Reviewerly/exHarmony huggingface dataset:
A Learning-to-Rank Based Approach for Improving Regression Test Case Prioritization

Many prior studies with attempt to improve regression testing adopt test case prioritization (TCP). TCP generally arranges the execution of regression test cases according to specific rules with the goal of revealing faults as early as possible. It is noted that different TCP algorithms adopt different metrics to evaluate test cases&#x0027; priority so that they may be effect at revealing faults early in different faulty programs. Adopting a single metric may not generally work well. In this decade, learning-to-rank (LTR) strategies have been adopted to address some software engineering problems. This study also uses a pairwise LTR strategy XGBoost to combine several existing metrics so as to improve TCP effectiveness. More specifically, we regard the metrics adopted by TCP techniques to evaluate test cases&#x0027; priority as the features of the training data and adopt XGBoost to learn the weights of the combined metrics. Additionally, in order to avoid overfitting, we use a fuzzy inference system to generate additional features for data augmentation. The experimental results show that our approach achieves more excellent effectiveness than the existing TCP techniques with respect to the selected subject programs.
Dy-HIEN

Many video websites offer packages composed of paid videos. Users who purchase a package become members of the website, and thus can enjoy the membership service, such as watching the paid videos. It is practically important to predict which users will become members so that the website can recommend them the suitable packages for purchasing. Existing works generally predict the purchase behavior of users through capturing their interests in items. However, such works cannot be directly applied to the studied problem due to the following challenges. First, some important features of videos and packages change over time, such as the number of clicks and the update of the videos. Existing methods are not capable to capture such dynamic features. Second, a user's purchasing intention is very hard to capture. A user watching a video does not necessarily mean that he/she would like to purchase the corresponding package. In this paper, we propose a Dynamic Evolution based Deep Hierarchical Intention Network (Dy-HIEN for short) for membership prediction, which contains two modules. In the first module, we design a dynamic embedding learning method, applying multi-relational heterogeneous information network and attention mechanism to effectively represent the embedding of videos and packages. In the second module, a hierarchical method is proposed to extract the purchase intention of users. First, the video play history is divided into sessions based on the clicks on packages, and then time-order encoder and kernel functions are applied to mine the intention pattern associated with the package clicked in each session. Extensive experiments on real-world datasets are conducted to demonstrate the advantages of the proposed model on a variety of evaluation metrics.
An Overview of Social Media and Sentiment Analysis

A wide range of frameworks are used in generation of enormous measure of information in various data formats like facts, content, sound, video. The social media accounts are major platform where people share their perspectives or u can say sentiments in above mentioned formats. In order to investigate the field of sentiment analysis social media platforms are major place that must be investigated. The increment and acquirement of applications that works on social media can be considered which provided both chances and difficulties to researchers of this field. The enormous measure of information produced by people by utilizing social media platforms is result of the combination of their experiences and every day actions. These social platforms produce information that can be very useful for any field. In this paper we are talking about investigation of social media which is best place for finding opinions, emotions and sentiments. We focus in this paper on sentiment analysis over social media. It will inspire researchers to focus over social media analysis and its major issues.
City Vision: CCTV Images Based Public Surveillance Model

A large number of criminals can be identified with the help of the forensic information derived from CCTV images, which is common in modern cities. CCTV cameras are commonly used to detect suspects of interest. Using this technique, it is also possible to obtain other types of forensic evidence and intelligence information. If CCTV images are used as forensic evidence, the reliability of the images must be determined. Case studies, empirical experiments, and phenomenological inquiry are all used to investigate this new type of evidence. Investigating the reliability of photographic evidence requires an interdisciplinary and multifaceted approach. According to this study, the use of CCTV images as forensic evidence is fraught with serious reliability problems. According to the study, reliability will be improved by establishing more robust methods that enhance the scientific model of repeatability and validation. As part of the research, a new theoretical model is developed that examines the relationship between photographic and physical proof and the application of photographic evidence in criminal investigations. In addition, the work develops a method for more reliable photo interpretation.
Digital Health Emergency Management—Pandemics and Beyond

Bandar Al Knawy, MD; Mollie Marian McKillop, MPH; Joud Abduljawad, MD; Sasu Tarkoma, PhD; Mahmood Adil, MD; Louise Schaper, PhD; Adam Chee, PhD; David W. Bates, MD; Michael Klag, MD; Uichin Lee, PhD; Zisis Kozlakidis, PhD; George Crooks, MBChB; Kyu Rhee, MD, MPP
SEPoW: Secure and Efficient Proof of Work Sidechains

