Reviewerly/exHarmony huggingface dataset:
Joint Sentiment Topic Model with Word Embeddings for Fake Review Detection

Abstract —Instead of relying exclusively on suppliers to excavate information about products and services, Internet users are now capable of attaining valuable information from a large number of reviews contributed by other consumers in the online community. Unfortunately, the increasing reliance on reviews of both customers and companies has resulted in the birth of fake reviews. Fake review is hard to be spotted due to its invisibility and diversity, which increases the difficulty of developing effective fake review identification methods. To overcome these challenges, recent studies have made lots of efforts to uncover fake reviews. However, two important points neglected are the topic information and sentiment information contained in sentences. In this paper, a novel fake review identification method joint sentiment topic model combining word embeddings (JSTE) is proposed. It derives sentiment and topics from the review text, while extending the semantic feature of topics by using word embeddings. From the method level, this paper combines joint sentiment topic features with linguistic features and utilizes LSTM as the classifier to classify fake reviews. Experimental results on three common datasets show that the proposed model improves the accuracy of fake comment identification by 5.27% in average compared with other baseline models.
Investigating Emotion Style in Human Faces Using Clustering Methods

For the past decade, performance-driven animation has been a reality in games and movies. While capturing and transferring emotions from human beings to avatars is a reasonably solved problem, it is accepted that humans express themselves in different ways, with personal styles, even when performing the same action. This article proposes a method to extract the style of human beings' facial movement when expressing emotions in posed images. We hypothesize that personal facial styles may be detected by clustering methods based on the similarity of individuals' facial expressions. We use the K-Means and Gaussian mixture model clustering methods to group emotion styles. In addition, extracted styles are considered to generate facial expressions in virtual humans and are tested with users. After an evaluation using both quantitative and qualitative criteria, our results indicate that facial expression styles do exist and can be grouped using quantitative computational methods.
Feature Fusion for Kinship Verification Based on Face Image Analysis

This paper proposes the fusion of two new features for improving kinship verification based on face image analysis. Combined features are the Gradient Local Binary Patterns (GLBP), which associates gradient and textural information. The second descriptor is the Histogram Of Templates (HOT), which is a shape descriptor. These features are utilized with the support vector machines classifier to develop the kinship verification. Experiments are carried out on Cornell and Kinface W-II datasets. Results obtained highlight the effectiveness of the proposed system which provide competitive and sometimes better performance than the state of the art.
Accept or Not? An Empirical Study on Analyzing the Factors that Affect the Outcomes of Modern Code Review?

Background: When submitting patches for code review, a patch is expected as the patch that is accepted in the fewest reworks (rounds of revisions) possibly. However, in practice, not all submitted patches can be accepted and integrated into the codebase the first time, since their quality is unsatisfactory or even poor, which might not pass patch review or need multiple revisions. Aims: In order to improve the effectiveness and efficiency of modern code review, this paper aims to identify the factors that impact the the patch acceptance and the rounds of revisions. Method: We first extract 29 factors from four categories: author, patch, static code quality and review. Then, we apply statistical approach to examine which of and how these factors impact the patch acceptance and the rounds of revisions, respectively. Results: Through case studies of 16,950 patches of 18 projects from Android and Eclipse systems, the result shows that: 1) the number of accepted/reviewed patches by an author has a statistically significant effect on the patch acceptance; 2) the kinds of languages and the number of invited reviewers have a statistically significant effect on the rounds of the revisions; 3) static code quality of patches has little impact on both patch acceptance and the rounds of revisions, although complexity has impact on the number of invited reviewers. Besides, there are other factors that have impact on the patch acceptance and rounds of revisions for different systems. Conclusions: Based on the analysis result, this study offers some suggestions for researchers and practitioners to improve the quality of submitted patches and the process of code review.
Cloud Workload Turning Points Prediction via Cloud Feature-Enhanced Deep Learning

