Reviewerly/exHarmony huggingface dataset:
An approach and benchmark to detect behavioral changes of commits in continuous integration

When a developer pushes a change to an application's codebase, a good practice is to have a test case specifying this behavioral change. Thanks to continuous integration (CI), the test is run on subsequent commits to check that they do no introduce a regression for that behavior. In this paper, we propose an approach that detects behavioral changes in commits. As input, it takes a program, its test suite, and a commit. Its output is a set of test methods that capture the behavioral difference between the pre-commit and post-commit versions of the program. We call our approach DCI (Detecting behavioral changes in CI). It works by generating variations of the existing test cases through (i) assertion amplification and (ii) a search-based exploration of the input space. We evaluate our approach on a curated set of 60 commits from 6 open source Java projects. To our knowledge, this is the first ever curated dataset of real-world behavioral changes. Our evaluation shows that DCI is able to generate test methods that detect behavioral changes. Our approach is fully automated and can be integrated into current development processes. The main limitations are that it targets unit tests and works on a relatively small fraction of commits. More specifically, DCI works on commits that have a unit test that already executes the modified code. In practice, from our benchmark projects, we found 15.29% of commits to meet the conditions required by DCI.
Experimental investigation of soiling impact on grid connected PV power

Algeria has large territory which is around 2,382 million km with an average solar power of 2650 kWh/m2/year on more than 80 % of the total surface of country. Back in 2015, the Algerian government has adopted a program for electricity production with an objective to insert 13GW of photovoltaic (PV) systems, which corresponds to an estimated modules area of 90 km2 distributed over the national territory. The PV systems choice is justified by the availability of a great solar potential. Nonetheless, the Sahara regions are characterized by frequent sandstorms. But the Algerian Northern regions are characterized by exhaust emissions of carbon particles. The main objective of our study is to show the impact of the soil on grid connected PV performance in coastal regions. Hence, experiments have been conducted on clean and dirty PV modules glazing in natural conditions to determine the electrical characteristics. It was found, that the dirt can significantly minimize the power production during the day, for an exposure period of one month and half after the last cleaning. It is very important to indicate that, this experimental investigation can help forecasting the PV generator energy production by taking into account of dirt effects.
An Interactive e-Book for Physics to Improve Students' Conceptual Mastery

Rotational dynamics is one of topic in physics for high school students that require visualization of its concepts. This research developed an interactive e-book for physics learning with its features consisting of multimedia content such as audio, video, animation, and simple problem-solving activities. This e-book has been validated and recommended by a team of experts. This trial aims to determine the effectiveness of e-book by students in mastering concepts, using a. quasi-experiment. The sample was chosen using purposive sampling technique at senior high school with 42 students. Data were collected using multiple choice questions. The increase of student concept mastery is determined based on the N-gain score of pretest and posttest data. The results show that the average student response to e-book is 81% in the very good criterion. The N-gain scores for the equilibrium concepts of A and B classes are 0.79 and 0.70 in the high criteria, while, the concept of rotational dynamics are 0.66 and 0.66 respectively and in the medium criterion. The increase of conceptual mastery occurs in every cognitive domain (remembering, understanding, applying, analyzing, evaluating, and creating). In conclusion, the e-book has been effective in enhancing the mastery of student concepts.
Multiscale Estimation of Event Arrival Times and Their Uncertainties in Hydroacoustic Records from Autonomous Oceanic Floats

ABSTRACT We describe an algorithm to pick event onsets in noisy records, characterize their error distributions, and derive confidence intervals on their timing. Our method is based on an Akaike information criterion that identifies the partition of a time series into a noise and a signal segment that maximizes the signal-to-noise ratio. The distinctive feature of our approach lies in the timing uncertainty analysis, and in its application in the time domain and in the wavelet timescale domain. Our novel data are records collected by freely floating Mobile Earthquake Recording in Marine Areas by Independent Divers (MERMAID) instruments, midcolumn hydrophones that report triggered segments of ocean-acoustic time series.
Review of local binary pattern operators in image feature extraction

&lt;span&gt;With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.&lt;/span&gt;
Method of Classification of Tonal Estimations Time Series in Problems of Intellectual Analysis of Text Content

Time series of tonal estimations that occurs in the process of sentiment analysis of the flow of text messages (from messengers, social networks, etc.) can contain significant information about the dynamics of emotions of subjects that generate the message. Intellectual analysis of such time series enable to find certain patterns of emotions dynamics, for example, in responses of clients of the transport company, taxi clients, etc. In this article a method of classification of time series of tonal estimates of short text messages based on PCA is developed. Authors showed up that the process of sentiment analysis of sufficiently short time series of tonal estimates enabled to identify situations where customer feedback has a negative or positive trend. The use of such method by managers or chat-bots will increase the level of maintenance of the transport infrastructure, clients, etc.
Embodiment of Wearable Technology: Qualitative Longitudinal Study

