Reviewerly/exHarmony huggingface dataset:
TNM: A Tool for Mining of Socio-Technical Data from Git Repositories

Networks of collaboration between engineers are reflected in traces of developers' activity in version control systems (VCSs). Extracting data from Git repositories is an essential task for researchers and practitioners working on socio-technical analysis, but it requires substantial engineering work. With increasing interest in analysing socio-technical data and applying it in practice, there are no flexible and easily reusable tools to retrieve socio-technical information from VCSs. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from their core research questions.In this paper, we present TNM-an open-source tool for mining socio-technical data from Git repositories. TNM is fast, flexible, and easily extensible.TNM is available on GitHub: https://github.com/JetBrains-Research/tnm.
A Longitudinal Study of Students' Perceptions of Immersive Virtual Reality Teaching Interventions

A series of virtual reality (VR) sessions was developed to explore students' perceptions of the use of VR as a teaching intervention during lectures in an undergraduate taxation module. The study was based on the theoretical framework of the Technology Acceptance Model (TAM) and the Educational Framework for immersive Learning (EFiL) and made use of a longitudinal multi-method research design approach. Data was gathered from 566 students over the academic year using three questionnaires which were statistically analyzed. Written reflections were also collected from students and these reflections were thematically analyzed. The results show that students were positive about participating in a VR teaching intervention before they had been exposed to VR in the classroom and that they remained positive throughout the academic year as the VR interventions were rolled out on three different occasions. Students' reflections were also overwhelmingly positive, and students believe that VR is an effective and innovative way to enhance learning. The contribution of this research can be found in its use of a longitudinal study to provide understanding of the perceptions of undergraduate taxation students of the use of VR.
Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

The longest common subsequence (LCS) problem is a prominent NP–hard optimization problem where, given an arbitrary set of input strings, the aim is to find a longest subsequence, which is common to all input strings. This problem has a variety of applications in bioinformatics, molecular biology and file plagiarism checking, among others. All previous approaches from the literature are dedicated to solving LCS instances sampled from uniform or near-to-uniform probability distributions of letters in the input strings. In this paper, we introduce an approach that is able to effectively deal with more general cases, where the occurrence of letters in the input strings follows a non-uniform distribution such as a multinomial distribution. The proposed approach makes use of a time-restricted beam search, guided by a novel heuristic named Gmpsum. This heuristic combines two complementary scoring functions in the form of a convex combination. Furthermore, apart from the close-to-uniform benchmark sets from the related literature, we introduce three new benchmark sets that differ in terms of their statistical properties. One of these sets concerns a case study in the context of text analysis. We provide a comprehensive empirical evaluation in two distinctive settings: (1) short-time execution with fixed beam size in order to evaluate the guidance abilities of the compared search heuristics; and (2) long-time executions with fixed target duration times in order to obtain high-quality solutions. In both settings, the newly proposed approach performs comparably to state-of-the-art techniques in the context of close-to-uniform instances and outperforms state-of-the-art approaches for non-uniform instances.
Word Sense Disambiguation Using Context Dependent Methods

In NLP, the challenging and crucial task is Word Sense Disambiguation (WSD). Many Natural Languages have many ambiguous words with more than one sense. Depending on the context the sense of the ambiguous word is identified, this process is termed as word sense disambiguation (WSD). WSD algorithms are classified as context dependent and context independent algorithms. This article discusses about context dependent algorithms QEWTSS and QEGBCPR and their performances are compared using the evaluation metrics such as Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) metrics. The data set used is Lexical Knowledge Base (LKB), which is developed from training data and is used for evaluation process.
Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares

This paper introduces Prio+, a privacy-preserving system for the collection of aggregate statistics, with the same model and goals in mind as the original and highly influential Prio paper by Henry Corrigan-Gibbs and Dan Boneh (NSDI 2017). As in the original Prio, each client holds a private data value (e.g. number of visits to a particular website) and a small set of servers privately compute statistical functions over the set of client values (e.g. the average number of visits). To achieve security against faulty or malicious clients, unlike Prio, Prio+ clients use Boolean secret-sharing instead of zero-knowledge proofs to convince servers that their data is of the correct form and Prio+ servers execute a share conversion protocol as needed in order to properly compute over client data. This allows us to ensure that clients’ data is properly formatted essentially for free, and the work shifts to novel share-conversion protocols between servers, where some care is needed to make it efficient. Our overall approach is simpler than Prio and our Prio+ strategy reduces the client’s computational burden by at least two orders of magnitude (or more depending on the statistic) while keeping server costs comparable to Prio. Prio+ permits computation of exactly the same wide range of complex statistics as the original Prio protocol, including high-dimensional linear regression over private values held by clients. We report detailed benchmarks of our Prio+ implementation and compare these to both the original Go implementation of Prio and the Mozilla implementation of Prio. Our Prio+ software is open-source and released with the same license as Prio.
The Effect of Resnet Model as Feature Extractor Network to Performance of DeepLabV3 Model for Semantic Satellite Image Segmentation

