Reviewerly/exHarmony huggingface dataset:
Large Scale Evaluation of Natural Language Processing Based Test-to-Code Traceability Approaches

Traceability information can be crucial for software maintenance, testing, automatic program repair, and various other software engineering tasks. Customarily, a vast amount of test code is created for systems to maintain and improve software quality. Today's test systems may contain tens of thousands of tests. Finding the parts of code tested by each test case is usually a difficult and time-consuming task without the help of the authors of the tests or at least clear naming conventions. Recent test-to-code traceability research has employed various approaches but textual methods as standalone techniques were investigated only marginally. The naming convention approach is a well-regarded method among developers. Besides their often only voluntary use, however, one of its main weaknesses is that it can only identify one-to-one links. With the use of more versatile text-based methods, candidates could be ranked by similarity, thus producing a number of possible connections. Textual methods also have their disadvantages, even machine learning techniques can only provide semantically connected links from the text itself, these can be refined with the incorporation of structural information. In this paper, we investigate the applicability of three text-based methods both as a standalone traceability link recovery technique and regarding their combination possibilities with each other and with naming conventions. The paper presents an extensive evaluation of these techniques using several source code representations and meta-parameter settings on eight real, medium-sized software systems with a combined size of over 1.25 million lines of code. Our results suggest that with suitable settings, text-based approaches can be used for test-to-code traceability purposes, even where naming conventions were not followed.
Hand Gesture Recognition Based on Force Myography Measurements using KNN Classifier

Hand gesture recognition presents one of the most important aspects for human machine interface (HMI) development, and it has a wide spectrum of applications including sign language recognition for deaf and dumb people. Herein, force myography signals (FMG) are extracted using eight nanocomposite CNT/PDMS pressure sensors simultaneously. Data are collected from eight healthy volunteers for American sign language digits 0-9. Two sets of features are extracted, the first one is composed of mean, standard deviation and rms values for the raw FMG data for all 8 sensors individually. The second set is composed of the 2-norm of the raw FMG signal and three proportional features, where the FMG signals are studied with respect to the reference rest signal. Classification is performed using each of the seven individual features as well as the combination of features in each set. The combination of features in the second set gives better testing accuracy of 95%, 91.9% for k=2, k=3 using KNN classifier, respectively.
Solving Parallax Error for 3D Eye Tracking

Head-mounted eye-trackers allow for unrestricted behavior in the natural environment, but have calibration issues that compromise accuracy and usability. A well-known problem arises from the fact that gaze measurements suffer from parallax error due to the offset between the scene camera origin and eye position. To compensate for this error two pieces of data are required: the pose of the scene camera in head coordinates, and the three-dimensional coordinates of the fixation point in head coordinates. We implemented a method that allows for effective and accurate eye-tracking in the three-dimensional environment. Our approach consists of a calibration procedure that allows to contextually calibrate the eye-tracker and compute the eyes pose in the reference frame of the scene camera, and a custom stereoscopic scene camera that provides the three-dimensional coordinates of the fixation point. The resulting gaze data are free from parallax error, allowing accurate and effective use of the eye-tracker in the natural environment.
An improved registration strategy for aligning incomplete blade measurement data to its model

Among the existing global registration methods, the principal components analysis (PCA) combined with iterative closest point (ICP) algorithm is probably the easiest and efficient way to implement global alignment. However, this method has limitations in processing the registration of two-point clouds with large difference in shape. To aiming this problem, an improved MRPCA-ICP method is proposed to align the incomplete blade measurement data to its CAD model. In this method, we firstly define and extract the major region (MR) of blade using the constraint plane constructed on the tenon of blade. Then the MRPCA method is presented to replace the conventional PCA to achieve coarse alignment, and a simple and efficient algorithm is particularly developed to ensure the consistent orientation of corresponding principal directions. Finally, the ICP-based fine registration is accelerated for the better initial input provided by MRPCA. Experimental results demonstrate the superiority of our method in terms of the registration efficiency and robustness.
Deep Learning Model for Personality Traits Classification from Text Emphasis on Data Slicing

