Reviewerly/exHarmony huggingface dataset:
Blockchain Technology: A Critical Review and Its Proposed Use in E-Voting in India

In 2009, the evolution of the blockchain technology was the foundation of the first ever cryptocurrency, bitcoin. It not just changed the financial industry but also proved to be a major break-through for peer-to-peer data transfer, information exchange having high security, efficiency, and transparency. Blockchain is a database of records which is distributed on the network, or we can say, its a public ledger of all transactions that have executed and are shared between every user on that blockchain network. Some of its characteristics include anonymity, irreversibility, immutability, decentralization and persistence. It has found applications and use in almost all fields which require data sharing but with secure authentication, anonymity and permanence. Some of the areas of its application are finance, real-estate and IoT. In this paper we discuss the blockchain technology and its fundamentals, its working, types of blockchain, applications, advantages, disadvantages and some other aspects in details. In this research paper, we introduce a model of e-voting as a CASE study which is developed in decentralised environment on the Ethereum blockchain. Using the concept of Blockchain, e-voting will assist to strengthen security and integrity of the election system reducing the cost while increasing the privacy. The use of encryption helps to provide security to the system. No intruder can gain access to the voting system and tamper it and in this way, EVMs can’t be accused of being fixed.
An Open Source Platform for Computational Histopathology

Computational histopathology is a fast emerging field which converts the traditional glass slide based department to a new examination platform. Such a paradigm shift also brings the in silico computation to the field. Much research have been presented in the past decades on the algorithm development for pathology image analysis. On the other hand, a comprehensive software platform with advanced visualization and computation capability, large developer community, flexible plugin mechanism, and friendly transnational license, would be extremely beneficial for the entire community. In this work, we present SlicerScope: an open platform for whole slide histopathology image computing based on the highly successful 3D Slicer. We present rationale on the choice of such an architecture, introducing new modules/tools for giga-pixel whole slide image viewing, and four specific analytical modules for qualitative presentation, nucleus level analysis, tissue scale computation, and 3D pathology. The entire software is publicly available at https://slicerscope.github.io/, facilitating the algorithmic, clinical, and transnational researches.
Experiential Learning: Folklore as a Learning Experience

New Education policies are designed keeping in mind the increased significance of acquiring 21st century skills. The system of rote-learning and text-based learning that was introduced by the British in India. is being replaced by new pedagogical skills and teaching methodologies. Learning is now affected through direct or virtual experiences or based on actual hands on activities. This paper intends to study the concept of Experiential Learning as expounded by Kolb. It recommends the various activities that can be used for Experiential Learning. It also throws light on the various parameters of defining and assessing the outcome of an experience. The paper also briefly traces the history of folklore and discusses the potential of folk stories and other tales from the past as an effective medium in Experiential Learning. Stories have the innate ability to keep the learner enthralled and ensure active involvement and reflection of the learner. Besides giving opportunities for developing language and other skills, exposure to folklore can also bring in awareness for environmental, social and gender related issues. Use of folklore can help the learners to weave across continents and culture and to take the experience beyond a classroom.
Optimized Hyperparameter Tuned Random Forest Regressor Algorithm in Predicting Resale Car Value based on Grid Search Method

The price of a car depreciates right from the time it is bought. The resale value of cars is influenced by many factors and influences both buyers and sellers, making it a prominent problem in the machine learning field. Diverse methodologies in machine learning can help us use all the varied factors and process a large amount of data to predict the cost. For our dataset, the Random Forest Regression algorithm shows a significant increase in the prediction rate. In order to optimise the Random Forest Regressor model, best hyperparameters can be found using hyperparameter tuning strategies. On comparing Grid Search and Randomized Search, a better prediction rate is accounted for using the former. These parameters are then passed to the algorithm as hyperparameter tuning can help collect the best batch of decision trees in the random forest for the most optimised prediction rate.
Demonstrating the Use of Rapid Touch Interaction in Virtual Reality for Prolonged Interaction in Productivity Scenarios

