Reviewerly/exHarmony huggingface dataset:
CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering Approach

The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT .
Advanced Chinese Character Detection for Natural Scene Based on EAST

Abstract Currently in the field of image vision processing, text detection and text recognition under natural scenes is a challenging task. At present, most of the research on text positioning is mainly aimed at English, and the detection and recognition of Chinese characters is relatively few. EAST text detection algorithm is simple in structure and high in accuracy. However, because of the feature extraction network, there is a small sense field, which cannot be well adapted to the text detection of Chinese characters. In order to adapt it to the natural scene Chinese character text detection, the feature extraction network of the modified EAST algorithm is Mobilnet-V2 and Resnet50, which increases the depth of the network and extracts more image features. The modified algorithm is tested on the dataset MSRA-TD500, and the obtained accuracy shows that the improved network performance is better than the original algorithm.
Modified Adaptive Synthetic SMOTE to Improve Classification Performance in Imbalanced Datasets

The oversampling technique in the data preprocessing has been utilized to mitigate the imbalanced data problem in the real research scenario. This imbalance may reduce the ability of classification algorithms to recognize cases of interest leading to misclassification of positive samples as negative class or the false positive generation. Synthetic Minority Oversampling Technique (SMOTE) is one of the oversampling techniques existing and the Adaptive Synthetic (Adasyn) SMOTE is one of its many variants. K-Nearest Neighbor (KNN) is incorporated in Adasyn. In this study, Manhattan distance is applied in the KNN computations. This modified Adasyn was evaluated in terms of its effectiveness in the performance measure of overall accuracy, precision, recall and F1 measure on the six imbalanced datasets using logistic regression as the classification algorithm. The modified Adasyn dominated over SMOTE and the original Adasyn by 66.67 percent of the total performance metric count. It leads the accuracy and recall count with 4 out of 6, precision count with 3 out of 6, and the F1 measure count with 5 over 6. Thus, proving that the modified Adasyn can provide an efficient solution in decreasing misclassification on imbalanced datasets.
A Virtual Trial Room using Pose Estimation and Homography

Rapid urbanization and digitization, increasing income and lifestyle changes of the middle class are leading to a major revolution in the retail industry, especially shopping malls. Today almost every city in India has several shopping malls and with the increase in population, the queues for trying on clothes in these shopping malls are increasing rapidly. This paper is a research in overcoming this problem and enhancing the customer's shopping experience. We have built an android based mobile application along with the technologies of OpenCV and TensorFlow lite which will allow the customers to try on the clothes virtually without waiting in a queue for a long time. The application uses OpenCV to map the clothes onto the customer's body, the customer can then have a look at it and decide whether to buy the clothes or not. The results obtained show that the mapping is done accurately onto the user's body and that it can avoid the need to go to trial rooms. Thus our application provides a quick, easy, and accurate way to try clothes and thus we believe it will prove instrumental in shaping the retail industry.
Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level solutions. This paper deals with the second approach. In particular, this paper shows a new proposition for calculating the weighted score function to use in the integration phase of the multiple classification system. The presented research includes experimental evaluation over multiple, open-source, highly imbalanced datasets, presenting the results of comparing the proposed algorithm with three other approaches in the context of six performance measures. Comprehensive experimental results show that the proposed algorithm has better performance measures than the other ensemble methods for highly imbalanced datasets.
Remote Sensing Image Inpainting with Generative Adversarial Networks

We investigate generative adversarial neural networks (GAN) for remote sensing image inpainting. We are considering a generative neural network with a contour predictor. We use this neural network to inpainting of the natural remote sensing images obtained by “Canopus”, “Meteor”, “AIST”, “Resurs” aircrafts, as well as Google Earth images. As a basis for comparison, we use an exemplar-based algorithm. We experimentally prove the effectiveness of the generative neural network with the contour predictor for remote sensing image inpainting, in particular for generation forgery Earth remote sensing data.
Wireless Communication Jamming Recognition Based on Lightweight Residual Network

