Reviewerly/exHarmony huggingface dataset:
A Zero Attentive Relevance Matching Network for Review Modeling in Recommendation System

User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two groups: the siamese models that build static user and item representations from their reviews respectively, and the interaction-based models that encode user and item dynamically according to the similarity or relationships of their reviews. Although the interaction-based models have more model capacity and fit human purchasing behavior better, several problematic model designs and assumptions of the existing interaction-based models lead to its suboptimal performance compared to existing siamese models. In this paper, we identify three problems of the existing interaction-based recommendation models and propose a couple of solutions as well as a new interaction-based model to incorporate review data for rating prediction. Our model implements a relevance matching model with regularized training losses to discover user relevant information from long item reviews, and it also adapts a zero attention strategy to dynamically balance the item-dependent and item-independent information extracted from user reviews. Empirical experiments and case studies on Amazon Product Benchmark datasets show that our model can extract effective and interpretable user/item representations from their reviews and outperforms multiple types of state-of-the-art review-based recommendation models.
Exploring the communication functions of comments during bug fixing in Open Source Software projects

Bug fixing is a frequent and important task in Open Source Software (OSS) development and involves the communication of messages, which can serve for multiple purposes and affect the efficiency and effectiveness of corrective software activities. This work is aimed at studying the communication functions of bug comments and their associations with fast and complete bug fixing in OSS development. Over 500K comments and 89K bugs of 100 OSS projects were extracted from three Issue Tracking Systems. Six thousand comments were manually tagged to create a corpus of communication functions. The extracted comments were automatically tagged using machine learning algorithms and the corpus of communication functions. Statistical and correlation analyses were performed and the most frequent comments communicated during fast and successful bug fixing were identified. Significant differences in the distribution of comments of fixed and not fixed bugs were found. Variations in the distribution of comments of bugs with different fixing time were also found. Referential comments that provided objective information were found to be the most frequent messages. Results showed that the percentages of conative and emotive comments are greater when bugs are resolved without the requested fixes and when fixes are implemented in a long time. Associations between communication functions and bug fixing exist. The results of this work could be used to improve corrective tasks in OSS development and some other specific linguistic aspects should be studied in detail in OSS communities.
Augmented Reality (AR) subject Natural Science media for human framework topics

Abstract The purpose of this study is develop media based on augmented reality for the subject of Natural Sciences on the topic of human skeletons. The research and development method used is the ADDIE model, focusing on the more technical design and development sections. This research produces augmented reality media by using Corel draw application, personal unity, blended 3d, vuforia free edition. With this augmented reality media design to facilitate users in understanding the human framework.
Technical analysis of adaptive neuron fuzzy intelligent system in tennis serve

Serving is the most important hitting technique in tennis, and a good service receiving can instantly reverse the active and passive relationship between serve and receive on the tennis court, and control the rhythm of the court. The purpose of this study is to use an adaptive neuron fuzzy intelligent system to analyze some techniques of tennis serve. In this study, eight male players from the school tennis team were selected as the experimental subjects, whose sports level was above the national tennis level II. Ten weeks before the simulation test, the training time and frequency of 8 subjects were the same. In other words, 5 times a week, 2.5 hours±0.5 hours. The work engineering of adaptive fuzzy system firstly, in the off-line modeling stage, the adaptive fuzzy system uses the rule self splitting technology to generate the initial fuzzy rules, and uses the improved adaptive neural network algorithm to optimize the calculation; then according to the error between the system input and the predicted output, the independent variable is adjusted and replaced; at the same time, the adaptive fuzzy system is further used for calculation In the process of tennis serving, the nonlinear control variables are obtained online and applied to the fuzzy system for control. Next, in the experiment, the system was used to record the body’s movement and service scores during service. The experimental results show that during the service process, the maximum trunk torsion amplitude can reach 48.26 ° and the minimum is only 5.41 ° and the service score accounts for 81.41% and 80.47% of the total scores of the two sections respectively. This shows that the fuzzy system in this study can effectively analyze the service posture and score of athletes. It is concluded that the accurate calculation and analysis of tennis serve by adaptive neuron intelligent fuzzy system in this study is conducive to improve the tennis serviceability and competition performance of players. This research has made a certain contribution to the intellectualization of sports.
Systematic Review Of Virtual Reality &amp; Its Challenges

