Reviewerly/exHarmony huggingface dataset:
Experiments for a better Gini index splitting criterion for Data Mining Decision Trees algorithms

The paper attempts to find splitting criteria derived from the Gini index criterion that would improve the performance of the Gini index criterion. The splitting criteria are used in the Data Mining Decision Tree (DT) training process. Thus, in the process of training of DT on seven databases we used the Gini index (G) criterion and two criteria derived from it. On these databases, for each of the three splitting criteria, three kinds of DT were executed: unpruned, error-based pruned and pessimistic pruned. We considered the classification error rate and the number of decision rules corresponding to each of the nine types of DT built and executed on the seven databases. Following the experiments, it turned out that in the case of DT induced by the symmetric Gini index (SG) splitting criterion the decision rules number and the accuracy of the classification on the test records are better than in the case of the G splitting criterion and modified Gini index (MG) splitting criterion.
On Applying Graph Database Time Models for Security Log Analysis

For aiding computer security experts in their work, log files are a crucial piece of information. Especially the time domain is of interest, since sometimes, timestamps are the only linking points between associated events caused by attackers, faulty systems or similar. With the idea of storing and analyzing log information in graph databases comes also the question, how to model the time aspect and in particular, how timestamps shall be stored and connected in a proper form. This paper analyzes three different models in which time information extracted from log files can be represented in graph databases and how the data can be retrieved again in a form that is suitable for further analysis. The first model resembles data stored in a relational database, while the second one enhances this approach by applying graph database specific amendments while the last model makes almost full use of a graph database's capabilities. Hereby, the main focus points are laid on the queries for retrieving the data, their complexity, the expressiveness of the underlying data model and the suitability for usage in graph databases.
Research on Multimodal Algorithms for Multi-routes Planning Based on Niche Technique

Multiple routes strategy, classified as a typical multimodal optimization, is an effective method for AGV to tackle the real-world problem. This article majorly explores the traditional optimization algorithm and the evolutionary algorithm for the routes planning problem. Compared with the traditional path planning method, the evolutionary algorithm based on niche technique can successfully avoid the particle interference in the multimodal condition, thereby ensuring the solution diversity. However, various restrictions may be produced when the niche technique cooperates with the evolutionary algorithm. Hence, this paper summarizes some niche techniques with evolutionary algorithms to analyze their merits for solving the multimodal problem. Also, several benchmark functions are involved for further discussing the effectiveness of the niche technology on the multimodal problem and some improvements are conducted for the algorithm to achieve a more effective work.
Research on The Innovation of Hybrid Teaching under The Platform of Rain Classroom ——Take “Advanced Business English Course” As an Example

“Internet + Education” has promoted the rapid development of education in China, and Rain Class platform is welcomed by both teachers and students for its practical and specific online and offline hybrid teaching methods. This paper states firstly the academic research status of MOOC and Rain Classroom in China. Then it discusses three design principles of hybrid teaching in college English Rain Classroom, and based on the principles it analyzes the micro design of hybrid teaching in “Advanced Business English Course” under the platform of Rain Classroom, including specific requirements and content of the high-level, innovation and challenge of the course, the overall process of the Rain Classroom, and all-round interactive links. Through this research, we hope to further promote the extensive application of the platform of Rain Classroom and practical teaching of Business English.
Experiences and Learned Lessons from an Air Free-Cooled Tropical Data Center Testbed

The air free-cooling has been long thought infeasible in tropics due to the unique challenges of year-round high ambient temperature and relative humidity. In recent years, the increasing availability of servers that can tolerate higher temperatures and relative humidity levels sheds light upon the feasibility of the air free-cooling to enhance the data center energy efficiency. However, building an air free-cooled data center in the tropics requires extensive experiments to understand the details of how the tropical environment conditions will affect data center power consumption, computing throughput, and server hardware reliability. Thus, together with multiple partners in data center industry and research, we conducted a project that designs, builds, and experiments with an air free-cooled data center testbed consisting of three server rooms hosting 12 server racks with 60 kW total power rating. This paper presents the key observations, experiences and learned lessons obtained from our project. The experiments show that (1) the air free-cooling design that uses fans only can reduce the power usage effectiveness (PUE) by 38%, compared to the global average PUE, (2) the tropics' year-round high temperatures up to 37°C do not impede the air free-cooling, and (3) the implementation of the air free-cooled data centers in tropics requires special cares to deal with airborne contaminants to avoid fast corrosion rate and dust-induced server faults.
eDBLP

