Reviewerly/exHarmony huggingface dataset:
Neural dynamics based complete grid coverage by single and multiple mobile robots

Abstract Navigation of mobile robots in a grid based environment is useful in applications like warehouse automation. The environment comprises of a number of free grid cells for navigation and remaining grid cells are occupied by obstacles and/or other mobile robots. Such obstructions impose situations of collisions and dead-end. In this work, a neural dynamics based algorithm is proposed for complete coverage of a grid based environment while addressing collision avoidance and dead-end situations. The relative heading of the mobile robot with respect to the neighbouring grid cells is considered to calculate the neural activity. Moreover, diagonal movement of the mobile robot through inter grid cells is restricted to ensure safety from the collision with obstacles and other mobile robots. The circumstances where the proposed algorithm will fail to provide completeness are also discussed along with the possible ways to overcome those situations. Simulation results are presented to show the effectiveness of the proposed algorithm for a single and multiple mobile robots. Moreover, comparative studies illustrate improvements over other algorithms on collision free effective path planning of mobile robots within a grid based environment.
Computing absolutely normal numbers in nearly linear time

A real number x is absolutely normal if, for every base b≥2, every two equally long strings of digits appear with equal asymptotic frequency in the base-b expansion of x. This paper presents an explicit algorithm that generates the binary expansion of an absolutely normal number x, with the nth bit of x appearing after npolylog(n) computation steps. This speed is achieved by simultaneously computing and diagonalizing against a martingale that incorporates Lempel-Ziv parsing algorithms in all bases.
Emergency Rescue Training System for Earthquakes Based on Immersive Technology

In order to quickly and accurately implement emergency rescue after earthquake and save lives and property, this paper proposes an emergency rescue training system based on immersion technology. The system uses virtual reality, augmented reality and mixed reality technology to construct a real and virtual interwoven training environment. Compared with the traditional emergency rescue training, this method is not limited by time and site, and enables the trainees to immerse themselves in the earthquake environment for operation. The system consists of four main functional modules: cognition of fault movement, recognition of earthquake precursor, simulation of earthquake scene, and training of emergency rescue.
Virtual and digital traces: a new approach in understanding

Over the past decade, there has been a significant increase in crimes committed using high technologies, which requires a new theoretical approach to understanding the trace picture in General and individual types in particular. The article reveals the relevance of studying virtual traces in domestic criminology as an independent type of traces, forms an approach to understanding the virtual space, established the differences between virtual traces and digital ones, which are a variety of material traces, noted the relationship of virtual and digital traces, which is especially important when they are removed and fixed. At the same time, virtual traces are considered as part of digital traces, which have a complicated basis, which requires significant efforts to detect and seize them for subsequent use in the process of proving in criminal cases. Such an approach in understanding a new type of traces in forensic science is relevant not only for the Russian Federation, but also for a number of foreign countries.
HyPA: A Hybrid Password-Based Authentication Mechanism

User authentication is the process of verifying identity of a user. A user’s identity can be verified by using different types of authentication mechanisms such as text-based password, graphical password, and biometrics. Password-based authentication is the primary line of defense against intruders and cyberattacks. Text-based password is widely used for authentication purpose, but it is vulnerable to different kind of security attacks, such as brute force attack, dictionary attack, shoulder surfing attack etc. To overcome such risks, we propose a hybrid password authentication (HyPA) mechanism in this paper. In our proposed method we combine graphical password with text-based password. Using HyPA user needs to provide text-based password (alphanumerical character) along with image-based password. The analysis proves the efficiency, effectiveness, and security of HyPA. We also show that our proposed mechanism is secure against brute force attack, dictionary attack, and shoulder surfing attack.
Visual Question Answering using Data Mining Techniques for Skeletal Scintigraphy in medical domain - VQADMSS

Understanding about the medical images of patients is a very tedious task. Doctors should convey their patient through the image of the questions asked by the patient. Large amounts of labeled data are required for training in traditional approaches for VQA (Visual Question Answering). Also, the description of clinic trial text in English and in multilingual contexts is one of the challenges in the medical field. To present the clarification about the images, doctors are required to provide the related images. It is better for comparison with the patient's previous report and current report. This paper contributes to solve the problems related to VQA for better description of the image and accuracy related images through the answer of the questions, also to make it easy to convey the users with any kind of images. Question answer process should be more descriptive for easy to understand and traceable. This system helps to identify the types of images which are captured by any scanner. The better accuracy is a visualization method which projects the answers as a baseline that shows the corresponding region with various colors, which is easier to note the answers in visual method for the appropriate questions. This proposed framework focuses on Radiology image for Skeletal Scintigraphy to transform and generate a model using Data Mining Techniques. This system suggests that the effective medical Visual Question answering techniques is better to assist doctors in clinical analysis and diagnosis. This also will help the hospital services to grow the medical domain.
Language Independent Searching Tools for Cultural Heritage on the QueryLab Platform

