Reviewerly/exHarmony huggingface dataset:
Automated Precision Tuning in Activity Classification Systems

The greater availability and reduction in production cost make wearable IoT platforms perfect candidates to continuously monitor people at risk, like elderly people. In particular these platforms, along with the use of artifical intelligence algorithms, can be exploited to detect and monitor people's activities, in particular potentially harmful situations, such as falling. However, wearable devices have limited computational power and battery life.
Analysis of Human Motion Based on AI Technologies: Applications for Safeguarding Folk Dance Performances

Analysis of human motion is an important research area in computer vision with numerous applications. Recent projects, such as EU i-Treasures and TERPSICHORE projects conduct research in this field to improve the capture, analysis and presentation of Intangible Cultural Heritage (ICH) using ICT-based approaches. The final goal is to document these forms of intangible heritage and to capture the associated knowledge in order to safeguard and transmit this information to the next generations. In addition, these approaches can give rise to new services for research, education and cultural tourism. They can also be used by creative industries (e.g. companies performing film, video, TV or VR applications production), as well as by local communities, creating new local development opportunities by promoting local heritage. This paper first reviews some very recent state of the art approaches based on deep learning which can achieve impressive results in recovering human motion (2D or 3D) and structure (skeleton with joints or realistic 3D model of the human body). Based on such approaches, we then propose a dance analysis approach, currently under development in TERPSICHORE project. Preliminary results are presented and, finally, some conclusions are drawn.
Improving the estimation of parameters in induction motors using an evolutionary computation algorithm

Induction Motors (IM) are the most used electrical machines in the industry, where they use significant energy percentages. The control of IM requires the knowledge of their behavior; in this sense, it is necessary to accurately estimate the internal parameters that control their performance. This process involves the optimization of linear models with different constraints. Evolutionary Algorithms (EA) are proven techniques designed to obtain better results than classical optimization methods. Most of EA suffer some limitations such as slow convergence; they also have a large number of parameters that need to be set by the designer. Therefore, to solve this problem, this paper proposes an alternative method to estimate the parameters of IM using the Electromagnetism-Like Optimization (EMO) algorithm. EMO has the advantage of using a small number of iterations. The experimental results and comparisons show that the proposed approach gives better results than related methods, regarding accuracy and convergence.
Building Undirected Influence Ontologies Using Pairwise Similarity Functions

The recovery of influence ontology structures is a useful tool within knowledge discovery, allowing for an easy and intuitive method of graphically representing the influences between concepts or variables within a system. The focus of this research is to develop a method by which undirected influence structures, here in the form of undirected Bayesian network skeletons, can be recovered from observations by means of some pairwise similarity function, either a statistical measure of correlation or some problem-specific measure. In this research, we present two algorithms to construct undirected influence structures from observations. The first makes use of a threshold value to filter out relations denoting weak influence, and the second constructs a maximum weighted spanning tree over the complete set of relations. In addition, we present a modification to the minimum graph edit distance (GED) [1], which we refer to as the modified scaled GED, in order to evaluate the performance of these algorithms in reconstructing known structures. We perform a number of experiments in reconstructing known Bayesian network structures, including a real-world medical network [2]. Our analysis shows that these algorithms outperform a random reconstruction (modified scaled GED ≈ 0.5), and can regularly achieve modified scaled GED scores better than 0.3 in sparse cases and 0.45 in dense cases. We argue that, while these methods cannot replace traditional Bayesian network structure-learning techniques, they are useful as computationally cheap data exploration tools and in knowledge discovery over structures which cannot be modelled as Bayesian networks.
The Influence of Light Environment on Eye-Control Human-Computer Interaction

The eye-controlled operation has attracted more and more attention in recent years, especially in the field of advanced human-computer interaction. It is often used to analyze the friendliness of human-computer interface design and guide human-computer interface design. In addition, the eye movement tracking method based on eye tracker can directly or indirectly capture the user's intention, habit, interest area, historical eye movement track, and other information. However, few people study the influence of light environment on eye control operation. This paper aims to study the influence of light environment on eye-control operation behavior during human-computer interaction task, and design human-computer interaction experiments under different illumination and color temperature conditions.
Developing and Analysis of Cyber Security Models for Security Operation Center in Myanmar

