Reviewerly/exHarmony huggingface dataset:
TTNet: Tabular Transfer Network for Few-samples Prediction

Tabular learning has been widely used in practical scenarios to handle tabular data such as the type of data in a spreadsheet or a CSV file. In many applications, it is necessary to transfer knowledge from the abundant source tabular data to the few target data, since models on the few target data are always easy to overfit. However, existing tabular learning methods have not touched the problem of transfer learning between different tabular datasets based on deep neural networks. To this end, we propose in this paper a new Tabular Transfer Network (TTNet for short) to enable effective and efficient knowledge transferring on tabular data. To enable network structures adaptive for transferring learning, TTNet first designs a BucTab network which integrates multi-bucket network with imitated tree-based network structures. In addition, TTNet uses a meta-transfer learning strategy to enable fast adaption by pre-training in the source domain of tabular data. Experiments on benchmark and real-world datasets show that TTNet significantly outperforms the state-of-the-art approaches in terms of effectiveness and efficiency, e.g., a new-shop prediction task testing on the e-commerce platform Ele.me shows that TTNet brings 9% MAE reduction comparing with the baselines.
Semi-quantum key agreement based on maximally three-qubit entangled states

In this paper, a semi-quantum key agreement (SQKA) protocol based on the three-particle maximally entangled GHZ state is proposed. In this SQKA protocol, the classical parties Alice and Bob can only operate the qubits with classical basis, while the quantum party Charlie has full manipulation on qubits. By introducing semi-quantum approach, the requirement for device is reduced. Moreover, each of parties has equal contribution to key formation. The protocol can effectively resist outsider attacks even participant attacks. Through the efficiency analysis, our protocol has a higher qubit efficiency than the previous semi-quantum key agreement.
A Brief Introduction of the Text Classification Methods

Text classification is basically to categorize text data into different groups. Text classification has been applied in various domains, including news filtering and organization, document organization and retrieval, opinion mining, and email classification and spam filtering. However, in the Era of Big Data, in which text data is generated every second, it is almost impossible to classify text manually. This paper will briefly talk about common task classification methods with greater emphasis on DL classification models, CNN and RNN. CNN is good at extracting word pattern of the text. A classification is done by detecting particular words&#x0027; presence and location. RNN converts the text into a word embedding vector and processes the vector as a sequential data. It makes a classification by a series of computation based on the word sequence. This paper will also analyze several research papers which further explore the nature of CNN and RNN classification models. Finally, it will address the problems that current text classification models face.
An Online English Practice System Based on Diagnosis Learning and ASP.NET

In the online learning system, there is a lack of teachers to evaluate the learners' learning status, and the learners' learning efficiency is not high. To solve such problem, this paper analyzes the data collected in the practice process of CET-4, and realizes a network-based diagnostic evaluation model. We construct a knowledge system for assignment in exercise database, then use logistic function model to diagnose learners' ability value. Then, the logistic function model is used to diagnose the learners' ability value, and the difficulty value of the exercise is deduced. At last, the detailed scheme is provided by combining the functional modules and database structure of the system, and the specific system operation and analysis are provided with .NET + MVC. The experimental results show that our system can better diagnose the learners' weakness in question type, and provide better practice guidance and test recommendation according to their learning status and knowledge point question type obstacles, to improve the learners' learning level and examination ability.
Scanned Document Image Enhancement Method Based on Lightweight Convolutional Neural Networks

Aiming at the problem of image degradation in the process of digitization of traditional paper documents, an enhancement method for document image resampling by scanning or photographing is proposed to improve the subjective clarity of document image and character recognition. In order to meet the requirements of readability and OCR accuracy of scanned document images, a lightweight convolutional neural network model is proposed firstly. Secondly, by analyzing the degradation of a large number of actual samples, a combined random degradation model is proposed to simulate the real degradation process, and a small sample data set is generated by the degradation model to train the model. The results show that the recall rate of the processed image is 91.5%, which is 18.3% and 12% higher than that of Laplace and Roberts operators, and the recognition accuracy is 89.0%, which is 43.3% and 30.2% higher than that of Laplace and Sobel operators.
Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS

This paper focuses on the covering mechanism which generates a new if-then rule when the input data does not match the rules in the XCS Classifier System (XCS), a rule-based machine learning system, and discusses how the new rule should be generated from the viewpoint of "inheritance" and "expansion" of the generalization degree of the nearest neighbor rule in the continuous space. For this purpose, this paper proposes the two covering mechanisms based on the "inheritance" and "expansion" of the generalization degree of the nearest neighbor rule and compares their results by applying them to XCS for real-valued input spaces (XCSR). Through the intensive experiments on three types of problems with the different characteristics, the following implications have been revealed: (1) the new rules should be generated by inheriting the generalization degree of the nearest neighbor rule in comparison with expanding it in the continuous space; and (2) XCSR with the "inheritance" based covering mechanism achieves higher classification accuracy with fewer rules than the conventional XCSR, which achieves higher classification accuracy than XCSR with the "expansion" based covering mechanism.
Affordance-Derived Declarative Interaction Models for Context Adaptation

Automatically adapting an interactive application to its use context is highly dependent on the existence of a declarative model. The Model-Based User Interface Development research made important progress in fully declarative specifications on interactive applications. However, the Abstract User Interface declarative models, such as task-based or communication-based models, are unfamiliar to designers and developers. This paper presents early explorations into a research program aimed at achieving fully declarative interactive applications: outlining a static concrete user interface and deriving the interaction from its affordances. The basic assumption is that for a well-designed user interface, the UI function can be derived from its form through affordance mechanisms. As the static aspects like the UI initial form are already being described declaratively in industrial practice, fully declarative interactive applications would result from the new research program.
How to Educate to Build an Effective Cyber Resilient Society

The possibility that computers, in particular, personal computers, can be used for harmful actions affecting global computer systems as a whole, due to two main reasons: (1) hardware and / or software failures, which are caused by problems related to their manufacture which must be solved by their respective manufacturers and (2) failures due to actions or inactions of their users, in particular people with low computer skills, people of very low age groups, e.g. children, or very old age groups, e.g. ageing people, or others without a minimum of computer skills. This problem is aggravated by the continuous proliferation of equipment, namely mobile devices, IOT devices and others that have Internet connectivity, namely through a browser. There are the possible ways in the area of cyber education that can contribute to cyber resilience of society and these are developed in this work.
Bitcoin during COVID-19:Could Internet Searches Explain Its Performance?

The purpose of this note is to present a behavioral and computational analysis of the performance of Bitcoin (BTC) during the COVID-19 period 31.12.2019-31.03.2021. In order to quantitatively include a health fear index into our analysis, we employ a code which is based on the Google searches for terms related to COVID-19 (the higher the index, the more the fear due to the pandemic). Moreover, we examine if increased interest, as expressed with an increased number of internet searches for Bitcoin terms, has an impact on the performance of BTC. The empirical findings confirm our assumptions that the suggested fear index explains the negative impact of COVID-19 fear on the performance of BTC, and that the increased interest regarding BTC is associated with increases in BTC prices. The methodology and the outcome of our study could be useful to investors, practitioners and scholars whose intention is to examine the performance of financial markets/assets.
Machine Learning and Deep Learning for Hardware Fingerprinting

Device or machine fingerprinting is the process of collecting information on a (part of a) device for its identification. This can be done under different scenarios and using information from different hardware and software layers of the device. Hardware fingerprinting typically refers to device fingerprinting using information collected from the hardware layer. Hardware fingerprinting can have nefarious usages related to privacy abuse, as well as many positive ones such as soft authentication, indoor positioning systems, and others. Here we introduce some of the uses of hardware fingerprinting, with special emphasis on those related to commonly available devices, and explain how machine learning and deep learning have enabled and/or improved them. Additionally, we discuss some of their limitations and possibilities for improvement.
Custom Components

In Chapter 7, you should have learned that Facelets <ui:composition>, <ui:include>, and <ui:decorate> are useful when you want to split main page layout fragments into reusable templates, such as header, menu, main content, and footer. And that Facelet tag files such as <t:field> are useful when you want to have a reusable group of components in order to minimize code duplication. And that composite components such as <t:inputLocalTime> are useful when you want to create a custom component with a single responsibility based on existing components and, if necessary, a bunch of HTML.
Application of UAV Remote Sensing Technology in the Construction of Modern Smart Farm