Virtual reality is a system that replaces the reality in a computer world and could be a game or a video that you play in a VR device like a VR headset. It is the utilization of PC innovation to establish a simulated environment to become a part and live that experience. The idea of virtual reality is to emerge into another world that is a virtual world. Virtual reality optimizes the user experience & is the way to meet and feel like a real-life community in the virtual world.Increased Reality (IR) is a technology that allows an individual to interact with a re-acting device regardless of whether this is a re-examination of today's reality or a conjured up environment. It is the way we perceive, feel and enter the past, the future and the present. The phase by which reality becomes a distorted reality is called VR. It could stretch from a PC game, from walking around our dream home, to walking around an untouchable universe. It could enter the globe. This paper provides a historical overview of virtual reality, list the basic terms and classes of VR systems, followed by entertainment applications of this technology. Typical VR systems are analyzed in a comprehensive manner. The entire implementation of VRs and their interrelationships: input devices, output devices and applications are extensively investigated. Furthermore, human factors and their consequences for the design problem of VE has been discussed.
Analysis of learning multimedia development needs for network infrastructure architecture

Abstract Analysis of the need for media is expected to help the school in terms of the use of technologies based on its development. The use of media in learning following the characteristics of students is one of the factors that support the media in the learning process. The need analysis of this media can be used as a reference for making decisions in using multimedia as one of the supporting factors of learning. Avoiding technological developments now is not an option, neither is learning. Inadequate interactive learning media learning is less effective. Analysis of the needs of this media can be one of the factors that support the media in the learning process. Based on the data that has been obtained, the researchers found the results of the achievement of targets from the analysis of the need for media in learning to be in the “needed” category. The results of this data processing confirm that the need for media in learning is “needed” to facilitate the learning process in the classroom.
An Effective Learning Method for Automatic Speech Recognition in Korean CI Patients’ Speech

The automatic speech recognition (ASR) model usually requires a large amount of training data to provide better results compared with the ASR models trained with a small amount of training data. It is difficult to apply the ASR model to non-standard speech such as that of cochlear implant (CI) patients, owing to privacy concerns or difficulty of access. In this paper, an effective finetuning and augmentation ASR model is proposed. Experiments compare the character error rate (CER) after training the ASR model with the basic and the proposed method. The proposed method achieved a CER of 36.03% on the CI patient’s speech test dataset using only 2 h and 30 min of training data, which is a 62% improvement over the basic method.
An Exploratory Study on the Impact of Collective Immersion on Learning and Learning Experience

This paper aims to explore the impact of a collective immersion on learners’ engagement and performance. Building on Bandura’s social learning theory and the theory on the sense of presence, we hypothesise that collective immersion has a positive impact on performance as well as cognitive, emotional and behavioural engagement. Ninety-three participants distributed in four conditions took part in the experiment. The four conditions manipulated the collective and individual dimensions of the learning environment as well as the high and low immersion of the learning material. The two conditions that offered a high immersion setting used two types of the novel immersive dome: a large one for collective immersion and a small one for individual use. All participants were presented with the same stimuli, an 8-min-long video of a virtual neighbourhood visit in Paris in the 18th century. The participants’ reactions were measured during and after the task. The learning outcome, as well as the cognitive, emotional and behavioural engagement, were measured. Final results showed that collective immersion learning outcomes are not significantly different, but we find that collective immersion impacts the cognitive, emotional and behavioural engagement of learners.
Lambek Calculus with Classical Logic

One of the most natural extensions of the Lambek calculus augments this logic with connectives of classical propositional logic. Actually, the resulting logic can be treated as a classical modal logic with binary modalities. This paper shows several basic properties of the latter logic in two versions: nonassociative and associative (axiom systems, algebras and frames, completeness, decidability, complexity), and some closely related logics. We discuss certain earlier results and add new ones.
Semantically Synchronizing Multiple-Camera Systems with Human Pose Estimation

Multiple-camera systems can expand coverage and mitigate occlusion problems. However, temporal synchronization remains a problem for budget cameras and capture devices. We propose an out-of-the-box framework to temporally synchronize multiple cameras using semantic human pose estimation from the videos. Human pose predictions are obtained with an out-of-the-shelf pose estimator for each camera. Our method firstly calibrates each pair of cameras by minimizing an energy function related to epipolar distances. We also propose a simple yet effective multiple-person association algorithm across cameras and a score-regularized energy function for improved performance. Secondly, we integrate the synchronized camera pairs into a graph and derive the optimal temporal displacement configuration for the multiple-camera system. We evaluate our method on four public benchmark datasets and demonstrate robust sub-frame synchronization accuracy on all of them.
Supervised Cross-Database Transfer Learning-Based Facial Expression Recognition

