Reviewerly/exHarmony huggingface dataset:
Metrics for reusability of java language components

Software reuse is a promising opportunity for a software development process that helps in achieving an improved quality of the developed software system. Improved quality also promotes software architecture reuse opportunities. From the past few decades, software reuse is actively gaining popularity in the form of Component Based Software Development (CBSD). The components being the basic unit in CBSD, accessing their reusability is a useful and open area of investigation. The extent of the reusability can be quantified based on the extent of intra and inter-connectivity of the attributes/ characteristics of the component. This can be computed by defining suitable metrics to measure the reusability of a component. Although, there exist some metric suites such as CK, MOOD, Halstead, McCabe’s metric, etc. for measuring the reusability of the object-oriented software systems, but they are not directly reusable in the CBSD environment. This is due to the fact that these metrics are applicable only at the class level and they do not target the interfaces of the component that are important and define the composability of the component in the CBSD environment. Therefore, this paper aims to study the reusability, flexibility, composability, and adaptability aspects of the software component. Further, based on the study conducted, we propose a reusability metric suite specially designed for the JavaBeans. This metric helps to measure the architecture level reusability of the JavaBeans. The JavaBeans component standard is specially targeted in this paper because components based on this standard are not fully explored and only a few metric suites exist for measuring the reusability of the JavaBeans. The proposed component reusability metric suite is validated both experimentally as well as against Briand’s framework.
Toward Backdoor Attacks for Image Captioning Model in Deep Neural Networks

Deep neural networks perform well in image recognition, speech recognition, and text recognition fields. The image caption model provides captions for images by generating text after image recognition. After extracting features from the original image, this model generates a representation vector and provides captions for the image by generating text through a recursive neural network. However, this image caption model has weaknesses in the backdoor sample. In this paper, we propose a method for generating backdoor samples for image caption models. By adding a specific trigger to the original sample, this proposed method creates a backdoor sample that is misrecognized as a target class by the target model. The MS-COCO dataset was used as the experimental dataset, and Tensorflow was used as the machine learning library. When the trigger size of the backdoor sample is 4%, experimental results show that the average attack success rate of the backdoor sample is 96.67%, and the average error rate of the original sample is 9.65%.
Code red

Code quality remains an abstract concept that fails to get traction at the business level. Consequently, software companies keep trading code quality for time-to-market and new features. The resulting technical debt is estimated to waste up to 42% of developers' time. At the same time, there is a global shortage of software developers, meaning that developer productivity is key to software businesses. Our overall mission is to make code quality a business concern, not just a technical aspect. Our first goal is to understand how code quality impacts 1) the number of reported defects, 2) the time to resolve issues, and 3) the predictability of resolving issues on time. We analyze 39 proprietary production codebases from a variety of domains using the CodeScene tool based on a combination of source code analysis, version-control mining, and issue information from Jira. By analyzing activity in 30,737 files, we find that low quality code contains 15 times more defects than high quality code. Furthermore, resolving issues in low quality code takes on average 124% more time in development. Finally, we report that issue resolutions in low quality code involve higher uncertainty manifested as 9 times longer maximum cycle times. This study provides evidence that code quality cannot be dismissed as a technical concern. With 15 times fewer defects, twice the development speed, and substantially more predictable issue resolution times, the business advantage of high quality code should be unmistakably clear.
SAR Ship Image Speckle Noise Suppression Algorithm Based on Adaptive Bilateral Filter

