Reviewerly/exHarmony huggingface dataset:
Blockchain: Technologies for Facilitating Cyber-Physical Security in Smart Built Environment

In smart built environments, people’s lives digitally connect with city services, buildings, infrastructures, and utilities through information and communication technologies (ICTs). Since the ICT applications in the smart built environments are continually increasing and evolving, the potential benefits that the smart built environments can provide are also growing. This chapter discusses the definition of each layer of smart built environments and reviews the literature regarding the cyber-physical risks according to the type. Some studies addressed the cyber-physical security risks from the aspects of risk cognition and information security management (ISM). The AECO industry is advancing towards the smart building paradigm in which IoT devices and networks are used to improve occupants’ comfort, reduce lifecycle costs, and optimize the operation of building systems. Cyber attacks can impact one or multiple smart building cyber security goals, involving confidentiality, authenticity, integrity, authorization, and availability, non-repudiation. The data decentralization of blockchain can effectively prevent the single point of failure and data loss and breach.
Case study in which the Deutsch-Jozsa algorithm responds with pure states

Abstract A theoretical essay is presented on a particular type of balanced functions that allow the Deutsch-Jozsa algorithm to produce pure state instead of mixed states. Such results occur if and only if the balanced functions are linear combinations (modulo 2) of a set of balanced clock functions, that is, clock functions with periods that are power of 2.
Multi-classification of microblog’s comments based on feature combination

Text classification algorithm develops rapidly in recent years, especially with the promotion of deep learning, the accuracy of various algorithms has reached a certain height. However, an obvious drawback of deep learning is that a large number of labeled samples is needed. In the case of unbalanced labeled samples, the result is not quite accurate of the category which has the minimum quantity. We consider that among a large number of comments, many of the top comments are positive but without involving valuable opinion. For the order depends on the user's level and the number of praises, while some really valuable comments may sink, it's hard for people to find them. The main work of this paper is to classify the comments into four types: valuable, negative, useless and normal. This paper has chosen ST-SVMs model, which realizes multi-classification through using SVM three times. At the same time, we proposed a concept of "Topic Proximity" which used LDA to extract the topic then used word2vec to calculate the similarity between the comments and topic. Then we give a combined feature, which consist of vector for comments text, sentiment intensity and topic proximity as the input features of the ST-SVMs model.
Human Activity Recognition Using Long Short-Term Memory

The aim of this project is to perform human activity recognition using recurrent neural networks by long short-term memory. Human activity recognition involves dividing the accelerometer input sequences into well-defined movements. The project uses tensor flow which is a math library that performs programming tasks across a wide range of objectives. Recurrent neural networks are a type of artificial neural network that are designed to identify patterns in data sequences. The project uses deep learning architecture. The problem of long-term dependencies is solved using a long short-term memory mechanism of recurrent neural networks, also known as LSTM. The advantage of using LSTM for the model is that it can learn directly from the time series data. The project also develops an Android application to study the real-life application of the model by directly using data from the accelerometer in the device. Additionally, a GUI was also developed to display the results.
Application of relativistic quantum mechanics in radial problems and E/M equations

Abstract This research is pure research in the field of pure physics. The background of the research that I did there was doing pure research in the field of physics. The subject of this research is to use the e/m measurement tool. This study aims to (1) study the influence of the magnitude of the electron velocity and the magnitude of the magnetic field on the electron path, (2) determine the e/m ratio in calculations. The results showed that the magnitude of the electron path radius is directly proportional to the magnitude of the electron velocity and inversely proportional to the magnitude of the magnetic field acting on it. Meanwhile, the calculation result for fixed current is 16.84% and the fixed voltage is 14.15%, in this case the size of the electron path is directly proportional to the magnitude of the speed of the electron path, and inversely proportional to the magnitude of the magnetic field acting on the electron.
One-Shot Quantum State Redistribution and Quantum Markov Chains

We revisit the task of quantum state redistribution in the one-shot setting, and design a protocol for this task with communication cost in terms of a measure of distance from quantum Markov chains. More precisely, the distance is defined in terms of quantum max-relative entropy and quantum hypothesis testing entropy. Our result is the first to operationally connect quantum state redistribution and quantum Markov chains, and can be interpreted as an operational interpretation for a possible one-shot analogue of quantum conditional mutual information. The communication cost of our protocol is lower than all previously known ones and asymptotically achieves the well-known rate of quantum conditional mutual information. Thus, our work takes a step towards an optimal characterization of the resources required for one-shot quantum state redistribution, an important open problem in quantum Shannon theory.
Application of Primary Component Analysis and Feature Map fusion to Improve Real-time Prostate Capsula Detection

