Reviewerly/exHarmony huggingface dataset:
Trends in software reuse research: A tertiary study

The reuse of software has been a research topic for more than 50 years. Throughout that time, many approaches, tools and proposed techniques have reached maturity. However, it is not yet a widespread practice and some issues need to be further investigated. The latest study on software reuse trends dates back to 2005 and we think that it should be updated. To identify the current trends in software reuse research. A tertiary study based on systematic secondary studies published up to July 2018. We identified 4,423 works related to software reuse, from which 3,102 were filtered by selection criteria and quality assessment to produce a final set of 56 relevant studies. We identified 30 current research topics and 127 proposals for future work, grouped into three broad categories: Software Product Lines, Other reuse approaches and General reuse topics. Frequently reported topics include: Requirements and Testing in the category of Lifecycle phases for Software Product Lines, and Systematic reuse for decision making in the category of General Reuse. The most mentioned future work proposals were Requirements, and Evolution and Variability management for Software Product Lines, and Systematic reuse for decision making. The identified trends, based on future work proposals, demonstrate that software reuse is still an interesting area for research. Researchers can use these trends as a guide to lead their future projects.
Concept-Driven Visual Analytics

Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
The Impact of Code Review on Architectural Changes

Although considered one of the most important decisions in the software development lifecycle, empirical evidence on how developers perform and perceive architectural changes remains scarce. Architectural decisions have far-reaching consequences yet, we know relatively little about the level of developers' awareness of their changes' impact on the software's architecture. We also know little about whether architecture-related discussions between developers lead to better architectural changes. To provide a better understanding of these questions, we use the code review data from 7 open source systems to investigate developers' intent and awareness when performing changes alongside the evolution of the changes during the reviewing process. We extracted the code base of 18,400 reviews and 51,889 revisions. 4,171 of the reviews have changes in their computed architectural metrics, and 731 present significant changes to the architecture. We manually inspected all reviews that caused significant changes and found that developers are discussing the impact of their changes on the architectural structure in only 31% of the cases, suggesting a lack of awareness. Moreover, we noticed that in 73% of the cases in which developers provided architectural feedback during code review, the comments were addressed, where the final merged revision tended to exhibit higher architectural improvement than reviews in which the system's structure is not discussed.
Exploiting Energy-Accuracy Trade-off through Contextual Awareness in Multi-Stage Convolutional Neural Networks

One of the promising solutions for energy-efficient CNNs is to break them down into multiple stages that are executed sequentially (MS-CNN). In this paper, we illustrate that unlike deep CNNs, MS-CNNs develop a form of contextual awareness of input data in initial stages, which could be used to dynamically change the structure and connectivity of such networks to reduce their computational complexity, making them a better fit for low-power and real-time systems. We suggest three run-time optimization policies, which are capable of exploring such contextual knowledge, and illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Moreover, we propose variable and dynamic bit-length fixed-point conversion to further reduce the memory footprint of the MS-CNNs.
AutoVAPS

With the rapid development of Internet-of-Things, sensors and devices are connected enabling a variety of applications. One of the most attractive applications is Video Analysis for Public Safety (VAPS), which has got massive attention from both research community and industry. However, there are still challenges in the system design and implementation of the VAPS service, especially in the mobile environment. For example, law enforcement officers are equipped with body-worn camera when they are on duty, how to connect body-worn cameras with the law enforcement vehicle and enable the law enforcement vehicle to perform near real-time video analysis for the officer are still open questions. Inspired by the promising edge computing technology, we propose an IoT-Enabled public safety service called AutoVAPS which integrates body-worn cameras and other sensors on the vehicle for public safety. In AutoVAPS, we propose a reference architecture that consists of the data layer for data management, the model layer for edge intelligence, and the access layer for privacy-preserving data sharing and access. Object detection is implemented as a case study of AutoVAPS. Early evaluation illustrated the applicability and challenges of AutoVAPS.
Coordinated behavior of cooperative agents using deep reinforcement learning

In this work, we focus on an environment where multiple agents with complementary capabilities cooperate to generate non-conflicting joint actions that achieve a specific target. The central problem addressed is how several agents can collectively learn to coordinate their actions such that they complete a given task together without conflicts. However, sequential decision-making under uncertainty is one of the most challenging issues for intelligent cooperative systems. To address this, we propose a multi-agent concurrent framework where agents learn coordinated behaviors in order to divide their areas of responsibility. The proposed framework is an extension of some recent deep reinforcement learning algorithms such as DQN, double DQN, and dueling network architectures. Then, we investigate how the learned behaviors change according to the dynamics of the environment, reward scheme, and network structures. Next, we show how agents behave and choose their actions such that the resulting joint actions are optimal. We finally show that our method can lead to stable solutions in our specific environment.
Naturalness- and information-preserving image recoloring for red–green dichromats

