Reviewerly/exHarmony huggingface dataset:
Implementation of Solar Tracking Design Using FPGA Tool

This paper shows a research for enhancing the rays of sunlight towards solar panels by designing the Field Programmable Gate Array (FPGA) that works on the solar tracking control system. The mechanism of whole design keeps the solar panel in horizontal rotation in order to get maximum energy from the sun during the day. The mechanical assemble of prototype solar cell is manufactured to change the position of panel from 180 degree East to West. Also we used the electronic circuit, which has quality for sensing the sunlight as well as controlling the servo motor. The tracking system is operated by using the software Altera Quartus-II. The process is gone through the programming and loaded into Altera DE2 FPGA board and tested successfully in the laboratory. This system has more energy concentration and has more improvement factor rather than using fixed solar panel system.
[Invited papers] Interactive Face Retrieval Framework for Clarifying User's Visual Memory

This paper presents an interactive face retrieval framework for clarifying an image representation envisioned by a user. Our system is designed for a situation in which the user wishes to find a person but has only visual memory of the person. We address a critical challenge of image retrieval across the user's inputs. Instead of target-specific information, the user can select several images that are similar to an impression of the target person the user wishes to search for. Based on the user's selection, our proposed system automatically updates a deep convolutional neural network. By interactively repeating these process, the system can reduce the gap between human-based similarities and computer-based similarities and estimate the target image representation. We ran user studies with 10 participants on a public database and confirmed that the proposed framework is effective for clarifying the image representation envisioned by the user easily and quickly.
Use of Cloud Computing Technology and Neural Network in College Students’ Psychological Testing

Strengthening the psychological health education of college students is an urgent need to promote the all-round development and the healthy growth of college students in the new era, and also an important measure to actively promote the quality education in colleges and universities. The timely, accurate and comprehensive understanding of college students’ psychological status is the basis of the mental health education. In order to improve the level of the psychological testing and speed up the construction of the information management, we have developed a psychological testing system for college students. This paper discusses the development objectives, the overall structure, the main functions and the application effects of the system. The progress of our society brings us many pressures and challenges. Therefore, this paper designs and implements an online psychological counseling platform with the auxiliary diagnostic system for colleges and universities.
Direct measurement of the recovery time of SNSPDs and its application for quantum communication

In this paper we demonstrate a simple and highly sensitive method to characterize the recovery time of efficiency for different kind of superconducting nanowire single- photon detectors. We also describe several applications of these detectors.
Detection of Online Malicious Behavior: An Overview

Online malicious behavior is performed in a certain kind of pressure, availability of opportunity, and through rationalized way. Web systems are accessed through browser and integrated with database so they usually face many types of vulnerabilities and online threats. The survey is focused on categorization of online malicious behavior on certain web platforms such as education, information technology, finance, and government. The characteristics of malicious behavior are explained. The research purpose is to gather, observe, compare, and study different malicious behavior, detection systems, tools and technologies used, results, and their drawbacks. The numerical observations of malicious behavior are given in order to understand severity of this behavior and its impact. The systems are observed comparatively to point out the challenges. The possible suggestions are mentioned about current requirements in online malicious behavior detection systems. The mind condition behind all malicious behavior is dishonesty and it is contagious by nature. The hybrid detection model is required which will detect malicious behavior in real time, will be flexible enough to configure newly arrived malicious behavior with good accuracy, and will work on multiple domains.
Crossing the Line: Online Violence

The Internet has a dark side that disproportionately affects women. In this chapter I will look at three forms of online abuse: (1) Posting non-consensual nude photographs or videos, (2) Cyberstalking, and (3) Doxing—publishing private information that could lead to identity theft. The result can be loss of jobs, electronic surveillance, threatened rape or murder, scare tactics and being targeted for unwanted attention. I discuss the sorts of problems—and harms—of each of these and note some of the steps that have been taken to address online violence.
Improving Response Quality in a Knowledge-Grounded Chat System Based on a Sequence-to-Sequence Neural Network