Background Current technology innovations, such as wearables, have caused surprising reactions and feelings of deep connection to devices. Some researchers are calling mobile and wearable technologies cognitive prostheses, which are intrinsically connected to individuals as if they are part of the body, similar to a physical prosthesis. Additionally, while several studies have been performed on the phenomenology of receiving and wearing a physical prosthesis, it is unknown whether similar subjective experiences arise with technology. Objective In one of the first qualitative studies to track wearables in a longitudinal investigation, we explore whether a wearable can be embodied similar to a physical prosthesis. We hoped to gain insights and compare the phases of embodiment (ie, initial adjustment to the prosthesis) and the psychological responses (ie, accept the prosthesis as part of their body) between wearables and limb prostheses. This approach allowed us to find out whether this pattern was part of a cyclical (ie, period of different usage intensity) or asymptotic (ie, abandonment of the technology) pattern. Methods We adapted a limb prosthesis methodological framework to be applied to wearables and conducted semistructured interviews over a span of several months to assess if, how, and to what extent individuals come to embody wearables similar to prosthetic devices. Twelve individuals wore fitness trackers for 9 months, during which time interviews were conducted in the following three phases: after 3 months, after 6 months, and at the end of the study after 9 months. A deductive thematic analysis based on Murray’s work was combined with an inductive approach in which new themes were discovered. Results Overall, the individuals experienced technology embodiment similar to limb embodiment in terms of adjustment, wearability, awareness, and body extension. Furthermore, we discovered two additional themes of engagement/reengagement and comparison to another device or person. Interestingly, many participants experienced a rarely reported phenomenon in longitudinal studies where the feedback from the device was counterintuitive to their own beliefs. This created a blurring of self-perception and a dilemma of “whom” to believe, the machine or one’s self. Conclusions There are many similarities between the embodiment of a limb prosthesis and a wearable. The large overlap between limb and wearable embodiment would suggest that insights from physical prostheses can be applied to wearables and vice versa. This is especially interesting as we are seeing the traditionally “dumb” body prosthesis becoming smarter and thus a natural merging of technology and body. Future longitudinal studies could focus on the dilemma people might experience of whether to believe the information of the device over their own thoughts and feelings. These studies might take into account constructs, such as technology reliance, autonomy, and levels of self-awareness.
Virtual Avebury: exploring sense of place in a virtual archaeology simulation

&lt;p class="VARAbstract"&gt;This paper describes and discusses creating and evaluating a virtual reality simulation of Avebury Stone Circle and Henge complex as it might have appeared and sounded circa 2300 BCE. Avebury is a Neolithic heritage site in the UK which is part of the Stonehenge, Avebury and associated sites UNESCO World Heritage Site. The overall aim of the project was to better understand the sense of place and presence that visitors can experience in virtual simulations of heritage sites. We investigated how virtual spaces might become experienced as places by visitors through their exploration, active participation, sensory stimulation and communication with other visitors in the simulation. More than 1200 members of the public experienced the simulation, both at Avebury itself and at three public exhibitions. The specific objectives of the project were to explore if and how the believability of a simulation was associated with feeling a sense of place in the virtual landscape, and if some personal characteristics, viz. age, disability, sex, immersive tendency, familiarity with IT and frequency of playing computer games, were associated with levels of enjoyment in, and learning from, the simulation. We analysed the data from a detailed questionnaire completed by 388 of the 702 visitors to Avebury from June to September 2018 who experienced the simulation, supported by observational data from all participants at all events. We found that believability was associated with a sense of place in the simulation, i.e. that the more believable the simulation appeared, the greater the sense of place experienced by the participants. We also found that personal characteristics had very little influence upon visitor reactions to the simulation, suggesting that such simulations might have wide appeal for heritage and museum visitors, regardless of age, gender or familiarity with technology.&lt;/p&gt;&lt;p class="VARAbstractHeader"&gt;Highlights&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;p&gt;More than 1200 members of the public experienced a 3D, fully immersive simulation of Avebury Henge, Wiltshire, UK over a nine-month period.&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;We found patterns of use and familiarity with information technology (IT), and using mobile technologies for gaming, that did not follow age and gender stereotypes.&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;We found little correlation between age, gender and IT familiarity with reactions to Virtual Avebury, suggesting that such simulations might have wide appeal for heritage site visitors.&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;
Deep Open Set Recognition Using Dynamic Intra-class Splitting

