Reviewerly/exHarmony huggingface dataset:
Coordinated DVFS and Precision Control for Deep Neural Networks

Traditionally, DVFS has been the main mechanism to trade-off performance and power. We observe that Deep Neural Network (DNN) applications offer the possibility to trade-off performance, power, and accuracy using both DVFS and numerical precision levels. Our proposed approach, Power-Inference accuracy Trading (PIT), monitors the server's load, and accordingly adjusts the precision of the DNN model and the DVFS setting of GPU to trade-off the accuracy and power consumption with response time. At high loads and tight request arrivals, PIT leverages INT8-precision instructions of GPU to dynamically change the precision of deployed DNN models and boosts GPU frequency to execute the requests faster at the expense of accuracy reduction and high power consumption. However, when the requests' arrival rate is relaxed and there is slack time for requests, PIT deploys high precision version of models to improve the accuracy and reduces GPU frequency to decrease power consumption. We implement and deploy PIT on a state-of-the-art server equipped with a Tesla P40 GPU. Experimental results demonstrate that depending on the load, PIT can improve response time up to 11 percent compared to a job scheduler that uses only FP32 precision. It also improves the energy consumption by up to 28 percent, while achieving around 99.5 percent accuracy of sole FP32-precision.
The fentanyl crisis &amp; the dark side of social media

Fentanyl sale is often promoted on social media, but there is very little empirical and systematic research on this issue. This study attempts to fill a gap in the literature by utilizing big data and computational as well as qualitative analyses to better understand this phenomenon. The study's sample includes a unique dataset of over 6 million tweets and Instagram posts referencing fentanyl including 5687 messages (856 tweets and 4831 Instagram posts) that promoted fentanyl sale and provided some instructions on where and how to purchase it. From a theoretical point of view, it discusses the concept of Dark Social Networking Sites (DSNS) by building and expanding on the previous literature on dark social media. Second, it offers an assessment of the content posted and major strategies used by opioid drug dealers on social media which mostly involve exchanging contact information of mobile communication apps in order to arrange for money transfers and deliveries.
Software Engineering Repositories

Defining and classifying software requirements are critical tasks for determining software functionality and overall software architecture. In this sense, several types of research are being developed aiming to automate the classification of software requirements through the use of machine learning algorithms. However, the feasibility of such studies runs counter to the existence of a public database that is adequate in terms of quantity and quality of sample requirements. A requirement base widely used in this type of task is the PROMISE. However, the number of requirements is considered low for practical applications involving machine learning. This research presents an expansion of the PROMISE corpus. New software requirements were incorporated, and the resulting dataset was evaluated through the use of well-known machine learning algorithms. We observed some improvement in the performance of these algorithms regarding the identification of some types of software requirements.
Analytical modeling of performance indices under epistemic uncertainty applied to cloud computing systems

The extent of epistemic uncertainty in modeling and analysis of complex systems is ever growing, mainly due to increasing levels of the openness, heterogeneity and versatility in cloud-based applications that are being adopted in critical sectors, like banking and finance. State-of-the-art approaches for model-based performance assessment do not embed such uncertainty in analytic models, hence the predicted results do not account for the parametric uncertainty. In this paper, we develop a method for incorporating epistemic uncertainty of the input parameters (i.e., the arrival rate λ and the service rate μ) to the M/M/1 queueing models, that are commonly used to analyze system performance. We consider two steady state and average output measures: the number of entities in the system and the response time. We start with closed-form solutions for these measures that enable us to study the propagation of epistemic uncertainty in input parameters to these output measures. We demonstrate the suitability of our method for the performance analysis of a cloud-based system, where the epistemic uncertainty comes from continuous re-deployment of applications across servers of different computational capabilities. System simulation results validate the ability of our models to produce satisfactorily accurate predictions of system performance indices under epistemic uncertainty.
Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.
Virtual reality driving simulator for user studies on automated driving

Nowadays, HCI Research on automated driving is commonly carried out using either low-quality setups with 2D monitors or expensive driving simulators with motion platforms. Furthermore, software for user studies on automated driving is often expensive and hard to modify for different scenarios. We plan to fill this gap by proposing a low-cost, high-fidelity immersive prototyping simulator by making use of virtual reality (VR) technology: AutoWSD - Automated driving simulator for research on windshield displays. We showcase a hybrid software and hardware solution as well as demonstrate how to design and implement scenarios for user studies, and thereby encourage discussion about potential improvements and extensions for AutoWSD, as well as the topic of trust, acceptance, user experience and simulator sickness in automation.
SMS Phishing and Mitigation Approaches