Aiming at the shortcomings of traditional bilateral filtering in suppressing speckle noise in SAR ship images, especially strong speckle noise and loss of image edge details, it proposes an improved bilateral filtering algorithm based on fast adaptive threshold and variable window in this paper. The algorithm was used to suppress speckle noise in SAR ship images. The traditional bilateral filtering cannot effectively filter out the strong speckle noise, but the SAR image has strong speckle noise because of the defects of its imaging principle. To solve these problems, an image sample truncation method based on fast adaptive truncation statistical characteristics is designed to adjust the photometric similarity weight characteristics to realize the adaptive adjustment of spatial standard deviation and gray standard deviation. After the local reference window is modified and truncated according to the local characteristics of the image, the adjusted combined similarity weight value greatly reduces the impact of strong speckle noise on the image. It is smoothed into speckle signal with strong impulse noise. In the traditional bilateral filtering, in order to enhance the effect of smoothing noise, it is necessary to specify a large value of geometric diffusion factor and gray similarity diffusion factor, resulting in the loss of image details. Based on the variable window size filtering method, when the extended local reference window is in the case of nonuniform edge, its window can be enlarged to make the speckle noise stronger. When an extended window contains details such as edges and textures, its size is not expanded to maintain image detail. This method can further smooth the speckle noise in the uniform region while preserving the edge details of the image. Finally, the adaptive truncated sample is used as the input of the bilateral filter. The image sample truncation method based on fast adaptive threshold can effectively eliminate the strong speckle noise information that affects the photometric similarity and weight accuracy of the image. The method based on variable window can greatly enhance the smoothness of the edge area of the image. The experimental results show that improved adaptive bilateral filtering algorithm improves the speckle noise removal ability by 16.06% compared with the traditional bilateral filtering algorithm in the speckle noise suppression of the SAR ship image, and the preservation performance of the image edge after filtering is improved by 5.41%. Compared with the original image, the filtered image has a 1.2% improvement in structural similarity. The algorithm can effectively suppress speckle noise and has a good ability to retain edge and texture information of the SAR ship image, which has strong practicability.
Dam safety assessment through data-level anomaly detection and information fusion

The anomaly detection and safety assessment of dams have attracted increasing attentions. To assess the safety of dams, a novel dam safety assessment model is proposed. The safety assessment model consists of data-level anomaly detection and information fusion. In the anomaly detection, a novel model based on the generative adversarial network and variational autoencoder is proposed. Through the stepwise training, the anomaly scores with reconstruction and discrimination losses can be obtained. Considering the multi-source and complexity of information system, a two-step fusion model is proposed. In the multi-source and multi-attribute fusion, the neighborhood rough set model is employed to construct the neighborhood granular structure. Through the idea of granular computing, the uncertainty of these sources and attributes is measured. Then the Sup-Inf fusion functions and fusion weights are employed to integrate the information. Finally, the anomaly scores with interval form are obtained. For verification, an arch dam is taken as an example. Through the analysis of detection and fusion results, the proposed model can detect the abnormal signals accurately and have the comprehensive assessment of the dam.
A Two-channel model for relation extraction using multiple trained word embeddings

As an essential task in the field of knowledge graph, relation extraction (RE) has received extensive attention from researchers. Since the existing RE methods only adopt one trained word embedding to obtain sentence representation, the polysemy problem cannot be well solved. In order to alleviate the polysemy in RE, this paper proposes a Two-channel model by adopting multiple trained word embeddings, in which one channel is a bidirectional long-short-term memory network based on an attention mechanism (Bi-LSTM-ATT), and the other channel is a convolutional neural network (CNN). Furthermore, a two-channel fusion method is proposed based on this model to deal with polysemy problem in RE. As a result, the Two-channel model achieves 85.42% and 62.2% F1-scores on the Semeval-2010 Task 8 dataset and KBP37 dataset, respectively. The experiment results show that the Two-channel model performs better than most existing models under the condition without using the external features generated by natural language processing (NLP) tools. On the other hand, the two-channel fusion method also obtains a better performance than either concatenation or addition on the two channels.
Predictive Analytics for Supporting Environmental Sustainability and Disaster Management

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 predictive analytics solution for supporting environmental sustainability. In particular, we focus on environmental sustainability and disaster management related to water levels. To elaborate, low water levels may lead to unsustainability in water supply. In contrast, high water levels may lead to hazards or disasters like floods. Thus, having a reliable predictive analytics solution that gives accurate estimates of water levels is important. Our solution integrates different categories of weather data collected from distributed rich data sources. Evaluation on real-life data from a Canadian city demonstrates the practicality of our solution in predicting water level, and thus supports environmental sustainability and disaster management.
Improving Human Activity Recognition using ML and Wearable Sensors

