Reviewerly/exHarmony huggingface dataset:
The significance of bug report elements

Abstract Open source software projects often use issue repositories, where project contributors submit bug reports. Using these repositories, more bugs in software projects may be identified and fixed. However, the content and therefore quality of bug reports vary. In this study, we aim to understand the significance of different elements in bug reports. We interviewed 35 developers to gain insights into their perceptions on the importance of various contents in bug reports. To assess our findings, we surveyed 305 developers. The results show developers find it highly important that bug reports include crash description, reproducing steps or test cases, and stack traces. Software version, fix suggestions, code snippets, and attached contents have lower importance for software debugging. Furthermore, to evaluate the quality of currently available bug reports, we mined issue repositories of 250 most popular projects on Github. Statistical analysis on the mined issues shows that crash reproducing steps, stack traces, fix suggestions, and user contents, have statistically significant impact on bug resolution times, for ∼70%, ∼76%, ∼55%, and ∼33% of the projects. However, on avarage, over 70% of bug reports lack these elements.
Heterogeneous Dynamic Graph Attention Network

Network embedding (graph embedding) has become the focus of studying graph structure in recent years. In addition to the research on homogeneous networks and heterogeneous networks, there are also some methods to attempt to solve the problem of dynamic network embedding. However, in dynamic networks, there is no research method specifically for heterogeneous networks. Therefore, this paper proposes a heterogeneous dynamic graph attention network (HDGAN), which attempts to use the attention mechanism to take the heterogeneity and dynamics of the network into account at the same time, so as to better learn network embedding. Our method is based on three levels of attention, namely structural-level attention, semantic-level attention and time-level attention. Structural-level attention pays attention to the network structure itself, and obtains the representation of structural-level nodes by learning the attention coefficients of neighbor nodes. Semantic-level attention integrates semantic information into the representation of nodes by learning the optimal weighted combination of different meta-paths. Time-level attention is based on the time decay effect, and the time feature is introduced into the node representation by neighborhood formation sequence. Through the above three levels of attention mechanism, the final network embedding can be obtained.Through experiments on two real-world heterogeneous dynamic networks, our models have the best results, proving the effectiveness of the HDGAN model.
Data Science for Big Data Applications and Services: Data Lake Management, Data Analytics and Visualization

Huge amounts of useful data are easily generated and gathered currently at a rapid rate from a broad range of rich data sources in numerous applications and services in the real world. Data science applies database techniques, scientific and engineering methods, mathematical and statistical models, data mining algorithms, and/or machine learning tools to manage data, extract the useful information and discover the new knowledge from these big data. This explains why data science for big data applications and services has become a fundamental technology in providing novel solutions in various areas in business, engineering, health, humanities, natural sciences, social sciences, etc. (e.g., healthcare, manufacturing, social life). Usually, data science focuses on big data management, analytics and visualization. Once big data are managed (i.e., captured, curated, managed and processed), big data are analyzed with an aim to discover interesting knowledge and information, which is usually presented in text or table form. Consistent with a proverb that "a picture is worth a thousand words", big data visualization as well as visual analytics helps to reveal and explain the discovered interesting knowledge and information. In this paper, we present (a) big data management with focus on information fusion and the data lake; (b) big data analytics and mining, with focus on frequent patterns; as well as (c) big data visualization with focus on a few visual analytic systems for visualizing big data and mined frequent patterns. For illustration, we discuss these three aspects of data science on coronavirus disease 2019 (COVID-19) data. This highlights some important aspects of data science for big data analyses, services, and smart data.
Optical cryptosystem scheme for hyperspectral image based on random spiral transform in gyrator domains

A novel chaotic cryptosystem for hyperspectral image, in this paper, is proposed by using a 4D hyperchaotic system in gyrator transform domains. In encryption process, a random spiral phase function (RSPF) is designed to generate variable spiral phase mask, which can be used for phase encoding with the hyperspectral image. Subsequently, the 4D hyperchaotic system is utilized to generate the chaotic sequences and these chaotic data is employed in designing RSPF, scrambling the hyperspectral image and synthesizing complex ciphertext, respectively. Finally, an improved spiral phase transform (SPT) in gyrator domains is implemented to obtain the ciphertext. To enhance the security of the encryption scheme, the ciphertext is reorganized according to the hyperchaotic attractor. Theoretical simulation analysis is given to demonstrate the validity and robustness of the proposed cryptosystem.
Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos

Abnormal event detection aims to identify the events that deviate from expected normal patterns. Existing methods usually extract normal spatio-temporal patterns of appearance and motion in a separate manner, which ignores low-level correlations between appearance and motion patterns and may fall short of capturing fine-grained spatio-temporal patterns. In this paper, we propose to simultaneously learn appearance and motion to obtain fine-grained spatio-temporal patterns. To this end, we present an adversarial 3D convolutional auto-encoder to learn the normal spatio-temporal patterns and then identify abnormal events by diverging them from the learned normal patterns in videos. The encoder captures the low-level correlations between spatial and temporal dimensions of videos, and generates distinctive features representing visual spatio-temporal information. The decoder reconstrucccts the original video from the encoded features representing by 3D de-convolutions and learns the normal spatio-temporal patterns in an unsupervised manner. We introduce the denoising reconstruction error and adversarial learning strategy to train the 3D convolutional auto-encoder to implicitly learn accurate data distributions that are considered normal patterns, which benefits enhancing the reconstruction ability of the auto-encoder to discriminate abnormal events. Both the theoretical analysis and the extensive experiments on four publicly available datasets demonstrate the effectiveness of our method.
LNBot: A Covert Hybrid Botnet on Bitcoin Lightning Network for Fun and Profit

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. LN is a payment channel network operating on top of Bitcoin network for faster Bitcoin transactions with negligible fees. Exploiting various anonymity features of LN, we designed a scalable two-layer botnet which completely anonymize the identity of the botmaster. In the first layer, the botmaster sends commands anonymously to the C&C servers through LN transactions. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multihop LN payments, where the commands are encoded with ASCII or Huffman encoding to provide covert communications. In the second layer, C&C servers further relay those commands to the bots they control in their mini-botnets to launch any type of attacks to victim machines. We implemented a proof-of-concept on the actual LN and extensively analyzed the delay and cost performance of LNBot. Our analysis show that LNBot achieves better scalibility compared to the other similar blockchain botnets with negligible costs. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot activities and minimize its impacts.
Performance Evaluation on Blockchain Systems: A Case Study on Ethereum, Fabric, Sawtooth and Fisco-Bcos

Blockchain technology is currently receiving increasing attention with widely used in many fields such as finance, retail, Internet of Things, and intelligent manufacturing. Although many blockchain applications are still in the early stage, this technique is very promising and has great potential. Blockchain is considered as one of the core technologies to trigger a new round of disruptive changes after Internet. In the future, it is expected to change the development prospects of many industries. However, the current blockchain systems suffer from poor performance which affects large-scale application. In order to better understand the performance of the blockchain systems, in this paper, we analyze four mainstream blockchain systems (Ethereum, Fabric, Sawtooth and Fisco-Bcos), and then perform a performance comparison through open source blockchain benchmarking tools. After that, we propose several optimization methods and discuss the future development of blockchain technique.
An Empirical Study on Dynamic Typing Related Practices in Python Systems

The dynamic typing discipline of Python allows developers to program at a high level of abstraction. However, type related bugs are commonly encountered in Python systems due to the lack of type declaration and static type checking. Especially, the misuse of dynamic typing discipline produces underlying bugs and increases maintenance efforts. In this paper, we introduce six types of dynamic typing related practices in Python programs, which are the common but potentially risky usage of dynamic typing discipline by developers. We also implement a tool named PYDYPE to detect them. Based on this tool, we conduct an empirical study on nine real-world Python systems (with the size of more than 460KLOC) to understand dynamic typing related practices. We investigate how widespread the dynamic typing related practices are, why they are introduced into the systems, whether their usage correlates with increased likelihood of bug occurring, and how developers fix dynamic typing related bugs. The results show that: (1) dynamic typing related practices exist inconsistently in different systems and Inconsistent Variable Types is most prevalent; (2) they are introduced into systems mainly during early development phase to promote development efficiency; (3) they have a significant positive correlation with bug occurring; (4) developers tend to add type checks or exception handling to fix dynamic typing related bugs. These results benefit future research in coding convention, language design, bug detection and fixing.
Temporal Textual Localization in Video via Adversarial Bi-Directional Interaction Networks

