Reviewerly/exHarmony huggingface dataset:
Path Planning Algorithm for Initially Unknown Indoor Environment Navigation

Allowing Robots to navigate automatically in unknown environments where human being cannot access is a needed characteristic in many fields, such as military, industry, civil engineering. This paper presents an efficient and fast path-planning algorithm for mobile robots, which navigate in a priori unknown indoor environments based on their local capacities of sensing and acting. For navigate in a priori unknown environments, re-planning the path it’s necessary each time the current one cut off by an obstacle, thus the path-planning algorithm must had the ability to re-plan the path once the tracked path become unavailable (online planning). The proposed path-planning algorithm deploy a grid map to characterize the environment, where the environment described by an occupancy and trajectory grids, which smooth the planning task. In order to make the planning faster we had split up the algorithm into two modules: obstacle avoiding and path re-planning. The experiments results had shown the applicability and the fusibility of the proposed approach.
BeiDou Satellite Navigation Terminal Effectiveness Evaluation Based on Cloud Theory

With the continuous development of the Beidou satellite navigation system, the Beidou satellite navigation terminal has been transformed from “bus slot + board card” to modularized and chip-based. Its functions, performance and practicability have been continuously improved, and it has been more and more widely used in military and civil fields. The effectiveness evaluation of the Beidou navigation terminal can accurately grasp the capabilities of the equipment in the process of use, and can provide an important basis for the design demonstration, appraisal and finalization of Beidou navigation terminal equipment. Effectiveness evaluation indicator system for BeiDou equipment is constructed based on the function of the equipment, and index weights are calculated through AHP method. Considering the randomness and uncertainty of the BeiDou equipment effectiveness evaluation, an integrated evaluation method which feasibility is studied by an example, is given using backward cloud and integrated cloud. The evaluation example and result analysis show that this method combining qualitative and quantitative analysis, is not only reduces the subjective factors in the evaluation, but also gets overall and visualized evaluation results, which provides a new way for effectiveness evaluation of BeiDou user equipment.
Real-Time Retail Smart Space Optimization and Personalized Store Assortment with Two-Stage Object Detection Using Faster Regional Convolutional Neural Network

In the present-day scenario of the retail environment, there is a tendency of customers to do prolonged shopping. During their constant efforts to purchase products of their choice, they bat around the entire store. They choose a product and continue exploring for more. While their further exploration, there is a possibility that they might encounter a better product that may satisfy their needs. So, there is a tendency that they may pick up the new product, compare with the existing product and leave the latter behind if they find the new product to be of a better purpose to their use, causing the initial product to be misplaced. There might be a possibility that empty spaces be created between products that might look sparse and lower stock display if not properly monitored. The primary goal of this research is smart space management and personalized store assortment by using computer vision. That is, wherever there are empty spaces created, we constantly monitor, and whenever there is any product misplaced, we send an automated notification to corresponding staff. We use state-of-the-art computer vision technology to address this issue. All the processing is done in real time and the system is found to be functionally very stable and works under all ideal conditions.
Global Perception Attention Network for Fine-Grained Visual Classification

Based on convolutional neural networks (CNNs), recent fine-grained visual classification (FGVC) are solved by locating the most discriminative parts, aggregating the most remarkable feature maps and learning high-order encoding. These works have proposed various interaction methods between global and local information. Compared with extracting global information, however, tracing discriminative local parts of objects breaks receptive field integrity and loses neighboring relationships. In some complex scenarios, recent FGVC models still face with the challenge of encoding whole objects integrally with less fine-grained local details. These factors motivate us to rethink rich feature utilization in FGVC task. In this paper, we propose Global Perception Attention Network (GPANet) for FGVC, a novel framework with a focal locator (FL) module and a global perception attention (GPA) module. The proposed network follows end-to-end location-classification designing paradigm for coarse-to-fine classifying. The FL works as a weakly-supervised location module by aggregating activation map and searching highest-response region. The GPA module is a core feature refinement module in our proposed method, consisting of a modified global perception module (mGPM) and a squeeze-and-excitation (SE) channel attention block, assembled through a residual structure. We perform a module-by-module ablation study for GPANet to prove its design effectivity and modular flexibility. Benchmark experiments are conducted on three public FGVC datasets. The proposed method achieves competitive performance compared with the state-of-the-arts and presents favorable generalization ability of several fine-grained recognition tasks.
Programming SBCs Using Python

Python is an important tool for programming and accessing the hardware of the SBCs. This chapter deals on python programming logic and hardware accessing such as GPIO operations as high/low, PWM operations, etc. The chapter provides details on circuits driving individual pins using Python and C.
Visual and Semantic Feature Coordinated Bi-Lstm Model for Unsupervised Video Summarization

