Reviewerly/exHarmony huggingface dataset:
Feature Recommendation by Mining Updates and User Feedback from Competitor Apps

Competition in mobile applications (i.e., apps) is becoming more and more intense with the increase in popularity of smart phones and mobile devices. Previous research shows that app developers spent considerable amount of time in exploiting user feedback to improve their apps. However, relying on own user feedback is insufficient for app survival in such competitive environment. It is highly important for an app developer to learn from competitors in order to keep the rank higher or become topper in the store (e.g., Google Play1). In this work, we present an approach to automatically classify and rank popular and un-popular features from the competitor apps. We follow 3 steps- (1) extract features from competitor app updates (i.e., whatsNew) and user feedback (i.e, reviews), (2) filter and group the review features that are relevant to whatsNew features, then, classify whatsNew features to binary classes, such as, popular and unpopular, (3) rank and prioritize those popular and unpopular features from competitors in order to recommend developers to adopt or avoid those features. The ranking of whatsNew features are done based on whatsNew-to-review relevance, user sentiments, and popularity of those features. We conduct extensive experiments on 840 updates and 262000 reviews of 84 different competitor apps of 10 categories. We found encouraging results from the experiments and the empirical evaluation validates the efficacy of our approach, hence, potential usefulness to the developers.
PDF Text Sentiment Analysis

Nowadays the internet has become a great source in terms of unstructured data. In the sentiment inspection, unprocessed text is operated, and it has brought different issues in computer processing. To avoid such issues, various steps and tactics are done. The paper gives an insight into the ground of sentiment inspection targeting today’s analysis works—lexicon-based work, context less categorization, and deep analysis. Sentiment mining, a main newbie subcategory in inspection, is discussed in this project. The main objective of the project is to describe a brief introduction to this emerging issue and to represent complete research of all major survey problems and the present increment in the ground. As a proof of that, this project requires greater than 400 links from all important journals. However the ground works with the natural language text, which is generally counted in the unprocessed data, this project has done a structured line or option in describing the difficulty with the objective of linking the unprocessed and processed ground and doing qualitative and quantitative analysis of emotions. It is major and important for practical applications.Pradhan, Rahul Gangwar, Kushagra Dubey, Ishika
Compression of Grayscale Images in DRPE-based Encrypted Domain

Compressing the encrypted images is considered an important issue in many applications such as cloud computing. From this perspective, this paper introduces an efficient approach for compression processing of images in the encrypted domain. The images are optically encrypted using the Double Random Phase Encoding (DRPE). The Joint Photographic Experts Group (JPEG) and the Set Partitioning in Hierarchical Trees (SPIHT) compression schemes have been used to compress the encrypted images. The process starts by converting the original image into an optical signal by an optical emitter like an optical source and encrypting it with DRPE. The DRPE applies two-phase modulations on both time and frequency domains to get the encrypted image. The JPEG and SPIHT compression techniques are utilized with different bit rates. The effect of compressing encrypted images with the DRPE is studied. Security analysis results show that the encrypted images using the DRPE are adequately compressed by SPIHT at a low bit rate, and can be adequately compressed by JPEG at a high bit rate. Also, experimental outcomes and security study prove that the multipurpose encryption and SPIHT compression scheme is more secure and effective.
On the Digital Image Correlation Technique

This study has been focused in the introduction of the theoretical and practical foundations of the Digital Image Correlation DIC technique to undergraduate students at EPN through the implementation of a Laboratory DIC practice. In the experimental test, an elastomer with different hole shapes is used as a test specimen, which should have a pattern of random speckles/dots in its surface, where the load applied is sequential or staggered to obtain images. The project evaluates three specialized image correlation packages: Digital Image Correlation Engine (DICe), Matlab's open source 2D Digital Image Correlation software Ncorr, and finally the GOM correlate software. The stress concentrator values result around different hole shapes are compared with analytically solutions of theory of elasticity and other formulas found in literature. Finally, a numerical simulation is carried out on a commercial FEA software to further compare experimental dossier. Data suggest a good agreement between procedures of analysis that allow the correct introduction of theoretical and experimental foundations of the Digital Image Correlation DIC among EPN community.
Machine Learning Based Classification of Academic Stress Factors