The paper describes the tools for searching and visualizing local and web inventories related to intangible and tangible cultural heritage in the QueryLab platform. The pandemic outbreak has made more evident the need to offer users tools to query and enjoy interesting and educative websites by their homes. The tools presented are useful for users who are not experts in the domain of the inventories, offering predefined queries and semantic query expansion to interact with the archives. The visual suggestions, in the form of word clouds of the tags of the selected archives, help in querying the archives and retrieving the elements that come closest to the user's interests. As one of the QueryLab aims is to continue to add inventories, in the languages they are stored, visual suggestions help to overcome the language distance between the archives and the users to allow an easy and successful interaction. This paper presents QueryLab tools for searching, browsing, and displaying multimedia data, with some preliminary results.
Accurate Estimation of Analog Circuit Parameters by CMA-ES Method

This paper presents a method to accurately estimate analog circuit parameters from observed amplitude characteristics. In analog circuits, it is not always possible to obtain the expected characteristics due to the existence of errors between the design value and the actual value of circuit parameters. It requires a huge amount of resources to measure all the actual values of those parameters. We propose a method to accurately estimate those values using the Covariance Matrix Adaptive Evolution Strategy (CMA-ES), and evaluate its performance in comparison with some other methods.
The Fog of Software Design

Your software projects never quite go as smoothly as the textbooks promise, do they? The article explains where the fog of design comes from and tells you how to handle it.
Modeling of cognitive processes based on gaze transition during programming debugging

This study proposes a method for analyzing gaze features and modeling cognitive processes during program debugging. We first use an eye-tracking device to extract gaze transitions between editor components and code blocks. We then create transition diagrams based on a Markov process and a Z-test. Experiments demonstrate the possibility of estimating gaze transitions and cognitive processes required for error resolution by comparing transition diagrams from advanced and beginner programmers.
A Technological Framework for Rapid Prototyping of X-reality Applications for Interactive 3D Spaces

Room-sized immersive environments and interactive 3D spaces can support powerful visualizations and provide remarkable X-reality experiences to users. However, designing and developing applications for such spaces, in which user interaction takes place not only by gestures, but also through body movements is a demanding task. At the same time, contemporary software development methods and human-centered design mandate short iteration cycles and incremental development of prototypes. In this context, traditional design and software prototyping methods can no longer cope up with the challenges imposed by such environments. In this paper, we introduce an integrated technological framework for rapid prototyping of X-reality applications for interactive 3D spaces, featuring real-time person and object tracking, touch input support and spatial sound output. The framework comprises the interactive 3D space, and an API for developers.
Medical model estimation with particle swarm optimization

In this paper, a nonlinear medical model based on observational variables has been produced and the particle swarm optimization (PSO) technique, which is an e¤ective technique to predict optimum parameters of the biomedical model, has been used.This study has been conducted on a dataset consisting of 539 subjects.For comparison purposes, nonlinear regression analysis, nonlinear deep learning, and nonlinear regression neural network methods are also considered and the PSO results appear to be slightly better than that of other methods.Built on observational variables and …ndings, the model is expected to be a good guide for healthcare professionals in diagnosing pathologies and planning treatment programs for their patients.It is therefore strongly believed that the article will be particularly useful for those interested in emerging biomedical models in various medical modelling areas such as infectious and hematological diseases such as anemia.
Unsupervised Image Segmentation Using THMRF Model

In this paper, a segmentation algorithm of brain magnetic resonance imaging is proposed. The proposed method rests on Tweedie hidden Markov random field processing and the Expectation- Method of moments and Maximization algorithm. The algorithm was validated on synthetic and real images. The basic merit of our proposed method of segmentation lies in its ability to quantify the complex structure as brain tumor.
Scene Text Detection with Feature Aggregation and Receptive Field Enhancement

