Reviewerly/exHarmony huggingface dataset:
Comparing ϕ and the F-measure as performance metrics for software-related classifications

Abstract Context The F-measure has been widely used as a performance metric when selecting binary classifiers for prediction, but it has also been widely criticized, especially given the availability of alternatives such as ϕ (also known as Matthews Correlation Coefficient). Objectives Our goals are to (1) investigate possible issues related to the F-measure in depth and show how ϕ can address them, and (2) explore the relationships between the F-measure and ϕ . Method Based on the definitions of ϕ and the F-measure, we derive a few mathematical properties of these two performance metrics and of the relationships between them. To demonstrate the practical effects of these mathematical properties, we illustrate the outcomes of an empirical study involving 70 Empirical Software Engineering datasets and 837 classifiers. Results We show that ϕ can be defined as a function of Precision and Recall , which are the only two performance metrics used to define the F-measure, and the rate of actually positive software modules in a dataset. Also, ϕ can be expressed as a function of the F-measure and the rates of actual and estimated positive software modules. We derive the minimum and maximum value of ϕ for any given value of the F-measure, and the conditions under which both the F-measure and ϕ rank two classifiers in the same order. Conclusions Our results show that ϕ is a sensible and useful metric for assessing the performance of binary classifiers. We also recommend that the F-measure should not be used by itself to assess the performance of a classifier, but that the rate of positives should always be specified as well, at least to assess if and to what extent a classifier performs better than random classification. The mathematical relationships described here can also be used to re-interpret the conclusions of previously published papers that relied mainly on the F-measure as a performance metric.
Do Not Read the Same News! Enhancing Diversity and Personalization of News Recommendation

Personalized news recommendation by machine is one of the widely studied areas. As the production of news articles increases and topics are diversified, it is impractical to read all the articles available to users. Therefore, the purpose of the news recommendation system should be to provide relevant news based on the user's interest. Unlike other recommendation systems, explicit feedback from users on each item such as ratings is rarely provided in news recommendation systems. Most news recommendation systems use implicit feedback such as click histories to profile user interest, which leads to biased recommendation results towards generally popular articles. In this paper, we suggest a novel news recommendation model for more personalized recommendations. If a user reads news not widely clicked by others, the news reflects the user's personal interest rather than other popular news clicked. We implement two user encoders, one to encode the general interest of the set of users and another one to encode the user's individual interest. We also propose regularization methods that induce two encoders to encode different types of user interest. The experiment on real-world data shows that our proposed method improves the diversity and the quality of recommendations for different click histories without any significant performance drops.
A Generative Approach for Financial Causality Extraction

Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports. Each financial causality contains a cause span and an effect span. Previous works proposed sequence labeling approaches to solve this task. But sequence labeling models find it difficult to extract multiple causalities and overlapping causalities from the text segments. In this paper, we explore a generative approach for causality extraction using the encoder-decoder framework and pointer networks. We use a causality dataset from the financial domain, FinCausal, for our experiments and our proposed framework achieves very competitive performance on this dataset.
STO Estimation for OFDM System Using CDM

In this letter, we propose a novel scheme for symbol timing offset (STO) estimation by using a convolutional neural network (CNN)-deep neural network (DNN) model (CDM) architecture for the orthogonal frequency division multiplexing (OFDM) system over different fading channel models. The proposed scheme estimates STO in the presence of carrier frequency offset (CFO) and without prior knowledge of the channel, modulation format, and transmitted OFDM signal parameters. The CDM architecture achieves better estimation accuracy gain compared to that of statistical-based and pilot-assisted CNN-based methods. Finally, the proposed CDM is validated over a radio frequency testbed, and the desired constellation diagram is obtained.
Time-aware Service Recommendation with Social-powered Graph Hierarchical Attention Network

Driven by Service-Oriented Computing techniques, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users&#x0027; dynamic preferences. Recent studies have revealed that users&#x0027; social connections may help better model their dynamic preferences. However, two phenomena exist to influence users&#x0027; dynamic preferences of service selection. First, users and their friends may only share preferences in certain services, which means not every service in the friends&#x0027; consumed mashups has the same impact on a target user&#x0027;s dynamic preference. Second, for a target user, friends in his social network with similar interests and behaviors may contribute more influence intensities. To cover the above phenomena synergistically, this paper proposes a Social-powered Graph Hierarchical Attention Network (SGHAN), as a deep learning model capable of learning similar behaviors from proper friends during mashup development. SGHAN is powered by the reciprocity between its two core components: a service-level attentional encoder captures users&#x0027; interested services in friends&#x0027; mashups, while a friend-level graph attention network selects informative friends and propagates the friends&#x0027; social influences. Extensive experiments show that the SGHAN model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.
Energy-Efficient Multi-Version Concurrency Control (EEMVCC) for Object-Based Systems