Smishing is an attack targeted to mobile devices in which the attacker sends text messages containing malicious links, phone numbers or E-Mail IDs to the victim and the attacker aims to steal sensitive user data like bank account details, passwords, user credentials, credit card details, etc through this message. Through this message, the attacker prompts the user to click on the link or contact the phone number or E-mail ID provided in the SMS. In this paper, we have discussed various mobile smishing attacks. We have also suggested various best policies which can be followed by the user to mitigate the smishing attacks. In addition to this, various techniques that are useful in mitigating and identifying smishing messages are also discussed. Finally, we gave an overview of methodologies proposed by other researchers in this field to differentiate smishing messages from legitimate ones. This paper would enable users to be more careful while clicking on the links provided in the SMS for downloading and installing apps and filling up fake user interfaces. Also, this paper would be a great contribution to the study on how to identify mobile smishing attacks.
U-Net with optimal thresholding for small blob detection in medical images

Biomarkers identified from medical images are valuable for disease diagnosis and staging. Object detection and segmentation are important for improving the accuracy of biomarker identification. It can be challenging to detect objects, especially small objects or “blobs,” from 3D images secondary to low image resolution, noise, and overlap. Traditional small blob filters generate significant false positives as noise components can be misidentified as regions of interest. Recent advancements in U-Net-a deep learning model-have yielded more effective denoising capabilities that make it more suitable for small blob detection. However, unless the intensity threshold is appropriately established, U-Net tends to “under-segment To address under-segmentation, we propose a combination of U-Net paired with optimal threshold detection via Otsu's thresholding. Two sets of experiments have been performed to compare this approach with both Hessian-based blob detection and U-Net with standard thresholding. The first experiment evaluated 20 simulated 3D images-comprising gloms with a varied size distribution, and noise. The second experiment examined MR images from 11 mouse kidneys with the objective of detecting all glomeruli. Our results support the conclusion that the proposed U-Net with optimal thresholding outperforms the Hessian-based detection and U-Net with standard thresholding approaches.
The benefits and challenges of general data protection regulation for the information technology sector

Purpose The implementation of European data protection is a challenge for businesses and has imposed legal, technical and organizational changes for companies. This study aims to explore the benefits and challenges that companies operating in the information technology (IT) sector have experienced in applying the European data protection. Additionally, this study aims to explore whether the benefits and challenges faced by these companies were different considering their dimension and the state of implementation of the regulation. Design/methodology/approach This study adopts a quantitative methodology, based on a survey conducted with Portuguese IT companies. The survey is composed of 30 questions divided into three sections, namely, control data; assessment; and benefits and challenges. The survey was created on Google Drive and distributed among Portuguese IT companies between March and April of 2019. The data were analyzed using the Stata software using descriptive and inferential analysis techniques using the ANOVA one-way test. Findings A total of 286 responses were received. The main benefits identified by the application of European data protection include increased confidence and legal clarification. On the other hand, the main challenges include the execution of audits to systems and processes and the application of the right to erasure. The findings allow us to conclude that the state of implementation of the general data protection regulation (GDPR), and the type of company are discriminating factors in the perception of benefits and challenges. Research limitations/implications This study has essentially practical implications. Based on the synthesis of the benefits and challenges posed by the adoption of European data protection, it is possible to assess the relative importance and impact of the benefits and challenges faced by companies in the IT sector. However, this study does not explore the type of challenges that are placed at each stage of the adoption of European data protection and does not take into account the specificities of the activities carried out by each of these companies. Originality/value The implementation of the GDPR is still in an initial phase. This study is pioneering in synthesizing the main benefits and challenges of its adoption considering the companies operating in the IT sector. Furthermore, this study explores the impact of the size of the company and the status of implementation of the GDPR on the perception of the established benefits and challenges.
A fast and automated segmentation method for detection of masses using folded kernel based fuzzy c-means clustering algorithm