More than 100 million individuals around the world suffer from color vision deficiency (CVD). Image recoloring algorithms have been proposed to compensate for CVD. This study has proposed a new recoloring algorithm to make up shortages of contrast enhancement and naturalness preservation of the state-of-the-art methods. The recoloring task is formulated as an optimization problem that is solved by using the colors in a simulated CVD color space to maximize contrast and to preserve the original color as much as possible. In addition, the dominant colors are extracted for recoloring. They are then propagated to the whole image so that the optimization problem could be solved at a reasonable cost independent of the image size. In the quantitative evaluation, the results of the proposed method are competitive with those of the best existing method. The evaluation involving subjects with CVD demonstrates that the proposed method outperforms the state-of-the-art method in preserving both the information and the naturalness of images.
Designing Participatory Sensing with Remote Communities to Conserve Endangered Species

The increasing loss of species globally calls for effective monitoring tools and strategies to inform conservation action. The dominant approach to citizens engagement has been smart phone and platform-centric, tasking crowds to collect and analyze data. However, many critically endangered species inhabit remote areas, characterized by sparsely populated communities with poor internet connectivity. Approaches need to garner high engagement relative to population size, with data collection and knowledge synthesis suited to the local context. We conducted a field study in remote communities to understand how to enhance conservation of Bhutan's critically endangered White-bellied heron by exploring existing monitoring practices and trialing acoustic sensing technologies. We found that knowledge about the species is partial, heterogeneous, situated within and across communities and rooted in cultural beliefs. Sensors, acoustic interfaces, and playful probes provided new ways for the community to 'see' and discuss their local environment fostering them to share and grow their knowledge together. We contribute a synthesis of key considerations for designing effective participatory sensing to conserve species in remote communities.
Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model

Understanding social perception is important for designing mobile devices that are socially acceptable. Previous work not only investigated the social acceptability of mobile devices and interaction techniques but also provided tools to measure social acceptance. However, we lack a robust model that explains the underlying factors that make devices socially acceptable. In this paper, we consider mobile devices as social objects and investigate if the stereotype content model (SCM) can be applied to those devices. Through a study that assesses combinations of mobile devices and group stereotypes, we show that mobile devices have a systematic effect on the stereotypes' warmth and competence. Supported by a second study, which combined mobile devices without a specific stereotypical user, our result suggests that mobile devices are perceived stereotypically by themselves. Our combined results highlight mobile devices as social objects and the importance of considering stereotypes when assessing social acceptance of mobile devices.
Analyzing The Emotions of Crowd For Improving The Emergency Response Services

Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man-made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take an emotional change detection approach and focus on the users’ emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ improved emotion analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS.
Trust fall: data breach perceptions from loyalty and non-loyalty customers

Due to increased technological development in the service industry, protecting consumers' personal data has become a critical concern. When companies' information systems are attacked and customers' profiles are compromised after data breaches, customers may feel more vulnerable about their information privacy. This study uses hotel data breaches as circumstances and aims to understand whether loyalty and non-loyalty program customers would distinguish differently the perceived severity, vulnerability, trust depletion, and crisis response strategies of an organization in response to a data breach. The results indicated that a data breach does not cause significant differences in perceived vulnerability and perceived severity between the two groups. However, loyalty program customers significantly reduce their trust toward the organization after a data breach crisis. This study provides implications to address the importance of customer data protection and crisis responses as part of customer relationship management.
Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation

With the increased complexity in various applications of Digital Image Correlation (DIC), the requirement for its performances as a key technique of optical metrologies becomes more and more crucial, especially in terms of accuracy, efficiency, and robustness. However, the existing DIC methods mainly rely on a calculation path to conduct the DIC analysis, which limits their further improvement in the measurement accuracy, computational efficiency, and robustness to complex deformation. To promote the implementation of a real-time and wide-applied DIC system, this study presents an automatic, fast and robust point-wise DIC (PW-DIC) algorithm. This work introduces an enhanced feature-based path-independent initial value estimation (PI-IVE) method for an accurate subpixel iterative algorithm, which is achieved by a variant of PatchMatch algorithm in the feature image generated from the speckle pattern image. The feature image is derived from DAISY descriptor, which extracts the descriptor value at every pixel effectively rather than some detected keypoints. This method calculates reliable full-field initial values for all points of interest (POIs) simultaneously without an initial guess transfer path, which is path-independent. It cooperates with the state-of-the-art inverse compositional Gauss-Newton (IC-GN) algorithm to perform an accurate DIC analysis for each POI independently. Moreover, the proposed PW-DIC method can solve the problem of low calculation efficiency in path-dependent DIC methods. Due to the high accuracy and flexibility of the proposed PW-DIC method, it outperforms the standard reliability-guided DIC (RG-DIC) methods for large, discontinuous or rotational deformations. Extensive experiments and analysis are conducted to demonstrate the accuracy, effectiveness, and robustness of the proposed PW-DIC method.
Effects of Local Latency on Game Pointing Devices and Game Pointing Tasks