Since the advent of sidechains in 2014, they have been acknowledged as the key enabler of blockchain interoperability and upgradability. However, sidechains suffer from significant challenges such as centralization, inefficiency and insecurity, meaning that they are rarely used in practice. In this paper, we present SEPoW, a secure and efficient sidechains construction that is suitable for proof of work (PoW) sidechain systems. The drawbacks for the centralized exchange of cross-chain assets in the participating blockchains are overcome by our decentralized SEPoW. To reduce the size of a cross-chain proof, we introduce merged mining into our SEPoW such that the proof consists of two Merkle tree paths regardless of the size of the current blockchain. We prove that the proposed SEPoW achieves the desirable security properties that a secure sidechains construction should have. As an exemplary concrete instantiation we propose SEPoW for a PoW blockchain system consistent with Bitcoin. We evaluate the size of SEPoW proof and compare it with the state-of-the-art PoW sidechains protocols. Results demonstrate that SEPoW achieves a proof size of 416 bytes which is roughly 123 $$\times $$ , 510 $$\times $$ and 62000 $$\times $$ smaller than zkRelay proof, PoW sidechains proof and BTCRelay proof, respectively.
Unity-Rate Coding Improves the Iterative Detection Convergence of Autoencoder-Aided Communication Systems

A forward error correction (FEC) and unity-rate coded (URC) autoencoder (AE)-assisted communication system is proposed for the first time, which relies on soft iterative decoding for attaining a vanishingly low error probability. The AE-demapper is specifically designed for directly calculating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extrinsic</i> logarithmic likelihood ratios (LLRs), which can be directly entered into the URC decoder for soft iterative decoding. This avoids the potential degradation due to the conversion of symbol probabilities to bit LLRs. A comprehensive capacity analysis of the AE is performed, which demonstrates the capacity advantage of the AE-aided constellation design over its conventional quadrature amplitude modulation (QAM)/ phase shift keying (PSK) counterpart. Furthermore, we carry out its EXtrinsic Information Transfer (EXIT) chart analysis, which indicates that as a benefit of our URC, the EXIT curve always reaches the [1,1] point of perfect convergence, leading to a vanishingly low error probability. More explicitly, our bit error ratio (BER) and block error ratio (BLER) results demonstrate that the proposed FEC-URC-AE system achieves significant iterative gains both in additive white Gaussian noise (AWGN) and Rayleigh channels, outperforming both its model-based FEC-AE and its conventional coded QAM/QPSK counterparts.
Improving the Result of Question Answering System with Semantic Similarity Method Based on Hierarchy in Ontology

Processing of question sentences is necessary to get an answer with the closest syntactic closeness. However, sentence processing only with syntactic proximity alone may not necessarily provide a satisfactory answer. Therefore, it is necessary to look at question sentences with semantic closeness. By applying the Semantic Similarity, it can improve the results of the Question Answering System. This work proposes The QASSOBI as The Question Answering System based on the Semantic Web using Ontology as knowledge with a hierarchical-based search. The results obtained precision of 84.21% and a recall of 80%. It has improved 60.71% compared to the process without Semantic Similarity.
Bessel regression and bbreg package to analyse bounded data

Summary Beta regression has been extensively used by statisticians and practitioners to model bounded continuous data without a strong competitor having the same main features. A class of normalised inverse‐Gaussian (N‐IG) process was introduced in the literature and has been explored in the Bayesian context as a powerful alternative to the Dirichlet process. Until this moment, no attention has been paid to the univariate N‐IG distribution in the classical inference. In this paper, we propose the bessel regression based on the univariate N‐IG distribution, which is an alternative to the beta model. The estimation of the parameters is done through an expectation–maximisation (EM) algorithm and the paper discusses how to perform inference. A useful and practical discrimination procedure is proposed for model selection between bessel and beta regressions. A new R package called bbreg is developed for fitting both bessel and beta regression models based on the EM‐algorithm and further providing graphical tools for model adequacy and model selection as well. Proper documentation for this package is available. The performances of the models are evaluated under misspecification in a simulation study. An empirical illustration is explored to confront results from bessel and beta regressions by using the new R package bbreg .
An Evaluation of Graph Databases and Object-Graph Mappers in CIDOC CRM-Compliant Digital Archives

