Reviewerly/exHarmony huggingface dataset:
Selection Issues of Kernel Function and Its Parameters of Hard Margin Support Vector Machine in a Real-World Handwriting Device

We investigate the characteristics of hard margin support vector machine using a real-world handwriting data. The number of support vectors and distance of margin are two investigated objectives in this work. The structural risk of support vector machine is influenced by the distance of margin that is decided by settings of the kernel function and it parameter. We found that number of support vectors are as the same when the parameter of kernel functions are changed, but the distance of margin is highly influenced by the parameter of kernel functions, which is related to the structural risk of established support vector machine. The handwriting data are not always mixed together in their original representation space. Some of them are linear separable, but some of them are not. We should design a group of classifiers to handle this real-world handwriting application by considering these characteristics and the primary discoveries of this work.
A Graph-Based Approach for Semantic Medical Search

Arguably one of the biggest breakthroughs in medical domain was the widespread adoption of medical search technologies. However, many traditional information retrieval technologies are performed poorly when they are employed in medical domain. This paper present a new approach with the goal of advancing the state-of-the-art in medical information retrieval by placing the question of document ranking in medical domain of the broader question of query-document relevance. This is achieved by two factors: query understanding and scoring the document importance. For query understanding, we focus on understand query intentions by semantic information (concepts and relations between them) from queries. For document importance, we propose a novel strategy is build the semantic linkages between concepts distributed in target documents and reference documents that considered not only the aspect importance but also the aspect similarity. The final ranking list is produced by integrate above two factors. Finally, we present a detailed performance analysis of our approach in comparison to existing models for medical search, and the experimental results show that our approach is greatly improve rank accuracy.
Detecting the position of the sealring and the edge on the sensor chip

ALICE (A Large Ion Collider Experiment) is one of the physics projects developed by CERN. The project aims to observe the results of collisions between proton and proton, proton and nucleus, and nucleus with nucleus. Thousands of sensor chips are used to record the collision trajectory. Visual inspection with digital image processing has been developed to analyze the conditions of cutting the sensor chip, using the Hough Transform, Edge Detection, and Template Matching methods. The distance from the edge to the sealring chip will be used as a reference to determine how the condition of chip cutting. The results obtained are quite good and give a support for the next research process.
A Moderately Deep Convolutional Neural Network for Relation Extraction

Relation extraction in text data is considered as an important task in the field of natural language processing. So far, distant supervision is widely adopted in relation extraction to get labeled data. However, such a method is often lack of semantic information, and thus may bring wrong labelling problem. In this paper, a moderately deep convolutional neural network (CNN) is proposed to tackle the difficulty in relation extraction. The proposed CNN integrates low-level features of text sentences with high-level ones. The proposed CNN-based model has been evaluated on the NYT freebase larger dataset and the results show that our model is superior to the popular models such as CNN+ATT, PCNN+ATT, and ResCNN-9.
HD 180 FPS FPGA Processor to Generate Depth Image with Clear Object Boundary

This paper proposes a novel fast method of depth image generation with super-pixels (SPs) that introduces a matching algorithm on SP basis, one-way check using only unidirectional disparities, and a cost filter on SP basis. In architecture design, an SP segmentation circuit that performs four iterations for whole image in one pass with four processing elements (PEs) is introduced. An SP-based stereo matching circuit is configured by serially connecting the same number of PEs as the maximum disparity, and performs parallel matching of all disparities for a left pixel, while suppressing memory access of the right pixel to 1 pixel / cycle. We implemented an FPGA processor with OpenCL and achieved high throughput of HD 180 fps with a disparity range of 192 pixels.
Baby Touch: Quantifying Visual-Haptic Exploratory Behaviors in Infants of Sensory-Motor Development

We propose a method to quantify an infant’s exploratory behavior by taking a video of his/her actions and analyzing them. Exploratory behaviors of infants are known to be necessary for the development of cognitive functions and language acquisition. Multiple studies on exploratory behaviors have been conducted; however, exploratory behaviors of infants have been commonly classified and quantified manually, requiring ample efforts. Advances in computer vision research using machine learning in recent years have made it possible to automatically analyze captured videos and register movements of the human body. In this study, we developed a measurement system that enables the quantification of exploratory behaviors of infants by combining OpenPose, OpenFace, and a computer vision library. First, we created a video-capturing environment suitable for capturing an infant’s behavior. Second, we integrated the computer vision library to analyze infants fixating on and touching objects placed in front of them. As a result, we are now able to quantify some aspects of infants’ exploratory behavior. Our measurement system will be useful for investigating the exploratory behavior of six to fifteen-month old children using visual-haptic modalities, and also it will also be valuable in comprehending the developmental stages of each child.
Accuracy of Data Fusion: Interval (and Fuzzy) Case

