Reviewerly/exHarmony huggingface dataset:
An approach of method‐level bug localization

Bug localization is an important field in software engineering research. The traditional bug localization approaches based on information retrieval separate words through lexical analysis. In this way, the comments of the source code are ignored or treated as plain text, which will lose some semantic information. In this paper, MBL_SHL, an automatic Method-level Bug Localization approach, which utilises code Summarization, Historical fixed bugs and code Length, is presented. Based on the code summarization technology, this approach first supplements the comment for uncommented code, and then calculates the Word2vec vector and Term Frequency–Inverse Document Frequency vector for the bug report, methods and comments, respectively. After that the authors calculate separately the similarity between the bug report and each method, the bug report and each comment. The code length information and historical fix information are also considered as a weight and a part of the score, respectively, to calculate the final score of each method. Finally, the scores are sorted to determine the list of methods that may need to be modified when fixing the software bugs. We built a method-granular bug localization dataset, which contains five open-source projects. The experimental results show that the proposed approach significantly outperforms the existing approaches on the method level.
Recommendations for repositories and scientific gateways from a neuroscience perspective

Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective.
Learning to Mitigate Rowhammer Attacks

Rowhammer is a vulnerability that arises due to the undesirable interaction between physically adjacent rows in DRAMs. Existing DRAM protections are not adequate to defend against Rowhammer attacks. We propose a Rowhammer mitigation solution using machine learning (ML). We show that the ML-based technique can reliably detect and prevent bit flips for all the different types of Rowhammer attacks considered here. Moreover, the ML model is associated with lower power and area overhead compared to recently proposed Rowhammer mitigation techniques for 26 different applications from the Parsec, Pampar, and Splash-2 benchmark suites.
Unbalanced fourth-order interference beyond coherence time

Interferometry has been used widely in sensing application. However, the technique is limited by the finite coherence time of the light sources when the interference paths are not balanced. Higher-order interference effects involve intensity correlations between multiple detectors and may have the advantage over the traditional second-order interference effect exhibited in only one detector. We discuss various scenarios with unbalanced delays in different paths in fourth-order interference exhibited in coincidence between two detectors. We find, in some cases, interference effect persists even when the delays are much larger than the coherence time of the sources. We also extend the discussion to nonstationary pulsed fields, which need to consider the pulse shape and require a different treatment. These results will be useful in remote sensing applications.
The Effect of Persuasive Design on the Adoption of Exposure Notification Apps: Quantitative Study Based on COVID Alert

The adoption of contact tracing apps worldwide has been low. Although considerable research has been conducted on technology acceptance, little has been done to show the benefit of incorporating persuasive principles.This research aimed to investigate the effect of persuasive features in the COVID Alert app, created by Health Canada, by focusing on the no-exposure status, exposure status, and diagnosis report interfaces.We conducted a study among 181 Canadian residents, including 65 adopters and 116 nonadopters. This study was based on screenshots of the 3 interfaces, of which each comprised a persuasive design and a control design. The persuasive versions of the first two interfaces supported self-monitoring (of exposure levels), and that of the third interface supported social learning (about how many other users have reported their diagnosis). The 6 screenshots were randomly assigned to 6 groups of participants to provide feedback on perceived persuasiveness and adoption willingness.A multivariate repeated-measure ANOVA showed that there is an interaction among interface, app design, and adoption status regarding the perceived persuasiveness of the interfaces. This resulted in a 2-way ANOVA for each interface. For the no-exposure interface, there was an interaction between adoption status and app design. Among adopters, there was no significant difference P=.31 between the persuasive design (mean 5.36, SD 1.63) and the control design (mean 5.87, SD 1.20). However, among nonadopters, there was an effect of app design (P<.001), with participants being more motivated by the persuasive design (mean 5.37, SD 1.30) than by the control design (mean 4.57, SD 1.19). For the exposure interface, adoption status had a main effect (P<.001), with adopters (mean 5.91, SD 1.01) being more motivated by the designs than nonadopters (mean 4.96, SD 1.43). For the diagnosis report interface, there was an interaction between adoption status and app design. Among nonadopters, there was no significant difference P=.99 between the persuasive design (mean 4.61, SD 1.84) and the control design (mean 4.77, SD 1.21). However, among adopters, there was an effect of app design (P=.006), with participants being more likely to report their diagnosis using the persuasive design (mean 6.00, SD 0.97) than using the control design (mean 5.03, SD 1.22). Finally, with regard to willingness to download the app, pairwise comparisons showed that nonadopters were more likely to adopt the app after viewing the persuasive version of the no-exposure interface (13/21, 62% said yes) and the diagnosis report interface (12/17, 71% said yes) than after viewing the control versions (3/17, 18% and 7/16, 44%, respectively, said yes).Exposure notification apps are more likely to be effective if equipped with persuasive features. Incorporating self-monitoring into the no-exposure status interface and social learning into the diagnosis report interface can increase adoption by >30%.
From Audio to Animated Signs

