Reviewerly/exHarmony huggingface dataset:
A lock-free algorithm of tree-based reduction for large scale clustering on GPGPU

Recently, the art of concurrency and parallelism has been advanced rapidly. However, conventional techniques still suffer of the drawback of lock contention. To name a few, atomic instruction has relatively low scalability as the number of iterations are increasing. This causes a serious slowdown when programmer cope with large-scale data mining processing such as clustering billions of data with numerous iterations. This paper proposes a Lock-free technique of tree-based reduction for large scale clustering on GPGPU. Proposal method is divided into two steps: fine reduction and coarse reduction. In the first reduction step, the clustering program obtain K * N intermediate array where K is the number of clusters and N is the number of blocks. In the following step, new mean value is calculated over N blocks. By doing this, the clustering program can evade using atomic instruction which causes lock contention in coping with large scale clusters. In experiment, the performance of native GPU kernel with atomic instruction, Thrust template libraries and proposal method is compared and evaluated.
The mineral factory: how to build a giant quartz reef

SummaryGiant quartz reefs can develop as part of a larger scale ‘mineral factory’ within a specific space and time, due to a series of interlinking processes and mechanisms. Ascertaining the criteria for giant quartz reef formation is based on data obtained from multidisciplinary, multi-scale analysis. The results show that a ‘quartz reef window’ is met when the following conditions are optimal (i) accommodation space; (ii) permeability; (iii) significant fluid supply; (iv) considerable Time Integrated Fluid Fluxes; (v) temperature conditions; (vi) SiO2 oversaturation; and (vii) cap rock/seal is present. These elements are key to understanding the evolution of giant quartz reef formations, as well as identifying and targeting these mineral lodes.
Mining Vehicle Failure Consumer Reports for Enhanced Service Efficiency

Troubleshooting a vehicle often requires some form of customer service; to help guide a customer towards a resolution. Consumer reporting of faulty vehicular components can be made via a telephone- based service call, or gathered through telemetry via embedded intelligent transportation systems (ITS). During data transmission, free text is created in the dialogue between the customer and the service representative. While free text is generated, key details of the discussion can be extracted and recorded. This paper describes methods used to process the recorded free text data. This method can help classify and direct the call to the correct channel of support tools and resources. An anonymous customer service report consisting of 75,000 calls was used for feature extraction. Five thousands of the calls were used in supervised learning to support data classifications. The matrix of data was evaluated for accuracy and repeated to minimize error and increase accuracy. By incorporating hand-crafted features using domain expertise, our natural language processing (NLP) based approach can achieve 85 percent accuracy in classifying the service calls.
Evaluation Model of Rural Primary and Middle School Education Informatization Based on Data Sharing

With the continuous development of science and technology, a huge project of China's primary and secondary school education system, which has attracted worldwide attention, is being carried out in Shenzhou at a dizzying speed. In today's information education, how to do a good job of information education in rural primary and secondary schools is particularly important. This paper puts forward an evaluation model of rural primary and secondary school education informatization based on data sharing. According to the mining results of rural primary and secondary school education informatization evaluation information, the frequent itemsets association rules of rural primary and secondary school education informatization evaluation data are reconstructed, the information of rural primary and secondary school education informatization is extracted, the process optimization control method is used to evaluate the rural primary and secondary school education informatization, and the big data analysis model of rural primary and secondary school education informatization evaluation is constructed combined with descriptive statistical analysis method. The regional linear statistics and process optimization scheduling method are used to realize the evaluation and data sharing design of rural primary and secondary school education informatization. The empirical analysis results show that this method has the advantages of high accuracy, good adaptability, high confidence level, accurate and reliable evaluation results, and improves the evaluation ability of rural primary and secondary school education informatization.
A Remote Controllable Supporting Device for Deaf-blind Person using Handwritten Character