This study investigated the potential for using principal component analysis (PCA) and feature map fusion to improve real-time prostate cpasula detection algorithms. Some objective detection algorithms can realize real-time object detection, but the mean average precision (mAP) is not ideal. PCA is processed firstly to make dimensionality reduction. After that, the processed images are sent to the new network called Almost Fully Feature Fusion Single Shot Multibox Detector (AFFSSD) to train. Feature map size at 150×150, 75×75, 38×38, 19×19, 10×10, 5×5, 3×3, 1×1 are all utilized for feature collection by feature map fusion and feature pyramid therein. Results showed that PCA and AFFSSD has the highest mAP for the prostate capsula images detection, by 76.23%, compared with Faster R-CNN, R-FCN, YOLOv3, SSD, FSSD applied independently. The proposed architecture fused many low-level features, so the confidence score are very high, almost 100 percent.
Lessons Learned from Distance Collaboration in Live Culture

Classical music is an important part of European intangible cultural heritage. The musical knowledge, experience and artistic interpretation are passed between generations by teachers, students and artists. The communication between musicians across Europe is very important for sharing different views and enriching experience. When distant musicians are connected by audiovisual transmissions with sufficiently low latency and high quality, new types of distant collaboration are possible. In this paper, we describe some lessons learned from using low-latency network audiovisual transmissions to connect distance musicians which enables novel scenarios of real-time collaboration and distance learning.
GDAssister: Graphic Design Assistant System with Optimal Algorithm of Associated Rule Mining

In the process of modern graphic design, designers usually get inspiration from a large number of art graphs and then choose graphic elements for graphic design. However, the process is generally manual, limiting the alternatives a designer can explore. In this paper, we propose GDAssister, a graphic design assistant system with optimal algorithm of associated rule mining. The system can automatically generate corresponding graphs according to the requirements of designers and use style transfer technology to change the style (color and texture) of the graphs. The system introduces four modules referring to the process of graphic design (e.g., graphic element extraction, association rule mining, automatic layout, style transform). Association rule mining is the core module of the system, and the efficiency of association rule mining algorithm determines the running efficiency of GDAssister. We design an improved association rule mining algorithm (IFP-Growth), which can reduce time consumption and memory usage. We use Chinese ethnic apparel database to verify the feasibility and efficiency of the system. Our experiment shows that GDAssister: (1)helps designers create more graphic alternatives to explore design creativity and (2)helps designers generate different styles of graphs more quickly to shorten the design cycle.
Natural language processing based identification of Related Short Forum Posts Through Knowledge Based Conceptualization

Online communities collaborate and users share their views using online forums. The experience and ideas shared by the users in the forum are rich but finding relevant forum posts is laborious and frustrating. This research is targeted towards comparing a post at hand to find forum posts related to it. The conventional methods for identifying text similarity are not as efficient as they do not conceptualize the short text and lead to poor performance in finding related content. This paper proposes a novel scheme for the identification of related short forum posts in discussion forums. Contrary to the use of fixed vocabulary sets in the existing schemes, the proposed method uses distinct words in the forum post pair to form a joint word set dynamically. The knowledge base is used for deriving a raw semantic vector for each forum post. Further, the two semantic vectors are used for the computation of semantic similarity. The proposed framework uses inverted indexing to improve the efficiency of retrieving relevant forum posts by reducing the search space with synonyms of the forum post at hand. It is proven to be efficient in finding related forum posts in discussion forums with a recall of 90% through a set of tests conducted. It is also observed that precision can be improved with the Named Entity Recognition method.
A Traceable Anonymous Authentication Method For Mimic Defense

Mimic-Defense enables active defense of systems by building DHR (dynamic heterogeneous redundancy) structure. The existing authentication between modules in DHR is based on specific identity information and lacks the protection of the identity information of the heterogeneous executor. In this paper, we integrated the direct anonymous authentication protocol DAA with Mimic-Defense, and proposed the traceable anonymous authentication method Track-DAA for Mimic-Defense, which combines the dispatcher and arbitrator in Mimic-Defense with Issuer and Verifier in the DAA protocol respectively. We added the new Track process which enables Issuer to trace the identity information of a specific platform. The safety of Track-DAA is verified, and its operational efficiency is analyzed. The results show that Track-DAA is an efficient traceable anonymous authentication for Mimic-Defense.
Making BBS-Net More Efficiency for RGB-D Salient Object Detection

