Reviewerly/exHarmony huggingface dataset:
Personalized Recommendation for Cold-Start Users via Cluster-Level Latent Feature Model

Making accurate and personalized recommendations for the cold-start users is a significant yet challenging problem in the cross-domain recommendation systems. In collaborative filtering methods, personalised recommendations are concluded from user history data. Therefore, a large volume is necessary for an accurate recommendation system. In this paper, we extend Wang et al.'s work on latent feature mapping model in single auxiliary domain into two auxiliary domains for the cold-start users who are in the target domain. First of all, in order to better describe users in the sparse domains, we make full use of K-means algorithm to extract user's feature between two auxiliary domains and handle the rating matrix in the target domain with matrix factorization by combining user similarities. Next, to perform knowledge transfer across different domains, we employ a neighborhood based multi-layer perceptron (MLP) approach to learn feature mapping function of users. Finally, the preference of the cold-start user in the target domain could be predicted based on the mapping function and his/her latent factors in the auxiliary domain. In addition, experimental results on user data obtained from Amazon platform reveal the edge of our new model against other relevant methods.
Detection of Intent-Matched Questions Using Machine Learning and Deep Learning Techniques

Questions which are syntactically different, yet having the same intent, give a poor encounter to both the writer of an answer as well as the individual who searches for the answer in social Q&A online platforms such as Quora, Yahoo Answers, and StackOverflow. In order to maintain a rich and diverse database of answers, ensuring the uniqueness of every question on such Q&A platforms is of utmost necessity. The objective of this proposed work is to eliminate redundancy/duplicacy of user-entered questions so as to increase the relevance of the answer(s) provided to semantically similar questions. This problem is a closed challenge taken from Kaggle, an online platform to learn and compete in data science challenges. The dataset worked upon in this paper has been obtained from Kaggle and LSTM with Euclidean Distance outperform other algorithms with log loss of 0.14.
Future Generation Education Technological Model

Today's economy is inconceivable without the application of new technologies. The need to keep up with the new requirements of the labor market is growing rapidly, and the need for further education is increasing. Continuous lifelong education and skills developing for adults becomes an integral part of everyday life. Adult learning requires additional motivation, facilitated by easy-to-understand and engaging technological solutions. Mutual cooperation is also becoming increasingly important. New individually oriented smart learning education model has been developed based on main psychological and pedagogical theories for fast growing information and technological society.
On the Blockchain-Based General-Purpose Public Key Infrastructure

The past few years have witnessed unprecedented advancements in the Distributed Ledger Technology (DLT) and blockchain - a form of DLT. DLT has clearly expanded the applications landscape in various sectors of our lives ranging from banking to business, finance, industry, education, and so on. On the other hand, security plays a crucial part in the successful realization of such applications and services. To this end, cryptography is the primary mean to protect the applications, networks, infrastructure, and services from cyber-threats. However, the existing Public Key Infrastructure (PKI) is based on central Certificate Authority (CA) that can become a bottleneck and may affect the efficiency of the cryptographic protocols because of the overhead incurred by the verification of cryptographic signatures and certificates. Recently, blockchain has also been leveraged to aid PKI without the need for a central authority. In this spirit, in this paper, we develop and implement a blockchain-based PKI using open-source Hyperledger Sawtooth. The proposed blockchain-based approach helps to address the problems of the existing PKI such as compromised and misbehaving CAs.
Quantum control in infinite dimensions and Banach-Lie algebras

In finite dimensions, controllability of bilinear quantum control systems can be decided quite easily in terms of the "Lie algebra rank condition" (LARC), such that only the systems Lie algebra has to be determined from a set of generators. In this paper we study how this idea can be lifted to infinite dimensions. To this end we look at control systems on an infinite dimensional Hilbert space which are given by an unbounded drift Hamiltonian H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and bounded control Hamiltonians H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,...,H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> . The drift H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is assumed to have empty continuous spectrum. We use recurrence methods and the theory of Abelian von Neumann algebras to develop a scheme, which allows us to use an approximate version of LARC, in order to check approximate controllability of the control sysPtem in question.
A Decentralized Approach to Architecture-Based Self-Protecting Software Systems

