Reviewerly/exHarmony huggingface dataset:
Long-term Static Analysis Rule Quality Monitoring Using True Negatives

Static application security testing (SAST) tools have found broad adoption in modern software development work-flows. These tools employ a variety of static analysis rules to generate recommendations on how to improve the code of an application.Every recommendation consumes the time of the engineer that is investigating it, so it is important to measure how useful these rules are in the long term. But what is a good metric for monitoring rule quality over time? Counting the number of recommendations rewards noisy rules and ignores developers’ reactions. Measuring fix rate is not ideal either, because it over-emphasizes rules that are easy to fix.In this paper, we report on an experiment where we use the frequency of true negatives to quantify if developers are able to learn a static analysis rule. We consider a static analysis rule to be ideal if its recommendations are not only addressed, but also internalized by the developer in a way that prevents the bug from recurring. That is, the rule contributes to code quality not only at present, but also in the future. We measure how often developers produce true negatives, that is, code changes that are relevant to a rule but do not trigger a recommendation, and we compare true-negative rate against other metrics. Our results show that measuring true negatives provides insights that cannot be provided by metrics such as fix rate or developer feedback.
Developing Software for Low Socio-Economic End Users: Lessons Learned from A Case Study of Fisherfolk Communities in Bangladesh

As part of a large Information and Communication Technology for Development (ICT4D) programme, we conducted a number of research projects to empower fisherfolk in Bangladesh. Due to their low socio-economic status, low level of digital as well as general literacy and many other similar factors, fisherfolk are very diverse as end users of software. It was important to understand their characteristics before designing any software for them. We started with exploratory research in reviewing literature and hearing from experts who closely work with fisherfolk. Based on the identification of some of the challenges, we designed a prototype software for tacit knowledge transfer among captains of boats. We conducted number of focus groups with the captains of the boats and adopted a co-creation process where the functionality and usability of the prototype software were decided by the end users themselves. From our experience of working with this diverse group of end users we propose specific recommendations for future software development for end users with a low socio-economic background.
On the Need for Artifacts to Support Research on Self-Adaptation Mature for Industrial Adoption

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community
Relation of Energy Consumption in Green Cloud Computing with Big Data

Cloud computing is already a part of the daily lives of both people and companies and as such, there is a concern about how it impacts the environment. The union of these two realities provides an opportunity for the emergence of Green Cloud Computing with new proposals, approaches, and metrics to make data centers more efficient, mainly in terms of energy, as well as to reduce CO2 emissions and its environmental impact. Knowing the metrics that can be used to measure the energy cost and environmental impact of data centers are fundamental in Green Computing, this article studies the relationship between energy consumption and execution times, architecture, and costs of a cloud environment. Using simulators it was possible to see an improvement in energy efficiency above 44%, as well as to get a cost reduction of at least 17%, and a reduction of of 55% in the environmental impact from the emission of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . It is important to note that there was no impact on processing times, by using the adoption of algorithms for complex problems, in the scheduling of Virtual Machines.
Design and experimental validation of an IoT data-logger device for indirect measurement of solar irradiance based on a correlation model

The present work aimed to develop an autonomous instrument for indirectly measuring solar irradiance from silicon sensors. Through laboratory tests, using a precision digital solar power meter device (SM206-SOLAR), with an error range of ±10% per reading and ±0.38% W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> per °C, and photovoltaic trainer equipment (Lucas Nulle), a statistical correlation model was developed between the short-circuit current of the NPA5S-12H solar module and the measured artificial irradiance. A significant linear correlation was determined with a mean absolute percentage error (MAPE) of 6.97%, an average root-mean-square error (RMSE) of ±26 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> per °C and a relative percentage root mean square error (rRMSE) of 10.76%. For real-time data collection purposes, a data-logger device with wireless communication functions based on a Wi-Fi link and IoT MQTT protocol was developed. Finally, a SQL server and web application were configured on a public Linux server to collect and present data in real-time.
Assessing Gender Differences In Using Reading Strategies In Classroom