Abstract BBS-Net is a novel bifurcated backbone strategy network based on cascaded refinement, which achieves the most advanced performance in RGB-D salient object detection models. However, the performance of the model in multi-scale feature extraction and depth feature enhancement is not strong enough, and the generated saliency maps contain a lot of background distraction and blurred edges. In this paper, based on BBS-Net framework, we adopt more effective feature extraction network and depth feature enhancement module. Specifically, 1) Use the Res2Net-50 structure that constructs hierarchical residual-like connections within a single residual block as a feature extraction network; 2) Use ECA module as channel attention to extract depth information and enhance cross-modal compatibility. The comprehensive experiments of our method on 6 datasets show that it outperforms the original BBS-Net and 7 current state-of-the-art methods in 4 evaluation metrics.
PENGGUNAAN METODE KOMPRESI TEKS UNTUK LAYANAN SMS OVER GPRS

The increasing number of short message service which has grown rapidly has bad impact towards its service quality. Delay in the system could increase while message delivered because the network gets burdened. Aplication Comresion text metode ( iD-Code companding algorithm) for predictive text has mode to reduce bits representation for each character. In the other hand by choosing new route like General Packet Radio Service (GPRS) will also significantly help to reduced burden of the network and also may achieve high rate of message delivery process. Based on analysis obtained that iD-Code Algorithm could truncates bit representation of predictive text file until 50% of value, with character average length only 4 bits. The implementation of iD-Code Algorithm also lead to the decreasing of packet holding time that also have impact reducing the packet delay time queue and further it will lead to the channel using efficiency.
Analysis of Tree Structure for Secure Group Communication Using LKH Approach

In secure Group communication for applications such as pay per view, teleconferencing, and video conferencing, key management method with logarithmic computation, storage time and bandwidth efficiency are expected. Major aspects with key management are initializing the group with a group key and rekeying the group. The server maintains a key tree. Rekeying is mandatory due to dynamic group nature (join/leave operations) or periodic rekeying. Logical Key Hierarchy has a logarithmic rekeying cost if the key tree is balanced, over multiple rekeying operations key tree is not balanced and LKH yields worst Rekeying cost. To achieve logarithmic cost either keep the key tree balanced or restructure the key tree in such a way that it will still result in the best rekeying cost. This paper explores tree structures using the Logical Key Hierarchy (LKH) approach to find an optimal tree structure for an application.
Research On Visual Question Answering Based On Deep Stacked Attention Network

Abstract Aiming at the problem that the existing visual question answering model has a language bias in high-level logical reasoning, the model describes images or answers questions with low accuracy. A visual question answering model based on deep stacked attention networks is proposed. At the same time, the attention mechanism is introduced to help the model fully mine the image information; secondly, the problem attention mechanism is introduced to pay attention to the image and the question information at the same time when the problem feature is extracted; finally, the features of the image and the question are integrated and the answer is selected in the classifier. Taking the Visual Genome and VQA2.0 data sets as examples for empirical analysis, the results show that the accuracy of predicting answers is 1.13% higher than the existing best model, which proves the effectiveness and applicability of the model.
Problem-Value Analysis of Authentic Literary Texts in the Context of Dialogue Space of Foreign-Language Education (Language Specialisations)

The purpose of this study is to describe the problem-value analysis of authentic literary texts (hereinafter ALT), aimed at value orientation of students in language specialisations, in correlation with the dialogic space. The article addresses the functional component of using ALT in teaching foreign lan-guages at university and also describes the main and transitional stages of the problem-value analysis of ALT as a tool for recreating multidimensional development environment, which is the dialogic space. The study clarifies the notion of the dialogic space as applied to foreign-language education and describes its structural components. Scientific novelty of the study lies in the fact that the stages of analysing authentic literary texts are presented in correlation with components of the dialogic space and with the techniques of working with an authentic literary text in the course of teaching a foreign language to students in language specialisations. As a result of the study, it is found that the problem-value analysis includes three main stages (motivational-preparatory, analytical-prospective, value-forming), which correlate to varying degrees with the components of the dialogic space of foreign-language education.
Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades

Ordinal classifier cascades (OCCs) are basic machine learning tools in the field of ordinal classification (OC) that consist of a sequence of classification models (CMs). Each of the CMs is trained in combination with a specific subtask of the initial OC task. OCC architectures make use of a data set’s ordinal class structure by simply arranging the CMs with respect to the corresponding class order (e.g., small - medium - large). Recently, we proposed bidirectional OCC (bOCC) architectures that combine two basic one-directional OCCs, based on a person-independent pain intensity recognition scenario, in combination with support vector machines. In the current study, we further analyse the effectiveness of bOCC architectures. To this end, we evaluate our proposed approach based on different OC benchmark data sets. Additionally, we analyse the proposed bOCCs in combination with two different classification models. Our outcomes indicate that it seems to be beneficial to replace basic pairwise one-directional OCCs by the pairwise bOCC architecture, in general.
Smart Contract Satisfiability Checking for Blockchain Consistency