Present study proposes a fast, accurate and automated segmentation approach of mammographic images using kernel based fuzzy c-means (FCM) clustering technique. This approach exploits the significant regional features of mammograms which address the properties of different breast densities. The proposed segmentation approach captures those regional features using appropriate kernel and hence apply fuzzy clustering technique for segmenting the masses. This study also introduces kernel based FCM (KFCM) approach in a folded way to process a combination of significant features simultaneously. Suitable choice of kernel size also assists to collect all possible variations of regional features with minimum blocking effect in the output results. Performances of the proposed methodology are analyzed qualitatively and quantitatively in compare to other clustering-based segmentation techniques. Since the proposed approach is able to resolve uncertain and imprecise characteristics of mammograms, it performs superior to other techniques. Convergence time of the proposed method is also assessed and compared with other conventional clustering techniques. Kernel based approach of the proposed segmentation technique reduces the number of data points for clustering and hence convergence speed improves over the conventional algorithms. This study also shows a variation of convergence speed of the proposed segmentation method with different image sizes.
Scenario-Based Systems Engineering: An Approach Towards Automated Driving Function Development

In this contribution, we propose an approach for scenario-based systems engineering for automated driving functions. The growing complexity of future driving systems presents an enormous challenge for the development and testing processes, the validation and release procedure, as well as the organization of work. Accompanying, the end of development and the concerning effort is difficult to estimate. This often leads to postponements and rise of development costs. Furthermore, recent research activities show that established test methods such as statistical testing are not well suited for the validation/verification and the release process of automated driving functions. Thus, we propose a method for continuous usage of scenarios, embedded in the systems engineering process, to divide complex and intangible development goals in smaller solvable tasks. Furthermore, we suggest how scenarios can be used for validation and verification of the target system. In this approach, various artifacts are generated, which help to plan, organize, and trace the development and validation process. As a result, temporal expenditure and development costs get estimable. Moreover, the technical progress of the system-to-develop becomes apparent at an earlier point of time.
Self-denigration and the mixed messages of ‘ugly’ selfies in Instagram

Abstract This paper sets out a new, multimodal account (both visual and verbal analysis) of how self-denigration is conveyed through ‘ugly selfies’ as posted to the photo-sharing site Instagram. Drawing on 226 Instagram posts categorised by the poster themselves with the hashtag #uglyselfie, the visual analysis compared the persons and setting in the selfie, the person’s facial expression (eye brow position, mouth expression), eye gaze and camera angle. The verbal analysis included the self-denigration found in the image caption and the rapport enhancing or threatening responses found in the comments to the image posted by others. Ugly selfies are found to convey various types of self-denigration, including self-deprecation and self-mockery. The difference between self-deprecation and self-mockery are explained by the types of ‘mixed messages’ generated by the images. In self-deprecation, the mixed messages arise from a contrast between the visual image and the verbal caption. In self-mockery, the mixed messages arise from contrasts within the image itself, where visual exaggeration and incongruity lead to a jocular interpretation of the ugly selfie. Although the focus in this paper is on self-denigration and selfie-taking, the analysis of mixed messages can be used to analyse many different forms of politeness strategies and many kinds of images shared with increasing frequency and importance on social media sites.
Contextualized CNN for Scene-Aware Depth Estimation From Single RGB Image

Directly benefited from deep learning techniques, depth estimation from single image has gained great momentum in recent years. However, most of the existing approaches treat depth prediction as an isolated problem without taking into consideration high-level semantic context information, which results in inefficient utilization of training dataset and unavoidably requires a large number of captured depth data during the training phase. To ameliorate, this paper develops a novel scene-aware contextualized convolution neural network (CCNN), which characterizes the semantic context relationship at the class-level and refines depth at the pixel-level. Our newly-proposed CCNN is built upon the intrinsic exploitation of context-dependent depth association, including inner-object continuous depth and inter-object depth change priors nearby. Specifically, rather than conducting regression on depth in single CNN, we make the first attempt to integrate both class-level and pixel-level conditional random fields (CRFs) based probabilistic graphical model into the powerful CNN framework to simultaneously learn different-level features within the same CNN layer. With our CCNN, the former model will guide the latter one to learn the contextualized RGB-Depth mapping. Hence, CCNN has desirable properties in both class-level integrity and pixel-level discrimination, which makes it ideal to share such two-level convolutional features in parallel during the end-to-end training with the commonly-used back-propagation algorithm. We conduct extensive experiments and comprehensive evaluations on public benchmarks involving various indoor and outdoor scenes, and all the experiments confirm that, our method outperforms the state-of-the-art depth estimation methods, especially for the cases where only small-scale training data are readily available.
Efficient Single-Stage Pedestrian Detector by Asymptotic Localization Fitting and Multi-Scale Context Encoding

