Reviewerly/exHarmony huggingface dataset:
An Efficient Quantum Multi-Collision Search Algorithm

In this article, we propose an efficient quantum k-collision search algorithm with low quantum memory O(n). The previous quantum k-collision algorithms can not be converted into a low quantum memory k-collision algorithm directly, because the time complexity of the converted algorithm is larger than the basic k-collision algorithm. To solve this problem, we shall not only divide our low memory quantum k-collision algorithm into several subroutines, but also need to achieve some balances between these subrou tines. The time complexity of our k-collision search algorithm is O(2(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -2)n/2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> -3 ), and the classical memory and quantum memory complexities are O(2(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -2)n/2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> -3 ) and O(n) respectively. In addition, we propose an efficient k-claw search algorithm, which can output a k-claw with O(n) qubits. Given 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> quantum processors, we can construct our quantum k-collision and k-claw parallel algorithm with the time of O(2(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -2)n-(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> -2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k-1</sup> -3)s/2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> -3 )while the classical memory and quantum memory complexities are O(2(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k-1</sup> -1)n+s-2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k-2</sup> /2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> -3 ) and O(n), respectively.
Text Entry in Virtual Reality: A Comparison of 2D and 3D Keyboard Layouts

Text entry is an important task in most interactive technologies in use today. Virtual Reality (VR) is becoming increasingly popular and is used in a variety of contexts, including tasks that involve text entry. With this being the case, it has become increasingly important to determine what the best keyboard layout is for text entry tasks in VR environments. To address this need, the current study compared two keyboard layouts, 2D (flat UI) and 3D (curved UI), with respect to text entry performance in VR. Results indicated that, compared to the 3D keyboard layout, using the 2D keyboard layout for the text entry task led to a greater number of words per minute, fewer corrections, and fewer redundant key presses while typing. These results indicate that the 2D keyboard layout was more efficient in VR text entry performance, compared to the 3D keyboard layout. Implications for the design and development of VR text entry tasks are discussed.
Harmonization Shared Autoencoder Gaussian Process Latent Variable Model With Relaxed Hamming Distance

Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches.
Implementation of Information Security System in Service and Trade

Abstract Usage of information technologies in service and trade industry is increasing daily which is accompanied by information leakage risks. Information security system can prevent threats from employee’s lack of skills and detect vulnerabilities on early stages. To implement information security system, general steps are to build conceptual model of the subject area, build a semantic network to follow decision making process, adept current situation in company to ISO 27001 requirements and choose information system to atomize business processes. EPC diagram given in this paper shows the steps that service and trade companies can follow to get ISO 27001 certificate. The article describes such security information and event management systems as IBM QRadar security intelligence platform, Splunk Enterprise Security and RSA NetWitness Suite. The results of the research show how implementation of information security policy impact company’s indicators and reputation. Cyber security system’s implementation can lead to low expenses caused by interruption in service or data leakage, increased reliability and security of systems and improved business processes’ optimization.
Exploring the Potential of Virtual Reality for Child Pedestrian Safety Training: A Case Study in China

This study explores the potential of using virtual reality in conducting child pedestrian safety training by designing and implementing a VR-based training intervention in three elementary schools in China, located in urban, rural, and fringe area respectively. The results suggested enhanced authenticity and interactivity as two technical affordances of VR, which enabled risky behavior diagnosis and personalized remedial instruction. The increased pedestrian performance among children during and after the VR intervention supported its efficacy as an innovative training program. Additionally, the empirical results indicated a limited impact of school location and family SES on children's pedestrian performance, highlighting the need for a universal pedestrian safety training curriculum for elementary schools nationwide.
The Mass Size Effect on the Breast Cancer Detection Using 2-Levels of Evaluation

Breast cancer is one of the most dangerous cancers and with the tremendous increase in the mammograms taken daily, computer-aided diagnosis systems play an important role for a fast and accurate prediction. In this paper, we propose three phases to detect and classify breast tumors. First, is the data preparation for converting DICOM files to images without losing data. Then, they are divided into mammograms with large and small masses representing the input to the second model training phase. The third phase is the model evaluation through two testing levels, first is the large masses checking and the second level is the small masses checking to output the detection results for large and small masses. The two testing levels using the trained small and large masses model overcomes the recent YOLO based detection work and the combined sizes trained model by achieving an overall accuracy of 89.5%.
A Comparison of Tools and Libraries for In-Class Face Detection and Emotion Recognition

Human factors, such as emotions, have been demonstrated as influential factors in educational teaching and learning. In order to exploit the relationship between students' in-class emotions and their learning performance, our preliminary work in this paper aims to try and compare different tools and libraries for in-class face detection and emotion recognition. We deliver our findings and insights about the popular libraries for these purposes.
SPEdu: A Toolbox for Processing Digitized Historical Documents

