Reviewerly/exHarmony huggingface dataset:
Side Channel Attack resistant Cross-User Generalized Deduplication for Cloud Storage

Cross-user deduplication is an effective technique to improve the efficiency of cloud storage by blocking redundant uploading of duplicate data. However, the deterministic response of this technique actually creates a side channel for attackers, which is able to be utilized to infer existence privacy of the target data in cloud storage. Because of the difficulty in achieving complete obfuscation in deduplication response, such kind of attack still cannot be well resisted, thus is becoming a big obstacle in using this technique. To deal with this challenge, we propose a cross-user generalized deduplication scheme for cloud storage, which takes the lead to achieve security against side channel attack by template extraction strategy. Specifically, we perform source-based deduplication to extracted templates and target-based strategy to the corresponding deviations. Because of the generalization strategy introduced, existence privacy of the target file cannot be inferred from deduplication response any more. The experimental results show that, with security against side channel attack guaranteed, the proposed scheme is more efficient in both communication and storage comparing with the state-of-the art.
Dual-path joint correction network for underwater image enhancement

Acquired underwater images often suffer from severe quality degradation, such as color shift and detail loss due to suspended particles' light absorption and scattering. In this paper, we propose a Dual-path Joint Correction Network (DJC-NET) to cope with the above degenerate issues, preserving different unique properties of underwater images in a dual-branch way. The design of the light absorption correction branch is to improve the selective absorption of light in water and remove color distortion, while the light scattering correction branch aims to improve the blur caused by scattering. Concretely, in the light absorption correction path, we design the triplet color feature extraction module, which balances the triplet color distribution of the degraded image through independent feature learning between R, G, and B channels. In the light scattering correction path, we develop a dual dimensional attention mechanism to extract the texture information from the features, aiming to recover sufficient details by more effective feature extraction. Furthermore, our method utilizes the multi-scale U-net to adaptively fusion features from different paths to generate enhanced images. Extensive visual and objective experimental results demonstrate that our method outperforms state-of-the-art methods in various underwater scenes.
Safety Helmet Detection based on YOLOV4-M

In the intelligent monitoring of the construction site, there is often the problem of not wearing a safety helmet. In order to effectively judge the wearing condition of the helmet, a detection method of YOLOV4-M is proposed. This method replaces the backbone network in YOLOV4 with MobileNetV3, uses depthwise separable convolution to reduce the amount of parameters of the backbone network, and uses H-swish to improve the performance of the model. Experiments show that the final detection accuracy rate is 98.2%, and the FPS reaches 40 frames, which can be well applied to the detection of helmets.
Foreign Assistance, Sustainable Development, and Commercial Law: A Comparative Analysis of the Impact of Corruption on Developing Economies

The essential purpose of this research is to investigate the relationship between foreign assistance and economic growth. It also analyzes the role corruption might play in influencing this relationship in aid-recipient countries that have similar real gross domestic product (GDP) per capita, but different development patterns. An analysis is performed on specific samples from 2000 to 2019. The model is split into three sections for this purpose: i.e., all developing economies, sub-Saharan Africa (SSA), and the most corrupt countries from regions other than SSA. The difference generalized method of moments (GMM) panel framework is used for empirical analysis. The study concludes that foreign aid does not result in encouraging and significant changes in overall economic growth in developing economies. By contrast, corruption has a powerful impact on foreign aid effectiveness. It is also observed from the analysis that SSA economies receive high levels of foreign assistance, but still cannot extract maximum benefit due to various economic and social problems. Furthermore, foreign assistance effectiveness is almost insignificant in most corrupt economies from other regions.
Shopping with Voice Assistant: Understanding Consumer Intention and the Mediating Role of Trust

Voice assistants are a new smart tool in the market to assist and ease the shopping process for consumers. A voice assistant, along with its in-built artificial intelligence, makes consumer and device interaction more humanlike and personal. Despite a promising debut in the Indian market, there has been limited research on the Indian consumers’ intention to use VA for online shopping. Most of the studies are exploratory in nature and lack a clear theoretical framework. This study aimed to tap the growing trend of the use of voice assistants in the Indian market. The study focused on finding what factors determine consumer intention to shop online using a voice assistant. How does trust upon a voice assistant affect the relationship between these factors and intention? This is a cross-sectional quantitative study. A convenience sample of 121 respondents participated in the study using an online structured questionnaire. The hypotheses testing was done using AMOS SEM. The tests confirmed that information quality and system quality impacted intention only when mediated through trust. Interaction quality, however, only had a direct effect, and no mediation through trust was determined. The study explained how more sincere efforts by VA in information searching and delivering the right information to consumers lead to the development of trust in the VA.
A University-Led Contact Tracing Program Response to a COVID-19 Outbreak Among Students in Georgia, February–March 2021

