Reviewerly/exHarmony huggingface dataset:
Sensor-Based Obstacle Avoidance for Autonomous Mobile Robots: Experimental Study

One of the highly essential issues in robotics is to let the mobile robot reach a predetermined location in the presence of obstacles. Many algorithms had been implemented for obstacle avoidance in an unknown environment. In this research, an algorithm has been presented for the mobile robot to perform this task. This algorithm is based on the use of sensors. The robot was programmed to use various sensors such as ultrasound and infrared sensors. The proposed algorithm was implemented in many environments, which contains several obstacles. The experiments show that the mobile robot has successfully avoided the obstacles located on its path to the predetermined target.
A Chinese Character-Level and Word-Level Complementary Text Classification Method

Text classification is a basic but important task in many natural language processing tasks. Nowadays, the mainstream classification methods mostly use deep learning technology, which shows better accuracy and stability in English text classification. Different from English text, Chinese text classification task involves the granularity of feature description in text decomposition. The two commonly used feature granularity are word-level feature and character-level feature. The former will bring semantic loss in the process of word segmentation, while the latter can't use the advanced semantic feature in the pre-trained word vector. We propose a method to fuse the word-level and the character-level information with attention mechanism. We train the CWC-Net, which combines the features to make the embedded information of characters and words complementary, so as to improve the semantic understanding ability of the network for Chinese text and reduce semantic loss. The comparative experiments on four Chinese text datasets, which involving topic classification and emotion analysis show that our model is more accurate than the traditional model which only relies on word-level features or character-level features. That verifies the effectiveness of the fusion of word-level features and character-level features on the improvement of model capability.
Computational Intelligence Techniques for Energy Management in Cloud Data Centers

Energy management in cloud datacenters attracts significant attention due to the high power consumption of datacenter. The cloud datacenter consumes a large amount of energy while transmitting and computing the data applications, which increases the maintenance cost for cloud datacenters. In order to solve these issues, several optimization techniques have been applied for load balancing. This paper details various computational intelligence methods used for energy management in cloud datacenters with its advantages and limitations. The three major aspects for energy management in cloud datacenters are load balancing, resource allocation, and predictive models. Machine learning techniques were used in the prediction of load and applying optimization technique to prevent service-level agreement (SLA) violation. Hybrid fruit fly optimization method, genetic algorithm, and multi-objective grey wolf optimization methods shows higher efficiency in minimizing energy consumption in datacenters. The hybrid method of firefly and improve particle swarm optimization (PSO) provides the solution with less computational time. Many existing methods suffer from the problem of local optimum and poor convergence along with SLA violations while considering energy efficiency.
ICT for Cyber Security in Business

Abstract In recent years, the financial value of the stored information repeatedly exceeds the one of the equipment which is needed for its storage. Therefore, the process of business development needs the implementation of up-to-date ICT for data security from cyber-attacks. The scalability of IT infrastructures in this direction leads to development of malware creators. Thus, file servers become inefficient and give away to private clouds. Attacks against data stored on file servers and workstations are becoming more widespread. Standard security methods (for example, backup) are not always secure enough and can also be the target of a cyber-attack if not properly managed. In addition, information from the time of the last backup to the time of attack will be lost. The paper aims to research and propose approaches which can be used for recovery of the information arrays. We consider some software for implementation of private cloud which correspond to the ISO 27001 standard. The methods for accessing data will be analyzed and the techniques by which clouds deal with some of cyber-attacks will be addressed.
Knowledge-Driven Framework for Designing Visual Analytics Applications

Machine learning and data analysis are becoming an essential part of the decision-making process in modern organizations. Even though new and improved analytics algorithms are developed frequently, organizations are struggling to develop analytics applications that can stay up-to-date with changing business requirements and technology innovations. The rapid development of ad-hoc programs to conduct machine learning tasks at hand has resulted in creating more expenses and efforts in the long term, a phenomenon referred to as technical debt in literature. This paper addresses the technical debt associated with data analytics applications by proposing a knowledge repository that captures analytics-related knowledge, which can be developed and maintained separately from the organization's IT infrastructure and used to design analytics applications with visual interfaces. This way, organizations can develop dynamic and adaptable analytics applications with easy-to-follow front-ends and can accommodate new data sources or machine learning models. We evaluate the proposed approach by conducting a case study that develops an application for the acquisition and management of high-frequency financial market data.
Illusion of Reality as Basis for Dramaturgic Conflict in A. Volodin’s Creative Work