Text analytics is the progression of find out insight knowledge over written text such as consumer attitude, product criticisms, emotion analysis, customer response, etc. Previous works fundamentally based on organized data, then again, writings, (for example, tweets) are inexactly organized, have poor spelling, regularly contain linguistic use errors, and they are multilingual. This made the development of much more perplexing and fascinating. The two common methodologies are used, Text-based analytics and Bag of Words. Sentiment parsing emphasis on the structure and grammar of words where Bag of words model neglects the grammar and word type and, in its place, concentrating on signifying text (a sentence, document, and tweet etc.) as the bag of words. In this research work, we have proposed a deep learning model for personality traits classification based on text. In this research paper, we emphasis on pre-processing over input data, meant for dimension reduction which may improve the classification accuracy and will reduce the computation time. In result section we will show the input data dimension reduced after pre-processing.
Enhanced active VM load balancing algorithm using fuzzy logic and K-means clustering

With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.
Fine-grained Program Partitioning for Security

Complex software systems are often not designed with the principle of least privilege, which requires each component be given the minimum amount of privileges to function. As a result, software vulnerabilities in less privileged code can lead to privilege escalation, defeating security and privacy. Privilege separation is the process of automatically partitioning a software system into least privileged components, and we argue that it is effective at reducing the attack surface. However, previous privilege-separation systems do not provide fine-grained separation of privileged code and non-privileged code co-existing in the same function for C/C++ applications. We propose a fine-grained partitioning technique for supporting fine-grained separation in automatic program partitioning. The technique has been applied to a set of security-sensitive networking and interactive programs. Results show that it can automatically generate executable partitions for C applications; further, partitioned programs incur acceptable runtime overheads.
Blind Denoising of Fluorescence Microscopy Images Using GAN-Based Global Noise Modeling

Fluorescence microscopy is a key driving force behind advances in modern life sciences. However, due to constraints in image formation and acquisition, to obtain high signal-to-noise ratio (SNR) fluorescence images remains difficult. Strong noise negatively affects not only visual observation but also downstream analysis. To address this problem, we propose a blind global noise modeling denoiser (GNMD) that simulates image noise globally using a generative adversarial network (GAN). No prior information on noise properties is required. And no clean training targets need to be provided for noisy inputs. Instead, by simulating real image noise using a GAN, our method synthesizes paired noisy and clean images for training a denoising deep learning network. Experiments on real fluorescence microscopy images show that our method substantially outperforms competing state-of-the-art methods, especially in suppressing background noise. Denoising using our method also facilitates downstream image segmentation.
On Hamilton–Jacobi PDEs and Image Denoising Models with Certain Nonadditive Noise

We consider image denoising problems formulated as variational problems. It is known that Hamilton–Jacobi PDEs govern the solution of such optimization problems when the noise model is additive. In this work, we address certain nonadditive noise models and show that they are also related to Hamilton–Jacobi PDEs. These findings allow us to establish new connections between additive and nonadditive noise imaging models. Specifically, we study how the solutions to these optimization problems depend on the parameters and the observed images. We show that the optimal values are ruled by some Hamilton–Jacobi PDEs, while the optimizers are characterized by the spatial gradient of the solution to the Hamilton–Jacobi PDEs. Moreover, we use these relations to investigate the asymptotic behavior of the variational model as the parameter goes to infinity, that is, when the influence of the noise vanishes. With these connections, some non-convex models for nonadditive noise can be solved by applying convex optimization algorithms to the equivalent convex models for additive noise. Several numerical results are provided for denoising problems with Poisson noise or multiplicative noise.
VSRNet: End-to-end video segment retrieval with text query

Users are sometimes interested in specific segments of an untrimmed video when using the video search engine. Targeting at this demand, we explore a novel research topic of text query based video segment retrieval (VSR). Different from the conventional video retrieval task or localizing text descriptions in a single video, it requires the retrieval of the most relevant video from a large collection as well as localizing the start and end timestamps of a segment that matches the text query best from the video. A direct solution is to perform video-level matching first, and then apply description localization among such video candidates. Such two-stage based methods are not able to utilize complementary information of each stage, and are time-consuming in inference. In this paper, We propose VSRNet, an end-to-end framework that efficiently retrieves video at segment granularity with two branches. In the first branch, individual videos and texts are mapped to a common space for stand-alone ranking. In the second branch, we propose a supervised text-aligned attention mechanism and calculate the response of every frame to the text query, from which the frames with high scores are aggregated as segment proposals. Extensive experiments conducted on ActivityNet Captions and DiDeMo verify the effectiveness of our method and show that our solution significantly outperforms the state of the art.
ExpMRC: explainability evaluation for machine reading comprehension