"Handwritten characters" are a means of communication with deaf--blind person. "Handwritten characters" are written by caregivers with fingers on the palm of a deaf--blind person. "Handwritten characters" have the feature of being easy to learn because a person can recognize hiragana and katakana intuitively. Many deaf--blind person can communicate using this method. However, because the caregiver must stay by the person's side, it imposes a heavy burden. For this study, we developed a support device that is able to recreate handwritten characters remotely without the assistance of a caregiver. The system consists of a mobile terminal on the input side, a server, and an output device. On the input side, characters are input using a mobile terminal on which a dedicated application is mounted. Applications are supported by Windows OS (Microsoft Corp.) and Android OS (Alphabet Inc.). Users have two modes of inputting characters: keyboard input and writing characters directly one by one. For keyboard input, a user can send more than one character at a time. The server uses Raspberry Pi. When the server receives character data via the network, it transmits character coordinates to the output device based on the data. In addition, a character file that records the coordinates of the character is stored in advance. When writing characters directly, the received data are converted to a format that can be read out by the output device. Then the data are transmitted to the output device. For keyboard input, the sent character strings are decomposed one character at a time. For the decomposed characters, the coordinate data are read out from the character file corresponding to each character and are transmitted to the output device. The output device performs a process of reproducing the handwritten character to the deaf--blind person. We used software (Makeblock mDrawBot; Makeblock Co., Ltd.) embedded in the device, which performs character output using coordinate data. This output device, which operates based on the coordinate data transmitted from the server, is connected to the server via a wire. Makeblock mDrawbot is plotter software that draws based on X and Y coordinates. We are studying the mechanism of adding Z coordinates so that it can be drawn according to the hand position. Development of this device demonstrated that the input characters were output correctly. Additionally, it reduces the labor of writing directly. This device can reduce burdens on caregivers.
Comprehensive Survey of Algorithms for Sentiment Analysis

The growth of the web results with a wider increase in online communications. These online communications include reviews, comments and feedbacks that are posted online by the internet users. It is important to discover and analyse their opinion for a better decision making. These opinionated data are analysed using sentiment analysis. Because of its importance in numerous areas, sentiment analysis was adopted as a subject of increasing research interest in the recent years. It is a technique adopted to extract the useful information and identify the user views either as positive or negative. This paper develops a survey on various approaches used in sentiment analysis and a comparative study has also been made along with the elucidation of recent research trends in the sentiment analysis.
Quantum Automated Object Detection Algorithm

Automated object detection algorithm is an important research challenge in many applications such as computer vision and urban surveillance systems. Recently, quantum computing and information processing become emerging technologies which attract researchers' increasing attention and some work has been attempted in image detection using quantum computing approach. In this paper, we propose a novel quantum automated object detection algorithm for urban surveillance systems. This novel quantum based method is shown to be capable of detecting an object in images accurately and is able to deal with the measurement errors caused by the quantum measurements in the image retrieving process.
An Anti-fraud Framework for Medical Insurance Based on Deep Learning

Given rising medical costs, medical expense control has become an important task in the healthcare domain. To solve the shortage of medical reimbursement mechanisms based on medical service items, single-disease payment models have been extensively studied. However, the approach of payment via a single-disease model is also flawed, and fraud may occur. Herein, we present an anti-fraud framework for medical insurance based on deep learning to automatically identify suspicious medical records, ensure the effective implementation of single-disease charges, and reduce the workload of medical insurance auditors. The framework first predicts the probabilities of diseases according to patients’ chief complaints and then evaluates whether the disease codes written in medical records are reasonable via the predicted probabilities; finally, medical records with unreasonable disease codes are selected as abnormal cases for manual auditing. We conduct experiments on a real-world dataset from a large hospital and demonstrate that our model can play an effective role in anti-fraud for medical insurance.
Topology Inference and Signal Representation Using Dictionary Learning

This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is no information about the transform domain. Five performance measures are used to compare JGLSR with two conventional algorithms and show its higher performance.
CARTOONNET: Caricature Recognition of Public Figures

Recognizing faces in the cartoon domain is a challenging problem since the facial features of cartoon caricatures of the same class vary a lot from each other. The aim of this project is to develop a system for recognizing cartoon caricatures of public figures. The proposed approach is based on the Deep Convolutional Neural Networks (DCNN) for extracting representations. The model is trained on both real and cartoon domain representations of a given public figure, in order to compensate the variations in the same class. The IIIT-CFW (Mishra et al., European conference on computer vision, 2016) [] dataset, which includes caricatures of public figures, is used for the experiments. It is seen from these experiments that improving the performance of the model can be achieved when it is trained on representations from both real and cartoon images of the given public figure. For a total of 86 different classes, an overall accuracy of 79.65% is achieved with this model.
Assessing the Readability of Vietnamese Texts Through Comparison (Extended Abstract)

