Reviewerly/exHarmony huggingface dataset:
Monogamy of nonconvex entanglement measures

One of the fundamental traits of quantum entanglement is the restricted shareability among multipartite quantum systems, namely monogamy of entanglement, while it is well known that monogamy inequalities are always satisfied by entanglement measures with convexity. Here we present a measure of entanglement, logarithmic convex-roof extended negativity (LCREN) satisfying important characteristics of an entanglement measure, and investigate the monogamy relation for logarithmic negativity and LCREN both without convexity. We show exactly that the $\alpha$th power of logarithmic negativity, and a newly defined good measure of entanglement, LCREN, obey a class of general monogamy inequalities in multiqubit systems, $2\otimes2\otimes3$ systems and $2\otimes2\otimes2^{n}$ systems for $\alpha\geq4\ln2$. We provide a class of general polygamy inequalities of multiqubit systems in terms of logarithmic convex-roof extended negativity of assistance (LCRENoA) for $0\leq\beta\leq2$. Given that the logarithmic negativity and LCREN are not convex these results are surprising. Using the power of the logarithmic negativity and LCREN, we further establish a class of tight monogamy inequalities of multiqubit systems, $2\otimes2\otimes3$ systems and $2\otimes2\otimes2^{n}$ systems in terms of the $\alpha$th power of logarithmic negativity and LCREN for $\alpha\geq4\ln2$. We also show that the $\beta$th power of LCRENoA obeys a class of tight polygamy inequalities of multiqubit systems for $0\leq\beta\leq2$.
Forecasting of Precipitation in India by Different Data Types using Investigation of Radial Basis Function Neural Network Model

There is a rainy season occurs during the period from June to august in almost all geographical parts of India. Moreover, some of the states like Uttarakhand, Cherapunji, Mumbai, Tamil Nadu etc. may suffer from some natural disaster. If we early predict such misfortunes through the variety of big data collected for such distinct positions at a particular amount of time then certainly can save the life and goods from such big natural calamities. Such normalized data can be updated at a regular interval of time. In view of this, the time series data analysis provides a method to early aware and protects the life of people from such natural disasters. The proposed method exploited the use the Radial Basis Function Neural Network Model with back propagation algorithm to make compatible with time series data analysis to forecast the predication of rainfall for the state of Punjab, India. In this technique, two types of predictions are used which are based on fifteen and twenty days. The comparison results reveal those fifteen days prediction provides more effective classification accuracy than twenty days prediction.
Application Analysis on PSO Algorithm in the Discrete Optimization Problems

Abstract Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzing the application of PSO algorithm in discrete optimization problem, which limits the development of discrete PSO algorithm. To address the challenge, we first give an investigation of PSO algorithm from the perspective of spatial search, then, try to give a novel analysis of the key feature changes when PSO algorithm is applied to discrete optimization, and propose a classification method to summary existing discrete PSO algorithms.
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation

While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy.Although several approaches recently proposed augmenting the training set by copying user utterances and replacing the real slot values with other possible or even similar values, they are not effective at improving the performance of existing DST models.To address these challenges, we propose a Turnbased Loss Function (TLF) that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns in order to improve joint goal accuracy.We also propose a simple but effective Sequential Data Augmentation (SDA) algorithm to generate more complex user utterances and system responses to effectively train existing DST models.Experimental results on two standard DST benchmark collections demonstrate that our proposed TLF and SDA techniques significantly improve the effectiveness of the state-of-the-art DST model by approximately 7-8% relative reduction in error and achieves a new state-of-the-art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and Multi-WOZ2.2, respectively.
On the Design and Development of Vision-Based Autonomous Mobile Manipulation

This paper investigates image-feature based visual tracking systems for autonomous mobile manipulation applications. First, we briefly present various visual tracking methods and their components for autonomous tracking applications. Second, we introduce the development process for the most popular image-feature based autonomous visual tracking system for grasping and mobile manipulation applications. Then, the application scenario for evaluation is provided with the detailed software and hardware components for the fire-fighting application. Finally, the evaluation results on a 6-DOF UR5 mobile robot manipulator arms are presented for autonomous grasping and manipulation for valve turning applications.
RF Synchronized Active LED Markers for Reliable Motion Capture