Few reports have described how university programs have controlled COVID-19 outbreaks. Emory University established a case investigation and contact tracing program in June 2020 to identify and mitigate transmission of SARS-CoV-2 in the Emory community. In February 2021, this program identified a surge in COVID-19 cases. In this case study, we present details of outbreak investigation, construction of transmission networks to assess clustering and identify groups for targeted testing, and program quality metrics demonstrating the efficiency of case investigation and contact tracing, which helped bring the surge under control. During February 10-March 5, 2021, Emory University identified 265 COVID-19 cases confirmed by nucleic acid testing in saliva or nasopharyngeal samples. Most students with COVID-19 were undergraduates (95%) and were affiliated with Greek life organizations (70%); 41% lived on campus. Network analysis identified 1 epidemiologically linked cluster of 198 people. Nearly all students diagnosed with COVID-19 (96%) were interviewed the same day as their positive test result. Of 340 close contacts, 90% were traced and 89% were tested. The median time from contact interview to first test was 2 days (interquartile range, 0-6 days); 43% received a positive test result during their quarantine. The surge was considered under control within 17 days, after which new cases were no longer epidemiologically linked. Early detection through systematic testing protocols and rapid and near-complete contact tracing, paired with isolation and quarantine measures, helped to contain the surge. Our approach emphasizes the importance of early preparation of adequate outbreak response infrastructure and staff to implement interventions appropriately and consistently during a pandemic.
Attenuating Catastrophic Forgetting by Joint Contrastive and Incremental Learning

In class incremental learning, discriminative models are trained to classify images while adapting to new instances and classes incrementally. Training a model to adapt to new classes without total access to previous class data, however, leads to the known problem of catastrophic forgetting of the previously learnt classes. To alleviate this problem, we show how we can build upon recent progress on contrastive learning methods. In particular, we develop an incremental learning approach for deep neural networks operating both at classification and representation level which alleviates forgetting and learns more general features for data classification. Experiments performed on several datasets demonstrate the superiority of the proposed method with respect to well known state-of-the-art methods.
Research on an improved PSO algorithm with dual self-adaptation and dual variation

Particle swarm optimization (PSO) algorithm is a global optimization evolutionary algorithm which simulates the foraging behavior of birds. In view of the problems of premature improved particle swarm optimization algorithm with dual self-convergence and prematurity of standard PSO algorithm, an adaptation and dual variation is proposed, to overcome the shortcomings of the existing improved PSO algorithm with linear inertia weight and motion direction variation. A nonlinear self-adaptive improvement of inertia weight and a linear self-adaptive improvement of learning factor are presented to improve the scope and precision of the optimal search for the balanced population, to strengthen the global search ability in the initial stage of optimization and to enhance the local search ability in the later stage of optimization. Moreover, in the later period of motion, the variation from the motion directions of some particles and the variation from initial values of a few particles are used to increase the diversity of the population and enlarge the searching range of the particles, so as to prevent the particles from falling into the local optimal solution. The improved PSO algorithm with dual self-adaptation and dual variation is applied to function optimization. The simulation results show that the algorithm can effectively improve the convergence rate and precision of the algorithm, and avoid premature convergence.
Digital marketing and public relations: A way to promote public relations value

The objective of this study is to determine the relationship of digital marketing as a wide emerging marketing tool, in developing public relation values for modern business in the globalized competitive era of mature markets. For this study, the cross-sectional data were collected by 450 respondents including the people as customers, and the managerial staff of the different well-reputed organizations in the United Arab Emirates (UAE). The findings of this study highlight that there is a clear and strong relationship between digital marketing in developing public relations values, and modern businesses need this technique to develop equity and provide a distinct message about the vision and mission of the organization in the target market. The significance of this study is it addresses the theoretical gap in the literature, and the practical gap in business practices by providing the insight to utilize digital marketing not only for the promotion of products and services but could be used to promote public relation values.
Blockchain Implementation Challenges in Developing Countries: An evidence-based systematic review and bibliometric analysis

