Reviewerly/exHarmony huggingface dataset:
How Graduate Computing Students Search When Using an Unfamiliar Programming Language

Developers and computing students are usually expected to master multiple programming languages. To learn a new language, developers often turn to online search to find information and code examples. However, insights on how learners perform code search when working with an unfamiliar language are lacking. Understanding how learners search and the challenges they encounter when using an unfamiliar language can motivate future tools and techniques to better support subsequent language learners.
Comparative Analysis of Package Warpage Using Confocal Method and Digital Image Correlation

Optical measurement techniques are commonly used in order to inspect the thermal warpage behavior and the deformation of packages during reflow. JEDEC standard has offered four types of methods to measure the warpage: Confocal, Digital Image Correlation (DIC), Shadow Moiré, and Digital fringe projection. In this paper, the recently emerged new confocal imaging system is introduced and compared with DIC method in different aspects.The objection of the comparison is not to decide which tool is better yet to provide a general guide for both advantages and disadvantages of two kinds of methods under different situations of applications. First, the same component is measured by confocal as well as DIC method in order to verify the accuracy of the new confocal inspection system. For this measurement, surface treatment is needed for DIC method yet the confocal method does not have such a requirement. Another application for confocal method is that except for the substrate warpage, it can also measure the bump top behavior like coplanarity during cycling, which DIC is not able to measure. However, the DIC is capable of inspecting the in-plane and out-of-plane deformation so that it has the CTE measurement function that confocal method does not possess.
Real-time Lexicon-free Scene Text Retrieval

In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos.
Solar powered water pump with single axis tracking system for irrigation purpose

Now a day the most convenient available energy is a solar energy used by the photo voltaic cell that converts heat energy into an electrical energy directly from the sun. Present research concerned with the solar tracking, conversion of Direct Current to Alternating Current and to run the agricultural motor equipped with the timer. Tracking is to align the solar panel always perpendicular to the solar beams to charge the battery effectively where the intensity of the light is higher and energy is in form of Direct Current to convert Alternating Current by inverter. Required energy from battery is to run the motor. To automate the stepper motor, which has silicon based solar panel mounted on its shaft, controlled by the microcontroller. Two sensors are connected to the micro controller one which senses the position of the sun and the second one for sensing whether it is day or night. The first sensor finds the location of the sun which is mounted on the solar panel, and the stepper motor is controlled accordingly such that the beams fall onto the solar panel always perpendicular. For this logic, used AT89C51 microcontroller and the embedded C-program coding is provided in the KEIL micro vision editor. Analyzed average amount of energy available from pump, efficiency achieved by the panel to work.
The Power of String Solving

The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user's purpose. Firstly, there is a multitude of string solving, each addressing essentially some subset of the general problem. Generally, the addressed fragments are relevant and well motivated, but the lack of comparisons between the existing tools on an equal set of benchmarks, cannot go unnoticed, especially as a common framework to compare solvers seems to be missing. In this paper we gather a set of relevant benchmarks and introduce our new benchmarking framework ZaligVinder to address this purpose.
OpenSZZ

The accurate identification of defect-inducing commits represents a key problem for researchers interested in studying the naturalness of defects and defining defect prediction models. To tackle this problem, software engineering researchers have relied on and proposed several implementations of the well-known Sliwerski-Zimmermann-Zeller (SZZ) algorithm. Despite its popularity and wide usage, no open-source, publicly available, and web-accessible implementation of the algorithm has been proposed so far. In this paper, we prototype and make available one such implementation for further use by practitioners and researchers alike. The evaluation of the proposed prototype showed competitive results and lays the foundation for future work. This paper outlines our prototype, illustrating its usage and reporting on its evaluation in action.
Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes

