Reviewerly/exHarmony huggingface dataset:
Machine Learning based Sentiment Analysis of Coronavirus Disease Related Twitter Data

Newly discovered coronavirus has resulted in an infectious disease named Coronavirus disease (COVID-19). The outbreak of Coronavirus has caused many nations to exercise lockdowns to curb the spread of this virus. In this situation, there has been an upsurge in the usage of the internet and social media platforms. Therefore, amidst this pandemic, another crisis has also evolved; caused by the spread of incomplete and often inaccurate information which keeps leading to mass fear and anxiety. Hence it is high time to address this informational crisis and find the polarity of people's sentiments towards COVID-19 situation so that authorized bodies can take suitable actions to cope with the situation and also review the prevalent systems. Currently, most of the sentiment analysis focus on texts, therefore we extracted sentiment from real-time tweets as well as from individual input image/video. By making use of PySpark and the libraries within it, we enabled scalable and fault-tolerant stream processing of live data stream. This study uses classification algorithms such as logistic regression, decision tree, random forest, Naive Bayes. After evaluation of all the models, logistic regression model was seen to have the highest values for accuracy and F_score. Hence this model has been used for the final sentiment analysis.
Serendipity in the city: User evaluations of urban recommender systems

Abstract The contemporary city is increasingly being labeled as a smart city consisting of both physical and virtual spaces. This digital augmentation of urban life sets the scene for urban recommender systems to help citizens dealing with the abundance of digital information and corresponding choice overload, for example, by recommending the best place to have dinner based on your personal profile. There are, however, concerns that this kind of algorithmic filtering could lead to homogenization of urban experiences and a decline of social cohesion among citizens. To overcome this issue, scholars increasingly encourage the introduction of serendipity in all types of recommender systems. Nonetheless, it remains unclear how this can be achieved in practice. In this work, we study user evaluations of serendipity in urban recommender systems through a survey among 1,641 citizens. More specifically, we study which characteristics of recommended items contribute to serendipitous experiences and to what extent this increases user satisfaction and conversion. Our results align with findings in other application domains in the sense that there is a strong relation between the relevance and novelty of recommendations and the corresponding experienced serendipity. Moreover, serendipitous recommendations are found to increase the chance of users following up on these recommendations.
A Work-Centered Approach to System User-Evaluation

New systems are often based on optimistic assumptions of how they will improve human performance. In the cognitive engineering tradition, these assumed benefits are regarded as hypotheses that need to be tested. An important element of a system user evaluation is to determine whether the hypothesized benefits are realized. Evaluation may also uncover unsupported aspects of performance or unanticipated side-effects of introducing the new technology that need to be addressed. We present a work-centered approach to user evaluation intended to meet these objectives, focusing specifically on design of tailored user-feedback questionnaires ( work-centered questionnaires) that are intended to be diagnostic of how specific system elements do, or do not, support work. We summarize two recent evaluation studies we have conducted that illustrate our approach and the diagnostic power of work-centered questionnaires. We discuss how the goals and approach of a work-centered evaluation differ from more traditional approaches to usability evaluation that emphasize the use of standardized questionnaires and broad assessments of usability.
An ontology matching approach for semantic modeling: A case study in smart cities

Abstract This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching‐based Instances (COMI) and Pattern mining for Ontology Matching‐based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart‐city data into similar clusters using the generic k‐means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state‐of‐the‐art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large‐scale data in smart‐city environments.
A Chaotic-based Encryption/Decryption System for Secure Video Transmission

