Reviewerly/exHarmony huggingface dataset:
An Ironic View on a Convoluted Reality: Part B

The feeling of uncontrolled confusion that envelops the Western world of today was the main reason for working on this thesis.The current COVID-19 pandemic came just in time to increase this sense of obscureness.This essay follows the first part of the work that referred to the behavior issues.Thus, the Author calls for deepening the study of individual inner-self as the key for better understanding human-beings behavior.For attaining this goal, it is suggested to involve some techniques taken from the Quantum Theory in Brain Research.After discussing the issues of naming and labeling the Author explains why today, one word cannot describe a whole act, event, or capacity and why a more descriptive term is required.For this purpose, the Matrix Model could be of great help.Further the complex of human inner-self, its urges, drives and motives are discussed, explaining the complicated influence of internal and external factors on behavior.Then the meaning of Self-esteem and Self-confidence and their influence on the person's conduct is examined.Finally, mediocrity, its origin, appearance, practices and threats are explored.The essay ends with a call to reconstruct a revised social order, based on solidarity, togetherness and human compassion.
Application of Artificial Intelligence Technology in Personnel File Management

With the continuous progress of science and technology, information technology has penetrated into all areas of our life and work. In the field of management, especially in the management of employee files has made great progress. Through the use of digital smart archives, it can identify various types of files, such as pictures, audio and video, and can be used to effectively collect, search and classify files. On this basis, this paper gives a brief overview of the application of artificial intelligence technology to personnel file management, combined with the traditional file management problems, and proposes to use artificial intelligence technology to manage personnel files. This paper proves that the application of artificial intelligence technology in personnel file management has highly degree of satisfaction. Diagnostic and analytical artificial intelligence technology can provide high-quality file services because it can intelligently evaluate the value of employee files and help ensure the security of man-made files.
Research on cloud platform architecture of flight big data

Based on the flight big data environment, the existing problems of current flight training management are analyzed. This paper designs the cloud platform architecture based on flight big data, and points out the key technology research that should be carried out under this architecture. With the help of this cloud platform, flight big data can be effectively analyzed and processed to provide decision support for flight training management, flight safety assessment and risk early warning.
Overview of the Supporting and Understanding of Conversational Dialogues (SUD) Workshop

The workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) seeks to encourage researchers to investigate automated methods to analyze and understand conversations, and also explore methodologies for proactively providing assistance to the communicating parties during conversations, ranging from summarizing the minutes of meetings to automatically keeping track of action items etc. The workshop will have (1) a regular research paper track, and a more focused (2) data challenge track, inviting papers on a specific task of contextualizing entities of interest from conversation dialogues.
FastCredit: Expediting Credit-based Proactive Transports in Datacenters

Recent proposals have leveraged emerging credit-based proactive transports to achieve high throughput low latency datacenter network transports. Particularly, those transports that employ hop-by-hop credits have the merits of fast convergence, low buffer occupancy, and strong congestion avoidability. However, they fairly transmit long flows and latency-sensitive short flows, which will cause the transmission latency of short flows and the average flow completion time increased. Although flow scheduling mechanisms have studied extensively to accelerate short flow transmission, they are hard to be directly applied in credit-based transports. The root cause is that most traditional flow scheduling mechanisms mainly work in the long queue containing flows in various sizes, while credit-based proactive transports maintain the extremely short bounded queue, near zero. Based on this observation, this paper makes the first attempt to accelerate short-flow scheduling in credit-based proactive transport, and proposed FastCredit. FastCredit can be used as a general building block to expedite short flows in credit-based proactive transports. In FastCredit, we schedule credit transmission at both receivers and switches to indirectly perform flow scheduling, and develop a mechanism to mitigate credit waste and improve network goodput. Compared to the state-of-the-art credit-based transport protocol, FastCredit reduces average flow completion time to 0.78x and greatly improves the short flow transmission latency to 0.51x in realistic workloads. Especially, FastCredit reduces average flow completion time to 0.76x under incast circumstances and 0.62x in many-to-one traffic mode. Furthermore, FastCredit still maintains the advantages of short queue and high throughput.
An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.
A Study on the Elements of Basic Education and It’s Application to the National Curriculum