While dealing with user-created video, the prior methods suffer from the problem of high redundancy among keyframes. To address the critical issue, we present a Visual and Semantic Feature coordinated Bi-LSTM (VSFB) model for unsupervised video summarization. First, a novel Salient-Area-Size-based spatial attention model is presented to extract frame-wise visual features on the observation that humans tend to focus on sizable and moving objects. Second, the visual features are integrated with semantic features processed by Bi-LSTM to refine the frame-wise probability of being selected as keyframes. Finally, an index adjusted diversity and representativeness reward is utilized to reinforce the learning operation of the VSFB model in the video summarization. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of the F-score.
Video Transmissions in Networks with Packet Loss

Packet loss is one of the main factors that can affect and/or impair the video’s quality, with a direct influence on the Quality of Experience (QoE) perceived by the end-user. For this reason, investigating the effects of packet loss on the quality of the video is crucial and could lead to a better understanding of the QoE perceived by the end-user. In this context, for purposes of teaching and learning on computer networks, this paper discusses a practical study on video transmissions in networks with packet loss. For this, based on a reference scenario, a computational environment is used for purposes of experimentation and discussion, with video transmissions under normal network conditions and with packet loss emulation of 5%, 10%, 15%, and 20%. The experiments and discussions are based in a computer network laboratory carried out with second-year students of an undergraduate computer course to help them understand the effects of packet loss on the quality of the video, as well as on the QoE perceived by the end-user. This work aims to complement other approaches to teaching and learning on computer networks.
Least-Restrictive Multi-agent Collision Avoidance via Deep Meta Reinforcement Learning and Optimal Control

Multi-agent collision-free trajectory planning and control subject to different goal requirements and system dynamics has been extensively studied, and is gaining recent attention in the realm of machine and reinforcement learning. However, in particular when using a large number of agents, constructing a least-restrictive collision avoidance policy is of utmost importance for both classical and learning-based methods. In this paper, we propose a Least-Restrictive Collision Avoidance Module (LR-CAM) that evaluates the safety of multi-agent systems and takes over control only when needed to prevent collisions. The LR-CAM is a single policy that can be wrapped around policies of all agents in a multi-agent system. It allows each agent to pursue any objective as long as it is safe to do so. The benefit of the proposed least-restrictive policy is to only interrupt and overrule the default controller in case of an upcoming inevitable danger. We use a Long Short-Term Memory (LSTM) based Variational Auto-Encoder (VAE) to enable the LR-CAM to account for a varying number of agents in the environment. Moreover, we propose a sample efficient meta-reinforcement learning framework with a novel reward function based on a Hamilton-Jacobi value function to train the LR-CAM. The proposed method is fully meta-trained through a ROS based simulation and tested on real multi-agent system. Our results show that LR-CAM outperforms the classical least-restrictive baseline by 30%. In addition, we show that even if a subset of agents in a multi-agent system use LR-CAM, the success rate of all agents will increase significantly.
Early Detection of Javanese Phonological Awareness of Grade 4 Elementary School Students

This study aim to detect the phonological awareness of Javanese in elementary school grade 4 students early through the distance learning process. To be able to determine the level of awareness of children's phonology, one indicator is to look at the child's ability to write and read Javanese text. This research is a descriptive qualitative type. Data was collected by archiving all assignments related to writing and reading Javanese text sent by students through the WhatsApp application. The collected data were then analyzed descriptively. The results showed that grade 4 elementary school students made many phonological errors when writing and reading Javanese text. The most common mistakes are when writing vowels /a/ and reading consonants /dh/ and /th/. Based on the many errors, it can be concluded that the phonological awareness of Javanese elementary school students is still low, or it can be said to be less aware.
Boneh-Boyen IBE

Identity-based encryption (IBE) is a form of public key infrastructure (PKI) which has become a hot topic of research among the research community as IBE enables senders to encode data without interfering with the public key certificate. IBE computes the public key from the unique recipient’s identity and authenticates by a third party so as to overcome the limitations faced by traditional cryptographic techniques. In 2004, D. Boneh and X. Boyen worked towards IBE without using random oracles which is one of the most popular and efficient IBE techniques. This chapter focuses on the Boneh-Boyen IBE technique which is based on selective identity and gives good results in standard model. It also enlightens on the basic IBE model introduction and its working to get a clear idea about IBE. Later on, this document covers the Boneh-Boyen basic scheme that is classified into two categories, additive scheme and multiplicative scheme. These schemes are explained in detail with the help of four main stages: parameter setup, extraction, encryption, and decryption process. Apart from the basic scheme, Boneh-Boyen full scheme is explained here which helps to compute the public key of the receiver. At the end, the chapter emphasizes on the security concept of the Boneh-Boyen IBE.
Substantiation of Methods and Development of an Algorithm for Neural Network Estimation of Object Classification Parameters in Aerial Imagery