ABSTRACTThis research is motivated by the use of reading strategies by English students based on gender in the classroom. The study used a survey research design involving 38 students of English education study program consisting of 15 male students and 23 female students. Data were collected using the Survey of Reading Strategies (SORS) questionnaire developed by Mokhtari and Sheory (2002). Data were analyzed using a survey and statistical test independent sample t-test was conducted to determine the differences between women and men. The results of the analysis using the independent sample t-test showed that there was no significant difference in all subscales of gender differences in the use of reading strategies. Thus, gender does not play a role in determining the reading strategy used.Keywords: Gender, English Students, Reading Strategy.
A Comprehensive Survey on Software as a Service (SaaS) Transformation for the Automotive Systems

Over the last few decades, automotive embedded Information and Communication Technology (ICT) systems have been used to enhance vehicle performance and enrich people's driving experience, increasing the panel of software features within them.However, even though until now automakers have kept up with the innovation pace in terms of the functionalities that have been offered to passengers, the majority of automakers' efforts have concentrated on bringing in these new functionalities by adding an unceasingly larger set of ECUs.All of this has been done without evolving any of the embedded software architecture consequently, due to budgetary constraints, legislative limitations, retro-compatibility problems, and a lack of awareness of the trending IT innovation.This unbalanced progress has then led to a substantial increase in in-vehicle architectural complexity, which has become a major concern for automakers nowadays as it makes the vehicle repairing process more complex, decreases software traceability and clashes with the objective of having higher business flexibility, modularity, and dynamicity within the vehicles.In this paper, we are going to go through literature, both academic and industrial, and propose a comprehensive study into automotive system transformation.We begin by giving a detailed analysis of the causes of evolution under five axes -i.e., society, business, industry, application, and technical.Then, we discuss the convergence of cars and software life cycles and propose a three-layered analysis of automotive ICT systems consisting of architecture design, software pipelines, and run-time management.Finally, we are going to propose certain detailed guidelines on the evolution perspectives for automotive systems through deriving from the convergence of advances in IT, as well as current and future automotive environmental constraints.
Classification Network-Guided Weighted <i>K</i>-Means Clustering for Multitouch Detection

In mobile devices, multitouch recognition technology is utilized to interact with the touch screen. Recognizing the coordinates of finger touches is the most important task in multitouch recognition. Several studies have been conducted to detect touch coordinates. However, these studies have been unable to detect exact coordinates when the big-finger problem occurs. The big-finger problem is a phenomenon in which a hole is generated in the center of the touch area when a large object approaches the touch screen, and it becomes more serious in a low-ground-mass environment. To solve the big-finger problem, we propose a novel method that detects touch coordinates using the clustering algorithm. We perform clustering on the touch data by treating the pixels of the touch data as data points. However, simply using the conventional clustering methods results in biased coordinate detection when touches of different sizes occur. Therefore, we propose the pixel intensity-weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering (PIKC) to prevent detection of biased coordinates. PIKC can detect unbiased coordinates by allocating touch pixel intensity values as weights in the centroid update process of clustering. Moreover, we accurately classify the number of touches using an efficient CNN-based multitouch classification network on adjacent-touch data. The predicted number of touches is used to determine the number of clusters for coordinate detection to further enhance the performance of our algorithm in adjacent-touch cases. Experiments demonstrate that our algorithm detects the touch coordinate most accurately compared to other algorithms in all touch scenarios.
Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU Platforms

The tremendous computing capacity of GPU offers significant potential in processing hierarchically compressed text data without decompression. However, current GPU techniques offer only traversal-based text data analytics; random access is exceedingly inefficient, limiting their utility significantly. To address this issue, we develop a novel and widely applicable solution that prompts random access to hierarchically compressed text data without decompression in GPU memory. We address three main challenges for enabling efficient random access to compressed text data on GPUs. The first challenge is designing GPU data structures that facilitate random access. The second challenge is efficiently generating data structures on GPU. The CPU is inefficient when generating data structures for random access, and this inefficiency increases considerably when PCIe transmission is incorporated. The third challenge is query processing on compressed text data in GPU memory. Random accesses, such as data updates, cause massive conflicts among countless threads. In order to address the first challenge, we develop several compressed GPU data structures, including indexing within the intricate GPU memory hierarchy. To handle the second challenge, we propose a two-phase process for producing these data structures on GPU. For the third challenge, a double-parsing design is proposed as a solution to avoid conflicts. We evaluate our solution on three platforms, two server-grade GPU platforms and one edge-grade GPU platform, using five real-world datasets. Experimental results show that random access operations on GPU achieve an average speedup of 52.98× compared to the state-of-the-art solution.
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework

Recently, numerous pancreas segmentation methods have achieved promising performance on local single-source datasets. However, these methods don't adequately account for generalizability issues, and hence typically show limited performance and low stability on test data from other sources. Considering the limited availability of distinct data sources, we seek to improve the generalization performance of a pancreas segmentation model trained with a single-source dataset, i.e., the single-source generalization task. In particular, we propose a dual self-supervised learning model that incorporates both global and local anatomical contexts. Our model aims to fully exploit the anatomical features of the intra-pancreatic and extra-pancreatic regions, and hence enhance the characterization of the high-uncertainty regions for more robust generalization. Specifically, we first construct a global-feature contrastive self-supervised learning module that is guided by the pancreatic spatial structure. This module obtains complete and consistent pancreatic features through promoting intra-class cohesion, and also extracts more discriminative features for differentiating between pancreatic and non-pancreatic tissues through maximizing inter-class separation. It mitigates the influence of surrounding tissue on the segmentation outcomes in high-uncertainty regions. Subsequently, a local-image-restoration self-supervised learning module is introduced to further enhance the characterization of the high-uncertainty regions. In this module, informative anatomical contexts are actually learned to recover randomly-corrupted appearance patterns in those regions. The effectiveness of our method is demonstrated with state-of-the-art performance and comprehensive ablation analysis on three pancreas datasets (467 cases). The results demonstrate a great potential in providing a stable support for the diagnosis and treatment of pancreatic diseases.
ILLATION: Improving Vulnerability Risk Prioritization By Learning From Network

Network administrators face the challenge of efficiently patching overwhelming volumes of vulnerabilities with limited time and resources. To address this issue, they must prioritize vulnerabilities based on the associated risk/severity measurements (i.e., CVSS). Existing solutions struggle to efficiently patch thousands of vulnerabilities on a network. This paper presents ILLATION, a proof-of-concept model that provides network-specific vulnerability risk prioritization to support efficient patching. ILLATION integrates AI techniques, such as neural networks and logical programming, to learn risk patterns from adversaries, vulnerability severity, and the network environment. It provides an integrated solution that learns and infers adversaries' motivation and ability in a network while also learning the constraints that restrict interactions between vulnerabilities and network elements. An evaluation of ILLATION against CVSS base and environmental metrics shows that it reflects changes in vulnerability scores and prioritization ranks as the same pattern as the CVSS model while identifying vulnerabilities with similar risk patterns to given adversaries better. On a simulated network with up to 10k vulnerable hosts and vulnerabilities, ILLATION can assess 1k vulnerabilities in about 4.5 minutes total, with an average running time of 0.19 seconds per vulnerability on a general-purpose computer.
DGRNet: A Dual-Level Graph Relation Network for Video Object Detection

Video object detection is a fundamental and important task in computer vision. One mainstay solution for this task is to aggregate features from different frames to enhance the detection on the current frame. Off-the-shelf feature aggregation paradigms for video object detection typically rely on inferring feature-to-feature (Fea2Fea) relations. However, most existing methods are unable to stably estimate Fea2Fea relations due to the appearance deterioration caused by object occlusion, motion blur or rare poses, resulting in limited detection performance. In this paper, we study Fea2Fea relations from a new perspective, and propose a novel dual-level graph relation network (DGRNet) for high-performance video object detection. Different from previous methods, our DGRNet innovatively leverages the residual graph convolutional network to simultaneously model Fea2Fea relations at two different levels including frame level and proposal level, which facilitates performing better feature aggregation in the temporal domain. To prune unreliable edge connections in the graph, we introduce a node topology affinity measure to adaptively evolve the graph structure by mining the local topological information of pairwise nodes. To the best of our knowledge, our DGRNet is the first video object detection method that leverages dual-level graph relations to guide feature aggregation. We conduct experiments on the ImageNet VID dataset and the results demonstrate the superiority of our DGRNet against state-of-the-art methods. Especially, our DGRNet achieves 85.0% mAP and 86.2% mAP with ResNet-101 and ResNeXt-101, respectively.
Revisiting the Regularizers in Blind Image Deblurring With a New One