This article is to study the application and optimization of UAV remote sensing technology in the construction of smart farms. Multiparty calculations, circuit design, remote sensing, and remote control are used wirelessly, with a wide field of vision and easy evasion. Its transmitting frequency, transmitting power, interference electromagnetic wave intensity, receiving sensitivity, and gain coefficient are all factors that affect wireless remote sensing. Stress and strain are also important factors for the timely response and control of remote sensing technology. This article first outlines smart farms and drones, and then describes how to design drone circuits. Its smallest UAV circuit design includes UAV reset circuit, UAV crystal oscillator circuit, UAV upgrade circuit, UAV PPM decoding module circuit diagram, and then explains the attitude calculation and complementary filtering of the multiwing UAV. The complementary filtering algorithm is applied to the attitude calculation, and the error is reduced through the analysis of the coordinate system, the quaternion method, and the calculation of Euler angles. Then establish a wireless communication propagation model for smart farmland monitoring. Among them, the Egli model is selected. After data monitoring, it is obvious that the Egli model can monitor higher path loss, higher accuracy, and stronger propagation strength. Then through experiments, the static data of the two different tail transmissions under different loads are analyzed, the stress-strain trend line is observed, and the performance of the two transmission systems is analyzed. It can be seen that under the same conditions, the bevel gear tail transmission system has higher comprehensive performance, good reaction force characteristics, and excellent stress-strain performance. The power ratio of the synchronous belt tail drive system is 31.22, and the power ratio of the bevel gear is 29.52, a difference of 5.76%. The power ratio wind speed value of the tail part of the timing belt is 0.01014, and the power ratio wind speed value of the bevel gear is 0.009851, a difference of 2.93%.
Fusion of Data from Impulse-Radar Sensors and Depth Sensors

This chapter is devoted to the identification of the effectiveness of fusing the data from the impulse-radar sensors and from the depth sensors, when applied for monitoring of elderly persons in their home environment. Sensors of both types enable the unobtrusive monitoring of movements but—since each type of sensors operates according to a different physical principle—they have specific complementary advantageous and disadvantageous features. In this chapter, three basic non-probabilistic methods for fusion of data are compared with three advanced methods based on Bayesian inference. The results of the experiments show that the fusion of the position estimates, obtained on the basis of data acquired by means of both types of sensors, alleviates the problems which occur if only single-type sensors are used for monitoring, and that the application of the Bayesian methods for data fusion can reduce the uncertainty of the position estimates more significantly than the use of the reference methods.
Design of Student Management System Based on Smart Campus and Wearable Devices

In view of the current military cadets' daily teaching management information level is not high, service function is weak, cannot effectively grasp the students' individual physiological and psychological state changes, the design of military cadets intelligent teaching accurate management system based on wearable devices. Relying on the smart campus wireless network platform, the system uses intelligent wearable devices, RFID technology and data analysis technology to collect the basic data of students' daily movement track, going out, exercise class, training, sleep and emotion, and master the comprehensive physiological and psychological state through big data analysis, so as to provide means and tools for accurate management of students under the condition of smart campus.
A Method of Determining of Switching Instants for Discrete-Time Control Systems

This article discusses dynamical polysystems with piecewise constant controls. Assuming that a dynamical system is controllable in continuous time, the controllability of this system is investigated in discrete time. Discrete-time controls are constructed by using the theory of multidimensional continued fractions. Discrete switching instants were found by using continued fractions approximating continuous switching instants.
Research on Keypoint-based Object Detection

With the help of deep learning, especially the development of convolutional neural networks, object detection has been greatly promoted and developed. Keypoint-based object detection approach uses keypoints for objection detection and is a new research focus. This paper sorts out the development of keypoint-based object detection, analyzes and compares representative networks, and summarizes loss functions in training procedures, and discusses the problems and development of keypoint-based object detection trends.
Design and Specification of a Blockchain-based P2P Energy Trading Platform

During the past years, we observe that the topic of blockchain and its application to various domains is gaining momentum. Thereby, blockchain clearly goes beyond crypto-currencies and offers a distributed ledger platform for implementing different services in a transparent and trustful manner. Potential applications can encompass the domains of mobility, supply chains, international trade, financial products, voting systems and especially the domain of energy, aspects of which are at the heart of this paper. In the current publication, we present a peer-2-peer (P2P) energy trading system based on the Ethereum blockchain and dedicated architecture and components that realize transparent, secure and trustful trading processes. The customers of the P2P trading system are provided by stakeholders (e.g. private house/apartment owners) that are able to consume and share renewable energy (e.g. originating from solar panels). We present the design, specification, technological stack and implementation of the Ethereum based P2P energy trading system, provide a use case flow which is demonstrated in our lab and measure some performance indicators of our prototype. Finally, we evaluate the design decisions based on our experiences.
CIAFP