Ultrasonic communication is an alternative communication method of transmitting information through solids. Sending video streams can deliver more information than a single sensor, and video monitoring shows great potential in ultrasonic communication applications. An efficient and secure cryptosystem is needed to protect the sensitive video stream. In this paper, we propose a novel Chaotic-based encryption scheme utilizing 1-D and 2-D iteration models for secure video streaming. This algorithm is based on the Arnold Cat Map and the Logistic Map and has good confusion and diffusion properties. The Arnold Cat Map transforms the dataset into a pseudo-random state over several iterations and is reversible, while the Logistic Map introduces a specific external key to replace and recover the pixels value during encryption and decryption. Both the encryption and decryption processes are presented and formulated in our cryptosystem scenario. The proposed method maintains a good encryption quality, provides key sensitivity and has low correlation between pixels. The results of a secured video frame using separate Chaotic Maps and the novel encryption scheme are compared and discussed. Experiments and analysis demonstrate that the Chaotic-based algorithm is best suited for the ultrasonic video communication system and is resilient to security attacks.
GEO: Generation and Execution Optimized Stochastic Computing Accelerator for Neural Networks

Stochastic computing (SC) has seen a renaissance in recent years as a means for machine learning acceleration due to its compact arithmetic and approximation properties. Still, SC accuracy remains an issue, with prior works either not fully utilizing the computational density or suffering from significant accuracy losses. In this work, we propose GEO - Generation and Execution Optimized Stochastic Computing Accelerator for Neural Networks, which optimizes stream generation and execution components of SC, and bridges the accuracy gap between stochastic computing and fixed-point neural networks. It improves accuracy by coupling controlled stream sharing with training and balancing OR and binary accumulations. GEO further optimizes the SC execution through progressive shadow buffering and architectural optimizations. GEO can improve accuracy compared to state-of-the-art SC by 2.2-4.0% points while being up to 4.4X faster and 5.3X more energy efficient. GEO eliminates the accuracy gap between SC and fixed-point architectures while delivering up to 5.6X higher throughput and 2.6X lower energy.
Cybersecurity and Privacy Risk Assessment of Point-of-Care Systems in Healthcare—A Use Case Approach

Point-of-care systems are generally used in healthcare to respond rapidly and prevent critical health conditions. Hence, POC systems often handle personal health information; and consequently, their cybersecurity and privacy requirements are of crucial importance. While, assessing these requirements is a significant task. In this work, we propose a use case approach to assess specifications of cybersecurity and privacy requirements of POC systems in a structured and self-contained form. Such an approach is appropriate since use cases are one of the most common means adopted by developers to derive requirements. As a result, we detail a use case approach in the framework of a real-based healthcare IT infrastructure that includes a health information system, integration engines, application servers, web services, medical devices, smartphone apps and medical modalities (all data simulated) together with the interaction with participants. Since our use case also sustains the analysis of cybersecurity and privacy risks in different threat scenarios, it also supports decision making and the analysis of compliance considerations.
Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization

In recent years, even though Stochastic Gradient Descent (SGD) and its variants are well-known for training neural networks, it suffers from limitations such as the lack of theoretical guarantees, vanishing gradients, and excessive sensitivity to input. To overcome these drawbacks, alternating minimization methods have attracted fast-increasing attention recently. As an emerging and open domain, however, several new challenges need to be addressed, including 1) Convergence properties are sensitive to penalty parameters, and 2) Slow theoretical convergence rate. We, therefore, propose a novel monotonous Deep Learning Alternating Minimization (mDLAM) algorithm to deal with these two challenges. Our innovative inequality-constrained formulation infinitely approximates the original problem with non-convex equality constraints, enabling our convergence proof of the proposed mDLAM algorithm regardless of the choice of hyperparameters. Our mDLAM algorithm is shown to achieve a fast linear convergence by the Nesterov acceleration technique. Extensive experiments on multiple benchmark datasets demonstrate the convergence, effectiveness, and efficiency of the proposed mDLAM algorithm.
Edge-Enabled Distributed Deep Learning for 5G Privacy Protection

