Reviewerly/exHarmony huggingface dataset:
A New Structure of Blockchain to Simplify the Verification

Blockchain is first introduced in Bitcoin and has good performance in cryptocurrencies. With the growth of chain, the height of blockchain in Bitcoin has reached five hundred thousand, and the entire capacity of chain is more than 500 GB. If a light node wants to verify a transaction, it has to spend nearly one week to download the data of whole chain with download rate of 1 MB/s, which seriously hinders the usage of blockchain. In order to resolve this problem, checkpoint is used to reduce the capacity of data, but this centralized scheme obviously violates the decentralization of blockchain. Other schemes are proposed will change the structure of blockchain may suffer the risk that adversary can fork a blockchain to cheat the light nodes. In this paper, we propose a novel blockchain architecture, which simplifies the verification in blockchain and is compatible with most consensus mechanism. We append backlinks in some blocks, such that blocks not only look like a chain but also like a binary tree. We also introduce a challenge mechanism for against the forking attack on light nodes.
Dynamic Content Mining

Neural networks and convolutional neural networks can be considered as functions which take as input a vector and compute a distribution over the set of possible classes. Such networks have no notion of order in time nor in memory. That is they are not suitable for dynamic content mining like speech recognition, video processing, etc. In this chapter we introduce models able to handle temporality of visual content.
Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

Abstract The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.
Digital Crime Evidence

With the advent of computers, there came computer-related crimes; hence, there comes the need for cybercrime judicial proceedings. And for any trial, evidence plays an instrumental role in bringing the victim to justice. So, there is a need for digital evidence. Digital crime evidence forms a core for the field of computer forensics. Breaking down the term digital crime evidence to be understood in simple words, it is the collection of data and information that plays a crucial role in digital crime investigation and that is usually stored and transmitted in electronic formats. Digital evidence is defined as any data stored or transmitted using a computer that supports or refute a theory of how an offense occurred or that address critical elements of the offense such as intent or alibi. This data is commonly a combination of text, audio, images, and videos. This evidence is generally invisible, fragile, time-sensitive, and integrity will be lost if they are mishandled.
Convolutional Neural Networks as Image Analysis Tool

After studies of fundamental operations of convolution and sub-sampling in previous chapter, we introduce here convolutional neural networks and consider those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and finally briefly overview most popular convolutional neural networks architectures.
Design Big Data Analysis System - Bigdeepexaminator

Big Data is a term used for such data sets, which at the same time are characterized by high volume, di-versity, real-time stream inflow, variability, complexity, as well as require the use of innovative technolo-gies, tools and methods in order to extracting new and useful knowledge from them. Big Data is a new challenge and information possibilities. Correct interpretation of data can play a key role in the global and local economy, social policy and enterprises. We present a data analysis system design with the use of ar-tificial intelligence that will help in obtaining valuable information from big data.
Aprendizaje Mixto en Entornos Virtuales de Enseñanza-Aprendizaje en la Asignatura Introducción a la Pedagogía en la Carrera de Contabilidad y Finanzas

Nowadays, Information and Communication Technologies (ICTs) are playing a leading role in the socioeconomic development, and heavily influencing every job, piece of research, service, and even everyday life of people from all over the world. The impact of these Technologies on universities and colleges requires new educational settings to be created, where ICTs may promote training processes in open, flexible, and effective environments. In this work, the results obtained by incorporating, for three academic years, ICTs into the Introduction to Pedagogy course as part of the Accounting and Finance Curriculum offered by the University of Havana are presented. Blended learning as an approach to education that combines a virtual learning environment specially created for this course, the learning platform Moodle, and face-to-face classroom practices was used.
Anomaly Detection Modeling Based on Self-Adaptive Threshold Voting Integrating DBN-LRs

Due to the imbalance of positive and negative samples, high data dimension and huge data volume, anomaly detection for real multi-source operation and maintenance data is challenging. Thus, an anomaly detection model is proposed based on integrated learning of Deep Belief Nets. It solves the imbalance problem of positive and negative samples in data set, and makes use of the good feature extraction function of Deep Belief Net to effectively reduce the dimension of the multi-source KPIs data. Combined with Logistic Regression and Restricted Boltzmann Machine, the anomaly detection model is constructed. During the process of integrating multiple weak classifiers, a self-adaptive threshold voting algorithm is put forwards, it integrates multiple weak classifiers and improves the generalization of the model. The approach presented in this paper is evaluated based on a real operational data set. The accuracy of the anomaly detection model is more than 99.23%, and the recall of the model is 99.38%.
Social aspects of human-computer interactions in the media