The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. They are the extensions from the PairNet, which is a Convolutional Neural Network (CNN) capable of carrying out simple pairing operations with low computational complexities. Three different types of feedforward NNs, termed Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are proposed for the extension. They are the PairNet operating in conjunction with short-cut connections and/or inception modules for achieving high classification accuracy and low computation complexity. A prototype system based on smart phones for remote control of home appliances has been implemented for the performance evaluation. Experimental results reveal that the PairNet has superior classification accuracy over its basic CNN and Recurrent NN (RNN) counterparts. Furthermore, the Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are able to further improve classification hit rate and/or reduce recognition time for hand gesture recognition.
Utilizing emotion scores for improving classifier performance for predicting customer's intended ratings from social media posts

Purpose Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data. Design/methodology/approach This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes. Findings Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively. Practical implications Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts. Originality/value The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.
Invisible Boundaries for VR: Auditory and Haptic Signals as Indicators for Real World Boundaries

Maintaining awareness of real world boundaries whilst being immersed in virtual reality (VR) with head mounted displays (HMDs), is a necessity for the physical integrity of the user. This paper explores whether individual human senses can be allocated to the real and the virtual world and what effect this has on workload, presence, performance and perceived safety. We present the results of a lab study (N=33) where the auditory and haptic sense of participants was trained to be an indicator for real world boundaries, while their visual sense was bound to a VR experience with an HMD. Our results suggests that allocating senses increases workload. However, while performance is comparable to purely visual indications of boundaries, sense allocation seems to improve presence. Participants prefer the signals to be separate or combined subsequently, depending on the priority and proximity to the boundary. This exploratory study is valuable for developers and researchers who want to start including audio and haptic signals as indicators for real world boundaries.
Visual Grounding Via Accumulated Attention

Visual grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. Generally, it requires the machine to first understand the query, identify the key concepts in the image, and then locate the target object by specifying its bounding box. However, in many real-world visual grounding applications, we have to face with ambiguous queries and images with complicated scene structures. Identifying the target based on highly redundant and correlated information can be very challenging, and often leading to unsatisfactory performance. To tackle this, in this paper, we exploit an attention module for each kind of information to reduce internal redundancies. We then propose an accumulated attention (A-ATT) mechanism to reason among all the attention modules jointly. In this way, the relation among different kinds of information can be explicitly captured. Moreover, to improve the performance and robustness of our VG models, we additionally introduce some noises into the training procedure to bridge the distribution gap between the human-labeled training data and the real-world poor quality data. With this "noised" training strategy, we can further learn a bounding box regressor, which can be used to refine the bounding box of the target object. We evaluate the proposed methods on four popular datasets (namely ReferCOCO, ReferCOCO+, ReferCOCOg, and GuessWhat?!). The experimental results show that our methods significantly outperform all previous works on every dataset in terms of accuracy.
Positional Change of the Eyeball During Eye Movements: Evidence of Translatory Movement

Purpose: To investigate the positional change of the eyeball induced by horizontal and vertical gazing to deduce translatory movement, using three-dimensional (3D) magnetic resonance imaging (MRI). Methods: In this prospective observational study participants underwent orbital MRI during central, right, left, up, and down gazing. MRI scans were processed using self-developed software; this software enabled 3D MR image reconstruction and the superimposition of reconstructed image sets between different gazes. After acquiring the coordinates of the eyeball centroid in each gaze, the changes in centroid coordinates from central gaze to the other gazes were estimated, and correlations with associated factors were evaluated. Results: The mean distance of centroid movement was 0.69 ± 0.27 mm in abduction, 0.68 ± 0.27 mm in adduction, 0.43 ± 0.23 mm in elevation, and 0.44 ± 0.19 mm in depression. The mean angle of centroid movement in horizontal gaze, measured in terms of the movement of the left eye centroid in the axial plane, was 228.7° in abduction and -4.2° in adduction. In vertical gaze, the mean angle of centroid movement was -96.8° in elevation and 101.8° in depression. Axial length and ocular volume were negatively correlated with the distance of centroid movement in horizontal gaze. Conclusions: The position of the eyeball moved in the same direction as the gaze during horizontal gaze, but in the opposite direction during vertical gaze. For accurate eye movement analyses, such as the measurement of the deviation angle in strabismus, translation should be considered in addition to rotation.
Consistency of adaptive importance sampling and recycling schemes