In this paper we describe an approach for visualizing the textual information archived in the DBLP and the static and dynamic relations contained in it. Those relations are existing between authors and co-authors, between keywords, but also between authors and keywords. Visually representing them provides a way to quickly get an overview about emerging or disappearing topics as well as researchers and researcher groups. To reach our goal we apply node-link diagrams, word clouds, heatmaps, and area plots to the preprocessed and transformed DBLP data. All visualizations are equipped with interaction techniques and are built by using the functionality of the Bokeh library in Python, which enables the users to run the eDBLP in a web browser and to explore the dataset in an interactive and intuitive way. Finally, we discuss limitations and scalability issues of our approach.
Искренность, аффект, эмпатия: поэтические сообщества и новые контексты публичности

This article, written on the basis of long-standing ethnographic field research, describes the features of those new poetic cultures that arose in the space of social networks (and especially the VKontakte network, popular in Russia), but that developed in a regime of intensive interaction between online and offline presentation modes. The author considers characteristic features of literary sociality, rituals of collective interaction, forms and modes of legitimation, emotional norms, and modes of interaction between the public and the private. The new poetic cultures are examined in the broad historical context of the social existence of poetry and transformations of global literary culture in a postmodern society.
Online Course Quality Evaluation Based on BERT

In order to evaluate the quality of online courses, this paper proposes a framework based on online course feature extraction and sentiment analysis, and applies this framework to the online courses of MOOC. Extract the <feature word, opinion word> word pair of the review data through the word frequency syntactic dependency, and merge the word pair into the sentiment classification of the BERT model to realize the fine-grained feature analysis of the online course review data, so as to obtain online courses in each Use this aspect to evaluate the quality of the course. Experiments conducted on MOOC online course reviews show that the BERT model incorporating binary features has improved accuracy, recall, and F1 values compared to traditional machine learning methods.
Survey: Affective Recommender Systems Techniques

Recommender systems (RSs) are software tools or techniques that support the user in the decision-making process by suggesting possibilities that the system predicts. RSs are associated with numerous applications such as Amazon.com, for a book recommendation, compact disks, and other items, MovieLens, for the movie, and VERIFIED technologies, for news articles recommendation. There's a critical range as of late showed up which is an affective recommender framework. The affective recommender is related to human behaviors. Due to this combination and distinctive interests, more statement is required, since it is in awkward organize and creating as compared to other ranges. So we have done a literature survey within affective recommender systems techniques. In affective recommender field, we tried to illustrate how the affective behavior of the user can be used in recommender systems.
Research on children's cognitive development for learning disabilities using recommendation method

Abstract In order to achieve effective cognitive training that can significantly improve the learning abilities of children with learning disabilities, we consider the computer‐based assistive technologies and propose a personalized cognitive training items recommendation method based on children's preferences and evaluation values. First, we devise an improved cognitive ability evaluation method to evaluate children's cognitive ability values. Second, we transform these values to corresponding cognitive levels using K‐means clustering algorithm and form comprehensive cognitive level maps to reveal the strengths and weaknesses of children in terms of cognitive abilities. Finally, an improved collaborative filtering recommendation method combining children's preferences and evaluation values are represented to generate a superior training proposal. The proposed method is implemented on our cognitive development platform (CogDaily) which is an assistive system and used for testing and practicing. The fifth‐grade students with learning disabilities in mathematics in a primary school in Hangzhou are selected as subjects. They are divided into test group and control group for 2 weeks of testing through this platform. The analysis results of the changes of cognitive abilities before and after the experiment show that the accurate recommendation proposal can effectively enhance children's cognitive abilities and improve their learning abilities.
Learning from Fuzzy System Demonstration: Autonomous Navigation of Mobile Robot in Static Indoor Environment using Multimodal Deep Learning

