Reviewerly/exHarmony huggingface dataset:
Light Field Imaging Distance Measurement Method Based on BP Neural Network

As light field camera can concurrently record light space and angle information, it is possible to use a single image to measure the distance of objects. It is of great significance to three-dimensional reconstruction, image guidance and other practical application. In this article, we analyze the imaging principle of plenoptic 1.0 camera to infer the refocus formula containing distance coefficient. In the unceasing refocusing process, ,the imaging quality of the same object continually varies with the change of refocusing coefficient. For acquiring the refocusing coefficient value of object at the clearest moment, we put forward a BP neural network-based focusing assessment model to quantify image quality, and adopt TID2013 data set to train the model in combination with tester grade . The imaging distance can be calculated according to refocusing coefficient. After bringing the imaging distance into Gaussian imaging formula, we derive the actual distance of the object. Moreover, we respectively test the algorithm and analyze error with simulated light field data and real light field data. The final outcomes suggest that the algorithm shows good distance measurement ability and edge recognition ability in a specific scope.
Identifying Victims of Human Sex Trafficking in Online Ads

Social media and the interactive web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. A prototype was designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management to generate actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. The prototype was used to analyze data sets generated from online ads. An unexpected outcome of the second data set was the discovery of the use of emojis in an expanded ontology. The final prototype used the expanded ontology to identify potential victims. The results of applying the prototypes suggest a viable approach to identifying victims of human sex trafficking in online ads.
The impression of virtual experience

Mobile devices have become an enormous platform for augmented reality (AR) technology to meet the need of users to experience the charm of AR technology in anytime and anywhere. However, the development of mobile AR technology is currently in a dilemma. Both extensibility and real-time are often challenging to be taken into account. In our study, we explored cloud-based solutions and designed two systems, S-MARC, and D-MARC. The first system boldly tried the idea of cloud technology supporting mobile AR and explored more possibilities for combining location information. The cloud-based mobile AR design optimization scheme is proposed in our study details. Besides, we invited some users to carry out experimental studies, and their user evaluations showed that the mobile AR cloud solution was implemented in both systems and received excellent feedback in the review. The results show that the proposed two schemes are superior to others in the performance and efficiency, and help to improve the current dilemma of mobile AR and build up users' confidence in the application of mobile AR.
Phoneme Sequence Extraction of Learner’s Pronunciation Errors for Foreign Language Learning

This paper presents a method for extracting phoneme sequences in foreign language pronunciation learning that a learner is likely to mispronounce. Some conventional methods show a learner's phoneme errors using speech recognition technology. Those systems simply show only one phoneme error that the learner makes; they show no analytical results of phoneme errors that arise from previous or later phoneme sequences. A feature of the proposed method is the ability to extract a phoneme that a learner is likely to mispronounce as an n-gram sequence rather than a single phoneme. We confirm the feasibility of our method with several experiments using a prototype of our learning system.
YanuX

We currently live surrounded by many different computing devices. Therefore, it is important to take better advantage of those devices by coming up with smart ways of integrating and combining them. We have been exploring the possibility of building applications that present user interfaces pervasively distributed across different co-located devices. We designed the YanuX framework, which generalizes and supports the development of this new type of applications. A key issue is the automatic distribution of user interface (UI) components among co-located devices. We created the tools set needed to describe the capabilities of each of the devices present in the environment and the requirements of each of the applications' components as configured by the developers. Restrictions of the components should match the capabilities of the devices leading to a UI component distribution decision that should reflect developers' intentions and expectations of the users. Besides detailing YanuX's components, the paper also presents YouTube Viewer as a proof-of-concept application based on YanuX. The application was also used in a user study to evaluate the concept and the experience supported by the framework. The results presented here are positive and very promising.
Pedestrian Flow Estimation Using Sparse Observation for Autonomous Vehicles

One of the major challenges that autonomous cars are facing today is the unpredictability of pedestrian movement in urban environments. Since pedestrian data acquired by vehicles are sparse observed a pedestrian flow directed graph is proposed to understand pedestrian behavior. In this work, an autonomous electric vehicle is employed to gather LiDAR and camera data. Pedestrian tracking information and semantic information from the environment are used with a probabilistic approach to create the graph. In order to refine the graph a set of outlier removal techniques are described. The graph-based pedestrian flow shows an increase of 61.29 % of coverage zone, and the outlier removal approach successfully removed 81 % of the edges.
The Role of Self-Efficacy for Satisfaction with Career Counselling and Goal Attainment Among Career Counsellors Working at Schools