Public education has the responsibility of guaranteeing educational opportunities, minimizing educational gaps and conflicts, and adapting to future society. In this regard, the purpose of this study is to set the elements of the Basic Education that students should learn for the future advancement and career in elementary and junior high school and to explore the direction of reflecting the national curriculum. To achieve the purpose, this study utilized the research methods such as literature analysis, expert council, Delphi survey, and survey analysis. The results of this study are as follows: First, this study derived literacy, numeracy, social competence, and inquiry ability as the elements of the Basic Education. Second, the Educational policy on the Basic Education should be established as a support policy for school education that should be providedat the national and local level. Finally, this study suggested the following as a follow-up study. First, the definition and contents of each element of Basic Education should be systematically explored. Second, the relationship between subject education, core competencies, cross-curricular themes, and Basic Education should be clearly set. Third, the Basic Education should be designed and implemented as a device for the curriculum to support a fair starting point for public education.
A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction

Unlike Emotion Cause Extraction (ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction (ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network (IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.
Efficient Privacy-Aware Federated Learning by Elimination of Downstream Redundancy

This article introduces a low-communication-overhead technique targeted toward federated learning systems using additively homomorphic encryption.
Strengthening speech content authentication against tampering

It is vital to authenticate the content of speech signals to prevent the framing of innocent individuals. Furthermore, lack of speech content authentication may lead to repudiation. The research problem of authenticity vs falsehood is timely considering the recent worldwide interest in fake news and viral manipulated media. Liu & Wang proposed an algorithm to detect tamperings in speech content, based on semi-fragile watermarking. These techniques embed a watermark imperceptibly in the speech signal. Semantic changes to the speech will destroy the watermark, while signal processing operations will not affect the watermark. One main criterion for these speech content authentication schemes is the ability to withstand tampering attacks. We present a cryptanalysis attack framework for watermark based media authentication schemes. To exemplify this, two media authentication schemes and corresponding attacks are modelled within this framework. We discuss the main reasons leading to the attacks and propose a strategy to strengthen the Liu & Wang algorithm against such attacks.
Computer Vision Based Campus Surveillance

Security is an important aspect for all of the human race and the organizations that are run by them. In this paper, an automated multipurpose security and surveillance system is proposed which is useful at highly critical places such as borders or highly restricted areas where tracking of each and every object is important. This system works on real time video footage captured using drone cameras or Closed Circuit Television (CCTV) systems and with the use deep learning object detection techniques detects buildings, trees, vehicles, water bodies, playground and slums. The system uses Faster R-CNN architecture and with the help of transfer learning the top layers of the architecture are fine tuned to detect system specific objects at a high accuracy. The video frames are given as input to convolution neural network (CNN) layers for classification of live footage that reveals the count of all objects detected. It approximately gives the percentage of each object class identified.
Solving forward and inverse kinematics problem for a robot arm (2DOF) using Fuzzy Neural Petri Net (FNPN)

Abstract At a time the robots become an important part of our lives by carrying them many tasks that require strength and accuracy, controlling these robots and giving them high accuracy and performance has become the focus of attention of workers in this field. In this research, one of the problems of controlling the robot arm is represented, where the problems of the forward and inverse kinematics have been addressed and find solutions to it using the artificial intelligence algorithm fuzzy Neural Petri net. The Matlab program was used to simulate the forward and reverse kinematics equations and then use the simulation results as data (inputs, outputs) for the proposed algorithm and run the program to update weights and give the required results depending on the ratio mean square error given.
Evolutionary Algorithm based Encoder Decoder Network Design for Semantic Inpainting and Noise Vector Mapping in Generative Adversarial Network