Architecture-based self-protection (ABSP) allows for the detection and mitigation of security threats informed by an architectural representation of the system maintained at runtime. This approach to self-protection has proven very effective through the ability at the architectural level to reason about the impact of a potential security threat, assess the overall security of a system and achieve defense in depth. However, existing methods to ABSP are centralized resulting in a single point of failure of the self-protecting subsystem. This paper describes a decentralized approach to ABSP based on DARE, an existing decentralized architecture-based framework for providing self-configuration and self-healing within distributed software systems. This paper examines how DARE was extended to provide self-protection capabilities and describes the detailed experimental validation of its approach. The experimental results illustrate DARE's decentralized capability of dynamically providing self-protection within a distributed software system.
Special issue on pattern recognition and cognitive assistants

This special issue proposes a space for researchers to discuss the challenges of bridging pattern recognition and cognitive assistants. This bridge was travelled in both sides. From one side, how pattern recognition methods can help intelligent systems to become more cognitive. And from the other side, how cognitive models can improve pattern recognition methods so that the final system can better assist users. So, this special issue presents research work on these topics, aiming to observe their interrelations in order to create theoretical approaches, methodologies and computational tools to advance work on cognitive assistants and pattern recognition.
Speckle Noise Removal by Total Variation and Curvelet Coefficient Shrinkage of Residual Noise

This paper proposed a Spatio-Frequency approach to remove multiplicative noise using total variation (TV) and Curvelet coefficient shrinkage of the residual noise. Prior to that, it also experimentally validated the importance of phase in speckle noise removal. We have shown that the phase of any complex transformation is more immune to speckle, than its magnitude. The proposed analysis on different phases encouraged the authors to apply threshold on Curvelet magnitudes to obtain the suppressed image details due TV minimization. The experimental results of our algorithm (on the simulated speckle noise) are compared with some of the recently reported image despeckling techniques. Two standard measures namely: Peak Signal to Noise Ratio (PSNR) and Structural Similarity Measure Index (SSIM) are adopted to analyze these methods quantitatively. Finally, the reference (noise-free) images of 3 different databases (with more than 500 images of wide variety of natural scenes, human faces, objects and textures) are considered to generate noisy image with varying number of looks; L= 4, 8, 12,16. The obtained denoising results demonstrate that the proposed technique provides significant improvement in noise suppression with high PSNR and SSIM values compared to other methods.
Second Order Subspace Statistics for Adaptive State-Space Partitioning in Multiple Particle Filtering

One of the main challenges in nonlinear Bayesian filtering is the so-called curse of dimensionality, that is, the computational complexity increase and associated performance degradation in high-dimensional systems. In the context of particle filtering (PF), a possible solution to mitigate such performance loss is the multiple PF (MPF) approach, where the original state is partitioned into several lower dimensional subspaces, and a set of interconnected PFs are used to characterize the marginal subspace posteriors. Two key issues are: i) how to partition the state, which is application dependent, and ii) how to let the filters (i.e., subspaces) fuse or merge depending on the time-varying conditions of the system, in order to improve the overall estimation performance. We propose a probabilistic approach to the adaptive state-partitioning problem within the MPF, which is based on the computation of subspace second order statistics. An illustrative multiple target tracking example is considered to support the discussion.
Application of Virtual Reality in Building Interior Decoration Engineering Practice

In recent years, the public pays more and more attention to the quality of house to flourish the interior design industry. In accordance with the relevant laws and regulations, those engaging in interior decoration and construction businesses should give priority to obtain the Certificate of Interior Decoration & Repairs Management of a Building, which is also one of the objectives of vocational college education. However, due to the restrictions to space and equipment on campus as well as safety considerations, it is difficult to provide the opportunity for students experiencing on-site constructions. However, virtual reality can provide such assistance. For this reason, this study aims to develop the related VR auxiliary materials for interior decoration practice based on the requirement of technical and vocational education for students in technology universities. Meanwhile, the participants are also trained to learn related technical skills and research development capabilities during the implementation of the project, so as to truly implement the spirit of technical and vocational education.
Lexical and Syntactic Simplification for Indonesian Text

