Reviewerly/exHarmony huggingface dataset:
Transforming abstract to concrete repairs with a generative approach of repair values

Software models, often comprise of interconnected diagrams, change continuously, and developers often fail in keeping these diagrams consistent. Detecting inconsistencies quickly and efficiently is state of the art. However, repairing them is not trivial, because there are typically multiple model elements that need to be repaired, leading to an exponentially growing space of combinations of repair choices. Despite extensive research on consistency checking, existing approaches either provide abstract repairs only (i.e., identifying the model element but failing to describe the change), which is not satisfactory. This paper presents a novel approach that provides concrete repair choices based on values from the inconsistent models. Thus, our approach first retrieves repair values from the model, turn them to repair choices, and groups them based on their effects. This grouping lets our approach explore the repair space in its entirety, providing quick example-like feedback for all possible repairs. Our approach and its tool implementation have been empirically assessed on 10 case studies from industry, academia, and GitHub to demonstrate its feasibility and scalability. A comparison with three versioned models shows that our approach identifies useful repair values that developers have chosen.
Automatic Stopwords Identification from Very Small Corpora

Natural Language Processing tools use language-specific linguistic resources, that might be unavailable for many languages. Since manually building them is complex, it would be desirable to learn these resources automatically from sample texts. In this paper we focus on stopwords, i.e., terms which are not relevant to understand the topic and content of a document. Specifically, we compare the performance of different techniques proposed in the literature when applied to very small corpora (even single documents), as may be the case for very local languages lacking a wide literature. Experiments show that simple term-frequency is an extremely reliable indicator, that outperforms other more complex approaches. While the study is conducted on Italian, the approach is generic and applicable to other languages.
Effectiveness of Peer Tutoring on The Academic Achievements of Tutors and Tutees With Respect to Knowledge, Comprehension and Application Levels of Cognitive Domain

Peer tutoring provides an opportunity to students of diverse abilities to work on academic tasks in pairs, in a democratic, cooperative and friendly approach. In this context, the study was an attempt to analyse how peer tutoring played the said role in enhancing the academic achievements of tutors and tutees at secondary level in reference to the key levels (knowledge, comprehension and application) of the cognitive domain. The study explored data from 40 purposefully selected participants of ANSI school Mardan, by applying the Pre-test Post-test Equivalent Group Design and conducting achievement tests before and after the treatments in order to accomplish the target objectives. The analysis of the data through inferential statistics (t-test) confirmed that though the tutors of the experimental group achieved better score at knowledge and application levels of the cognitive domain; however, they were not better in the comprehension level on post-test in compare to the high achievers of the control group. In the same way, the performance of the tutees of the group under investigation was significantly better with regards to academic ability, knowledge and comprehension at cognitive domain; however, at the application level there was no significant difference as compare to their correspondent low achievers of the controlled group. In the light of the findings it was concluded that the academic performance of the tutors as well as the tutees was significantly affected by peer tutoring.
Online Product Reviews: Is a Finer-Grained Rating Scheme Superior to a Coarser One?

Product review platforms in online marketplaces differ with respect to the granularity of product quality information they provide. While some platforms provide a single overall rating for product quality (also referred to as the single-dimensional rating scheme), others provide a separate rating for each individual quality attribute (also referred to as the multidimensional rating scheme). The multidimensional rating scheme is superior to the single-dimensional rating scheme, ceteris paribus, in reducing consumers’ uncertainty about product quality and value. However, we show that, when sellers respond to product ratings by adjusting their prices, compared to the single-dimensional rating scheme, the multidimensional rating scheme does not always benefit consumers, nor does it necessarily benefit sellers or society. The uncertainty associated with quality attribute rating and the extent of differentiation between competing products determines whether a finer-grained multidimensional rating scheme is superior to a coarser-grained single-dimensional rating scheme from the consumer, seller, and social planner perspectives. The main driver of the results is that more (less) granular and less (more) uncertain information exposes (hides) underlying differentiation, or a lack thereof, between competing products, which, in turn, alters upstream price competition in the presence of heterogeneous consumer preferences. The results demonstrate that focusing on the information transfer aspect of rating schemes provides only a partial understanding of the true impacts of rating schemes.
Cross-Modality Medical Image Retrieval with Deep Features

In medical imaging, modality refers to the technique and process used to create visual representations of a particular part of the body, organs, or tissues. Conventional modalities include X-ray, CT-scan, Magnetic Resonance Imaging (MRI), Ultrasound, and Positron Emission Tomography (PET). Depending on the modality used, the same disease can be detected differently, making a modality an essential filter in evaluating the relevance of search results when retrieving similar medical images. Traditionally, texture features have been used for content-based medical image retrieval. However, texture features are limited in capturing the semantic similarity between medical images, let alone their modalities. This paper explores deep features (features extracted by deep convolutional neural networks (CNN)) and analyzes their effectiveness in retrieving similar medical images, semantic-wise and modality-wise, from a collection with different medical image modalities. We have examined CNNs of different architectures pre-trained in natural images and CNNs we fine-tuned and fully-trained from scratch in medical images to extract deep features. Based on retrieval performance evaluation, we show that deep features, even though extracted by CNN pre-trained in natural images, still outperform texture features. On the other end, we show that deep features extracted by a smaller, simpler, and yet computationally efficient CNN we trained in medical images can compete with large and complex ImageNet CNNs fine-tuned or fully trained in medical images.
RETRACTED ARTICLE: Influence of the social and economic characteristics of Saudi women on their attitudes toward empowering them in online labor market