Semantic image segmentation is an interesting problem in Computer Vision with many potential applications. The DeepLab model is combined with two other networks: Resnet and Conditional Random Field networks, making the DeepLab model a fairly deep network structure to increase semantic segmentation performance. Many previous studies argued that there are some limits on the deep learning model&#x0027;s depth as the deep structure may lead to vanishing/exploding gradient, which the model&#x0027;s performance. This paper presents an experimental study to compare the effect of several ImageNet pre-trained Resnet variant models with different network layers used as feature extractor in DeepLab model to solve semantic image segmentation task. In this study, three Resnet34, Resnet50, and Resnet101 models as network extractor of DeepLabV3were explored. The experiment found that semantic image segmentation model performance measured by the best accuracy and average accuracies of DeepLabV3- Resnet34, DeepLabV3-Resnet50, and DeepLabV3-Resnet101 are (0.87, 0.86) (0.86, 0.84), and (0.92, 0.88) respectively. Based on the experiment, DeepLabV3-Resnet101 achieved the best semantic segmentation performance than the other models
Analysing the efficiency of solar roads within settlement areas in Germany

Solar roads are solar panels to be placed on existing roads, where you can drive and walk on. The application of solar roads is very expensive at the beginning, but can pay for itself after several years and even pay-off. Nevertheless, for municipalities this might be a big financial effort. The aim of this project was analysing the potential of solar roads in the German city Bensheim. In this area for each street section different parameters like Sky View Factor, slope gradient and exposure, orientation of the street and parking space were defined. Finally, it could be shown, that some segments are more preferable – wide main streets with a north-south orientation – than others, such as north facing slopes or historical sites in the city centre for the installation of solar roads. Also advantages and disadvantages for solar roads could be summed up.
An Analysis of learners’ affective and cognitive traits in Context-Aware Recommender Systems (CARS) using feature interactions and Factorization Machines (FMs)

Massive Open Online Courses (MOOCs) have witnessed a fast emergence in recent years thanks to their open and massive nature. A huge number of learners with different profiles, knowledge and learning objectives follow the same online courses with different personalized experiences. With this rapid development, course recommendation based on traits and characteristics becomes a necessity during the learning process to determine potentially suitable learning objects while analyzing user's behaviours. Context-Aware Recommender Systems (CARS) have been shown to be effective in recommending items according to user's interests and affect. In this paper, we propose a novel approach for Context-Aware Recommender Systems based on Factorization Machines (FMs). We propose a new algorithm called Unsupervised Graph Predictor Factorization Machines (UGPFMs) that models feature interactions during the recommendation process. UGPFMs apply Convolutional Neural Networks (CNNs) in the graph predictor to model interactions between context features in the rating matrice, then it employs the Factorization Machine (FM) to select the suitable items for the recommendation. UGPFM is a generic model that extends FM to improve the accuracy of recommendation, it outperforms state-of-the-art feature interactions-based FM techniques using MSE, RMSE, MAE and R-Squared metrics. We applied UGPFMs on MOOC while considering the sentiment, the cognition and the confusion as contexts for the recommendation. We compared different context combinations' impact on the recommendation accuracy, then we computed each context-to-context feature interactions to better understand each context efficiency on e-learning recommendation.
Viewport-aware dynamic 360° video segment categorization

Unlike conventional videos, 360° videos give freedom to users to turn their heads, watch and interact with the content owing to its immersive spherical environment. Although these movements are arbitrary, similarities can be observed between viewport patterns of different users and different videos. Identifying such patterns can assist both content and network providers to enhance the 360° video streaming process, eventually increasing the end-user Quality of Experience (QoE). But a study on how viewport patterns display similarities across different video content, and their potential applications has not yet been done. In this paper, we present a comprehensive analysis of a dataset of 88 360° videos and propose a novel video categorization algorithm that is based on similarities of viewports. First, we propose a novel viewport clustering algorithm that outperforms the existing algorithms in terms of clustering viewports with similar positioning and speed. Next, we develop a novel and unique dynamic video segment categorization algorithm that shows notable improvement in similarity for viewport distributions within the clusters when compared to that of existing static video categorizations.
Exploring Workers' Subjective Experiences of Habit Formation in Cybersecurity: A Qualitative Survey