A knowledge-grounded chat model based on a sequence-to-sequence neural network is proposed to perform more natural question answering. The previous chat model suffered from the same words being repeated during decoding. To reduce this word repetition, we propose a new decoding mechanism that considers the generation distribution of previously generated words. In experiments with question-answering sentences that are semi-automatically constructed, the proposed model outperformed a representative knowledge-grounded chat model, with a better accuracy of 4.35% p in finding answer phases. In addition, it demonstrated 1.63-2.05%p better results in all evaluation measures such as BLEU and ROUGE for evaluating the quality of responses and 2.83%p better results in evaluation measures such as distinct-1 for evaluating the diversity of responses.
Thoughts on the Path of New Media Technology Helping the Transformation of Newspaper Industry

New media has a certain impact on the survival and development of the traditional newspaper industry. The transformation and development of the traditional newspaper industry is imperative. At present, the new media technology can promote the transformation and development of the traditional newspaper industry, so the rational use of new media technology is conducive to the transformation and development of the traditional newspaper industry. Starting from the stages and problems of newspaper transformation, this paper briefly discusses the way of newspaper transformation.
Heterogeneous Item Recommendation for the Air Travel Industry

Analyzing the travel behaviors and patterns of air passengers have always been of great significance to the air travel industry. Understanding the demands and interests of passengers behind their behaviors is a crucial and fundamental task for many applications. However, this task is challenging due to the lack of customer information, data sparsity, and the long-tail distribution. In this paper, we investigate the problem of heterogeneous item recommendation by learning representations of items and passengers in a shared latent space. Specifically, we first establish a heterogeneous information network (HIN) through statistical analysis, where the edges represent the interactions between different nodes. Each node also contains some auxiliary attribute information that describes its travel behavior or that of its passenger groups. Then we devise a joint matrix factorization model to learn node representations based on the HIN, where both the heterogeneous edges and the node attributes are incorporated into the learning process. Moreover, a weighting strategy is further utilized to deal with the long-tail distribution of passenger behaviors based on the implicit feedback information. Experimental results conducted on a real-world passenger name record (PNR) dataset demonstrate the effectiveness of the proposed method.
Graph refining via iterative regularization framework

Graph-based methods have been widely applied in clustering problems. The mainstream pipeline for these methods is to build an affinity matrix first, and then use the spectral clustering methods to construct a graph. The existing studies about such a pipeline mainly focus on how to build a good affinity matrix, while the spectral method has only been considered as an end-up step to achieve the clustering tasks. However, the quality of the constructed graph has significant influences on the clustering results. Unlike most of the existing works, our studies in this paper focus on how to refine the original graph to construct a good graph by giving the number of clusters. We show that spectral clustering method has a good property of block structure preserving by giving the priori knowledge about number of clusters. Based on the property, we provide an iterative regularization framework to refine the original graph. The regularization framework is based on a well-designed reproducing kernel Hilbert spaces for vector-valued (RKHSvv) functions, which is in favor of doing kernel tricks on graph reconstruction. The elements in RKHSvv are multiple outputs affinity functions. We show that finding an optimal multiple outputs function is equivalent to construct a graph, and the associated affinity matrix of such a graph can be obtained in a form of multiplication between a kernel matrix and an unknown coefficient matrix.
Zero-Shot Language Transfer for Cross-Lingual Sentence Retrieval Using Bidirectional Attention Model

We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval also for pairs of resource-lean languages, without any parallel data.
Modelling of cross organizational manufacturing resource service chain based on service supply-demand dynamic matching network

In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.
INTEGRATION OF MEDICAL PHOTOGRAMMETRY AND GAS NEURAL NETWORK FOR INTELLIGENT DISEASE DIAGNOSIS