Jamming recognition is an important part of communication anti-jamming. To precisely identify various wireless jamming, a lightweight residual network model was proposed. Firstly, signals received were transformed into time frequency images through Short Time Fourier Transform. Then, inputting time frequency images into recognition model, the lightweight residual network based on dilated kernel takes the images as input and directly outputs recognition results, achieving an "end-to-end" identification. The model was able to achieve an 81.21% accuracy even when JSR is -9dB. Compared with common method with manually-designed features and convolutional neural network with plain kernels, the lightweight residual network utilizing the dilated kernel effectively improve performance in recognition with a few of extra computational complexity added.
Generalization of Agent Behavior through Explicit Representation of Context

In order to deploy autonomous agents in digital interactive environments, they must be able to act robustly in unseen situations. The standard machine learning approach is to include as much variation as possible into training these agents. The agents can then interpolate within their training, but they cannot extrapolate much beyond it. This paper proposes a principled approach where a context module is coevolved with a skill module in the game. The context module recognizes the temporal variation in the game and modulates the outputs of the skill module so that the action decisions can be made robustly even in previously unseen situations. The approach is evaluated in the Flappy Bird and LunarLander video games, as well as in the CARLA autonomous driving simulation. The Context+Skill approach leads to significantly more robust behavior in environments that require extrapolation beyond training. Such a principled generalization ability is essential in deploying autonomous agents in real-world tasks, and can serve as a foundation for continual adaptation as well.
Bridge-surface panoramic-image generation for automated bridge-inspection using deepmatching

Visual inspection is important for the efficient maintenance of bridge structures and has recently been supplemented with the use of image-processing techniques that can localize and quantify damages using images captured from bridges. A series of overlapping bridge images can be combined for constructing a panoramic bridge-surface image in which the locations and sizes of the damages can be noted. Despite the excellent performance of image-processing techniques, generating panoramic images from a series of bridge-surface images is challenging as bridge-surface images may not possess distinct patterns or patterns that can act as reference feature points for stitching adjacent images. To address this issue, this paper presents a general method for stitching bridge-surface images using Deepmatching, which determines a pixel-wise correspondence between an image pair in comparison with conventional feature-wise matching methods. To employ Deepmatching for panoramic-image generation, (1) image matching pair search using 2D Delaunay triangulation, (2) parametric model for optimal image stitching were developed, and (3) field validation was conducted in this study. First, possible image matching pairs are organized using the two-dimensional Delaunay triangulation, and then Deepmatching is used to determine the matching points between possible image pairs. The developed parametric model refines the valid image matching pair, which is used for obtaining optimal global homographies for panoramic-image generation. For the validation of the proposed method, a lab-scale experiment on a flat concrete wall and a field experiment on a concrete bridge were conducted. The experimental validation demonstrates that the proposed method successfully identifies dense matching points between image pairs and generates a panoramic image while minimizing the occurrence of ghosting and drift.
Partial domain adaptation based on shared class oriented adversarial network

Most existing domain adaptation methods assume that the label space of the source domain is the same as the label space of the target domain. However, this assumption is generally untenable due to the differences between the two domains. Therefore, a novel domain adaptation paradigm called Partial Domain Adaptation (PDA), which only assumes that the source label space is large enough to subsume the target label space has been proposed recently to relax such strict assumption. Previous partial domain adaptation methods mainly utilize weighting mechanisms to alleviate negative transfer caused by outlier classes samples. Though these methods have achieved high performance in PDA tasks, all the heterogeneous data is retained during the whole training process, which still contributes to negative transfer. In this work, we propose a shared class oriented adversarial network (SCOAN) for partial domain adaptation. Outlier samples are excluded from training process via weighting strategy to entirely circumvent negative transfer and positive transfer is performed by combining adversarial network and Maximum Mean Discrepancy (MMD) to bridge domain gap. Multi-classifier module is proposed to further improve the generalization ability of the network. Extensive experiments show that SCOAN achieves state-of-the-art results on several benchmark partial domain adaptation datasets.
Enhancing the Student Perception on Software Project Management in Computer Science