Employee behaviors remain at the center of the cybersecurity of workplaces, despite the challenges they face in doing so. Time pressures and competing demands mean that users tend to rely on habitual behaviors that often run counter to good cybersecurity practice. One possible solution may be to encourage positive habit formation. Designing such interventions, however, relies on knowledge of the perception and experience of habit formation in the context of cybersecurity. To this end, a qualitative survey containing open-ended questions was completed by 195 participants (mean age = 35.51, 53 percent female) recruited via an online participant panel. Participants were asked what cybersecurity behaviors they perform at work and how they believe any habits were prompted, formed, and maintained. Thematic analysis identified three over-arching themes: (a) forming habits unavoidably or unconsciously (some were mandated, or formed without conscious awareness), (b) consciously cultivating habits (including the roles of intrinsic motivation and external prompts), and (c) social and organizational influences (including the influence of occupational culture, social modeling, previous experiences, and information gathering practices). Based on these findings, we present guidelines for supporting workplace cybersecurity habit formation reflecting these subjective experiences, namely introducing automatic solutions, facilitating external cues, fostering interest in cybersecurity issues among employees, creating a positive cybersecurity occupational culture and highlighting positive behavior, and providing access to accessible cybersecurity information to employees. These results constitute a first step in identifying how habits can be exploited for positive cybersecurity behavior change in a way that accounts for the reliance on habitual behaviors in busy, time-pressured workplaces.
Target relational attention-oriented knowledge graph reasoning

In knowledge graph reasoning, the existing graph attention mechanisms tend to distribute attention to certain high-frequency relations. In this work, we design a target relational attention-oriented reasoning model, which focuses more on the relations that match the target relation. We propose a hierarchical (node-level and relational subgraph-level) attention mechanism to aggregate the information of multi-hop neighbors, and to thereby obtain a better node-embedding representation, (with high-order propagation characteristics). The mechanism also relieves over-smoothing to a certain extent. Node-level information aggregation uses the classical graph-attention mechanism, and the distribution of attention in the subgraph-level information aggregation is determined according to the relation in the reasoning task. In other words, we must give these relations different attentions according to the reasoning relation in the task. Experiments show that our model significantly outperforms current state-of-the-art methods. We further study the influence of encoder parameters on the model performance; increasing the number of attention-heads, layers, or hidden out-dimensions can effectively improve the performance.
Improving Action Recognition via Temporal and Complementary Learning

In this article, we study the problem of video-based action recognition. We improve the action recognition performance by finding an effective temporal and appearance representation. For capturing the temporal representation, we introduce two temporal learning techniques for improving long-term temporal information modeling, specifically Temporal Relational Network and Temporal Second-Order Pooling-based Network. Moreover, we harness the representation using complementary learning techniques, specifically Global-Local Network and Fuse-Inception Network. Performance evaluation on three datasets (UCF101, HMDB-51, and Mini-Kinetics-200) demonstrated the superiority of the proposed framework compared to the 2D Deep ConvNets-based state-of-the-art techniques.
Micro-expression recognition based on the fusion between optical flow and dynamic image

Micro-expression (ME) is a subtle and involuntary facial expression that reveals the human's concealed emotion. Psychology studies pointed out that research of ME can build potential applications in many fields. Therefore, ME recognition (MER), one of the two main ME analysis tasks, has been becoming an attractive topic recently. However, the work of MER is still needed to consider due to several limitations related to performance and dataset. This paper proposes a feature fusion between optical flow and dynamic image to create a robust ME representation, which can be learned effectively from deep learning techniques. Experiments from two public datasets, CASME-II and SAMM, show that our method obtains higher performance than several existing studies and is very promising for future research.
Convex formulation for multi-task L1-, L2-, and LS-SVMs

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific models.
Machine Learning-Based Monitoring System With IoT Using Wearable Sensors and Pre-Convoluted Fast Recurrent Neural Networks (P-FRNN)