Cloud workload turning point is either a local peak point standing for workload pressure or a local valley point standing for resource waste. Predicting such critical points is important to give warnings to system managers to take precautionary measures aimed at achieving high resource utilization, quality of service (QoS), and profit of the investment. Existing researches mainly focus more on the workload's future point value prediction only, whereas trend-based turning point prediction is not considered. Moreover, one of the most critical challenges during the prediction is the fact that traditional trend prediction methods which succeed in financial and industrial areas, etc., have a weak ability to represent the cloud features, which means that they cannot describe the highly-variable cloud workloads time series. This article introduces a novel cloud workload turning point prediction approach based on cloud feature-enhanced deep learning. First, we establish a turning point prediction model of cloud server workload considering cloud workload features. Then, a cloud feature-enhanced deep learning model is designed for workload turning point prediction. Experiments on the most famous Google cluster demonstrate the effectiveness of our model compared with state-of-the-art models. To the best of our knowledge, this article is the first systematic research on turning point-based trend prediction of cloud workload time series by cloud feature-enhanced deep learning.
Neural Network-Enhanced Optical Phase Conjugation for Nonlinearity Mitigation

Using a multi-layer perceptron to equalise the residual nonlinearity from the transmission of PDM 28 Gbaud 64QAM over 400km of SSMF employing midlink optical phase conjugation, we demonstrate 12-fold reduction in the BER.
Bug Report Classification into Orthogonal Defect Classification Defect Type using Long Short Term Memory

Software systems are being used in many businesses for performing critical operations such as financial operations. A bug in these systems can lead to financial losses. By identifying the type of such bugs, developers can easily take an action to fix a bug. Orthogonal defect classification model is a popular model for classifying bug reports in various attributes. In this paper, we proposed a bug report classification method that classify into their type as defined by ODC based on long short term memory, a RNN which is used in many classification task. The proposed method outperforms the classical approach such as bag of words and TF-IDF based classification models.
An Efficient Crypto Processor Architecture for Side-Channel Resistant Binary Huff Curves on FPGA

This article presents an efficient crypto processor architecture for point multiplication acceleration of side-channel secured Binary Huff Curves (BHC) on FPGA (field-programmable gate array) over GF(2233). We have implemented six finite field polynomial multiplication architectures, i.e., (1) schoolbook, (2) hybrid Karatsuba, (3) 2-way-karatsuba, (4) 3-way-toom-cook, (5) 4-way-toom-cook and (6) digit-parallel-least-significant. For performance evaluation, each implemented polynomial multiplier is integrated with the proposed BHC architecture. Verilog HDL is used for the implementation of all the polynomial multipliers. Moreover, the Xilinx ISE design suite tool is employed as an underlying simulation platform. The implementation results are presented on Xilinx Virtex-6 FPGA devices. The achieved results show that the integration of a hybrid Karatsuba multiplier with the proposed BHC architecture results in lower hardware resources. Similarly, the use of a least-significant-digit-parallel multiplier in the proposed design results in high-speed (in terms of both clock frequency and latency). Consequently, the proposed BHC architecture, integrated with a least-significant-digit-parallel multiplier, is 1.42 times faster and utilizes 1.80 times lower FPGA slices when compared to the most recent BHC accelerator architectures.
Face Mask Recognition Based on YOLOv3-tiny

The use of computer vision to realize non-contact face mask wearing testing and identify the standardization of wearing can improve the efficiency of mask wearing inspection, which is of great significance to reduce the spread of COVID-19. Based on the YOLO v3 framework in deep learning, this project conducts an in-depth study on the accuracy and efficiency of face mask detection, introduces its structure and principle, and explores and experiments its application based on TensorFlow. The evaluation and identification rate of the experiment was 78*, which verified the feasibility and practicability of the method.
RouteInfer: Inferring Interdomain Paths by Capturing ISP Routing Behavior Diversity and Generality