This paper discusses the stress caused by activities related to academics, especially the stress experienced by new students. These days academics are one of the most dominant causes of stress in a student. Hence this paper proposes a system that would categorize students based on their stress levels. The system is built by combining the various questionnaires and scoring metrics that are famous worldwide and have been developed and backed by leading researchers.The basic structure of the proposed study is to build a system that presents a questionnaire with a defined scoring metric. Based on the answers received from the students, the system will be able to categorize the student’s stress levels. This research revolves around studying various parameters, psychological methodologies, and metrics that are currently being used to monitor users suffering from academic stress. Both global and local research papers and various publications have been studied to find the right set of assessments for the proposed system. For implementation, a questionnaire is used that asks the user various questions based on various parameters - Firstly, Student Academic Stress Scale (SASS). The scales evaluated are – Affective, Behavioural, Cognitive, and Physiological. Secondly, Stress in University Students (SUS) and lastly, Questionnaire of Academic Stress in Secondary Education (QASSE). The research is conducted on various colleges of UT Chandigarh, and results are discussed in the end.
Salp Swarm Algorithm based Semi-supervised Metric Fuzzy Clustering

Semi-supervised Metric Fuzzy Clustering (SMUC) is known for taking advantage of prior information of membership to guide clustering. However, SMUC has the following problem: it is very easy to lead to lower utilization effect of prior information of membership due to sensitivity to the initial cluster centers. In order to solve the problem, we propose a hybrid approach for Salp Swarm Algorithm based Semi-supervised Metric Fuzzy Clustering (SSA-SMUC). Firstly, several data samples were randomly selected as the salp population from all data samples to obtain the optimal initial cluster centers. Secondly, the optimal initial cluster centers are obtained by the Salp Swarm Algorithm using the DB index as its fitness function. Finally, applying the optimal cluster centers as the initial cluster centers of SMUC for clustering, we can improve SMUC's utilization effect of prior information and get the better clustering result. Further experiments conducted in UCI data show that the proposed clustering algorithm can derive a better performance.
Face Segmentation, Face Swapping, and How They Impact Face Recognition

Face swappingFace swapping refers to the task of changing the appearance of a face appearing in an image by replacing it with the appearance of a face taken from another image, in an effort to produce an authentic-looking result. We describe a method for face swappingFace swapping that does not require training on faces being swapped and can be easily applied even when face images are unconstrained and arbitrarily paired. Our method offers the following contributions: (a) Instead of tailoring systems for face segmentationFace segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentation, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations for robust face swappingFace swapping under unprecedented conditions, without requiring subject-specific data or training. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure how intra- and inter-subject face swappingFace swapping affect face recognitionFace recognition. We show that intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with established perceptual studies, we show that better face swappingFace swapping produces less recognizable inter-subject results (see, e.g., Fig. 2.1). This is the first time this effect was quantitatively demonstrated by a machine vision method. Some of the material in this chapter previously appeared in [47].
On augmenting database schemas by latent visual attributes

Abstract Decision-making in our everyday lives is surrounded by visually important information. Fashion, housing, dating, food or travel are just a few examples. At the same time, most commonly used tools for information retrieval operate on relational and text-based search models which are well understood by end users, but unable to directly cover visual information contained in images or videos. Researcher communities have been trying to reveal the semantics of multimedia in the last decades with ever-improving results, dominated by the success of deep learning. However, this does not close the gap to relational retrieval model on its own and often rather solves a very specialized task like assigning one of pre-defined classes to each object within a closed application ecosystem. Retrieval models based on these novel techniques are difficult to integrate in existing application-agnostic environments built around relational databases, and therefore, they are not so widely used in the industry. In this paper, we address the problem of closing the gap between visual information retrieval and relational database model. We propose and formalize a model for discovering candidates for new relational attributes by analysis of available visual content. We design and implement a system architecture supporting the attribute extraction, suggestion and acceptance processes. We apply the solution in the context of e-commerce and show how it can be seamlessly integrated with SQL environments widely used in the industry. At last, we evaluate the system in a user study and discuss the obtained results.
Recognition of Overlapped Frequency Hopping Signals Based on Fully Convolutional Networks