Achieving human-level performance on some Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). However, it is necessary to provide both answer prediction and its explanation to further improve the MRC system's reliability, especially for real-life applications. In this paper, we propose a new benchmark called ExpMRC for evaluating the textual explainability of the MRC systems. ExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE+, and C3, with additional annotations of the answer's evidence. The MRC systems are required to give not only the correct answer but also its explanation. We use state-of-the-art PLMs to build baseline systems and adopt various unsupervised approaches to extract both answer and evidence spans without human-annotated evidence spans. The experimental results show that these models are still far from human performance, suggesting that the ExpMRC is challenging. Resources (data and baselines) are available through https://github.com/ymcui/expmrc.
Securing Optical Networks Using Quantum-Secured Blockchain: An Overview

The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.
What are friends for? The impact of friendship on communicative efficiency and cortisol response during collaborative problem solving among younger and older women

Conversation is a skilled activity that depends on cognitive and social processes, both of which develop through adulthood. We examined the effects of age and partner familiarity on communicative efficiency and cortisol reactivity. Younger and older women interacted with familiar or unfamiliar partners in a dyadic collaborative conversation task (N = 8 in each group). Regardless of age, referential expressions among familiar and unfamiliar partners became more efficient over time, and cortisol concentrations were lower for speakers interacting with familiar partners. These findings suggest that communicative effectiveness is largely preserved with age, as is the stress-buffering effect of a familiar partner.
Explainable Internet Traffic Classification

The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.
Forensic analysis of Twitch video streaming activities on Android

Abstract Video streaming abuses range from copyright infringement and piracy to child sexual abuse and murder. This paper performs proactive forensics of Twitch video streaming activities on Android devices. We designed our experiments using a methodology based on black‐box testing principles and conducted them on Android devices using a forensic framework. The results of our experiments suggest that forensic investigators can extract evidences of streams broadcast, shared, and watched by the user, and associate them with the Twitch account of the user. We detail all the recoverable artifacts of forensic significance, provide mechanisms to extract them based on the identified anchors, and interpret their significance in forensic investigations.
An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

Abstract An advanced hybrid algorithm ( h aDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed h aDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, h aDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in h aDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed h aDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.
Colonoscopic Image Synthesis For Polyp Detector Enhancement Via Gan And Adversarial Training

Computer-aided polyp detection system powered by deep neural networks has achieved high performance but also suffers from data insufficiency. To address this problem, recent researches focus on synthesizing new colonoscopic images by Generative Adversarial Network (GAN). However, the synthesized images follow the same distribution as that of the training dataset, which limits the performance of the detectors re-trained on it. Recent studies show that adversarial examples can expand the data distribution and thus adversarial training can effectively improve the robustness of deep neural networks. Inspired by these two factors, this paper proposes a data augmentation framework to directly produce false negative colonoscopic images via GAN and the adversarial attack. The synthesized polyps are natural and experiments on three popular detectors show that compared with using GAN alone, producing false negative images by the adversarial attack can further improve the performance of the re-trained detectors.
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games

In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of the research is based on StarCraft II environment because it is the most well-known RTS games in world-wide. In our proposed MADRL-based algorithm, distributed MADRL is fundamentally used that is called QMIX. In addition to QMIX-based distributed computation, we consider state categorization which is a novel preprocessing method for representation of graph attention. Furthermore, self-attention mechanisms are used for identifying the relationship among agents in the form of graphs. Based on these approaches, we propose a categorized state graph attention policy (CSGA-policy). As observed in the performance evaluation of our proposed CSGA-policy with the most well-known StarCraft II simulation environment, our proposed algorithm works well in various settings, as expected.
Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

Purpose In recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises. Design/methodology/approach This study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter. Findings This research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public. Originality/value This study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.