The MVCC (Multi-Version Concurrency Control) is proposed to increase the concurrency of multiple conflicting transactions and the scalability of a distributed computing system. On the other hand, the larger number of transactions are concurrently performed in a system, the larger amount of electric energy is consumed on servers. In this paper, the EEMVTO (Energy-Efficient Multi-Version Timestamp Ordering) algorithm is newly proposed to reduce the total electric energy consumption of servers by not performing meaningless write methods. The evaluation results show the total electric energy consumption of servers can be reduced in the EEMVTO algorithm.
The Time Travel Exploratory Games Approach: An Artificial Intelligence Perspective

The authors deploy Artificial Intelligence techniques for the design of digital games aiming at affective and effective education. There is a case of environmental education studied in some detail. The games in focus are time travel exploratory games that enable learners to find data from the past. The narrative is traveling back in time – virtually – and exploring the past – really. Successful learners return with valuable findings. But sometimes, human learners fail. Not everyone is familiar with time travel. How might a learner behave when finding herself back in time in a foreign virtual world? Preparing the exploratory digital game for unforeseeable learners' behavior is an involved planning task. For this purpose, advanced technologies of Artificial Intelligence for planning in dynamic environments such as complex industrial processes are adopted and adapted. This leads to storyboarding of learners' experiences. From the Artificial Intelligence perspective, the time travel exploratory game itself becomes an AI system. It adapts to the learners needs and desires aiming at fun when playing the game and at success and effectiveness.
Blockchain-based Food Tracking for Intelligent Farming using IoT

In various applications and disciplines, blockchain is a growing subject of study. It becomes genuinely disruptive, particularly when paired with the Internet of Things (IoT), exploring new action plans, boosting involvement, and transforming numerous industries, notably farming. This research work suggests a blockchain-based, self-organized, open, and ecological food tracking system, which incorporates all players in an intelligent agriculture environment, even if they do not trust each other. Due to the blockchain network’s ability to implement smart contracts and consensus, unauthorized individuals cannot carry out any erroneous transactions. Further, the IoT and blockchain technologies are combined to increase the application progress in real-time. However, maintaining the sensors attached to sensing and mining the blockchains present in an IoT world will always remain as a technological fault. This research study intends to examine the distributed ledger technology’s essential aspects and characteristics. The conceptual model is provided for intelligent farming by combining IoT and Blockchain, and this resulted in developing a different integrated framework.
The Potential of Virtual Reality for Aerospace Applications

In the past decade, virtual reality (VR) technologies have become increasingly affordable, flexible, and accessible with the release of consumer versions such as the Oculus Rift or the HTC Vive. The immersive and interactive experiences enabled by this technology offer relevant, innovative use cases and training methods in a wide variety of fields. Therefore, VR is becoming more relevant in many areas outside classic entertainment. These include, for example, medical applications, educational environments, industrial applications, and design applications. In particular, immersive simulations and experiences that would often be too dangerous, expensive, difficult or even impossible to perform can be experienced in a safe environment. In this paper, the potential of VR technologies for different areas of aerospace applications is demonstrated and discussed. A systematic literature review based on the PRISMA guideline is conducted to identify relevant publications and applications from the databases IEEE, ACM, and Scopus. The analysis focuses on identifying various use cases of VR in aerospace research, design, manufacturing, operations, maintenance and education. Furthermore, the benefits, potential challenges and relevant technologies are discussed. This paper is designed to give readers a good overview of the state-of-the-art in VR, focusing on use cases and the potential and challenges, and discussing relevant future scenarios for this application field.
Duplicate Bug Report Detection Using an Attention-Based Neural Language Model