In the field of facial expression recognition (FER), the main aim is to classify the expressions into the emotions being communicated by humans. One of the most significant fields of computer vision for real-life application is FER. The datasets of FER are mostly laboratory-posed datasets which creates FER in real-life harder to detect. Transfer learning is applied in many fields of computer vision where there is a lack of labeled dataset. In this paper, we have studied the effect of labeled data for training in FER when applied on deep residual networks. We have used two networks of 50 and 152 layers, pretrained on two datasets ImageNet, and VGG using cross-databases. The performance evaluation of the model is done on benchmark FER2013 dataset. Our approach has achieved better performance outcome than the existing approaches in the field of FER using transfer learning.
Fuzzy c-means clustering with conditional probability based K–L information regularization

Fuzzy c-means with regularization by K–L information (KLFCM) is an objective function method for clustering, which is regarded as a fuzzy counterpart of Gaussian mixture models (GMMs) with EM algorithm when the regularization parameter λ equals 2. However, KLFCM method extracts very close or even coincident clusters in many cases because the K–L information term in its objective function is used to minimize the dissimilarity between the membership degrees and the proportions of data belonging to the clusters. To deal with this problem, we propose a new model called fuzzy c-means clustering with conditional probability based K–L information regularization (CKLFCM) which incorporates the conditional probability distributions and the probabilistic dissimilarity functional into the conventional KLFCM algorithm in order to assign appropriate membership degrees to each data point. CKLFCM technique does not suffer from obtaining the unexpected close or coincident clusters. Several experiments are presented to show the effectiveness of the proposed algorithm.
Identifying e-books in promoting literacy for children in general and children with disabilities

Abstract ICT-based media have been chosen by people within various contexts. One of the easiest media to access nowadays is electronic books (e-books). This article seeks to identify e-books effectiveness to promote literacy digitally. E-books from various online platform were acquired and analysed. The results showed that e-books had been revealed to be effective in promoting literacy for children with and without disabilities. It is implied that e-books can be an effective virtual tool in the era of industrial revolution 4.0.
A model of computing with road traffic dynamics

Reservoir computing derived from recurrent neural networks is more applicable to real world systems than deep learning because of its low computational cost and potential for physical implementation.Specifically, physical reservoir computing, which replaces the dynamics of reservoir units with physical phenomena, has recently received considerable attention.In this study, we propose a method of exploiting the dynamics of road traffic as a reservoir, and numerically confirm its feasibility by applying several prediction tasks based on a simple mathematical model of the traffic flow.
Optimizing Design of Smart Workplace through Multi-Objective Programming

Smart environments have proven very supportive to the improvement of the performance of people in different workplaces. Plenty of applications have been introduced spanning different settings including healthcare, ambient assisted living, homes, offices, and manufacturing environment, etc. However, subjectivity and ambiguity prevail in the majority of research, and still, up to date, rare approaches found quantitatively and objectively constructing or assessing the impact of smart enabling technologies on the performance of the subject environment. Further, no approaches have considered optimizing the adoption of those smart technologies with respect to objectives achievement. This article presents a novel optimization methodology for designing a smart workplace environment in conditions of ambiguity or fuzziness. The methodology begins with defining and weighing the overall goals and objectives of the workplace. The Prometthe multi-criterion decision-making technique is used to weigh the operational objectives with respect to the overall workplace goals. Next, the relation among basic building blocks of the model; namely: the operational objectives, smartness features, and smart enabling technologies are quantified, utilizing fuzzy relations. Then, the fuzzy goal programming techniques will be utilized to optimize the impact relation values while considering the budget constraint. The proposed optimization methodology is implemented on the development and optimization of the smart clinic, as a typical instance of the workplace.
Exploration of multimodal alternative access for individuals with severe motor impairments: Proof of concept