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.
Encoding-free Incrementing Hough Transform for High Frame Rate and Ultra-low Delay Straight-line Detection

High frame rate and ultra-low delay straight-line detection plays an increasingly important role in highly automated factories that call for straight-line features to achieve swift locations in real scenes. However, vision systems based on CPU/GPU have a fixed delay between image capture and detection, making straight-line detection challenging to reach an ultra-low delay. Achieving detection nearly simultaneous with capture on the same image is considered. This paper proposes (A) an encoding-free incrementing Hough transform and (B) a partially compressed line parameter space to implement a straight-line detection core on an FPGA board. The encoding-free incrementing Hough transform directly calculates line parameters only by incrementing and initialization while capturing an image. Furthermore, the partially compressed line parameter space reduces the required memory resources and the path delay under the premise of accurate vote recordings for every line feature. The evaluation result shows that the proposals achieve as accurate detection (RMSE of θ on 0.0057, and RMSE of p on 2.09) as standard Hough transform (RMSE of θ on 0.0057, and RMSE of p on 2.13) and the designed detection core processes VGA (640 × 480) videos at 1.358 ms/frame delay.
Optimized Dependency Weights in Source Code Clustering

Some methods use the dependencies between source code entities to perform clustering to, e.g., automatically map to an intended modular architecture or reconstruct the implemented software architecture. However, there are many different ways that source code entities can depend on each other in an object-oriented system, and it is not likely that all dependencies are equally useful. We investigate how well an optimized set of weights for 14 different types of dependencies perform when automatically mapping source code to modules using an established mapping technique. The optimized weights were found using genetic optimization. We compare the F1 score of precision and recall to uniform weights and weights computed by module relation ratio in eight open-source systems to evaluate performance. Our experiments show that optimized weights significantly outperform the others, especially in systems that seem not to have been designed using the low coupling, high cohesion principle. We also find that dependencies based on method calls are not useful for automatic mapping in any of the eight systems.
Lost in the matrix: Dialectical tensions in facilitating virtual video groups during COVID-19 pandemic

The research phenomenologically explored the experience of facilitating virtual video groups during the COVID-19 pandemic. Research questions addressed relational processes in virtual video groups, including emotional presence, interpersonal communication, and intimacy. Specifically, we asked how facilitators can intervene effectively to promote these processes in the virtual space, within the context of social distancing. Semi-structured group interviews were held with 26 female group facilitators from various professional backgrounds during the first wave of COVID-19 in Israel in May 2020. Phenomenological analysis yielded five main themes addressing dialectical tensions that operate simultaneously in the virtual space, both enabling and hindering relational processes in virtual video groups: intimacy and intrusion in the domestic space; sharp transitions from presence to absence; fragmented processing despite abundant information; sterility and clarity in group communication; and the hyper-aware self – being a participant and an observer at the same time. Moving groups into a virtual sphere challenged the traditional role of facilitators, who struggled to create a safe space in an unstable virtual setting where the boundaries between personal and professional lives were reduced. Findings also point to the potential of the domestic space to promote closeness and intimacy and suggest the virtual space requires facilitators to embrace multiplicity as a state of mind when intervening. Facilitators must work with permeable boundaries between inner and outer group spaces, accept discontinuity as a basic property of the virtual, and acknowledge the limitations caused by multiple stimuli.
Seeking stability by being lazy and shallow: lazy and shallow instantiation is user friendly

Designing a language feature often requires a choice between several, similarly expressive possibilities. Given that user studies are generally impractical, we propose using stability as a way of making such decisions. Stability is a measure of whether the meaning of a program alters under small, seemingly innocuous changes in the code (e.g., inlining).
On-chip integration of reconfigurable quantum photonics with superconducting photodetectors

Scaling up quantum optics experiments requires on-chip reconfigurable quantum photonics, but their integration with detectors is a challenge. We show microelectromechanical reconfiguration of photonic circuits with on-chip superconducting single–photon detectors and demonstrate key applications.
Gynecological Disease Diagnosis Expert System Using the Web-Based Dempster Shafer Method

