Reviewerly/exHarmony huggingface dataset:
Human Fall Detection using Built-in Smartphone Accelerometer

Falls are serious health hazard issue among the aged people around the world. It's a common accident for the elderly people living alone. Obviously, this accident can be timely reduced by using accurate fall detection method in order to reduce injuries and loss of life. For this purpose, we used smartphone-based fall detection method using the features of triaxial acceleration values of x, y and z which is obtained from the built-in accelerometer sensor embedded on our smartphones. We do a lot of daily activities like sitting, walking, standing, lying, and running. These were collected through accelerometer data. An app was used called Physics Toolbox Sensor Suite to take the data values which consist of accelerometer. The data values were taken through two positions one in chest pocket and another in pant pocket for both falls and non-falls. Also, intentional falls were also taken like fall -forward, fall-backward, right lateral fall, left lateral fall and so on. All these data were collected together to distinguish between fall and non-fall. These falls and non-falls were submerged together in a given time set keeping its frequency fixed along 6000samples from each data set through MATLAB. Then by using the Neural Net Pattern Recognition app leads us solving data classification problem using two-layer feed forward network. Using our data, we trained, validated and test the data through Neural Network Pattern Recognition, and achieved our classification accuracy to 90.6%. Using 67 data consisting of 26 falls and 41 non-falls. Basically, we classified and predict the data's through offline activity recognition. Once the falling victim is detected his positions along its locations will be tracked. And instantly will send an alert to the caregivers for immediate assistance.
An approach to evaluating the efficiency of a solution for visualization problem

Summary In recent years, various approaches to visualization have been adopted for decoding the significance implicit in data. Although the effectiveness of these approaches has been evaluated, scant attention has been paid to their efficiency. In this study, an interactive system was designed based on a two‐component visualization system. In the proposed model, resource libraries were installed on a computer, and correlations were drawn between human libraries and user characteristics. An interactive visualization system can be used to create different visualization problems. For each problem, the input is a dataset, and the output is the information or knowledge gleaned by the user from the visual pattern representing the dataset. Each visualization problem may be solved using many methods and various visual patterns, with varying cognitive loads. The efficiency of a solution is evaluated by comparing the output worth with the cognition cost of the solution. In this article, a quantitative algorithm was proposed to evaluate the efficiency of a visualization solution to enable the user in determining the most efficient solution according to the purpose of the visualization.
Self induced transparency pulses in transmon base quantum metamaterials

In this paper, we analyze the possible utilization of the short electromagnetic pulse for qubit state storage and for qubit manipulation. We analyze the properties of the electromagnetic pulse properties on characteristic preparation of the qubit register as well as on the pulse duration and determined the conditions under which it can be used as a medium for storing the qubit state, as well as for the qubit state manipulation.
Genetic algorithm for minimization of ESOP representations for multiple-output logic functions

Abstract This article concerns ESOP representations for multiple-output logic functions. Exclusive-or sum-of-products expression (ESOP) is a formula representing a logic function as an exclusive-or sum of products of variables and their negations. EXOR-based realization leads to reduction in size of logic circuits for many practical logic functions and improves their testability. Minimization of a logic function is a problem of constructing ESOP representation for the given function having the minimal number of summands. We introduce a correspondence between ESOPs for multiple-output logic functions and single-output logic functions and propose genetic algorithm obtaining close to minimal ESOP representations for multiple-output logic functions.
Man-Machine Translation—Future of Computer-Assisted Translation

Abstract The development of artificial intelligence has given birth to machine translation, which greatly improves translation efficiency. However, the quality of machine translation is not satisfactory, especially in terms of proprietary terms, cultural vocabulary, and text logic. Human-machine translation has become an innovative product of computer-assisted translation, which automatically saves corpus modified or stored by the translator and grows together with it, constituting a unique translation library. After that the machine can automatically identify and retrieve the correct translation in subsequent translation practices, maintaining quality while ensuring quantity and improving translation efficiency.
A Polytopic Box Particle Filter for state estimation of Non Linear Discrete-Time Systems

