Reviewerly/exHarmony huggingface dataset:
Chinese Text Feature Extraction and Classification Based on Deep Learning

With the rapid development of deep learning, neural networks have been widely used in natural language processing tasks and achieved good results. Since convolutional neural networks can acquire high-level features that can better represent textual semantic information, convolutional neural networks (CNN) and convolutional recurrent neural networks (CRNN) are used to establish feature extraction models to extract text features. At the same time, tf-idf and word2vec methods are used to represent text features, and then feed them into SVM and Random forest classifier to classify Chinese academic papers dataset. Experimental results show that the classification results obtained by using the CNN and CRNN feature extraction model are better than using the TF-IDF and Word2vec feature extraction methods. In addition, the classification results obtained by using SVM and Random forest classifier are better than that of the original neural network.
Conceptual Models and Their Foundations

There is no common agreement which artifact should (not) be considered to be a conceptual model although the term 'conceptual model' is used for more than for five decades in computer science and for more than one century in science and engineering. A team from all faculties at our university has been able to develop a notion of model that covers all model notions known in the disciplines of this team. We now introduce three notions of conceptual model in this paper: light, slim, and concise versions of the notion of conceptual model. The paper answers the following questions: Are all models also conceptual models? What is a conceptual model? Is there a formal notion of a conceptual model? What is not yet a conceptual model? What will never be a conceptual model? What is a concept? Which philosophical and scientific foundations we should consider while modelling? Is the existence of an ontology a necessary prerequisite for the being as conceptual model?
Extracting Definitions and Hypernyms with a Two-Phase Framework

Extracting definition sentences and hypernyms is the key step in knowledge graph construction as well as many other NLP applications. In this paper, we propose a novel supervised two-phase machine learning framework to solve both tasks simultaneously. Firstly, a joint neural network is trained to predict both definition sentences and hypernyms. Then a refinement model is utilized to further improve the performance of hypernym extraction. Experiment result shows the effectiveness of our proposed framework on a well-known benchmark.
Application of improved particle swarm optimization algorithm in solving camera extrinsic parameters

A hybrid particle swarm optimization algorithm for determining extrinsic parameters of the camera, with the intrinsic parameters known, is presented in this paper. An adaptive inertia weighting strategy is designed, and improved crossover and mutation operation are performed. Taking the result of the particle swarm optimization algorithm as the initial value, the extrinsic parameters of the camera are optimized by Gauss–Newton method. The simulation results demonstrate that, when the standard deviation of image noise is 0.1 pixels, the mean relative error of rotation angle is less than 0.04844% and the mean relative error of translation vector is less than 0.05583%. The experimental results show that the mean reprojection error of the proposed algorithm is no more than 0.06045 pixels. The algorithm can achieve high precision and robustness in practical applications. Besides, the algorithm is computational efficient.
DGA and DNS Covert Channel Detection System based on Machine Learning

In recent years, the application of covert channel to ensure network security has attracted more and more attention from scholars and network attackers, but the research on its detection is relatively few. We propose a DGA and DNS covert channel detection system based machine learning, using improved TF-IDF, specificity score and other algorithms to detect malicious domain names. The experimental results show that the accuracy of the system in detecting malicious domain names is 99.92%.
Customer satisfaction prediction with Michigan-style learning classifier system

Abstract Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.
Student Action Recognition Based on Multiple Features

Student action analysis plays an important role in learning and teaching. However, manual observation does not meet the needs of big educational data analysis. So it is necessary for us to design an automated way to identify student actions. In this paper, a student action recognition method based on multiple features, which consists of Local Log-Euclidean Multivariate Gaussian (L2EMG) and Scale-Invariant Feature Transform (SIFT), is proposed. L2EMG can model low and high order statistics of local images. SIFT descriptor has strong expansibility, which can be combined with L2EMG algorithm to better describe the features of the image. The experimental results based on self-built dataset have demonstrated that the proposed method has better performance for student action recognition.
A Binary Variational Autoencoder for Hashing

Searching a large dataset to find elements that are similar to a sample object is a fundamental problem in computer science. Hashing algorithms deal with this problem by representing data with similarity-preserving binary codes that can be used as indices into a hash table. Recently, it has been shown that variational autoencoders (VAEs) can be successfully trained to learn such codes in unsupervised and semi-supervised scenarios. In this paper, we show that a variational autoencoder with binary latent variables leads to a more natural and effective hashing algorithm that its continuous counterpart. The model reduces the quantization error introduced by continuous formulations but is still trainable with standard back-propagation. Experiments on text retrieval tasks illustrate the advantages of our model with respect to previous art.
Invoice Discounting: A Blockchain-Based Approach

