Reviewerly/exHarmony huggingface dataset:
Does Deep Learning improve the performance of duplicate bug report detection? An empirical study

Do Deep Learning (DL) techniques actually help to improve the performance of duplicate bug report detection? Prior studies suggest that they do, if the duplicate bug report detection task is treated as a binary classification problem. However, in realistic scenarios, the task is often viewed as a ranking problem, which predicts potential duplicate bug reports by ranking based on similarities with existing historical bug reports. There is little empirical evidence to support that DL can be effectively applied to detect duplicate bug reports in the ranking scenario. Therefore, in this paper, we investigate whether well-known DL-based methods outperform classic information retrieval (IR) based methods on the duplicate bug report detection task. In addition, we argue that both IR- and DL-based methods suffer from incompletely evaluating the similarity between bug reports, resulting in the loss of important information. To address this problem, we propose a new method that combines IR and DL techniques to compute textual similarity more comprehensively. Our experimental results show that the DL-based method itself does not yield high performance compared to IR-based methods. However, our proposed combined method improves on the MAP metric of classic IR-based methods by a median of 7.09%–11.34% and a maximum of 17.228%–28.97%.
Hierarchical context-agnostic network with contrastive feature diversity for one-shot semantic segmentation

One-shot semantic segmentation aims at distinguishing pixels of an unseen category from the background, using merely one annotated image from the same category. However, most previous works neglect the feature diversity of foreground and the context information of background by using the masked average pooling. To solve these issues, we propose the Hierarchical Context-Agnostic Network (HCNet). It mainly includes two modules: (1) a Hierarchical Pyramid Supportive (HPS) module that generate the hierarchical supportive prototypes from coarse to fine to ensure feature diversity, and (2) a Background Exclusion Supportive (BES) module that explicitly introduces the contrastive information from the background for more precise category features. We conduct extensive experiments on Pascal-5i and COCO-20i to evaluate the performance of HCNet. HCNet achieves the mIoU score of 62.1% on Pascal-5i and 40.7% on COCO-20i and outperforms other works for the challenging one-shot segmentation, which has proved the efficiency of the whole network. Code is available at https://github.com/fangzy97/hcnet.
Semi-supervised node classification via fine-grained graph auxiliary augmentation learning

Node classification has become an important research topic in recent years. Since there are always a few training samples, researchers improve the performance by properly leveraging the predictions of unlabeled nodes during training. However, suffering from the model degradation resulted from the accumulative error of pseudo-labels, there is limited improvement. In this paper we present fine-grained Graph Auxiliary aUgmentation (GAU). It trains the primary task together with an automatically created auxiliary task which is a fine-grained node classification task. And an auxiliary augmentation strategy is designed to enlarge the labeled set for the auxiliary task by utilizing the pseudo-labels of the primary task. Comprehensive experiments show that GAU alleviates the sensitivity of the model to the pseudo-label quality, so more unlabeled nodes can participate in the training. From the perspective of co-training, the fine-grained auxiliary task which is trained by much more unlabeled nodes helps to learn better node representations from a different view, thereby boosting the final performance. Extensive experiments verify the superior performance of the GAU on different GNN architectures when compared with other state-of-the-art approaches.
Fine-Grained Human Activity Recognition - A new paradigm

Nowadays, fine-grained Human Activity Recognition (HAR) has become extremely interesting among researchers due to its applications in fields such as healthcare, security, sports, and smart environments. In this paper, we provide a brief overview of the State of the Art approaches in fine-grained human activity recognition. We also discuss the characteristics, complexities, and scarcity of inertial datasets related to fine-grained and coarse-grained activities. To mitigate this scarcity, we collect our inertial dataset, consisting of 17 participants performing 4 fine-grained tasks while interacting with an Inertial Measurement Unit (IMU) sensor embedded in a solid object. Next, we test the most commonly used machine learning classifiers (e.g., kNN, XGboost) on the collected dataset and present the results. Finally, we demonstrate the necessity of a new approach to deal with the recognition of fine-grained activities, and we state our future research directions in this context.
‘Can you Set It Up On Your Own?’ – Investigating Users’ Ability To Participate in Remote-Based Virtual Reality Studies

