Reviewerly/exHarmony huggingface dataset:
Research on the Hybrid First-rate Course Construction Mode in 5G Era

This paper analyses the background of the construction of first-rate courses. It can be seen that the blended first-rate courses are the most important part of the curriculum construction, so it is necessary to conduct in-depth research on them. But the existing research results can't meet the need of the construction of hybrid first-class curriculum in 5g era, in this case, we contrast state of first-rate courses of macroscopic standard and the construction of excellent courses, and then put forward the realization of high order, innovative and challenges of hybrid first-rate course construction mode and three aspects of the school curriculum constructions combining with the actual situation. In order to pass the 6 + 3 hybrid first-rate research course construction mode, hybrid first-rate course construction mode can provide reference for counterparts.
Semi-Paired Asymmetric Deep Cross-Modal Hashing Learning

Hashing has been effectively applied in large-scale multimedia retrieval tasks due to the characteristics of fast calculation speed and low storage cost. However, most existing cross-view hashing methods require well paired views, where one sample in one view can always be associated with one sample in another view. Nevertheless, such fully-paired setting is difficult to hold in practice. The cross-view retrieval task for semi-paired data, in which only a portion of the sample is paired, remains more challenging and less explored. In this case, we design an unsupervised deep hashing for large-scale data, named semi-paired asymmetric deep cross-model hashing (SADCH) to address the challenging task. SADCH is a novel asymmetric end-to-end deep neural network model. Specifically, SADCH trains deep network by using query points to improve the training efficiency, and directly learns hash codes of database. A similarity matrix is constructed by a novel cross-view graph to explore the underlying data structures, such that the similarities of paired points and unpaired points can be preserved. As such, the deep features and similarity graph matrix can be jointly used to design an alternating algorithm to efficiently generate more differentiated hash codes. SADCH is evaluated for large-scale cross-view approximate nearest neighbor search on three benchmark datasets and compared against several state-of-the arts. The results of extensive experiments demonstrate the superiority of the proposed SADCH for semi-paired hashing in the unsupervised setting.
Automatic sound synthesis using the fly algorithm

Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies’representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language.
Joint Alignment of Image Faces

Researches on face alignment have made great progress, which benefits from the use of prior information and auxiliary models. However, that information lacks in a single face image has always affected the further development of these researches. The methods considering multiple face images provide a feasible way to solve the problem undoubtedly. Joint alignment where multiple face images are considered was presented in the paper. Face alignment was used for each face, and joint face alignment was used for optimizing the alignment results of all faces further. During joint alignment, both rigid variations of faces and non-rigid distortions were considered, however, they were regarded as two independent stages. Joint face alignment was a process where optimization was performed iteratively. In each iteration, both rigid variations and non-rigid distortions were performed sequentially, and moreover, the results of rigid variations were used as input of non-rigid distortions. At the stage of rigid variations, the key points of a face were divided into five groups to reduce the effect of global constraints which was imposed by face shape. After several iterations, the optimal solution of joint alignment can be obtained. The experimental results show that the joint alignment can obtain the optimal results than joint alignment using phased global rigid variations and non-rigid distortions and that using iterative global rigid variations and non-rigid distortions, and it can be used as a novel method for joint alignment.
Enabling Hardware Affinity in JVM-Based Applications: A Case Study for Big Data

Java has been the backbone of Big Data processing for more than a decade due to its interesting features such as object orientation, cross-platform portability and good programming productivity. In fact, most popular Big Data frameworks such as Hadoop and Spark are implemented in Java or using other languages designed to run on the Java Virtual Machine (JVM) such as Scala. However, modern computing hardware is increasingly complex, featuring multiple processing cores aggregated into one or more CPUs that are usually organized as a Non-Uniform Memory Access (NUMA) architecture. The platform-independent features of the JVM come at the cost of hardware abstraction, which makes it more difficult for Big Data developers to take advantage of hardware-aware optimizations based on managing CPU or NUMA affinities. In this paper we introduce jhwloc, a Java library for easily managing such affinities in JVM-based applications and gathering information about the underlying hardware topology. To demonstrate the functionality and benefits of our proposal, we have extended Flame-MR, our Java-based MapReduce framework, to provide support for setting CPU affinities through jhwloc. The experimental evaluation using representative Big Data workloads has shown that performance can be improved by up to 17% when efficiently exploiting the hardware. jhwloc is publicly available to download at https://github.com/rreye/jhwloc .
Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data from the clinical data warehouse of Rennes and from other sources. The dataset included hospital stays based on the criteria of the French national methodology for the 30-day readmission rate (i.e., patients older than 18 years, geolocation, no iterative stays, and no hospitalization for palliative care), with a similar pre-processing for all algorithms. We calculated the area under the ROC curve (AUC) for 30-day readmission prediction by each model. In total, we included 259114 hospital stays, with a readmission rate of 8.8%. The AUC was 0.61 for the Logistic Regression, 0.69 for the Gradient Boosting, 0.69 for the Random Forest, and 0.62 for the Neural Network model. We obtained the best performance and reproducibility to predict readmissions with Random Forest, and found that the algorithms performed better when data came from different sources.
Gait Identity Verification Using Equipped Smartphone Sensors