The more information we have about a quantity, the more accurately we can estimate this quantity. In particular, if we have several estimates of the same quantity, we can fuse them into a single more accurate estimate. What is the accuracy of this estimate? The corresponding formulas are known for the case of probabilistic uncertainty. In this paper, we provide similar formulas for the cases of interval and fuzzy uncertainty.
A Predictive Control Solution for Contingency Motion Planning for Autonomous Vehicle

With a model-based prediction control approach to generate optimal trajectory and avoid collision in many situations that cannot perform emergency braking, this paper proposes a solution to optimal path planning and safety assurance to avoid collision for autonomous vehicle by identifying the possible motion trajectories of the vehicles ahead when participating in traffic. The safety factor to avoid collision is performed in each motion phase of the vehicle, with the development of a nonlinear constraint between the brake and the steering angle for the vehicle's contingency motion planning. The efficiency of this solution is evaluated through assumpted simulations with different scenarios, then there are orientations toward applied research on the problem of autonomous vehicles in reality.
Joint index and cache technique for improving the effectiveness of a similar image search in big data framework

Nowadays, the exponentially increasing amount of digital images available imposes a great challenge to a content-based image retrieval (CBIR) system due to the requirement of extensive-computing. Considering this challenge, this paper presents an approach to achieve effectiveness and scalability of a CBIR system in a large-scale dataset. To do that, we propose a cache mechanism to spare the distance computation efforts of a retrieval task in the CBIR system. Additionally, a MapReduce technique is presented to exploit the cached data in a parallel facility, thereby not only improving the performance of a CBIR system but also ensuring scalability for the system. Additionally, a collaborative caching service has been introduced for enhancing the data availability, thus decreasing the network traffic load due to fetching data remotely in the distributed environment. Moreover, by clustering the dataset before a search, this system can be efficient at responding to a user query since only a portion of the dataset is actually operated at a time. Through experiments, our approach obtains significant efficiency gains compared to other methods in terms of response time and achieves an acceptable accuracy ratio, which is applicable in the practical environment.
Nick Monroe – Stalks Resisting Hate with Lies

At Resisting Hate we are very used to people writing silly articles about us. We see lies posted about us every day. We see our names linked incorrectly ...
Scientific Experimental Data Representation Standard through Knowledge Metadata Representation Model

The growth of data from scientific experiments is increasing nowadays. These data came from different experiments done through various laboratory instruments or machines. It became an issue to manage and analyse scientific experimental data because of the heterogeneous nature of data structure and format. This paper proposed a knowledge metadata representation model to standardise the scientific experimental data representation to make it a standard structure. We discussed the methodology of the proposed model and gives the analysis of results. The evaluation and validation of the knowledge metadata representation model, as well as the verification of the metadata elements extraction, show promising results.
Research and Improvement on the Linkage of 2D and 3D Based on Multi-touch Technology

As more mature technology, multi-touch appears strong interactive experience. This paper introduced multi-touch interactive technology into the linkage of 2D and 3D. Its natural and efficient features was adopted to solve the existing problem in virtual reality interaction. OTSU algorithm was applied to low the error rate of multi-touch recognition. Then defined gestures generic class and the designed linkage framework realize the linkage of 2D and 3D in multi-touch mode and promote the interaction experience.
Dual Dominant Local Binary Descriptor for Face Recognition

Recent years, many LBP variants tried to improve its discrimination and robustness, however, brought about extra cost. In this paper, we propose a compact yet highly robust local binary descriptor Dual Dominate Patterns (DDP) for face recognition which focuses on horizontal, vertical and inclined 45-degree orientations. For each orientation, pixels' comparisons of diverse scales are gathered, then the dominate one in two perpendicular orientations is encoded to highlight the key gradient and restrain noises. With the simple feature selection method for each pixel, DDP shortens the descriptor without significant information loss. The excellent resistance and strong discriminative power under the condition of intermediate to high level of noises, blur and severe lighting on FERET and CAS-PEAL-R1 databases suggests that our algorithm is a strong candidate for local binary face recognition.
Randomized Benchmarking of Quantum Gates on a GPU

