Reviewerly/exHarmony huggingface dataset:
Input Interface Using Wrinkles on Clothes for Wearable Computing

Although wearable interfaces can enable computer operation anywhere and at any time, many current devices are too cumbersome or socially unacceptable to wear in daily life. In contrast, a ridge of cloth produces a wrinkle that forms naturally on clothes, and the shape of these wrinkles can be recognized by their tactile sensations. Hence, they can achieve eye-free input and output functions. We therefore propose an interface that uses the wrinkles on clothes to input and output information. The interface generates several wrinkles on an item of clothing when an application requires input. The user senses the current state and number of choices by touching these wrinkles. The user can then input commands by selecting one wrinkle. The wrinkles disappear after the operation is complete. We implemented a prototype of this interface and investigated the social acceptance of the device position. The results show that the various device positions (excluding the armpit) achieve a moderate level of social acceptance. We mounted the prototype on the front of the right thigh of a pair of trousers and evaluated the recognition accuracy of the wrinkle patterns as well as the learning curve with respect to input accuracy and time. The participants achieved a recognition rate of 89.4%, input accuracy of 95% or more, and an input time of approximately 2s. This device has the potential to provide unobtrusive and eyes-free operation and the current results indicate directions for its future development.
Evaluating Portable Mechanisms for Legitimate Execution Stack Access with a Scheme Interpreter in an Extended SC Language

Scheme implementations should be properly tail-recursive and support garbage collection. To reduce the development costs, a Scheme interpreter called JAKLD, which is written in Java, was designed to use execution stacks simply. JAKLD with interchangeable garbage collectors was reimplemented in C. In addition, we have proposed an efficient C-based implementation written in an extended C language called XC-cube, which features language mechanisms for implementing high-level programming languages such as “L-closures” for legitimate execution stack access, with which a running program/process can legitimately access data deeply in execution stacks (C stacks). L-closures are lightweight lexical closures created from nested function definitions. In addition to enhanced C compilers, we have portable implementations of L-closures, which are translators from an extended S-expression based C language into the standard C language. Furthermore, we have another mechanism for legitimate execution stack access, called “closures”. Closures are standard lexical closures created from nested function definitions. Closures can also be implemented using translators. In this study, JAKLD was reimplemented in an extended SC language (S-expression based C language) that features nested functions to evaluate (L-)closures and their implementations, including translators.
A Proposed Architecture for Parallel HPC-based Resource Management System for Big Data Applications

Big data can be considered to be at the forefront of the present and future research activities.The volume of data needing to be processed is growing dramatically in both velocity and variety.In response, many big data technologies have emerged to tackle the challenges of collecting, processing and storing such large-scale datasets.Highperformance computing (HPC) is a technology that is used to perform computations as fast as possible.This is achieved by integrating heterogeneous hardware and crafting software and algorithms to exploit the parallelism provided by HPC.The performance capabilities afforded by HPC have made it an attractive environment for supporting scientific workflows and big data computing.This has led to a convergence of the HPC and big data fields.However, big data applications usually do not fully exploit the performance available in HPC clusters.This is so due to such applications being written in high-level programming languages and do not provide support for exploiting parallelism as do other parallel programming models.The objective of this research paper is to enhance the performance of big data applications on HPC clusters without sacrificing the power consumption of HPC.This can be achieved by building a parallel HPC-based Resource Management System to exploit the capabilities of HPC resources efficiently.
Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces

Learning mobile robot space and navigation behavior, are essential requirements for improved navigation, in addition to gain much understanding about the navigation maps. This chapter presents mobile robots feature-based SLAM behavior learning, and navigation in complex spaces. Mobile intelligence has been based on blending a number of functionaries related to navigation, including learning SLAM map main features. To achieve this, the mobile system was built on diverse levels of intelligence, this includes principle component analysis (PCA), neuro-fuzzy (NF) learning system as a classifier, and fuzzy rule based decision system (FRD).
Reliable Colour Detection for Matching Repaint Application

This paper presents the design and development of a system that creates the optimal conditions to sense colour for matching repaint application.The effectiveness of eliminating, and then recreating the lighting conditions to a desired level in a controlled enclosure is explored.The desired artificial light is introduced into the enclosure, providing optimal, constant conditions to measure surface reflectance.The tests conducted showed that the proposed system worked well.The testing results were good and showed that the relative results between samples taken at exactly the same location were an accurate match and consistent.
Application of similarity analysis in PV sources generation forecasting for energy clusters

The resultant photovoltaic installation powers significantly affect the process of cluster coordination in terms of balancing, which is associated with the need for the most accurate forecast of photovoltaic generation. This article describes the application of similarity analysis in order to use commonly available meteorological data for predicting generation level from photovoltaic sources on the example of several selected installations and their corresponding real production profiles.
Intellectics Fundamentals in a Professional Activity of a Future Bachelor