Accurate inference of interdomain paths between arbitrary source and destination is the foundation for many research areas, especially for the security of the Internet routing system. The widely used method to solve this problem is using standard policies based on the business relationship model, but it is far from satisfactory. We conduct an in-depth analysis on the inherent limitations of the path inference by standard routing policies and show that the routing behaviors of ISPs are diverse and standard import policies are oversimplified. Then we develop RouteInfer, an algorithm for accurately inferring interdomain paths by capturing ISP routing behaviors diversity and generality. RouteInfer uses a 3-layer policy model to extract the fine-grained policies and coarse-grained policies of ASes and can achieve high accuracy as well as good generalization ability. After extracting policies, we find another inherent challenge that there is still a huge number of ASes without inferred policies. To overcome this challenge, RouteInfer formulates the prediction of route decisions as a ranking problem and develops a learning-based approach especially for predicting route decisions. We carefully design node, link, and path features based on the understanding of actual route decisions. Overall, on average, RouteInfer achieves 81.64 $$\%$$ accuracy. Compared with state-of-the-art inference algorithms, RouteInfer increases the inference accuracy by about 30.04 $$\%$$ to 182.3 $$\%$$ . Furthermore, we analyze the inferred policies and the route decision model to understand routing behaviors deeply. We find that many ASes set fine-grained policies for CDN ASes. Besides, most of the violations of the standard preference rule are related to p2p links in European IXPs.
Breathe-to-Pair (B2P)

We propose Breathe-to-Pair (B2P), a protocol for pairing and shared-key generation for wearable devices that leverages the wearer's respiration activity to ensure that the devices are part of the same body-area network. We assume that the devices exploit different types of sensors to extract and process the respiration signal. We illustrate B2P for the case of two devices that use respiratory inductance plethysmography (RIP) and accelerometer sensors, respectively. Allowing for different types of sensors in pairing allows us to include wearable devices that use a variety of different sensors. In practice, this form of sensor variety creates a number of challenges that limit the ability of the shared-key establishment algorithm to generate matching keys. The two main obstacles are the lack of synchronization across the devices and the need for correct noise-induced mismatches between the generated key bit-strings.
An efficient system using implicit feedback and lifelong learning approach to improve recommendation

Abstract This paper presents a new technique for contextual item-to-item Collaborative Filtering-based Recommender System, an improved version popularised by e-commerce giant Amazon two decades back. The concept is based on items also-viewed under the same browsing session. Users’ browsing patterns, locations, and timestamps are considered as the context and latent factors for each user. The algorithm computes recommendations based on users’ implicit endorsements by clicks . The algorithm does not enforce the user to log in to provide recommendations and is capable of providing accurate recommendations for non-logged-in users and with a setting where the system is unaware of users’ preferences and profile data (non-logged-in users). This research takes the cue from human lifelong incremental learning experience applied to machine learning on a large volume of the data pool. First, all historical data is gathered from collectable sources in a distributed manner through big data tools. Then, a long-running batch job creates the initial model and saves it to Hadoop Distributed File System (HDFS). An ever-running streaming job loads the model from HDFS and builds on top of it in an incremental fashion. At the architectural level, this resembles the big data mix processing Lambda Architecture . The recommendation is computed based on a proposed equation for a weighted sum between near real-time and historical batch data. Real-time and batch processing engines act as autonomous Multi-agent systems in collaboration. We propose an ensemble method for batch-stream the recommendation engine. We introduce a novel Lifelong Learning Model for recommendation through Multi-agent Lambda Architecture. The recommender system incrementally updates its model on streaming datasets to improve over time.
Can blockchains and data privacy laws be reconciled?

Due to the advancing digitalization, the importance of data is constantly increasing. Application domains such as smart cars, smart cities, or smart healthcare rely on the permanent availability of large amounts of data to all parties involved. As a result, the value of data increases, making it a lucrative target for cyber-attacks. Particularly when human lives depend on the data, additional protection measures are therefore important for data management and provision. Blockchains, i. e., decentralized, immutable, and tamper-proof data stores, are becoming increasingly popular for this purpose. Yet, from a data protection perspective, the immutable and tamper-proof properties of blockchains pose a privacy concern. In this paper, we therefore investigate whether blockchains are in compliance with the General Data Protection Regulation (GDPR) if personal data are involved. To this end, we elaborate which articles of the GDPR are relevant in this regard and present technical solutions for those legal requirements with which blockchains are in conflict. We further identify open research questions that need to be addressed in order to achieve a privacy-by-design blockchain system.
Lip print‐based identification using traditional and deep learning