We propose a scene text detection with feature aggregation and receptive field enhancement. It mainly improves the problem of wrong text segmentation. Specifically, a Feature Aggregation and Receptive Field Enhancement Module (FARE) adopts a ladder structure and constructs multi-scale feature aggregation with multiple branches to enhance the feature representation ability of texts of different scales. We add dilated convolution to expand the range of receptive field, which increases the detection of large-scale texts in low-level feature and obtains more accurate position information. Extensive experiments on the ICDAR 2015, ICDAR 2017 MLT, and CTW1500 show that the proposed method effectively improves the detection performance of the network. Notably, our proposed method has detected a precision of 87.4% on the curved text dataset CTW1500.
Analysis of On-site Evaluation Methods of Network Security in the Evaluation of Information Security Level Protection

Network security on-site evaluation is not only an important content of the information security level protection evaluation project, but also a major difficulty. It should accurately select network evaluation objects and evaluation methods for different user access paths. In view of the incomplete technical information in the current network field security evaluation, the limited ability and non-cooperation of the technical personnel of the evaluated party, this article provides some suggestions on how to achieve efficient, fast and accurate evaluation.
Activity Monitoring in the Digital Learning Environment of a Modern University: Technological Tasks of Psychological Support

The paper reviews the existing approaches to using digital footprints in the digital learning environment. Monitoring digital footprints of university students can help to design smart learning environment and predict models of interaction between this environment and the user. The article covers the main analysis tools that can be applied to activity monitoring in LMS Moodle, including datasets as a convenient resource for distant learning. The authors studied authentication techniques that are based not on one’s knowledge but on the confirmation of one’s digital profile. The research results revealed some personal styles and patterns of cognitive behavior that reflect students’ work in the digital learning environment. The research results can be used to develop new psychological support of activity monitoring of the digital university environment, as well as to create new effective cognitive user-friendly interfaces.
A dependently typed calculus with polymorphic subtyping

A polymorphic subtyping relation, which relates more general types to more specific ones, is at the core of many modern functional languages. As those languages start moving towards dependently typed programming a natural question is how can polymorphic subtyping be adapted to such settings. This paper presents the dependent implicitly polymorphic calculus (λI∀): a simple dependently typed calculus with polymorphic subtyping. The subtyping relation in λI∀ generalizes the well-known polymorphic subtyping relation by Odersky and Läufer (1996). Because λI∀ is dependently typed, integrating subtyping in the calculus is non-trivial. To overcome many of the issues arising from integrating subtyping with dependent types, the calculus employs unified subtyping, which is a technique that unifies typing and subtyping into a single relation. Moreover, λI∀ employs explicit casts instead of a conversion rule, allowing unrestricted recursion to be naturally supported. We prove various non-trivial results, including type soundness and transitivity of unified subtyping. λI∀ and all corresponding proofs are mechanized in the Coq theorem prover.
A COMPARATIVE RESEARCH ON G-HMM AND TSS TECHNOLOGIES FOR EYE MOVEMENT TRACKING ANALYSIS

Eye movement analysis provides a new way for disease screening, quantification and assessment. In order to track and analyze eye movement scanpaths under different conditions, this paper proposed the Gaussian mixture-Hidden Markov Model (G-HMM) modeling the eye movement scanpath during saccade, combing with the Time-Shifting Segmentation (TSS) method for model optimization, and also the Linear Discriminant Analysis (LDA) method was utilized to perform the recognition and evaluation tasks based on the multi-dimensional features. In the experiments, 800 real scene images of eye-movement sequences datasets were used, and the experimental results show that the G-HMM method has high specificity for free searching tasks and high sensitivity for prompt object search tasks, while TSS can strengthen the difference of eye movement characteristics, which is conducive to eye movement pattern recognition, especially for search tasks.
Inequality Thresholds, Governance and Gender Economic Inclusion in sub-Saharan Africa

Inequality and gender economic exclusion are major policy concerns facing sub-Saharan Africa in the post-2015 development agenda. The study provides critical masses of inequality that should not be exceeded if governance is to promote gender economic participation. The research focuses on 42 countries in sub-Saharan Africa using annual data spanning from 2004 to 2014. The empirical evidence is based on the Generalized Method of Moments. The following findings are established. First, inequality (i.e. the Gini coefficient) levels that completely nullify the positive effect of governance on female labour force participation are 0.708 for political stability, 0.601 for voice & accountability, 0.588 for government effectiveness, 0.631 for regulatory quality, 0.612 for the rule of law, and 0.550 for corruption-control. Second, inequality thresholds at which female unemployment can no longer be mitigated by governance channels include: 0.561 (for political stability) and 0.465 (for the rule of law). Third, inequality levels that completely dampen the positive impact of governance on female employment are 0.608 for political stability, 0.580 for voice & accountability, 0.581 for government effectiveness, and 0.557 for the rule of law. As the main policy implication, for good governance to promote gender economic inclusion, inequality levels should not exceed established thresholds.
Filtering Method based on Speed Prior Information by a Fixed Single Observer Using Bearing-only Measurements