Due to the limited storage and computing power, edge devices at the network edge cannot train deep learning models locally. Traditional deep learning training requires users to upload a local dataset to a cloud center, and trains the data using massive computation resources of the cloud center. However, it results in two bad effects: uploading a local dataset to a centralized cloud center controlled by a third party leaves user data privacy at risk; and uploading multimedia data will consume huge bandwidth resources of mobile users and storage resources of the cloud center, resulting in low scalability in term of the number of edge devices. To deal with these two problems, we propose an edge-enabled distributed deep learning platform by dividing a general deep learning training network into a front and back subnetwork. Specifically, the front subnetwork consisting of several layers is deployed close to input data and is trained separately at each edge device using the local dataset, and the outputs of all front subnetworks are sent to the back subnetwork for later training at a cloud center; while the back subnetwork is deployed at the cloud center, and its output is sent to each front subnetwork. As no original dataset is transferred from edge devices to the cloud center, the platform can protect data privacy and has high scalability. Above that, another two measures are taken to ensure data privacy: asymmetric encryption technology is adopted to guarantee the safety and integrality of the transferred parameters between edge servers and the cloud center; and blockchain technology is used to monitor the actions of the stakeholders in this platform and thereby ensure trust among the stakeholders. Experimental results show the validation of the proposed method.
Machine Learning for Credit Card Fraud Detection

Moumeni, Loubna Saber, Mohammed Slimani, Ilham Elfarissi, Ilhame Bougroun, ZinebE-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Many errors are lost due to fraudulent card transactions each year. The development of performance fraud detection methods is obligatory to minimize such losses. This article examines the usefulness of applying different learning approaches for detecting credit card fraud. Three algorithms will be applied on a database of an American bank and the data will be exploited based on supervised and unsupervised learning techniques, namely the MLP (multilayer perceptron), LR (logistic regression) and PCA (Principal Component Analysis). The main purpose of the study is comparing the classification performance of each algorithm using real dataset of fraudulent user accounts in a telecommunication network.
News Video Summarization Combining SURF and Color Histogram Features

Because the data volume of news videos is increasing exponentially, a way to quickly browse a sketch of the video is important in various applications, such as news media, archives and publicity. This paper proposes a news video summarization method based on SURF features and an improved clustering algorithm, to overcome the defects in existing algorithms that fail to account for changes in shot complexity. Firstly, we extracted SURF features from the video sequences and matched the features between adjacent frames, and then detected the abrupt and gradual boundaries of the shot by calculating similarity scores between adjacent frames with the help of double thresholds. Secondly, we used an improved clustering algorithm to cluster the color histogram of the video frames within the shot, which merged the smaller clusters and then selected the frame closest to the cluster center as the key frame. The experimental results on both the public and self-built datasets show the superiority of our method over the alternatives in terms of accuracy and speed. Additionally, the extracted key frames demonstrate low redundancy and can credibly represent a sketch of news videos.
Two-stage routing with optimized guided search and greedy algorithm on proximity graph

As interest surges in large-scale retrieval tasks, proximity graphs are now the leading paradigm. Most existing proximity graphs share the simple greedy algorithm as their routing strategy for approximate nearest neighbor search (ANNS), but this leads to two issues: low routing efficiency and local optimum; this because they ignore the special requirements of different routing stages. Generally, the routing can be divided into two stages: the stage far from the query (S1) and the stage closer to the query (S2). S1 aims to quickly route to the vicinity of the query, and the efficiency is dominant. While S2 focuses on finding the results accurately, so the accuracy is staple. We carefully design tailored routing algorithm for each stage, then combine them together to form a Two-stage routing with Optimized Guided search and Greedy algorithm (TOGG). For S1, we propose optimized guided search to quickly locate the query’s neighborhood by the guidance of the query direction. While for S2, we propose optimized greedy algorithm to comprehensively visit the vertices near the query by agilely detecting explicit and implicit convergence paths. Finally, using theoretical and experimental analysis, we demonstrate the proposed method can achieve better performance of efficiency and effectiveness than state-of-the-art work.
The forgotten election administrator of internet voting: lessons from Estonia