The concept of biometric identification is centred around the theory that every individual is unique and has distinct characteristics. Various metrics such as fingerprint, face, iris, or retina are adopted for this purpose. Nonetheless, new alternatives are needed to establish the identity of individuals on occasions where the above techniques are unavailable. One emerging method of human recognition is lip-based identification. It can be treated as a new kind of biometric measure. The patterns found on the human lip are permanent unless subjected to alternations or trauma. Therefore, lip prints can serve the purpose of confirming an individual's identity. The main objective of this work is to design experiments using computer vision methods that can recognise an individual solely based on their lip prints. This article compares traditional and deep learning computer vision methods and how they perform on a common dataset for lip-based identification. The first pipeline is a traditional method with Speeded Up Robust Features with either an SVM or K-NN machine learning classifier, which achieved an accuracy of 95.45% and 94.31%, respectively. A second pipeline compares the performance of the VGG16 and VGG19 deep learning architectures. This approach obtained an accuracy of 91.53% and 93.22%, respectively.
Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

Solar resource assessment is of paramount importance in the planning of solar energy applications. Solar resources are abundant and characterization is essential for the optimal design of a system. Solar energy is estimated, indirectly, by the processing of satellite images. Several analyses with satellite images have considered a single variable—cloudiness. Other variables, such as albedo, have been recognized as critical for estimating solar irradiance. In this work, a multivariate analysis was carried out, taking into account four variables: cloudy sky index, albedo, linke turbidity factor (TL2), and altitude in satellite image channels. To reduce the dimensionality of the database (satellite images), a principal component analysis (PCA) was done. To determine regions with a degree of homogeneity of solar irradiance, a cluster analysis with unsupervised learning was performed, and two clustering techniques were compared: k-means and Gaussian mixture models (GMMs). With respect to k-means, the GMM method obtained a smaller number of regions with a similar degree of homogeneity. The multivariate analysis was performed in Mexico, a country with an extended territory with multiple geographical conditions and great climatic complexity. The optimal number of regions was 17. These regions were compared for annual average values of daily irradiation data from ground stations using multiple linear regression. A comparison between the mean of each region and the ground station measurement showed a linear relationship with a R2 score of 0.87. The multiple linear regression showed that the regions were strongly related to solar irradiance. The optimal sites found are shown on a map of Mexico.
Trac <sup>2</sup> Chain

With the built-in tamper-proofness and trusted decentralized properties, blockchain has become a new way of building applications and is viewed an attractive platform for provenance graph storage. However, provenance graphs have the graph structure containing sensitive information including the records as well as the linkages between the records. Both need to be kept secret from the unauthorized users including the blockchain peers. Storing the record linkages on blockchain in a privacy-preserving manner is challenging because (1) past records cannot be modified due to the tamper-proofness, (2) efficient graph tracking/tracing on blockchain is necessary for a provenance system on blockchain, and (3) the size of the records' pointer may leak the volume of linkages. Existing approaches either fail to provide linkage privacy protection to the provenance graph, could only support less-complicated structure such as tree, or leak the volume of linkages from the block sizes. We present Trac2Chain, a framework that provides complete linkage privacy as well as full tracking/tracing functions for the provenance graphs stored on blockchain. The novel graph storage design in Trac2Chain allows generation of the provenance dependencies of arbitrary graph structure while still enjoying the tamper-proofness of blockchain. The evaluation and comparison show that Trac2Chain achieves the best functionality and privacy guarantees among the state-of-the-art approaches and has comparable overhead.
Evaluating fork after withholding (FAW) attack in Bitcoin

Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network.
Privacy-Preserving Sports Wearable Data Fusion Framework

When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.
Assessing the Value of Course-Assigned E-Books

Many academic libraries have historically limited textbook purchases. Given recent calls to support expanded online education programs and textbook affordability initiatives, however, many academic libraries have changed course to invest considerable amounts of money to license electronic books (e-books) that are assigned in courses. This study explores the value of these materials by leveraging commonly used evaluative methods, such as benefit-cost ratio and usage statistics, as well as novel criteria, including a quality analysis using Resources for College Libraries (RCL) and an analysis of the persistence of assigned titles across semesters. The findings suggest that assigned e-books are a wise investment of library funds within the authors’ institutional context. By investigating value from diverse perspectives, the authors assert the importance of approaching such a complex question with multimodal and inclusive methods.