The Portuguese General Directorate for Book, Archives and Libraries (DGLAB) has selected CIDOC CRM as the basis for its next-generation digital archive management software. Given the ontological foundations of the Conceptual Reference Model (CRM), a graph database or a triplestore was seen as the best candidate to represent a CRM-based data model for the new software. We thus decided to compare several of these databases, based on their maturity, features, performance in standard tasks and, most importantly, the Object-Graph Mappers (OGM) available to interact with each database in an object-oriented way. Our conclusions are drawn not only from a systematic review of related works but from an experimental scenario. For our experiment, we designed a simple CRM-compliant graph designed to test the ability of each OGM/database combination to tackle the so-called “diamond-problem” in Object-Oriented Programming (OOP) to ensure that property instances follow domain and range constraints. Our results show that (1) ontological consistency enforcement in graph databases and triplestores is much harder to achieve than in a relational database, making them more suited to an analytical rather than a transactional role; (2) OGMs are still rather immature solutions; and (3) neomodel, an OGM for the Neo4j graph database, is the most mature solution in the study as it satisfies all requirements, although it is also the least performing.
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies

Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a single numerical score, e.g., ratings or sentiment scores. This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e.g., the Food and Service of a restaurant. Understanding consumers' opinions toward different aspects can help evaluate business performance in detail and strategize business operations effectively. Specifically, we aim to answer interventional questions such as What will the restaurant popularity be if the quality w.r.t. its aspect Service is increased by 10%? The defining challenge of causal inference with observational data is the presence of "confounder'', which might not be observed or measured, e.g., consumers' preference to food type, rendering the estimated effects biased and high-variance. To address this challenge, we have recourse to the multi-modal proxies such as the consumer profile information and interactions between consumers and businesses. We show how to effectively leverage the rich information to identify and estimate causal effects of multiple aspects embedded in online reviews. Empirical evaluations on synthetic and real-world data corroborate the efficacy and shed light on the actionable insight of the proposed approach.
Speech Disorders Classification in Phonetic Exams with MFCC and DTW

Recognizing disordered speech is a challenge to Automatic Speech Recognition (ASR) systems. This research focuses on classifying disordered speech vs. non-disordered speech through signal processing coupled with machine learning techniques. We have found little evidence of ASR that correctly classifies disordered vs. ordered speech at the level of expert-based classification. This research supports the Automated Phonetic Transcription - Grading Tool (APTgt). APTgt is an online E-Learning system that supports Communications Disorders (CMDS) faculty during linguistic courses and provides reinforcement activities for phonetic transcription with the potential to improve the quality of students&#x0027; learning efficacy and teachers&#x0027; pedagogical experience. In addition, APTgt generates interactive practice sessions and exams, automatic grading, and exam analysis. This paper will focus on the classification module to classify disordered speech and non-disordered speech supporting APTgt. We utilize Mel-frequency cepstral coefficients (MFCCs) and dynamic time warping (DTW) to preprocess the audio files and calculate the similarity, and the Support Vector Machine (SVM) algorithm for classification and regression.
Program Verification Enhanced Precise Analysis of Interrupt-Driven Program Vulnerabilities

Due to the non-deterministic occurring of interrupt service routines, vulnerabilities of interrupt-driven programs, such as data race and atomicity violation, are usually hard to discover. Static analysis is an effective method for vulnerability analysis of interrupt-driven programs. However, existing techniques usually produce a large number of false alarms, which limits the application of static analysis in practice. To achieve high precision in vulnerability analysis of interrupt-driven programs, this paper proposes a program verification enhanced precise analysis method. For each potential vulnerability detected by static analysis, we propose a vulnerability validation approach which employs program verification to further automatically verify its feasibility. We have implemented a prototype of our method on top of CBMC. Experimental results on both an academic benchmark and 24 real-world programs show that our method can successfully identify true vulnerabilities and achieve a high precise analysis.
Symmetric-light Photometric Stereo

This paper presents symmetric-light photometric stereo for surface normal estimation, in which directional lights are distributed symmetrically with respect to the optic center. Unlike previous studies of ring-light settings that required the information of ring radius, we show that even without the knowledge of the exact light source locations or their distances from the optic center, the symmetric configuration provides us sufficient information for recovering unique surface normals without ambiguity. Specifically, under the symmetric lights, measurements of a pair of scene points having distinct surface normals but the same albedo yield a system of constrained quadratic equations about the surface normal, which has a unique solution. Experiments demonstrate that the proposed method alleviates the need for geometric light source calibration while maintaining the accuracy of calibrated photometric stereo.
Path-Analysis-Based Reinforcement Learning Algorithm for Imitation Filming