Uterine disease is a disease that generally attacks female reproduction. Most women know a disease that is in their womb when the disease is declared severe or so-called advanced stage. With this expert system, it is hoped that human uterine diseases can be detected early and can minimize the occurrence of more dangerous diseases. The method used in this expert system is the Dempster Shafer calculation method. This expert system works by entering the symptoms experienced by the patient after which the system will work according to the calculations determined by the expert who entered the knowledge base. The result of this research is to build an expert system for diagnosis of gynecology using a website-based dempster shafer. This website can be used as the use of technology so that it can be used to diagnose uterine diseases precisely and accurately, so that people with gynecological diseases are handled more quickly from an early age.
Self-Supervised Deep Fisheye Image Rectification Approach using Coordinate Relations

With the ascent of wearable camera, dashcam, and autonomous vehicle technology, fisheye lens cameras are becoming more widespread. Unlike regular cameras, the videos and images taken with fisheye lens suffer from significant lens distortion, thus having detrimental effects on image processing algorithms. When the camera parameters are known, it is straight-forward to correct the distortion, however, without known camera parameters, distortion correction becomes a non-trivial task. While learning-based approaches exist, they rely on complex datasets and have limited generalization. In this work, we propose a CNN-based approach that can be trained with readily available data. We exploit the fact that relationships between pixel coordinates remain stable after homogeneous distortions to design an efficient rectification model. Experiments performed on the cityscapes dataset show the effectiveness of our approach. Our code is available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/MasakHosono/SelfSupervisedFisheyeRectification.
Study on the Construction of English ICAI Course Based on BP Neural Network Algorithm

Abstract Artificial neural network is also referred to as neural network or connection model, it’s a model animal neural network, the mathematical model of distributed and information processing algorithm. Because of its good abstract classification, Has been used in all aspects of life. And in recent years, the traditional English teaching mode in colleges and universities has been gradually replaced by CAI new teaching methods, much progress has been made, Increasing resources to open schools and curricula, effectively promote the sharing of quality teaching resources, it promotes the improvement of students’ English listening, speaking, reading, writing and translation. But the general problems of English CAI teaching have gradually emerged, for example, curriculum design does not pay enough attention to individual differences, online teaching lacks interaction between teachers and students, lack of effective supervision and assessment of learning results. At present, the student-centered teaching concept based on the class-flipping classroom and the “online and offline” mixed teaching model is becoming popular, which has had a great impact on the traditional foreign language teaching model. Designing English intelligent computer-assisted teaching (intelligent computer-assisted teaching) can intelligently meet the individual needs of students according to the learning effect, realize the interaction between teachers and students through human-computer interaction, and facilitate the management and evaluation of the teaching process by managers. This is referred to as ICAI. It is an urgent problem in English teaching, especially in the teaching of college English public courses. This paper designs an English ICAI language curriculum system based on BP neural network algorithm and SSH architecture.
Multiple-Model Approach to Over-the-Horizon Radar Tracking Based on Target Perceivability

For over-the-horizon radar (OTHR)-based target tracking, the reflecting height of the ionosphere which reflects radar signals is important. Existing methods assume that this height is exactly known a priori. In practice, however, we can only determine its range, not its value. To circumvent this problem, we propose to use a multiple-model approach in which each model corresponds to a specific height for OTHR tracking. Further, existing methods do not provide a systematic and effective solution to track confirmation and termination, and mean-square-error (MSE) matrices of state update calculated by these methods are in error. To overcome these drawbacks and estimate the target state, an autonomous multiple-model target perceivability (TP)-based multipath probabilistic data association (TP-MPDA) tracker is proposed, where TP probabilities are derived for track confirmation, termination, and refinement, and MSE matrices of state update are corrected. Seeing that a small fixed set of models does not cover a large height range well, an expected-mode augmentation TP-MPDA tracker is proposed, which augments a basic model set by an online adaptive expected height. Since the model is nonlinear in the height, we also present a best model augmentation TP-MPDA tracker, where a basic model set is augmented by the best model that minimizes a Kullback–Leiber divergence. Simulation results demonstrate that the proposed algorithms are effective and have better performance than the existing MPDA algorithm.