The paper aims to provide an insight into specificity of A. Volodin’s artistic worldview by analysing dramaturgic conflict in his creative work. The study is novel in that it is the first to analyse content of the plays “The Factory Girl”, “Five Evenings” and “The Appointment” from the modal-evaluative perspective: such a semantic category as illusion of reality is considered as a basis for dramaturgic conflict in these plays. The research findings consist in identifying specificity of the axiological orientation peculiar to the author’s worldview of Volodin the dramatist, in revealing that modal frame of illusoriness highlights the issue of losing emotional and spiritual ties in the society whose representatives live in different realities.
"The Canton Canon" Digital Library Based on Knowledge Graph - Taking the Revolutionary Archives of Canton in the Republic of China as an Example

In order to more efficiently meet people's in-depth needs for knowledge acquisition and research, and to more effectively develop and use "the Canton Canon" historical archives, we designed and constructed the Canton Revolutionary History Knowledge Graph based on the revolutionary archives of Canton during the Republic of China. We associated the knowledge graph with the document corpus, transformed and combined the traditional digital library from document information management to knowledge management, and proposed a double-Layer retrieval mode of the document corpus based on the knowledge graph. At the same time, we developed "the Canton Canon" digital library based on the knowledge graph, and introduced the design and application of the digital library. It provides a great example of how to use huge archives more efficiently and build a more open digital library. What's more, it also opens up new ideas for the transformation of Chinese digital libraries from information management to knowledge management.
Causal Maps for Multi-Document Summarization

Concept maps are concise graphical representations of text data which have been shown to be applicable as a tool for text summarization and downstream tasks. Most prior work has either focused on the generation of concept maps for small corpora or require expensive training data to implement. In this work, we focus on generating causal maps, a subset of concept maps in which only semantically causal relationships are considered. We propose a map generation framework which utilizes a novel mixture model to simultaneously derive concepts and links. This method is computationally efficient and therefore scalable to large datasets, and is fully unsupervised, which makes it suitable for practical applications. We show that our method performs better than a commonly used unsupervised text summarization algorithm, and has results which are comparable to the state-of-the-art supervised method.
Computational entropy

research-article Share on Computational entropy Author: Salil Vadhan View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 693–726https://doi.org/10.1145/3335741.3335767Published:04 October 2019Publication History 0citation72DownloadsMetricsTotal Citations0Total Downloads72Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Disambiguation of Japanese Idiomatic Expressions Using Bias in Occurrence of Verbs

Some idiomatic expressions have both an original meaning and a literal meaning. It is important in understanding the context to determine which meaning of an expression is used in a sentence. An OpenMWE corpus has been used to determine the meaning of Japanese idiomatic expressions, but methods that use this corpus are not able to determine the meaning of expressions that are not included in the corpus. The only way to solve the problem with these methods is to increase the number of corpus examples by hand, which is time-consuming. In this paper, we propose a method for determining the meaning of idiomatic expressions by using the frequency of the occurrence of verbs and their related nouns in a sentence. The system consists of two phases: a learning phase and a disambiguation phase, in which a decision tree model is constructed and used to determine whether an expression in a sentence is idiomatic or literal. We also tested a method using literal usage representation, which has been used in previous studies. Evaluation experiments show that the combination of our method and a method of previous studies had the highest F value (0.795). An advantage of this method is that it is able to determine even idiomatic expressions that are not included in the corpus.
Profitable day trading Bitcoin futures following continuous bullish (bearish) candlesticks

We explore the profitability of day trading Bitcoin futures following diverse consecutive 3 bullish (bearish) one-minute candlesticks and then adopt stop-loss or take-profit only as exits. We reveal that different from our cognition, adopting take-profit strategies would have a notable average positive profit per trade (APPT), which is robust by employing the upward trend out-of-sample data different from the downward trend in-sample data. We infer that such impressive findings might result from temporary rising (falling) prices likely manipulated for appealing to investors pursuing long (short) positions. As a result, investors may realize profit instead of suffering loss frequently by adopting take-profit exits since mean reversion might often occur after such manipulation. Moreover, we argue that stop-loss might be redundant for day trading such futures because stop-loss seems enclosed in the mechanism of day trading.
Changing the narrative perspective: From deictic to anaphoric point of view