The paper presents a novel optical motion capture technique based on the detection of differences between successive images of a scene tagged with active LED markers that are wirelessly synchronized with camera shutter. By design the method provides an unambiguous identification of individual LED markers and thus reliable registration of tracked points of interest within a set of successive frames or among multiple cameras if a 3D capture is required. It eliminates a laborious cleanup of capture data and can provide marker coordinates in real-time without human intervention. The paper describes principles of the method and technical implementation proving functionality of the solution.
Unity as a Physics Simulator: Calculating Mean Free Path for Hard Disk Gas

In a typical physics course for non-physics majors, students analyze and solve problems that are simple and perceived by students as idealized to the extent that they are only remotely related to real-world problems. This perception hurts their motivation to learn fundamental physics. In this work, we show how students can use the Unity Physics Engine as a physics simulator to solve and analyze more complex and exciting physics problems than the standard paper-and-pencil problems. We provide an example of such a problem: calculating the mean free path for hard disk gas. By delegating the hard and tedious work of performing the time evolution of the system of gas particles to the Unity Physics Engine, students can concentrate on acquiring and analyzing data. In this way, students can engage with the physics of complex problems that could motivate them to learn fundamental physics. This approach to teaching physics is interesting in particular for a computer science major because it also helps students develop their programming skills.
Design Heuristics For In-Crisis Users

Any interface designed for in-crisis users needs to address their state of mind and physiological arousal. However, to date no clear guidance exists for the specific needs of in-crisis users. An opportunity to redesign a regional domestic abuse website inspired us to adapt Nielsen’s 10 heuristics by including three heuristics for in-crisis users, specifically domestic abuse victims. Given the lack of research we used existing research to address users’ cognitive and motor processing impairments during high anxiety and arousal. To see how well current domestic abuse websites follow these heuristics, we evaluated a pseudo-randomly selected set of 98 domestic abuse helplines. The result showed overall poor results. Irrelevant information, poor readability, and failure to highlight crucial information were among the main contributors for the poor assessment. The data illustrates the need for improved design guidelines to create a safe and effective option for in-crisis users.
Problems of the Digital Learning Formation in a Distance Format

The article deals with the problems of translating the educational process into a digital format. The objective necessity and demand for education digitalization has been substantiated. The measures of state support for distance learning during the period of restrictive measures are described, examples of the most accessible digital means of supporting the educational process are given. Considerable attention is paid to the issues of teachers’ readiness to work in remote and mixed formats. The analysis of the teachers’ difficulties in the implementation of an indirect form of educational activity. The forms and methods of training a teacher for remote work are proposed. On the basis of the competence-based approach, the goals and objectives of the educational process are formulated. Structural models of teacher and student competencies are proposed, on the basis of which a methodological system for the formation of subject and general competencies in a digital format should be developed. The conclusion is made about the advisability of improving the competencies of teachers in the field of remote work in a digital format, the subsequent development of the topic in scientific pedagogical research.
Survey on Recent Active Learning Methods for Deep Learning

The motivation of active learning is that by providing limited labeled training samples, a machine learning algorithm can provide higher accuracy. The provided training samples are selected from a large or streaming dataset. The selection procedure often incorporates some measure of informativeness of samples. This measure is also defined based on the machine learning model itself. The data used in active learning is usually unlabeled; hence, the selected samples have to be labeled by an oracle (e.g., a human or a machine annotator). This is in case that labeling data is time-consuming or expensive in some way. In this paper, active learning is first introduced, a general introduction is given, and several query strategy frameworks are reviewed. Several recent papers on the topic of active learning and deep learning are studied, analyzed, and categorized based on their query strategy and applications. Specially, the overview of active learning and recent deep learning techniques are explored.
The Variegated Applications of Deep Learning Techniques in Human Activity Recognition