1. Introduction The present age is filled with science and technology. Today’s modern civilization is the gift of seamless and robust technological development. Therefore, we are going through an innovative flow of amazing technology that spreads among various sectors, like manufacturing, retail, and financial services (Chang et al., 2019; Hou et al., 2020). Blockchain is a
Breast Cancer Prediction Using Advance Machine Learning Algorithms

Breast cancer is very common type of cancer now a day. It is observed in many of the women and responsible for many deaths in recent days. In this work the power of machine learning classifiers is applied in prediction of breast cancer. Three machine learning classifiers LR, KNN and Decision Tree are applied on a standard data set and their prediction accuracy is calculated. From the experiment it is concluded that the LR algorithms is best for breast cancer prediction.
Cloud computing resource node allocation algorithm based on load balancing strategy

At present, cloud computing has developed into a high-performance computing service model integrating the technical advantages of distributed computing, utility computing, load balancing, parallel computing, network storage, hot backup redundancy and virtualization. However, due to the lack of effective algorithm at the beginning of DAG task scheduling to ensure the load balance of resource node allocation during task scheduling, this paper proposes a cloud computing resource node allocation algorithm (NA-LB) based on load balancing strategy. The algorithm records the changes of virtual machine operating parameters in the cloud computing resource pool as vector values of spatial vectors, Therefore, the mathematical calculation method of load balance is given and used as the load balance index to measure the allocation of resource nodes. At the same time, the task replication strategy is used to further optimize the task resource node allocation process. The experimental results show that NA-LB algorithm can effectively shorten the DAG data processing time and show good load balance.
xASP: An Explanation Generation System for Answer Set Programming

In this paper, we present a system, called xASP, for generating explanations that explain why an atom belongs to (or does not belong to) an answer set of a given program. The system can generate all possible explanations for a query without the need to simplify the program before computing explanations, i.e., it works with non-ground programs. These properties distinguish xASP from existing systems such as $$\texttt{xClingo}$$ , $$\texttt{DiscASP}$$ , $$\textit{exp}(\textit{ASP}^c)$$ , and $$\textit{s(CASP)}$$ , which also generate explanations for queries to logic programs under the answer set semantics but simplify and ground the programs (the three systems $$\texttt{xClingo}$$ , $$\texttt{DiscASP}$$ , $$\textit{exp}(\textit{ASP}^c)$$ ) or do not always generate all possible explanations (the system $$\textit{s(CASP)}$$ ). In addition, the output of xASP is insensitive to syntactic variations such as the order conditions and the order of rules, which is also different from the output of $$\textit{s(CASP)}$$ .
DoS Attacks on Blockchain Ecosystem

Denial of Service (DoS) attacks are a growing threat in network services. The frequency and intensity of DoS attacks are rapidly increasing day by day. The immense financial potential of the Cryptocurrency market is a prevalent target of the DoS attack. The DoS attack events are kept on happening in cryptocurrencies and the blockchain ecosystem. To the best of our knowledge, there has not been any study on the DoS attack on the blockchain ecosystem. In this paper, we identify ten entities in the blockchain ecosystem and we scrutinize the DoS attacks on them. We also present the DoS mitigation techniques applicable to the blockchain services. Additionally, we propose a DoS mitigation technique by the use of verifiable delay function (VDF).
An Optimized Feature Selection Method for E-Learning Recommender System Using Deep Neural Network based on Multilayer Perceptron

An effective E-Learning system must identify learning content appropriate for the needs of the specific learner from among the many sources of learning content available.The learning content can consist of different types of media content namely Text, Video and Image and to determine a rating.Recommendation systems become increasingly used in information systems and especially in e-learning platform.These systems are used to propose and recommend content of these platforms to users according to needs of the later in order to allow them to have the maximum information for learning.In this paper, a recommendation system based on data mining and deep learning has been proposed to help the learner by recommending the appropriate learning content.To improve the recommendations a content matrix is created and based on this, the logistic regression and deep learning methods could classify recommendations.Experimental results show that the precision, recall and f measure for Top 5 & 10 Recommendations using Deep Neural Network performsby 1.439% better than Top 5 & 10 Recommendations using Logistic Regression among Chi Squared, SA, PSO and ICA methods.
A High-Accuracy Calibration Method for a Telecentric Structured Light System

