexing and browsing of videos. In the last decade, many researchers contributed for the shot boundary detection for different types of videos. Their remarkable efforts and outcomes have been studied and its comparison is showcased in this paper. This paper contains the survey of different methodologies implemented to achieve the task of Shot Boundary Detection along with key performance measurement parameters. This includes different preprocessing techniques, feature extraction methodologies, similarity computation techniques, etc. The outcomes of different approaches are framed with reference to accuracy, speed of computation and along with comparison of precision, recall and F1 score.
Machine Translation Decoding beyond Beam Search

Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Lespiau Jean-Baptiste, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
A New Configuration of Roof Photovoltaic System for Limited Area Applications—A Case Study in KSA

Increased world energy demand necessitates looking for appropriate alternatives to oil and fossil fuel. Countries encourage institutions and households to create their own photovoltaic (PV) systems to reduce spending money in electricity sectors and address environmental issues. Due to high solar radiation in the Kingdom of Saudi Arabia (KSA), the government urges people and institutions to establish PV systems as the best promising renewable energy resource in the country. This paper presents an optimal and complete design of a 300 kW PV system installed in a limited rooftop area to feed the needs of the Ministry of Electricity building, which has a high energy consumption. The design has been suggested for two scenarios in terms of adjusting the orientation angles. The available rooftop area allowed to be used is insufficient if a tilt angle of 22o is used, suggested by the designer, so the tilt angle has been adjusted from 22o to 15o to accommodate the available area and meet the required demand with a minimum shading effect. The authors of this paper propose a modified scenario “third scenario” which accommodates the available area and provides more energy than the installed “second scenario”. The proposed panel distribution and the estimated energy for all scenarios are presented in the paper. The possibility of changing tilt angles and the extent of energy production variations are also discussed. Finally, a comparative study between measured and simulated energy is included. The results show that August has the lowest percentage error, with a value of 2.7%, while the highest percentage error was noticed in November.
Robust Self-Testing of Multiparticle Entanglement

Quantum self-testing is a device-independent way to certify quantum states and measurements using only the input-output statistics, with minimal assumptions about the quantum devices. Because of the high demand on tolerable noise, however, experimental self-testing was limited to two-photon systems. Here, we demonstrate the first robust self-testing for multiphoton genuinely entangled quantum states. We prepare two examples of four-photon graph states, the Greenberger-Horne-Zeilinger states with a fidelity of 0.957(2) and the linear cluster states with a fidelity of 0.945(2). Based on the observed input-output statistics, we certify the genuine four-photon entanglement and further estimate their qualities with respect to realistic noise in a device-independent manner.
proScript: Partially Ordered Scripts Generation

Scripts -prototypical event sequences describing everyday activities -have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.However, to date they have proved hard to author or extract from text.In this work, we demonstrate for the first time that pre-trained neural language models can be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake).To do this, we collect a large (6.4k) crowdsourced partially ordered scripts (named proScript), that is substantially larger than prior datasets, and develop models that generate scripts by combining language generation and graph structure prediction.We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partialorder) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script.Our experiments show that our models perform well (e.g., F1=75.7 on task (i)), illustrating a new approach to overcoming previous barriers to script collection.We also show that there is still significant room for improvement toward human level performance.Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.
Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?

There is an exponential increase in the range of digital products and devices promoting aging in place, in particular, devices aiming at preventing or detecting falls. However, their deployment is still limited and only few studies have been carried out in population-based settings owing to the technological challenges that remain to be overcome and the barriers that are specific to the users themselves, such as the generational digital divide and acceptability factors specific to the older adult population. To date, scarce studies consider these factors. To capitalize technological progress, the future step should be to better consider these factors and to deploy, in a broader and more ecological way, these technologies designed for older adults receiving home care to assess their effectiveness in real life.
A Novel Distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams

The concurrence of state-of-the-art Industrial 5G, Cyber-Physical Systems, Smart-Systems, Industrial Internet of Things, and Additive Manufacturing paves the next-level digital remodeling. However, the transfiguration unwittingly tailpiece an operational onus on the smart-environment operators. The multiplicity and classes of IoT devices operating in the intelligent environment are myriad. The characterization of ingress network traffic and the accurate classification of devices is necessary to efficiently manage the devices and offer cutting-edge security solutions and quality of Service (QoS). The paper addresses these challenges by offering a novel intelligent framework for traffic classification leveraging behavioral attributes of IoT traffic. The paper’s contributions to the research community are fourfold. Firstly, the paper proposes a novel IoT classification framework based on Stack-Ensemble for real-time high-volume IoT traffic. The experimental results indicate that the proposed novel Stack Ensemble model can extract the best out of base models and demonstrate an accuracy of 99.94%. The intelligent models are evaluated over multiple dimensions to project the isometric view of the model performance and the experimental results. To achieve that goal, all the performance metrics that most researchers most often miss have been elucidated. Secondly, the paper comprehends the flow-level statistical characteristics of IoT devices. Third, the paper offers the distributed, scalable, and portable framework architecture. The architecture is horizontally scalable, distributing the computational load. The framework offers an end-to-end industry-grade machine-learning pipeline and triumphs the vulnerabilities of existing solutions. Finally, the paper discusses the statistical insights into the intelligent model and the results of the experimentation study. The proposed work paves the opportunities for researchers, smart-environment operators, and developers to unfold the architecture and supplement the security solutions against cyber-attacks.
A Novel Configurable High-precision and Low-cost Circuit Design of Sigmoid and Tanh Activation Function

This paper proposes a configurable Sigmoid and Tanh activation function circuit design by second-order approximation and deviation compensation. The proposed circuit is synthesized in TSMC 180nm technology within a 7218 um <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area at 500MHz, it is also deployed on Xilinx ZC706 FPGA by 158 LUTs and 46 FFs at 333MHz. It achieves almost 4.68× better precision and 20.2% less area than previous works. The experimental results show that our method has almost no accuracy loss on LSTM Chinese speech recognition tasks and the YOLOv3 image recognition post-processing task.
A Modification of the Imperialist Competitive Algorithm With Hybrid Methods for Constrained Optimization Problems

This paper studies a modification of the imperialist competitive algorithm to solve constrained optimization problems with hybrid methods. The imperialist competitive algorithm is a kind of evolutionary algorithm based on the colonial competition mechanism of imperialism, which is a type of social heuristic optimization algorithm. However, this algorithm needs to be modified because of some problems, including the decreasing number of empires, which leads to easily falling into a local optimum, and a lack of information exchange among countries leading to the search ability being insufficiently strong. To solve these problems and improve the search ability of the algorithm, a newly improved imperialist competitive algorithm with hybrid methods is proposed based on the imperialist competitive algorithm in this paper. In this algorithm, a crossover mechanism is introduced as the empire interaction to boost the information exchange among countries. Two groups of combinatorial operators are constructed in the process of empire assimilation and revolution to improve the algorithm’s global and local search ability. Moreover, a step of empire splitting is added based on the imperialist competitive algorithm in which the number of empires is decreased rapidly by empire competition in imperialist competitive algorithm, which leads to a decrease in the population diversity. Then, the proposed algorithm is tested on 12 benchmark functions and 4 engineering problems, and the constraints are addressed by Deb’s rules. The results obtained by the proposed algorithm are compared with those of algorithms that have obtained good results in recent years, and it is shown that the proposed method obtains promising and comparable results on the constrained optimization benchmarks in terms of the solution quality and robustness.
Super-encoder with cooperative autoencoder networks

Dimensionality reduction plays a crucial role in classification, object detection, and pattern recognition tasks. Its main objective is to decrease the dimension of the original data while retaining the most distinctive information. With the emergence of deep learning, an autoencoder has become a state-of-the-art non-linear dimensionality-reduction method. Nonetheless, as the existing autoencoder models are devised to follow the data distribution and employ similarity techniques, preserving distinctive information can be problematic. To tackle this issue, we propose super-encoder (SE) networks trained in a supervised and cooperative manner. The SE consists of an encoder, separator, and decoder networks. The encoder combined with separator networks are dedicated to generating separable latent representation based on the label, and the decoder network should be able to reconstruct it to the original data simultaneously. Herein, we introduce a novel cooperative learning mechanism with a new loss function; therefore, the encoder, separator, and decoder networks can cooperate to achieve these objectives. Extensive experiments using benchmark datasets were conducted. The results indicated that the SE is more effective in extracting separable latent code than the existing supervised and unsupervised dimensionality-reduction models. Furthermore, as a generator, it can obtain highly competitive realistic images.
Incremental few-shot object detection via knowledge transfer