The development of Particle Filters has made possible state estimation of dynamic systems presenting non-linear dynamics and potential multi-modalities. However, the efficiency of these approaches depends tightly of the required number of particles which may prove very high to approximate large range of uncertainty on the process or the measurements. To overcome this issue, the Box-Particle Filter (BPF) combines the versatility of the Particle Filter and the robustness of set-membership algorithms. The particles are replaced by boxes which represent in a compact way large variations of the estimates. Although this filter presents various advantages and requires a small number of boxes to estimate the state, the resulting estimates may prove pessimistic, as the uncertainty description as unions of axis-aligned intervals can be rather rough and doesn’t account for potential dependencies between the resulting estimate components. In the proposed paper, a new version of the BPF is proposed. Boxes are replaced by polytopes (multidimensional polygons) in the filter algorithm, so that they can adapt to represent state components dependency. This modification tends to ameliorate the estimation precision (i.e. the size of the final set that includes the true state decreases) while keeping the number of required polyhedrons small. Several examples illustrate the benefits of such an approach.
Coherent H∞ control for Markovian jump linear quantum systems

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).
Development Trend of TV Drama Aesthetic Form from the Perspective of Big Data

Abstract After entering the era of big data, the aesthetic form of TV plays is bound to enter a new round of changes. It is of great strategic significance to conduct in-depth theoretical research on the changes of its development in time. Based on this, this paper analyzes the development trend of TV drama aesthetic form from the perspective of big data. In the research, we use the method of literature analysis, through consulting relevant information to understand the aesthetic form of Chinese TV drama, and summarize the current situation of aesthetic form of Chinese TV drama. At present, the development of online drama has a great impact on the aesthetic form of TV drama. Therefore, we use the form of questionnaire survey to compare and analyze the aesthetic form of traditional TV drama and online drama, and use cluster analysis to sort out and evaluate the research results, so as to obtain more direct research results. The results of this study show that the traditional TV series score 61 on openness, 65 on topic, 54 on convenience and 71 on diversity. On the other hand, the score of online drama is 90 in openness, 86 in topic, 83 in convenience and 86 in diversity. Thus, it can be seen that the network drama has a great influence on the development of people’s TV drama aesthetic form.
Set-based Scheduling for Highway Entry of Autonomous Vehicles

This paper proposes a framework for generation of collision-free reference trajectories in a cooperative multi-agent setting. The approach is hierarchical: a high-level controller schedules groups of cooperating agents, for which trajectories are then determined by a lower-level trajectory planner. Admissible behaviors of a cooperative group are encoded by so-called maneuvers, which are modeled by hybrid automata. This allows to plan trajectories by solving hybrid optimal control problems. Controllable sets characterize the solution sets of these problems and are used for quick assessment of feasibility prior to planning. This enables the high-level controller to quickly assess the feasibility of different maneuver options and cooperative groups. Special emphasis is put on safety and feasibility of the framework. The efficacy of the approach is demonstrated by simulation of a highway entry scenario for autonomous vehicles.
Building Probabilistic Causal Models using Collective Intelligence

The purpose of this paper is to show a novel approach to automatically generating Probabilistic Causal Models (Bayesian Networks (BN)) by applying Natural Language Processing (NLP) techniques to a corpus of millions of digitally published news articles in which views by different authors are expressed on the future states of economic and financial variables, and geopolitical events. The BNs that we will show how to derive will represent the wisdom-of-the-crowds forward-looking point-in-time views on various variables of interest and their dependencies. These Bayesian Networks are likely to be of interest to asset managers and to economists who want to gain a better understanding of the current drivers of an economy based upon a rigorous probabilistic methodology. Additionally, in an asset allocation context, the BNs we derive can be fed to an optimization engine to construct a forward-looking optimal portfolio given the constraints of the asset manager (e.g., budget, short constraints etc.). We demonstrate various automatically derived Bayesian Networks in a financial context
Development of New Behavior Patterns for Animals in Minecraft

This article is devoted to the development of a modification for the video game Minecraft, which adds new behavior patterns to animals in this game. Minecraft is a cross-platform game, but the object of the research is Minecraft: Java Edition. The main methods of mod development represented in the research are the Java programming language and the application programming interface MinecraftForge.
D. S. MEREZHKOVSKY AS A STUDENT OF THE THIRD GYMNASIUM (COMMENTATOR’S NOTES). Part 1

The article introduces the academic community to a vast collection of the unknown documents from the archives of the Third Gymnasium (Classical School) and from Merezhkovsky’s personal foundation, which contains the experiments of his early years. As the result, a picture emerges that depicts the school routine, featuring the portraits of the professors and the analysis of humanitarian subjects. Besides, a detailed commentary on the «Gymnasium» fragments of the autobiographical poem The Ancient Octaves is offered, and conclusions are drawn regarding the reliability of the assessments of education contained in it.
Comparative Evaluation of Several Classification Algorithms on News Posts Using Reddit Social Network Dataset