Humans are vivacious and obstinate in that they are plagued by a constant need to be motile and sprightly which gives us a goldmine of data to work on. This constant stream of ever-changing activities performed by us could be dissected fastidiously to gain insight into the specifications of that activity. This could spur the use of IoT, automated devices and real-time monitoring. A variety of techniques some of which are video camera feeds which could be sourced from CCTVS and sensors, could be put to use to efficaciously procure data. This paper will delve into the various techniques proposed by various researchers and compare their performance on various deep learning and machine learning models to analyse them intrinsically. We will also showcase our own model consisting of the use of a 3D tempo-spatial dataset called the UCI-HAR dataset employing various deep learning models like LSTM, SVMs and more. The deep learning model will be improved upon by architectural and hyper parameter improvements. Other sections will discuss the related works including the datasets used in Human Activity Recognition. Also contained in the discussion section are the technicalities of the papers like the accuracy and the relevancy of the deep learning models being used. A proposed hybrid models using both video feed and sensor data for recognition will be floated. A panoply of industries including the health and defence sectors stand to gain from the rapid recognition of human activities.
An IoT-based context-aware model for danger situations detection

On a daily basis, people perform planned or routine activities related to their needs, such as going to the office, playing sports and so on. Alongside them, unpleasant unforeseen situations can take place such as being robbed on the street or even being taken hostage. Providing information related to the crime scene or requesting help from the competent authorities is difficult. That is why, mechanisms to support users in such situations, based on their physical status, would be of great importance. Based on such idea, a context-aware model for detecting specific situations of danger is proposed. It is characterized by a set of defined features related to the body posture, the stress level and geolocation whose values are gathered through a smartphone and a smartwatch, as enabling technologies for the local computation. A machine learning technique was adopted for supporting body posture recognition, whereas a threshold-based approach was used to detect the stress level and to evaluate of user’s location. After the description of the proposed model, the achieved results as well as current limits are also discussed.
Dynamic Transaction Storage Strategies for a Sustainable Blockchain

As the core technology behind Bitcoin, Blockchain’s decentralize, tamper-proof, and traceable features make it the preferred platform for organizational innovation. In current Bit-coin, block reward is halved every four years, and transaction fees are expected to become the majority of miner revenues around 2140. When transaction fee dominates mining rewards, strategic deviations such as Selfish Mining, Undercutting, and Mining Gap could threaten the integrity and security of the Blockchain. In this paper, we propose a set of Dynamic Transaction Storage (DTS) strategies for maintaining a sustainable Blockchain under transaction-fee regime. Through the systematic simulation, we demonstrate that block incentive volatility can be reduced by applying DTS strategies, and thus avoid strategic deviations. With DTS, public Blockchains such as Bitcoin become sustainable when the mining reward is solely based on transaction fee.
Design and Implementation of Autonomous Mobility Algorithm for Home Service Robot Based on Turtlebot

To design a home service robot, the Turtlebot robot based on ROS platform is used to achieve the autonomous navigation and obstacle avoidance functions. The result is shown in simulation experiment. During the experiment, to complete SLAM mapping, the Gmapping algorithm is applied. On the other hand, the Dijkstra algorithm is used for global path planning, and the dynamic window algorithm is used for local path planning. Both of them are used to realize the autonomous navigation function. By carrying the depth camera Astra, the robot can autonomous navigate and avoid obstacles in the indoor environment. In addition, by writing scripts, the SLAM mapping process, which originally required manual control, has also been improved to autonomous SLAM without manual control, making it more convenient in practical applications.
A Novel Hybridization Model for Human Activity Recognition using Stacked Parallel LSTMs with 2D-CNN for Feature Extraction

Recognizing human activity is a difficult task in certain applications, including video retrieval, human-computer interactions, autonomous driving, video surveillance, medical field, educational purpose, and abnormal activity recognition etc. However, extracting reliable and timely information from video recordings of human actions and behaviours is the most challenging issue in a ubiquitous computing environment. The purpose of this work is to present a novel hybridization deep model for comprehending and interpreting videos in HAR systems. The proposed hybridization model is built using 2D-Concolutional neural networks (2D-CNN) and stacked parallel bidirectional Long short-term memory (SPBD-LSTM). Its capable of learning prolonged sequence and processing longer videos that are sequentially synchronized by assessing attributes over a specified time interval. Experiments are carried out with two benchmark datasets UCF101 and SPHAR (Surveillance Perspective Human Action Recognition) to validate the contributions of proposed work. Finally, our hybridization model exhibits outstanding performance for analysing complicated human activities.
Smart and Secure Blockchain-based Healthcare System Using Deep Q-Learning