Text readability is a measure of how easy/difficult a text is to read. Studies on readability have been noticed for a long time in English and other popular languages. In Vietnamese, studies on the text readability are still quite limited, mainly because of the lack of corpora with capacity large enough to carry out the examination. In this paper, we present a method for assessing the readability of Vietnamese texts that does not need too much cost to develop a training corpus, based on comparing the correlation difficulty between texts but still achieving positive results.
Node Criticality Assessment in a Blockchain Network

Blockchain systems are being rapidly integrated in various technologies, with limited work on the effect of the underlying network topology on the blockchain performance. In this work, we investigate the significance of each network node on the overall blockchain performance. This is assessed by selecting critical nodes according to different criticality metrics, and investigating, using simulations, the degradation in performance incurred upon removing these nodes. The most critical nodes are the ones that incur the greatest degradation in performance. The considered performance metrics are the blockchain size and the packet drop rate. Criticality metrics such as Betweennes Centrality, Closeness Centrality and Degree Centrality are compared. It is found that the Sign Change Spectral Partitioning approach, enhanced with Blockchain Specific traffic flow information, is able to identify critical nodes better in the sense that higher degradation in performance is reported upon their removal.
Petrographic investigation and geochemical survey of rocks in Inteet area (Nile River State, Sudan)

Abstract The aim of the work is to estimate the Inteet area for search for gold mineralization. This area is located between Umm Trambishi and Alsalam area in Nile River State, Sudan. Surrounding territory is a gold-mining region, in which there are known deposits with estimated resources, as well as promising places where local prospecting is carried out. For a preliminary assessment of the prospects of the site is necessary to conduct geological and geochemical studies. Evaluation of the geological environment was carried out both during field work and with a petrographic microscope. 11 thin sections of rock samples have been prepared and petrographically investigated under polarized microscope the mineral compositions, textures, microstructures, degree of alteration and metamorphism have been studied. Geochemical studies were conducted on 32 samples from 25 sampling points. The main results of petrographic investigation and geochemical survey show the presence of hydrothermal alteration zones, which are associated with a high content of elements-indicators of gold mineralization. In general, the territory can be assessed as promising, but requiring further exploration and assessment work.
A Moving Human Detection and Tracking using Combination of HOG and Color Histogram

Detection and tracking of a moving target in a crowded area using camera surveillance is still an open topic that to be resolved. Determining a single target instance known among other objects in the room becomes a problem. We propose a color-based human tracking algorithm by considering the correlation distance from the color histogram used as the target selection. Furthermore, we also create a new dataset based on the INRIA dataset to minimize the detection area. Then, we apply the HOG extraction feature to the human detection system. We apply the Recall-Precision curve to evaluate the performance. The accuracy and precision rate has increased by 10 times. Experimental results are also provided in performance metrics to prove the reliability of our proposed method.
Performance analysis of chinese cursive character recognition based on convolutional neural network

Chinese cursive characters written in old books are more difficult to recognize than other Chinese Characters such as handwritten Chinese character because they have many various styles. For this reason, it needs a software-based recognition model or service that can recognize Chinese cursive character more easily to assist and support many related researchers. In this paper, we will show the performance analysis results of Chinese cursive character recognition based on Convolutional Neural Network (CNN). In the performance evaluation, we used a dataset collected directly from many old books. Data augmentation technique was applied because our dataset is relatively small dataset when compared to other deep learning datasets. Our optimized recognition model showed about 91.6% recognition accuracy.
Using VR to Simulate Interactable AR Storytelling

This paper describes a system that simulates location based AR storytelling in a VR environment and explores how VR simulation might be used as a prototyping and user-testing tool. The system is currently developed and used for testing possible future versions of local news distribution with AR devices- However it can be used as a more general tool for testing what, where and how to augment reality in other contexts.
HPM-FSI: A High-Performance Algorithm for Mining Frequent Significance Itemsets

