Reviewerly/exHarmony huggingface dataset:
Generalized Collaborative Personalized Ranking for Recommendation

Data sparsity is a common problem in collaborative ranking for personalized recommendation with implicit feedback. Several previous work tried to ‘borrow’ feedback information from users’ neighborhood as their prior preferences to alleviate this problem. However, they emphasize the overlapping interests of users and their neighborhood while de-emphasize the importance of users’ own specific taste, which leads to under-personalization. In addition, they ignore the collaborative influence among items which is also important for preference learning.To solve these problems, we propose an effective collaborative ranking method G eneralized C ollaborative P ersonalized R anking (GCPR), which utilizes the collaborative influence among users and items in a unified framework. It strengthens the specific taste of users by using inner-basket influence of items to enhance the personalization. In addition, it utilizes cross-basket influence of items to dig more collaborative items to further alleviate the sparsity problem. Then, we utilize generalized AUC to learn a confidence-based listwise preference, and propose a post-training based on self-paced learning to solve the top-biased problem of the generalized AUC. Experimental results on four public real-world datasets show that GCPR achieves better performance than traditional collaborative filtering (CF) methods and state-of-the-art collaborative ranking methods.
Playful User-Generated Treatment

Virtual reality exposure therapy (VRET) is a promising approach in treating phobias such as fear of heights (acrophobia). VRET provides an effective, cost-efficient, scalable and individually adaptable alternative to traditional exposure therapy. To further foster the potential of VRET, a novel concept called Playful User-generated Treatment (PUT) was derived from expert interviews and literature review. In this paper, we provide additional insights regarding the applicability of PUT in real therapy scenarios. For that purpose, practicing psychotherapists (n=13) participated in an online survey and shared their assessments regarding PUT. By conducting qualitative content analysis (inductive category formation), we identified opportunities and challenges that should be considered for the design of playful VRET systems. Opportunities were seen for preparatory habituation, increased control and self-efficacy, improved interaction, economic usage and a realistic display of anxiety-inducing environments. Challenges included lack of direct communication and realism as well as pseudo-habituation to virtual environments.
Generating Vietnamese Language Caption Automatically for Scene Images

Generate automatically the image caption is describing content over an image using the machine learning algorithms. It is still a challenging problem since the knowledge of computer vision and text processing is needed. This paper presents a new approach to generate the Vietnamese language caption for scene images using deep learning. Even the image caption has good results in the English language. However, for the Vietnamese language, it is one of the first researches and experiment results are promising.
Big Data in Art Design

With the rapid development of the information age, big data is gradually applied to all walks of life. In the field of education, big data has brought strong technical support to the construction of databases and scientific research. The application of big data to the humanities and social sciences, especially in the art design discipline, is particularly effective, and can further promote the sustainable and healthy development of art design teaching. The purpose of this article is to study the application of big data in art design. This paper firstly improves the traditional Apriori algorithm, proposes the Apriori algorithm based on Hadoop, and introduces the idea of the improved Apriori algorithm. Then introduced the workflow of distributed parallel computing framework. The experimental results show that when the number of pictures is very large, the use of distributed retrieval can obviously improve the retrieval efficiency. When the data set size is 54 KB, 3.97 MB, 33.87 MB, the image retrieval time is only 100 ms, 150 ms and 1000 ms.
Privacy Through Data Recolouring

Current anonymization techniques for statistical databases exhibit significant limitations, related to the utility-privacy trade-off, the introduction of artefacts, and the vulnerability to correlation. We propose an anonymization technique based on the whitening/recolouring procedure that considers the database as an instance of a random population and applies statistical signal processing methods to it. In response to a query, the technique estimates the covariance matrix of the true data and builds a linear transformation of the data, producing an output that has the same statistical characteristics of the true data up to the second order, but is not directly linked to single records. The technique is applied to a real database containing the location data of taxi trips in New York. We show that the technique reduces the amount of artefacts introduced by noise addition while preserving first- and second-order statistical features of the true data (hence maintaining the utility of the query output).
An Exchange Center Based Digital Cash Payment Solution

The paper proposes a digital cash (Dcash) online payment system based on exchange center that represents central bank to manage and monitor issuance, payment and refund of Dcash. Difference Dcash could be exchanged when its issuer registered at the exchange center. Then it introduces architecture of the system and a series of transaction procedures. This paper models the payment activity using Color Petri Nets. At last, by analyzing the state space reports of the model, we can verify and check this system and get the results that the flow charts of these entities are controllable, realizable and feasible.
MFV: Application software for the visualization and characterization of the DC magnetic field distribution in circular coil systems