Recent advances in deep learning and high-performance computational hardware, have shown great potential for solving mobile robot navigation and obstacle avoidance problems. In autonomous navigation, mobile robots use onboard sensors to perceive the environment and extract useful information to safely navigate to the target goal. Thus, fusion of multiple modalities improves the navigation system performance significantly. Multimodal deep learning networks are concerned with learning richer representations from multiple modalities which improves network's predictive ability compared to unimodal networks. In this paper, we propose a multimodal deep learning network for autonomous navigation of mobile robots in a static indoor environment. Further, a fuzzy-based behavior navigation system is designed to collect datasets for training the designed multimodal network. The evaluation of the trained models showed that the network was able to control mobile robot motion and navigate safely to the target goal. Further, the network mimics the cognitive decision-making ability of the fuzzy system and generalizes to unseen scenarios.
Advanced Technologies to Support Service Discovery in Service-Oriented Systems

The development of information technology encourages the service industry to revolutionize service-oriented businesses. Various service industries provide web-services as IT services and register it to several service directories, so other parties can use and integrate these services into existing systems to create more value. Performing service discovery from a collection of services in the service directory is a challenge that must be resolved. Understanding the latest technology to support service discovery could help them determine the technology that can be used. This article presents a review of previous studies related to service discovery methods carried out on service-oriented systems. It summarizes selected previous related studies and categorizes them based on qualitative approaches. The aim is to present some of the latest research and provide a comprehensive vision of designing service discovery engines. This paper provides theoretical contribution and the understanding related to service discovery from previous studies through the systematic literature review. Thirty-eight articles were selected based on search results and then reviewed. It can be used as a guide to build the service discovery engine for service-oriented system.
Video Semantic Segmentation leveraging Dense Optical Flow

Semantic segmentation is a key step in scene understanding for any autonomous system, as it provides the class of each pixel of an image. The best results on this task are achieved by deep neural networks, however they require in training a significant number of annotated frames which are difficult to create. This work is focused on improving the semantic segmentation network on sparsely labeled video sequences by including optical flow information between consecutive frames, and to provide a meaningful evaluation using densely labeled sequences. The system is composed of a static semantic segmentation, an optical flow and a linking network, which are chosen from existing architectures based on their high accuracy and low computational needs, but were never used together before. After conducting various experiments on the novel Virtual KITTI 2 dataset, we find that the optical flow compensates for the sparsity of annotations in video training sets, and by using just 25% of the labels we achieve the same video semantic segmentation quality as the static network trained on the fully labeled dataset. Since fewer annotations are needed, the cost of ground truth generation is reduced significantly. Moreover, taking advantage of the optical flow ground truth availability, we find the limits of the flow approach despite future advances, when more accurate optical flow networks will be developed. The system's improved capabilities are also validated on the Cityscapes dataset.
Semisupervised Multiscale Generative Adversarial Network for Semantic Segmentation of Remote Sensing Image

Semantic segmentation of remote sensing images based on deep neural networks has gained wide attention recently. Although many methods have achieved amazing performance, they need large amounts of labeled images to distinguish the differences in angle, color, size, and other aspects for small targets in remote sensing data sets. However, with a few labeled images, it is difficult to extract the key features of small targets. We propose a semisupervised multiscale generative adversarial network (GAN), which not only utilizes the multipath input and atrous spatial pyramid pooling (ASPP) module but leverages unlabeled images and semisupervised learning strategy to improve the performance of small target segmentation in semantic segmentation when labeled data amount is small. Experimental results show that our model outperforms state-of-the-art methods with insufficient labeled data.
Brain Magnetic Resonance Imaging Generation using Generative Adversarial Networks

Magnetic Resonance Imaging (MRI) is nowadays one of the most common medical imaging technology, due to its non-invasive nature and the many kind of supported sequences (modalities), that provide unique insights about a particular disease. However, it is not always possible to acquire all the sequences required, for several reasons such as prohibitive scan times or allergies to contrast agents. To overcome this problem and thanks to the recent improvements in Deep Learning, in the last few years researchers have been studying the application of Generative Adversarial Networks, a promising paradigm in deep learning, to generate the missing modalities. In this work we developed and trained two models of Generative Adversarial Networks, called MI-pix2pix and MI-GAN, to solve the problem of generating missing modalities for brain MRIs. In particular, our approaches are multi-input generative models, as they exploit as input several MRI modalities to generate the missing one. Our results are promising and show that the developed models are able to generate rather realistic and good quality images.
Interactive and Explainable Point-of-Interest Recommendation using Look-alike Groups

Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability and contextuality particularly in cold start situations. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs "mindsets", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness.
The Variational InfoMax AutoEncoder

The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator. In order to solve such an issue, we provide a learning objective, learning a maximal informative generator while maintaining bounded the network capacity: the Variational InfoMax (VIM). The contribution of the VIM derivation is twofold: an objective learning both an optimal inference and generative model and the explicit definition of the network capacity, an estimation of the network robustness.
On Computational Shortcuts for Information-Theoretic PIR

Information-theoretic private information retrieval (PIR) schemes have attractive concrete efficiency features. However, in the standard PIR model, the computational complexity of the servers must scale linearly with the database size. We study the possibility of bypassing this limitation in the case where the database is a truth table of a "simple" function, such as a union of (multi-dimensional) intervals or convex shapes, a decision tree, or a DNF formula. This question is motivated by the goal of obtaining lightweight homomorphic secret sharing (HSS) schemes and secure multiparty computation (MPC) protocols for the corresponding families. We obtain both positive and negative results. For "first-generation" PIR schemes based on Reed-Muller codes, we obtain computational shortcuts for the above function families, with the exception of DNF formulas for which we show a (conditional) hardness result. For "third-generation" PIR schemes based on matching vectors, we obtain stronger hardness results that apply to all of the above families. Our positive results yield new information-theoretic HSS schemes and MPC protocols with attractive efficiency features for simple but useful function families. Our negative results establish new connections between information-theoretic cryptography and fine-grained complexity.
A two-stage procedure for the automated identification of micro-earthquakes: implementation on single-station 3C passive seismic data

SUMMARY In this paper, we propose a two-step procedure for the automated detection of micro-earthquakes, using single-station, three-component passive seismic data. The first step consists of the computation of an appropriate characteristic function, along with an energy-based thresholding scheme, in order to attain an initial discrimination of the seismic noise from the ‘useful’ information. The three-component data matrix is factorized via the singular value decomposition by means of a properly selected moving window and for each step of the windowing procedure a diagonal matrix containing the estimated singular values is formed. The ${L_2}$-norm of the singular values resulting from the above-mentioned windowing process defines the time series which serves as a characteristic function. The extraction of the seismic signals from the initial record is achieved by following a histogram-based thresholding scheme. The histogram of the characteristic function, which constitutes its empirical probability density function, is estimated and the optimum threshold value is chosen corresponds to the bin that separates the above-mentioned histogram in two different areas delineating the background noise and the outliers. Since detection algorithms often suffer from false alarms, which increase in extremely noisy environments, as a second stage, we propose a new ‘decision-making’ scenario to be applied on the extracted intervals, for the purpose of decreasing the probability of false alarms. In this context, we propose a methodology, based on comparing among autoregressive models estimated both on isolated seismic noise, in addition to the detections resulting from the first stage. The performance and efficiency of the proposed technique is supported by its application to a series of experiments that were based on both synthetic and real data sets. In particular, we investigate the effectiveness of the characteristic function, along with the thresholding scheme by subjecting them to noise robustness tests using synthetic seismic noise, with different statistical characteristics and at noise levels varying from 5 down to –5 dB. Results are compared with those obtained by the implementation of a three-component version of the well-known STA/LTA algorithm to the same data set. Moreover, the proposed technique and its potential to distinguish seismic noise from the useful information through the proposed decision making scheme is evaluated, by its application to real data sets, acquired by three-component short-period recorders that were installed for monitoring the microseismic activity in areas characterized by different noise attributes.
Hyperparameter Optimization for AST Differencing

Computing the differences between two versions of the same program is an essential task for software development and software evolution research.AST differencing is the most advanced way of doing so, and an active research area.Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness.In this paper, we present a novel approach named DAT (Diff Auto Tuning) for hyperparameter optimization of AST differencing.We thoroughly state the problem of hyper-configuration for AST differencing.We evaluate our data-driven approach DAT to optimize the edit-scripts generated by the state-of-the-art AST differencing algorithm named GumTree in different scenarios.DAT is able to find a new configuration for GumTree that improves the editscripts in 18.7% of the evaluated cases.