The study aims to investigate the attitudes of Saudi women toward empowering them in the online labor market and study the impact of the social and economic characteristics on their attitudes toward empowering. The study adopted a survey approach, the questionnaire was distributed online to a number of private companies of the women’s sectors at Hail city. The responses in the questionnaire was 262. The study discovered that Saudi women’s have positive attitudes toward their empowerment. It was found that there is no statistically correlation between Methods of empowering women online in KSA and social characteristics. It was found that marriage is the most important social factor that contributes to encouraging women to empower in the online labor market. It was found that high-income women are the most used group to work online, as it is an ideal and effective environment for them for achievements and successes.
Learning to Make Decisions via Submodular Regularization

Many sequential decision making tasks can be viewed as combinatorial optimization problems over a large number of actions. When the cost of evaluating an action is high, even a greedy algorithm, which iteratively picks the best action given the history, is prohibitive to run. In this paper, we aim to learn a greedy heuristic for sequentially selecting actions as a surrogate for invoking the expensive oracle when evaluating an action. In particular, we focus on a class of combinatorial problems that can be solved via submodular maximization (either directly on the objective function or via submodular surrogates). We introduce a data-driven optimization framework based on the submodular-norm loss, a novel loss function that encourages the resulting objective to exhibit diminishing returns. Our framework outputs a surrogate objective that is efficient to train, approximately submodular, and can be made permutation-invariant. The latter two properties allow us to prove strong approximation guarantees for the learned greedy heuristic. Furthermore, we show that our model can be easily integrated with modern deep imitation learning pipelines for sequential prediction tasks. We demonstrate the performance of our algorithm on a variety of batched and sequential optimization tasks, including set cover, active learning, and Bayesian optimization for protein engineering.
Leveraging Side Information to Anime Recommender System using Deep learning

The rapid development of the Japanese animation industry has produce tons of anime movies which made interest to groups of people. Each anime movie has its own characteristic which complies with specific user's interests. Therefore, a personalization engine was needed to provide recommendations. The use of collaborative filtering based recommender system that only takes into account historic explicit interactions (such as rating) was able to provide recommendations. However, we might able to improve the personalization by taking into account the users' and items' side information. Our contributions in this paper are follows. First, we collected 301,136 ratings provided by 116,126 users to 9,444 anime works in which crawled from MyAnimeList, as well as users' and items' side information. Second, we proposed a deep learning method that incorporates side information from both users and anime works into a hybrid model. This model learns the embedding separately for users and anime, in which we also add a LSTM layer to extract information from long text feature like Synopsis which will be combined and feed into a deep neural network to predict the rating of given user and anime work. And finally, we experimented and calculated the performance. The result shows that the model with side information gain result around 5% better than the SVD model.
Revisiting Temporal Modeling for Video Super-resolution.

Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and industrial communities. Although many deep learning-based VSR methods have been proposed, it is hard to directly compare these methods since the different loss functions and training datasets have a significant impact on the super-resolution results. In this work, we carefully study and compare three temporal modeling methods (2D CNN with early fusion, 3D CNN with slow fusion and Recurrent Neural Network) for video super-resolution. We also propose a novel Recurrent Residual Network (RRN) for efficient video super-resolution, where residual learning is utilized to stabilize the training of RNN and meanwhile to boost the super-resolution performance. Extensive experiments show that the proposed RRN is highly computational efficiency and produces temporal consistent VSR results with finer details than other temporal modeling methods. Besides, the proposed method achieves state-of-the-art results on several widely used benchmarks.
Predicting eye movement and fixation patterns on scenic images using Machine Learning for Children with Autism Spectrum Disorder

This study uses eye-tracking experiment data to predict the fixation points for children with Autism Spectrum Disorder (ASD) and Typically Developing (TD) for 14 ASD and 14 TD subjects for 300 scenic images [1]. Based on explanatory Logistic Regression models, it is evident that fixation patterns for both ASD and TD subjects focus on the center of each scenic image. Using gradient boosting the researchers successfully identify 31.7% and 39.5% of all fixation points in the top decile of predicted fixation points for ASD and TD subjects respectively. Results conclude that TD subjects have less variability in their eye movement and fixation points leading to increased accuracy in predicting where they will look.
I Can Think Like You! Towards Reaction Spoofing Attack on Brainwave-Based Authentication