Close range photogrammetry is an image-based method of measurement that can be used to create a three-dimensional model of objects. This method is very popular due to its high speed, low cost, and non-invasiveness as a measurement tool in medicine. The main weakness of these systems is the lack of topology between points, which is especially important in the medical field. The neural gas network can learn the topology of the points associated with the 3D model of objects. Considering the capabilities mentioned regarding the photogrammetry and the neural gas network, a combination of these can be used as a diagnostic tool in such a way that in addition to considering the points of the model, neighboring points are also considered in the diagnostic process. Accordingly, in this study, we want to design a system by combining these two tools that can recognize diseases by their apparent symptoms. Moreover, the use of the neural gas network and the possibility of local and general examination of the organs increase the accuracy of diagnosis. Diagnosis of foot disease has been used as a case study in this system. The results showed that the neural gas network has a high degree of flexibility for modeling the human body compared to previous methods, and provides a better approximation. Also, the accuracy of reconstruction of the 3D model of the object is effective in the process of diagnosis and influences the level of intelligence of the system as well. Finally, in the system implemented, the results showed that the disease was correctly diagnosed in all 5 feet of the patient. Also, in 4 cases, the location of the disease was correctly detected and in one case the location of the disease was misdiagnosed.
Designing a Dual-Layer 3D User Interface for a 3D Desktop in Virtual Reality

We present a new dual-layered 3D user interface for a 3D desktop environment in Virtual Reality (VR). VR technology has proven its potential in supporting higher levels of engagements and intuitive interactions between the human and the machine. Natural and intuitive interaction is still considered as one of the most important user interaction issues in VR. Our novel dual-layered 3D user interface allows the user to interact with multiple screens portrayed within a curved 360-degree effective field of view available for the user. Downward gaze allows the user to raise the interaction layer that facilitates several traditional desktop tasks. We perform a quantitative and qualitative user study to validate the usability and user experience of the new user interface. We show that our 3D user interface is well suited for a VR 3D desktop environment.
Incorporating Mobile Multimedia in School Student Management

Under the new situation, the information management has gradually become the focus of the school management. With the increase of the students in schools, the burden of the student management becomes heavier. To this end, strengthening the design and research of the school management system and improving the efficiency of the student management have become the focus of the school development. At present, with the development of the big data technology, the management of college students also needs the corresponding changes. Based on the student management, this paper analyzes the current situation and the existing problems of the student information management, and expounds the principles and effective ways of developing the student management information construction, which can effectively promote the modernization of the student management.
Exploiting Global Impact Ordering for Higher Throughput in Selective Search

We investigate potential benefits of exploiting a global impact ordering in a selective search architecture. We propose a generalized, ordering-aware version of the learning-to-rank-resources framework [9] along with a modified selection strategy. By allowing partial shard processing we are able to achieve a better initial trade-off between query cost and precision than the current state of the art. Thus, our solution is suitable for increasing query throughput during periods of peak load or in low-resource systems.
Layer-Edge Patterns Exploration and Presentation in Multiplex Networks: From Detail to Overview via Selections and Aggregations

Multiplex networks have been widely used to describe the multi-type connections of entities in the real world. However, researches for multiplex networks visualization unilaterally focus on the presentation of topological structure, lacking of specific high-level information presentation for quantitative comparison of interlayer structure. Users cannot participate in the exploration and freely choose the layers (or sub-graphs, regions, etc.) of interest for structural comparison. Contraposing the layer-edge patterns visual analysis tasks of multiplex networks, this paper puts forward a novel solution for exploration and analysis that tightly couples topological structure and high-level patterns. It mainly contains a multi-force directed model to realize the balanced layout of nodes in multi-layer topology, as well as two kinds of high-level patterns of which the visual representations are, respectively, designed by a familiar metaphor—that is, the similar pattern representation based on the area-proportional Venn diagrams and the interaction pattern representation based on the directed arrows. Furthermore, views association is implemented through underlying data sharing and multiple interactions which can be used to gain insights through the creation of selections of interest and produce high-level infographic-style overviews simultaneously. The experiments on real-world data demonstrate the support of the proposed method for layer-edge patterns analysis tasks in multiplex networks and the effectiveness for analyzing the multi-layer structure of multiplex networks.
Distortion evaluation method for the progressive addition lens–eye system

