Reviewerly/exHarmony huggingface dataset:
An Empirical Study on the Use of Defect Prediction for Test Case Prioritization

Test case prioritization has been extensively re-searched as a means for reducing the time taken to discover regressions in software. While many different strategies have been developed and evaluated, prior experiments have shown them to not be effective at prioritizing test suites to find real faults. This paper presents a test case prioritization strategy based on defect prediction, a technique that analyzes code features - such as the number of revisions and authors - to estimate the likelihood that any given Java class will contain a bug. Intuitively, if defect prediction can accurately predict the class that is most likely to be buggy, a tool can prioritize tests to rapidly detect the defects in that class. We investigated how to configure a defect prediction tool, called Schwa, to maximize the likelihood of an accurate prediction, surfacing the link between perfect defect prediction and test case prioritization effectiveness. Using 6 real-world Java programs containing 395 real faults, we conducted an empirical evaluation comparing this paper's strategy, called G-clef, against eight existing test case prioritization strategies. The experiments reveal that using defect prediction to prioritize test cases reduces the number of test cases required to find a fault by on average 9.48% when compared with existing coverage-based strategies, and 10.4% when compared with existing history-based strategies.
Two-Step Artificial Bee Colony Algorithm Enhancement for QoS-Aware Web Service Selection Problem

This paper presents an enhanced artificial bee colony (ABC) algorithm for solving the web service selection problem. The proposed algorithm searches the best possible combination of web services to satisfy user requirements. An adapted neighborhood selection and replacement process and a swapping process are used to improve the ABC behavior. Neighboring nodes are employed to enhance ABC performance by encouraging exploration in early iterations, where bees have no knowledge regarding the search space, and by encouraging exploitation in later iterations to exploit bee knowledge of the search space. The swapping process is used to enhance ABC performance by randomly swapping portions among the best two solutions randomly. The idea behind this swap is to exploit the characteristics of the best solutions to generate new solutions. We compared the proposed algorithm with other algorithms in terms of quality and execution time using 60 different datasets. These datasets have different numbers of tasks and web services. The results indicate that the proposed algorithm finds better solutions compared to other algorithms. In addition, the results' summarization on 60 datasets with 30 different executions shows that the proposed algorithm outperforms the threshold-based algorithm by 6% and the enhanced ABC by 3% in terms of solutions' quality.
Zero impact: a large-scale study of uncitedness

This paper presents a large-scale study of the phenomenon 'uncitedness'. A literature review indicates that uncitedness is related to at least three factors: Field, document type, and time. To explore these factors and their mutual influence further, and at much larger scale than previous studies, the paper focuses on seven subject areas (arts and humanities; social sciences; computer science; mathematics; engineering; medicine; physics and astronomy), seven document types (articles; reviews; notes; letters; conference papers; books; book chapters), and a 20-year publication window (1996–2015). Documents are searched in Scopus, and retrieved year-by-year, discipline-by-discipline, and for each individual document type (total: 29,472,184 documents; 7,508,741 uncited documents). The results show great variance in uncitedness ratios between subject areas and document types. This is probably caused by a somewhat tacitly agreed upon genre hierarchy existing in all subject areas, yet with important local traits and differences. The importance of the time-dimension is documented. Time to first citation varies a great deal between subject areas, and the uncitedness ratio is consequently shown to be quite sensitive to the length of citation windows.
A Tool, not a Toy: Using Virtual Reality to Evaluate the Communication Between Autonomous Vehicles and Pedestrians

Although the main market for Virtual Reality (VR) is currently the gaming industry, advantages of using virtual environments in research and development have been already demonstrated e.g. for car industry or urban planning. Especially when no prototype is feasible or available, VR constitutes an advantageous alternative since it allows tests in laboratory conditions with high flexibility and ensured safety for test participants. In the presented study, it is investigated how VR can be used as a tool for Usability Tests to evaluate Human Machine Interfaces (HMI) for communication between autonomous vehicles and pedestrians. Singapore with its regulations and requirements has been selected as reference. Beyond the findings that explicit HMI concepts improve the communication between autonomous vehicles and pedestrians, VR was validated as suitable tool to conduct Usability Tests. Further studies plan to integrate additional case studies as well as improved immersion of test participants within the virtual environment.
A novel method for topic linkages between scientific publications and patents