Given a natural language description, temporal textual localization aims to localize the most relevant segment in an untrimmed video, which is a natural and imperative extension of temporal action localization. Most existing temporal textual localization works neglect the long-range semantic modeling in video contents and lack accurate textual understanding. Moreover, they remain in single-task learning and fail to exploit multi-view supervised information. Based on these observations, we introduce a novel adversarial bi-directional interaction network, which is a global framework to retrieve the target segment directly. Specifically, we propose a bi-directional attention mechanism to build bi-directional information interaction, which captures long-range semantic dependencies from video context and enhances textual representation learning. After localization, we further advise an auxiliary discriminator network to verify the localization result and boost the performance by adversarial training process. We adopt multi-task learning approach to train our model, including: (1) predicting coordinate probability distribution task, which selects start and end frame to localize target segment; (2) predicting frame-level correlation distribution task, which calculates the correlation between frame and description; (3) auxiliary adversarial learning task, which calculates matched score between localization and description to boost the performance. The extensive experiments on ActivityNet Captions and TACoS show the significant effectiveness and efficiency of our method.
Website quality: An analysis of scientific production

A range of different methods and tools have been proposed by both academics and professionals in recent years for evaluating the quality of websites. Some are of general application and can be used to assess any type of website, while others have been adapted to the specialized characteristics of the websites employed in a given sector. This paper undertakes an analysis of existing scientific production in this field, with the aim of identifying its most relevant publications, its principal authors and the specific sectors served by the sites under evaluation. By triangulating review methods, 716 texts, published between 2000 and 2018, were identified in Scopus, Web of Science and other databases and examined. In addition to basic bibliographic information, the number of citations received by each text was recorded using Google Scholar. The area of knowledge in which each author works was also categorized based on his or her specific affiliations. The results point to a growing interest in website quality in a scientific community that has a presence in more than 70 countries. Its authors are drawn from various disciplines, although the highest number of publications is recorded in computer science, business and medical informatics. The most frequently cited texts are in fact seminal books in the associated disciplines of usability, information architecture and user experience. However, as of 2007, the number of texts describing evaluation tools for the websites of specific sectors – most notably, education (33%), health (27%) and commerce (21%) – increased their share. In conclusion, it is evident that website quality is a field of study undergoing constant growth and increasing specialization and one that offers ample opportunities for research.
Single Object Trackers in OpenCV: A Benchmark

Object tracking is one of the fundamental tasks in computer vision. It is used almost everywhere: human-computer interaction, video surveillance, medical treatments, robotics, smart cars, etc. Many object tracking methods have been published in recent scientific publications. However, many questions still remain unanswered, such as, which object tracking method to choose for a particular application considering some specific characteristics of video content or which method will perform the best (quality-wise) and which one will have the best performance? In this paper, we provide some insights into how to choose an object tracking method from the widespread OpenCV library. We provide benchmarking results on the OTB-100 dataset by evaluating the eight trackers from the OpenCV library. We use two evaluation methods to evaluate the robustness of each algorithm: OPE and SRE combined with Precision and Success Plot.
Deterministic Model Fitting by Local-Neighbor Preservation and Global-Residual Optimization

Geometric model fitting has been widely used in many computer vision tasks. However, it remains as a challenging task when handing multiple-structural data contaminated by noises and outliers. Most previous work on model fitting cannot guarantee the consistency of their solutions due to their randomness, precluding them from many real-world applications. In this research, we propose a fast two-view approximately deterministic model fitting scheme (called LGF), to provide consistent solutions for multiple-structural data. The proposed LGF scheme starts from defining preference function by preserving local neighborhood relationship, and then adopts the min-hash technique to roughly sample subsets. By this way, it is able to cover all model instances in data in the parameter space with a high probability. After that, LGF refines the previous sampled subsets by globalresidual optimization. Furthermore, we propose a simple yet effective model selection framework to estimate the number and the parameters of model instances in data. Extensive experiments on real images show that the proposed LGF scheme is able to observe superior or very competitive performance on both accuracy and speed over several state-of-the-art model fitting methods.
Customized Federated Learning for accelerated edge computing with heterogeneous task targets