The article describes a new course on “Intellectics fundamentals in a professional activity of future Bachelors” -into the curricula of Russian Universities. The following issues are solved: the reasons for a new course introduction; its contents; possibilities and forms of its introduction into the current curricula; the place of the course in the system of the professional student training. The purpose of the study is enhancing bachelors’ intellectual culture as basis of professional university training. The study results are: Research methods are theoretical, empirical, and pilot testing. Key conclusions: the concepts “intellectics” of a professional activity, “intellectual” culture have been disclosed, their social role has been shown; the course has been designed and tested. These modules are a part of basic and variable components of a fundamental professional educational program (FPEP). The course can be applied for professional competences development with the main factor – enhancement of bachelors’ professional education within the university and new employers’ requirements.
Establishment of cellular automata image model and its application in image dimension measurement

Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.
Utilising &amp;lt;i&amp;gt;Grasia&amp;lt;/i&amp;gt; to Increase Pupils’ Motivation and Participation in Grammar Lesson

Grammar lessons can be dull especially when the lessons are conducted in a one-way interaction where pupils are required only to listen and absorb the information conveyed by the teacher.This lacklustre teaching practice should be changed.This research was intended to create a new way of teaching and learning grammar which is a transition from boring drilling towards exciting grammar lesson.Employing Action Research design, this research aimed to test a new method of teaching and learning grammar which is using Grasia (Grammar through Tarsia) in English language grammar lesson.The respondents of the research were from primary and secondary school pupils.19 pupils from two primary schools in Daro and Lubok Antu, as well as, 21 pupils from a secondary school in Baram district were chosen as participants through convenient sampling method.The data of this research were obtained through teacher observation questionnaire and self-assessment survey which were carried out before and after Grasia was implemented in grammar lesson.The findings indicated a positive outcome among the participants after teaching and learning session through Grasia.This finding is fundamental to give an insight to all the teachers that a change is needed in order to carry out a fun and meaningful grammar lesson.Grasia provides alternative methods for teachers to introduce grammar items in a fun and engaging way rather than chalk and talk lesson.
Deep RNN Architecture: Design and Evaluation

Sequential data labelling tasks, such as handwriting recognition, face two critical challenges. One is in great needs of a large-scale well-annotated training data. The other is how to effectively encode both the current signal segment and the contextual dependency. Both needs many human efforts. Motivated to relieve such issues, this chapter presents a systematic investigation on architecture design strategies for recurrent neural networks in two different perspectives: the pipeline perspective and micro core perspective. From the pipeline perspective, a deep end-to-end recognition architecture is proposed, allowing a fast adaptation to new tasks with minimal human intervention. Firstly, the deep architecture of RNN gave a high nonlinear feature representation. Through hierarchical representations, effective and complex features are expressed in terms of other, simpler ones. Moreover, a hybrid unit is used to encode the long contextual trajectories, which comprise of a BLSTM (bidirectional Long Short-Term Memory) layer and a FFS (feed forward subsampling) layer. Secondly, the CTC (Connectionist Temporal Classification) objective function makes it possible to train the model without annotating the data in character level. Thirdly, a modified CTC beam search algorithm integrates the linguistic constraints wisely during decoding in a unified formulation. In such an end-to-end style, a thin and compact network with high accuracy can be designed and ready to industry-scale applications. From the micro core perspective, a new neuron structure named gated memory unit (GMU) is presented, inspired by both LSTM and GRU. GMU preserves the constant error carousels (CEC) which is devoted to enhance a smooth information flow. GMU also lends both the cell structure of LSTM and the interpolation gates of GRU. Our methods are evaluated on CASIA-OLHWDB 2.x, a publicly available Chinese handwriting database. Comparing with state-of-the-art methods, it shows that: (1) our pipeline demonstrates over 30% relative error reductions on test set in terms of both correct rate and accurate rate; (2) better results can be achieved by our pipeline at certain feasible assumption on competition set; (3) GMU is of potential choice in handwriting recognition tasks.
Experience of using atomic-emission spectrometers with MAÉS in an accredited laboratory

A system of the set rules, procedures and regulatory documents is developed in the Central Analytical Laboratory of the JSC «EZ OCM» for the method of atomic emission spectral analysis of precious metals with arc excitation of the spectrum to meet all the requirements of the accrediting body «Rosakkreditatsiya». The features of managing the measuring instruments — spectrometers with MAÉS analyzers, auxiliary equipment and microclimate are considered. The requirements to the personnel of the laboratory for spectral analysis are considered. The rules for developing measurement procedures, instructions for laboratory technicians, measuring programs, methods for performing routine analysis of the samples, and ensuring the traceability of measurement results are specified. The availability of using «ATOM3.3» software for solving everyday problems of the laboratory is shown.
Face recognition using assemble of low frequency of DCT features

Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor.
BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines

The BoltzMM package (Version 0.1.3;https://CRAN.R-project.org/package=BoltzMM) for the R statistical programming environment (R Core Team, 2018) provides a complete suite of functions for application and estimation of fully-visible Boltzmann Machine (FVBM) models.The FVBM was first considered in Hyvarinen ( 2006), and can be described as follows.
A Novel Interactive Fusion Method with Images and Point Clouds for 3D Object Detection

This paper aims at tackling the task of fusion feature from images and their corresponding point clouds for 3D object detection in autonomous driving scenarios based on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird’s Eye View (BEV) LIDAR point clouds and their corresponding RGB images. Differing in existing fusion methods, which are simply the adoption of the concatenation module, the element-wise sum module or the element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and their corresponding image feature maps by designing a novel structure, where single level feature maps and utilize multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss compared to the existing fusion method used on the KITTI 3D object detection benchmark.
Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.
Viterbi Approximation of Latent Words Language Models for Automatic Speech Recognition

This paper presents a Viterbi approximation of latent words language models (LWLMs) for automatic speech recognition (ASR). The LWLMs are effective against data sparseness because of their soft-decision clustering structure and Bayesian modeling, so LWLMs can perform robustly in multiple ASR tasks. Unfortunately, implementing an LWLM to ASR is difficult because of its computation complexity. In our previous work, we implemented an n-gram approximation of LWLM for ASR by sampling words according to a stochastic process and training word n-gram LMs. However, the previous approach cannot take into account a latent word sequence behind a recognition hypothesis. Our solution is the Viterbi approximation that simultaneously decodes both the recognition hypothesis and the latent word sequence. The Viterbi approximation is implemented as a two-pass ASR decoding in which the latent word sequence is estimated from a decoded recognition hypothesis using Gibbs sampling. Experiments show the effectiveness of the Viterbi approximation in an n-best rescoring framework. In addition, we investigate the relationship of the n-gram approximation and the Viterbi approximation.
Q Learning with Quantum Neural Networks

Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic.There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL).However, quantum RL has made the least progress when compared to the other two areas.In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment.RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards.Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces.This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.
A New Privacy-Preserving Searching Model on Blockchain

It will be convenient for users if there is a market place that sells similar products provided by different suppliers. In physical world, this may not be easy, in particular, if the suppliers are from different regions or countries. On the other hand, this is more feasible in the virtual world. The Global Big Data Exchange in Guiyang, China, which provides a market place for traders to buy and sell data, is a typical example. However, these virtual market places are owned by third parties. The security/privacy is a concern in addition to the expensive service charges. In this work, we propose a new privacy-preserving searching model on blockchain which enables a decentralized and secure virtual search-and-match market place. The core technical contribution is a new searchable encryption scheme for blockchain. We adopt the similarity preserving hash and leverage smart contracts to protect the system from the forgery attack and double-rewarding attack. We formally prove the security and privacy of our protocol, and evaluate our scheme on the private net of Ethereum platform. Our experimental results show that our protocol can work efficiently.
Detecting Micro-Expressions in Real Time Using High-Speed Video Sequences

Micro-expressions (ME) are brief, fast facial movements that occur in high-stake situations when people try to conceal their feelings, as a form of either suppression or repression. They are reliable sources of deceit detection and human behavior understanding. Automatic analysis of micro-expression is challenging because of their short duration (they occur as fast as 1/15–1/25 of a second) and their low movement amplitude. In this study, we report a fast and robust micro-expression detection framework, which analyzes the subtle movement variations that occur around the most prominent facial regions using two absolute frame differences and simple classifier to predict the micro-expression frames. The robustness of the system is increased by further processing the preliminary predictions of the classifier: the appropriate predicted micro-expression intervals are merged together and the intervals that are too short are filtered out.
RFID in Libraries: Automatic Identification and Data Collection Technology for Library Documents

The chapter describes the main features of the use of the radio-frequency identification (RFID) technology in library activities. The technical capabilities of RFID equipment for unique identification of library documents in information systems for various purposes are shown. The questions of development of library technologies due to the full-featured use capabilities of automatic and radio-frequency identification systems in libraries are presented. The possibility of development of library RFID systems in the direction of the concept of Electronic Product Code (EPC) through the modification of the regulatory framework on the basis of existing harmonized standards is considered. It is shown that this approach to the formation of the regulatory framework will create conditions for increasing the availability of RFID technology for libraries. Development of library RFID systems in the direction of the Internet of things (IoT) concept will significantly increase the integration of the traditional library collections, along with electronic documents, in the modern information space. This will increase the availability of printed documents to readers and contribute to development of library technologies towards development of a global integrated system of library and information support for human activities. This will allow libraries themselves to integrate more fully into the global information space, at the next stage of development of information technologies.