Contribution: This article presents a research for enhancing the computer science students' perception on the software project management subject. The main objective of this article is double. The first one is to redesign the current course by including new teaching and new learning methods that encourage students to learn the project management competences and skills. The second one is to analyze the satisfaction of the students regarding the new software project management course. The design of the new course has been based on the improvement proposals from the students, which were collected throughout a grounded theory (GT) study during two academic years. Background: Students must be motivated with both the technical and other transversal competencies provided by the different subjects in the computer science curricula. It is not easy to teach a management subject because students do not clearly find the relationship with their studies. However, it is widely accepted that computer science students need to be trained in project management to be able to work at full capacity in the industry. There is not much literature on how to increase the motivation of students in management subjects. Research Question: Which are the causes and consequences of a low level of interest for software project management in computer science studies? Methodology: Two different experiments were carried out. The first one consisted of determining, during two academic years, the main reasons why computer science students do not get to comprehend the need for such this subject in the curricula. From the GT results, a set of innovative actions were introduced in order to redesign the subject. In the second experiment, during the following two academic years, the satisfaction of the students regarding the changes introduced in the course was analyzed. Findings: The first finding was obtained from the GT study conducted. Four causes influencing students to do not give enough importance to software project management, two consequences derived from these causes and three contingencies to potentially mitigate the consequences were identified. The second finding was the corroboration that the new course design contributes to student motivation and engagement. Through the evaluation of the new course, students can better understand project management techniques and their application to projects. Data collected show that students are aware of the importance of these skills and competencies for their background and professional profile.
Magical Realist Design

We expand the critical scope of HCI by discussing features of Magical Realism, a body of literature with roots in Latin America but now global in scope. Through the discussion of our failures to treat the canon of Magical Realism as a resource for designing an augmented reality app for children we expand to consider how the world view of Magical Realist literature presents challenges for design and new critical perspectives particularly around temporality, techno-politics, and reality and presence. We conclude by asking what a truly Magical Realist design practice that resists moves to treat literature as a resource to be used preferring instead to approach is as a context of engagement.
The Analysis of Using Active Learning Technology in Institutions of Secondary Vocational Education

The purpose of the research is to analyse the use of active learning technologies in training students and to assess the effectiveness of their use in the teaching process in secondary vocational education.In order to verify the effectiveness of the developed pedagogical methods, methods of experimental work and analysis of the obtained data were applied.The authors also used survey and testing methods among students of an educational organization for professional motivation, obtaining a significant amount of data.In order to identify the resulting changes, testing of students was repeated at the final stage of the study.The authors have substantiated the use of active learning technologies in the formation of career motivation of students of secondary vocational education.The essence of technology is in enhancement of the student's position as a subject in the educational process.The effectiveness of pedagogical environment for active learning technologies in the formation of career motivation in secondary vocational education students has been experimentally substantiated.
Secure and Privacy-Preserving Attribute-Based Sharing Framework in Vehicles Ad Hoc Networks

In the vehicle communications, vehicle-to-vehicle interconnection has extraordinary significance for improving the quality of transportation services and ensuring effective and safe information sharing between vehicles. However in the vehicular Ad hoc networks (VANETs), messages shared between vehicles can be easily spoofed and tampered. How to ensure the authenticity, integrity and non-repudiation of the message is an important issue for VANETs. In order to solve the above problems, a privacy-preserving VANETs sharing system based on attribute-based cryptography is proposed in this paper. In the proposed scheme, the attributes of the vehicles are divided into static attributes and dynamic attributes according to the characteristics of them. And sensitive attribute values that may leak user privacy are hidden to protect user privacy. To achieve the verifiability, integrity, non-repudiation and traceability of the message, a new group signature on the ciphertext is introduced. Additionally, for the calculation limitation of the vehicle terminal, only 6 bilinear pairing operations are needed in the data recovering phase of the scheme. Finally, security analysis and performance evaluations are given to show that the proposed scheme has higher security and efficiency than those in the literature.
MiSoSouP

We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new formulation of these measures as functions of averages, that makes it possible to approximate them using sampling. We then discuss how pseudodimension, a key concept from statistical learning theory, relates to the sample size needed to obtain an high-quality approximation of the most interesting subgroups. We prove an upper bound on the pseudodimension of the problem at hand, which depends on characteristic quantities of the dataset and of the language of patterns of interest. This upper bound then leads to small sample sizes. Our evaluation on real datasets shows that MiSoSouP outperforms state-of-the-art algorithms offering the same guarantees, and it vastly speeds up the discovery of subgroups w.r.t. analyzing the whole dataset.
Decentralized E-Learning Marketplace: Managing Authorship and Tracking Access to Learning Materials Using Blockchain