Though Faster R-CNN based two-stage detectors have witnessed significant boost in pedestrian detection accuracy, they are still slow for practical applications. One solution is to simplify this working flow as a single-stage detector. However, current single-stage detectors (e.g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks. Accordingly, a structurally simple but effective module called Asymptotic Localization Fitting (ALF) is proposed, which stacks a series of predictors to directly evolve the default anchor boxes of SSD step by step to improve detection results. Additionally, combining the advantages from residual learning and multi-scale context encoding, a bottleneck block is proposed to enhance the predictors' discriminative power. On top of the above designs, an efficient single-stage detection architecture is designed, resulting in an attractive pedestrian detector in both accuracy and speed. A comprehensive set of experiments on two of the largest pedestrian detection datasets (i.e. CityPersons and Caltech) demonstrate the superiority of the proposed method, comparing to the state of the arts on both the benchmarks.
On the influence of Test Smells on Test Coverage

Software testing is a key practice in the software quality assurance process. Usually, the quality of a test is not analyzed before its execution, i.e., there are no tests to check the tests. When the quality of tests is not guaranteed, it may impair the quality of the software. Test Smells are an alternative to indicate problems in the test code that can affect test maintainability, more specifically readability and comprehension. This study investigates correlations between test coverage and test smells types. We also introduce the JNose Test, a tool to automate test smells detection. We analyzed 11 open source projects and detected 21 types of smells and 10 different test coverage metrics to each test class. We identified 63 out of 210 calculated correlations. Our results show that there is a relationship between test smells and test coverage, in which test smells may influence code coverage. Our findings might support software testers and help them understand the behavior and consequences of poorly written and designed tests.
Mobile Immersive Computing: Research Challenges and the Road Ahead

By its name, immersive computing creates a sense of immersion by blurring the boundary between the digital and the physical worlds. Typical immersive computing technologies include VR, 360-degree videos, AR, MR, and so on. This article discusses and highlights several research challenges when consumers experience immersive media content on mobile devices. It focuses on the recent developments for improving network efficiency and quality of user experience for mobile immersive applications. It then briefly introduces related standardization activities. Finally, it points out several opportunities and directions for further innovation, such as edge-accelerated AR applications.
Optimization of time constrained N-version programming service components with competing task execution and version corruption processes

This paper models a software service component implementing the N-version programming (NVP) redundancy on the cloud computing platform to enhance the service reliability. Specifically, multiple versions of the same service component are activated in parallel on different servers of the cloud to perform the requested service. At required service response time, the output is determined based on a threshold first-past-the-post voting rule (output with the most votes and the number of these votes exceeds a predetermined threshold). However, effectiveness of the NVP approach can be greatly compromised by co-residence attacks, a common type of cyber-attacks launched to corrupt user's service through co-residing user's and attacker's virtual machines on the same cloud server. This paper formulates and solves an optimization problem, particularly, a minmax game problem that finds the number of service component versions (SCVs) and the threshold to maximize the user's utility while considering a strategic attack behavior aiming to maximize the attacker's utility. The solution methodology encompasses a probabilistic model of evaluating the service success probability (SSP) and corruption attack success probability (CAP), two performance metrics used in the computation of the user's and attacker's utilities. Examples are analyzed to demonstrate influences of different model parameters on SSP, CAP, and solutions to the considered optimization problem.
Evidence for Area as the Primary Visual Cue in Pie Charts

The long-standing assumption of angle as the primary visual cue used to read pie charts has recently been called into question. We conducted a controlled, preregistered study using parallel-projected 3D pie charts. Angle, area, and arc length differ dramatically when projected and change over a large range of values. Modeling these changes and comparing them to study participants' estimates allows us to rank the different visual cues by model fit. Area emerges as the most likely cue used to read pie charts.