Abstract This paper provides a generic deep learning method to solve open set recognition problems. In open set recognition, only samples of a limited number of known classes are given for training. During inference, an open set recognizer must not only correctly classify samples from known classes, but also reject samples from unknown classes. Due to these specific requirements, conventional deep learning models that assume a closed set environment cannot be used. Therefore, special open set approaches were taken, including variants of support vector machines and generation-based state-of-the-art methods which model unknown classes by generated samples. In contrast, our proposed method models unknown classes by atypical subsets of training samples. The subsets are obtained through intra-class splitting (ICS). Based on a recently proposed two-stage algorithm using ICS, we propose a one-stage method based on alternating between ICS and the training of a deep neural network. Finally, several experiments were conducted to compare our proposed method with conventional and other state-of-the-art methods. The proposed method based on dynamic ICS showed a comparable or better performance than all considered existing methods regarding balanced accuracy.
Improved covering-based collaborative filtering for new users’ personalized recommendations

Abstract User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF and could provide satisfactory recommendations to an active user which often has sufficient rating information. However, different from an active user, a new user in RSs often has special characteristics (e.g., fewer ratings or ratings concentrating on popular items), and the previous CBCF approach cannot provide satisfactory recommendations for a new user. In this paper, aiming to provide personalized recommendations for a new user, through a detailed analysis of the characteristics of new users, we reconstruct a decision class to improve the previous CBCF and utilize the covering reduction algorithm in covering-based rough sets to remove redundant candidate neighbors for a new user. Furthermore, unlike the previous CBCF, our improved CBCF could provide personalized recommendations without needing special additional information. Experimental results suggest that for the sparse datasets that often occur in real RSs, the improved CBCF significantly outperforms those of existing work and can provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.
Optimal weighted bilateral filter with dual‐range kernel for Gaussian noise removal

The bilateral filter is a classical technique for edge-preserving smoothing. It has been widely used as an effective image denoising approach to remove Gaussian noise. The performance of bilateral filtering highly depends on the accuracy of its range distance estimation, which is used for pixel-neighbourhood similarity measurement. However, in the conventional bilateral filtering approach, estimating the range distance directly from noisy observation results in the degradation of denoising performance. To address this issue, the authors propose a novel bilateral filtering scheme with a dual-range kernel, which provides a more robust range of distance estimation at various noise levels compared with existing methods. To further improve the denoising performance, they employ a linear model to retrieve the remaining image details from the method noise and add them back to the denoised image by employing an optimal approach based on Stein's unbiased risk estimate. Experiments on standard test images demonstrate that the proposed method outperforms conventional bilateral filter and its major state-of-the-art variants.
BiGAN: Collaborative Filtering with Bidirectional Generative Adversarial Networks

Recently, GAN-based collaborative filtering methods have gained increasing attention in recommendation tasks which can learn remarkable user and item representation. However, these existing GAN-based methods mainly suffer from two limitations: (1) Their trainings are not comprehensive given the fact that the discriminator may be trained misleadingly and over-early converging since the generator may accidentally sample real items as fake ones, resulting in the emergence of contradicting labels for the same items. (2) They fail to consider implicit friends (users with the same interests.), leading to severe limitations of recommendation performance. In this paper, we propose BiGAN, an innovative bidirectional adversarial recommendation model which can alleviate the limitations mentioned above in recommendation tasks. It consists of two GANs, namely ForwardGAN and BackwardGAN. Specifically, ForwardGAN learns to generate a group of possible interacted items given a specific user, it aims to ensure that the discriminator Df can be trained effectively. Furthermore, BackwardGAN fully exploits implicit friends with similar behaviors, then propagates them back to ForwardGAN, where a similarity exploration strategy is implemented to gain more outstanding user representation. Therefore, two GANs are trained jointly in a circle, where the augment of one GAN will enhance another one, leading to the promising user and item representation. In the experimental part, we demonstrate that our model is superior to other state-of-the-art recommenders.
Statistical ZAPR Arguments from Bilinear Maps