The characterization of the magnetic field distribution is essential in experiments and devices that use magnetic field coil systems. We present an open-source application software, MFV (Magnetic Field Visualizer), for the visualization of the distribution of the magnetic field produced by circular coil systems. MFV models, simulates, and plots the magnetic field of coil systems composed by any number of circular coils of any size placed symmetrically along the same axis. Therefore, any new design or well-known coil system, such as the Helmholtz or the Maxwell coil, can be easily modeled and simulated using MFV. A graph of the homogeneity of the magnetic field can be also produced, showing the work region where the magnetic field is homogeneous according to a percentage of homogeneity given by the user. A standardized input and output file format are employed to facilitate the exchange and archiving of data. We include some results obtained using MFV, showing its applicability to characterize the magnetic field in different coil systems. Furthermore, the magnetic field results provided by MFV were validated by comparing them with results obtained experimentally in a commercial Helmholtz coil. Obtaining the maximum variability between the experimental and simulation magnetic flux density values along the axis of symmetry is 0.87%.
Sports Application Practice and Innovation Based on Computer Aided Information Technology

Abstract The use of modern computer aided information technology teaching means can activate students’ interest in learning, stimulate students’ thirst for knowledge, and make students devote themselves to learning in a pleasant and relaxed mood. The domestic related research mainly focused on the modern computer aided information technology used to strengthen the traditional school physical education, only took the computer aided information technology as the auxiliary means to affect the level of school physical education, and failed to penetrate the modern computer aided information technology into the teaching elements. The research on school physical education informatization is lack of systematicness, and the research is mainly focused on local research, with less overall grasp. It attaches importance to the study of individual cases and practical problems, tends to be localized and technical, and the theoretical research is not systematic enough. [1]
Deep Face Recognition Based on Penalty Cosface

Face recognition has achieved great progress because of the advancement of deep convolutional neural networks (CNNs) techniques. The Softmax loss is one of the most popular loss function for deep learning models. In many situations, face images are captured in the unconstrained environments with changing poses and illuminations, making face recognition very challenging because of the dramatic appearance variations. The models trained with the Softmax loss may fail to extract discriminative information for the face images with extreme illumination or pose conditions. Recently, Cosface has been proven effective for improving the generalization ability of the Softmax loss. Derived from Cosface, we propose a novel method named Penalty Cosface to address the unconstrained face recognition challenges and learn discriminative features. Specifically, we design a variant of Cosface that remove radial variations by penalizing \(\ell _2\)-normalized constraints of the features and weights. Therefore, the discriminative ability of the Penalty Cosface is guaranteed by the large margin of the Cosface, and the penalty term is beneficial to simplifying the gradient calculations. Experimental results show that the Penalty Cosface improves the discriminative power of deep networks and outperforms the other variants of Softmax loss.
2D Vector Map Fragile Watermarking with RST Invariance and Region Location

For 2D vector map fragile watermarking, tolerating RST (rotation, scaling and translation) operations and locating all tampered regions at the same time is a challenging task. In this paper, we propose a fragile watermarking method that not only tolerates RST operations, but also locates all tampered regions. The method divides the features of the vector map into groups. For each group, a watermark that is composed of location-bits and check-bits is generated. The location-bits are generated to locate the RST version of the group’s original region, while the check-bits are formed to be invariant to RST operations and sensitive to malicious attacks. Using a RST invariant data hiding method, the check-bits are embedded into the group itself, and the location-bits are embedded into its mapping group. If a watermarked vector map has suffered from malicious attacks and RST operations during transmission, all the tampered groups’ current regions and the RST version of their original regions can be located. Experimental results are provided to demonstrate the effectiveness of our method.
A Similarity Calculation Model of Weak Link Web Pages Based on Keyword Location Influence

Weakly linked web pages are a group of related pages with weak connections. With the advent of the self-media era, the related information links between various platforms have become weaker, the calculation of the similarity of weakly linked web pages is becoming more and more important. The current method for calculating the similarity of weakly linked webpages is to combine the similarity of webpage content and the relevance of links, and then simply add the results. There is a large deviation in the calculation results of this method. To this end, this paper proposes a similarity calculation model for weak link webpages based on the influence of keyword position (WIWKLI). First, we start with the division of web pages, through URL matching deduplication, keyword similarity calculation to form a keyword sequence of web pages, then through the improvement of the RNN model to calculate the positional influence of keywords, so as to realize the precise definition of the webpage and form a link pseudo-network between related webpages accordingly, Finally, the similarity between web pages is calculated through the pseudo-network. By applying this calculation model to the focused crawler experiment, Experimental results show that the method proposed in this paper can calculate the similarity of weakly linked web pages, and the accuracy and page relevance have been improved to a certain extent.
Region-Free Cloud Recognition Based on Center Net