In counteraction to the increasing threat of cyber terrorism, the modeling to be predicted in guessing the predictive models for estimating the incidence of cyber-attacks for enterprise network in Myanmar are seriously needed. Although we need these models, there is no record of attacks, defenseless, outcome and threat to utilize the developing predictive models and authentication. The main purpose of this research is to determine whether SOC (Security Operation Center) manager uses cyber security model by using SOC results figures to prepare further cyber defense and incident response plan. The goal of this study was achieved by conducting experiments on various cyber-attacks occurred in security operation center of Industrial Control System (ICS).
e-Consult: The Effectiveness of Thesis Advisory Model Using Management Information Systems in IAIN Bukittinggi

Abstract This study aims to obtain accurate data about the effectiveness of thesis advisory model using management information systems or e-Consult in IAIN Bukittinggi. This model has been developed in previous research in a series of research and development and has been validated by several experts with the result of very valid category. The effectiveness testing in this study involved 30 students whose thesis are advised by the researcher himself. It was done to facilitate the data selection. They were divided into the experimental group and the control group, each consisting of 15 students. This study was conducted through pre-experimental design with Static Group Comparison model. The data were analyzed by using non-parametric statistics through Binomial test. From this statistic test, the obtained level of effectiveness of this model is in very effective category.
Time-Offset Estimation in Multisensor Tracking Systems

In this paper, a new algorithm is proposed for time-offset estimation in multisensor target tracking systems. First, the time offset pseudo-measurement equation is derived and calculated in both centralized and distributed scenarios, where measurements and local tracks are available at the fusion center, respectively. Second, the observability of time offset is analyzed theoretically with constant velocity (CV) and constant acceleration (CA) targets, showing that only relative time offsets between sensors are observable. Then, a two-stage relative time-offset estimation method is developed with two different formulations corresponding to different target dynamic models. Finally, simulation results show that the proposed algorithm meets the corresponding posterior Cram&#x00E9;r-Rao lower bound (PCRLB), demonstrating the validity of the proposed algorithm.
Effective Distributed Representation of Code-Mixed Text

As an increasing number of people embrace social media, mining data generated from the same has become an important task. Possible applications range from opinion mining, sentiment analysis to hate speech detection. More importantly, analyzing code-mixed multilingual text has gained popularity due to the reason that it holds important socio-cultural clues that may be lost in translation. Methods to effectively analyse code-mixed Hindi/English(Hinglish) text have been explored in this paper. Firstly, we generate a large scale code-mixed corpus that would aid in further research of code mixed text on social media. High-quality word embeddings are trained on this code-mixed text. Finally, we demonstrate the efficacy of our proposed method by training machine learning models that improve upon the previous state-of-the-art using a much lighter and explainable architecture. Our main intention behind training the classifier model was not only high performance but also good model explainability and speed.
Comparison of Linear Filters in the Presence of Biased Measurements

In addition to the typical random errors that vary between consecutive measurements, the measurements for most all sensors used for target tracking include bias errors that remain fixed during a target tracking episode and are typically characterized by an a priori mean and covariance. Several extensions to the Kalman filter have been developed to consider the bias error statistics when calculating a state error covariance. In this paper, several of these extensions are compared by deriving analytical forms of the steady-state covariance with nearly fixed and nearly constant velocity targets in a scalar coordinate. A novel method for computing the measurement covariance inflation required to contain the bias and random errors at the output of the filter is presented and illustrated.
A Prefatory Study on Data Channelling Mechanism towards Industry 4.0

Data are increasing in volume, variety and velocity in this Internet of things and big data era. It applies from industry (or manufacturing) process monitoring control to video surveillance analysis to track human and machines activities. Therefore, fast and accurate approaches in data channelling are needed to effectively deal with these big data. This paper presents practical methods to manage and transfer the data from industry manufacturing site to a centralized data processing hub. In this hub, data are transformed into understandable information, which can assist human in understanding and monitoring manufacturing situation autonomously. These data are collected and channelled to desired location for analysis through Open Platform Communication Unified Architecture (OPC UA). Industrial protocols and standards are used to interpret the data channelling methods and tested on several industrial machines. Result shows that size of data and number of OPC UA Client that connects to OPC Server affects the data channelling speed.
Knowledge Maps of Tourism Big Data Research in China Based on Visualization Analysis