The difficulty in protecting and enforcing Intellectual Property (IP) rights has been a major obstacle to sharing intellectual works in digital forms. In this work, we present a prototype design for a decentralized e-learning market-place where teachers, authorized publishers and authors can publish their learning materials, establish conditions for allowing access, and grant or revoke access based on adherence or infringement of their rights. Using the Blockchain of Learning Logs (BOLL) and a digital book reader (BookRoll), we demonstrate how authors can be empowered to deploy smart contracts that protect and enforce these rights on their intellectual works in a distributed manner. We particularly use the blockchain in order to enable trust, trace-ability, rights protection, transparency and collaboration between authors and users of their work with no third party interference. Finally, we examine the implications of our proposed design, its limitations and directions for future work.
Staying on Track: a Comparative Study on the Use of Optical Flow in 360° Video to Mitigate VIMS

Visually Induced Motion Sickness (VIMS), when the visual system detects motion that is not felt by the vestibular system, is a deterrent for first-time Virtual Reality (VR) users and can impact its adoption rate. Constricting the field-of-view (FoV) has been shown to reduce VIMS as it conceals optical flow in peripheral vision, which is more sensitive to motion. Additionally, several studies have suggested the inclusion of visual elements (e.g., grids) consistent with the real world as reference points. In this paper, we describe a novel technique dynamically controlled by a video’s precomputed optical flow and participants’ runtime head direction and evaluate it in a within-subjects study (N = 24) on a 360° video of a roller coaster. Furthermore, based on a detailed analysis of the video and participant’s experience, we provide insights on the effectiveness of the techniques in VIMS reduction and discuss the role of optical flow in the design and evaluation of the study.
On the application of digital optical microscopy in the study of materials structure and deformation

Digital optical microscopy (DOM) has revolutionised many aspects of industrial inspection and metrology through its ability to acquire and manipulate accurate images of samples using the power of computers. We report the application of HokusAI®, a suite of techniques for digital optical microscopy developed at the Hierarchically Structured Materials (HSM) lab at the Skolkovo Institute of Science and Technology (Skoltech), to elasto-plastic deformation and fracture mechanics analysis of samples of stainless steel AISI 304. Focus Stacking Optical Microscopy (FSOM) is combined with image stitching and Digital Image Correlation (DIC) to collect exhaustive and precise description of sample geometry, surface roughness, fracture surface morphology, and the evolution of deformation during in situ tensile testing. The implications of these advances for the analysis of deformation and fracture of advanced engineering materials are discussed.
Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping

Several works have been conducted regarding the reduction of the energy consumption in electrical circuits. Reversible circuit synthesis is considered to be one of the major efforts at reducing the amount of power consumption. The field of reversible circuit synthesis uses a large number of proposed algorithms to minimize the overall cost of circuits synthesis (represented in the line number and quantum cost), with minimal concern paid for synthesis time. However, because of the iterative nature of the synthesis optimization algorithms, synthesis time cannot be neglected as a parameter which needs to be tackled, especially for large-scale circuits which need to be realized by cascades of reversible gates. Reducing the synthesis cost can be achieved by Binary Decision Diagrams (BDDs), which are considered to be a step forward in this field. Nevertheless, the mapping of each BDD node into a cascade of reversible gates during the synthesis process is time-consuming. In this work, we implement the idea of the subtree-based mapping of BDD nodes to reversible gates instead of the classical nodal-based algorithm to effectively reduce the entire reversible circuit synthesis time. Considering Depth-First Search (DFS), we convert an entire BDD subtree in one step into a cascade of reversible gates. A look-up table for all possible combinations of subtrees and their corresponding reversible gates has been constructed, in which a hash key is used to directly access subtrees during the mapping process. This table is constructed as a result of a comprehensive study of all possible BDD subtrees and considered as a reference during the conversion process. The conducted experimental tests show a significant synthesis time reduction (around 95% on average), preserving the correctness of the algorithm in generating a circuit realizing the required Boolean function.