Studies have shown certain game tasks such as targeting to be negatively and significantly affected by latencies as low as 41ms. Therefore it is important to understand the relationship between local latency - delays between an input action and resulting change in the display - and common gaming tasks such as targeting and tracking. In addition, games now use a variety of input devices, including touchscreens, mice, tablets and controllers. These devices provide very different combinations of direct/indirect input, absolute/relative movement, and position/rate control, and are likely to be affected by latency in different ways. We performed a study evaluating and comparing the effects of latency across four devices (touchscreen, mouse, controller and drawing tablet) on targeting and interception tasks. We analyze both throughput and path characteristics, identify differences between devices, and provide design considerations for game designers.
A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar (SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process. In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio (SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
Security Managers Are Not The Enemy Either

Security managers are leading employees whose decisions shape security measures and thus influence the everyday work of all users in their organizations. To understand how security managers handle user requirements and behavior, we conducted semi-structured interviews with seven security managers from large-scale German companies. Our results indicate that due to the absence of organizational structures that include users into security development processes, security managers unintentionally obtain a negative view on users. Their distrust towards users leads to the creation of technical security measures that cannot be influenced by users in any way. However, as previous research has repeatedly shown, rigid security measures lead to frustration and discouragement of users, and also to creative (but usually insecure) methods of security circumvention. We conclude that in order to break through this vicious cycle, security managers need organizational structures, methods and tools that facilitate systematic feedback from users.
An Exploration of Bitcoin Mining Practices

Bitcoin blockchain technology is a distributed ledger of nodes authorizing transactions between anonymous parties. Its key actors are miners using computational power to solve mathematical problems for validating transactions. By sharing blockchain's characteristics, mining is a decentralized, transparent and unregulated practice, less explored in HCI, so we know little about miners' motivations and experiences, and how these may impact on different dimensions of trust. This paper reports on interviews with 20 bitcoin miners about their practices and trust challenges. Findings contribute to HCI theories by extending the exploration of blockchain's characteristics relevant to trust with the competitiveness dimension underpinning the social organization of mining. We discuss the risks of collaborative mining due to centralization and dishonest administrators, and conclude with design implications highlighting the need for tools monitoring the distribution of rewards in collaborative mining, tools tracking data centers' authorization and reputation, and tools supporting the development of decentralized pools.
Using a Simple Expert System to Assist a Powered Wheelchair User

A simple expert system is described that helps wheelchair users to drive their wheelchairs. The expert system takes data in from sensors and a joystick, identifies obstacles and then recommends a safe route. Wheelchair users were timed while driving around a variety of routes and using a joystick controlling their wheelchair via the simple expert system. Ultrasonic sensors are used to detect the obstacles. The simple expert system performed better than other recently published systems. In more difficult situations, wheelchair drivers did better when there was help from a sensor system. Wheelchair users completed routes with the sensors and expert system and results are compared with the same users driving without any assistance. The new systems show a significant improvement.
Brick

Augmented reality (AR) games have been growing in popularity in recent years. However, current AR games offer limited opportunities for a synchronous multiplayer experience. This paper introduces a model for designing AR experiences in which players inhabit a shared, real-time augmented environment and can engage in synchronous and collaborative interactions with other players. We explored the development of this model through the creation of Brick, a two-player mobile AR game at the room scale. We refined Brick over multiple rounds of iteration, and we used our playtests to investigate a range of issues involved in designing shared-world AR games. Our findings suggest that there are five major categories of interactions in a shared-world AR system: single-player, intrapersonal, multiplayer, interpersonal, and environmental. We believe that this model can support the development of collaborative AR games and new forms of social gameplay.
Toward Accurate Quality Estimation of Screen Content Pictures With Very Sparse Reference Information

The screen content (SC) pictures, such as webpages, serve as a visible and convenient medium to well-represent the Internet information, and therefore, the visual quality of SC pictures is highly significant and has attained a growing amount of attention. Accurate quality evaluation of SC pictures not only provides the fidelity of the conveyed information, but also contributes to the improvement of the user experience. In practical applications, a reliable estimation of SC pictures plays a considerably critical role for the optimization of the processing systems as the guidance. Based on these motivations, this paper proposes a novel method for precisely assessing the quality of SC pictures using very sparse reference information. Specifically, the proposed quality method separately extracts the macroscopic and microscopic structures, followed by comparing the differences of macroscopic and microscopic features between a pristine SC picture and its corrupted version to infer the overall quality score. By studying the feature histogram for dimensionality reduction, the proposed method merely requires two features as the reference information that can be exactly embedded in the file header with very few bits. Experiments manifest the superiority of our algorithm as compared with state-of-the-art relevant quality metrics when applied to the visual quality evaluation of SC pictures.