OBJECTIVE:We scientifically analyze knowledge structure, development stages, research hotspots and research frontiers of tourism big data in China to provide practical and useful references for researchers to understand the research status and development trends of this field.METHODS:Published journal literatures were retrieved. A scientific collaboration analysis was conducted to visualize the relations of authors and institutions. A co-occurrence analysis was used to visualize the network of key words that was classified by the clustering analysis. Burst detection was conducted to visualize emerging words across the entire research field.RESULTS:We retrieved 964 literatures, from which 668 literatures were identified after screening. Wang Dong has published the most papers. A cooperative group of scientific research institutions with Beijing Union University as the core has been formed. The key words were classified into 6 clusters, and the frequency of "tourism industry" is the largest, and top 14 key words with the highest emergence intensity were detected.CONCLUSIONS:The literature of tourism big data research in China has been increasing rapidly since 2016. Three cooperative groups with Wang Dong, Liu Ligang and Pan Xinqin as the core respectively were formed, and a cooperative group of scientific research institutions with Beijing Union University as the core has been formed. The research hotspots of tourism big data in China mainly focus on six aspects: tourism industry development, key technologies of tourism big data, global tourism, tourism public service, tourists behavior, problems and countermeasures. The evolution of in this field can basically be divided into three stages: exploration (before 2012), start-up (2013-2016) and rapid development (from 2017 to present).
Improving Classification Accuracy Using Hybrid of Extreme Learning Machine and Artificial Algae Algorithm with Multi-Light Source

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers.
Security Tag Fields and Control Flow Management

Cybersecurity community now faces advanced threats: Rowhammer, Spectre, Meltdown, Foreshadow, PortSmash, and future unknown threats. Current cybersecurity threats have led to a renewed interest in security tag fields and program control flow. Security tag fields have their roots in 1950's era hardware for system reliability. Today, there is great interest in tags for hardware-based cybersecurity. Current commodity microprocessors execute instructions. Future cybersecurity architectures need to understand and manage security. We investigate using security tag fields to enforce some control flow properties.
Data Analysis for a Hailstorm Event Based on FY-4 Satellite Observation

Observations from the FY-4 satellite products (cloud type (CLT), cloudtop height (CTH), cloudtop pressure (CTP), cloudtop temperature (CTT)) and C-band Doppler weather radar are compared and analyzed for the process of a hailstorm in Bayan Nur from 15:00 to 18:00 on August 9,2019 (BJT). Analyze and research the characteristics of occurrence, development and change of hailcloud during this hail werther. The results are as follows: (1) The terrain of the Yinshan Mountains has a special strengthening effect on the formation of convective clouds. (2) When the convective cloud matures, the height of the CTH reaches 14km, the CTP is 110hPa, the CTT is 208 K, and the CLT are mainly mixed clouds, cirrus clouds (translucent ice clouds) and supercooled water clouds, and the radar echo intensity reaches 50dBZ. (3) Satellite products have better consistency with radar observations.
A description language for chess

The game of chess involves patterns. In this article we develop a simple description language for chess positions as patterns. It seems useful to catalogue similar positions in a generic form, based on common elements, in order to simplify the search for chess positions. A simple chess pattern lang uage is used here to develop a computer program to find similar chess configurations. The aim is to avoid a commonly-used brute force approach. Further, this language could explain why some typical maneuvers actually work in some patterns. This could also be a step forward to model and build chess programs, which perform in a way closer to what a human being does.
Cross-Domain Speaker Recognition using Cycle-Consistent Adversarial Networks

Speaker recognition systems often suffer from severe performance degradation due to the difference between training and evaluation data, which is called domain mismatch problem. In this paper, we apply adversarial strategies in deep learning techniques and propose a method using cycle-consistent adversarial networks for i-vector domain adaptation. This method performs an i-vector domain transformation from the source domain to the target domain to reduce the domain mismatch. It uses a cycle structure that reduces the negative influence of losing speaker information in i-vector during the transformation and makes it possible to use unpaired dataset for training. The experimental results show that the proposed adaptation method improves recognition performance of a conventional i-vector and PLDA based speaker recognition system by reducing the domain mismatch between the training and the evaluation sets.
Pendidikan Pengguna Di Perpustakaan Daerah Jawa Tengah Menggunakan Animasi 3 Dimensi Berbasis Muvizu