As a dominant edge intelligence technique, Federated Learning (FL) can reduce the data transmission volume, shorten the communication latency and improve the collaboration efficiency among end-devices and edge servers. Existing works on FL-based edge computing only take device- and resource-heterogeneity into consideration under a fixed loss-minimization objective. As heterogeneous end-devices are usually assigned with various tasks with different target accuracies, task heterogeneity is also a significant issue and has not yet been investigated. To this end, we propose a Customized FL (CuFL) algorithm with an adaptive learning rate to tailor for heterogeneous accuracy requirements and to accelerate the local training process. We also present a fair global aggregation strategy for the edge server to minimize the variance of accuracy gaps among heterogeneous end-devices. We rigorously analyze the convergence property of the CuFL algorithm in theory. We also verify the feasibility and effectiveness of the CuFL algorithm in the vehicle classification task. Evaluation results demonstrate that our algorithm performs better in terms of the accuracy rate, training time, and fairness during aggregation than existing efforts.
Depth occlusion perception feature analysis for person re-identification

Person re-identification (ReID) has achieved significant improvement under the setting of matching two holistic person images. However, persons are easily occluded by the various objects and other persons in real-world scenarios, making Person ReID a challenging task. In this paper, we propose a novel method named Pose-Driven Visibility Model (PDVM) to effectively solve the degradation of recognition performance caused by occlusion. Firstly, we extract non-occluded human body features through pose estimation, pay attention to the salient features of non-human parts through self-attention mechanism, and obtains the final feature representation after the combination. Secondly, we more accurately locate person body parts by utilizing the detected human keypoints in different occlusion situations, effectively reducing the impact of unalignment and realizing better matching for persons. We implement extensive experiments on Occluded-DukeMTMC and Partial-REID. Our proposed method achieves state of the art performances which reaches 53.0% Rank-1 accuracy on Occluded-DukeMTMC dataset and ablation analysis also verify the effectiveness of our method. 2020 Elsevier Ltd. All rights reserved
Learning to utilize auxiliary reviews for recommendation

Review-based recommender systems represent users and items with reviews associated with them. As such, the recommender systems are highly dependent on the number of reviews, which is usually few in number. Thus, they produce inaccurate recommendations to users who rarely wrote reviews. An approach to generate better recommendations for the cold-start users is to augment the scarce reviews using other reviews, which are called auxiliary reviews. In this work, we address two research questions when leveraging auxiliary reviews: 1) How to find important auxiliary reviews? 2) How to combine the auxiliary reviews with their original reviews?, and propose a method that learns to utilize auxiliary reviews (LUAR) to tackle these questions. LUAR learns to automatically focus on important auxiliary reviews from data via neural attention mechanism. For the review combination issue, a self-attentive module in LUAR combines auxiliary reviews and original reviews considering their level of contribution. The module dynamically computes the level of contribution of each review based on its relative importance compared to others. Experimental results show that LUAR outperforms the state-of-the-art review-based recommender systems on 7 real-world datasets. Qualitative analyses also show that LUAR can accurately focus on important auxiliary reviews.
Player-Centered AI for Automatic Game Personalization: Open Problems

Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design.
Integrating blockchain and Internet of Things systems: A systematic review on objectives and designs

Recent years have witnessed the emergence of the Internet of Things (IoT) systems that incorporate blockchain (BC) elements in their architecture. Due to discrepancies between the requirements of IoT systems and the characteristics of BC networks, the motivations and design of these blockchain-enabled IoT systems (BC-IoT) are not only intriguing from a research perspective but also invaluable in practice. This paper presents an inductive study of the "why" and "how" of BC-IoT systems through a Systematic Literature Review of 120 peer-reviewed studies. To capture the diverse nature of BC-IoT integration, we proposed and applied a multi-perspective framework to analyse the existing systems. Regarding their motivations, we studied the improvement objectives and technical problems that drive the integration of BC. Regarding the design, we captured the position of BC within IoT systems as well as the content and processes that IoT systems offload to BC. As these dimensions are not mutually exclusive, they constitute a rich and multi-angle view of BC-IoT integration. Based on these findings, we defined 10 archetypes of BC-IoT systems that embody the core patterns of usage and configuration of BC in IoT systems.