Nearly 5% of the world population is hearing-impaired and uses a sign language as their primary mode of communication. Unfortunately, sign languages are not commonly understood, and this poses a great communication barrier between the hearing-impaired and the rest. This paper builds, based on prior wok, a novel approach that breaks the barrier one-way with accessibility and accuracy as the key objectives. It achieves accessibility using mobile devices for user-facing interactions and accuracy using a transformer model. The system uses a four-stage pipeline to enable one-way communication between hearing-gifted and hearing-impaired: audio capture, audio-to-text conversion, text-to-gloss transliteration, and gloss animation. The user-facing first and last stages of the pipeline are implemented on mobile devices to ensure wide accessibility, while the computationally intensive middle stages are implemented on cloud servers. Empirical evaluations show that the approach has a high accuracy.
Towards Interoperable Enclave Attestation: Learnings from Decades of Academic Work

Secure enclave technology has during the last decade emerged as an important hardware security primitive in server computer cores, and increasingly also in chips intended for consumer devices like mobile phones and PCs. The local or remote user of the enclave will rely on attestation protocols to confirm the isolation and other security properties of the enclave. In this paper, we analyze different attestation architectures and techniques as well as ways to present attestation evidence and metrics. We find that existing industry efforts to make attestation interoperable across platforms and trust roots can benefit from the wealth of research around remote attestation that has taken place since the 1990’s.
Full-stack quantum computing systems in the NISQ era: algorithm-driven and hardware-aware compilation techniques

The progress in developing quantum hardware with functional quantum processors integrating tens of noisy qubits, together with the availability of near-term quantum algorithms has led to the release of the first quantum computers. These quantum computing systems already integrate different software and hardware components of the so-called “full-stack”, bridging quantum applications to quantum devices. In this paper, we will provide an overview on current full-stack quantum computing systems. We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design to extract the most from noisy intermediate-scale quantum (NISQ) processors which are both error-prone and severely constrained in resources. As an example of co-design, we will focus on the development of hardware-aware and algorithm-driven compilation techniques.
Towards multi-sensory design: Placemaking through immersive environments – Evaluation of the approach

Multi-Sensory Design is an experimental project that explores the potential and limitations of immersive environments as a means to support the incorporation of intangible sensory aspects of place integrated into the design process. The study investigates how sensory exploration of designs in VR can be integrated into decision making and design concept assessment. An interactive mixed-media approach adopted for this project, allowed users to tap into visual, aural and kinesthetic human senses; and provided an additional level of engagement by adding a temporal dimension to the space. The manuscript details results of the evaluation study, assessing key features of the proposed sensory design approach and reports on the identified limitations and opportunities for future studies.
Code smells detection via modern code review: a study of the OpenStack and Qt communities

Code review plays an important role in software quality control. A typical review process involves a careful check of a piece of code in an attempt to detect and locate defects and other quality issues/violations. One type of issue that may impact the quality of software is code smells - i.e., bad coding practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in modern code review and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews by analysing reviews from four, large open source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We manually checked a total of 25,415 code review comments obtained by keywords search and random selection; this resulted in the identification of 1,539 smell-related reviews which then allowed the study of the causes of code smells, actions taken against identified smells, time taken to fix identified smells and reasons why developers ignored fixing identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, 4) developers generally followed those recommendations and actioned the changes, 5) once identified by reviewers, it usually takes developers less than one week to fix the smells and 6) the main reason why developers chose to ignore the identified smells is that it is not worth fixing the smell. Our results suggest the following: 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project’s development team) and 3) program context needs to be fully considered in order to make a decision of whether to fix the identified code smell immediately.
Evaluation of Sustainable Digital Currency Exchange Platforms Using Analytic Models