The introduction of new voting channels, voting technologies and other voting innovations are often thought to improve voter participation in elections and democracy. However, it frequently happens at the expense of administrators, who needs to deliver even more complex elections. This article traces how the introduction of a new voting channel, Internet voting, affects frontline administrators through a qualitative in-depth case study of the 2017 local elections in Estonia. Findings show that the local election administration plays a substantial role in delivering Internet voting, despite the centralized election hierarchy. The case shows little evidence to support the expectation that Internet voting decreases the administrative burden of local election officials. The article outlines the vulnerabilities in Internet voting administration, resulting from the complexity of delivering multi-channel elections, particularly the ones integrating Internet- and paper-based voting channels. The article makes important recommendations for improving the implementation of electronic voting and improving the quality of elections.
ESRFuzzer: an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities

Abstract SOHO (small office/home office) routers provide services for end devices to connect to the Internet, playing an important role in cyberspace. Unfortunately, security vulnerabilities pervasively exist in these routers, especially in the web server modules, greatly endangering end users. To discover these vulnerabilities, fuzzing web server modules of SOHO routers is the most popular solution. However, its effectiveness is limited due to the lack of input specification, lack of routers’ internal running states, and lack of testing environment recovery mechanisms. Moreover, existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper, we propose a solution ESRFuzzer to address these issues. It is a fully automated fuzzing framework for testing physical SOHO devices. It continuously and effectively generates test cases by leveraging two input semantic models, i.e., KEY-VALUE data model and CONF-READ communication model, and automatically recovers the testing environment with power management. It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities. With the guidance of the two semantic models, ESRFuzzer can work in two ways: general mode fuzzing and D-CONF mode fuzzing. General mode fuzzing can discover both issues which occur in the CONF and READ operation, while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode fuzzing.We ran ESRFuzzer on 10 popular routers across five vendors. In total, it discovered 136 unique issues, 120 of which have been confirmed as 0-day vulnerabilities we found. As an improvement of SRFuzzer, ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types, and 23 of them have been confirmed as 0-day vulnerabilities by vendors. The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.
Code Augmentation for Detecting Covert Channels Targeting the IPv6 Flow Label

Information hiding is at the basis of a new-wave of malware able to elude common detection mechanisms or remain unnoticed for long periods. To this aim, a key approach exploits network covert channels, i.e., abusive communication paths nested within a legitimate traffic flow. The increasing diffusion of IPv6 makes it attractive for an attacker, especially for the presence of the Flow Label field, which can be manipulated to contain up to 20 secret bits per packet. Unfortunately, gathering data to implement a standalone detection mechanism or to support third-party security tools is a poorly generalizable process and often leads to scalability issues. This paper showcases how to take advantage of code augmentation features (i.e., the extended Berkeley Packet Filter) to detect covert channels targeting the IPv6 Flow Label. To prove its effectiveness, the proposed approach has been tested against Internet-wide traffic traces collected in the wild. Results indicate that it is possible to spot the channel while mitigating the memory footprint and the computational burden (e.g., the processed traffic only experience an additional delay of a few nanoseconds).
An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph aggregation

For the context-dependent Text-to-SQL task, the generation of SQL query is placed in a multi-turn interaction scenario. Each turn of Text-to-SQL must take historical interactive information and database schema into account. Accordingly, how to encode and integrate these different types of texts (the question sentence, the corresponding SQL query, and database schema) is a tough problem. In previous work, these series of texts are usually concatenated into sequences and encoded by various variants of recurrent neural networks (RNN). However, the RNNs cannot model the intrinsic relationship of the text directly. To this end, we propose an interaction-modeling mechanism to represent and aggregate these texts. Firstly, different types of texts are represented as individual graphs. Then, heterogeneous graph aggregation is used to capture the interactions and aggregate graphs into a holistic representation. Finally, the corresponding SQL query is generated based on the current question and the aggregated information. We evaluate our model on the SparC and CoSQL dataset to demonstrate the benefits of interaction-modeling. Experimentally, our model has a competitive performance and space–time cost.
Visual Clustering Factors in Scatterplots

Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets.
A new method for image classification and image retrieval using convolutional neural networks

Abstract This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel‐1k, Corel‐10k, Caltech‐101 object and Scene‐67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state‐of‐the‐art in the field.