Reviewerly/exHarmony huggingface dataset:
Collaborative Representation Guided Graph Learning for Visual Classification

Graph construction plays a crucial role in graph learning, and a good graph should well reveal the real similarities of data. Recently, Collaborative Representation (CR) is leveraged to construct a higher quality graph called L_2-graph instead of the conventional Euclidean distance-based ones. Such L_2-graph enjoys many merits of CR and can give a smooth encoding of data. Inspired by its remarkable successes in clustering and subspace learning, we attempts to bring such advantages towards to the graph-based classifier. We follow the Graph Transduction (GT) framework to present a novel semi-supervised graph-based classifier called L_2-Graph Transduction (L_2GT). Moreover, since the graph cut in GT can be deemed as the predicted labels associated with different categories, we put a linear mapping between the visual feature space and such predicted label space to inductively derive a purely supervised graph-based classifier called L_2-Graph Inductive Classifier (L_2GIC). Four popular image databases, namely, ORL, AR, JAFFE and Caltech256, are used for evaluating the proposed classifiers. Experimental results demonstrate the superiorities of these classifiers in comparison with some state-of-the-art graph-based classifiers.
Results and Lessons of the Question Answering Track at CLEF

The Question Answering track at CLEF ran for 13 years, from 2003 until 2015. Along these years, many different tasks, resources and evaluation methodologies were developed. We divide the CLEF Question Answering campaigns into four eras: (1) Ungrouped mainly Factoid question factoid questions asked against monolingual newspapers (2003–2006), (2) Grouped questions asked against newspapers and Wikipedia Wikipedias (2007–2008), (3) Ungrouped questions against multilingual parallel-aligned EU legislative documents (2009–2010), and (4) Questions about a single document using a related document collection as background information (2011–2015). We provide the description and the main results for each of these eras, together with the pilot exercises and other Question Answering tasks that ran in CLEF. Finally, we conclude with some of the lessons learnt along these years.
Training Platform for Continuous Casting Based on Multi-Model Interactions

Abstract Currently, the human-computer interaction technology, as the core technology of virtual reality, has been developing rapidly. By analyzing the advantages and disadvantages of the mainstream interactive equipment used in today’s market and combining the advantages of the equipment, this paper posed a continuous casting training platform with multiple interactive modes. In this platform, the keyboard and mouse interaction mode, contact interaction mode and VR device interaction mode could be simultaneously applied to continuous casting training, which enables to give play to the characteristics of different interaction modes. Comparing with the single interaction mode, it proved that this platform would improve the efficiency of user interaction and enhance the training effect, as well as to propose a reference for human-computer interaction mode.
Video question answering by frame attention

In recent years, Visual Question Answering (VisualQA) has gradually become one of the research hotspots of video understanding, but most of the researches are mainly focused on Image Question Answering (ImageQA), while fewer researches pay attention to Video Question Answering (VideoQA). Inspired by the ImageQA model, we propose a model, which utilizes videos and questions to generate answers. We also redesign and simplify the Joint Sequence Fusion (JSFusion) model for our soft-attention mechanism called Frame Attention which can refines its attention on the frame object with the help of questions. Frame Attention first fused the multi-modal features by the Hadamard product, and then generated attention probability by encoding. In addition, a new training strategy for the ZJL dataset is also proposed, and can take full advantage of all the answers of the questions for training. Experiments show the advantages of our model and accuracy of 0.509 is achieved.
Graph coloring for determining angklung distribution

Abstract Angklung is a traditional instrument originated from West Java, Indonesia. It is played by shaking it and each angklung can only produce one note. A set of angklungs are needed in order to produce a whole song. An angklung player can hold no more than seven angklungs because of the size of the instrument. A player also cannot hold two overlapping angklungs since shaking more than one angklung at the same time is nearly impossible. These restrictions create problems that can be modeled as mathematical problem. It is called the angklung distribution problem, which is the problem of distributing angklungs to each player based on a given song so that no player holds two or more overlapping angklungs with the least possible number of players. In this paper, we model this problem into a graph theoretical problem where we use the concept of graph coloring to solve this problem. We also develop a method to lower the number of players suggested from the graph coloring by viewing the song as an array of symbols.
Data Clustering Using Variational Learning of Finite Scaled Dirichlet Mixture Models with Component Splitting

We have developed a variational learning approach for finite Scaled Dirichlet mixture model with local model selection framework. By gradually splitting the components, our model is able to reach convergence as well as obtain the optimal number of clusters. By tackling real life challenging problems including spam detection and object clustering, the proposed model’s flexibility and performance are validated.
The Pressure Control System for Tea Rolling Based on Fuzzy Control