This study presents an analytic model to support the general public in evaluating digital currency exchange platforms. Advances in technologies have offered profitable opportunities, but the general public has difficulty accessing appropriate information on digital currency exchange platforms to facilitate their investments and trading. This study aims to provide a decision support system using analytic models that will guide the public in deciding the appropriate digital currency exchange platform for trading and investment. The overarching objective is to support the public in embracing the new era of a dependable, trustworthy, and sustainable digital society. Particularly, this study offers an analytics model that compares numerous well-known digital currency exchange platforms based on the opinions of 34 human expert members on six main criteria to identify the most suitable platform. In this study, the analytic hierarchy process approach, which is a multiple-criteria decision-making method, and Expert Choice software were used for decision support. Using pairwise comparisons of exchanges with respect to the criteria in the software, the weight of each exchange was determined, and these weights became the basis for prioritizing the exchange platform. This study provides valuable insight into how an analytics-driven expert system can support the public in selecting their digital currency exchange platform. This work is an integral part of an effort to help disruptive digital technology become widely accepted by the general public.
STCA: Utilizing a spatio-temporal cross-attention network for enhancing video person re-identification

Video-based re-identification (ReID) is a crucial task in computer vision that draws increasing attention due to advances in deep learning (DL) and modern computational devices. Despite recent success with CNN architectures, single models (e.g., 2D-CNNs or 3D-CNNs) alone failed to leverage temporal information with spatial cues. This is due to uncontrolled surveillance scenarios and variable poses leading to inevitable misalignment of ROIs across the tracklets, which is accompanied by occlusion and motion blur. In this context, designing temporal and spatial cues for two different models and their combinations can be beneficial, considering the global of a video-tracklet. 3D-CNNs allow encoding of temporal information while 2D-CNNs extract spatial or appearance information. In this paper, we propose a Spatio-Temporal Cross Attention (STCA) network to utilize both 2D-CNNs and 3D-CNNs that calculate the cross attention mapping both from the layer of 3D-CNNs and 2D-CNNs along a person's trajectory to gate the following layers of 2D-CNNs; and highlight relevant appearance features for the person ReID. Given an input tracklet, the proposed cross attention (CA) is able to capture the salient regions that propagate throughout the tracklet to obtain the global view. This provides a spatio-temporal attention approach that can be dynamically aggregated with spatial features of 2D-CNNs to perform finer-grained recognition. Additionally, we exploit the advantage of utilizing cosine similarity while triplet sampling as well as for calculating the final recognition score. Experimental analyses on three challenging benchmark datasets indicate that integrating spatio-temporal cross attention into the state-of-the-art video ReID backbone CNN architecture allows for improving their recognition accuracy.
Software engineering, bridging theory and practice in an agile learning environment

The case study presented in this paper, investigates the conditions for implementing an agile learning environment. The course Software Engineering in Project Form for the second-year bachelor program in Computer Science at the KTH-Royal Institute of Technology, Sweden, has undergone different iterations to increase attendance rate and student engagement and motivation and closing the gap between theory and practice. For the current academic year of 2021/2022, changes includes converting traditional lectures into student conference seminars and teachers into mentors, in line with the agile learning approach and values. Focusing on group working skills, communication skills and expected learning outcomes, the students were asked to assess their previous experiences and their own abilities in group work and communication, estimate the required qualities for software engineers, rate their preferences for learning and group work, and describe their overall expectations for the course. For this purpose, a survey was distributed to the 211 students, 117 responded. The results suggests that the students seems to prioritize most of the agile values, with the exception of customer collaboration. It can therefore be concluded that the conditions for introducing an agile learning environment will be fruitful. For the duration of the course, until May 2022, further evaluations will be made including interviews with mentors and another student survey after the course has finished, to investigate if, and if so how, the agile approach could increase student engagement whilst reducing the barriers between theory and practice.
Reliability of Google's Tensor Processing Units for Embedded Applications