Distortion is one of the most significant defects in image optical systems. Herein, a method for comprehensively evaluating progressive addition spectacle lenses based on the modulation transfer function (MTF) and distortion of the lens–eye system is proposed. A lens–eye system is established, and the parameters of the system were simulated and measured simultaneously. The human eye model was compared with and without progressive addition lenses to obtain the image quality of the lenses. It was observed that eyes with different diopters and different lens designs show different trends; however, good improvement was observed in all cases after progressive addition lens correction. In terms of image clarity and deformation, the distant field was caused by a negative distortion value, the near area was caused by a positive distortion value. and the optical distortion was found to be proportional to the diopter. Moreover, the progressive corridor distortion value was minimal. With a larger field of view, the distortion of the lens became larger; thus, when an object was viewed through the lens, large distortion was observed at the edges.
Multivariate Denoising and Missing Data Estimation for Heavy-Tailed Signals

This study focuses on heavy-tailed noise reduction in multivariate signals, with no knowledge of their forms. We propose a non-parametric multivariate denoising technique which is robust to heavy-tailed noise. Using a univariate robust linear regression, we construct a multivariate non-parametric method. We design a robust matrix decomposition and, consequently, propose a robust procedure including this new decomposition. In addition, we develop a robust procedure for the imputation of the missing points of the signals. The key advantage of our methods over the previous tools is the robustness to the heavy-tailed observations. The results of our simulation study confirm the good performance of the proposed methods.
HE Stain Image Segmentation Using an Innovative Type-2 Fuzzy Set-Based Approach

HE stain images are widely used in medical diagnosis and often considered a gold standard for histology and pathology laboratories. A proper analysis is needed to have a critical decision about the status of the diagnosis of the concerned patient. Segmentation is always considered as an advanced stage of image analysis where objects of similar properties are put in one segment. But segmentation of HE stain images is not an easy task as these images involve a high level of fuzziness with them mainly along the boundary edges. So, traditional techniques like hard clustering techniques are not suitable for segmenting these images. So, a new approach is proposed in this chapter to deal with this problem. The proposed approach is based on type-2 fuzzy set and is new. The experimental results prove the superiority of the proposed technique.
An Optimal Travel Route Recommendation System for Tourists’ First Visit to Japan

As the Tokyo Olympics approach, increasing numbers of foreigners are planning to travel to Japan. It is not easy for these tourists to make travel plans by searching for information about unfamiliar Japanese sightseeing spots on the Internet. In particular, it is difficult to search for the relevant tourism information, such as names, categories, or open ratings. In this study, we aim to facilitate recommendations of sightseeing routes with high satisfaction. Tourists’ satisfaction is judged by various restrictions, such as travelers’ multiple purposes, staying time at the tourism site, travel time, and purpose. According to the users’ requirements, the optimal sightseeing route is selected by fitness function score. Finally, we will take the recommended route for the user.
Node Scheduling: A Blockchain-Based Node Selection Approach on Sapiens Chain

Recently, blockchain techniques have been widely used in finance, e-commerce and sharing economy, owing to its decentralization, openness and permission-less properties. In cybersecurity, a blockchain-based framework Sapiens Chain is proposed, which aims to provide decentralized and trustable cybersecurity services while protecting the privacy of the anonymous users. However, how to select an optimal service provider in Sapiens Chain remains challenging. In this paper, we tackle the novel problem of scheduling nodes in Sapiens Chain, which covers and rewards the nodes based on their performance with the history of providing cybersecurity services, and thus attracting more and more users to anticipate cybersecurity services. Under the scheduling algorithm, we propose to select nodes for different tasks by unifying the interest of each user, such as the bandwidth, the computing resource, and the activation degree. The experimental results demonstrate the efficiency and effectiveness of our method.
Hand Posture Detection of Smartphone Users Using LSTM Networks

Automatic hand posture detection of smartphone users is important for adaptive user interface design, context aware application development, and activity analysis. This paper presents a method for hand posture and phone placement detection from data produced by accelerometer, magnetometer and gyroscope of a smartphone using LSTM networks. Real-time testing results indicated that LSTM network is effective in hand posture and phone placement prediction, and the proposed method outperformed existing methods by significant margins.