Rolling is a key procedure in the tea primary processing. The appearance and quality of the final tea produced depends largely on the rolling procedure. The pressure control of tea rolling machine is an issue that often plagues the tea processing enterprises. To solve this problem, a method based on fuzzy control technology has been proposed here. Fuzzy control techniques are used to control the pressure of rolling in this paper. Compared with the traditional PID control method, the pressure control system using fuzzy control has the characteristics of short time to achieve stability and small overshoot. The rolling pressure control system based on fuzzy control proposed in this paper promotes the intelligent control of tea rolling machine.
Handsfree for Web

The predominance of exclusively manual interaction required when interacting with a browser and websites is presented as a restrictive condition. This limitation was the starting point and the main motivation for the development of a web navigation model and a tool that can be used to browse the web via voice commands. This tool is proposed as a hands-free solution for web navigation, it provides new interaction mechanisms and new ways of accessing web content.
A Fast Approach for Text Region Detection from Images on Online Social Media

Microblogging sites are very popular sources of real-time information, which includes both textual information, images, and videos. Since individual posts on such sites are very small, they can convey only a small amount of information. Hence, in situations like an ongoing emergency or disaster, users wishing to share a large amount of information often resort to including the text in an image and then sharing the image. Utilizing such textual information within images requiresLayek, Ashish Kumar a text-detection mechanism that not only needs to be accurate, but also very fast in order to process the hundreds of imagesMandal, Sekhar posted on social media in real-time. In this work, we propose such a text-detection algorithm from images. Experiments over images postedGhosh, Saptarshi on Twitter during a recent disaster event show that the proposed method achieves competitive accuracy with a state-of-the-art method, while being much faster.
PREANALYTICAL INFLUENCES OF TIME LAPSE AND TYPE OF SAMPLE ON INTACT PTH ASSAYS OF PLASMA AND SERUM ON ADVIA CENTAUR XPT IMMUNOASSAY ANALYSER

BACKGROUNDMeasurement of PTH is important in metabolic bone disorders.We conducted a prospective study for a month on the preanalytical influences of time lapse and type of sample on intact PTH assays of plasma and serum.We wanted to study the preanalytical influences of time lapse and type of sample on Intact PTH Assays of Plasma and Serum on ADVIA Centaur XPT immunoassay analyser. METHODSThis prospective study was conducted in the laboratory of Department of Biochemistry, Osmania Medical College, Hyderabad, for a period of one month in December 2018.Venous blood samples of patients attending General Medicine OPD were included in the study.Venous blood samples from the patients attending OPD were collected from 20 patients and each patient provided both serum and plasma in two separate tubes and at centrifugation; ten specimens were labeled as Cold and another ten as Room Temperature.The Cold set (Groups B & D) are immediately placed on ice and centrifuged at 4 °C and separated in to four aliquots for measurement at different timings and stored at 4°C till assayed.The second set (A & C) specimens were treated in same manner at room temperature.Total number of samples, 160 aliquots (N=10 X 4 X 4=160) were assayed [baseline hr, 24 hr, 48 hr, and 72 hr] on ADVIA Centaur XPT © Siemens immunoassay analyser. RESULTSPTH values from plasma samples were higher than from corresponding serum, irrespective of temperature effect (p < 0.001).Time had no significant effect on PTH measurements in EDTA sample both at RT and Cold.At room temperature, serum iPTH was declining with the time lapse by 54%. CONCLUSIONSPreanalytical issues can influence the PTH and affect the clinical decisions; hence laboratories need to set preanalytical protocols.
A hybrid FCM-VIKOR approach to family economic policy-making

Purpose This paper aims to present a systematic methodology to study different economic labor policies and their impacts on women and families. As women entered into the labor market, the traditional division of family labor vanished. Now, families need to make the best decision to both improve the family economy and enhance family's main functions. In addition, the government is responsible toward the consequences of the family policies. Design/methodology/approach The content analysis, fuzzy cognitive map, scenario-planning and vlse kriterijumsk optimizacija kompromisno resenje (VIKOR) have been combined to deal with the studied problem. As a case study, the focus has been on the Iranian family. According to the developed methodology, different family-oriented policies have been simulated and their results are analyzed. Findings Findings show, considering the effects that the division of the couples’ labor has on meeting the material/non-material family needs, the best policy is to support women's home-based businesses. This way, the economic factors will be improved, the couples’ dependence on meeting their needs will be more favorably affected and the family unity will be strengthened. Originality/value In this study, “family” has been analyzed as a single socioeconomic system. Never have the family economic studies been analyzed with a systematic approach by considering all the economic and non-economic factors together. This objective can be realized by applying the methodology proposed in this research because it can help to predict the consequences of any policy toward the family and provide a platform for proposing better policies and making the related decisions in this area.
Application of an improved regularized super resolution reconstruction method in infrared detection system