Animation technology have developed in the last few decades rapidly. Many animation tools can be used to make a 3D animations by users easily one of which is Muvizu software. It can be used without specialist skills, so users can reduce the time needed to create 3D animation movie. Muvizu is used for the develope a 3D animation that used as a media of user education programs in the library, so the program can use at any time. The research method used ADDIE models beginning with analysis, design, development, implementation which is tested to experts and evaluation at each step. This research is foccus on designing and developing 3D animation using muvizu software consist of story development, pre-production, production, and post-production. The results of the research from material expert showed very good results on general material aspects and user education content. While the results media expert showed very good on the aspects of software engineering, audiovisual communication, and learning design.
Bypassing Full Disk Encryption with Virtual Machine Introspection

Full Disk Encryption (FDE) is a common practice today to reduce the risk of unauthorized access to personal data in public cloud environments. Some research works demonstrated that a malicious hypervisor employing Virtual Machine Introspection (VMI) can bypass FDE and perform unwanted file operations. However, these works provide restricted OS support, enable access only to user level files and may not support complex uses cases. In this paper, we present a new approach for bypassing FDE using VM kernel functions instrumentation. Our approach is portable over different FDE solutions, supports Linux and Windows OSes and provides fast access to user and system files on the VM disk. In addition it enables with no modification existing applications on the host OS to transparently bypass FDE and operate on the VM disk.
Shift to Knowledge

In the previous chapter we outlined the background of this book — constructive design research — and unpacked how constructive design research, being partly based on design practices, needs to pay respect to both professional and academic worlds of design. We introduced the concept of ‘drifting’ as an ostentatious way of articulating how processes of design change and adopt as new knowledge is build and the need for reframing its concerns emerges. Stated metaphorically: not as driftwood, but as in car rally; intentionally and controlled. The chapter also outlined how constructive design research in increased self-efficacy bridges ideas and ways of working from other disciplines in its particular ways.
Transferring Multi-Channel Convolutional Neural Network Model for Cross-Domain Sentiment Analysis

Analyzing sentiment analysis with deep learning requires massive labeled datasets where such data is not always available. The annotation process is also time-consuming and tedious. Further, even after we train the sentiment analysis, it creates another problem. Because this model is domain-dependent, the performance in another domain estimated to perform poorly. In this paper, we present the transfer learning approach to transfer knowledge gained from the source dataset into the target dataset with the expectation to improve the target model. Multichannel Convolutional Neural Network deploys different n-grams as the input channel in a single CNN model to grasp meaningful features from the text. This method has proven to perform well in sentiment analysis problems. We train our three datasets with different domains using this method as the baseline. The largest dataset then becomes the source model for transfer learning and other datasets as the target. Fine-tuning our source model also needed when retraining it into the target dataset. From the evaluation, we show that several transfer learning strategies outperform the domain-specific model, even when the data is imbalanced. We also highlight certain failing strategies that inflict lousy results on the target model performance.
Partitioned Update Binomial Gaussian Mixture Filter

Gaussian Mixture Filters (GMFs) are approximations of the Bayesian filter for nonlinear estimation. A GMF consists of a weighted sum of Gaussian components. Each component is propagated and updated with a Kalman-type filter. When the nonlinearity is small in the update step, the required number of components to yield an accurate approximation is small and vice versa. In this paper, we propose multiple improvements to GMF that reduce the computational load and increase the estimation accuracy. The new filter processes measurements so that the least nonlinear measurements will be applied first, this reduces the need for new components. After splitting a Gaussian component, the update is done so that the measurement function is evaluated only in nonlinear directions, which reduces computational load. Finally we propose a new faster algorithm for reducing the number of components after measurements are applied. Results show that the proposed improvements make the algorithm faster and improve the estimation accuracy with respect to a GMF that is used as a basis for development.
Removal of Nuisance Signals in Electric Grid Disturbance Data

An iterative algorithm for filtering out nuisance signals in recorded fault and disturbance data is presented. The intended application of the algorithm is removal of transient signal components (due to instrument transformers and network transient processes) as well as interharmonics in short-duration digital records of voltage and current signals. Output of the algorithm is a system frequency dynamic phasor which can be used in the automated disturbance data analysis in power systems. Commonly used, the Fourier transform based signal processing algorithms are not able to efficiently filter such components. Due to short lengths of recorded signals, frequency resolution of the Fourier transform based algorithms is not sufficient for this application. To demonstrate practicality and accuracy of the proposed algorithm several simulation experiments are devised and results are presented in the paper.