Reviewerly/exHarmony huggingface dataset:
Smart Notice Board

A notice board is a surface intended for the posting of public messages, for example, to advertise items wanted or for sale, announce events, or provide information. Notice boards are mandatory asset used in institutes and organizations or public places. The process of notice board handling is a time consuming and hectic. To overcome this problem a new concept of digital notice board is introduced in this paper. This concept provides digital way of displaying notices using Android application and Wireless technology.
Height estimation from single aerial images using a deep ordinal regression network

Understanding the 3D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essential building block for various applications such as 3D digital city modeling, change detection, and city management. Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching. These methods require two or more images from different perspectives to reconstruct 3D coordinates with camera information provided. In this paper, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the great success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image by training a deep CNN model with large-scale annotated datasets. These methods treat height estimation as a regression problem and directly use an encoder-decoder network to regress the height values. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem, using an ordinal loss for network training. To enable multi-scale feature extraction, we further incorporate an Atrous Spatial Pyramid Pooling (ASPP) module to extract features from multiple dilated convolution layers. After that, a post-processing technique is designed to transform the predicted height map of each patch into a seamless height map. Finally, we conduct extensive experiments on ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate significantly better performance of our method compared to the state-of-the-art methods.
On Universalized Adversarial and Invariant Perturbations

Convolutional neural networks or standard CNNs (StdCNNs) are translation-equivariant models that achieve translation invariance when trained on data augmented with sufficient translations. Recent work on equivariant models for a given group of transformations (e.g., rotations) has lead to group-equivariant convolutional neural networks (GCNNs). GCNNs trained on data augmented with sufficient rotations achieve rotation invariance. Recent work by authors arXiv:2002.11318 studies a trade-off between invariance and robustness to adversarial attacks. In another related work arXiv:2005.08632, given any model and any input-dependent attack that satisfies a certain spectral property, the authors propose a universalization technique called SVD-Universal to produce a universal adversarial perturbation by looking at very few test examples. In this paper, we study the effectiveness of SVD-Universal on GCNNs as they gain rotation invariance through higher degree of training augmentation. We empirically observe that as GCNNs gain rotation invariance through training augmented with larger rotations, the fooling rate of SVD-Universal gets better. To understand this phenomenon, we introduce universal invariant directions and study their relation to the universal adversarial direction produced by SVD-Universal.
Learning quantitative representation synthesis

Software systems often use specialized combinations of data structures to store and retrieve data. Designing and maintaining custom data structures particularly concurrent ones is time-consuming and error-prone.We let the user declare the required data as a high-level specification of a relation and method interface, and automatically synthesize correct and efficient concurrent data representations. We present provably sound syntactic derivations to synthesize structures that efficiently support the interface.We then synthesize synchronization to support concurrent execution on the structures. Multiple candidate representations may satisfy the same specification and we aim at quantitative selection of the most efficient candidate. Previous works have either used dynamic auto-tuners to execute and measure the performance of the candidates or used static cost functions to estimate their performance. However, repeating the execution for many candidates is time-consuming and a single performance model cannot be an effective predictor of all workloads across all platforms. We present a novel approach to quantitative synthesis that learns the performance model. We developed a synthesis tool called Leqsy that trains an artificial neural network to statically predict the performance of candidate representations. Experimental evaluations demonstrate that Leqsy can synthesize near-optimum representations.
PECULIARITIES OF CREATING A POPULAR INTERNET MEDIA

The article deals with the peculiarities of creating an online media, using different advantages of Internet.Since its launch, the Internet has been picking the audience in the traditional media, forcing or thinking about survival issues.The adaptation of traditional media to digital age conditions are doing, largely by switching from one sector med in a market a few.Accordingly, such forms of editorial work have arisen when journalists prepare their material in several kinds, given that the information will be used both on the website, in the newspaper or magazine, and on radio and/or television.The internet has now provided an opportunity, on the one hand, to effectively manage the information coming from different sources, and on the other hand to supply it for different platforms.The multiplication of dissemination channels has allowed the media to practice new schemes of media interactions.Following the media integration came media convergence, which means combining the production of media messages of different formats due to the use of common technologies and unified organizational principles.In view of these developments, the massive emergence of convergent editions, which were able to simultaneously generate content for almost any type of media, transforming it accordingly, subsequently led to the emergence of entire media holdings that were able to equally represent themselves at all playgrounds: television, radio, press, of course, on the Internet.However, the oversaturation of such positioning of such media gradually tired the users who, being in a constant information noise, did not want to receive the same information several times, submitted differently.They spend more time online, choosing resources with interesting information for themselves.In fact, the audience wanted to see the most complete material, with comments and explanations, links and supplements in the form of photos and videos -that is, to get everything in one place.
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes

We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning frameworks, which assume fully known state transition models, we design a method that eliminates such a strong assumption, which is oftentimes extremely difficult to engineer in reality. To achieve this, we first apply the second-order Taylor expansion of the value function. The Bellman optimality equation is then approximated by a partial differential equation, which only relies on the first and second moments of the transition model. By combining the kernel representation of value function, we then design an efficient policy iteration algorithm whose policy evaluation step can be represented as a linear system of equations characterized by a finite set of supporting states. We have validated the proposed method through extensive simulations in both simplified and realistic planning scenarios, and the experiments show that our proposed approach leads to a much superior performance over several baseline methods.
Deep RNN Based Obstacle Avoidance Control for Redundant Manipulators

In this chapter, we consider the obstacle avoidance problem of redundant robot manipulators with physical constraints compliance, where static and dynamic obstacles are investigated. Both the robot and obstacles are abstracted as two critical point sets, respectively, relying on the general class-K functions, the obstacle avoidance problem is formulated into an inequality in speed level. The minimal-velocity-norm (MVN) is regarded as the cost function, converting the kinematic control problem of redundant manipulators considering obstacle avoidance into a constraint-quadratic-programming problem, in which the joint angles and joint velocity constraints are built in velocity level in form of inequality. To solve it, a novel deep recurrent neural network based controller is proposed. Theoretical analyses and the corresponding simulative experiments are given successively, showing that the proposed neural controller does not only avoid collision with obstacles, but also track the desired trajectory correctly.
Crypto-Semantic System for Text Information Protection

Introduction.Information security is one of the most important areas of computer science.Often it is necessary to transmit messages containing sensitive or secret information that a third party must not intercept and recognize.A crucial task is to create the methods for constructing cryptographically strong cryptosystem.Purpose.A method of constructing cryptographically strong cryptosystem, which provides a formal opportunity to encrypt unlimited volumes of text messages.Methods.Lexicographic methods of information security Results.Usage of the lexicographic mechanism for enlarging the alphabet of the textual information's language allows increasing the unicity distance, which is the main threshold indicator for the cryptosystem to belong to the class of cryptographically strong security systems with theoretically proven ideal information-theoretic stability.Images' space of a given subject area of an information system application is mapped onto the space of semantic images of this system, taking into account the semantic relationships between them, which are determined by a given restrictive conditions.This is the basis of the thesaurus structure, which allows during encryption to replace the message to be encrypted with other plausible messages that reflect the true meaning of outgoing messages. Conclusion.For the first time at a formal level, a synthesis of the method of constructing a lexicographic cryptosystem with theoretical absolute stability has been carried out.A security system has been built, which consists of two parts: a semantic thesaurus developed for a specific application area, and software and hardware tools for implementing encryption/ decryption operators using the created thesaurus.
Complex Network Filtering and Compression Algorithm Based on Triangle-Subgraph

Compressing the data of a complex network is important for visualization. Based on the triangle-subgraph structure in complex networks, complex network filtering compression algorithm based on the triangle-subgraph is proposed. The algorithm starts from the edge, lists nodes of the edge and their common node sets to form a triangle-subgraph set, parses the triangle-subgraph set, and constructs new complex network to complete compression. Before calculating the set of triangle-subgraph, node importance ranking algorithm is proposed to extract high- and low-importance nodes and filter them to reduce computational scale of complex networks. Experimental results show that filtering compression algorithm can not only improve the compression rate but also retain information of the original network at the same time; sorting result analysis and SIR model analysis show that the sorting result of node importance sorting algorithm has accuracy and rationality.
White Library: FreeSources: Free Resources During COVID-19

Looking for free (or really inexpensive) resources during the COVID19 crisis? Look no further! This page aims to bring together all the free content provided by e-resource and software vendors during the time of the COVID-19 pandemic. These free resources will only be available for this limited time.
Axiological Foundations of the Management Field: In Search of Good Management