Among Monte Carlo techniques, the importance sampling requires fine tuning of a proposal distribution, which is now fluently resolved through iterative schemes.Sequential adaptive algorithms have been proposed to calibrate the sampling distribution.Cornuet et al. [Scand.J. Stat.39 (2012) 798-812] provides a significant improvement in stability and effective sample size by the introduction of a recycling procedure.However, the consistency of such algorithms have been rarely tackled because of their complexity.Moreover, the recycling strategy of the AMIS estimator adds another difficulty and its consistency remains largely open.In this work, we prove the convergence of sequential adaptive sampling, with finite Monte Carlo sample size at each iteration, and consistency of recycling procedures.Contrary to Douc et al. [Ann.Statist.35 (2007) 420-448], results are obtained here in the asymptotic regime where the number of iterations is going to infinity while the number of drawings per iteration is a fixed, but growing sequence of integers.Hence, some of the results shed new light on adaptive population Monte Carlo algorithms in that last regime and give advices on how the sample sizes should be fixed.
Emoji in open‐ended questions: A novel use in product research with consumers

Abstract The aim of this work was to explore the inclusion of emoji in open‐ended descriptions in food‐related consumer research. A total of 119 consumers evaluated six samples of chocolates of different flavors using an open‐ended question with the option of including emoji if desired. A list of 39 emoji were available to include in the responses. The great majority of the participants used emoji to describe at least one of the chocolate samples. Although up to 12 emoji were used in product descriptions, the great majority of responses included one or two emoji. Emoji were mainly used to emphasize information that was also expressed in the text, but in 20% of the responses that included emoji at least part of the meaning was conveyed exclusively by emoji. Furthermore, emoji were used to convey a wide range of meanings, from hedonic reactions to the product to emotional and conceptual associations. Practical Applications This research presents a new methodological variant of open‐ended questions, in which participants are given the option to include emoji in their product descriptions. Results showed that consumers indeed use emoji in their responses to convey a wide range of meanings, which suggests that this alternative is probably regarded as “natural” and could enhance consumer engagement during product evaluation. Hence, the use of emoji in open‐ended questions seems to be a good methodological alternative.
STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels

The Deep learning of optical flow has been an active area for its empirical success. For the difficulty of obtaining accurate dense correspondence labels, unsupervised learning of optical flow has drawn more and more attention, while the accuracy is still far from satisfaction. By holding the philosophy that better estimation models can be trained with betterapproximated labels, which in turn can be obtained from better estimation models, we propose a self-taught learning framework to continually improve the accuracy using self-generated pseudo labels. The estimated optical flow is first filtered by bidirectional flow consistency validation and occlusion-aware dense labels are then generated by edge-aware interpolation from selected sparse matches. Moreover, by combining reconstruction loss with regression loss on the generated pseudo labels, the performance is further improved. The experimental results demonstrate that our models achieve state-of-the-art results among unsupervised methods on the public KITTI, MPI-Sintel and Flying Chairs datasets.
Controlling the Sense of Embodiment for Virtual Avatar Applications: Methods and Empirical Study

Background The sense of embodiment (SoE) is the feeling of one’s own body, and research on the SoE extends from the rubber hand illusion to the full-body ownership illusion with a virtual avatar. Objective The key to utilizing a virtual avatar is understanding and controlling the SoE, and it can be extended to several medical applications. In this study, we aimed to clarify these aspects by considering the following three subcomponents of SoE: sense of agency, ownership, and self-location. Methods We defined a human avatar (HA), point light avatar (PLA), and out-of-body point light avatar (OBPLA) and compared them in three user studies. In study 1, 28 participants were recruited and the three avatar conditions (HA, PLA, and OBPLA) were compared. In study 2, 29 new participants were recruited, and there were two avatar conditions (HA ad PLA) and two motion synchrony conditions (synchrony and asynchrony). In study 3, 29 other participants were recruited, and there were two avatar conditions (PLA and OBPLA) and two motion synchrony conditions (synchrony and asynchrony). Dependent measures included sense of agency, ownership, and self-location; emotional response; presence; and simulator sickness. Results The findings of study 1 showed that the three avatar generation methodologies can control the sense of ownership and self-location in a stepwise manner while maintaining a high sense of agency. In studies 2 and 3, we found dependencies among the three subcomponents of SoE and observed that they affected users’ subjective experiences. Conclusions Our findings may have implications for boosting the effects of virtual avatar applications in medical areas, by understanding and controlling the SoE with a full-body illusion.
Comparison of Virtual Reality Visualizations With Traditional Visualizations in Hotel Settings