It is increasingly important to build topic linkages between scientific publications and patents for the purpose of understanding the relationships between science and technology. Previous studies on the linkages mainly focus on the analysis of nonpatent references on the front page of patents, or the resulting citation‐link networks, but with unsatisfactory performance. In the meanwhile, abundant mentioned entities in the scholarly articles and patents further complicate topic linkages. To deal with this situation, a novel statistical entity‐topic model (named the CCorrLDA2 model), armed with the collapsed Gibbs sampling inference algorithm, is proposed to discover the hidden topics respectively from the academic articles and patents. In order to reduce the negative impact on topic similarity calculation, word tokens and entity mentions are grouped by the Brown clustering method. Then a topic linkages construction problem is transformed into the well‐known optimal transportation problem after topic similarity is calculated on the basis of symmetrized Kullback–Leibler (KL) divergence. Extensive experimental results indicate that our approach is feasible to build topic linkages with more superior performance than the counterparts.
Smart wheelchair integrating head gesture navigation

Science and technology have been improving day by day. Smart technology is augmenting the living standard of the human. Smart wheelchair is a paradigm of smart technology. The main ambition behind the titled project is to introduce a smart wheelchair which will be navigated with different head gestures of the user. The proposed wheelchair has been associated with exclusive features to make the movement more comfortable and convenient. Accelerometer sensor has been used to move the wheelchair in five different directions according to different head gestures. Two DC motors have been incorporated to move the wheels in different position. Arduino UNO controls the direction of the wheelchair using Relay as the motor driver. The wheelchair has been powered by solar panel thus providing emphasize on the use of green energy. LDR has been used as light detection sensor which would help the user to move when there is inadequate light. Seat belt sensor has been used as a switch to provide secure movement. Ultrasonic sensor has been used as the obstacle detector for making the movement safer. At last, the operation of the entire system has been observed and commendable outcomes have been achieved to make the smart wheelchair efficient and effective for the user.
Towards Collaborative Intelligence Friendly Architectures for Deep Learning

Modern mobile devices are equipped with highperformance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile devices. However, most mobile devices are still not capable of performing real-time inference using very deep models. Computations associated with deep models for today's intelligent applications are typically performed solely on the cloud. This cloud-only approach requires significant amounts of raw data to be uploaded to the cloud over the mobile wireless network and imposes considerable computational and communication load on the cloud server. Recent studies have shown that the latency and energy consumption of deep neural networks in mobile applications can be notably reduced by splitting the workload between the mobile device and the cloud. In this approach, referred to as collaborative intelligence, intermediate features computed on the mobile device are offloaded to the cloud instead of the raw input data of the network, reducing the size of the data needed to be sent to the cloud. In this paper, we design a new collaborative intelligence friendly architecture by introducing a unit responsible for reducing the size of the feature data needed to be offloaded to the cloud to a greater extent, where this unit is placed after a selected layer of a deep model. This unit is referred to as the butterfly unit. The butterfly unit consists of the reduction unit and the restoration unit. The outputs of the reduction unit is offloaded to the cloud server on which the computations associated with the restoration unit and the rest of the inference network are performed. Both the reduction and restoration units use a convolutional layer as their main component. The inference outcomes are sent back to the mobile device. The new network architecture, including the introduced butterfly unit after a selected layer of the underlying deep model, is trained end-to-end. Our proposed method, across different wireless networks, achieves on average 53x improvements for end-to-end latency and 68 x improvements for mobile energy consumption compared to the status quo cloud-only approach for ResNet-50, while the accuracy loss is less than 2 %.
Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding breast cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. The analysis of our experiments shows an advantage of the linear Support Vector Regression, Lasso regression, Kernel Ridge regression, K-neighborhood regression, and Decision Tree regression—these models achieve most accurate survival prognosis results. The cross-validation for accuracy demonstrates best performance of the same models on the studied breast cancer data. As a support for the proposed approach, a Python-based workflow has been developed and the plans for its further improvement are finally discussed in the paper.
Understanding How and Why Developers Seek and Analyze API-Related Opinions

