Reviewerly/exHarmony huggingface dataset:
Revisiting reopened bugs in open source software systems

Reopened bugs can degrade the overall quality of a software system since they require unnecessary rework by developers. Moreover, reopened bugs also lead to a loss of trust in the end-users regarding the quality of the software. Thus, predicting bugs that might be reopened could be extremely helpful for software developers to avoid rework. Prior studies on reopened bug prediction focus only on three open source projects (i.e., Apache, Eclipse, and OpenOffice) to generate insights. We observe that one out of the three projects (i.e., Apache) has a data leak issue – the bug status of reopened was included as training data to predict reopened bugs. In addition, prior studies used an outdated prediction model pipeline (i.e., with old techniques for constructing a prediction model) to predict reopened bugs. Therefore, we revisit the reopened bugs study on a large scale dataset consisting of 47 projects tracked by JIRA using the modern techniques such as SMOTE, permutation importance together with 7 different machine learning models. We study the reopened bugs using a mixed methods approach (i.e., both quantitative and qualitative study). We find that: 1) After using an updated reopened bug prediction model pipeline, only 34% projects give an acceptable performance with AUC $\geqslant $ 0.7. 2) There are four major reasons for a bug getting reopened, that is, technical (i.e., patch/integration issues), documentation, human (i.e., due to incorrect bug assessment), and reasons not shown in the bug reports. 3) In projects with an acceptable AUC, 94% of the reopened bugs are due to patch issues (i.e., the usage of an incorrect patch) identified before bug reopening. Our study revisits reopened bugs and provides new insights into developer’s bug reopening activities.
Factors affecting Agile adoption: An industry research study of the mobile app sector in Saudi Arabia

Agile is an established software development methodology that helps develop software by improving time to market, quality, customer engagement and reducing costs. Factors underpinning its adoption have been widely researched. However, most of these studies have been conducted in developed countries, particularly Europe and North America, with only a handful carried out in developing countries, including the Middle East. This is problematic given the strategic and economic importance of the software industry in such places as Saudi Arabia, where Agile adoption remains in the early stages, despite heavy investment in this industry in recent years to diversify its oil-dependent economy. Therefore, this study empirically investigates the factors influencing Agile adoption by Saudi Arabian software small and medium-sized enterprises (SMEs). To this end, in-depth interviews were conducted with 12 software practitioners from three software SMEs. Our findings reveal that human factors, such as customer involvement, team capability, organisational culture and national culture, are considered the most impactful factors affecting the adoption of Agile as opposed to technical ones, such as the availability of specific tools or techniques.
The Use of Discord Servers to Buy and Sell Drugs

The focus of current research on social media drug markets is the use of mainstream platforms such as Facebook, Snapchat and Instagram. No research currently exists examining how lesser-known social media platforms may facilitate online drug supply. This paper presents the first analysis of the use of the social media platform Discord to buy and sell illegal drugs. The study utilizes observational data and qualitative interviews with Discord drug market participants in New Zealand, including sellers and a drug server administrator ( n = 12). Our findings demonstrate that the Discord platform, which was initially established for gaming, is also being used to facilitate drug transactions. Discord is used to establish local drug selling groups called “servers,” which can be joined by accessing an “invite-link.” The advantages of Discord drug servers cited by interviewees included competitive prices and the ability to greatly expand local seller and customer bases beyond pre-existing personal networks. However, accessibility, server size and management varied considerably between drug servers, giving rise to a range of issues and concerns. We use drug market typologies based on theory of “open” and “closed” markets to understand how “lower tier” and “higher tier” Discord drug servers provided different buying and selling environments. “Lower tier” drug servers were generally characterized by greater ease of entry, larger size, higher rates of opportunism among participants and variable server management. Conversely, “higher tier” drug servers typically involved tighter market entry controls, more active server management and were generally smaller in size. The emergence of Discord drug servers illustrates how the evolution of social media platforms presents their users with new spaces that can be adapted to function as drug markets and the tensions that may emerge during the process of learning to buy and sell in a new social media space.
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection

Despite the recent advances of deep neural networks, object detection for adverse weather remains challenging due to the poor perception of some sensors in adverse weather. Instead of relying on one single sensor, multimodal fusion has been one promising approach to provide redundant detection information based on multiple sensors. However, most existing multimodal fusion approaches are ineffective in adjusting the focus of different sensors under varying detection environments in dynamic adverse weather conditions. Moreover, it is critical to simultaneously observe local and global information under complex weather conditions, which has been neglected in most early or late-stage multimodal fusion works. In view of these, this paper proposes a Global-Local Attention (GLA) framework to adaptively fuse the multi-modality sensing streams, i.e., camera, gated, and lidar data, at two fusion stages. Specifically, GLA integrates an early-stage fusion via a local attention network and a late-stage fusion via a global attention network to deal with both local and global information, which automatically allocates higher weights to the modality with better detection features at the late-stage fusion to cope with the specific weather condition adaptively. Experimental results demonstrate the superior performance of the proposed GLA compared with state-of-the-art fusion approaches under various adverse weather conditions, such as light fog, dense fog, and snow.
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning

While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering the safety critical applications of c-MARL, such as traffic management, power management and unmanned aerial vehicle control, it is crucial to test the robustness of c-MARL algorithm before it was deployed in reality. Existing adversarial attacks for MARL could be used for testing, but is limited to one robustness aspects (e.g., reward, state, action), while c-MARL model could be attacked from any aspect. To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms. First, motivated by Markov Decision Process (MDP), MARLSafe consider the robustness of c-MARL algorithms comprehensively from three aspects, namely state robustness, action robustness and reward robustness. Any c-MARL algorithm must simultaneously satisfy these robustness aspects to be considered secure. Second, due to the scarceness of c- MARL attack, we propose c-MARL attacks as robustness testing algorithms from multiple aspects. Experiments on SMAC environment reveals that many state-of-the-art c- MARL algorithms are of low robustness in all aspect, pointing out the urgent need to test and enhance robustness of c-MARL algorithms.
PereiraASLNet: ASL letter recognition with YOLOX taking Mean Average Precision and Inference Time considerations

Sign language essentially allows for communication without the need to explicitly say words. It was developed by the American School for the Deaf in the early 90’s. It is a naturally generated language which incorporates facial movements and hand gestures to convey thoughts and ideas. In modern times, it is used predominantly by people who are deaf and hard of hearing. Unlike most languages, ASL isn’t widely taught which makes it difficult for the general population to communicate effectively with those people who predominantly use ASL as the sole means of communication. Therefore, arises the need for a system which detects and predicts letters from images and which can then be used in real time to overcome this language barrier. This research aims to develop a sign language recognition system atop of YOLOX, which is built on top of YOLOV3, which contains in its architecture, convolutional neural networks. Using the various backbones of YOLOX, this paper introduces and provides six models on every end of the accuracy-testing time spectrum from least accurate/fastest response time to the most accurate/slowest response time. I thereby propose PereiraASLNet, which trains YOLOX with custom classes from the letters A-Z and a Pascal VOC XML American Sign Language dataset developed by Roboflow and variants of YOLOX have been developed, taking into consideration the mean average precision and inference times of all the YOLOX backbone architectures namely the YOLOX-nano, YOLOX-tiny, YOLOX-small, YOLOX-medium, YOLOX-large and YOLOX-xlarge. The testing mean average precision for the models were found to be – 0.9046, 0.9070, 0.9227, 0.9304, 0.9329 and 0.9578 and the testing inference time was found to be 3.50ms, 12.97ms, 34.86ms, 64.56ms, 83.23ms and 97.56ms respectively
Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum

While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a “blockchain” sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection methods in blockchain mainly concentrate on manual features and graph analytics, which first construct a homogeneous transaction graph using partial blockchain data and then use graph analytics to detect anomaly, resulting in a loss of pattern information. In this brief, we mainly focus on Ponzi scheme detection and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFAug</i> , a generic Heterogeneous Feature Augmentation module that can capture the heterogeneous information associated with account behavior patterns and can be combined with existing Ponzi detection methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFAug</i> learns the metapath-based behavior characteristics in an auxiliary heterogeneous interaction graph, and aggregates the heterogeneous features to corresponding account nodes in the homogeneous one where the Ponzi detection methods are performed. Comprehensive experimental results demonstrate that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFAug</i> can help existing Ponzi detection methods achieve significant performance improvement on Ethereum datasets, suggesting the effectiveness of heterogeneous information on detecting Ponzi schemes.
Quantifying through-thickness J for plane strain specimens using digital image correlation considering constraint effects