The Internet of Things (IoT) generates massive amounts of data everywhere through sensors of every kind which are disseminated in a variety of objects. This data contains incredibly valuable information useful for multiple applications. Knowing the context in which it was generated is extremely important and constitutes one of the first steps in extracting the knowledge it contains. Thereby, Context-Aware Learning (CAL) has become an important area of research as machine learning (ML) is a fast and ever-evolving technology. Wearable devices, ranging from accelerometers (ACC), frequently used, to magnetic field sensors, are used to monitor and recognize human activities (HA). Beyond ML Algorithms (MLA), accurate Human Activities Recognition (HAR) or context identification, depends not only on the kinds of sensors used but also on their location. In this paper, we study the impact of three types of sensors: ACC, gyroscope (GYR), and magnetometer (MAG); and their locations on the performance of MLA for HAR. Our results show that magnetic field sensors, less frequently used in the literature, placed at a specific location, provide the best performance in terms of HAR. Using a publicly available dataset, PAMAP2, we implement and evaluate the performance of HAR using five MLA: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLA), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). Our results show that the success rate of these algorithms is 98.3%, 90.4%, 97.6%, 99.9%, and 100% respectively, which exceeds the results obtained in a previous work based on the same dataset.
Continuous software security through security prioritisation meetings

Software security needs to be a continuous endeavour in current software development practices. Frequent software updates, paired with an ongoing flow of security breaches, requires software companies to address software security throughout development and post deployment. Prescriptive software security approaches do not match well with agile software development and its emphasis on self-management. Agile approaches are however in favour of meetings as a coordination and problem-solving strategy. This article investigates the role of regular security meetings centred on making security priorities and decisions for achieving continuous software security. Through technical action research and an observational case study, we studied variations of such meetings in three companies. We found that such meetings can reach key stakeholders, make security more visible, and contribute to ongoing security prioritisation. Thus, security meetings are a promising approach, especially for small and medium sized development companies with basic yet immature security competence. Future research should investigate further the role of such meetings and how best to organise them for different contexts and needs. For this we outline implications for research and practice, e.g., related to participants and how to organise the discussions and prioritisations in the meeting.
Secure Identity-Based Designated Verifier Anonymous Aggregate Signature Scheme Suitable for Smart Grids

Safe and efficient collection of users’ electronic consumption data is a basic function of the smart grid. Anonymous aggregate signature is a fine option to realize this feature, which can not only reduce the computing cost of the data center (DC) but also protect the user’s identity information from leaking. The security proofs of all known identity-based aggregate signature (IBAS) schemes were completed in the random oracle model (ROM). Regrettably, these schemes are not necessarily secure in real life. In this article, we first propose the system model and security demands of an identity-based designated verifier anonymous aggregate signature (IB-DVAAS) scheme for smart grids. We then come up with a concrete scheme, only the designated DC can check the validity of an aggregate signature, but it cannot recognize the real identity of the signers. Next, we display the security proofs in the standard model. Finally, we conduct an analysis on performance for six aggregate signature schemes. Only three pairing operations are required and hash-to-point operations are not used, the calculation cost of the new scheme is lower than that of other schemes.
Autonomous Critical Help by a Robotic Assistant in the Field of Cultural Heritage: A New Challenge for Evolving Human-Robot Interaction

Over the years, the purpose of cultural heritage (CH) sites (e.g., museums) has focused on providing personalized services to different users, with the main goal of adapting those services to the visitors’ personal traits, goals, and interests. In this work, we propose a computational cognitive model that provides an artificial agent (e.g., robot, virtual assistant) with the capability to personalize a museum visit to the goals and interests of the user that intends to visit the museum by taking into account the goals and interests of the museum curators that have designed the exhibition. In particular, we introduce and analyze a special type of help (critical help) that leads to a substantial change in the user’s request, with the objective of taking into account the needs that the same user cannot or has not been able to assess. The computational model has been implemented by exploiting the multi-agent oriented programming (MAOP) framework JaCaMo, which integrates three different multi-agent programming levels. We provide the results of a pilot study that we conducted in order to test the potential of the computational model. The experiment was conducted with 26 real participants that have interacted with the humanoid robot Nao, widely used in Human-Robot interaction (HRI) scenarios.
Pinpoint: A Record, Replay, and Extract System to Support Code Comprehension and Reuse

Block-based programming environments, such as Scratch and Snap!, engage users to create programming artifacts such as games and stories, and share them in an online community. Many Snap! users start programming by reusing and modifying an example project, but encounter many barriers when searching and identifying the relevant parts of the program to learn and reuse. We present Pinpoint, a system that helps Snap! programmers understand and reuse an existing program by isolating the code responsible for specific events during program execution. Specifically, a user can record an execution of the program (including user inputs and graphical output), replay the output, and select a specific time interval where the event of interest occurred, to view code that is relevant to this event. We conducted a small-scale user study to compare users&#x2019; program comprehension experience with and without Pinpoint, and found suggestive evidence that Pinpoint helps users understand and reuse a complex program more efficiently.
Evaluating Gender Bias in Pair Programming Conversations with an Agent