A strong and convenient identity verification method is always demanded. Nowadays, the advanced sensing features like gyroscope, accelerometer and rotation information are easily available through a smartphone. These prominent and hidden features (orientation and force) created simultaneously against any activities on a smartphone that can be used in user identity verification instead of considering morphological features (video and image). Two similarity measure algorithms (Euclidean and Manhattan) have been used on the collected dataset from 63 users in 5 sessions with 4 repetitions and we got 8.7% of EER (Equal Error Rate). This study also explains the data collection tools, data capture procedure and anomaly detection for further development in this topic area.
A Novel Method for Embedding Multiple Video Frames into A Single Video Stream by Utilizing Genetic Algorithm

The modern age is driven by video streaming services. Video streaming vendors allow the viewers to view the digital content on televisions, desktops, laptops, phones, electronic gadgets, etc. The video streaming platforms run on the monthly subscription model, wherein the streaming services acquire licensing rights to the digital content and further make the content available to the customers based on the type of subscription chosen by a user. As the over-the-top (OTT) platforms gain popularity in video streaming services, delivery of preference-based digital content to the consumers seem to bring about a paradigm shift in video streaming services. However, the consumer can view only one content at one time as the transmission station broadcasting the composite video signal accommodates separate video channels on separate frequency windows by applying modulation techniques. The paper discloses a novel method for embedding multiple video frames into a single video stream by utilizing a genetic algorithm, such that multiple consumers can view multiple preference-based video contents at one time.
A Combination of Hill Cipher and LSB for Image Security

The maximum value obtained to test the encrypt hill cipher uses the avalanche effect with modification of one, two, three, and four key matrices 35.71%, therefore an additional security technique is needed. The Least Significant Bit (LSB) method is used to insert the ciphertext that has been generated from the hill cipher algorithm, has been testing using RGB, CMY, CMYK and YUV shapes with 6142 characters in 128 x 128 character images producing the highest PSNR value of 51.2826 dB in CMYK images. Steganography technique is applied because it has advantages in terms of imperceptibility, for example the results of a stego image are so similar to the original image that it is difficult to be distinguished by the human senses. Tests were carried out with 10 images, five images measuring 512 x 512 and five images measuring 16 x 16. While the messages to be inserted were 240, 480, and 960 characters for images measuring 512 x 512 and 24, 48 and 88 characters for images measuring 16 x16. Test results that I have done are calculated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) with a minimum PSNR of 51.2907 dB which means the resulting image is quite good. Computation time is calculated using tic toc on Matlab by encrypting 2000 and 6000 characters, and also computation time.
RETRACTED: Design of Travel Itinerary Planning System Based on Artificial Intelligence

Abstract The existing travel information on the Internet is cluttered, repetitive, and singular. It cannot provide tourists with accurate travel information and help tourists plan their trips rationally. To this end, this article establishes a travel planning system based on artificial intelligence. This article conducted a multi-threaded performance test for the route search submodule, and tested the tendency of the vertical search crawler to crawl the webpage as the number of multi-threads increased. In addition, this article conducted a stress test to test the system’s increase in the number of concurrent users. System performance changes. The test results show that the system function basically meets the needs of users, and has good stability and efficiency.
Understanding the Impact of Mutable Global State on the Defect Proneness of Object-Oriented Systems

A key requirement for complex software systems is for them to not be susceptible to error. However, little research has been done thus far on what makes a system defect prone. Static constructs have already been proven to have a negative impact on several software quality aspects (such as maintainability or testability) therefore it would be interesting to see if they also affect error proneness.This article investigates the presence of mutable global state in object-oriented systems and tries to establish whether or not the classes that utilize it are more prone to error. We propose an approach that can be followed to study these aspects which includes: 1) detection strategies for identifying mutable global state instances and their users, 2) a method to determine if a particular commit is a bug-fix, and 3) a process for extracting fine-grained source code changes and using them to assess if mutable global state users are more defect prone than other production classes. The feasibility of the approach is demonstrated through an empirical study conducted on 5 open-source projects. The results show that mutable global state is present in the production code of the analyzed systems. They also prove that classes which utilize mutable global state instances tend to have a higher proneness to error compared to other classes.
Corpus Analysis: A Case Study on Kadazandusun Newspaper Archive