We introduce the task of changing the narrative point of view, where characters are assigned a narrative perspective that is different from the one originally used by the writer. The resulting shift in the narrative point of view alters the reading experience and can be used as a tool in fiction writing or to generate types of text ranging from educational to self-help and self-diagnosis. We introduce a benchmark dataset containing a wide range of types of narratives annotated with changes in point of view from deictic (first or second person) to anaphoric (third person) and describe a pipeline for processing raw text that relies on a neural architecture for mention selection. Evaluations on the new benchmark dataset show that the proposed architecture substantially outperforms the baselines by generating mentions that are less ambiguous and more natural.
RoseTrajVis: Visual Analytics of Trajectories with Rose Diagrams

In this paper we propose RoseTrajVis, a visual analytics system for studying trajectories using rose diagrams, inspired in a classic visualization technique for aggregating data. The rose diagram is composed of sectors (petals) that aggregate trajectories given their direction and show their speeds within an area around a point in a map. It also includes colored arrows on the border that point to the origin and destination of the trajectories and show their average speed. RoseTrajVis enables users to place rose diagrams over a map view and interact with them by applying time, speed and distance to origin/destination filters, and by adjusting the aggregation radius, allowing the visualization of commuting patterns in a city. We prepared a prototype of RoseTrajVis with taxi data, and evaluated the completion times and errors of users carrying out trajectory analysis tasks, and also collected feedback from a domain expert. Results revealed that completion times varied with task complexity, being longer for tasks requiring exploratory analysis of the trajectories on a map, and that almost no errors were made by users. The expert mentioned that RoseTrajVis provides an innovative way of analysing aggregate trajectories and has the potential to be useful in several professional areas.
Automatic Detection of Anomalies in Video Surveillance using Artificial Intelligence

Abstract The significance of security in day to day life is increasing, and hence the use of a video surveillance system is becoming commonly accepted in almost every public places. Even though by placing surveillance system in public places will help to trace out the culprits of the anomalous situation, it is hard to trace it out. The knowledge of the incidence time is needed to identify the event, and also, it requires an enormous data handling task. To identify the anomalous situation in real-time will help the authorities to decrease the consequences and loss during the anomalous event. The paper proposes an in-depth study of various automatic anomaly detection techniques which helps to reduce the loss occurred of the anomalous situation. The advancements in Artificial Intelligence help in the quick and automatic identification of nominal and anomalous events. The sequential and incremental learning approach in feature extraction will help to generate a model that will provide much accurate classifications and predictions of anomalies.
Response to: ‘Disruptive innovation and technology enhanced learning’

In this response article, the author argues that the notion of 'disruptive innovation' should not be a guiding light for higher education First and foremost, proponents of this idea would do well to recognise that much broader discourses of revolutionary change have characterised the field of education technology for decades In short, it is an old and rather tired narrative The difficulty with accepting 'disruptive innovation' as any more prescient than the many other narratives of crisis and technical solution in education, is that it seems to be particularly indicative of a historical amnesia that is antithetical to the very foundations of higher education The notion of 'disruptive innovation' is firmly aligned with a view that would see a reduced role for the state, if one at all, in future educational governance The current COVID-19 pandemic is a very real crisis, not only for education, but also for almost every other aspect of life Nevertheless, it seems clear that many in the edtech industry have viewed it as an unrivalled opportunity for expansion Such emergency responses to the current pandemic should not be promoted as the moment to finally realise 'disruptive innovation' in education They would be better viewed as opportunism about which those that care for public education should be wary [For the original article, Disruptive Innovation and Technology Enhanced Learning, see EJ1288163 ]
Near term implementation of Shor's Algorithm using Qiskit