The work presented in this paper focuses on the use of Evolutionary Algorithms (EA) in two different fields of interest where researchers are keenly working on - Encoder Decoder and Generative Adversarial Network architectures. These architectures have brought in wonders to the field of Image processing. They gain interests of researches in various other fields like image domain mappings, interactive generation, security, detection of patterns and processing etc. The key aspects that determine a good performance of a system are its model architecture and optimal hyper parameters that drives it. This paper proposes two new analysis with Evolutionary Algorithms 1) Hyper parameter optimization of Encoder Decoder Model for the task of Image Inpainting and 2) Addressing the input noise vector of the Generative Adversarial Network model. In the first analysis, the research optimizes the task of semantic inpainting. The aim of inpainting is to do the semantic filling of the missing regions in original image. Inpainting task is used across various domains like medical imaging to reconstruct the damaged body parts and tissues, satellite imaging and surveillance to have semantic filling of images taken in uncertain weather conditions and in photography which allows the range of fillings possible to better fulfill the customer needs etc. The research found a set of hyper parameters which can perform similar to/outperform the manually hard coded parameters. It is really difficult task for a person to try out all the combinations and evolutionary algorithms tend to do it very easily. In second analysis, the research focuses on the input noise vector to the generator of Generative Adversarial Network. After training the generator is capable of mapping the random noise vector to an image. This study addresses grouping all those vectors together which are capable of mapping to a particular class of generation using evolutionary algorithms. This way the present study addressed the phenomenon of mapping of Generative Adversarial Networks. For both the above analysis CIFAR10 Dataset is used.
Sciit: Embedding issue tracking in source control management

Sciit is a distributed issue tracker. Distributed issue tracking eliminates much of the friction that is otherwise necessitated by separately maintaining source code in a source control management system (SCM) and task information in an issue tracker. Sciit goes beyond the state of the art in distributed issue tracking by treating issues as first class change control items, represented as fragments of text anywhere within the SCM. This approach treats issues as representations of work in progress alongside other project artefacts, allowing much of the metadata about an issue, such as status, affected components and participants to be inferred directly from the state of the SCM, rather than requiring additional maintenance by a developer.
Using static web technologies and git-based workflows to re-design and maintain a library website (quickly) with non-technical staff

In 2018, a university-wide brand update prompted the University of Idaho Library to reexamine their website development practices and move toward a static web approach that leverages librarian skillsets and provides the library greater control over its systems and data. This case study describes the methodological reasons behind the decision to use the static site generator Jekyll over a Content Management System (CMS) and the practical steps taken to create a sustainable and agile development model. The article details the ways this static web approach (nicknamed “Lib-STATIC”) facilitates cross-departmental communication, collaboration, and innovative feature development for library staff members of varying technical abilities.
PR-SEO: An Innovative Approach for Product Ranking in E-Commerce

This chapter proposes an approach for product ranking with SEO (PR-SEO). PR-SEO is such a way with which all the product in an e-commerce site can get a rank. Search engine optimization (SEO) is now become one of the most useable technique because of the e-commerce business. With the development of technology, nowadays online shopping has become more attractive to all the users. To represent the product in front of all the user, search engine optimization (SEO) helps a lot by ranking the product. In recent few days, the search engine optimization (SEO) becomes more popular because of only the e-commerce sites. Now in this era of digital marketing, all the e-commerce sites are in a competition because of their profit for the product. For generating a new lead of organic traffic, all the e-commerce sites need to be available in the top search when a customer is willingly searching for it without this new lead of organic traffic will not be generated. This proposed product ranking with SEO (PR-SEO)mainly focuses on the SEO tactics for digital marketing as well as e-commerce sites.
Innovative Development of Combination of Digital Media Technology and Virtual Reality Technology