While quantum computing has shown great promise in the field of computer science, a lack of actual practical quantum hardware means that mainstream research must rely on simulations. As such, a wide number of quantum computing simulation libraries have been developed, each with their own strengths and weaknesses. A good simulator must not just be accurate, but fast as well. This is especially relevant for quantum systems since the problem size growth for quantum systems is super-exponential. For this paper, we introduce a quantum computing simulation system that takes advantage of multiple gpus to achieve up to 400 times faster simulation time. We discuss the implementation details and provide analysis of its performance. We also demonstrate how the real-world phenomenon of quantum gate incoherence can be accurately simulated by varying the floating point precision and demonstrate it by using a precision of 9 bits, which we evaluate using Randomized Benchmarking.
Realization and Improvement of Bayes Image Matting

The image matting technology is an image separation method, and there are many ways to implement foreground extraction. The Bayes matting is an interactive input matting algorithm. In the original image, the selected foreground area and background area are identified by different colors. The users can take the interactive matting algorithm to achieve the expected image segmentation. The realization of image matting has very good result and has a good distinction between the foreground and the background parts at the edges. But if the colors of current scene is closed to the background at the edges then the foreground image is confused. In the preparation work, firstly the algorithm identifies the face and draw the outline. The image of this scope is classified the part of the foreground without the calculation of the matting algorithm. This ensures the integrity of the prospect image. The result can quite precisely separate the two parts of the foreground area and the background area.
Enhanced Collaborative Filtering Through User-Item Subgroups, Particle Swarm Optimization and Fuzzy C-Means

Recommender systems are information filtering systems that assist users to retrieve relevant information from massive amounts of data. Collaborative filtering (CF) is the most widely used technique in recommender systems for predicting the interests of a user on particular items. In traditional CF preferences of all items from many users are collected in the prediction process and this may include items that are irrelevant to the active user (the user for whom the prediction is for). Recently, subgroup based methods have emerged which take into account correlation of users and a set of items to rule out consideration of superfluous items. In this paper our objective is to explore CF that considers only user-item subgroups which consist of only similar subset of users based on a subset of items. We propose a novel hybrid framework based on Particle Swarm Optimization and Fuzzy C-Means clustering that optimizes the searching behaviour of user-item subgroups in CF. The proposed algorithm is experimented and compared with several state-of-the-art algorithms using benchmark datasets. Accuracy metrics such as precision, recall and mean average precision is used to find the top N recommended items.
Face Recognition Using Texture Features and Multilayer Perceptron

Developing a robust system for face recognition is a challenge in the field of Computer Vision. Texture is one of low-level visual features that mostly used to represent a unique characteristic of an image in face recognition. In this study, a face recognition system is implemented using two well-known texture extraction methods, i.e., wavelet and Local Binary Pattern (LBP). The texture features of face image are then classified using multilayer Perceptron. The face dataset used in experiment consists of 2260 images from 113 people. The experiment is carried out by comparing the performance of the two texture extraction methods. Both methods achieve high accuracy in recognizing face images. The recognition rates achieved by the wavelet and LBP methods are 99.63 and 93.19%, respectively.
Open-Source Oriental Game and Endgame Database

Xiangqi (Chinese Chess) is one of the oldest games in China. In an earlier paper [1], we described an algorithm to help improve the performance of Xianqi using an Opening Book Database (OB) that aids in opening moves. We augment this work by also using a database at the back-end, Endgame Database (EG). Zobrist Hashing Algorithm [2] is used to translate a position into an index and use this index to access elements in the hash table of both OB and EG databases. We remark on storage efficiencies and the performance of the algorithm with the two databases, OB and EG.
Assessing optimization algorithms based on ant colony using adapted networks science metrics

This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO is a computational intelligence technique inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. In the ACO, artificial ants seek for solutions to the tackled problem, transversing the graph that represents the combinatorial problem. For each displacement, the ants deposit pheromone on the edges which they passed. The ants use the pheromone for indirect communication, and the amount of pheromone weighs the intensity of interaction between the ants. The graph of pheromones forms a network that evolves along the iterations. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient, and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient.
Forecasting the stock price time series via all components of multi resolution