This paper presents the analysis of text data acquired from News Sabah Times, which is the only Sabah's newspaper that has a section for Kadazandusun news. Currently, there is no text translation tool available to translate a sentence or large text specifically from Kadazandusun to other languages such as Bahasa Melayu. Thus, the first step is to develop such a system is to analyze the available corpus from the newspaper archive. The objective is to perform text analysis and then providing the possible ways of utilizing the knowledge. In this work, the purpose is to report the methodology and utilization of the fundamental corpus analysis related to text mining and not covering on the linguistics aspects and grammatical context. In addition, this paper also reports on the findings from the newspaper corpus analysis.
Using Systems of Dynamic Mathematics in Project Work of School Students

In this paper, we consider ways of using systems of dynamic mathematics (Geogebra and others) in the project activities of secondary school students. The advantages of the approach under consideration are compared both with the use of computer programs in the framework of the standard educational process, and in comparison with traditional approaches in the organization of project activities.
Normalizing Constant Estimation with Gaussianized Bridge Sampling

Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.
Sentiment Analysis—An Evaluation of the Sentiment of the People: A Survey

Online archives have received a great deal of attention in recent years from a person’s view and thoughts as primary platform. Set of circumstances gives rise to increasing interest in methods for automatically collecting and evaluating individual opinions from online documents such as customer reviews, Weblogs and comments on electronically accessible media, emphasis of current studies are mainly on attitude analysis. Interest of human beings is on designing a structure which can categorize feelings of individuals in the form of automated letter. Retrieving and determining beliefs from Web require appropriate mechanism that can be used to acquire and estimate thoughts of the desires of online consumers, which could be useful for economic or marketing research. An aspect of natural language processing (NLP), sentiment analysis (SA) has experienced a growing interest in the past decade. The difficulties and chances of this rising field are likewise talked about prompting our postulation that the analysis of multimodal sentiment has a significant untapped potential.
ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually. Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.
#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement

In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.
Balanced Off-Policy Evaluation in General Action Spaces

Estimation of importance sampling weights for off-policy evaluation of contextual bandits often results in imbalance - a mismatch between the desired and the actual distribution of state-action pairs after weighting. In this work we present balanced off-policy evaluation (B-OPE), a generic method for estimating weights which minimize this imbalance. Estimation of these weights reduces to a binary classification problem regardless of action type. We show that minimizing the risk of the classifier implies minimization of imbalance to the desired counterfactual distribution of state-action pairs. The classifier loss is tied to the error of the off-policy estimate, allowing for easy tuning of hyperparameters. We provide experimental evidence that B-OPE improves weighting-based approaches for offline policy evaluation in both discrete and continuous action spaces.
Construction of Identity Based Signcryption Using Learning with Rounding

Current technology, quantum computing is a big threat to the security based on essentially number-theoretic cryptographic constructions. Shor’s algorithm forces one to understanding and working on quantum resistant problems. Therefore, we need a reliable communication especially which can provide both confidentiality and authenticity in a single step. This paper presents an identity based signcryption using learning with rounding (IBSCLR) scheme in a random lattice. The security is based on the worst-case hardness of learning with rounding (LWR) problem. This scheme uses short signature and ensures security in widely acceptable standard model. Furthermore, the scheme illustrates security in the quantum era and can be applied to practical application vehicular, crowdsourcing, internet of things based structure in the modern computation world.
A SAR Image Data Augmentation Method Based on Generative Adversarial Network

Generative adversarial network (GAN) has been increasingly used in the SAR image simulation in recent year. However, the network is difficult to keep stable, and high-quality generated samples are not available. In order to solve the problem, a model based on the Wasserstein GAN with deep convolutional structure is proposed. Training on SAR image datasets, we show evidence that our method is superior to original deep convolutional generative adversarial network (DCGAN). We also build a verification framework by YOLOv3, and tests are done to validate generated samples. The results show that our method is more stable, and the diversity of generated samples is improved.
Block Difference of Inverse Probabilities Features for Chromoendoscopy Image Classification

Gastric or stomach cancer is one of the most common cancers in the world. It used to be the leading cause of cancer deaths before 1980s. Endoscopy is a less invasive method to screen gastric cancer than biopsy. In chromoendoscopy, one of endoscopy improvements, by spraying dyes over mucosal surface, abnormal regions are made more prominent visually. However, detection and classification of abnormal regions are not so easy tasks. Accuracy depends largerly on experience of doctors, physical status of doctors, and illumination variations. Nowaday, with computer-aided diagnosis (CAD) systems, gastric cancer can be detected and classified into different stages. In this paper, we propose using Block Difference of Inverse Probabilities (BDIP) and Support Vector Machine (SVM) to build an automatic and accurate yet simple classification algorithm for identifing whether a chromoendoscopy (CH) image is abnormal or not. Experimental results show that the proposed method has a classification accuracy of 87.3% and an area under the curve (AUC) value of 0.92 on the CH imageset obtained using an Olympus CV-180 endoscope at the Portuguese Institute of Oncology (IPO) Hospital in Porto, Portugal.