Super resolution reconstruction is a feasible technique to improve the image resolution without changing the existing infrared detection equipment. The original image usually contains noise, the traditional regularized super resolution method has the contradiction between restraining noise diffusion and protecting image details in the process of image reconstruction. In order to reduce the loss of reconstructed image information caused by noise suppression in the reconstruction process, this paper studied the reason of noise diffusion in original sequence image through image reconstruction model, proposed a method based on Lee's regularized method using Huber Markov random field as prior model in image reconstruction process, Experiment results show that the proposed method improved the images obtained by the infrared detection system, is superior to traditional method and Lee method in subjective visual and objective indicators, can meet the practical detection requirements.
Applications Research of Wireless Video Transmission Technology in Security Monitoring

Abstract With the development of monitoring industry from full analog monitoring to full digital monitoring system, monitoring transmission technology has also experienced from closed-circuit television transmission to lP wired network transmission. The security monitoring system based on wireless video is intuitive in form and rich in content. Wireless video intelligent monitoring and control part and display and recording part are applied in many places of society. Through the control operation of the camera host, the image captured by the camera is collected and sorted, and the image is input, stored and replayed through specific processing mode. Moreover, video intelligent analysis technology can greatly improve the efficiency of security monitoring and realize the unattended automatic security monitoring system. Gradually moving towards digital network development, the efficiency has been greatly improved and the security has been greatly enhanced. As an indispensable part of digital security system, wireless video transmission technology has become a powerful tool for modern management.
Natural Language Explanations of Classifier Behavior

Tools that enhance interpretability of classifiers tend to focus on the knowledgeable data scientist. Here we propose techniques that generate textual explanations of the internal behavior of a given classifier, aiming at less technically proficient users of machine learning resources. Our approach has been positively evaluated by a group of users who received its textual output.
The identifications of the availability of facilities and infrastructure at the temporary evacuation of Padang

Abstract The building of Temporary Evacuation Sites (shelter tsunami) has been done in West Sumatra. One of them is the Padang shelter, located in North Ulak Karang Village, Padang. The location has met the requirement of PEMA 646. However, the facilities and infrastructure are still insufficient. Consequently, this study is conducted to inventory, analyze and recommended the facilities and infrastructure so that the mitigation of tsunami disaster will be optimal. The method used is descriptive evaluative, method by doing field observations. The data in the field is tabulated and analysed based on Regulation of Public Works No 19 / PRT / M / 2001, Indonesian National Standard number 7766: 2012, and 7743: 2011. The results of the study show that the early warning facility (EWS) has functioned well, the logistics room, medical room, public kitchen, and warehouse have not got the equipment yet; clean water and electricity have not functioned. There are 22 evacuation road meeting the wide requirement of environment streets out of 71 existing streets, evacuation signs on 9 streets. To cope with tsunami disaster risk optimally, Padang shelter needs to be equipped with public facilities, additional evacuation maps, evacuation signs, and the improvement of evacuation streets to Padang shelter.
Random Inception Module and Its Parallel Implementation

Inception module is proposed in GoogLeNet, which improves performance by increasing the width of the network. Multiple branches are computed in parallel, which makes the inception module naturally take the advantage of GPU high-performance computing. In this paper, we propose a parallel implementation of the inception module to accelerate the training and test of the inception networks. However, convolution neural networks are prone to overfitting due to the huge amount of parameters. We propose random inception module to avoid overfitting and accelerate inception module. In order to demonstrate the effectiveness of the proposed methods, we compare the performance of the random inception module with original inception module on CIFAR-10 dataset. Experimental results show our parallel inception module obtains over 2.8 $$\times $$ speedup compared with Caffe. And our proposed RIM indeed behaves in a manner of regularization and speeds up convergence.
Automatic Vulnerability Identification and Security Installation with Type Checking for Source Code

Cyber security has been an important issue for control systems, however, there may be a shortage of security experts in the near future. Most of security engineering methods focus only at high-level such as architectures, and do not consider the availability of the control system. In addition, it is often difficult use them without security expertise. In this paper, the author proposed a novel method for solving this problem. The method automatically identifies vulnerabilities in a source code using a formal and static method (type-checking), and fixes them by installing security functions accordingly. Since this approach is entirely automated, it can be seamlessly used by regular engineers, i.e. without security expertise. The authors implemented the method and experimentally investigated its effectiveness on a simple example including the characteristics typical of communicating control systems.
A Research of Variable Selection Method within A Framework of Real-coded Genetic Algorithm