This paper proposes an approach to quantify the through-thickness J-integral for plane strain specimens using digital image correlation (DIC) and examines the constraint effects on DIC measurement. The DIC-based J estimation utilizes both the contour and domain integrals over the specimen free surface. The validation of the DIC algorithm deploys numerical results, by treating the nodal displacements as the DIC extracted data. The experimental study further compares the far-field DIC J value with through-thickness average J measured from η approach. This study demonstrates that the far-field domain integral from DIC represents closely the through-thickness average J for plane strain specimens.
MS-ML-SNYOLOv3: A robust lightweight modification of SqueezeNet based YOLOv3 for pedestrian detection

Pedestrian detection is a vital problem area in computer vision applications such as surveillance, automotive safety, and robotics. To continue the progress made in this field, a deep learning based approach is given in this work. A modified lightweight YOLOv3 is proposed, which is named as MultiScale-MultiLevel-SqueezeNetYOLOv3 (MS-ML-SNYOLOv3). An improved base network forms the basis of the proposed YOLOv3. Two Squeeze & Expand blocks are embodied in SqueezeNet at specific levels of the network to extract a hierarchical feature representation. An addition of a larger receptive field is made in the Expand section as well to encapsulate the feature information at various scales. The proposed method is compared against state-of-the-art detection methods, Faster RCNN, YOLOv2, YOLOv3, and SSD. The comparison also includes various base networks for YOLOv2 and YOLOv3. The performance metrics used for comparison are Detection Error Trade-Off, and Precision-Recall Curve. The proposed method is evaluated on benchmark datasets INRIA and Caltech Pedestrian datasets. A statistical analysis, Friedman Test is conducted to verify the application of MS-ML-SNYOLOv3 in real world data.
Online metaheuristic algorithm selection

The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation. The features extracted from analysis of fitness landscapes can be used to select the suitable algorithm for the given problem. However, these features are usually expensive and extracted prior to the optimization process which leads to a single algorithm to be selected. In this work, we propose an intelligent switch mechanism that enjoys an efficient non-convex ratio (ENCR) feature extracted online during the optimization to switch between two choices of algorithms, each favoring a type of landscape in terms of modality. For this work, two case studies including a pair of Harris hawks optimizer (HHO) and differential evolution (DE) and another pair of multiverse optimizer (MVO) and moth-flame optimizer (MFO) are selected among several algorithms to evaluate the performance of this framework. The proposed one-way and two-way switch algorithms take advantage of the merits of the two base algorithms to reach better final solutions and higher convergence rates in the majority of case studies. The overall comparison and ranking of the algorithms, including a random switch baseline, demonstrates the superiority of the intelligent switch mechanisms over the baselines.
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange

Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatio-temporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions. The unique higher-order mechanism further strengthens spatial information interaction between the encoder and the decoder. Considering the limited receptive fields in Convolutional LSTMs, we also introduce an attention module to highlight informative features for prediction. Anomalies are eventually identified by comparing the frames with their corresponding predictions. Evaluations on three popular benchmarks show that our framework outperforms most existing prediction-based anomaly detection methods.
MBNet: A Multi-Resolution Branch Network for Semantic Segmentation Of Ultra-High Resolution Images

Semantic segmentation of ultra-high resolution images is more challenging than ordinary images since high-resolution images need to be cropped into patches in training due to GPU memory limitation. To solve this problem, we design a multibranch structure to deal with multi-resolution inputs, called Multi-resolution Branch Network (MBNet). MBNet takes patches of various instead of only one resolution as inputs, so it can make the extracted features pure and different from each other so as to cover the complex scenes with tremendous variation. Moreover, to make full use of the multi-branch structure, we design a zoom module. Zoom module abandons the previous L2-norm before feature concatenation but combines different features according to the learned attention, which fully releases the advantages of multi-resolution. Results on two benchmark datasets show that our method improves significantly over the previous state-of-the-art methods.
Deliberation of Streaming RNN-Transducer by Non-Autoregressive Decoding