We propose a method for accurately calibrating a telecentric structured light system consisting of a camera attached to a bilateral telecentric lens and a pin-hole projector. The proposed method can be split into two parts: axial calibration and transverse calibration. The first part is used for building the relationship between phase and depth by means of a planar plate with ring markers on its surface at several different positions in the measuring volume. The second part is used for establishing the relationship between transverse coordinates and pixel positions with the depth offered by a translation stage and the extracted ring centers. Compared with existing methods that require projector calibration, the proposed method can avoid a propagation of the correspondence error between the camera imaging plane and projector imaging plane, thus increasing calibration accuracy. The calibrated telecentric structured light system is further used for three-dimensional (3D) reconstructions of a planar, a ruled surface, and complex surfaces. Experimental results demonstrate that the proposed system calibration method can be used for accurate 3D measurement.
Stacked Language Models for an Optimized Next Word Generation

Next word prediction task is the application of a language model in natural language generation that deals with generating words by repeatedly sampling the next word conditioned on the previous choices. This paper proposes a stacked language model for optimized next word generation using three models. In stage I, the meaning of a word is captured through learn embedding and the structure of the text sequence is encoded using a stacked Long Short Term Memory (LSTM). In stage II, a Bidirectional Long Short Term Memory (Bi-LSTM) stacking on top of the unidirectional LSTM encodes the structure of the text sequences, while in stage III, a two-layer Gated Recurrent Unit (GRU) is used to capture text sequences of data. The proposed system was implemented using Python 3.7, Tensorflow 2.6.0 with Keras and a Nvidia Graphical Processing Unit (GPU). The proposed deep learning models were trained using the Pride and Prejudice corpus from the Project Gutenberg library of ebooks. The evaluation was performed by predicting the next 3 words after considering 10 sets of text sequences. From the experiment carried out, the accuracy of the two-layer LSTM model measured 83&#x0025;, the accuracy of the Bi-LSTM stacking on unidirectional LSTM model measured 79&#x0025;, and the accuracy of the two-layer GRU model measured 81&#x0025;. Regarding predictions, the two-layer LSTM predicted the 10 sequences correctly, the Bi-LSTM stacking on unidirectional LSTM predicted 8 sequences correctly and the two-layer GRU predicted 7 sequences correctly.
StrokeStyles: Stroke-based Segmentation and Stylization of Fonts

We develop a method to automatically segment a font’s glyphs into a set of overlapping and intersecting strokes with the aim of generating artistic stylizations. The segmentation method relies on a geometric analysis of the glyph’s outline, its interior, and the surrounding areas and is grounded in perceptually informed principles and measures. Our method does not require training data or templates and applies to glyphs in a large variety of input languages, writing systems, and styles. It uses the medial axis, curvilinear shape features that specify convex and concave outline parts, links that connect concavities, and seven junction types. We show that the resulting decomposition in strokes can be used to create variations, stylizations, and animations in different artistic or design-oriented styles while remaining recognizably similar to the input font.
How centralized is decentralized? Comparison of wealth distribution in coins and tokens

Rapidly growing distributed ledger technologies (DLTs) have recently received attention among researchers in both industry and academia. While a lot of existing analysis (mainly) of the Bitcoin and Ethereum networks is available, the lack of measurements for other crypto projects is observed. This article addresses questions about tokenomics and wealth distributions in cryptocurrencies. We analyze the time-dependent statistical properties of top cryptocurrency holders for 14 different distributed ledger projects. The provided metrics include approximated Zipf coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient. We show that there are quantitative differences between the coins (cryptocurrencies operating on their own independent network) and tokens (which operate on top of a smart contract platform). Presented results show that coins and tokens have different values of approximated Zipf coefficient and centralization levels. This work is relevant for DLTs as it might be useful in modeling and improving the committee selection process, especially in decentralized autonomous organizations (DAOs) and delegated proof-of-stake (DPoS) blockchains.
Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter

Social media such as Twitter has now become very close to society. Twitter users can express current issues, their opinions, product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment of their products among the public as insight that will be used to evaluate what aspects of their products need to be further developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique (SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%.