Recently variable selection and parameter optimization are getting more and more important. Regarding parameter optimization, much attention has been paid to Real-coded Genetic Algorithms (RCGA) because of their good searching ability and high flexibility. As for variable selection, traditionally Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) are used quite often as selection criteria. These criteria estimate the relative quality of analysis models for a given set of data, but do not evaluate the importance of the variables themselves. This paper proposes a new variable selection method applying RCGA. This new variable selection method consists of 2 main components. The one is a new variable selection criterion utilizing the variances of genes in RCGA and the other is an estimation method of how far is in progress of RCGA optimization. The effectiveness of this new variable selection method is confirmed through application to a structural change model, which is one of discontinuous models.
Application of Clustering Algorithms for Discovering Bug Patterns in JavaScript Software

Applications developed with JavaScript language are increasing every day, not only for client-side, but also for server-side and for mobile devices. In this context, the existence of tools to identify faults is fundamental in order to assist developers during the evolution of their applications. Different tools and approaches have been proposed over the years, however they have limitations to evolve over time, becoming obsolete quickly. The reason for this is the use of a fixed list of pre-defined faults that are searched in the code. The BugAID tool implements a semiautomatic strategy for discovering bug patterns by grouping the changes made during the project development. The objective of this work is to contribute to the BugAID tool, extending this tool with improvements in the extraction of characteristics to be used by the clustering algorithm. The extended module of the BugAID extraction module (BE) that extracts the characteristics is called BE+. Additionally, an evaluation of the clustering algorithms used for discovering fault patterns in JavaScript software is performed. The results show that the DBScan and Optics algorithms with BE+ presented the best results for the Rand, Jaccard and Adjusted Rand indexes, while HDBScan with BE and BE+ presented the worst result.
PECULIARITIES OF DIGITALIZED LEARNING PROCESS ORGANISATION IN THE LEARNING MANAGEMENT SYSTEMS

The author reviews the peculiarities of digitalized learning process organisation in the hardware and software environment of learning management systems in the context of vocational education and training. The author notes the complexity of the transition to the Industry 4.0 and mentions the risks connected with the named process in the vocational education and training. In such conditions it is important to develop effective models of public and private partnership, to decentralize administration and financing and, above all, to assure the vocational education and training quality.At the same time the learning process taking place in such an environment becomes digitalized, which, on the one hand, gives it additional advantages (easy access to training data regardless of time and place, individualisation of students’ learning paths, broad multimedia resources, etc.) and, on the other hand, requires solving complex tasks in distance courses designing. The author focuses on the methodology of distance courses designing, so it is very important for vocational education and training institution to ensure the realisation of the following five main components: regulatory, financial, technical, personnel, teaching and methodological support. It is emphasised that distance course designing becomes the most important aspect of implementing the modern digitalized learning process. The structure of creative group in distance courses designing is grounded, which comprises: a specialist in the relevant vocational sphere, specialist in distance learning, technical specialist (system administrator or distance courses programmer) and digital content designer.
Augmented Reality (AR) for Utility Infrastructure: An Experiential Development Workflow

The process and product development phase of the research instrument for experiential action research is crucial in the success of the research. Due to time, space and resource limitations, fewer studies have concentrated on this development process. In this respect, research on Augmented Reality (AR) in the Architecture, Engineering and Construction (AEC) industry is no exception. This is more evident in subsurface, urban utilities and infrastructure sector. Furthermore, a limited number of studies on AR/VR have utilized mobile devices as their enabling technologies. This paper sets out to contribute to the state-of-the-art in AR research for urban utilities and infrastructure by outlining a generic procedural workflow to be used for designing AR experiments for experiential research in this area. Given the fact that workflow development research in AR is still limited, this research presents a unique contribution in this area to date.
Super-Resolution Reconstruction of Fine-Grained Fittings Image of Transmission Line Based on Compressed Sensing

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. Aiming at the low resolution problem of fine-grained fittings image of trans-mission line, an image super-resolution reconstruction algorithm based on compressed sensing is proposed. The K-SVD (K-Singular Value Decomposition) algorithm is used to implement sparse representation according to the theory of compressed sensing. The reconstruction is performed by OMP (Orthogonal Matching Pursuit) algorithm. The proposed algorithm has good de-noising effect and shortened processing time. The fine-grained fit-tings image that has correlation with the reconstructed image is trained to enhance the reconstruction effect and is used for high-quality recovery of the fine-grained fittings image of the transmission line. The simulation results verify the effectiveness of the proposed algorithm, and the reconstructed image has a better improvement in subjective visual effects and objective evaluation indicators.