In the coming period of Internet of Things (IoT), user authentication is one important and essential security mechanism to protect assets from unauthorized access. Textual passwords are the most widely adopted authentication method, but have well-known limitations in the aspects of both security and usability. As an alternative, biometric authentication has attracted much attention, which can verify users based on their biometric features. With the fast development of EEG (electro-encephalography) sensors in current headsets and personal devices, user authentication based on brainwaves becomes feasible. Due to its potential adoption, there is an increasing need to secure such emerging authentication method. In this work, we focus on a brainwave-based computer-screen unlock mechanism, which can validate users based on their brainwave signals when seeing different images. Then, we analyze the security of such brainwave-based scheme and identify a kind of reaction spoofing attack where an attacker can try to imitate the mental reaction (either familiar or unfamiliar) of a legitimate user. In the user study, we show the feasibility and viability of such attack.
Edge Detection and Compressive Sensing-Oriented Digital Image Block Watermarking Algorithm

This paper investigated the problem of the watermark algorithm for digital color image. An digital image watermark embedding and blind extraction algorithm is proposed based on edge detection and Compressive Sensing (CS) theory. Firstly, the carrier image is divided into non-overlapping image blocks. Then, the target image blocks suitable for concealing the watermark information are chosen by combining the edge detection algorithm. Finally, watermarks are embedded into target image blocks by utilizing the Singular Value Decomposition (SVD) and CS theory. The experimental result shows that the proposed digital images watermark algorithm has excellent invisibility and robustness performance in some scenarios.
Research on decoupling method of force sensor based on PSO-ELM

In minimally invasive surgical robot system, the implementation of the force feedback function can increase the flexibility of the surgeon during surgery and reduce the risk of damage to the tissues and organs of the patient. In order to achieve the force detection during surgical process, this paper designs a 3- axis force sensor based on fiber Bragg grating (FBG). When decoupling the sensor, a decoupling result superior to least square (LS) is achieved by combining extreme learning machine (ELM) and particle swarm optimization (PSO). By using PSO-ELM for decoupling, the average error rates in three mutually perpendicular directions are 1.35%, 1.07%, 5.70%, respectively.
The European Competency and the Teaching for Understanding Frameworks: Creating Synergies in the Context of Initial Teacher Training in Higher Education

The aim of this research was to analyze the rich, complex, interrelated relationship that could possibly be established between the Teaching for Understanding framework, pedagogical proposal belonging to Project Zero (Education research team of Harvard University, Boston, MA, US), and the European Competency Framework. Through an in-depth analysis of the constituent features that define and explain both proposals, and questions answered both by teachers with ample classroom experience and experts on the field, it shows that these are two complementary educational perspectives that share a similar vocation and that contribute to improving the teaching-learning process in Higher Education, specifically in the context of initial Teacher Training. Likewise, it analyzes the different factors to take into account when implementing them, particularly in the context of Higher Education; factors that make them complex proposals but also of very high educational value and with the potential to enrich the teaching-learning process, no matter the context.
Neighborhood Geometric Structure-Preserving Variational Autoencoder for Smooth and Bounded Data Sources

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder–decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.
Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization late-time resonant scattering electromagnetic signals of magnetic spheres provide diverse information and features, which result in superior object classification performance. For this purpose, first, time-domain late-time resonant scattering electromagnetic signals are generated numerically for single and multilayer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly convolutional neural networks trained with spheres having different material properties, a high multilayer object classification performance is achieved. Furthermore, by incorporating the frequency characteristics of the late-time resonant scattering electromagnetic signals to the classification process through Fourier transform and convolutional neural network layers for feature extraction, a convolutional neural network with long short term memory algorithm is developed. The outcome of the proposed algorithm design is shown to be particularly successful even in the case of limited available data on challenging targets. This extended strategy is also shown to outperform modern data augmentation and transfer learning techniques in terms of accuracy as well as the computational cost.
Design and Simulation of Remote Monitoring System of 3D Printer Based on Cloud Platform

In order to realize remote monitoring of 3D printer and man-machine separation, thus saving manufacturing cost and personalized manufacturing. The cloud platform was configured by the rented cloud server, and then the communication protocol and firewall were added to enable the cloud platform to remotely access and control the upper computer. The 3D printing process was transmitted to the cloud platform by WiFi, and users can monitor the printing process in real time on the client. In addition, the SSIM algorithm was used to compare the pictures in the printing process with the pictures of real articles, and the cloud platform remote control and monitoring system was designed and implemented to realize man-machine separation.
Product Label, Obstacle and Sign Boards Detection for Visually Impaired People

From the ancient days, visually impaired people had been facing many problems.In this scenario due to heavy traffic there is a chance for them to meet with an accident due to the absence of vision. In addition it becomes necessary for them to depend on others while crossing the roads, to recognize the product labels and to identify the directions in the one way roads. This work helps such people to overcome those problems.This work uses camera module for identifying directions and product labels. The camera captures the label image of the product which is in front of it, detects the product label image and then the image is separated using openCV library. It also uses ultrasonic sensors for identifying obstacles. The output can be heard by impaired people in the form of voice signals through earphones. All this work is carried out by using raspberry pi 3 module B+ with python software. This work helps the visually impaired people to find obstacles which appear in front of them. It also helps in identifying product label and sign boards on the road.