With the advent and proliferation of online developer forums as informal documentation, developers often share their opinions about the APIs they use. Thus, opinions of others often shape the developer's perception and decisions related to software development. For example, the choice of an API or how to reuse the functionality the API offers are, to a considerable degree, conditioned upon what other developers think about the API. While many developers refer to and rely on such opinion-rich information about APIs, we found little research that investigates the use and benefits of public opinions. To understand how developers seek and evaluate API opinions, we conducted two surveys involving a total of 178 software developers. We analyzed the data in two dimensions, each corresponding to specific needs related to API reviews: (1) Needs for seeking API reviews, and (2) Needs for automated tool support to assess the reviews. We observed that developers seek API reviews and often have to summarize those for diverse development needs (e.g., API suitability). Developers also make conscious efforts to judge the trustworthiness of the provided opinions and believe that automated tool support for API reviews analysis can assist in diverse development scenarios, including, for example, saving time in API selection as well as making informed decisions on a particular API features.
Health information systems in developing countries: some reflections on information for action

This short paper offers some reflections on the role, effectiveness and challenges for health information systems in developing countries. It is argued that such systems need to support the use of information for action, but that this is often compromised by poor quality data. In addition, good data and related ICTs are inadequate by themselves since effective action also needs knowledgeable people and supportive institutions. Some approaches are discussed on software philosophy, educating people and changing institutions. The paper also includes a brief reflection on theory and its role in relation to action in health information systems, and ends with some reflections for practice. Illustrative examples in the paper are drawn mainly from India, but the issues discussed are relevant to engagement across the globe.
Key Management Scheme for Secure Channel Establishment in Fog Computing

Fog computing is a promising extension of cloud computing, and enables computing directly at the edge of the network. Due to the decentralized and distributed nature of fog nodes, secure communication channels have to be supported in fog computing, which are generally realized through secure keys. Key management schemes are usually employed to generate, distribute and maintain the secret keys. In this paper, we propose a key management scheme called dynamic contributory broadcast encryption (DConBE) for secure channel establishment in fog computing. It allows a group of fog nodes that want to establish a fog system to negotiate a public encryption key and each node’s decryption key in one round without a trusted dealer. Any end user may encrypt messages under the public encryption key with short ciphertexts to any subset of the fog nodes in the system. Only selected fog nodes in the system can decrypt the encrypted messages using their respective decryption key. Our new key management scheme also achieves the properties of fog node dynamics, fully collusion-resistant and stateless.
Juror comprehension of forensic expert testimony: A literature review and gap analysis

Forensic scientists and commentators including academics and statisticians have been embroiled in a debate over the best way to present evidence in the courtroom. Various forms of evidence presentation, both quantitative and qualitative, have been championed, yet amidst the furor over the most "correct" or "accurate" way to present evidence, the perspective of the fact-finder is often lost. Without comprehension, correctness is moot. Unbeknownst to many forensic practitioners, there is a large, though incomplete, body of literature from the cognitive psychology domain that explores the question of what jurors understand when forensic scientists testify. This body of work has begun to test different proposed methods of testimony in an effort to understand which are most effective at communicating the strength of evidence that is intended by the expert. This article is a review of that literature that is intended for the forensic scientist community. Its aim is to educate that community on the findings of completed studies and to identify suggestions for further research that will inform changes in testimony delivery and ensure that any modifications can be implemented with confidence in their effectiveness.
Measuring Learning During Search

We investigate the relationship between search behavior, eye -tracking measures, and learning. We conducted a user study where 30 participants performed searches on the web. We measured their verbal knowledge before and after each task in a content-independent manner, by assessing the semantic similarity of their entries to expert vocabulary. We hypothesize that differences in verbal knowledge-change of participants are reflected in their search behaviors and eye-gaze measures related to acquiring information and reading. Our results show that participants with higher change in verbal knowledge differ by reading significantly less, and entering more sophisticated queries, compared to those with lower change in knowledge. However, we do not find significant differences in other search interactions like page visits, and number of queries.
Identifying Experts in Software Libraries and Frameworks Among GitHub Users