Historical-educational documentary sources have gained considerable attention in educational contexts. However, some sources suffer from serious problems such as inadequate infrastructure, poor preservation, and lack of qualified personnel. In addition, a large part of documents is not digitilized, making research difficult. As a consequence, there is a need for transcription, digitalization, and cataloging sources of information for the analysis of large volumes of data. To deal with this issue, we present SPEdu, a tool to digitalize sources of information demanded by research on the History of Education. The workflow of SPEdu is divided into three steps. Firstly, SPEdu adquires images from an information source. Secondly, the tool prepocesses the images and extracts features from them. Finally, a supervised machine learning module was built to classify images between text and non-text. To assess the viability of SPEdu, we used the Official Gazette of the State of Sergipe. Regarding the third step, we evaluated the performance of classification algorithms, such as J48, Logistic Regression, Multi-layered Perceptron (MLP), Naive Bayes, Random Forest, and Random Tree. Results have revealed that Random Forest outperformed remaining techniques with an average rate of 95\(\%\) of accuracy.
Method for Horizontal Alignment Deviation Measurement Using Binocular Camera Without Common Target

In the application of using binocular camera to measure the horizontal alignment deviation, due to the limited field of view of the camera, it is difficult for each camera to obtain all the target points to determine the position of the parts. To this end, a binocular position measurement model, where each optical camera corresponds to one cooperative target point, is established to determine the horizontal alignment deviation between the upper and lower parts. To overcome the problem of insufficient information in calculating the horizontal alignment deviation, the measurement data of a biaxial inclinometer and laser rangefinder are added. The Monte Carlo method is used to determine the error level for any position and attitude in the measurement range based on past work experience. The high accuracy of the position measurement method (the overall error does not exceed 2 mm) for a binocular camera without a common target meets the actual application requirements. This conclusion is verified by the experimental results.
POSTER: Data Collection via Local Differential Privacy with Secret Parameters

Local Differential Privacy (LDP) is attracting industries and researchers as a way of collecting data from individuals while preserving their privacy. LDP provides a strong privacy guarantee; however, it causes a decrease of utility so that we can only apply LDP to simple tasks such as heavy hitter estimation. In this paper, to solve the issue, we explore the amplification of the privacy of LDP with a small loss of utility. In LDP, a privacy parameter which decides the level of privacy protection is treated as the public information or common parameter in a data collection protocol. However, LDP requires that data providers perturb their data on their device, so naturally, data providers can choose and keep their preferred privacy parameter secret. In this paper, we study how the privacy level and utility change in a new privacy model, Parameter Blending Privacy, that the data providers keep their privacy parameter secret. The result concludes that this manipulation amplifies the privacy level with a small loss of utility, so it improves utility-privacy trade-off.
Free space continuous-variable quantum key distribution with practical links

The transmittance fluctuation resulting from free space optics will impart the continuous-variable quantum key distribution (CV-QKD). In this paper, we study the CV-QKD over free space by taking practical transmittance fluctuation into account. The transmittance is modeled following the Gaussian distribution, which reflects the most realistic fluctuation since all sources of perturbation are required to be incorporated together in practice. The comprehensive comparison between the practical model and existing models in secret key rate is drawn. The numerical results reveal that the existing model of uniform distribution underestimates the impact of air link for both slow and fast fading scenarios, and the practical secret key rate will suffer severer degradation. Further, as the variance of the Gaussian model decreases, the secret key rate degrades as well. Also, a comparison between heterodyne and homodyne detection schemes is made, and we found that the heterodyne detection outperforms the homodyne detection in the secret key rate for both the slow and fast fading air quantum channels.
Research on Autonomous and Controllable High-performance Consensus Mechanism of Blockchain

Based on the policies of developing the autonomous and controllable domestic technologies, this paper aims to build a blockchain consensus algorithm with security, stability, high-performance and expansibility in hope of forming one or more universally acknowledged and more unified blockchain consensus mechanisms. In addition, it attempts to make improvement and optimization based on POS consensus algorithm and with a focus on solutions to the problems of large data volume and blockchain network congestion. This paper is explored from four perspectives as follows: (1) Reducing the number of blockchain nodes participating in consensus. (2) Reducing the amount of broadcast data. (3) Reducing the number of transactions. (4) Other problems that may arise.
A 3DOF+ view rendering method for panoramic light field

We propose a new method of rendering panoramic light field within a certain range, which provides an effective way for the acquisition of virtual reality data. Different from existing panoramic light field reconstruction algorithms, we propose the concept of the ray sphere to render a panoramic light field. Using the internal and external parameters of the cameras in the acquisition system, all the light field data are uniformly expressed in the ray sphere. The constructed ray sphere enables the rendering of a panoramic light field of any view point within a 3DOF+ space, which can not be achieved with correlation methods. In addition, we design and build an acquisition system to capture real scenes to verify the effectiveness of our method. Experimental results show that our method can get panoramic light field rendering of any view point on the horizontal plane within half the radius of the acquisition system, and can effectively process the light field video data.
A Hybrid Model for Community-Oriented Lexical Simplification