The final decision on the nomenclature of the input parameters of the neural network is made as a result of rather lengthy comparative experiments. To reduce the number of these experiments, it is advisable to determine the importance of each of the possible parameters of computer vision. Since modern formalized methods for assessing the importance of the classification parameters of objects in aerial imagery do not meet the accuracy requirements, it was decided to use expert estimation to form the convolution parameters. It is proposed to use the method of paired comparisons, which is explained by its proven efficiency in cases of a large number of test objects, with which the parameters of object classification in aerial images are associated. In this case, the input data of the model is a vector, the elements of which will be matrices of expert assessments of the significance of computer vision parameters. As a result, a model of the processes of integration of object classification parameters on aerial imagery used to recognize unidentified objects and previously classified objects has been developed. The developed model is used to determine the parameters of computer vision, which can be used in neural network tools for optimizing scanners for processing airborne data based on the automation of an expert assessment of current parameters.
Case Study #1: Leadership and Values in Silicon Valley

Fridays are traditionally dress-down days, pandemic notwithstanding. But at one of the author's early jobs, the last day of the week brought Baileys Irish Cream in the morning coffee and mimosas for all staff. At Tantalize, the tech titans were brilliant jerks. To be sure, they had plenty of role models in the industry. The author was on the information security management team, reporting to the CISO. Her experience was a microcosm of the overall management culture, and of the overarching unreality of the enterprise itself. Throughout her career, the author has often reflected on her days at Tantalize. In the job she held after that, she managed five experienced staff, all men, some decades senior to her, and they established an open, mutually supportive, and respectful rapport. Four years on, the author now serve as Chief Strategy Officer and Head of People at Cobalt.
Human Identity in the Coordinates of Historical Time

The search for one’s own identity is a necessary condition for the existence of a person and, as a rule, is conducted in the aspect of his national, religious, and social characteristics. The great importance in this process has historical self-determination, which only in the mutual superposition of the coordinates of the beginning and the end of history is possible. The problem is these parameters are traditionally considered autonomously in relation to each other, which a balanced assessment of the state of modern humanity and its future complicates. The aim of this paper is to apply methodological approach that will provide a correlation of historical retrospective and outlook for self-determination of modern man. Since the historical location of modern human is the point between prehuman and post-human, it can be argued that his essence is not quite defined. It is multifaceted, but without soul is impossible, so anthropogenesis is a soul-genesis, which in the physical appearance of man is expressed, in the creation of tools, art, religion, morality, in various forms of symbolic activity. A not fully formed person begins to dehumanize. This is a symptom of the approaching of the “end” of history. Organic and intellectual regression is accompanied by the integration of human with a machine, which assumes not only the functions of the body, but also intellect. The primacy of creation is replaced by the dominance of consumption. In man “symbolic animal” dies, but “virtual animal” awakes. Religious faith degenerates into “secular” faith, language degrades to the level of “digital” phenomenon, beauty transforms into a commercial and entertainment product-simulacrum. There is the occurrence of a new era—post-verbal, post-value, post-human. But historical pluralism points to the asynchronous of the “beginning” and the “end” of history. Ending in one point, it begins in another, thus preserving the hope for the preservation of man.
Fuzziness and Vagueness in Natural Language Quantifiers: Searching and Systemizing Few Patterns in Predicate Logic

The study of quantification has become a significant area of research among logicians, philosophers, linguists, computer scientists, and others. Related to quantification, the form of generalized quantifiers is generally found in natural language. However, another type of quantifiers, which is called fuzzy and vague and they always need sort of proper treatment and resolution. Many proposals like (‘the fuzzy inclusion relation’ and ‘monotonicity in the ith argument’) have been introduced to deal such issue. Here, we especially focus on to a natural language (Punjabi) to study few but important fuzzy quantifier cases that do not find in Hindi. We select 29 predicates and compare them in two data sets with these (ਭੌਰਾ-ਕੁ [pòrɑ-kʊ]; ਮਾੜਾ-ਜਾ [mɑɽɑ-ɟɑ]; ਜਮ੍ਹਾ-ਈ [ɟəmɑ-i]; and ਮਸਾ-ਕੁ [məsɑ-kʊ]) fuzzy quantifiers and suggest the mapping plan. After comparing both languages (Punjabi and Hindi), we claim that Punjabi has comparatively strong quantification and it can be discussed and analyzed in a predicate logic.
Classical and Operant Conditioning