While pair programming conversational agents have the potential to change the current landscape of programming, they require vast amounts of diverse data to train. However, due to gender gaps in the Computer Science field, it is difficult to obtain data involving women in pair programming scenarios; this may result in a bias in a future agent. Furthermore, previous research has highlighted differences between men and women in problem solving, communication, creativity, and leadership styles, which are critical for the success of pair collaboration. Therefore, it is crucial to understand how the agent&#x2019;s performance is affected by the gender composition of training datasets. Using the transformer-based language model BERT, we created a natural language understanding (NLU) model for our future agent, and tested its intent classification performance when alternately trained and tested on datasets composed entirely of either men or women. We found that the model&#x2019;s performance was significantly higher when trained and tested on men datasets, indicating the presence of gender bias within the NLU model of a future agent.
Comparative Analysis of Fuzzy Logic-based Image Enhancement Techniques for MRI Brain Images

In the medical industry, a medical image like magnetic resonance imaging (MRI), mammogram, and X-ray etc. has a very important role because it helps medical professionals for early detection and effective diagnosis of diseases. But unfortunately, during image acquisition and processing, these medical images go through many issues like low-light exposure, noise, bad contrast, etc. which not only affects the overall quality but also makes it difficult to extract the right information from the medical image. Thus, it becomes important to enhance image quality by removing noise, improving image contrast for proper treatment of patients. To make medical images suitable for a variety of applications, image parameters like brightness, contrast, entropy, and visual quality are improved by utilizing suitable image enhancement techniques. Fuzzy logic is a powerful tool that efficiently deals with uncertainty. Utilizing fuzzy logic in the image enhancement process is the best way to get high-quality images. In our research paper, we have implemented fuzzy logic-based image enhancement techniques on MRI brain images. Traditional image enhancement techniques like histogram equalization are compared with fuzzy logic-based image enhancement technique like adaptive fuzzy histogram equalization (AFHE) and contrast-limited fuzzy adaptive histogram equalization (CLFAHE), and their performance is compared on the basis of visual quality, peak signal-to-noise ratio (PSNR), entropy, and structural similarity index measure (SSIM). Experimental results show that AFHE gives improved image quality with richer details and adequately enhanced contrast as compared to other techniques.
A Survey of Dummy-Based Location Privacy Protection Techniques for Location-Based Services

As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable convenience to users, but it also poses a significant threat to their privacy. A large number of research works have emerged to protect users' privacy. Dummy-based location privacy protection solutions have been widely adopted for their simplicity and enhanced privacy protection results, but there are few reviews on dummy-based location privacy protection. Or, for existing works, some focus on aspects of cryptography, anonymity, or other comprehensive reviews that do not provide enough reviews on dummy-based privacy protection. In this paper, the authors provide a review of dummy-based location privacy protection techniques for location-based services. More specifically, the connection between the level of privacy protection, the quality of service, and the system overhead is summarized. The difference and connection between various location privacy protection techniques are also described. The dummy-based attack models are presented. Then, the algorithms for dummy location selection are analyzed and evaluated. Finally, we thoroughly evaluate different dummy location selection methods and arrive at a highly useful evaluation result. This result is valuable both to users and researchers who are studying this field.
Exploring Organization of Computational Notebook Cells in 2D Space

Representing branching and comparative analyses in computational notebooks is complicated by the 1-dimensional (1D), top-down list arrangement of cells. Given the ubiquity of these and other non-linear features, their importance to analysis and narrative, and the struggles current 1D computational notebooks have, enabling organization of computational notebook cells in 2 dimensions (2D) may prove valuable. We investigated whether and how users would organize cells in such a "2D Computational Notebook" through a user study and gathered feedback from participants through a follow-up survey and optional interviews. Through the user study, we found 3 main design patterns for arranging notebook cells in 2D: Linear, Multi-Column, and Workboard. Through the survey and interviews, we found that users see potential value in 2D Computational Notebooks for branching and comparative analyses, but the expansion from 1D to 2D may necessitate additional navigational and organizational aids.