We propose to deliberate the hypothesis alignment of a streaming RNN-T model with the previously proposed Align-Refine non-autoregressive decoding method and its improved versions. The method performs a few refinement steps, where each step shares a transformer decoder that attends to both text features (extracted from alignments) and audio features, and outputs complete updated alignments. The transformer decoder is trained with the CTC loss which facilitates parallel greedy decoding, and performs full-context attention to capture label dependencies. We improve Align-Refine by introducing cascaded encoder that captures more audio context before refinement, and alignment augmentation which enforces learning label dependency. We show that, conditioned on hypothesis alignments of a streaming RNN-T model, our method obtains significantly more accurate recognition results than the first-pass RNN-T, with only small amount of model parameters.
Partially Fake Audio Detection by Self-Attention-Based Fake Span Discovery

The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly implemented biometric identification models and can be harnessed by in-the-wild attackers for illegal uses. The ASVspoof challenge mainly focuses on synthesized audios by advanced speech synthesis and voice conversion models, and replay attacks. Recently, the first Audio Deep Synthesis Detection challenge (ADD 2022) extends the attack scenarios into more aspects. Also, ADD 2022 is the first challenge to propose the partially fake audio detection task. Such brand new attacks are dangerous and how to tackle such attacks remains an open question. Thus, we propose a novel framework by introducing the question-answering (fake span discovery) strategy with the self-attention mechanism to detect partially fake audios. The proposed fake span detection module tasks the anti-spoofing model to predict the start and end positions of the fake clip within the partially fake audio, address the model’s attention into discovering the fake spans rather than other shortcuts with less generalization, and finally equips the model with the discrimination capacity between real and partially fake audios. Our submission ranked second in the partially fake audio detection track of ADD 2022.
A Framework to Enhance Object Detection Performance by using YOLO Algorithm

One of the most widely used applications in computer vision is object detection. It is a technique used for detecting and sculpting real-world objects. Despite the fact that there are numerous detection methods, their accuracy and efficiency are insufficient. This research work utilizes deep learning approaches to recognise objects using the YOLOv3 and YOLOv4 algorithms. The images of the two classes, Auto and Street Light, were combined to produce a dataset. Both training and testing are done using this dataset. This algorithm can recognise several things quickly and accurately, making it one of the most efficient methods for detecting a variety of objects. Various existing models are contrasted with Assessment methods such as Precision, Recall, F1-Score, average IOU, mean Average Precision (mAP), and others in the context of Object Detection.
End-to-End Speech Summarization Using Restricted Self-Attention

Speech summarization is typically performed by using a cascade of speech recognition and text summarization models. End-to-end modeling of speech summarization models is challenging due to memory and compute constraints arising from long input audio sequences. Recent work in document summarization has inspired methods to reduce the complexity of self-attentions, which enables transformer models to handle long sequences. In this work, we introduce a single model optimized end-to-end for speech summarization. We apply the restricted self-attention technique from text-based models to speech models to address the memory and compute constraints. We demonstrate that the proposed model learns to directly summarize speech for the How-2 corpus of instructional videos. The proposed end-to-end model outperforms the previously proposed cascaded model by 3 points absolute on ROUGE. Further, we consider the spoken language understanding task of predicting concepts from speech inputs and show that the proposed end-to-end model outperforms the cascade model by 4 points absolute F-1.
Progressive Learning for Image Retrieval with Hybrid-Modality Queries

Image retrieval with hybrid-modality queries, also known as composing text and image for image retrieval (CTI-IR), is a retrieval task where the search intention is expressed in a more complex query format, involving both vision and text modalities. For example, a target product image is searched using a reference product image along with text about changing certain attributes of the reference image as the query. It is a more challenging image retrieval task that requires both semantic space learning and cross-modal fusion. Previous approaches that attempt to deal with both aspects achieve unsatisfactory performance. In this paper, we decompose the CTI-IR task into a three-stage learning problem to progressively learn the complex knowledge for image retrieval with hybrid-modality queries. We first leverage the semantic embedding space for open-domain image-text retrieval, and then transfer the learned knowledge to the fashion-domain with fashion-related pre-training tasks. Finally, we enhance the pre-trained model from single-query to hybrid-modality query for the CTI-IR task. Furthermore, as the contribution of individual modality in the hybrid-modality query varies for different retrieval scenarios, we propose a self-supervised adaptive weighting strategy to dynamically determine the importance of image and text in the hybrid-modality query for better retrieval. Extensive experiments show that our proposed model significantly outperforms state-of-the-art methods in the mean of [email protected] by 24.9% and 9.5% on the Fashion-IQ and Shoes benchmark datasets respectively.