The objective of the current study was to investigate how virtual reality (VR) visualizations compare with traditional visualizations such as two-dimensional (2D) photos as a marketing tool for hotel websites. The specific research question was “How do VR visualizations compare with 2D photos in terms of (1) affective responses, (2) attitudinal/behavioral responses, and (3) cognitive load—that is, task efforts and experiences associated with technology?” A quasi-experimental study was conducted over two time periods with independent sample pools. A boutique hotel lobby and a guest room were captured in 2D and VR formats. Subjects were randomly assigned to one of the two types of visualizations. The sample comprised 84 university undergraduate students from the South-Central United States. Nonparametric tests were used to test the proposed hypotheses. There was a statistically significant difference between two visual conditions for several variables—that is, pleasure, satisfaction, word of mouth, and so on. However, those differences were not always consistent between the two time periods. Overall findings suggest that the use of VR visualizations as a promotional tool might not be more effective than traditional photos. Limitations of the study include using students as a proxy for the general population of travelers, small sample size, self-reported measures, and narrow conditional scope. Those limitations should be addressed in future studies.
Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image

The detection of the region of interests (ROIs) on Whole Slide Images (WSIs) is one of the primary steps in computer-aided cancer diagnosis and grading. Early and accurate identification of invasive cancer regions in WSI is critical in the improvement of breast cancer diagnosis and further improvements in patient survival rates. However, invasive cancer ROI segmentation is a challenging task on WSI because of the low contrast of invasive cancer cells and their high similarity in terms of appearance, to non-invasive regions. In this paper, we propose a CNN based architecture for generating ROIs through segmentation. The network tackles the constraints of data-driven learning and working with very low-resolution WSI data in the detection of invasive breast cancer. Our proposed approach is based on transfer learning and the use of dilated convolutions. We propose a highly modified version of U-Net based auto-encoder, which takes as input an entire WSI with a resolution of 320×320. The network was trained on low-resolution WSI from four different data cohorts and has been tested for inter as well as intra- dataset variance. The proposed architecture shows significant improvements in terms of accuracy for the detection of invasive breast cancer regions.
Data Integrity: A security threat for Internet of Things and Cyber-Physical Systems

Internet of Things (IoT), is emerging as the most evolving technology and contributing to the critical domain areas from transportation, to manufacturing, to energy, to automation, to space, to defence, to health care. IoT system architecture is an aggregation of Cyber-Physical Systems (CPS) that include multiple edge nodes like sensors, actuators or devices as primary components of the system. The crucial element in the existence and evolution of IoT is collecting rich and diverse data stream from the application of interest. Information extracted from data inference mostly drives the decisional policies of the cyber-physical system, which govern the functionality of critical application areas. Edge devices play the role of data generator, collector, or integrator of the system. The fact that these edge devices are ubiquitous and placed in easily accessible unsecured locations make then prone to cyber-attacks. The data sourced through these systems being safety-critical and high commercial value, draw the attention of adversary to attempt for cyber attacks unceasingly. This survey paper contributes in terms of identifying the security threats and challenges related to privacy and security of data in the context of IoT systems and CPS. Data tampering and malicious infusion are the prime sources of threats. The paper also outlines the current state of art research to identify and mitigate such data integrity threats and attacks.