In today's world, all the most important events and changes in economic and cultural relations take place within the information community. It is impossible to gain authority and a place among the partners of this community without the development of democracy in each particular state. It is also impossible without the use of computer technologies, but such use should not contradict international standards of human-computer interaction and standards of information protection. Special attention of researchers in the field of mass communications sociology and media psychology is attracted by the pervasion of human-computer interaction problems into mass media discourse, which accumulates a significant part of public discourse as a whole. The paper presents the results of an online expert survey of professional journalists discussing the impact of computer networks on their "actors", groups and communities. In the mainstream of interdisciplinarity, using the anthropocentric and sociocommunicative approaches, the social aspects of human-computer interactions in the media are studied. The chosen methodology allows to focus on the most relevant of them, such as: the need for humanization of communicative contacts in networks and mass media, recognition of changes in the structure of audiences, increasing the role of subjectivity and personification of contacts. The problem for future human-computer interactions (HCI) research is that the further computer science - and therefore technology - advances, the more often we have to think about the balance between benefits and harms caused to humans by active interaction with the computer.
Online memory learning for active object recognition

Traditional CNN-based recognition algorithms are trained for limited labeled data, which may not perform well in a different environment due to the lack of adaptivity of the CNN networks. So the traditional CNN-based recognition algorithms can not play a good role in robot applications because the robots have to work in different environments. However, the robot can continuously perceive new images during its mission. These images contain lots of environment-related features but lack of labels. So the robots must learn the environment-related features adaptively with unlabeled data to further improve the performance of CNN-based recognition algorithms. We call this ability as active object recognition (OBR). In this paper, we designed a dynamic memory structure (DMS) which can adaptively learn the environment-related features online and embedded DMS into a VGG-16 network to implement active object recognition. We also evaluate our dynamic memory network of CIFAR-10 and CIFAR-100 classification dataset. The results show that by learning environment-related features, dynamic memory network achieves a better performance on classification accuracy. More importantly, the network can have the ability to improve itself while many times testing.
An IOT Based Intelligent Medicine Box Using Vending Machine- Medical ATM

Medicines play vitally important role for well-being of a person, it helps to prevent illness and heals diseases. This paper helps to get acquainted with Any Time Medicine is a machine which delivers required medicine in emergency cases and ensures availability of 24x7. It can provide medicines for basic common health issues like fever, cold and cough, etc. First aid can be done easily using this system. Medicines provided by this machine are only for timely calmness and in emergency case and the person has to consult doctor later on. ATM will be functional in saving life in remote areas, rural areas and where the medical stores aren’t aired over in case of emergency. This paper shows the use of advanced RISC machine which controls the other sub systems like RFID reader. When card is inserted, features of particular user are read by RFID reader and will be displayed.
Information Technology in Multicultural Educational Environment

This paper discusses the challenges of learning in multicultural educational environment. It proposes approaches of using information technology tools such as on-line internet resources, mobile devises, mobile programs for calculations etc. in international students groups for teaching art students fractal geometry. Some exercises for easy comprehension of such mathematical concepts as self-similarity, iterative process, recursion, convergent and divergent sequences, infinite sequences are designed. The paper considers the phenomenon of using technology as medium of instruction in the international students groups. The challenge of considering and using the means of information technology when developing pedagogical tools, techniques and approaches to teach students in multicultural educational environment is posed.
Word Sense Guessing: A Knowledge Graph based Approach

In order to tackle issues of traditional corpus, such as low updating rate, we propose a novel corpus based on Knowledge Graph. In this paper we do experiments that are particularly designed to use one specific sense guessing model. This paper also compares the results of experiments which are respectively based on traditional and novel ones.
A Negative Similarity Based Hybrid Recommender System Using Apache Spark