Dwork and Naor (FOCS '00) defined ZAPs as 2-message witness-indistinguishable proofs that are public-coin. We relax this to ZAPs with private randomness (ZAPRs), where the verifier can use private coins to sample the first message (independently of the statement being proved), but the proof must remain publicly verifiable given only the protocol transcript. In particular, ZAPRs are reusable, meaning that the first message can be reused for multiple proofs without compromising security. Known constructions of ZAPs from trapdoor permutations or bilinear maps are only computationally WI (and statistically sound). Two recent results of Badrinarayanan-Fernando-Jain-Khurana-Sahai and Goyal-Jain-Jin-Malavolta [EUROCRYPT '20] construct the first statistical ZAP arguments, which are statistically WI (and computationally sound), from the quasi-polynomial LWE assumption. Here, we construct statistical ZAPR arguments from the quasi-polynomial decision-linear (DLIN) assumption on groups with a bilinear map. Our construction relies on a combination of several tools, including the Groth-Ostrovsky-Sahai NIZK and NIWI [EUROCRYPT '06, CRYPTO '06, JACM '12], "sometimes-binding statistically hiding commitments" [Kalai-Khurana-Sahai, EUROCRYPT '18] and the "MPC-in-the-head" technique [Ishai-Kushilevitz-Ostrovsky-Sahai, STOC '07].
The rubber hand illusion in hypnosis provides new insights into the sense of body ownership

Abstract Body ownership can be experimentally investigated with the rubber hand illusion (RHI), in which watching a rubber hand stroked synchronously with one’s own hidden hand induces a feeling of ownership over the rubber hand. The aim of this study was to investigate response to the RHI in high (N = 21) and low (N = 19) hypnotizable individuals in normal waking state and in hypnosis. Response to the RHI was measured via a question on the illusory feeling of ownership and with proprioceptive drift. The Highs expressed an overall feeling of more ownership over the rubber hand in both the normal waking state and hypnosis, although both groups gave higher ownership scores after synchronous than after asynchronous stroking and the difference between conditions was similar across groups. Conversely, the proprioceptive drift appeared to be differentially modulated by hypnosis and hypnotic suggestibility: it was increased in the Highs and decreased in the Lows after hypnosis induction. These findings hint at an interplay between hypnotic suggestibility and hypnosis in modulating response to the RHI. The selective breakdown of proprioceptive drift among the Lows suggests resistance to recalibrate one’s own limb in hypnosis.
A Quadral-Fuzzy Control Approach to Flight Formation by a Fleet of Unmanned Aerial Vehicles

This paper addresses the control of a fleet of unmanned aerial systems (UAVs), termed as drones, for flight formation problems.Getting drones to fly in formation is a relevant problem to be solved when cooperative cargo transportation is desired.A general approach for this problem considers the coordination of a fleet of UAVs, by fusing all information coming from several individual sensors posed on each UAVs.However, this approach induces a high cost as every UAV should have its advanced perception system.As an alternative, this paper proposes the use of a single perception system by a fleet composed of several elementary drones (workers) with primitive low-cost sensors and a leader drone carrying a 3D perception source.We propose a Quadral-Fuzzy approach to ensure that all drones fly in formation and will not collide with each other or with environment obstacles.We also develop a new way to compute potential fields based on possibility fuzzy (fuzziness) measure with the focus of avoiding collisions between the drones.The proposed approach encompasses four high-coupled intelligent controllers that respectively control the leader and worker drones' motion and implement obstacle and collision avoidance procedures.Simulation results using a fleet of four aerial drones are presented, showing the potential for solving usual problems to flights in formation, such as dodging obstacles, avoiding collisions between the drones, among others.
Optimal Speed Plan for the Overtaking of Autonomous Vehicles on Two-Lane Highways

In passing maneuvers on two-lane highways, assessing the needed distance and the potential power reserve to ensure the required speed mode of the passing vehicle is a critical task of speed planning. This task must meet several mutually exclusive conditions that lead to successful maneuvers. This paper addresses three main aspects. First, the issues associated with a rational distribution of the speed of the passing vehicle for overtaking a long commercial vehicle on two-lane highways are discussed. The factors that affect the maneuver effectiveness are analyzed, considering the safety and cost. Second, a heuristic algorithm is proposed based on the rationale for choosing the necessary space and time for overtaking. The initial prediction’s sensitivity to fluctuations of the current measurements of the position and speed of the overtaking participants is examined. Third, an optimization technique for the passing vehicle speed distribution during the overtaking time using the finite element method is presented. Adaptive model predictive control is applied for tracking the references being generated. The presented model is illustrated using a simulation.
Schwinger effect of Gaussian correlations in constant electric fields

We study the Schwinger effect of Gaussian correlations (quantum entanglement, discord and mutual information) of the continuous-variable two-mode squeezed states shared by Alice and Bob, paying special attention to the difference of the Schwinger effect of correlations between modes of fermion-fermion and qubit-bosonic fields studied previously. We also study the redistribution and conservativeness of the correlations under the Schwinger effect.