Many individuals with complex communication needs and severe motor impairments are unable to control technologies through conventional means and require alternative access techniques to achieve accurate and efficient access. With current alternative access techniques, individuals with severe motor impairments are limited in that they can only use one access technique at a time. The purpose of this project was to test proof of concept of a new multimodal access technique which integrated eye gaze and single switch scanning selection techniques. The aims were to investigate the learning patterns of two adults with severe cerebral palsy when first introduced to the multimodal access technique and then to compare the accuracy and efficiency of multimodal to single-modality access when selecting targets on an AAC visual scene display. The participants learned to use the multimodal access technique; they demonstrated improvements in their accuracy of selection across sessions and attained at least 80% accuracy within 3-15 training sessions. Both participants achieved similar accuracy with multimodal access compared to single-modality, but took longer to select targets with multimodal access compared to single-modality. The potential utility of multimodal access is explored as well as important avenues for future research.
Assessing User’s Susceptibility and Awareness of Cybersecurity Threats

Cybersecurity threats, including those involving machine learning, malware, phishing, and cryptocurrency, have become more sophisticated. They target sensitive information and put institutions, governments, and individuals in a continual state of risk. In 2019, phishing attacks became one of the most common and dangerous cyber threats. Such attacks attempt to steal sensitive data, such as login and payment card details, from financial, social, and educational websites. Many universities have suffered data breaches, serving as a prime example of victims of attacks on educational websites. Owing to advances in phishing tactics, strategies, and technologies, the end-user is the main victim of an attack scenario. According to several studies, the end-user can play a significant role in preventing a phishing attack. Therefore, this study was conducted to investigate the levels of user awareness regarding cyber threats and explore the relationship between the knowledge on cybercrimes and the awareness of phishing, within the context of cybercrime targeting educational websites. An observational experiment using ‘think aloud’ method was conducted with 20 students from Taif University. The results indicated that although the participants demonstrated an advanced level of information technology experience as specialists in computer science and computer engineering, their susceptibility to phishing was high. The results of this study will contribute to the cybersecurity research field in terms of proposing risk management plans, delivering embedded training to end-users, and improving spam detecting tools.
No keyword is an island: in search of covert associations

This paper describes how corpus-assisted discourse analysis based on keyword identification and interpretation can benefit from employing Market Basket Analysis (mba) after keyword extraction. mba is a data mining technique used originally in marketing that can reveal consistent associations between items in a shopping cart, but also between keywords in a corpus of many texts. By identifying recurring associations between keywords, we can compensate for the lack of wider context which is a major issue impeding the interpretation of isolated keywords (especially when analysing large data). To showcase the advantages of mba in ‘re-contextualising’ keywords within the discourse, we conducted a pilot study on the topic of migration, contrasting anti-system and centre-right Czech Internet media. The results show that mba is useful in identifying the dominant strategy of anti-system news portals: to weave in a confounding ideological undercurrent and connect the concept of migrants to a multitude of other topics (i.e., flooding the discourse).
Towards Practical Robustness Improvement for Object Detection in Safety-Critical Scenarios

Object detection has been widely applied to safety-critical scenarios such as autonomous driving. Recent works show that object detectors can be easily deceived by small adversarial examples. While adversarial training is one of the few effective methods to improve robustness, there is no practical adversarial training on large-scale DNNs (e.g., YOLO V3 and ResNet101) over large datasets (e.g., MS-COCO and ImageNet) due to the high cost of generating adversarial examples.
Frame-Level Specaugment for Deep Convolutional Neural Networks in Hybrid ASR Systems

Inspired by SpecAugment - a data augmentation method for end-to-end ASR systems, we propose a frame-level SpecAugment method (f-SpecAugment) to improve the performance of deep convolutional neural networks (CNN) for hybrid HMM based ASR systems. Similar to the utterance level SpecAugment, f-SpecAugment performs three transformations: time warping, frequency masking, and time masking. Instead of applying the transformations at the utterance level, f-SpecAugment applies them to each convolution window independently during training. We demonstrate that f-SpecAugment is more effective than the utterance level SpecAugment for deep CNN based hybrid models. We evaluate the proposed f-SpecAugment on 50-layer Self-Normalizing Deep CNN (SNDCNN) acoustic models trained with up to 25000 hours of training data. We observe f-SpecAugment reduces WER by 0.5-4.5% relatively across different ASR tasks for four languages. As the benefits of augmentation techniques tend to diminish as training data size increases, the large scale training reported is important in understanding the effectiveness of f-SpecAugment. Our experiments demonstrate that even with 25k training data, f-SpecAugment is still effective. We also demonstrate that f-SpecAugment has benefits approximately equivalent to doubling the amount of training data for deep CNNs.