Convolutional Neural Networks (CNNs) have become the most used and efficient way to identify and classify objects in a scene. CNNs are today fundamental not only for autonomous vehicles, but also for Internet of Things (IoT) and smart cities or smart homes. Vendors are developing low-power, efficient, and low-cost dedicated accelerators to allow the execution of the computational-demanding CNNs even in embedded applications with strict power and cost budgets. Google's Coral Tensor Processing Unit (TPU) is one of the latest low power accelerators for CNNs. In this paper we investigate the reliability of TPUs to atmospheric neutrons, reporting experimental data equivalent to more than 30 million years of natural irradiation. We analyze the behavior of TPUs executing atomic operations (standard or depthwise convolutions) with increasing input sizes as well as eight CNN designs typical of embedded applications, including transfer learning and reduced data-set configurations. We found that, despite the high error rate, most neutrons-induced errors only slightly modify the convolution output and do not change the CNNs detection or classification. By reporting details about the fault model and error rate, we provide valuable information on how to evaluate and improve the reliability of CNNs executed on a TPU.
Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification

Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer using histopathology images. This paper has performed the binary and multi-class classification of breast cancer using a transfer learning-based ensemble model. To analyze the correctness and reliability of the proposed model, we have used an imbalance IDC dataset, an imbalance BreakHis dataset in the binary class scenario, and a balanced BACH dataset for the multi-class classification. A lightweight shallow CNN model with batch normalization technology to accelerate convergence is aggregated with lightweight MobileNetV2 to improve learning and adaptability. The aggregation output is fed into a multilayer perceptron to complete the final classification task. The experimental study on all three datasets was performed and compared with the recent works. We have fine-tuned three different pre-trained models (ResNet50, InceptionV4, and MobilNetV2) and compared it with the proposed lightweight ensemble model in terms of execution time, number of parameters, model size, etc. In both the evaluation phases, it is seen that our model outperforms in all three datasets.
U-HAR

After the success of smartphones and smartwatches, smart glasses are expected to be the next smart wearable. While novel display technology allows the seamlessly embedding of content into the FOV, interaction methods with glasses, requiring the user for active interaction, limiting the user experience. One way to improve this and drive immersive augmentation is to reduce user interactions to a necessary minimum by adding context awareness to smart glasses. For this, we propose an approach based on human activity recognition, which incorporates features, derived from the user's head- and eye-movement. Towards this goal, we combine an commercial eye-tracker and an IMU to capture eye- and head-movement features of 7 activities performed by 20 participants. From a methodological perspective, we introduce U-HAR, a convolutional network optimized for activity recognition. By applying a few-shot learning, our model reaches an macro-F1-score of 86.59%, allowing us to derive contextual information.
Efficient Traveling Salesman Problem Solvers using the Ising Model with Simulated Bifurcation

An Ising model-based solver has shown efficiency in obtaining suboptimal solutions for combinatorial optimization problems. As an NP-hard problem, the traveling salesman problem (TSP) plays an important role in various routing and scheduling applications. However, the execution speed and solution quality significantly deteriorate using a solver with simulated annealing (SA) due to the quadratically increasing number of spins and strong constraints placed on the spins. The ballistic simulated bifurcation (bSB) algorithm utilizes the signs of Kerr-nonlinear parametric oscillators' positions as the spins' states. It can update the states in parallel to alleviate the time explosion problem. In this paper, we propose an efficient method for solving TSPs by using the Ising model with bSB. Firstly, the TSP is mapped to an Ising model without external magnetic fields by introducing a redundant spin. Secondly, various evolution strategies for the introduced position and different dynamic configurations of the time step are considered to improve the efficiency in solving TSPs. The effectiveness is specifically discussed and evaluated by comparing the solution quality to SA. Experiments on benchmark datasets show that the proposed bSB-based TSP solvers offer superior performance in solution quality and achieve a significant speed up in runtime than recent SA-based ones.