In the traditional frequent itemsets mining on transactional databases, which items have no weight (equal weight, as equal to 1). However, in real world applications are often each item has a different weight (the importance/significance of each item). Therefore, we need to mining weighted/significance itemsets on transactional databases. In this paper, we propose a high-performance algorithm called HPM-FSI for mining frequent significance itemsets based on approach NOT satisfy the downward closure property (a great challenge). The experimental results show that the proposed algorithms perform better than other existing algorithms on both real-life and synthetic datasets.
Data Interoperability

This poster provides an overview of an initial effort to examine the development of data interoperability from an infrastructural perspective applied to two geoscience examples, the International Geosample Number (IGSN) and EarthCube. Both activities represent efforts to develop data interoperability as part of computer supported cooperative work. In the case of the IGSN, the data interoperability provided by the cyberinfrastructure is arguably reaching the point of becoming a substrate for scientists. In the case of EarthCube, data interoperability through the envisaged cyberinfrastructure remains elusive. Recent themes in CSCW literature examine questions of infrastructure and infrastructuring [17]. A goal of this poster is to illustrate the potential to use analysis of geoscience infrastructure to add to these discussions. The relevance of data interoperability is significant given the large amounts of funding and effort being put towards this goal and the potential for data interoperability to foster new scientific insights. Finally, examining this topic is even more salient given the recent emphasis on "FAIR data."
Quality assessment framework for video contextualisation of personal videos

The world is witnessing rapid transformations in hardware technology. This will keep on improving day by day. The processing power of every handheld device is significantly improved as well as the storage capacity is increased. With all these advancements, the personal video capture and usage of videos have tremendously increased across many applications. The quality assessment of personal videos has become very important. It is a vital task to design a model for assessing the video quality. A novel methodology for detecting damaged video frames is proposed here. The primary objective of the research is to detect uni-coloured frames and frames with ice effect. The novel histograms bin comparison technique is proposed for inter- and intraframe analysis. The shakiness of the video is calculated using motion estimation. The video quality is also assessed using blur detection as well as contrast calculation to spot useful portion in the video. The proposed framework generates the video quality metadata and supports the contextualisation process.
A 3D Key for Encrypting 2D Images - A DNA Melded Chaotic Approach

The data security assumes a noteworthy job when transmitting the enormous measure of information by means of the web. Encryption assumes a huge task in keeping data unassailable. In this work, an image encryption depending on 3D chaotic map is proposed. Three chaotic sequences are generated from 3D chaotic map to perform confusion, diffusion, DNA encoding, and DNA decoding. The effectiveness of the suggested algorithm are examined with several specifically, statistical attack, key sensitivity, key space, chosen plain text and known plain text attacks. The developed encryption algorithm is performed on 256 × 256 grey scale image and has achieved entropy equivalent to 7.9973 and correlation closer to zero. Further, the execution of proposed algorithm is contrasted and compared with the state-of-art methods to prove the algorithm's strength.
Users Intention Based on Twitter Features Using Text Analytics

Online Social networks are widely used in current times. In this paper, we investigate twitter posts to identify features that feed in intention mining calculation. The posts features are divided into three sets: tweets textual features, users features, and network contextual features. In this paper, our focus is on tweets analysing textual features. As a result of this paper, we were able to create intentions profiles for 2960 users based on textual features. The prediction accuracy of three classifiers was compared for the data set, using ten intention categories to test the features. The best accuracy was achieved for SVM classifier. In the future, we plan to include user and network contextual features aiming at improving the prediction accuracy.
Economic Growth, Policy and Income Distribution

One of the major realizations among development economists has been that the dual goals of economic growth and economic equity may not be compatible. The Economic Structure of a country may tend to concentrate income in a few hands. This may have to do with the ownership of property and wealth, location of specific resources such as minerals. Direct governmental economic activity can have an impact upon inequality. Most studies of fiscal activities show that the exert only mild redistributive pressures within most economies. One of the primary factors associated with income distribution is the quantity and quality of human resources. Economists and policy makers have already declared income distribution to be a major concern in developing countries, occupying a status similar to economic growth. The role of small enterprises in economic growth and development is little understood.