Invoice discounting is a market with a double-digit potential growth rate in Europe and worldwide in the next years. The main benefit of invoice discounting is the acceleration of cash flow from customers to suppliers: suppliers get advance payments from the bank, rather than waiting for the customers to pay. Hence, thanks to the quick availability of capital, businesses can invest in expansion and growth. More specifically, one of the most relevant problems today is how to provide better and faster invoice discounting services while preventing the double spending and maintaining the risk low. The blockchain frameworks have the potential to provide the right solution and thus to revolutionize the invoice discounting process. The benefits for suppliers, customers and financial institutions are related to the increased transparency added to the whole discounting process and the following risk reduction for the banks due to the capability to enhance the entire process and to reduce the double spending. In our paper, we introduce a blockchain-based invoice discounting system, called Distributed Ledger Invoice, and we propose a novel assessment method for evaluating currently available blockchain solutions for the invoice discounting scenario. Moreover, we also discuss two main issues regarding the information accessibility and the interoperability. In particular, since blockchain is still an emerging technology interoperability is a key factor for the blockchain adoption in inter-banking processes, where different blockchains solutions might be used. In this work we propose a decoupling layer, based on the Attribute-Based Access Control language, to unify the access control to reserved information across heterogeneous blockchains.
Research on group animation design technology based on artificial fish swarm algorithm

Group animation is a technology that simulates group behavior by computer. It matures with the continuous development of computer graphics. The things that need to be described are expressed in the form of anime, which not only clearly reflects the process of the event, but also shows a more realis tic picture, and can also increase the cognitive range of group behavior to a certain extent. Based on the analysis of the advantages and disadvantages of artificial fish swarm algorithm, this paper improves the research on artificial fish swarm algorithm, and it is easy to fall into local optimum, and it is improved by using convergence and divergence behavior in social learning mechanism., to some extent, improve the search performance of the algorithm. Based on the collision detection and avoidance, the improved artificial fish swarm algorithm is used to plan the group behavior path, and the obtained path data is imported into Maya. The tools provided by Maya will be used to establish the characters, 3D scenes and planning. The paths are merged together to get the corresponding group animation effects.
Feature Selection in Credibility Study For Finance Sector

With the advances in the Information Technology field, banks can evaluate the credit requests of the customers via effective analytical methods and risk analysis. The software products, named Credit Scoring Systems, consist of collecting customer data based on pre-determined credit factors, processing the data with various statistical or machine learning methods, and conducting a credit risk analysis to make the final credit decision. The effects of the quantitative values of the properties formed in the data set vary according to the results. Determination a subset set of columns with a high impact on the outcome and meaningful and removal of irrelevant columns without effect according to the values in the features of a dataset is called the property selection. It is generally used for accuracy and scaling. In this study, it was studied to determine the areas that most impacted the credit result on the sample dataset to meet the need of the structure that can apply the credit to the consumers who apply for the loan and manage the assessment in the consumers. Information Gain and Gain Ratio algorithms were used to determine the most useful features. As a result of the study conducted on the data set, the valuable values of function were committed by using the Gain Ratio, and Information Gain algorithms, and the characteristics were listed according to their magnitude.
Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center

With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.
Standard compliant light field lenslet image coding model using enhanced screen content coding framework

With the recent advances in high computing capabilities and reliable delivery networks, it is realistic to expect emerging immersive media applications such as virtual reality (VR) and 360-deg video. ISO/IEC Motion Picture Experts Group (MPEG) has launched an MPEG-I project that focuses on not just the current but also the next-generation immersive media applications. Light field (LF) is one of the candidates under consideration by MPEG-I. A LF lenslet image contains plentiful repeating patterns since each microlens captures the same scene. On the other hand, intrablock copy (IBC) in screen content coding (SCC) increases the coding efficiency of screen content (SC) by finding the repeating patterns. SCC can be an efficient encoder for LF image compression. However, SCC is not optimized for compressing lenslet images. Therefore, we propose an efficient lenslet image coding (LIC) model using SCC to encode lenslet images based on the lenslet image characteristics. Experimental results show that our LIC model further improves the coding efficiency of SCC on lenslet images by 3.72% on average and up to 7.77% in bitrate reduction. For fast approach, LIC is faster than SCC by 44.12%. With our proposed LIC model, the encoded bitstreams are compliant with the SCC standard with an even faster decoding time.
Disability Interactions in Digital Games