Imitation filming has been applied to autonomous filming by mimicking human operators. To imitate the operation of cameramen when filming multiple human actions, existing methods plan the camera motion through time series prediction or train multiple models to handle a particular style in a specific situation. As a result, these methods require various settings to adapt to different scenarios. In this work, we overcome such limitations and propose an end-to-end imitation learning framework for drone cinematography systems. The framework consists of two main components: ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</b> ) an efficient motion feature extraction module for generating a compact motion feature space, ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</b> ) a path-analysis-based reinforcement learning (PABRL) algorithm for imitating multiple filming styles from demonstrations and incorporating aesthetical features for improved perspective shots. Our PABRL method is based on the actor–critic network, which regards multiple human motion variables, camera translations, and image composition as inputs and then outputs an aesthetical filming strategy related to the subject motion. In addition, we propose an attention mechanism and a long–short-term rewarding function to enhance the motion feature space and the integrity of the generated trajectory, respectively. Extensive experimental results in simulated and real outdoor environments demonstrate that compared with state-of-the-art methods, our method can achieve 69.8% higher performance in terms of trajectory planning accuracy while successfully incorporating aesthetical features into the captured videos.
Improved TF-IDF Algorithm Combined with Multiple Factors

The TF-IDF algorithm is often used for the extraction of keywords of articles, but it only considers the information of word frequency, which limits the choice of keywords. In order to improve the efficiency of the algorithm, an improved algorithm (TF-IDFIS) has been presented,which adds the synonyms of keywords trained by word2vec model to the word vector composed of keywords. Then the improved algorithm is given different weights based on the part of speech and the location information(TF-IDFISW). In order to verify the effectiveness of the improved algorithm, experiments were conducted on a standard data set. The experimental results show that the accuracy of TF-IDFIS and TF-IDISW is greatly improved compared with the traditional method.
Detecting Adversarial Examples Utilizing Pixel Value Diversity

The state-of-the-art deep learning methods can be vulnerable: imperceptibly elaborated perturbations will induce unexpected behaviors. In this paper, we introduce two novel adversarial example detection methods utilizing pixel value diversity. First, we propose two independent metrics to assess the pixel value diversity separately, which reflects the spread of the pixel values in an image. Then we observe that adversarial examples are different from clean images on both metrics, regardless of attack methods. Based on this observation, for either metric, we can set a threshold and compare the threshold with the value of an image on the metric to detect whether the image is an adversarial example. Against several popular attack methods, experimental results on a variety of datasets show that our approach achieves better performances in detecting adversarial examples, compared to the state-of-the-art detection method. We also show that our methods are reliable even against adaptive attack.
A New Encryption/Decryption Approach Using AES

Information security issues are the most important concerns in the information technology driven world. The security concern of electronic data becomes so important and the responsibility of cryptography has increased so much. In this paper we use AES 128/256 bit keys for encrypting/decrypting data by the use of cipher library of java. In the proposed scheme, message is encoded/decoded by the key provided by user. The AES implementation of 128/256 bit key using the libraries is presented so that the excess classes can be reduced. The proposed method can be very useful as a light weight encryption mechanism with minimum computational overheads for mobile devices as well.
Re-Compose the Image by Evaluating the Crop on More Than Just a Score

Image re-composition has always been regarded as one of the most important steps during the post-processing of a photo. The quality of an image re-composition mainly depends on a person’s taste in aesthetics, which is not an effortless task for those who have no abundant experience in photography. Besides, while re-composing one image does not require much of a person’s time, it could be quite time-consuming when there are hundreds of images to be recomposed. To solve these problems, we propose a method that automates the process of re-composing an image to the desired aspect ratio. Although there already exist many image re-composition methods, they only provide a score to their predicted best crop but fail to explain why the score is high or low. Conversely, we succeed in designing an explainable method by introducing a novel 10-layer aesthetic score map, which represents how the position of the saliency in the original uncropped image, relative to that of the crop region, contributes to the overall score of the crop, so that the crop is not just represented by a single score. We conducted experiments to show that the proposed score map boosts the performance of our algorithm, which achieves a state-of-the-art performance on both public and our own datasets.