The scientific study of conditioning dates back to the beginning of the twentieth century, to the experiments of Ivan Pavlov in Russia and Edward Thorndike in the United States, working in quite different traditions and in total ignorance of one another. Pavlov developed the general procedures for studying classical conditioning, invented a terminology to describe it that is in use, and advanced an account of what was happening in his experiments, many elements of which are widely accepted. According to Thorndike the principle of instrumental conditioning is the law of effect. A response that produces one class of consequence will increase in probability; one that produces another class will decrease in probability. Instrumental responses are modified by their consequences, as opposed to Pavlovian conditional response, which are simply elicited by a stimulus regardless of their consequences.
Blockchain and the Future of Securities Exchanges

In this paper, we analyze blockchain technology as an alternative to facilitate securities market transactions. We argue that the ability to lower transaction costs combined with the reduction of intermediaries and improvements in transaction efficiency will promote the implementation of blockchain technology in these markets. We use platform economics to illustrate how blockchain-based securities exchanges can reduce unfair rents that platform operators extract from the investing public under the current regulatory regime, and indicate how the economics of platforms might be used to implement a blockchain solution to enhance the exchange process. We argue that in order to reduce transaction costs and increase the potential for innovation, blockchain platforms should be moved from the supply-side to an intermediary position and operate as a neutral party that straddles both sides of the market. Finally, we find that open routing procedures are likely to positively impact the market and enhance the efficiency of securities market transactions.
Innovation of Employment and Entrepreneurship Education Based on K-means Clustering Algorithm

After more than ten years of exploration and development, entrepreneurship education in Colleges and universities in China has entered a new stage of diversification and connotation development. How to guide students’ personalized entrepreneurship education has become a hot issue in entrepreneurship education. This paper proposes to use the mature k-means algorithm in industry to analyze and evaluate students’ entrepreneurial thinking through big data, and explore a new way of personalized entrepreneurship education, and puts forward corresponding improvement measures from the main aspects of College Students’ entrepreneurship education, such as college students themselves, colleges and universities.
Text Mining for Factor Modeling of Japanese Stock Performance

Recent advances in natural language processing (NLP) have made a significant breakthrough in various NLP tasks. Word embeddings and pre-trained language models can convert unstructured textual data to computable numerical vectors, containing meanings and contextual information. However, most public language models are only for English, and therefore, financial applications are also in English. Few Japanese studies a direct approach, which predicts not attribute of a text but real-world information. This study aims to build a factor model based on Corporate Annual Securities Reports' textual data for the Japanese stock market and investigate its effectiveness. We found that textual data complement financial numerical data and improve the p-value of the Gibbons-Ross-Shanken Test, reducing the factor model's anomaly. Furthermore, BERT [1] is much better than word2vec [2] and LDA [3] for this factor modeling.
Collaborative Framework for Implementation of Accessible Digital Solutions

The paper presents an innovative framework for collaboration in the telecommunication sector that engages regulators, academia, telecommunication operators, and organizations of people with disabilities in the application of expertise, knowledge, and digital technologies to create wealth and welfare in society and increase public awareness of the communication issues of people with disabilities. The framework is one possible approach in dealing with practical implementations of digital accessibility solutions and a way to encourage all stakeholders in today’s digital world to consider certain steps that will significantly improve the quality of life of people with disabilities. Within the framework presented, several accessible digital solutions have been developed and published. The paper presents in detail the application developed to raise awareness about the importance of proper interaction with people with different types of disabilities (people with intellectual disabilities, deaf, blind, etc.).
Detection of Text from Video with Customized Trained Anatomy

With the influence of diverse architectures like ImageNet, VGGNet, ResNet for detection of objects in images, we are proposing a novel architecture for detection of text in video. It is challenging to detect text candidates due to its nature of properties that varies from normal objects in terms of contours, connectionist, size, scaling to motion occlusion, color contrast, poor illumination, etc. Also, it is not possible to apply the existing architecture for the proposed anatomy with incompatibility in targets, parameters. Hence, working on video takes different path of learning and validation. The proposed architecture reads the temporal data to train the sequence of learning features. These features are fed to periodic connectionist to learn successive features to obtain the text candidate. Later, representation of the features are fed to regional proposal network to obtain the regions of interest by comparing with the ground-truth data followed by pooling the text regions with bounding box and finding the probability of their occurrence. The proposed structure evaluated on an ICDAR 2013 “Text in Video” dataset of different indoor and outdoor videos achieves high detection rates and performed better than labeled features.