This research investigates a comparative evaluation of the results of different classification algorithms applied on the Reddit News Social Network Dataset. A program, which is well described was written in C# Dot Net that could run the classification using different parameters. These parameters which were input to the program, include the data size to read, training and testing size, class to predict, vector representation and the classification algorithm including Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), and N-Nearest Neighbor (NN). The classification was performed multiple times (various parameters). Also, the training and testing data were selected at random. That, to get a better judgment on the results. The performance metrics: precision, recall, accuracy, f-measure, sensitivity, and specificity were calculated for every time any classification algorithm is executed. Results showed that the precision for the classification Algorithms ranged from a value greater than 50 to 100%. Regarding the average precision results, in most of the results, DT had the highest average precision (4 times as the highest with one time together with LR but for different data size). Also, it was found that regarding the time required for a classification algorithm to produce results, for a data of size 200, all algorithms were fast (less than 5 s) except for LR (2–3 min). For a data of size 500, LR was the slowest with a time of 32 min. Then NN was the second slowest with a time of 2 min. All others including NB, NN and DT were the fastest (time in less than 5 s). For a data of size 1000, LR took 1 day and it was not even enough for the LR classifier to finish executing and produce any results. All remaining algorithms also did not complete the calculation of results in 1 day.
Control of Wheelchair on the Ramp Trajectory Using Bioelectric Impedance with Fuzzy-PID Controller

A person who has a severe disability, have difficulty walking, climbing, and descending stairs, so requires assistive devices for daily mobility. in particular, the paralysis of the upper and lower limbs resulted in them being unable to use the wheelchair properly. So that in this study, an electric wheelchair that can be used by persons with disabilities is made, which is able to move based on the bioelectrical impedance signal on the part of the body that can still be moved. The pre-existing research is controlling wheelchairs on flat roads, whereas when the road goes up or down, the wheelchair has an unstable speed. This research was developed by utilizing Fuzzy-PID control, which is able to control the wheelchair speed so that it is more stable. This electric wheelchair with Fuzzy-PID control is able to walk on an up and down ramp at an angle not exceeding 10°, at a stable speed. The trial subjects used were four people with different weights. The error rate at each speed increases as the ramp gets steeper, and the user load gets higher. Success in this trial is at an average success rate of 80%. This occurs because of the change in the width of the bioimpedance signal, which affects the slow and fast commands when given the command to move, and the tilt angle of the track which affects the motor speed when the wheelchair crosses the track.
Kannada Text-to-Speech System using MATLAB

The purpose of the proposed Kannada text-to-speech system is to design a device to convert written Kannada text-to-spoken output by generating speech. Optical character recognition as well as text-to-speech synthesizer contributes to making this system efficient. This paper describes the design, implementation, and experimental results of the device. This system is made of two parts: The first part is the character recognition and the second is the text-to-speech conversion. The Kannada text is segmented into smaller units using the concatenation method of speech synthesis. The system has been developed using MATLAB. This type of device mainly helps the blinded, and it can also be used by the people who suffer from dyslexia and reading challenges.
Research on Tracking Relationship Establishing Method of Train Control System Requirements Specification Based on Labeled-LDA

The system requirements specification is the basis for the design and development of the train control system, and the test cases are compiled according to system requirements specification, which are of great significance to the function test of the train control system. Establishing the tracking relationship between train control system requirements specification and test cases is an important part of achieving completeness verification of test cases and requirements change management. In order to solve the problem that the train control system specification texts contain a large number of terms in the field, this paper proposes a strategy that combines the train control system terms dictionary with the conditional random field model to extract the train control system terms. By analyzing the features of the relationship between the system requirements specification and the test cases, we select the Labeled Latent Dirichlet Allocation (Labeled-LDA) model to model the specification texts, and determine the requirements tracking relationship by calculating the similarity between the requirements and the test cases. It is proved that the corresponding relationship between the train control system requirements specification and the test cases obtained by the above method is basically correct, and the precision and recall are high. The results show that this method can realize the purpose of automatically establishing the system requirements specification tracking relationship.
Computer Technology in the Course of Probability and Mathematical Statistics