Because of the significant influence produced by different types of cloud on photovoltaic power, accurate cloud identification is developed widely to predict the variation of photovoltaic power generation. Classical cloud-recognition methods generally use texture, structure, and color simultaneously to capture the cloud characteristic. In this paper, a Center Net-based cloud image recognition method is applied to cloud identification for the first time to simplify the operation and enhance the detection speed. The proposed algorithm adopts Hourglass Resdcn18, Resdcn101, and DLA -34 networks respectively to generate heatmap, and picks the heatmap peaks as a center point for target identification. Eight kinds of clouds are selected to verify the detection performance of the proposed algorithm: altocumulus, altostratus, cumulus, cirrus, cirrostratus, cumulonimbus, cumulonimbus, stratocumulus. The effectiveness of identification is compared under the structure of each network. The cloud center location and confidence are visualized under the DLA-34 model with higher confidence. The results showed that the highest confidence level of DLA-34 network is 0.973, which is higher than that of other networks apparently. Compared with Faster R-CNN, the cloud image recognition speed of Center Net is faster and the confidence is higher.
A New Face Detection Framework Based on Adaptive Cascaded Network

In recent years, face detection and face recognition were widely used in fields like identity-verification system, security system and financial system. However, some of the real-time face detection tasks are still challenging due to the overflowed computational cost of a discriminative system. To resolve this issue, we propose a cascaded network based on CNN, which has simple architecture and light-weighted parameters. The network consists of three stages and operates with a image pyramid (same image with different resolutions), the first stage quickly discriminates faces (proposals) from background, and the other two stage specifically evaluate those proposals to determinate whether a proposal contains face or not. We also address an adaptive method that dynamically drops those redundant proposal regions generated in first stage to avoid prohibitive computation. The model we trained runs at 180 ms for processing a single image on CPU, and keeps an accuracy around 98.4%.
cARd: Mixed Reality Approach for a Total Immersive Analog Game Experience

In this paper, we explore the next generation of analog game (i.e., board and card games) experiences by combining a traditional analog game with mixed reality technology. First, we transfer an existing analog game into a virtual reality (VR) environment that enhanced the gameplay with an animated model, visual effects, and sound effects. Second, we implement a multi-player interaction to allow collaborative play in virtual environments even when players are isolated from each other physically. Third, we conduct an experiment to compare the VR environment with the original analog game. Finally, we showcase cARd, the resulting collaborative mixed reality version of the game that combines the strength of the traditional analog game with VR by using both VR and augmented reality (AR) technology. This mixed reality approach aims to enhance the player experience while keeping the benefits of the traditional analog game, in particular manipulating tangible objects.
Human-Computer Interaction as an Important Aspect of Software: A Tutorial

Personal computers and smartphones are now used by people belonging to different age groups and professions, and for different purposes. Human-computer interaction is a sub-discipline of computer science that studies how users interact with these devices and use the knowledge to design efficient user interfaces. In this tutorial article, we discuss the fundamentals of human-computer interaction and its relevance. We discuss the characteristic features of window-based user interface and explain why it is more popular than other types of user interfaces. We conduct an experiment to determine the ease of use of various widgets used in window-based user interfaces. Results of ANOVA show that the time taken by users to use different types of widgets vary significantly ( ) for desktop applications ( F=32.94644) as well as for smartphone apps ( F=54.97363). We conclude that user interface of software should be deigned systematically and using appropriate widgets.
A toolset to support a software maintenance process in academic environments

Software engineering and maintenance processes are designed to provide structure and organization around a set of activities involved in the production or maintenance of a software product. Understanding these processes and learning to follow them are important experiences for students in a software engineering course. But it can be difficult for both students and instructors to recognize when a process isn't being followed. Tools designed to guide a process can help. In these environments, tool support can also help ensure that students are using version control systems appropriately while fostering an environment in which students learn from their peers. The importance of tool support for agile processes has been recognized, but little such support has been designed for academic environments. "Co-Op" is a software maintenance-focused process and supporting toolset designed for use in academic environments. The toolset is implemented as a web application that focuses on change impact analysis, use of version control systems that adheres to the process, and communication amongst part-time developers. A brief overview can be seen at https://bit.ly/2YEbWvO.
An Effective Occlusion Edge Prediction Method in Light Field Depth Estimation