The availability of consumer-grade virtual reality (VR) devices allows user studies to be conducted remotely, that is in users’ homes. In this way, diverse populations can be reached and studies using virtual reality can be conducted in settings characterized by high ecologic validity. In this study (N=21) we investigate challenges participants face as they are required to set up and calibrate a virtual reality system in their home without assistance from experimenters. This allowed us to identify key reasons why participants struggle with this task. Our findings suggest that providing illustrative instructions and additional assistance on request can notably increase the success rate of setting up a VR environment for participating in a remote study. Interestingly, we also find that it is harder to recruit participants who do not have prior VR experience to participate in remote VR studies. We derive suggestions on how to support this task based on our findings.
Business Analytics and Competitive Advantage for SMEs in UAE: A Mediating Role of Technology Assets

The small and medium enterprises (SMEs) working in UAE are facing challenges to develop a competitive advantage. The bad decision-making by the management is the reason behind the failure of SMEs. Therefore, the purpose of this study is to identify the role of business analytics as a competitive advantage for SMEs in the UAE utilizing the quantitative data collected from the employees of SMEs in the UAE. 500 questionnaires were distributed to collect the data; 370 questionnaires were considered for data analyses. The findings revealed the important role of business analytics in developing the competitive advantage for SMEs. Besides, for business analytics, the role of business competition, the role of top management, and technological assets are critical for the right decision-making. Consequently, the study contributes to the body of knowledge with a theoretical framework defining the relationship between different variables of the study. The study also has theoretical and practical implications for enhancing the competitive advantage of SMEs in the UAE.
Using Suggestion Information in Exchange Solutions between Users in Interactive Evolutionary Computation Creating Blended Juices

Making products fit the feelings and preferences of many users is one of the targets of Kansei engineering. At present, it is still challenging to achieve the target. Interactive Evolutionary Computation (IEC) is a candidate for the techniques of making the products. IEC employs evolutionary computations’ searching ability for the best and good solutions to a particular problem. Some previous studies have proposed IECs where multiple users participated in the search. In some of them, the user’s basic search is performed independently, and sometimes good solutions are exchanged between the users. This study aims to propose a method using users’ suggestions in the exchange as a good communication opportunity between users. This study tried to achieve the users’ suggestion in IEC in the creation of juice by blending several source juices. The tasting experiment through seven generations was conducted with fourteen participants. Significant increases in mean and maximum fitness values were observed from the initial to the final generations. Furthermore, qualitative analysis is performed based on the exchange states and the reason for the suggestion with a thought of positive computing.
Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy

Adaptive Network-based Fuzzy Inference System (ANFIS) is a promising model of explainable neural networks, which constructs Takagi-Sugeno fuzzy inference system based on neuro learning, but cannot work well with incomplete datasets including missing values. In this research, a novel approach of making ANFIS robust against missing values is proposed by modifying the premise part with Fuzzy c-Means (FCM) utilizing the partial distance strategy. By calculating the premise fuzzy memberships of incomplete objects having missing values with FCM criterion using partial distances, the proposed ANFIS variant can produce ANFIS outputs even if some objects include missing values in their input observation. The characteristics of the proposed model are demonstrated through a numerical experiment with a time series data.
Temporal Convolution based Skip Connections for Online Arabic Handwriting Recognition

Handwriting recognition is an active research area in document analysis field. In the few last decades, a rapid growth and a frequent use of data entry devices was observed. Consequently, several approaches have focused on online handwriting modeling and recognition. In this paper, we presented a new system for online Arabic handwriting recognition based on beta-elliptic modeling and one dimensional Residual Neural Networks. Beta-elliptic model was applied to extract the dynamic and geometric characteristics of the trajectory, whereas the developed Residual Network is based on temporal convolution and skip connections and it has ability to represent the sequential aspect of the input data. The experiments have been done on the public Arabic handwriting dataset LMCA and showed the effectiveness of the proposed recognition model that reached the accuracy of 96.87%.
Detection of Topic Transition in Chat Dialogue System