At present, the teaching method of mathematics basic course is still the one to many teaching mode of multimedia and blackboard writing, but for the probability data analysis, the application of computer technology in practice is not popular. It is necessary to combine the probability research of data with the data processing of computer technology, so as to improve the progress of the course and the learning efficiency of students. Starting from the new characteristics of probability system frequency problem, this paper systematically expounds the basic principles of computer technology learning and the characteristics of applicable problems, summarizes the typical structure and training methods of computer technology learning, constructs the overall framework of computer technology learning based on artificial intelligence, analyzes and summarizes its application in frequency dynamic trend perception, frequency security and stability evaluation, frequency control and modulation application progress in the field of energy saving. This paper reviews the existing research from the aspects of problem characteristics, application demand changes, application necessity, application adaptability, etc. The results show that compared with the experimental class of grade 17, grade 18 has achieved better teaching effect and academic performance. It can be seen from the average score comparison that the proportion of grade 18 students in 80–90 and 90–100 is higher, and the average score is significantly higher than that of grade 17 students without teaching reform practice.
Analysis of the New Trend of Chinese Teaching Research in China

Taking the research results of Chinese teaching in senior high school in recent years as an example, the paper analyzes and interprets the research trend of Chinese teaching in China. Chinese teaching research embodies the characteristics of emphasizing "traditional culture" and "teaching strategy". The new trend of combining "traditional culture" with "teaching strategy" is just a manifestation of Chinese cultural self-confidence.
Invasive Ductal Carcinoma (IDC) Classification Based on Breast Histopathology Images Using Convolutional Neural Network

Invasive Ductal Carcinoma (IDC) is the most common sub-type of all breast cancers that affected adult women worldwide. IDC can spread to other areas of the body such as liver, lungs and even bones. The process of identifying and categorizing breast cancer sub-types accurately is a very important clinical task. IDC Diagnosis requires extremely serious measures, such as surgery and radiation therapy. Diagnosis based on pathological imagery is no less difficult, requiring a microscope and manual learning to classify it as positive or negative cancer. This process is very time consuming and conveys many errors due to human cognitive limitations. The existence of a system which can automatically perform such work, is expected to save time and reduce the error rate diagnose. This study proposed IDC and Non IDC classification by analyzing the Breast Histopathology Images using Convolutional Neural Network (CNN) method. The dataset consisted of 1020 IDC images, the same number is also used for Non-IDC. The model was composed of CNN with three hidden layers plus one fully connected layer with sigmoid activation. An evaluation is carried out to see the performance of the proposed method by using a matrix of precision, recall, F1, and accuracy. The experimental results show that the proposed method provides precision, recall, F1-score of 0.93 and 93% accuracy. This study is expected to be validated for later use in assisting medical authorities for conducting clinical diagnoses.
A Robust Progressive Text Line Segmentation Framework with Markov Line Descriptors

Text line segmentation (TLS) continues to be an unsolved problem in the field of document analysis, and this is especially true for lines of handwritten characters. In recent years, however, this challenging problem has seen a surge of interest. In this paper we introduce our work on a new robust framework for solving the TLS problem via a trial-and-error method within a divide-and-conquer paradigm. Specifically, we propose a set of candidate solutions from constrained optimal Markov line descriptors (COMLD) and then predict a candidate class using a classifier learned from training data. Our new framework will treat a multiple-line candidate as a new and independent TLS problem, and it will re-estimate method parameters to achieve better adaptations and thus better solutions. Our extensive experimental results indicate that this framework 1) reaches or outperforms state-of-the-art TLS solutions on various datasets; 2) is robust against various image variations including, but not limited to, rotation, noise, image, resolution, and text line spaces; and 3) is trainable for a new language/data and is self-adaptive to new testing data. This evidence suggests that our methodology is a highly promising solution to the TLS problem.
Towards Fine-grained Concept Generation

Constructing large-scale taxonomies are crucial for many knowledge-rich applications that need concepts to better understand texts. However, current taxonomies suffer from the scarcity of concepts. Specifically, many fine-grained concepts are missing, while these fine-grained concepts play important roles in understanding related instances more deeply. In this paper, we propose an unsupervised fine-grained concept generation framework called FGCGen, which takes advantages of knowledge bases to generate mass of fine-grained concepts. Specifically, instead of extracting concepts from corpus, FGCGen detects entity heads and modifiers from knowledge bases and combines them to generate fine-grained concepts. We identify critical challenges of this generation process and employ three novel modules to solve them. We evaluate proposed methods on both Chinese and English datasets to show the strength of FGCGen, especially on constructing large-scale high-quality fine-grained taxonomies. Extensive experiments are introduced to prove the efficiency and effectiveness of the modules in FGCGen.