The rapid technological advances in digital business after the millennium has exponentially boosted the options of available items to the users. Customized lists of recommendations assist people by limiting the number of options to a very few items that are related to the specific user and thus supporting them to have the final decision with ease. In this study, we used Apache Spark to show an effective parallel implementation of our hybrid recommendation algorithm. Our hybrid algorithm is based on Alternating Least Squares and Negative Similarity Collaborative Filtering methods. Alternating Least Squares is a type of algorithm for matrix factorization, which can be parallelized during runtime. It performs relatively well when processing large scale datasets. Negative Similarity Collaborative Filtering (NSCF) technique exploits the tastes of people at the opposite poles. The cascade hybridization method was used in the study to create a hybrid algorithm. In the evaluations, we observed that hybridization achieved significant accuracy performance improvement when compared to the performance of NSCF algorithm alone. Moreover, our hybrid method performed slightly better than the ALS algorithm alone in the overall accuracy of recommendations.
Low-Noise LLC Side-Channel Attack with Perf

Many cache side-channel attacks have been proposed, and they threaten sensitive programs in real-world. The success of the attacks depends on how accurately to decide whether a set of cache lines are in cache or not. However, external factors insert noise into cache attacks, and the noise disturbs the attacks’ decision process. Attacks in last-level cache (LLC) have more noise compared with core-dedicated caches. In this paper, we propose an attack method using Perf—a performance analyzing tool in Linux, attack$$_{Perf}$$, to achieve low-noise in cache side-channel attacks to LLC. The proposed method utilizes Perf to decide cache hits/misses when accessing memory. Since Perf gets the number of cache hits and misses from hardware performance counters, it can identify the cache hits/misses of memory accesses with the less noise. For evaluation, we compare the performance of attacks$$_{timer}$$ (existing attack method) and attacks$$_{Perf}$$ by implementing Flush+Reload and Prime+Probe. For the accuracy of Perf, we compare the clock cycles of the timer and the counts of Perf according to the victim’s access.
Monitoring behaviours with model divergence: emailing studies of users with cognitive impairments

Users with cognitive impairments use an assistive technology email system, CogLink, for socialisation and help in their activities of daily living. As users interact with the AT email client, the logged stream of events is monitored and analysed to aid decision-making. When caregivers receive monitor notifications, they know that the user has had a significant change in her emailing behaviour. Consequently, caregivers select adaptations to the email client that can challenge the user to gain still more emailing skills. The monitor in this emailing system analyzes user data to recognise when significant changes in user behaviour warrants caregiver attention. Moreover, the monitor can distinguish newly learned skills from new, but transient behaviours. This article summarises the CogLink assistive technology monitoring techniques, introduces the learning likelihood algorithm, which distinguishes transient from learned behaviour, and provides lessons learnt from a decade of monitoring CogLink users.
Three-dimensional nonrigid reconstruction based on probability model

Most nonrigid motions use shape-based methods to solve the problem; however, the use of discrete cosine transform trajectory-based methods to solve the nonrigid motion problem is also very prominent. The signal undergoes discrete transformation due to the transform characteristics of the discrete cosine transform. The correlation of the data is well extracted such that a better compression of data is achieved. However, it is important to select the number and sequence of discrete cosine transform trajectory basis appropriately. The error of reconstruction and operational costs will increase for a high value of K (number of trajectory basis). On the other hand, a lower value of K would lead to the exclusion of information components. This will lead to poor accuracy as the structure of the object cannot be fully represented. When the number of trajectory basis is determined, the combination form has a considerable influence on the reconstruction algorithm. This article selects an appropriate number and combination of trajectory basis by analyzing the spectrum of re-projection errors and realizes the automatic selection of trajectory basis. Then, combining with the probability framework of normal distribution of a low-order model matrix, the energy information of the high-frequency part is retained, which not only helps maintain accuracy but also improves reconstruction efficiency. The proposed method can be used to reconstruct the three-dimensional structure of sparse data under more precise prior conditions and lower computational costs.
Regression-Based Affect Recognition and Handling Using the Attribution Theory