As a challenging problem in machine learning, incremental few-shot object detection (iFSD) [1] aims to incrementally detect novel classes with few examples, while keeping the previous knowledge without revisiting base classes. Here, we propose two models based on the observation that when new memories come, new connections will be created between memory cells in the brain [2]. The first one, which is called the multi-class head (MCH) model, simulates how humans add new memory-connections that every time novel classes come, a classification branch is added to predict novel classification. And the second model, called the bi-path multi-class head (BPMCH) model, adds a new backbone, which is initialized with the weight of the base class backbone, to transfer more knowledge of the base class to the novel class. Considering accuracy and speed, we choose the Fully Convolutional One-Stage Object Detection (FCOS) [3] + Adaptive Training Sample Selection (ATSS) [4] detector as our baseline. Our models are first trained on the base classes with abundant examples and then finetuned on novel classes with few examples, which not only maintain the knowledge learned from the base class but also transfer the knowledge to the novel class. Extensive experiments show that our models outperform the state-of-the-art model ONCE [1] on the COCO [5] and PASCAL VOC [6] by a large margin.
Trust in Scholarly Communications and Infrastructure: Indigenous Data Sovereignty

Many Indigenous people have a deep mistrust of research, with some describing research as one of the "dirtiest" words in Indigenous language. The histories and experiences behind such mistrust are long and painful. Given what has been perceived as Indigenous objectification at the hands of largely Anglo-European others for research from which they fail to benefit, many communities now refuse research unless it is undertaken under certain, Indigenous-defined circumstances. Such refusal is a move away from others purposes and a move towards autonomy and self-determination. For some, this is a statement of sovereignty and it applies to all areas of endeavour, including the new frontiers of research and the structures that support them, such as datification of knowledge. This article examines data sovereignty from the perspective of Indigenous peoples. While data sovereignty has become a ubiquitous concern, Indigenous data sovereignty arises from contexts specific to Indigenous peoples. The focus of this article is to provide a brief overview of recent data sovereignty developments, along with the context that lies behind these activities. Through this examination, implications for trust in scholarly communications will be discussed.
Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
Robust Watermarking Scheme for Encrypted Images Based on Scrambling and Kronecker Compressed Sensing

Data hiding in encrypted images (DHEI) embeds data in the cipher state, which not only protects the confidentiality of the cover image content, but also hides the existence of the secret information, and has a broad application prospect. However, many DHEI schemes pursue high capacity at the expense of robustness. In this letter, we propose a robust watermarking scheme for encrypted images based on scrambling and Kronecker compressed sensing (CS). The scheme makes full use of the encryption and compression characteristics of CS to realize the image encryption and preprocessing at the same time, and utilizes the democratic characteristic of CS, Most Significant Bits (MSBs) replacement, image global scrambling and adjusted uniform quantization to improve the robustness. In addition, the two-dimensional Kronecker CS reduces the computational complexity of the encoding end and increases the encoding efficiency. Experimental results show that our scheme achieves good robustness against various noise attacks and clipping attacks, while maintaining high-capacity embedding and stable recovery.
Trademark Image Similarity Detection Using Convolutional Neural Network

A trademark is any recognizable sign that identifies products/services and distinguishes them from others. Many regional and international intellectual property offices are dedicated to dealing with trademark registration processes. The registration process involves examining the trademark to ensure there is no confusion or interference similarity to any other prior registered trademark. Due to the increasing number of registered trademarks annually, the current manual examining approach is becoming insufficient and more susceptible to human error. As such, there is potential for machine learning applications and deep learning, in particular, to enhance the examination process by providing an automated image detection system to be used by the examiners to facilitate and improve the accuracy of the examination process. Therefore, this paper proposed a trademark similarity detection system using deep-learning techniques to extract image features automatically in order to retrieve a trademark based on shape similarity. Two pretrained convolutional neural networks (VGGNet and ResNet) were individually used to extract image features. Then, their performance was compared. Subsequently, the extracted features were used to calculate the similarity between a new trademark and each of those registered using the Euclidean distance. Thereafter, the system retrieved the most similar trademark to the query according to the smallest distances. As a result, the system achieved an average rank of 67,067.788, a normalized average rank of 0.0725, and a mean average precision of 0.774 on the Middle East Technical University dataset, which displays a promising application in detecting trademark similarity.
Convolutional Blur Attention Network for Cell Nuclei Segmentation

Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (AJI) were used to evaluate the segmentation efficiency of these models. Overall, the proposal method in this paper has the best performance, the AJI indicator on the DSB dataset and MoNuSeg is 0.8429, 0.7985, respectively.