In a blockchain environment, smart contracts have been defined to be a way of setting up trust between parties by providing a self-executing equivalent of legal contracts. Besides, ensuring correctness and reliability of smart contracts is vital to checking trust in blockchain-based systems. In this paper, we discuss a logic-based approach in order to ensure the quality of the blockchain in terms of trustworthiness of smart contracts by considering some minors behaving as a logical satisfiability service. The service selection is designed as a Proof of Work of the minors.
Feasibility Study of Outlier Detection in Smart Manufacturing Applications

Nowadays, machine learning is becoming part of the manufacturing process. It is important to machine learning techniques that its training data or processing data likely need to be clear. Because of the quality of the data, the accuracy of the machine learning model is directly affected. Detecting outliers is important for such manufacturing process analysis. The complexity of the manufacturing process and a large amount of data challenges to detect outliers in the manufacturing system. In univariate outlier detection, most of methods are not available in real-time data streaming. We chose streaming peaks-over threshold (SPOT) and auto-regression anomaly detection (AAD) for getting results on the ceramic manufacturing process dataset because they are real-time-based methods.
Movie Recommendation System Addressing Changes in User Preferences with Time

With a rapid increase in the volume of data, it is extremely critical for a business to display users with personalized recommendations. Applications such as Youtube, Amazon Prime Video, etc., keep track of the user’s activity, preferences, and ratings in order to provide meaningful recommendations. Such recommenders are typically based upon long-term preference profiles. However, our proposed approach implements a deep learning model that overcomes the current limitations of content and collaborative based filtering approaches and achieved a root mean squared error of 0.8659. These intermediate recommendations are further filtered based upon the changes in user preferences over time and provides the final recommendations tailored to user’s recent interactions. The proposed approach obtains a hit rate of 63% taking the user’s current interests and behavior into account.
Polynomial Methods in Statistical Inference: Theory and Practice

This survey provides an exposition of a suite of techniques based on the theory of polynomials, collectively referred to as polynomial methods, which have recently been applied to address several challenging problems in statistical inference successfully. Topics including polynomial approximation, polynomial interpolation and majorization, moment space and positive polynomials, orthogonal polynomials and Gaussian quadrature are discussed, with their major probabilistic and statistical applications in property estimation on large domains and learning mixture models. These techniques provide useful tools not only for the design of highly practical algorithms with provable optimality, but also for establishing the fundamental limits of the inference problems through the method of moment matching. The effectiveness of the polynomial method is demonstrated in concrete problems such as entropy and support size estimation, distinct elements problem, and learning Gaussian mixture models.
Design of intelligent construction engineering frame based on communication information visualization

Design of the intelligent construction engineering frame based on the communication information visualization is presented in this paper. High-rise intelligent buildings are different from low-rise buildings. They have higher requirements for high and low voltage power distribution systems and lightning and fire protection. Therefore, when the actual electrical design is carried out, the optimization design should be focused on, so as to provide the necessary guarantee for people's living safety. Based on this, the paper proposes the 2 aspects of the novelties. (1) The realization of the cost minimization enables network engineers and designers to the focus on the network optimization, especially to make the changes in general mobile communication more obvious. The novel communication method is designed for the improvement of the system. (2) Correction warning information must be transmitted to the processing platform in the shortest time, and the processing results should be fed back to the alarm platform. The novel information mode is designed to be applied for the system.
AutoNotes: A Touch-Free Blink-Based Interactive Model for Generation of Notes from Lecture Videos

The recent explosion of interest in online courses can directly be attributed to the current pandemic which has collated to the adoption of Massive Open Online Courses. While the enforced social distancing protocol inevitably demands the involvement of technology in raising awareness, it further requires the imposition of preventive restrictions on touch-based systems. Visual gestures like simple blinks can account for a significant subset of possible actions in mobile application domains. The proposed model introduces a novel approach that facilitates the automated generation of relevant lecture notes and web-linked keywords from videos through a blink-controlled interface . Functions like auto-video pausing and auto-closing of the application based on eye attribute tracking have been integrated into the system for handling context switching. The experimental results reveal 84.17 usability score, an average of 89.55% of different types of blink detection accuracy, 91.56% of text generation accuracy and 70.41% of keyword detection accuracy along with low false positives and false negatives under different lighting conditions.