본 논문의 핵심적인 연구 질문은 ‘인사조직 분야의 가치론적 기반은 무엇인가?’이다. 이 논문은 ‘좋은 경영’을 진지하게 탐구함으로 이 물음에 해답을 제시한다. 따라서 본 연구의 목적은 인사조직 분야의 연구와 실무에서 요구되는 가치론적 기반을 제공하는 데 있다. 이 목적을 위해 좋은 경영을 가능하게 하는 조건들과 좋은 경영의 본성을 이루는 다양한 양상들에 대한 체계적인 탐구를 수행하였다. 이런 탐구를 위한 논리 구조에서는 우선 형이상학적 관점으로 인격적 비판실재론을 제시하는데, 형이상학적 대전제는 ‘인격적 비판실재론은 더불어 좋은 삶을 누리는 것을 지향하게 하는 형이상학적 토대를 제공한다’이다. 메타윤리적 관점으로는 다원적 비자연주의 입장을 가지는데, 이것이 제공하는 소전제는 ‘다원적 비자연주의는 좋음이 다원적 및 다층적 방식으로 실재하는 비자연적 속성을 가진다는 메타윤리적 토대를 제공한다’는 것이다. 이런 대전제와 소전제를 바탕으로 좋은 경영의 가능조건으로 존재론적으로는 ‘좋은 경영은 사회적 실체를 전제하는 경영’이어야 하고, 인식론적 조건으로는 ‘좋은 경영은 인격적 지식에 기반한 경영’이어야 하며, 가치론적으로는 ‘좋은 경영은 유기적 가치를 추구하는 경영’이어야 함을 제시하였다. 좋은 경영에 대한 이런 가능조건을 기반으로 ‘좋은 경영이라는 실체의 본성’을 제시하는데, 이 본성은 초월성, 내재성, 관계성 및 역사성의 양상들로 이루어져있다. 이 양상들은 인격의 본성의 양상에서 확장된 것으로 각각 경영의 높이, 깊이, 넓이 및 길이 차원에 해당한다. 결론적으로, 본 논문에서의 좋은 경영이란 인격과 경영의 양상인 초월성, 내재성, 관계성 및 역사성이 충분히 발현되어 좋은 삶을 실현하는 경영을 의미한다. 좋은 경영은 좋은 삶을 실현하여 인류의 번영을 가능하게 하는 경영인 것이다. 이런 결론을 도출하는 추론 과정은 인격과 경영의 본성을 네 가지 양상으로 파악하고, ‘좋은 삶’을 매개로 인격과 경영 간의 구조적인 동형성이 있음을 주장하는 방식이다. 주류 경영학에 속하는 인적자본 이론과 인격적 비판실재론 입장에 해당하는 인간가치이론을 비교하는 토론으로 마무리한다.The question ‘What is good management?’ is a fundamental question that guides all management research and activities. The purpose of this study is to provide an axiological foundation in the field of management by suggesting a theory of good management. This study takes ‘personal critical realism,’ the organic combination of personalism and critical realism, as the metaphysical foundation for good management. In addition, this paper suggests ‘pluralistic non-naturalism’ as the metaethical foundation for good management, referring to the position that good is pluralistic and is not purely reduced to natural properties. With this premise of ‘personal critical realism’ and ‘pluralistic non-naturalism,’ we propose the following as possible conditions for good management. First, ontologically speaking, good management should consider its objects to be socially real entities. Second, epistemologically, good management should be based on ‘personal knowledge,’ which is related to relational epistemology. Third, in terms of axiology, good management should pursue organic values. Next, we suggest four aspects of the nature of good management: transcendence, interiority, relationality, and historicality. Good management will emerge stronger if these aspects are simultaneously realized. With all of these presuppositions and propositions taken into account, we can move on to non-reductionist management theories. In conclusion, good management is defined as pursuing the good life through realizing four aspects of the nature of person and management. Finally, a few essential issues are discussed, such as the comparison between human capital theory from the mainstream perspective and human value theory from the perspective of personal critical realism.
A Method for Teaching Korean Traditional Music Creative Class with the Use of Teaching Aids

본고는 학생들이 국악 창작수업에서 다양한 방법으로 음악을 표현하는 환경을 만들어 주기 위하여 교구를 활용한 수업 지도 방안을 모색하였다. 이를 위하여 정간보의 기본 원리를 적용하여 만든 삼소박 카드와, 가락짓기 교구를 사용하였다. 삼소박 카드는 장단꼴과 말붙임새 창작활동 등에서 활용되며, 학생들은 카드를 생략ㆍ교환ㆍ추가 등의 형태로 조합하면서 장단꼴과 말붙임새의 관계 및 원리를 이해하게 된다. 가락짓기 교구는 2음ㆍ3음ㆍ육자배기토리ㆍ메나리토리 가락짓기로 활동하였으며, 교구판에 음카드와 음색깔 카드로 가락을 만들면서 선율이 가지고 있는 지역적 특징을 자연스럽게 이해하는데 도움이 되도록 하였다.This study developed a method for teaching Korean traditional music creative class with the use of teaching aids. For this purpose, a set of Samsobak cards that utilize the basic principles of Jeongganbo and a melody making tool are used. The results are as follows. Samsobak cards are used for rhythm formats and the utilization of lyrics, by omitting, exchanging, and adding rhythm cards, they are led to students’ understanding of the relationship and the principles of rhythm formats and the utilization of lyrics. The melody making tool is used for the melody making activity with 2 notes, 3 notes, yukjabaegitori, and menaritori rhythms. A melody is composed by using note cards and note color cards on the teaching aid board.
Building skills for organizing project activities in future educators