This paper presents a text simplification approach for Indonesian text by incorporating lexical and syntactic features that aims to improve documents accessibility for children. We employed the similar words information from word-embeddings model in lexical simplification and manually defined rules in syntactic simplification. We conducted the evaluation by presenting the simplified texts and several questions regarding text simplicity, fluency, and adequacy in form of a questionnaire to 137 fourth to sixth grade elementary students. The evaluation results show that our system is able to reduce the lexical and syntactic complexity, based on the simplicity and adequacy parameters, while we need to study the fluency parameter further.
Median based Multi-label Prediction by Inflating Emotions with Dyads for Visual Sentiment Analysis

Visual sentiment analysis investigates sentiment estimation from images and has been an interesting and challenging research problem. Most studies have focused on estimating a few specific sentiments and their intensities. Multi-label sentiment estimation from images has not been sufficiently investigated. The purpose of this research is to accurately estimate the sentiments as a multi-label multi-class problem from given images that evoke multiple different emotions simultaneously. We first introduce the emotion inflation method from six emotions defined by the Emotion6 dataset into 13 emotions (which we call `Transf13') by means of emotional dyads. We then perform multi-label sentiment analysis using the emotion-inflated dataset, where we propose a combined deep neural network model which enables inputs to come from both hand-crafted features (e.g. BoVW (Bag of Visual Words) features) and CNN features. We also introduce a median-based multi-label prediction algorithm, in which we assume that each emotion has a probability distribution. In other words, after training of our deep neural network, we predict the existence of an evoked emotion for a given unknown image if the intensity of the emotion is larger than the median of the corresponding emotion. Experimental results demonstrate that our model outperforms existing state-of-the-art algorithms in terms of subset accuracy.
Design of Resource Recommendation Model for Personalized Learning in the Era of Big Data

This paper proposes a personalized learning resource recommendation model based on big data. The design of the model consists of data storage, data analysis, resource matching, and the resource recommendation. In order to provide a suitable resource, data analysis is a more critical procedure that involves the analyses of basic information, learning style, learning status, learning behavior, and learning interest, which can be successfully analyzed by means of kafka and flume. Through an experiment, it shows that personalized resource recommendation platform really plays a positive role in improving students learning.
A Real-time and Online Multiple-Type Object Tracking Method with Deep Features

Object tracking is one of the most important things in intelligent vision system. Meanwhile, the most challenging issue in object tracking is how to keep the target's identity unchangeable with limited power consumption. In this paper, we propose a real-time and online tracking method to track multiple types of objects (e.g. pedestrian and car). Furthermore, to handle the ID switching problem, we provide a lightweight deep learning model which can recognize the similarity of objects. It can effectively solve the ID switching problem resulted from occlusion. Finally, we do some experiments to demonstrate that the proposed method achieves the state-of-the-art performance with less power consumption. The proposed method can solve the problem of high computation of tracking and keep the high accuracy of counting results with low ID switching rate. The experimental result shows that the average counting accuracy of the proposed method can reach more than 93% on pedestrian and vehicle counting applications. Also, it shows that the proposed method improves 68.2% on average of ID switching rate than previous works.
Behavior Analysis through Routine Cluster Discovery in Ubiquitous Sensor Data

Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of features for modeling user-specific characteristics. These characteristics, in turn, can be used for a number of tasks including resource management, power efficiency, and smart home applications. In recent years, the employment of topic models for BA has been found to successfully extract the dynamics of the sensed data. Topic modeling is popularly performed on text data for mining inherent topics. The task of finding the latent topics in textual data is done in an unsupervised manner. In this work we propose a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations. We evaluate three different techniques namely LDA, the Non-negative Matrix Factorization (NMF) and the Probabilistic Latent Semantic Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets namely, the Intel Lab Data, Kyoto Data, and MERL data. Through rigorous experiments, we achieve silhouette scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset and 0.8312 over the MERL dataset for clustering.
A Real-time Video Fusion Processing System Based On SIFT Algorithm and FPGA