Current camera-based VR headsets support free-hand mid-air inter-action or physical hand-held controllers for input, which can lead to fatigue during use, as users lack support for their arms and hands between interactions. In our demonstration, we showcase a novel approach to bring quick touch interaction to Virtual Reality, illustrating the beneficial use of rapid tapping, typing, and surface gestures for ongoing interaction in Virtual Reality, particularly in the con-text of content creation and productivity scenarios. The productivity scenarios that become possible using our approach are therefore reminiscent of apps that exist on today's phones and tablets. To reliably make touch interaction work in VR, we use a wrist-worn prototype to complement the optical hand tracking from VR headsets with inertial sensing to detect touch events on surfaces. Our prototype band TapID integrates a pair of inertial sensors in a flexible strap, from whose signals TapID reliably detects surface touch events and identifies the finger used for touch. This event detection is then fused with the optically tracked hand poses to trigger input in VR. Our demonstration comprises a series of VR applications, including UI control in word processors, web browsers, and document editors.
Multi-Keyword Privacy Security Protected Search Through Encrypted Data On Cloud Storage

Through the advancement of cloud storage, data providers are allowed to outsource their complicated data processing processes with considerable versatility and economic benefits since local locations to the private, public cloud. However, confidential data must be secured before outsourcing in order for data to be safe, which obsolescent common usage of data relying on plaintext search. Therefore, it is incredibly necessary to allow a secure cloud data search service Given the vast number of web users and cloud records, in order to be applicable to such keywords, several keywords should be acknowledged in the search request and return records. The searchable encryption-based research specializes in search of a particular keyword or in boolean keywords, seldom processing the outcomes of pursuit.  For the very first time, the problem of protecting the privacy of multi - keyword search for encrypted data in cloud storage is described as well as solved in this article. For such a safe cloud data usage scheme, we series a series of strict privacy criteria. We pick from the different multi-keyword semantics the effective coordinate matching similarity test, i.e. as various matches as likely, in order to catch the importance of the data papers to the search. We can use inner product similitude to test this resemblance attribute quantitatively. Initial, on the basis of stable internal commodity estimation and then, in two separate Vulnerability Models, we suggest a simple concept for the MRSE to reach multiple rigorous data protection standards. We expand these two frameworks to help further search terminology to improve our user experience for the data search service. Examination of data security and assurance of performance of suggested systems shall be carried out in-depth. Experiments in the real-world data set indicate that plans for numerical and connectivity structures effectively add small overheads.
Picture Preview Generation for Interactive Educational Resources

With the development of network technology, many online educational resource platforms have emerged, and the number of resources on these platforms is increasing dramatically. Compared with the traditional resources, the interactive educational resources (IERs) have hidden interactive information and dynamic content. Only when the users perform interactive operations correctly can they acquire the knowledge conveyed by the resources, which makes it a challenge to help the users understand the general content of resources and find the resources that they are interested in. Therefore, in this paper, we propose a method for generating picture previews of IERs. Firstly, we analyze the file that stores interactive resource information, obtain the interactive elements’ information, extract the dependencies, and generate an operation sequence. Secondly, we execute the operation sequence using an automated software tool to mimic the interaction process between the user and the resource and save the screenshots in the process to generate a picture preview of an IER. We apply this method to a dynamic geometry platform, NetPad, and conduct experiments. Experimental results demonstrate that the method can effectively extract interactive semantic information and simulate the interactive process, and the generated picture previews are of great help to the users to understand the content of IERs.
IMPACT OF ARTIFICIAL INTELLIGENCE IN CUSTOMER SERVICE: STATE OF THE ART

Artificial intelligence (AI) is rapidly transforming customer service. This paper aims to paint a state-of-the-art of AI and its impact in customer service organizations by conducting a literature review as well as exploring current AI solutions used by practitioners. We will also identify gaps in the research that need to be addressed to better understand the impact of AI in customer service
The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network

In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.
Federated Marginal Personalization for ASR Rescoring