We propose a method for detecting topic transitions in a chat dialogue system to provide a response that takes into account the topic. First, we conducted a questionnaire in which participants categorized transitions in dialogues as a transition, possible transition, or non-transition, and provided their reasoning. Next, for transition detection, we introduce the concept of a slight topic transition, interrogative sentence detection, and an order of priority for detection techniques. These were integrated with the baseline techniques which use the similarity between utterances, the intersection of nouns, and keywords. The results show that the interrogative sentence detection and prioritizing certain detection techniques were effective for detecting topic transitions. Furthermore, transitions which are considered ambiguous by people can be clarified by classifying them into one of the three types.
Estimating the conceptual distance between unknown words using machine learning

Measuring the distance between words has many applications in natural language processing. By using the conceptual distance using the thesaurus dictionary, it is possible to obtain a distance scale different from the word similarity using the cosine similarity. However, the distance cannot be measured except for the words listed in the thesaurus dictionary. To solve the problem, this study aims to estimate the conceptual distance between unknown words using machine learning. Through some experiments, the conceptual distance of an unknown word could be estimated with the same level of accuracy as that of a known word.
Variance based Averaging and Standard Normal Distribution for Improvement of SBT Quantization in Neural Network Coding

Deep Neural Network (DNN) models are undergoing active development, and the efforts to achieve higher task performance have caused models to become more complex and larger in size. This limitation causes difficulties of being used in low-power edge devices. To solve this issue, MPEG (Moving Picture Experts Group) of ISO/IEC has developed NNC (Neural Network Coding), a standard that can reduce model complexity and size, thus making facilitation of DNN models in low-power edge devices possible. This standard is designed as a toolbox of compression methods, including Stochastic Binary Ternary (SBT) quantization. SBT quantization is an approximation method that achieves extremely high coding gains by approximating all non-zero values within a weight tensor to the tensor’s mean. However, this technique can cause significant performance loss due to its inaccurate estimate of values. We therefore propose Variance based Optimal Averaging (VOA) and Standard Normal Distribution based optimization (SNDO), which are two different skills to enhance the performance of SBT quantization by searching for more accurate estimates of parameters. In this paper, we explain the mechanism of these two methods, and confirm their performance through experimental results.
Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage

Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verification-based approaches, they require specialist knowledge, and are time-consuming. Recently, we introduced HyperGI, a dynamic, hypertest-based approach that can detect and produce potential fixes for hyperproperty violations. In particular, we focused on violations of the noninterference property, as it results in information flow leakage. Our instantiation of HyperGI was able to detect and reduce leakage in three small programs. Its fitness function tried to balance information leakage and program correctness but, as we pointed out, there may be tradeoffs between keeping program semantics and reducing information leakage that require developer decisions.
Insight: Exploring Cross-Ecosystem Vulnerability Impacts

Vulnerabilities, referred to as CLV issues, are induced by cross-language invocations of vulnerable libraries. Such issues greatly increase the attack surface of Python/Java projects due to their pervasive use of C libraries. Existing Python/Java build tools in PyPI and Maven ecosystems fail to report the dependency on vulnerable libraries written in other languages such as C. CLV issues are easily missed by developers. In this paper, we conduct the first empirical study on the status quo of CLV issues in PyPI and Maven ecosystems. It is found that 82,951 projects in these ecosystems are directly or indirectly dependent on libraries compiled from the C project versions that are identified to be vulnerable in CVE reports. Our study arouses the awareness of CLV issues in popular ecosystems and presents related analysis results.
ASTOR: An Approach to Identify Security Code Reviews

During code reviews, software developers often raise security concerns if they find any. Ignoring such concerns can bring a severe impact on the performance of a software product. This risk can be reduced if we can automatically identify such code reviews that trigger security concerns so that we can perform additional scrutiny from the security experts. Therefore, the objective of this study is to develop an automated tool to identify code reviews that trigger security concerns.
Architectural Implementation of AES based 5G Security Protocol on FPGA