Occlusion is one of the most complicated and difficult problem to deal with in light field depth estimation. Previous methods usually use edge detection of central view as the basis for subsequent depth estimation. However, few algorithms take the correlation between light field sub-aperture images into consideration, which makes the incorrect depth estimation in the occlusion areas and object boundaries. In this paper, based on the refocusing theory, we explore three occlusion prediction methods and derive model to make full use of the correlation between different sub-aperture to handle the occlusions in depth estimation, the obtained occlusion map is used as a guidance for the depth estimation. Markov Random Field is built to regularize the depth map and smoothing filtering, which is used at the background area of the non-occlusion area to process noise in order to improve estimation results. Experimental results on the real light-field data sets show that the proposed algorithm can greatly improve the depth estimation results compared with state-of-the-art algorithms, especially in complex occlusion areas.
Runtime Analysis of Stochastic Gradient Descent

Stochastic gradient descent (SGD) is one of the most famous methods for minimization. However, there are few results about the runtime analysis of SGD because of its randomness. In this paper, we explain how to approximate SGD by stochastic differential equations (SDE). The theoretical foundation of mean first passage time (MFPT) is then used to determine the runtime of SGD. Concretely, we first consider a special case where SDE can be approximated as Ornstein-Uhlenbeck process. On the basis of the idea of integrals, we then generalize the relevant theories to analyze the runtime of General SDE. The experiment results indicate the theoretical analysis is close to the practical situation.
Beyond 160 applications of an expert system: key to a better usability

The most influential relevant thinkers have complained of the “poverty” of Expert Systems (ES) both in the past (Dreyfus and Dreyfus, 1986) and in recently studies as well (Müller and Bostrom, 2016). We developed our own AI-Based Expert System shell for rule-based and case-based reasoning three decades ago and now there are 160 Knowledge Engineering (KE) process behind us with this system. We hope that this experience give us the right to formulate an opinion about that what is the key to a better usability and user experience in understanding of the result of the decision making process. While we do not think that ES is an omnipotent panacea, we also do not think that its applicability is determined only by the shell capabilities. However, one ability is essential; namely, presenting the result as simply as possible in order to that the decision-maker also can understand it. Our finding is that ES shells are only able to be transparent if they are designed by people who have an understanding of the human thinking process instead of a strong math-based software development approach.
Research on Economic Law Teaching Reform Based on Big Data

Abstract The course of economic law, as a basic course of economics and management, plays an important role in the cultivation of students’ vocational ability. It is also helpful to realize the cultivation of students’ vocational ability, help them to set up a better view of employment and help them to establish their vocational view. At present, in the era of big data, more and more problems have emerged in the teaching of economic law for economic majors. Some experts and scholars put forward that the basic reform can be carried out according to their professional characteristics, pay attention to the analysis of typical cases of students in daily teaching, adopt diversified assessment standards, and constantly improve the teaching quality.
Neural Machine Translation Based on Improved Actor-Critic Method

Reinforcement learning based neural machine translation (NMT) is limited by the sparse reward problem which further affects the quality of the model, and the actor-critic method is mainly used to enrich the reward of the output fragments. But for low-resource agglutinative languages, it does not show significant results. To this end, we propose an novel actor-critic approach that provides additional affix-level rewards and also combines the traditional token-level rewards to guide the parameters update of the NMT model. In addition, for purpose of improving the decoding speed, we utilize an improved non-autoregressive model as the actor model to make it pay more attention to the translation quality while outputting in parallel. We achieve remarkable progress on two translation tasks, including the low-resource Mongolian-Chinese and the public NIST English-Chinese, while significantly shorting training time and accomplishing faster convergence.
Security Reconsideration and Efficiency Evaluation of Decentralized Multi-authority Anonymous Authentication Scheme

We consider the security definition of decentralized multi-authority anonymous authentication schemes (DMA-AAuth) which was proposed by Anada-Arita at ICICS2018. The security is against causing-misauthentication attack, and we modify it to capture a threat of corrupted key-issuing authorities. Then we prove that the concrete scheme proposed by Anada at CANDAR'19 is secure under the new definition. Next, we evaluate efficiency of the concrete scheme by implementation. We use the C programming language with the TEPLA library.