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> Users and developers use bug tracking systems to report errors that occur during the development and testing of software. The manual identification of duplicates is a tedious task especially with software that have large bug repositories. In this context, their automatic detection becomes a necessary task that can help prevent frequently fixing the same bug. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BERT-MLP</i> , a novel pretrained language model using bidirectional encoder representations from ransformers (BERT) for duplicate bug report detection (DBRD) with the aim of improving the detection rate compared to existing works. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> Our approach considers only unstructured data. These are fed into the BERT model in order to learn the contextual relationships between words. The output is fed into a multilayer perceptron (MLP) classifier, representing our base DBRD. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Our approach was evaluated on three projects: Mozilla Firefox, Eclipse Platform, and Thunderbird. It achieved an accuracy of 92.11, 94.08, and 89.03%, respectively, for Mozilla, Eclipse, and Thunderbird. A comparison with a dual-channel convolutional neural network (DC-CNN) model and other pretrained models, including RoBERTa and Sentence-Bert has been conducted. Results showed that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BERT-MLP</i> outperformed, the second best performing models (DC-CNN and Sentence-BERT) by 12% in accuracy for Eclipse and 9% for both Mozilla and Thunderbird, respectively.
A freely available system for human activity recognition based on a low-cost body area network

Over the last decade, Human Activity Recognition (HAR) has become a vibrant research field in various applications scenarios, ranging from sports, healthcare and well-being to smart cities, smart homes, and industry, mainly due to the widespread availability of devices as smartphones, smartwatches, and wearables. A key ingredient for sophisticated HAR systems is represented by the availability of high-quality datasets. These are generally gathered by dedicated Body Area Networks (BANs), and further elaborated through machine learning and deep learning algorithms. Thus, the BAN design plays a central role in such a context, where the main challenges are related to easiness of use, costs and energy constraints of their components. In this context, our paper presents a highly configurable HAR system, based on a low-cost and easy-to-use BAN. The system includes a CNN-based algorithm validated over a dataset, collected through the proposed BAN, on 12 persons performing 7 different human activities.
Quality Requirement Documentation Guidelines for Agile Software Development

In agile software development (ASD), where minimal documentation and rapid delivery are the focus, Quality requirements (QRs) are often underspecified and not documented. Guidelines supporting QR documentation task are scarce. The study developed the Agile QR-Doc QR documentation guidelines, which aim to support QR documentation in ASD. We applied a design science research methodology (DSRM) to build the Agile QR-Doc. We used a survey questionnaire and open discussion with ten software practitioners, from two ASD companies to validate the Agile QR-Doc. The practitioners evaluated the guidelines in terms of usefulness, relevance, understandability, and coverage of important aspects for supporting QR documentation and their impact on the agility of the software development process. Agile QR-Doc list 12 recommendations that are grouped into two categories. The first category introduces three recommendations that focus on raising awareness about the significance of QRs, their documentation and related challenges. The second category lists nine recommendations that introduce artifacts, practices and important aspects for documenting QRs. The validation reveals the relevance, understandability and usefulness of the guidelines to support QR documentation in ASD. It also indicates that the guidelines consider important aspects for documenting QRs and that they do not negatively affect the agility of the software development process. Practitioners can utilize the practices, artifacts and knowledge from the guidelines to support QR documentation in ASD. Researchers can benefit from the knowledge on QR documentation in ASD, and application of DSRM in building artifacts.
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification

Nuclei segmentation and classification is a crucial step utilized throughout various microscopy medical analysis applications. However, it has several challenges such as the segmentation of small objects, highly imbalanced data, and subtle differences between types of nuclei. In this paper, we propose a method to tackle the aforementioned problems. First, a new encoder &#x201C;ConvNeXt&#x201D; is proposed for the HoVer-Net architecture to leverage the major components of vision transformer to improve the network&#x0027;s encoding capabilities. Second, to better distinguish the differences between nuclei, a multi-channel color space-based approach is proposed to aid the model in extracting discriminative features. Third, affine transformations such as scaling and shearing are utilized to improve the model&#x0027;s gen-eralizability. Finally, Unified Focal Loss (UFL) and Sharpness-Aware Minimization (SAM) are tested in attempt to tackle the foreground-backward imbalance and further improve gen-eralizability, respectively. We show how our proposed method (ConvNeXt-S encoder &#x002B; affine transformation &#x002B; multi-channel color stacking) outperforms the original HoVer-Net network on the &#x201C;Lizard&#x201D; dataset by 2.6&#x0025; mPQ+.
A Data Science Solution for Mining Weather Data and Transportation Data for Smart Cities

As we are living in an uncertain world, the uncertainty may have significant and/or direct implications on various aspects of the computer industry. Hence, innovations of computers, software and applications have emerged as a pressing need. Data science solutions have been designed and/or developed for social good. For example, in this paper, we present a data science solution for mining weather and transportation data. The solution helps analyze the effect of common weather conditions (e.g., fog, rain, snow) on transportation. Evaluation on real-life data for a Canadian city demonstrates the practicality of our data science solution for mining weather and transportation data. Knowledge discovered helps in building a smart city.
Effectiveness, Policy, and User Acceptance of COVID-19 Contact-Tracing Apps in the Post–COVID-19 Pandemic Era: Experience and Comparative Study