Generally, lexical simplification replaces complex words in a sentence with simplified and synonymous words. Most current methods improve lexical simplification by optimizing ranking algorithm and their performance are limited. This paper utilizes a hybrid model through merging candidate words generated by a Context2vec neural model and a Context-aware model based on a weighted average method. The model consists of four steps: candidate word generation, candidate word selection, candidate word ranking, and candidate word merging. Through the evaluation on standard datasets, our hybrid model outperforms a list of baseline methods including Context2vec method, Context-aware method, and the state-of-the-art semantic-context ranking method, indicating its effectiveness in community-oriented lexical simplification task.
POSTER: Dynamic Software Vulnerabilities Threat Prediction through Social Media Contextual Analysis

Publicly available software vulnerabilities and exploit codes are often utilized by malicious actors to launch cyberattack to vulnerable targets. Therefore, organizations not only need to update their software to the latest version, they need to do effective patch management and also prioritize the patching schedule. In order to prevent future cyber threat based on publicly available resources on the Internet, this study propose a dynamic vulnerability threat assessment model to predict the exploited tendency for each vulnerability (i.e., CVE). The model considers many aspects of vulnerability which are gathered from multiple sources. Features range from profile information to contextual information of Twitter discussion about these vulnerabilities. When applied to predict the vulnerabilities exploitation in real world data, it showed better prediction accuracy using our approach and has been deployed into our threat intelligence platform as one of the analytic functions.
A robust CNN model for handwritten digits recognition and classification

At present, machine learning and deep learning models are playing a key role in various domains of image classification including handwritten numeral images. Handwritten digits recognition (HDR) via machine learning has received great attention of researchers due to ambiguity in learning methods. Hitherto, several researchers made significant efforts to improve the recognition process by selecting appropriate parameters and feature design. But there is always room for improvement in the conventional methods. Convolutional neural network (CNN) is a deep neural network most commonly applied to analyze image classification, object detection, face recognition, etc. To execute the task of HDR, robust CNN architecture is used for feature extraction and classification. In this paper, a Java-based framework known as Deeplearning4j (DL4J), is used for recognition and classification of the MNIST database. Results demonstrate that, compared to existing techniques, the proposed model is superior in terms of accuracy for handwritten digits recognition.
Multi-granularity Multimodal Feature Interaction for Referring Image Segmentation

Referring image segmentation aims to segment the entity referred by a natural language description. Previous methods tackle this problem by conducting multimodal feature interaction between image and words or sentence only. However, considering only single granularity feature interaction tends to result in incomplete understanding of visual and linguistic information. To overcome this limitation, we propose to conduct multi-granularity multimodal feature interaction by introducing a Word-Granularity Feature Modulation (WGFM) module and a Sentence-Granularity Context Extraction (SGCE) module, which can be complementary in feature alignment and obtain a comprehensive understanding of the input image and referring expression. Extensive experiments show that our method outperforms previous methods and achieves new state-of-the-art performances on four popular datasets, i.e., UNC (+1.45%), UNC+ (+1.63%), G-Ref (+0.47%) and ReferIt (+1.02%).
New Trends in Databases to NonSQL Databases

The appearance of the “big data” phenomenon has meant a change in the storage and information processing needs. This new context is characterized by 1) enormous amounts of information are available in heterogeneous formats and types, 2) information must be processed almost in real time, and 3) data models evolve periodically. Relational databases have limitations to respond to these needs in an optimal way. For these reasons, some companies such as Google or Amazon decided to create new database models (different from the relational model) that solve the needs raised in the context of big data without the limitations of relational databases. These new models are the origin of the so-called NonSQL databases. Currently, NonSQL databases have been constituted as an alternative mechanism to the relational model and its use is widely extended. The main objective of this chapter is to introduce the NonSQL databases.
Optimizing Cost and Maximizing Profit for Multi-Cloud-Based Big Data Computing by Deadline-Aware Optimize Resource Allocation

Cloud computing is most powerful and demanding for businesses in this decade. “Data is future oil” can be proved in many ways, as most of the business and corporate giants are very much worried about business data. In fact to accommodate and process this data, we required a very expensive platform that can work efficiently. Researchers and many professionals have been proved and standardize some cloud computing standards. But still, some modifications and major research toward big data processing in multi-cloud infrastructure need to investigate. Reliance on a single cloud provider is a challenging task with respect to services like latency, QoS and non-affordable monetary cost to application providers. We proposed an effective deadline-aware resource management scheme through novel algorithms, namely job tracking, resource estimation and resource allocation. In this paper, we will discuss two algorithms in detail and do an experiment in a multi-cloud environment. Firstly, we check job track algorithms and at last, we will check job estimation algorithms. Utilization of multiple cloud service providers is a promising solution for an affordable class of services and QoS.
Can Bitcoin Investors Profit from Buy, Hold, and Sell Recommendations by Crypto Analysts?

Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction changes relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
Double Cyclic Codes over \({\mathbb{F}_{q}+v\mathbb{F}_{q}}\)

In this paper, an algebraic structure of a type of double cyclic codes is investigated, which covers some existing codes as special cases. The paper presents generic results about the generating polynomials, minimal generating sets, matrices and dual codes of the proposed codes.