Career counsellors working at schools are expected to promote students’ social, emotional, academic, and career development (Lindwall &amp; Coleman, 2008). Despite the importance of career counselling at school (Anctil, Smith, Schenck, &amp; Dahir, 2012; Osborn &amp; Baggerly, 2004), school counsellors face barriers to implementing career counselling, including limited time because of competing demands, negative perceptions about career counselling from parents, teachers, and administration, and low school counsellor self-efficacy (Sanders, Welfare, &amp; Culver, 2017). Considering the importance of career counselling and challenging working conditions, studies usually focus on individual antecedents of effective career counselling (Sawyer et al., 2013). Existing research reveals that self-efficacious consultants provide higher-quality career consulting services to various groups of employees (Bodenhorn &amp; Skaggs, 2005; Larson &amp; Daniels, 1998), are more satisfied with their job and experience less stress (Lent &amp; Hackett, 1987). According to Larson and Daniels (1998), self-efficacy is the essential factor of successful career counselling. However, existing studies do not disclose the mechanism of why career consultants with higher self-efficacy perform consulting activities better. Therefore, this study is aimed to analyse the role of self-efficacy in the relationship between job resources (opportunity to develop and feedback), satisfaction with career counselling and goal attainment among career counsellors working at schools. The study was part of the project “Strategies to Utilise and Cultivate Positive Characteristics &amp; Employability Skills in Schools” (SUCCESS, 2017-12-LT01-KA201-035247). In total, 246 school career counsellors from Lithuania, Italy, Ireland, and Greece were surveyed online. Most of the participants (88.6 percent) were female, and their age varied between 25 and 60 years. In all countries, the professional experience of career counselling varied between 1 and more than 10 years.The results of the study revealed the importance of self-efficacy for satisfaction with career counselling and goal attainment. Self-efficacy moderated the relationship between the opportunity for development and feedback and satisfaction with career counselling, i. e. the opportunity for development and feedback and satisfaction predicted satisfaction with career counselling only when self-efficacy was high. Furthermore, the opportunity for development and feedback were indirectly related to goal attainment through satisfaction with career counselling only when self-efficacy was high. In other words, job resources are more important and better used by those career counsellors who rely on their abilities, can remain calm when facing difficulties in their job and find solutions when confronted with a problem. The limitations of the study together with practical implications are discussed.
Comparison of Different Rule Base Matrix in Fuzzy Logic Controller

Abstract The development of power electronics technology has been widely applied in various fields. Converters have been widely used in renewable energy and several industrial applications. Many control that used to operate the converter, one of these is Fuzzy Logic Controller (FLC). FLC is used because it has advantages such as fast response and good performance. FLC is a rule-based control system, so the basic rules are made to operate this system by input processing. Fuzzy variable 3x3 matrix rule base is less responsive in achieving steady state conditions. Therefore, a 5x5 matrix rule base fuzzy variable is modified, so that it gets smoother response and faster system to achieve steady state. In this paper, the response of the output voltage to changes in the fuzzy variable 3x3 matrix rule base will be compared to the 5x5 matrix rule base.
Application of Unsupervised Machine Learning to Cluster the Population Covered by Health Insurance

Unsupervised learning refers to training an artificial intelligence system based on unlabeled data. One of its main applications is clustering analysis, which is the study of techniques and algorithms used to create clusters from a set of data. Clustering methods can be applied in various fields, particularly in health insurance. Health insurance is one of the ways that individuals finance their medical needs and maintain a stable financial situation in case of illness, which shows the importance of subscribing to a medical insurance coverage. Therefore, studying the behavior of the population benefiting from it is essential. In this paper, we apply K-means algorithm, which is one of the most used and efficient unsupervised learning methods for data clustering, in order to cluster the subscribers covered by health insurance in homogeneous groups or clusters. The clusters created will serve as a decision support tool for policy makers in the insurance field.
Multi-layer Attention Neural Network for Sentence Semantic Matching

Sentence semantic matching, which used to determine the semantic relationship between two sentences, plays an important role in many tasks such as Natural Language inference (NLI), Machine Comprehension and Paraphrase Identification. In this paper, we propose the multi-layer attention neural network which employs multi-level attention between two sentences. To be specific, we employ two layers of soft-alignment attention-one after embedding layer and the other after encode layer. Then we aggregate the matching information from each attention layer and use both max-pooling and attention-pooling to obtain the final representation and feed it into a MLP classifier. We use four different datasets to evaluate the model and the results shows that our model has a significant performance.
Optimal quantization via dynamics