Software development increasingly depends on libraries and frameworks to increase productivity and reduce time-to-market. Despite this fact, we still lack techniques to assess developers expertise in widely popular libraries and frameworks. In this paper, we evaluate the performance of unsupervised (based on clustering) and supervised machine learning classifiers (Random Forest and SVM) to identify experts in three popular JavaScript libraries: facebook/react, mongodb/node-mongodb, and socketio/socket.io. First, we collect 13 features about developers activity on GitHub projects, including commits on source code files that depend on these libraries. We also build a ground truth including the expertise of 575 developers on the studied libraries, as self-reported by them in a survey. Based on our findings, we document the challenges of using machine learning classifiers to predict expertise in software libraries, using features extracted from GitHub. Then, we propose a method to identify library experts based on clustering feature data from GitHub; by triangulating the results of this method with information available on Linkedin profiles, we show that it is able to recommend dozens of GitHub users with evidences of being experts in the studied JavaScript libraries. We also provide a public dataset with the expertise of 575 developers on the studied libraries.
In natural interaction with embodied robots, we prefer it when they follow our gaze: a gaze-contingent mobile eyetracking study

Initiating joint attention by leading someone's gaze is a rewarding experience which facilitates social interaction. Here, we investigate this experience of leading an agent's gaze while applying a more realistic paradigm than traditional screen-based experiments. We used an embodied robot as our main stimulus and recorded participants' eye movements. Participants sat opposite a robot that had either of two 'identities'-'Jimmy' or 'Dylan'. Participants were asked to look at either of two objects presented on screens to the left and the right of the robot. Jimmy then looked at the same object in 80% of the trials and at the other object in the remaining 20%. For Dylan, this proportion was reversed. Upon fixating on the object of choice, participants were asked to look back at the robot's face. We found that return-to-face saccades were conducted earlier towards Jimmy when he followed the gaze compared with when he did not. For Dylan, there was no such effect. Additional measures indicated that our participants also preferred Jimmy and liked him better. This study demonstrates (a) the potential of technological advances to examine joint attention where ecological validity meets experimental control, and (b) that social reorienting is enhanced when we initiate joint attention. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.
Location-Velocity Attention for Pedestrian Trajectory Prediction

Pedestrian path forecasting is crucial in applications such as smart video surveillance. It is a challenging task because of the complex crowd movement patterns in the scenes. Most of existing state-of-the-art LSTM based prediction methods require rich context like labelled static obstacles, labelled entrance/exit regions and even the background scene. Furthermore, incorporating contextual information into trajectory prediction increases the computational overhead and decreases the generalization of the prediction models across different scenes. In this paper, we propose a joint Location-Velocity Attention LSTM based method to predict trajectories. Specifically, a module is designed to tweak the LSTM network and an attention mechanism is trained to learn to optimally combine the location and the velocity information of pedestrians in the prediction process. We have evaluated our approach against other baselines and state-of-the-art methods on several publicly available datasets. The results show that it not only outperforms other prediction methods but it also has a good generalization ability.
25.3 A 128b AES Engine with Higher Resistance to Power and Electromagnetic Side-Channel Attacks Enabled by a Security-Aware Integrated All-Digital Low-Dropout Regulator

Side channel attacks (SCA) exploit data-dependent information leakage through power consumption and electromagnetic (EM) emissions from cryptographic engines to uncover secret keys. Integrated inductive voltage regulators (IVR) with a randomized control loop [1] or switching frequency [2], and random voltage dithering [3] have demonstrated improved power side-channel analysis (PSCA) resistance. Simulation studies have shown PSCA resistance via shunt linear regulators [4]. This paper demonstrates improved power and EM SCA resistance of standard (unprotected) 128b AES engines with parallel (P-AES, 128b) and serial (S-AES, 8b) datapaths via an on-die security-aware all-digital series low-dropout (DLDO) regulator, commonly used for fine-grain SoC power management. The security-aware DLDO improves SCA resistance using control-loop induced perturbations in a baseline DLDO, enhanced by a random switching noise injector (SNI) via power stage control and a randomized reference voltage (R-VREF) generator coupled with all-digital clock modulation (ADCM).