In order to combine multi-views into one ultra-wide single-view for present surveillance desire, a video fusion scheme based on FPGA is proposed in this paper. The geometric transformation and image fusion algorithms used in our scheme are simulated by Matlab for verification and optimization. Then, a combination of two 640*480 video images captured by cameras is implemented in a Altera FPGA platform DE2-115. Moreover, the weighted fusion method is used to realize video fusion for better performance; and larger size of 1280*768@60Hz video images' combination are also realized in DE2-115. Experiments show that the proposed approach can realize the mosaic of two video images with a delay of 16 clock cycles, i.e. a total of 0.16us. The error induced by the fusion process is less than 1 pixel. The implementation in DE2-115 for two RGB 1280*768@60 pixel size image combination can be 1743 times faster than software implementation with a good splicing performance.
Generating Student Interest in Quantum Computing

This Research to Practice Full Paper discusses an approach to motivating graduate computer science and electrical engineering students in the study of quantum computing. The U.S. Congress and other governmental and industrial organizations have recognized that teaching Quantum Computing in university-level Computer Science and Electrical Engineering programs is strategically important for workforce development along with technology development. Success in this endeavor requires generating genuine interest in the topic and motivating potential students. Quantum Computing requires an understanding of relatively difficult related subjects including physics, math and computer science. Many computer science and electrical engineering students shy away from quantum computing because of its complexity. This paper discusses our success with generating interest in quantum computing among graduate students and motivating them through exploring, in related courses, some aspects of quantum computing that have the potential to generate discontinuities. The approach is also examined in terms of the Scientific Learning Cycle as discussed by Wankat & Oreovicz. Opportunities and challenges of adding Quantum Computing to Computer Science and Electrical Engineering programs were studied through a student small group project in a cybersecurity course. The work built on extensive research into the effectiveness of small group projects and the role of “play” in learning. Our small group project on quantum computing was introduced three years ago into a cybersecurity course which is required for all students in our university's Masters in Computer Science and Masters in Electrical Engineering programs. The basic goals of the project are to teach students some basics of quantum computing by allowing them to “play with it” and to alert students to the fact that quantum computing is a rapidly developing new field that they need to follow during at least the next decade. The small group project helped students to explore the potential of quantum computing to substantially undermine current Public Key Cryptography and, at the same time provide a new approach to encryption that is even more secure than Public Key Cryptography. A qualitative analysis of discussions and assigned tasks suggests that this strategy succeeded in generating significant interest in quantum computing among students. They became motivated and immersed in a difficult subject despite the requirement for investment of significant time and effort to understand a complex combination of physics, math and computer science.
Chaotic Encryption of ECG Signals with a Random Kernel Approach

Security of biometric data is very important in the healthcare domain. Due to uniqueness of electrocardiogram (ECG) is very high even in identical twins, so it can be used as biometric signature.This paper introduces a simple efficient ECG encryption technique based on random kernel approach. As ECG signals contain sensitive confidential information with details for patient identification, it needs to be encrypted before transmission through public network to avoid the data being breached and hacked. The security of the proposed system will depend on random kernel coefficients, substitution process and length of kernel filter. Simulation results show that the proposed system is capable of encrypting ECG signals for secure communication efficiently.
Image Stitching Using Green Coordinates and Robust Elastic Warping

In this study, we have proposed an improved GreenWarps, which is based on the combination of prewarping using Green coordinates and robust elastic warping. Firstly, an unaligned image is warped to the reference image by Green coordinates, and then the prewarped image is warped and locally registered using robust elastic warping. Finally, seam estimation is implemented to seamlessly blend the registered warped and reference images together. The experimental results confirm that our method can effectively stitch images with large parallax and projection distortion, which is difficult to realize using the state-of-the-art methods.
Constructing a Digital Signature Algorithm Based on the Difficulty of Some Expanded Root Problems

This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> . The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.
Multiple-Operation Image Anti-Forensics with WGAN-GP Framework

A challenging task in the field of multimedia security involves concealing or eliminating the traces left by a chain of multiple manipulating operations, i.e., multiple-operation anti-forensics in short. However, the existing anti-forensic works concentrate on one specific manipulation, referred as single-operation anti-forensics. In this work, we propose using the improved Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) to model image anti-forensics as an image-to-image translation problem and obtain the optimized anti-forensic models of multiple-operation. The experimental results demonstrate that our multiple-operation anti-forensic scheme successfully deceives the state-of-the-art forensic algorithms without significantly degrading the quality of the image, and even enhancing quality in most cases. To our best knowledge, this is the first attempt to explore the problem of multiple-operation anti-forensics.