The development of science and technology has brought tremendous changes to people Research and experiment of thermal water de-icing devices lives, and at the same time has brought information dissemination and media styles to a new stage. The purpose of this article is to conduct targeted research on whether the combination of digital media technology (DMT) and virtual reality technology (VRT) can be innovatively developed. This paper firstly analyzes the development of the VRT market and the application of the latest DMT in VRT, and clarifies the focus and direction of the research on the problems that need to be solved in running high-quality VRT under low energy consumption. The technology that consumes the most computing power in the operation of high-end VRT is the key to solving the problem. VRT uses many of the most popular algorithms at the moment, applying graph theory and ID3 algorithm, and uses post-pruning technology to realize the pruning of classification decision trees, generate classification rules, and complete the classification of DMT in VRT application scenarios. The experimental results show that the application analysis of innovative development research combining DMT and VRT in this article is more recognized and liked by consumers and experiencers. According to statistics, 50% of consumer experiences express their love for this.
A Very Lightweight Encryption Method Based on Multiple Substitution Tables

Abstract We present a very lightweight encryption method based on multiple substitution tables. The method does not use sophisticated integer arithmetic and is therefore suitable for small embedded systems and other applications using simple processing elements. Plain text is encrypted one byte at a time. Each byte is replaced by the substitution element in the current table, and the plain text byte also determines the table to be used for the next byte. Encrypting and decrypting one byte involves only a table look-up, no arithmetic or bit manipulation. The same plain text byte has very small probability of being encrypted the same way twice in a row. We also present additional techniques for making the method secure against attacks when used in applications where the messages may have special or restricted forms.
Security Evaluation for Stream Cipher Cryptosystem Based on Soft Computing and Theoretic Parameters

Abstract Security evaluation means an examination of a system to determine its degree of compliance with a stated security model, security standard, or specification. There are different models for security assessment, and the operation of selecting the appropriate model depends on the type of the cryptosystem. Consequently, the criteria for assessing security differ from one model to another. Information security contributes directly to increase the level of trust between users by providing an assurance of confidentiality, integrity, and availability. The main goal of this paper proposing an evaluation model of the cryptography systems. in this paper focuses the stream cipher started from its simple component LFSR and then add more than one register and using a linear feedback shift register (LFSR) with Langford arrangements to generate system to achieve randomness and complexity and evaluated based on statistical tests, information theory, soft computing (fuzzy logic), game theory. The results were acceptable and successfully passed the required tests.
A novel denoising algorithm for medical images based on the non‐convex non‐local similar adaptive regularization

Sparse representation is a powerful statistical image modelling technique and has been successfully applied to image denoising. For a given patch, a non-convex non-local similarity adaptive method is adopted for sparse representation of images. First, it uses the autoregressive model to perform dictionary learning from sample patch datasets. Second, the sparse representation of an image introduces non-convex non-local self-similarity as the regularization term. In order to make better use of the sparse regularization method for image denoising, the parameters used in this study are estimated using adaptive methods. This model is more efficient and accurate, Compared with K-means singular value decomposition (KSVD) algorithm, a generalized K-means clustering method, total variation of population sparsity (GSTV) algorithm, adaptive sparse domain selection (ASDS) algorithm, forward denoising convolutional neural network (DnCNNs), a fast and flexible Convolutional Neural Network image denoising method (FFNNet) and operator-splitting algorithm to minimize the Euler elastica functional (OSEEF). Image noise-reduction experiments confirmed that using the adaptive regularization method, the results in peak signal to noise ratio (PSNR) and visual opinion are better than other algorithms.
A Lightweight Hybrid Majority Vote Classifier Using Top-k Dataset

Human activity recognition on resource constrained device such as Smartphone is possible using small dataset. In this paper, we present our unique Split-then-join strategy to combine two data hungry algorithms (KNN and Naives Bayes) into Lightweight Hybrid Majority Vote algorithm for Smartphone using little compressed dataset. We simulated our proposed classier and compare it with state of the art classification algorithms (C4.5, K Nearest Neighbor, Support Vector Machine and Naïve Bayes, Random Forest) in R for machine learning with Caret, Rweka and e1071 packages using our top-k personalized dataset collected from 13 randomly selected subjects. The result our experimentation demonstrate the superiority of our algorithm over its predecessor Naive Bayes and KNN. Moreover it is comparative with Tree-Oriented and ensembles algorithms such as C4.5, Boosted Trees and Random Forest with accuracy and precision above 85% in all static and most complex human activities.