Although the fundamental principles of quantum computing have been known for decades, it is only within the past few years that practical quantum computers have become available. Since these systems are limited to a small number of qubits, they cannot demonstrate quantum advantage for many practical problems. Accordingly, there has been an effort to develop near term implementations of algorithms purely for research and education. In this paper, we discuss a near term implementation of Shor's Algorithm using the Qiskit language on an IBM Q System One quantum computer. We extend currently available documentation by providing a full circuit diagram for Shor's Algorithm including gates specific to the implementation using Qiskit. We present an implementation capable of factoring small two-digit prime numbers, and discuss the limitations of noise when using real quantum computers vs. simulations.
Empirical analysis of performance bottlenecks in graph neural network training and inference with GPUs

The graph neural network (GNN) has become a popular research area for its state-of-the-art performance in many graph analysis tasks. Recently, various graph neural network libraries have emerged. They make developing GNNs convenient, but the performance bottlenecks of GNNs on large datasets are not well studied. In this work, we analyze the performance bottlenecks in GNN training and inference with GPUs empirically. A GNN layer can be decomposed into two parts: the vertex and the edge calculation parts. We select four representative GNNs (GCN, GGNN, GAT, GaAN) for evaluation according to their computational complexity. We decompose their running time and memory usage, evaluate the effects of hyper-parameters and assess the efficiency of the sampling techniques. The experimental evaluation with PyTorch Geometric indicates that the edge-related calculation is the performance bottleneck for most GNNs, dominating the training/inference time and memory usage. The sampling techniques are essential for GNN training and inference on big graphs with GPUs, but their current implementation still has non-trivial overheads in sampling and data transferring.
Using Mixed Reality to Provide Experience for Internship Students in the Field of Auditing Studies during Covid 19

Internships or practical work for students currently in higher education or university has become a necessity. Internships are important because they introduce students to the world of work, so that students not only learn theory, but also practice. The current condition where the world is hit by the Covid 19 pandemic makes many students unable to physically intern. Students cannot come to work and to clients due to social distancing. Mixed reality is one of the solution so that students can get virtual internship experience. This research is a descriptive qualitative research. In this study, we present data in the form of literature reviews and observations regarding the use of mixed reality in providing internship experiences to students who specialize in the field of auditing science. The results of our research state that mixed reality is more widely used in providing virtual experiences, students can gain work experience without having to meet people physically, however, some challenges must also be considered in augmented development and virtual reality in audit learning, including: content problems, content development costs are still expensive, and also the issue of health problems after usage.
A Visual-Interactive Idiom to Diagnose Missing Data Mechanisms

With vast amounts of data, comes vast numbers of problems. The process of collecting data is far from perfect, either due to human factors or technological errors, which can lead to inaccuracies and uncertainties in the data. One such issue is missing data: the absence of information. Several methods can deal with missing values, but to choose the correct approach, it is necessary to diagnose the missing data mechanisms, which describe how the distribution of missingness in a given data variable correlates to other variables. This diagnosis can be made with statistical tests or data visualization techniques. However, statistical tests provide an uncertainty estimation that is often misinterpreted, and the visualizations readily available in data analysis packages have some scalability issues, such as cognitive overload and lack of screen space. Thus, this paper proposes a visual-interactive idiom for diagnosing missing data mechanisms. The proposed solution consists of a set of visual encodings and two derived metrics that synthesizes the missing data mechanisms and the uncertainty associated with this synthesis. We present the concepts behind the visual encodings, derived metrics, and interactions of the idiom.
Citation Recommendation based on Citation links Explanation

Abstract With the rapid increase of scientific papers, it is difficult for researchers to obtain appropriate references. Due to the black-box property of neural network, the existing content-based citation recommendation methods are mainly embedding text into a low-dimensional space, and citation links are not interpretable (e.g., paper A cites paper B because of topic X). To solve this problem, we propose a Weighted Citation Network with Citation Links Explanation (WCN-CLE) algorithm. Because citation links mainly depends on the content, we need to explore textual reasons for citation links. Firstly, we extract word fragments through citation network information for each citation link as its explanation. Secondly, we construct a weighted HIN network with two kinds of vertex (paper and author). The weight of each edge is calculated by its word fragments. Then the query document is linked to the HIN network through author information and word fragments similarity with candidate papers. Finally, each vertex’s feature representation is obtained by network representation learning method, and recommend papers through vector similarity. Experimental results on two real-world datasets show that WCN-CLE has better performance due to its integration of structural information and semantic information.