We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federated learning (FL). Instead of fine-tuning the parameters of NNLMs on personal data, FMP regularly estimates global and personalized marginal distributions of words, and adjusts the probabilities from NNLMs by an adaptation factor that is specific to each word. Our presented approach can overcome the limitations of federated fine-tuning and efficiently learn personalized NNLMs on devices. We study the application of FMP on second-pass ASR rescoring tasks. Experiments on two speech evaluation datasets show modest word error rate (WER) reductions. We also demonstrate that FMP could offer reasonable privacy with only a small decrease in speech recognition accuracy.
A novel framework for integrating mobile machine learning and L2 vocabulary acquisition

Cross-cultural interactions are increasingly important and encompass many types of activities including recreational, educational, financial, and scientific. Exchange of ideas during these activiti...
Non-Attractive Periodic Trajectory Formation Mechanism on Random and Chaotic Time Series

Pseudo-random number series extracted from chaotic and random time series from the chaotic and random neural network (CRNN) with fixed-point arithmetic has been the focus of attention for protecting the information security of IoT devices. Pseudo-random number series generated by a computer is eventually periodic, practically. The produced closed trajectory is not a limit cycle, because which does not divide the phase space into 2 regions. The closed trajectory in this work is called a non-attractive periodic trajectory (NPT) because it hardly attracts trajectories within the neighborhood. The method of preventing the closed trajectory formation has been proposed on the basis of the NPT formation mechanism in this paper. The method has extended the period of NPT considerably. It is expected to apply security applications for IoT devices.
المهارات القيادية الاستراتيجية وتأثيرها في الالتزام التنظيمي دراسة استطلاعية لآراء عينة من العاملين في الشركة العامة لمنتوجات الألبان / وزارة الصناعة العراقية

Aim of the research to find out what leadership skills available in General Company for Dairy Products, and knowledge of the relationship and influence between leadership skills and organizational commitment, the use of system the (SPSS) Statistical to get to the results, was chosen General Company for Dairy Products of field research , For being the most important Coefficient in Ministry of Industry and was search Community from (General Manager , Deputy General Manager, head of Department ) and the number (85) , but the number of questionnaires returned was (72) questionnaire , formulated the hypothesis link the main contact between leadership skills and organizational commitment ramifications of which Three hypotheses subsidiary, as formulated three hypotheses influence between leadership skills and organizational commitment The ramifications of each of the Three sub-hypotheses , the research found that all hypotheses link and influence between the variables and sub is a powerful and significant moral, as recommended by search a number of recommendations most important: to emphasize the importance of leadership skills in the Company plant because of its significant impact in support organizational commitment , a lack of focus on the skill without the other , but the use of all leadership skills at all administrative levels , and the intensification of seminars , conferences and training sessions on the themes of leadership skills and organizational commitment, and the emphasis on work teams in order to exchange experiences and skills.
Deep Focus Earthquake as a Result of a Hydraulic Shock in the Mantle of the Earth

The idea of the formation of deep-focus earthquakes does not fit into the framework of Mr. Reid's theory of Elastic recoil explaining the origin of earthquakes as the emission of the energy of elastic deformations by the rocks of the earth's interior due to the simple physical fact of the absence of elastic deformations at a depth of several tens of kilometers. More can be said, if humanity had not experienced the devastating catastrophes caused by deep-focus earthquakes, scientists, in the person of Cambridge University professor Mr. Jeffries, would still have argued that such events cannot take place in the bowels of the Earth. Indeed, it is a very, very dubious occupation to expect elastic deformations in hot, semi-molten rocks at depths of hundreds of kilometers. On the other hand, seismologists represented by Mr. C. Frohlich of the University of Austin, Texas continue to cling stubbornly to the idea of mythical elastic stresses of the rocks, allegedly occurring in the Wadati-Benioff subduction zones. As a result of the dispute between the two schools, deep-focus earthquakes turned out to be an established scientific fact, therefore, Mr. Jeffries and Co. were wrong. But Mr. Frohlich and Co turned out to be wrong, for the deep-focus earthquakes that occurred outside the subduction zones completely and without any hope destroyed the idea of elastic deformations at great depths. Is the intrigue preserved? Not! In fact, the process of forming a deep focus earthquake is simple and is a classic hydraulic shock. From hydrodynamics, it is known that where fluid flows through pipelines, in our case, magma flows through cracks in the Earth’s mantle, there have always been, are and will happen, hydraulic shocks.
Effective and Efficient ROI-wise Visual Encoding Using an End-to-End CNN Regression Model and Selective Optimization