In the post-COVID-19 pandemic era, many countries have launched apps to trace contacts of COVID-19 infections. Each contact-tracing app (CTA) faces a variety of issues owing to different national policies or technologies for tracing contacts.In this study, we aimed to investigate all the CTAs used to trace contacts in various countries worldwide, including the technology used by each CTA, the availability of knowledge about the CTA from official websites, the interoperability of CTAs in various countries, and the infection detection rates and policies of the specific country that launched the CTA, and to summarize the current problems of the apps based on the information collected.We investigated CTAs launched in all countries through Google, Google Scholar, and PubMed. We experimented with all apps that could be installed and compiled information about apps that could not be installed or used by consulting official websites and previous literature. We compared the information collected by us on CTAs with relevant previous literature to understand and analyze the data.After screening 166 COVID-19 apps developed in 197 countries worldwide, we selected 98 (59%) apps from 95 (48.2%) countries, of which 63 (66.3%) apps were usable. The methods of contact tracing are divided into 3 main categories: Bluetooth, geolocation, and QR codes. At the technical level, CTAs face 3 major problems. First, the distance and time for Bluetooth- and geolocation-based CTAs to record contact are generally set to 2 meters and 15 minutes; however, this distance should be lengthened, and the time should be shortened for more infectious variants. Second, Bluetooth- or geolocation-based CTAs also face the problem of lack of accuracy. For example, individuals in 2 adjacent vehicles during traffic jams may be at a distance of ≤2 meters to make the CTA trace contact, but the 2 users may actually be separated by car doors, which could prevent transmission and infection. In addition, we investigated infection detection rates in 33 countries, 16 (48.5%) of which had significantly low infection detection rates, wherein CTAs could have lacked effectiveness in reducing virus propagation. Regarding policy, CTAs in most countries can only be used in their own countries and lack interoperability among other countries. In addition, 7 countries have already discontinued CTAs, but we believe that it was too early to discontinue them. Regarding user acceptance, 28.6% (28/98) of CTAs had no official source of information that could reduce user acceptance.We surveyed all CTAs worldwide, identified their technological policy and acceptance issues, and provided solutions for each of the issues we identified. This study aimed to provide useful guidance and suggestions for updating the existing CTAs and the subsequent development of new CTAs.
High‐Throughput Multichannel Parallelized Diffraction Convolutional Neural Network Accelerator

Abstract Convolutional neural networks are paramount in image and signal processing, and are responsible for the majority of image recognition power consumption today, concentrated mainly in convolution computations. With convolution operations being computationally intensive, next‐generation hardware accelerators need to offer parallelization and high efficiency. Diffractive optics offers the promise of low‐latency, highly parallel convolution operations. However, thus far parallelism is only partially harvested, thereby significantly underdelivering in comparison to its throughput potential. Here, a parallelized operation high‐throughput Fourier optic convolutional accelerator is demonstrated. For the first time, simultaneous processing of multiple kernels in Fourier domain enabled by optical diffraction orders is achieved alongside input parallelism. The proposed approach can offer ≈100× speedup over the previous generation optical diffraction‐based processor and 10× speedup over other state‐of‐the‐art optical Fourier classifiers.
First come first served: the impact of file position on code review

The most popular code review tools (e.g., Gerrit and GitHub) present the files to review sorted in alphabetical order. Could this choice or, more generally, the relative position in which a file is presented bias the outcome of code reviews? We investigate this hypothesis by triangulating complementary evidence in a two-step study. First, we observe developers' code review activity. We analyze the review comments pertaining to 219,476 Pull Requests (PRs) from 138 popular Java projects on GitHub. We found files shown earlier in a PR to receive more comments than files shown later, also when controlling for possible confounding factors: e.g., the presence of discussion threads or the lines added in a file. Second, we measure the impact of file position on defect finding in code review. Recruiting 106 participants, we conduct an online controlled experiment in which we measure participants' performance in detecting two unrelated defects seeded into two different files. Participants are assigned to one of two treatments in which the position of the defective files is switched. For one type of defect, participants are not affected by its file's position; for the other, they have 64% lower odds to identify it when its file is last as opposed to first. Overall, our findings provide evidence that the relative position in which files are presented has an impact on code reviews' outcome; we discuss these results and implications for tool design and code review. Data and materials: https://doi.org/10.5281/zenodo.6901285