Quantization for probability distributions refers broadly to estimating a given probability measure by a discrete probability measure supported by a finite number of points. We consider general geometric approaches to quantization using stationary processes arising in dynamical systems, followed by a discussion of the special cases of stationary processes: random processes and Diophantine processes. We are interested in how close stationary process can be to giving optimal n-means and nth optimal mean distortion errors. We also consider different ways of measuring the degree of approximation by quantization, and their advantages and disadvantages in these different contexts.
The Application of the Big Data Age and the Artificial Intelligence in the Computer Network Technology

With the development and inheritance of the modern science and technology in our country, the application of the computer network technology is becoming more and more important. By changing the forms of the technology applications in the development of the computer network, the application of the technology in the network research and development in the new period is applied to the development of the computer network technology. For example, the big data technology and the artificial intelligence technology are the new technologies formed in the development of the computer network technology. The combination of the new technology and the computer network technology can meet the technical needs of the development of the computer network technologies. In view of this, this paper analyses the application research of the big data age and the artificial intelligence in the computer network technology, hoping to provide reference for the application of the big data technology and the artificial intelligence technology in the computer network technology with the help of this project.
Unsupervised Word Sense Disambiguation Using Word Embeddings

Word sense disambiguation is the task of assigning the correct sense of a polysemous word in the context in which it appears. In recent years, word embeddings have been applied successfully to many NLP tasks. Thanks to their ability to capture distributional semantics, more recent attention have been focused on utilizing word embeddings to disambiguate words. In this paper, a novel unsupervised method is proposed to disambiguate words from the first language by deploying a trained word embeddings model of the second language using only a bilingual dictionary. While the translated words are useful clues for the disambiguation process, the main idea of this work is to use the information provided by English-translated surrounding words to disambiguate Persian words using trained English word2vec; well-known word embeddings model. Each translation of the polysemous word is compared against word embeddings of translated surrounding words to calculate word similarity scores and the most similar word to vectors of translated surrounding words is selected as the correct translation. This method only requires a raw corpus and a bilingual dictionary to disambiguate the word under question. The experimental results on a manually-created test dataset demonstrate the accuracy of the proposed method.
An augmented real-world interactive classroom for developing learning numbers and counting skills for preschool children

High quality preschool can have large beneficial effects on educational outcomes, including social, physical, intellectual, cognitive, and emotional development. In this paper, we propose an augmented, real-world interactive learning-environment for preschool students based on Quick Response (QR) codes, mobile technologies, and pedagogical concepts. The proposed application attempts to integrate learners' interactive play with their learning environment. We evaluate our proposed learning method using the Android SDK, which allows preschool children to navigate and count a set of QR tagged learning objects using a one-to-one counting correspondence and cardinal counting principles. Preliminary results provide evidence for potential benefits of an interactive learning experience in an augmented real-world classroom environment for fostering children's number-learning and counting skills in preschool1.
Computational Complexity Analysis of Neural Network-Based Nonlinear Equalization for Short Reach Direct Detection Systems

We analyze the computational complexity of a Mayer neural network (NN)-based nonlinear equalizer for short-reach direct detection (DD) systems. The system bit-error-rate (BER) performance is also investigated using different NNs and receiver bandwidths.
Catalog Model for Experts on Procurement Projects to Avoid Using Experts Fictitiously in Electronic Procurement Systems

In general, government projects require the involvement of experts in certain fields. Usually experts are recruited from lecturers at a particular university, or at certain expertise. Especially in Indonesia, many facts show that the use of experts often still uses the method of borrowing the names of experts, without the involvement of expertise in the project concerned. In this study, we propose the use of catalog models for experts. The catalog model has so far been applied to the goods shopping model that has met certain requirements, both at the regional, institutional and central levels. This item catalog system has proven to be very easy for procurement units to be able to shop for goods easily and simply. To do this, a list of experts was obtained in collaboration with universities and certain expertise associations. Because the lecturer database is managed by Kemristekdikti, this database should be directly referenced by the government procurement electronic system. Likewise the expert roster of certain expertise associations can be used as a database for this catalog system.
Channel prediction based on adaptive structure extreme learning machine for UAV mmWave communications

In unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications, the inter-UAV wireless channel is fast varying because the high mobility of the UAV transmission platform. In such dynamic scenarios, it is very costly to obtain the inter-UAV channel state information (CSI) with the conventional pilot-aided channel estimation. Aiming to address this critical issue, in this paper, we propose a novel adaptive-structure extreme learning machine (ASELM) enabled fast channel predication to obtain the CSI in a proactive fashion, which can further support agile beam-based inter-UAV mmWave communication. In particular, ASELM copes with the channel variations by adaptively adjusting the number of neurons in the hidden-layer of ELM. Moreover, a sliding window prediction mechanism (SWPM) predicts subsequent-CSI by efficiently reuses the predicted concurrent-CSI to train the ASELM, which is able to save the pilot overhead for channel sampling (estimation) towards longer-range channel prediction and improve prediction accuracy at affordable costs. Simulation results show that the proposed ASELM enabled fast channel predication can achieve lower normalized mean square error than traditional prediction algorithm in the considered inter-UAV mmWave communication scenarios.
Multiway Attention for Neural Machine Translation

Neural machine translation (NMT) with source side attention has achieved remarkable performance. Nevertheless, all existing attention mechanisms employ only one attention function. However, several different attention functions have been proposed. They have different mechanisms and capture different information of the sentences, thus a single attention function does not perform well. In the paper, we propose the multiway attention neural machine translation model (MA-NMT) which employs multiple attention functions in the attention mechanism to calculate the weight of each source word when predicting the next target word. Specially, we design three attention functions to get the contextual information. Then we combine the features from all attention function to obtain the final semantic representation. The results of experiments on the English-German translation task demonstrate that the proposed MA-NMT improves the performance than the baseline NMT models.
Prerequisites for formation of intangible (non-material) resources area

The article presents a look at informatization, and then the digitalization of society from the perspective of its original foundation. Previously studied branches of science, representing independent research areas, are the basis of a new direction. Conceptual directions are given, and author's comments on them are presented.
SEMANTIC APPROACH FOR WEB-ROBOT DETECTION

Today modern researches suggest that robotic traffic on web resources prevails over user traffic in terms of volume and intensity. Web robots threaten data privacy, copyright, as well as affect performance, security, and affect statistics. There is a need to develop efficient detection and protection methods against web robots. Existing techniques involve the use of syntactic and analytical processing of web server logs to detect web robots. This article proposes to analyze the graph of visits of web robots, taking into account the time, as well as the connectivity of topics of the visited pages. In the article we provide an algorithm for data selection and cleansing, extracting semantic features of pages on a web resource, as well as the proposed detection parameters. We describe in detail the process of forming the ground truth and the principles of existing sessions labelling to the legit and robotic types. It is proposed to use the capabilities of a web server to identify sessions uniquely. The clustering procedure and the selection of a suitable classification model are discussed. For each of the studied models, the selection of hyper parameters and cross-validation of the results are made. The analysis of performance and detection accuracy, as well as comparison with the results of existing approaches is provided. Empirical results of the proposed method on web-resources show that this method leads to better web robot detection accuracy and precision comparing with the existing approaches.
Video Super-Resolution with Compensation in Feature Extraction

Conventional Convolutional Neural Network (CNN) based video super-resolution (VSR) methods heavily depend on motion compensation. Pixels in input frames are warped according to flow-like information to eliminate inter-frame differences. These methods have to make a trade-off between the distraction caused by spatio-temporal inconsistency and the pixel-wise detail damage caused by compensation. In this paper, we propose a novel video super-resolution method with a dynamic filter network based compensation module and a residual network based SR module. Unlike traditional VSR techniques, our method does not warp input pixels, but performs motion compensation during feature extractions. The experimental results demonstrate that our method outperforms the state-of-the-art VSR algorithms by at least 1.08 dB in terms of PSNR, and recovers more details together with superior visual quality.
Chinese Semantic Role Labeling with Hybrid Model

To this date, most of the Chinese Semantic Role Labeling (SRL) methods based on deep learning still rely on complex feature or architecture engineering, which more or less weaken the generalization ability of model and increase the complexity of this task. In this article, we proposed a deep hybrid model, which is composed of Bi-directional Long Short Term Memory (Bi-LSTM), Self-Attention and Conditional Random Field (CRF), as an end to-end system without using any pre-defined features. We just took the sentences that have been segmented as input, and depended on the competence of model to extract potential context dependency information. After that, we used sentence level tag information via a CRF layer to get the final labeling results. Experimental results on Chinese Proposition Bank (CPB) shows that our model is really effective for this task, which can achieve 81.2% F1 score.