Health issues of individuals has to be observed and diagnosed carefully as well as at the early stages and treatment has to be given using suitable medicines. Many health disorders can be detected at the earliest and thus reduce its activities before affecting the individuals severely. Recently, technology has been incrementally developed and thus numerous wearable devices to monitor human health are readily existing in the market. Even the specialist, clinicians and experts in the medical field, face difficulties in detecting the health issues and disease with the observed symptoms. Information of the patients gathered from Electronic Health Records (EHR), Internet of Things (IoT) and wearable as well as mobile devices helps in providing advancements in the technologies used. This research work introduces a novel monitoring system where the patient uses a wearable sensor connected with the database present in the hospital via IoT. The data obtained is classified with the help of the pre-convoluted fast recurrent neural networks. This classification helps to detect the abnormal data towards the health of the individuals with improved accuracy and reduced time consumption. Before the results of the medical test arrives, this classification approach has the ability to detect the patients’ abnormality in health. The results after classification will be sent to doctor when it is detected to be abnormal or else the parametric analysis is performed. The simulation results are optimized in comparison with the results of any other neural network and wearable device. the rate of classification achieved by P-FRNN is comparable and execution time achieved is appreciably low. Likewise, for ‘Sea’ dataset, ‘DEVDAN’ is better than P-FRNN as accuracy obtained is higher only when numerous parameters are considered. Similarly, dataset with P-FRNN do not perform well for ‘Learn++’ and ‘Learn++.NSE’ models where the accuracy obtained is over 93% when few features are used and moreover the execution time is comparatively low.
Generalized pyramid co-attention with learnable aggregation net for video question answering

• To handle the complexity of videos in V-VQA, we propose a generalized pyramid co-attention mechanism with diversity learning to explicitly encourage accuracy and diverse attention maps. For this generalized module, two possible ways are tried, Multi-path Pyramid Co-attention with diversity learning (MPC) and Cascaded Pyramid Transformer Co-attention with diversity learning (CPTC). This strategy benefits the capturing of distinct, complementary and informative features. • To aggregate the sequential features without destroying the feature distributions and temporal information, we propose a new learnable aggregation component. It imitates Bags-of-Words (BoW) quantization mechanism to automatically aggregate adaptively-weighted frame-level feature (or word-level feature). • We extensively evaluate the effectiveness of the overall model on two publicly available datasets (i.e., TGIF-QA and TVQA) for V-VQA task. The experimental results demonstrate that our model outperforms the existing state-of-the-art by a large margin and our extended CPTC performs better than MPC. Code and model have been released at: https://github.com/lixiangpengcs/LAD-Net-for-VideoQA . Video based visual question answering (V-VQA) remains challenging at the intersection of vision and language. In this paper, we propose a novel architecture, namely Generalized Pyramid Co-attention with Learnable Aggregation Net (GPC) to address two central problems: 1) how to deploy co-attention to V-VQA task considering the complex and diverse content of videos; and 2) how to aggregate the frame-level features (or word-level features) without destroying the feature distributions and temporal information. To solve the first problem, we propose a Generalized Pyramid Co-attention structure with a novel diversity learning module to explicitly encourage attention accuracy and diversity. And we first instantiate it into a Multi-path Pyramid Co-attention (MPC) to capture diverse feature. Then we find each attention branch of original co-attention mechanism does not interact with the others, which results in coarse attention maps. So we extend the MPC structure to a Cascaded Pyramid Transformer Co-attention (CPTC) module in which we replace co-attention with transformer co-attention. To solve the second problem, we propose a new learnable aggregation method with a set of evidence gates. It automatically aggregates adaptively-weighted frame-level features (or word-level features) to extract rich video (or question) context semantic information. With evidence gates, it then further chooses the most related signals representing the evidence information to predict the answer. Extensive validations on the two V-VQA datasets, TGIF-QA and TVQA show that both our proposed MPC and CPTC achieve the state-of-the-art performance and CPTC performs better under various settings and metrics. Code and model have been released at: https://github.com/lixiangpengcs/LAD-Net-for-VideoQA .
Learning to find naming issues with big code and small supervision

We introduce a new approach for finding and fixing naming issues in source code. The method is based on a careful combination of unsupervised and supervised procedures: (i) unsupervised mining of patterns from Big Code that express common naming idioms. Program fragments violating such idioms indicates likely naming issues, and (ii) supervised learning of a classifier on a small labeled dataset which filters potential false positives from the violations.