Considering the speed information as a prior constraint, constrained cubature kalman filter (CCKF) tracking filtering method based on model reduction (MR) method and perfect measurement (PM) method is studied and compared for the tracking problem of fixed single observer passive positioning using only angle pair constant speed targets. The simulation results show that the fixed single observer passive positioning system can track the moving target with constant speed by measuring angle only under the condition of speed prior information, and the perfect measurement method is more robust to the initial state of the filter than the model reduction method.
Video Information recantation using picture characteristic method

Abstract Information withdrawal is one of the art for many researchers, especially extracting the images one of the most inspiring for many investigators nowadays, because the complexity of the data set. Image semantic features, because these features tell how to use the corresponding patterns during the process of editing the viedo. The video arrangements which is a formal and explicit specification of events. Specifically, an event is combination of Position, period, moving, and image capturing practise. Extracting the similar contentment from the stored data set is called interpretation template. Here, a semantic design is a mixture of little –smooth structures such color, motion and audio. This video arrangement bring the new set of information that not extracted previously.
Survey on Crime Analysis Using Data Mining Based on Mobile Platforms

With a large rise in crime globally, there is a necessity to analysis crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. Crime analysis and spatial analysis using Geographic Information System(GIS) tools such as hot spot generation, zoning, navigation and crime profiling, detection of mobile locations and various web-based applications are well known and can be used scientifically for citizens advancement, while crime prediction and control can be used effectively. Crime data analysts will enable the law enforcement officials to speed up the crime resolution process. We can analyze previously unknown, helpful information from unstructured data by using the theory of data mining. The purpose of this work is to perform a survey on the crime analysis by using several data mining techniques that has been applied towards criminal activities over the years to be a valuable guide to young researchers that are interested in this field.
Research on Smart Learning System Model

Abstract With the development of computer science in our country, more and more intelligent products appear in people’s daily lives, bringing convenience to people’s lives. In the education industry, an intelligent learning system has also emerged. After learning the relevant knowledge of the construction of the intelligent learning system and the Apriori algorithm, this article builds the intelligent learning system based on the Apriori algorithm, and take the university physics department as an example to study and analyse the function and realization of the system.
Semantic Techniques to Support IoT Interoperability

Smart devices and sensors have reached a very high level of pervasiveness: we are practically surrounded by intelligent items, which continuously communicate with each other and collect information. One of the most challenging issues regarding the use of such sensors regards the possibility to seamlessly make them interoperate to reach a specific goal. This objective could be difficult to achieve, due to the lack of a universally accepted standard for sensor communications. In this paper, we present a prototype tool for the analysis of sensors’ API that, through a semantic graph representation, tries to overcome the possible interoperability issues that may arise in a sensor network, and provides instrument to support sensors’ orchestration and management.
Designing an Artificial Agent for Cognitive Apprenticeship Learning of Elevator Pitch in Virtual Reality

Developing social skills like elevator pitch requires being situated within authentic activities and contexts, which is difficult to achieve on a daily basis. In this work, we explore whether an artificial agent with embodied feedback in virtual reality (VR) can foster a situated learning experience. Previous works on computer-mediated feedback have shown that VR can foster oral presentation competence for preuniversity and junior undergraduates through delivering feedback. However, it is unclear how well the learning experiences are and how well students perceive an artificial coach in VR, especially for senior undergraduates and postgraduates seeking job and research opportunities. To inform the design of such a system, we conducted interviews with experts and observed real elevator pitches. We then designed a proof-of-concept VR coaching system with three embodied feedback strategies: immediate, after-action, and the combination of both. Through a between-subject experiment with 40 participants, we studied learners’ perceptions under the embodied feedback. We found that receiving embodied feedback can create a stronger sense of cognitive apprenticeship, i.e., coaching and helping from experts, and help improve the perception of the artificial agent and the effect of learning. We further investigated the pros and cons of different strategies and discussed room for improvement.