This chapter presents the automatic frustration detection module and the response to it through motivational messages using a learning theory of our social networking-based language learning system, called POLYGLOT. In POLYGLOT, students can declare their affective state among “happy”, “frustrated” and “neutral”. However, their interaction with the tutoring system, i.e. experiencing difficulty in a test or receiving a bad grade, can be a blockage of their goal and as such the reason of feeling a negative emotion, such as frustration. Hence, POLYGLOT can detect students’ frustration by using the linear regression model. The relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Finally, the POLYGLOT’s response on frustration is the delivery of motivational messages based on the Attribution Theory, involving a three-stage process underlying that behavior must be observed/perceived, must be determined to be intentional and is attributed to internal or external causes. With the use of motivational messages, the students are assisted in the educational process and are not willing to quit learning.
Metric space method for constructing splitting partitions of graphs

Abstract In an earlier work [6] the concept of splitting partition of a graph was introduced in connection with the maximum clique problem. A splitting partition of a graph can be used to replace the graph by two smaller graphs in the course of a clique search algorithm. In other words splitting partitions can serve as a branching rule in an algorithm to compute the clique number of a given graph. In the paper we revisit this branching idea. We will describe a technique to construct not necessary optimal splitting partitions. The given graph can be viewed as a metric space and the geometry of this space plays a guiding role. In order to assess the performance of the procedure we carried out numerical experiments.
Artificial Intelligence and Blockchain-Driven Islamic Capital Markets

New innovative digital and advanced technologies are transforming the way we access and use existing financial products and services. Fintech is becoming disruptive more and more by latest and advanced technologies such as blockchain, cloud computing, big data analysis, Internet of Things, robo-advisors, and artificial intelligence. This chapter discusses the crucial innovation, structural and institutional development for financial technologies in Islamic Finance. The blockchain proposes a new operational design, where all actors within the capital market can work from common ledgers, with collective datasets in almost real-time, and supporting operations which are more streamlined. The system will also incorporate fraud-detection and compliance enhancements, where applicable. This conceptual chapter also assesses the key features of the AI and blockchain architecture and protocol developments. It identifies areas where AI and blockchain can bring substantial transformational change, while at the same time, identifies some of the major barriers to adoption within the capital markets.
Depth Map Inpainting Using a Fully Convolutional Network

Emerging consumer depth cameras, such as Microsoft Kinect and ASUS Xtion Pro, are low-cost depth sensors which can capture the depth maps of the scene, and obtaining color images of the corresponding scenes at the same time. Depth information is very important for computer vision applications, and to acquire the accurate depth information is challengeable. However, due to the limitations of imaging principle, depth maps acquired by such devices are often noisy and will lose depth values for certain areas. In this paper, we present a novel method for depth inpainting problem using a Fully Convolutional Network (FCN). The network has two input branches. One branch is a color image that has been processed by a convoluted hidden layer, and the other one is the missing depth map. Besides, we add a shortcut in order to improve the feature extraction capability of the network. We have validated our method in the Middlebury Stereo Dataset and real-world scenes captured by Kinect 2.0. Experiments show that our method has better applicability and reliability than traditional methods in depth inpainting.
Feature Extraction Based on Frame Interval for Wireless Network Devices

The development of wireless communication technology has brought great convenience to our lives, however, in the fields of military communications, remote signal control and wireless signal transmissions, we still face huge challenges.
Improved Quality Aware Network Using Quality and Max Pooling Based Late Feature Fusion for Video-Based Person Re-Identification

Video-based person re-identification is an important research area in computer vision. Quality Aware Network (QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature. However, the QAN has poor ability to distinguish pedestrians with highly similar clothing. To address this problem, we analyze the advantages and shortcomings of Quality Pooling and Max Pooling in theory, and propose the Frame Quality and Max Pooling based Late Feature Fusion Method (QMFF). It aims at enhancing the QAN's ability to distinguish pedestrians with similar clothing. First, use Quality Pooling to get the initial ranking list. Then, for each Query, judge whether the top-N candidates in the ranking list are highly similar in clothing to one another. If the Query meets the similarity criterion, Max Pooling with Frame Quality Filter is used. Local salient features selected by Max Pooling help distinguish pedestrians more accurately. Experimental results on the iLIDS-VID dataset show that the proposed method is superior to the QAN and many recently proposed methods.