Objectives: This research study presents a forecasting model that integrates an efficient discrete wavelet transform and a Backpropagation Neural Network (BPNN) for predicting financial time series.Methods/Statistical analysis: The presented model uses the wavelet transform at several time instances based on local smooth B-Spline wavelets of order d(BSd) to decompose the financial time series data.So, an approximation (long-term trends) component and several details (shortterm deviations) components are obtained.Since the details components act as a complementary part of the approximation component, to prepare a prediction model which applies all decomposed components is very advantageous.Therefore, all components are used as smooth input samples of the neural network to forecast the future of the financial time series.Findings: The proposed model is designed to forecast the stock prices of five different companies, and according to the obtained results, the presented model outperforms a conventional model that uses only the approximation component as a wavelet de-noising-based model.The numerical results have shown the prediction accuracy.Applications/Improvements: The proposed model can predict future stock prices better than the de-noised based model in nearly 70%cases.
Optimized Adaptive Streaming with Intelligent Network Awareness

Video streaming service, considering both live as well as video-on-demand contents, has completely changed the internet world. However, buffering remains the biggest concern, which severely degrades the quality of experience. In particular, the amount of time spent in video buffering phase has the worst impact on the user engagement. This buffering phase becomes more visible while streaming in fluctuating networks, which is a common scenario when user watches streamed video while ravelling or during weather aberration. In this paper, we propose an intelligent network aware adaptive streaming method which estimates the past network trend, optimizes the video queue caching mechanism and enforces video quality in client device. By doing so, the algorithm is able to reduce buffering events by average 40% and quality switches by almost 45%, providing an almost seamless video streaming playback experience.
Entangled in Cryptoassets’ Legal Nature and Governance: Searching for Clear Boundaries or Working for their Removal?

The increase in the number of disputes before courts and regulatory authorities requires legal scholars to finally question the legal nature of cryptoassets. The few legislative frameworks already in force show the limits of a state-based regulation addressed both to captivate the FinTech industry through the ‘intangible asset’ of legal certainty and to avoid stifling the development of distributed ledger technology (DLT) and blockchains. However, these aims can conflict with regulatory capital market objectives, especially with market integrity and investor protection. ‘Exchange platforms’ and initial coin offerings can cause fraud and scams. Currently, these concepts affect ‘only’ private and commercial law, but what will happen when public law dimension is also affected? It is likely that the wait to find out will not be long if we consider the emergence of securities tokenization. Thus, the time has come to decide whether to clarify the legal boundaries between cryptoassets and fiat money, securities and commodities or to use this new technology to have more efficient and less costly financial markets by removing the legal border between crypto space and the ‘physical world economy’. Considering this aspect, the involvement of international organizations should be encouraged.
Case Study: Arc Flash Study of Wang Noi Combined Cycle Power Plant by Handy Arc Flash Calculator

Arc flash is one of the severe electrical hazard which causes injury or decease to workers. EGAT concern about this hazard and develop Handy Arc Flash Calculator (HAFC) used for incident energy calculation according to the IEEE 1584 standard which released on 2002. This document shows a case study of using HAFC compared to ETAP software for Wang Noi Combined Cycle 4 (WNCC4). The results of both methods are the same.Moreover, the warning label which suitable for Thailand is created for the awareness of worker and selection of proper Personal Protective Equipment (PPE) level.
MeasureOP: Sentiment Analysis of Movies Text Data

Sentiment analysis is a series of methods, techniques, and tools about detecting and extracting subjective information, such as opinion and attitudes, from language. The goal of our project was to classify movies’ reviews, by analyzing the polarity (positive or negative) of each paragraph in a review (Cui et al., Neurocomputing 187:126–132, 2016). We experimented with various RNN models on the Nervana Neon deep learning framework, an open-source framework developed by Nervana Systems, in order to improve accuracy in training and validation data. We experimented with network architecture, hyper parameters (batch size, number of epochs, learning rate, batch normalization, depth, vocabulary size) in order to find out which model works best for sentiment classification. This paper presents our findings and conclusions.
Bank Data Certification and Repurposing Using Blockchain

Privacy protection is one of the basic needs for supporting a good interaction in a globally interconnected society. It is important not just for business and government but also to a huge and increasing body of electronic or online societies. In such situations, a traditional digital ledger storage systems seems more centralized. Security of traditional digital ledger system has always been a greater concern when considered for implementing at a huge scale. When such sensitive data is at stake, there should be nothing doubtable about the system's strength to secure data and withhold itself against any potential attacks. Blockchain is one way through which such potential security issues can be solved. A blockchain, actually block chain, is basically a continuously increasing list of records, which are called blocks; these blocks are linked and secured mainly using cryptography. Every block typically has a cryptographic hash of the block previous to it, along with a timestamp and data of the transaction.