Digital games are a hugely popular activity enjoyed for the diverse experiences and relationships that they offer players. In 2019, games are more accessible to an increasingly diverse audience of disabled players through both new gaming technology and in-game options that allow people to tune their experiences. As a significant cultural medium, it is also challenging perceptions of disability in how characters are depicted. In this workshop, we aim to understand better the research challenges in making games for and with disabled players. We explore opportunities in games and disability through the lens of the new Disability Interaction (DIX) manifesto.
Semi-fragile watermarking for color image authentication in power internet of things

How to protect acquired images plays a significant role in power internet of things (PIoT). The semi-fragile watermark can be introduced into PIoT to authenticate the integrity of PIoT color images. A semi-fragile restorable watermarking technology based on weight-function on modulo operations is presented. First, the watermarking embedding is implemented at the end-device of PIoT. The 11-bit recovery watermark of each block in YUV components is generated by the significant coefficients. Using this weight-function on modulo operations, the recovery watermark is embedded by modulating 7 quantized coefficients. The steps of authentication and recovery requiring large computation resource are implemented in the cloud data center of PIoT. The tampered blocks are localized and restored by the interpolation and inpainting methods with the extracted recovery watermark. Experimental results on actual PIoT color images prove that higher invisibility can be achieved and this method detects tampered regions with a better performance.
Ultra-Short Term Load Forecasting Based on Elman Neural Network

The paper puts forward an ultra-short term load forecasting method based on the Elman neural network (ENN) to deal with such problems as low forecasting accuracy, poor timeliness and failures to take weather factors into account in current ultra-short term load forecasting algorithm. This method makes the best of dynamic recursion of the Elman neural network as well as arbitrary precision approximating of nonlinear mapping, with full consideration of the influence of real-time temperature, real-time rainfall, the type of working days and the historical load on current load. The prediction simulation calculation of Guangdong electric power grid’s actual data shows that the method is of high accuracy, strong robustness and adaptability, which creates conditions for the forthcoming electric spot market.
A generic construction of CCA-secure deterministic encryption

Deterministic public-key encryption (D-PKE), introduced by Bellare, Boldyreva, and O'Neill (CRYPTO'07), encrypts every plaintext into a unique ciphertext without using any randomness. Compared with the traditional randomized public-key encryptions, D-PKE has advantages in searching encrypted values across the database thanks to its lower computational overheads. In the literature, Bellare et al. gave the “strongest possible” notion of security for this primitive, called PRIV, and they also showed how this notion can be achieved in the random oracle model. Afterwards, Boldyreva, Fehr, and O'Neill (CRYPTO'08), followed by many other researchers, designed chosen ciphertext attack (CCA) secure D-PKE schemes in the standard model. These works are all based on lossy trapdoor functions and all-but-one trapdoor functions. In this paper, we present an alternative paradigm to design CCA-secure D-PKE. Our approach is a generic construction based on the identity-based lossy trapdoor functions (IB-LTDF), and it also can be extended to design CCA secure deterministic identity-based encryption schemes (D-IBE). To the best of our knowledge, this gives the first efficient construction of CCA-secure D-IBE schemes. Moreover, our approach can be considered as another application of the CHK transformation.
Lost &amp; Found: Gaze-based Player Guidance Feedback in Exploration Games

Gaze could be harnessed as a powerful tool for guiding players. By knowing where players are looking, a game could provide support players in finding relevant objects. With this assumption in mind, we made our first steps regarding the investigation of gaze-supported player guidance in a 3D first-person exploration game prototype called Lost & Found. Specifically, we investigated the feedback channels that could be combined with a gaze-based guidance approach. A comparative study was carried out to examine the impact of visual (i.e., vignette effect on screen), auditory (i.e., sound cues), and haptic (i.e., controller vibration) feedback on the players' game experience. Results show that visual and audio feedback appeared to be very appealing for players, While haptic feedback received relatively low scores. The next steps involve the development of more elaborated variants of the visual and auditory feedback.
Hotness- and Lifetime-Aware Data Placement and Migration for High-Performance Deep Learning on Heterogeneous Memory Systems