Object-oriented (OO) code has many dependencies among the classes and different types of changes that often have an impact during the maintenance of the software. In this paper, we proposed a technique for change impact analysis (CIA) with fault prediction (FP) using machine learning techniques for OO software. In proposed method an intermediate OO program representation is proposed using a graph which detects the difference between the original program and the modified program. For fault prediction, class level metrics are extracted from KC1 data set and 14 machine learning algorithms are trained on dataset. The genetic algorithm (GA) and correlation are used to extract significant features from the dataset. Trained prediction models are evaluated using classification accuracy (Acc). Our proposed approach consider various types of changes possible in the program, and test cases are selected to test the modified code during regression testing and finds the fault in classes. Among the 14 machine learning algorithms random forest (RF) giving best accuracy than other algorithms.
A Novel Radial Basis Function (RBF) Network for Bayesian Optimization

Gaussian process (GP) is the most popular surrogate model used in Bayesian optimization for solving computationally expensive problems. However, the computation time for constructing GP may become excessively long when the number of training samples increases. This study investigates multi-task learning with the radial basis function (RBF) network and proposes a multi-task learning network models based on RBF. Moreover, the proposed multi-task-RBF networks are applied to a Bayesian optimization framework and used to replace the GP for avoiding the covariance calculation. Experimental studies under several scenarios indicate that the proposed algorithm is competitive in performance compared with GP- and single-task-based Bayesian optimizations.
Smart Contract

A smart contract is a technology that allows the creation of a negotiation process capable of running independently, without human intervention. This chapter intends to frame the figure of the “smart contract” from a legal point of view. It shows that the smart contract is an advanced tool in the context of a contractual relationship. The possibility of making a smart contract “the contract” in a legal meaning opens up scenarios which have hitherto been unexplored for contract law. It is still difficult to determine to what extent current rules are adequate to govern this phenomenon. The chapter will therefore conclude with a review of the strengths and weaknesses of the smart contract technology and with some suggestions for a future smart contract law.
Simulation Model for Agile Software Project Management

Software engineering is one of the most important topics in software manufacturing, and its importance is due to how we obtain the application of the lower cost or we can say fixed cost, in the least time and the highest quality. Due to the existence of several ways to implement a specific project management plan, and here it is the responsibility of the project manager to find the appropriate approach to managing the proposed project in a way its implementation. This depends on the manager’s flexibility in accepting work under several different frameworks and approaches. The difficulty here it is complemented when the project manager is unable to adapt to work with different approaches. The basic idea here to adopt the methodology of work for the proposed project should not depend on one method that the manager depends on, but rather depends on several things, including the size of the work team, the size of the project, project requirements, cost, delivery time and flexibility in changing requirements
Automatic Monitoring System for Security Using IoT Devices and Smart Contracts

With the recent technological advances in the design of IoT devices and their widespread use, it has become increasingly common for alarm services that provide security for facilities to use IoT devices. However, the conventional centralized management of such IoT devices involves risks, such as data falsification that is vulnerable to cyber-attacks, as well as problems pertaining to the data volume, integrity, and reliability of the IoT devices themselves. Therefore, managing the logs of a large number of IoT devices is difficult. For this reason, blockchain is often used in forensics and other applications. However, to the best of our knowledge, there has been no research to-date that can automatically determine the actual level of anomalies from the logs of IoT devices using smart contracts. In this study, we propose a method to securely record the logs of IoT devices in a blockchain and use those logs to automatically detect anomalies. We also propose a method to estimate the degree of anomalies based on the logs using smart contracts and automate the sequence of events performed by the security companies.
On Digitizing the Greek Music Tradition: Designing the Cretan Lute for Mobile Devices

In this paper, an application, which simulates the playing of Cretan lute, is presented. This study is the continuation of a greater project, which started with the simulation of the musical instruments “Cretan lyre” and “mandolin” for mobile devices [1].The aim of the project is to create the appropriate mobile applications for Greek traditional musical instruments, both from an educational and cultural point of view, as well as for entertainment. The purpose of the application is not only the representation of the “Cretan lute” and the user’s familiarity with it, but also its use in a real play environment in combination with a (digital or real) Cretan lyre. Through the application the user becomes familiar with the form and sound of the Cretan lute, learns the two basic metrics used by musicians in Crete to perform the most well-known traditional rhythms (“syrtos”, “siganos”, “pentozali”, “maleviziotis”, “sousta”, “anogianos pidihtos”) and has the ability to record and reproduce his own compositions. Also, the connectivity of the device to an audio console enables the use of the instrument in combination with other instruments, thus creating an environment equivalent to that of a real concert or a traditional festival.