The article analyses the modern paradigm shift in education, treating it as a complex self-organizing system, interacting with other social systems, encourages the teaching community not only to develop technology that activates pedagogical resource person as the subject of life, but also to search for constructs and systems that allow the individual to adequately demonstrate their competence, success, ability, skills, and abilities in the competitive environment of professional activity.One of these constructs is to understand the mechanisms of this success through the appeal to the project technology, which has a significant potential in the development of the Patriotic sphere of the individual as a component of the system of basic fundamental values.
Use of Hybrid Data Mining in Identification of Crime Patterns and Trends in the Matatu Industry in Kenya

The aim of this study was to propose an automatable technological framework that identifies crime and misconduct patterns and trends in the matatu industry using data mining techniques for intelligence led policing in Kenya.The objectives of the study include to propose a framework for intelligent transport management system with patterns and trends identification capabilities, enhance formulation of policy developments, implementations and government regulations for the transport sector in Kenya, design model system for testing the framework to ascertain its practicability and effectiveness and identify challenges of the transport sector in Kenya.This was an application research which made use of dummy data.The study established that it is possible to use artificial intelligence to manage the transport sector by use of a system that will not only help identify the patterns and trends of matatus' on Kenyan roads but to answer the why's associated with the trends to help come up with meaningful applicable practical solutions to enhance security and integrity in the transport sector in general.The study also unearthed challenges in relation to the implementation of the above.Combination of classification and association rules based data mining approach was utilized for this study due to its effectiveness in bringing out patterns and trends that are interlinked and related to each other.
EFFECTIVE Teaching Approaches in Special Education Program: A Literature Review

To address the individual's differences and special needs, Special Education is the practice of educating students.It tailored to meet the needs of students with disabilities.Effective teaching approaches for Special Education Program are ways to identify and achieve the main goal on Special Education.This study examines the effective teaching approaches in Special Education Program with contextual variables included as controls are identified.These looked at the influences of personal characteristics in support for various educational teaching approaches and best practices.Data were gathered from data mining.Special education teacher must incorporate satisfying and effective teaching approaches in special education to improve the quality and learning process of education.
SENTIPUBLIKO: Sentiment Analysis of Repost Jejemon Messages using Hybrid Approach Algorithm

Jejemon language becomes a form of communication dialect.It was a form of expression used by a particular social group unknown as Jejemon.However, the Jejemon expression has different formats ranging from basic form of changing letter to number, lowercase letter to uppercase letter, inserting shortcut texts into more complicated format.This paper aims to classify Jejemon tweet whether it is a positive, negative or neutral sentiment through sentiment analysis techniques.Experiment included translation of Jejemon formatted tweet, reduction of sentiment scores on repost tweets and sentiment classification.Analysis of experiment results involves Paired T-Test, confusion matrix, precision, recall, f-score and accuracy.Evidently, translated Jejemon tweet resulted 78.5% similar from the actual message using cosine similarity algorithm.Furthermore, Paired T-Test shows no significant difference between new sentiment scores from translated expression and actual sentiment scores using Hybrid Algorithm.Sentiment analysis metrics such as precision, recall, f-score and accuracy show acceptable values of 71%, 76%, 71% and 73% respectively.
Voice Enabled Extended Reality

This electronic document presents an Idea around Augmented Reality, virtual reality and voice. The idea of voice applications and integrating voice with augmented and virtual reality is explained in this document. Applications are currently using touch based interactions as a medium for user-application interaction. There is a lack of a 3d platform for visualization. Moreover extended reality applications use reticle pointer for pointing and clicking. This paper explains how voice enabled extended reality can eliminate pointing issues in current extended reality applications and how extended reality can help people make daily lives easier, with easy to interact technology. This paper aims at explaining how better interaction medium and visualization can help both the businesses and the users.