Heterogeneous memory systems that comprise memory nodes with disparate architectural characteristics (e.g., DRAM and high-bandwidth memory (HBM)) have surfaced as a promising solution in a variety of computing domains ranging from embedded to high-performance computing. Since deep learning (DL) is one of the most widely-used workloads in various computing domains, it is crucial to explore efficient memory management techniques for DL applications that execute on heterogeneous memory systems. Despite extensive prior works on system software and architectural support for efficient DL, it still remains unexplored to investigate heterogeneity-aware memory management techniques for high-performance DL on heterogeneous memory systems. To bridge this gap, we analyze the characteristics of representative DL workloads on a real heterogeneous memory system. Guided by the characterization results, we propose HALO, hotness- and lifetime-aware data placement and migration for high-performance DL on heterogeneous memory systems. Through quantitative evaluation, we demonstrate the effectiveness of HALO in that it significantly outperforms various memory management policies (e.g., 28.2 percent higher performance than the HBM-Preferred policy) supported by the underlying system software and hardware, achieves the performance comparable to the ideal case with infinite HBM, incurs small performance overheads, and delivers high performance across a wide range of application working-set sizes.
THREAT: A Large Annotated Corpus for Detection of Violent Threats

Understanding, detecting, moderating and in extreme cases deleting hateful comments in online discussions and social media are well-known challenges. In this paper we present a dataset consisting of a total of around 30 000 sentences from around 10 000 YouTube comments. Each sentence is manually annotated as either being a violent threat or not. Violent threats is the most extreme form of hateful communication and is of particular importance from an online radicalization and national security perspective. This is the first publicly available dataset with such an annotation. The dataset can further be useful to develop automatic moderation tools or may even be useful from a social science perspective for analyzing the characteristics of online threats and how hateful discussions evolve.
Forensic Investigations in Vehicle Data Stores

This research locates vehicle data stores and analyzes their forensic information value based on digital forensic principles. Four distinct data store types were located in this process -- airbag Event Data Recorder (EDR), Electronic Control Unit (ECU), Telematic Platform and Infotainment System.
Research on Improved Localization and Navigation Algorithm for Automatic Guided Vehicle

Abstract Aiming at the problem of inaccurate positioning caused by the wheel slippage or “kidnapping” movement of the robot in the positioning navigation, an improved autonomous positioning navigation strategy based on the robot operating system (ROS) is proposed. Firstly, combined with adaptive Monte Carlo localization (AMCL) algorithm and laser-based point-and-line iterative closest/corresponding point (PLICP) pose estimation algorithm, the accuracy and robustness of positioning are effectively improved. Then, based on the path planning strategy combining A* algorithm and dynamic window algorithm (DWA), an improved navigation failure recovery method is proposed and integrated into the ROS navigation framework, which can effectively improve the efficiency of robot positioning navigation and task execution. Finally, the mobile robot model TurtleBot is used as the experimental platform. The simulation experiment and field test demonstrate that the improved algorithm is superior to the original algorithm. The improved algorithm can adapt to the inaccuracy of the odometer and can achieve accurate localization and navigation in long-distance environment.
Tell Me Where It is Still Blurry

Mobile devices such as smart phones are ubiquitously being used to take photos and videos, thus increasing the importance of image deblurring. This study introduces a novel deep learning approach that can automatically and progressively achieve the task via adversarial blurred region mining and refining (adversarial BRMR). Starting with a collaborative mechanism of two coupled conditional generative adversarial networks (CGANs), our method first learns the image-scale CGAN, denoted as iGAN, to globally generate a deblurred image and locally uncover its still blurred regions through an adversarial mining process. Then, we construct the patch-scale CGAN, denoted as pGAN, to further improve sharpness of the most blurred region in each iteration. Owing to such complementary designs, the adversarial BRMR indeed functions as a bridge between iGAN and pGAN, and yields the performance synergy in better solving blind image deblurring. The overall formulation is self-explanatory and effective to globally and locally restore an underlying sharp image. Experimental results on benchmark datasets demonstrate that the proposed method outperforms the current state-of-the-art technique for blind image deblurring both quantitatively and qualitatively.