In neuroscience, visual encoding based on functional magnetic resonance imaging (fMRI) has been attracting much attention, especially with the recent development of deep learning. Visual encoding model is aimed at predicting subjects’ brain activity in response to presented image stimuli . Current visual encoding models firstly extract image features through a pre-trained convolutional neural network (CNN) model, and secondly learn to linearly map the extracted CNN features to each voxel. However, it is hard for the two-step manner of visual encoding model to guarantee the extracted features are linearly well-matched with fMRI voxels, which reduces final encoding performance. Analogizing the development of the computer vision domain, we introduced the end-to-end manner into the visual encoding domain. In this study, we designed an end-to-end convolution regression model (ETECRM) and selective optimization based on the region of interest (ROI)-wise manner to accomplish more effective and efficient visual encoding. The model can automatically learn to extract better-matched features for encoding performance based on the end-to-end manner. The model can directly encode an entire visual ROI containing enormous voxels for encoding efficiency based on the ROI-wise manner, where the selective optimization was used to avoid the interference of some ineffective voxels in the same ROI. Experimental results demonstrated that ETECRM obtained improved encoding performance and efficiency than previous two-step models. Comparative analysis implied that the end-to-end manner and large volume of fMRI data are potential for the visual encoding domain.
A decision support healthcare system based on IoT and neural network technique

Purpose The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.
A Non-sequential Approach to Deep User Interest Model for CTR Prediction

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, and recently a lot of deep learning methods attempt to improve the performance through feature interaction or user interest models. However, the existing methods fails to explore the potential interest from user behavior sequence effectively. In this paper, we proposed a Non-sequential Interest Interaction Network (NIIN) to tackle this problem. Specifically, we first design the key-vector storage method to construct the sparse representation for any input behavior sequences. Moreover, our NIIN framework introduces a Time Interest Interaction (TII) module to explore interactions among interests. It first includes an efficient fine-grained attention mechanism to score items in behavior sequence for reserving user's diverse interests. After that, our TII module exploits multidimensional partition layer to segment behavior sequence in temporal dimension, and sum pooling operation obtains user's interest in different periods. Furthermore, several fully connect layers model interactions among multiple interests to mine potential interest and give the final score of the target item. Experiments on CTR datasets including Alipay, Taobao and Alimama datasets demonstrate that the NIIN framework outperforms the state-of-the-art methods.
Fundamentals of Digital Didactics: Specifics and Development at the Present Stage

A theoretical review of research and publications on digitalization of the educational space, including digital didactics, is presented. The authors consider the theoretical aspects of the definition of “digital didactics” in the light of recent domestic and foreign studies. The novelty of this study lies in the characterization of the main trends in the problem of digital didactics, highlighting several problems that modern Russian teachers face in the process of digital learning. The authors draw attention to the need to consider the basics of digital didactics in the context of meaning formation. More than 55 publications of domestic and foreign researchers were studied. The issues of defining the concept of digital didactics are considered, the General characteristics of digital didactics at the present stage, the most important problems and prospects for its study are given. This article will allow researchers working in the field of digital didactics to get acquainted with the most well-known concepts of digital learning today.
Privacy-preserving data splitting: a combinatorial approach

Privacy-preserving data splitting is a technique that aims to protect data privacy by storing different fragments of data in different locations. In this work we give a new combinatorial formulation to the data splitting problem. We see the data splitting problem as a purely combinatorial problem, in which we have to split data attributes into different fragments in a way that satisfies certain combinatorial properties derived from processing and privacy constraints. Using this formulation, we develop new combinatorial and algebraic techniques to obtain solutions to the data splitting problem. We present an algebraic method which builds an optimal data splitting solution by using Gröbner bases. Since this method is not efficient in general, we also develop a greedy algorithm for finding solutions that are not necessarily minimally sized.