Confidentiality and integrity security are the key challenges in future 5G networks. To encounter these challenges, various signature and key agreement protocols are being implemented in 5G systems to secure high-speed mobile-to-mobile communication. Many security ciphers such as SNOW 3G, Advanced Encryption Standard (AES), and ZUC are used for 5G security. Among these protocols, the AES algorithm has been shown to achieve higher hardware efficiency and throughput in the literature. In this paper, we implement the AES algorithm on Field Programmable Gate Array (FPGA) and real-time performance factors of the AES algorithm were exploited to best fit the needs and requirements of 5G. In addition, several modifications such as partial pipelining and deep pipelining (partial pipelining with sub-module pipelining) are implemented on Virtex 6 FPGA ML60S board to improve the throughput of the proposed design.
Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology.
Multi-Source Video Domain Adaptation With Temporal Attentive Moment Alignment Network

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios, which relaxes the assumption in conventional Unsupervised Domain Adaptation (UDA) that source data are sampled from a single domain and match a uniform data distribution. The MSDA is more challenging due to the existence of different domain shifts between distinct domain pairs. When considering videos, the negative transfer would be provoked by spatial-temporal features and can be formulated into a more challenging Multi-Source Video Domain Adaptation (MSVDA) problem. In this paper, we address the MSVDA problem by proposing a novel Temporal Attentive Moment Alignment Network (TAMAN) which aims for effective feature transfer by dynamically aligning both spatial and temporal feature moments. The TAMAN further constructs robust global temporal features by attending to dominant domain-invariant local temporal features with high local classification confidence and low disparity between global and local feature discrepancies. To facilitate future research on the MSVDA problem, we introduce comprehensive benchmarks, covering extensive MSVDA scenarios. Empirical results demonstrate a superior performance of the proposed TAMAN across multiple MSVDA benchmarks.
Only Once Attack: Fooling the Tracker With Adversarial Template

Adversarial attacks in visual object tracking aims to fool trackers via injecting invisible perturbations for the video frames. Most adversarial methods advocate generating perturbations for each video frame, but frequent attacks may increase the computational load and the risk of exposure. Unfortunately, less works are about only attacking the initial frame and their attack effects are insufficient. To tackle this, we focus on the initialization phase of tracking and propose an only once attack framework. It can effectively fool the tracker via only generating invisible perturbations for the initial template, rather than each frame. Specifically, considering the tracking mechanism of the Siamese-based trackers, we design the minimum score-based and the minimum IoU-based loss functions. Both of them are used for training the UNet-based perturbation generator instead of the tracker, achieving the non-targeted attack. Additionally, we propose the location and direction offsets as the base attacks of sophisticated targeted attack. Combined with the two basic attacks, the tracker can be easily hijacked to move towards the fake target predefined by users. Extensive experimental results demonstrate that our only once attack framework costs the least number of attacks yet achieves better attack effect, with the maximum drop of 68.7%. The transferability experiments illustrate that our attack framework with good generalization ability can be directly applicable to the CNN-based, Siamese-based, deep discriminative-based and Transformer-based trackers, without retraining.
Multi-Level Attention Based Coreference Resolution With Gated Recurrent Unit and Convolutional Neural Networks

Aiming at the diversity of the entity mentions in the field of animal husbandry in Tibet, the reference resolution model Att-GRU-CNN based on multi-level attention mechanism is proposed. The model uses GRU network for global semantic feature learning and knowledge memory, and uses CNN network to further extract local high-level semantic features. A word-level attention layer is added on the GRU hidden layer, the feature vector of entity mention does dot product operation with the feature vector of each word, and the result is normalized and used as the weight value distribution of the words, thus, the prior knowledge that the mention is the most important in the context is given to the network; A sentence-level attention layer is added on the CNN convolution layer, the convolutional layer output and entity name library matrix do association operation, and the result is used as the weight value distribution of the sentence, so as to strengthen the relation between the sentence where the mention is located and the entity scientific name. At the same time, the data enhancement technology is used to improve the generalization ability of the model. Finally, the ablation experiment on Tibetan animal husbandry dataset verified the effectiveness of each component of the model and the superposition effect after combination; the performance comparison experiment is carried out on public datasets MUC, B3 and CEAF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi _{4}$ </tex-math></inline-formula> . The experimental results show that this model has a significant improvement over other models in the accuracy rate, recall rate and F1 score.