Image deblurring and its counterpart blind problem are undoubtedly two fundamental tasks in computational imaging and computer vision. Interestingly, deterministic edge-preserving regularization for maximum-a-posteriori (MAP) based non-blind image deblurring has been largely made clear 25 years ago. As for the blind task, the state-of-the-art MAP-based approaches seem to also reach a consensus on the characteristic of deterministic image regularization, i.e., formulated in an L0 composite style or termed as L0+X style, where X is often a discriminative term such as dark channels-based sparsity regularization. However, with a modeling perspective as such, non-blind and blind deblurring are entirely disconnected from each other. Additionally, because L0 and X are motivated very differently in general, it is not easy in practice to derive an efficient numerical scheme. In fact, since the prosperity of modern blind deblurring 15 years ago, a physically intuitive yet practically effective and efficient regularization has been always desired. In this paper, representative deterministic image regularization terms in MAP-based blind deblurring are firstly revisited, with an emphasis on their differences from edge-preserving regularization for non-blind deblurring. Inspired by existing robust losses in the statistical and deep learning literature, an insightful conjecture is then made. That is, deterministic image regularization for blind deblurring can be naively formulated using a type of redescending potential functions (RDP), and interestingly, a RDP-induced blind deblurring regularization term is actually the 1rst -order derivative of a nonconvex edge-preserving regularization for non-blind image deblurring. An intimate relationship in regularization is therefore established between the two problems, differing much from the mainstream modeling perspective on blind deblurring. Via above principle analysis, the conjecture is demonstrated on benchmark deblurring problems in the final, accompanied with comparisons against several top-performing L0+X style methods. We note that, the rationality and practicality of the RDP-induced regularization is particularly highlighted here, aiming to open up an alternative line of possibility for modeling blind deblurring.
DeepBoot: Dynamic Scheduling System for Training and Inference Deep Learning Tasks in GPU Cluster

Deep learning tasks (DLT) include training and inference tasks, where training DLTs have requirements on minimizing average job completion time (JCT) and inference tasks need sufficient GPUs to meet real-time performance. Unfortunately, existing work separately deploys multi-tenant training and inference GPU cluster, leading to the high JCT of training DLTs with limited GPUs when the inference cluster is under insufficient GPU utilization due to the periodic inference workload. DeepBoot solves the challenges by utilizing idle GPUs in the inference cluster for the training DLTs. Specifically, 1) DeepBoot designs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive task scaling</i> (ATS) algorithm to allocate GPUs in the training and inference clusters for training DLTs and minimize the performance loss when reclaiming inference GPUs. 2) DeepBoot implements <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auto-fast elastic</i> (AFE) training based on Pollux to reduce the restart overhead by inference GPU reclaiming. Our implementation on the testbed and large-scale simulation in Microsoft deep learning workload shows that DeepBoot can achieve 32% and 38% average JCT reduction respectively compared with the scheduler without utilizing idle GPUs in the inference cluster.
Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

Demosaicing is a technique that reconstructs an RGB image from fragmentary color samples sensed by the image sensor. The color filter array (CFA), which is placed over the image sensor, determines the color of each pixel. The most used color filter array is the Bayer CFA. This paper proposes an Anti-Aliasing and Anti-Color-Artifact Demosaicing (AAACA) algorithm for the Bayer pattern and the resource-efficient very-large-scale integration (VLSI) architecture for the proposed algorithm. The AAACA comprises an anti-aliasing approach and a color artifacts filter named color difference-based median filter (CDMF). Compared to the traditional demosaicing methods that equally treat pixels on and not on edges, the AA reconstructs the pixels on edges with different strategies from those not on edges, significantly removing the aliasing around recovered edges. Then the CDMF is utilized to remove the color artifacts of the reconstructed RGB image based on the color difference after median filtering. We respectively simulate the quantitative evaluation and subjective visual quality on McMaster and Kodak datasets. Our experiments reveal that the proposed AAACA algorithm can significantly remove the visual aliasing around recovered edges and greatly reduce color artifacts in demosaiced images compared to the state-of-art demosaicing algorithms. The proposed VLSI architectures can achieve superior visual qualities compared with the previous VLSI implementations under the same process technology conditions with 180nm CMOS technology, an image resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1280\times 720$</tex-math> </inline-formula> (HD), and a working frequency of 200MHz.