Healthcare is one of the top priorities in modern society to provide better health facilities. Therefore, investments in health care systems increased rapidly, aligned with the population growth rate. Besides, the data generated from the health sectors is incomparable with the amount of data generated in other industries. Therefore, managing data processing and sharing between various healthcare stakeholders is essential. Blockchain is an emerging technology used heavily in various domains, including the healthcare sector, to facilitate secure data sharing. However, mapping the content requirements with the blockchain’s configuration was not addressed, especially when addressing security, delays, and cost in healthcare systems. This paper proposes a blockchain-based intelligent Healthcare system (BC-iHealth) to address the mapping between the blockchain entities’ needs with the blockchain’s configuration while maximizing the security and minimizing the overall delay and cost. The optimization model is formulated as a Markov Decision Process (MDP) and solved intelligently using a Deep Q-Learning approach. Simulation results confirm that the Deep Q-Learning optimizes the BC-iHealth system and outperforms two benchmark strategies: random selection and exhaustive search.
Building Spell-Check Dictionary for Low-Resource Language by Comparing Word Usage

Each language has its own vocabulary which is spoken by a corresponding group of speakers. There are generally languages that have better resources and thus Natural Language Processing methods typically perform generally better for such languages; whereas on other hand, in the case of a large number of low-resource languages - there is a lack of sufficient annotated data that can be used in order to efficiently use the unsupervised methods for NLP tasks. As a result, a spell checker is a necessity for composing any documentation in a language; typically, by identifying words that are typologically and grammatically correct as well as misspelled words in such a language. The aim of this paper is to present a spell-check dictionary for the Albanian language by comparing word usage among various texts. Furthermore, it aims to do so by defining words to be entered in the dictionary from a large text collection taken from experiments and then conducting a comparison review of word usage frequency. The corpora include 49k sentences for the Albanian language of different fields such as computer science, economics, law, medicine, politics, tourism, art, psychology, etc. This spell-check dictionary would further contribute to the ease of use of the Albanian language in electronic media. Noting that the Albanian language is a low-resource language, another aim of this paper and related further research relates to building a larger and better corpus of Albanian language on top of which the spell-checking dictionary could be continuously advanced and perfected.
Adversarial Attacks and defenses on Deep Learning Models in Natural Language Processing

Deep neural networks (DNNs) have achieved remarkable success in various tasks such as image classification, speech recognition, and natural language processing. However, DNNs have proven to be vulnerable to attacks from adversarial examples. These samples are generated by adding some imperceptible disturbances, which are used to mislead the output decision of the deep learning model and bring significant security risks to the system. However, previous research mainly focused on computer vision, thus neglecting the security issues of natural language processing models. Since the text data is discrete, the existing methods in the image field cannot directly use the text. This article summarized the research on adversarial attacks and defenses in natural language processing and looked forward to future research directions.
Framework for Block-Chain Deployment in Assembly of an Air-Craft or a SpaceCraft

The final assembly of an aircraft or a spacecraft is a rigorous task that requires full traceability of the parts installed, their fabrication processes, and the assembly work. This is also the case for each of its sub-assemblies. Safety and security go hand in hand in the production of these flying machines, and even more so for satellites and space-shuttles. The many parts and sub-assemblies that compose the final spacecraft, make full traceability a challenging fit, that is almost unsurmountable. Such traceability is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full spacecraft. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the space-craft assembly documentation. This paper describes a framework that harnesses smart-contract and blockchain technology to offer traceable and secure documentation of the assembly process. The concept of a Bill-Of-Assembly (BOA) is introduced which incorporates data from the bill of materials, the bill of operations and processes, and the bill of resources (machines and/or operators). The paper describes the use of the BOA with smart-contract and blockchain technology for getting full traceability, safety, and security for the spacecraft. Finally, we show that this BOA data structure could be the backbone of a digital twin of the spacecraft. Future research could extend the proposed approach to other assembly processes such as automotive assembly lines.
Devanagari manuscript labeling using feature extraction: A survey and future directions

Translation of multiple images or documents into editable text, that can be decomposed is termed as OCR. Different automated machine learning tools have been used in the literature that segregate the data into characters. This automatic segregation helps to convert the copies into electronic format. This paper deals with multiple feature extraction techniques and also the data labeling tools that have been used extensively for character recognition. This paper presents the present state of art techniques to provide further direction of research for the researchers.