Reviewerly/exHarmony huggingface dataset:
Policies Selection for Pedagogical Agent Based on the Roulette Wheel Algorithm

Pedagogical agents are computational entities that interact with users and facilitate learning opportunities. They usually need to be programmed to follow a set of commands for an effective personalized exchange of knowledge and tasks. In this study, we evaluate the effectiveness of a policy-based model and its level of satisfaction about the interaction without and with the pedagogical agent using the bio-inspired roulette selection algorithm. The approach is quantitative, with an exploratory and descriptive study. The results revealed that our agent achieved great acceptance among the users who rated it as intelligent, friendly, and reliable. It is evidenced that the agent can influence the attitude, perception, and behavior of the user to reach better self-regulated learning.
Base64 Color Code

QR code widely used as an url to link to a website or data that contains the essential text to fetch. Its nearly a decade has been used in day today life on products. An idea about re-inventing the qr code in such a way that making files in transformation of base64 (code text)which then formatted to a pattern of qr code which enables qr code has the content of the file. Now when a person scans the QR(Quick Response) code without the interaction with the internet he gets the app or the file in his device. While in this if the scanner of the app has the predefined AR(Augmented reality) object which helps the today's world to make an advertisable mode for attracting customers. Like adding a predefined cube with rotating animation. Now the user just has to add text on what side has to be placed. so image code just has to contain the text on which side and what speed the cube to rotate. By this a new way already defined library holding in decoder or scanner app helps the user interaction and content delivery easily. In this adding color QR code which gives the possibility to explore more data into it .
Mining Morphological Similarities for Translation Lexicon Augmentation

Bilingual subword correspondences have been proven to be significant in tackling the problem of out-of-vocabulary terms. In this paper, we present results of our study focused on automatic learning of bilingual subword units for translation lexicon augmentation. Experiments involve official Indo-Aryan languages including Hindi, Konkani and Sanskrit each of which are resource-poor and characterized by highly inflectional morphology. Bilingual subword units comprising of stem and suffix pairs are first learnt using morphologically similar surface translation forms and subsequently used in suggesting word-word translations. Bilingual subword units thus learnt are employed in generating phrase translations.
Low-Rank on Graphs Plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data

Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance, and urban traffic monitoring. Existing anomaly detection methods are most suited for point anomalies in sequence data and cannot deal with temporal and spatial dependencies that arise in spatiotemporal data. In recent years, tensor-based methods have been proposed for anomaly detection to address this problem. These methods rely on conventional tensor decomposition models, not taking the structure of the anomalies into account, and are supervised or semi-supervised. We introduce an unsupervised tensor-based anomaly detection method that takes the sparse and temporally continuous nature of anomalies into account. In particular, the anomaly detection problem is formulated as a robust low-rank + sparse tensor decomposition with a regularization term that minimizes the temporal variation of the sparse part, so that the extracted anomalies are temporally persistent. We also approximate rank minimization with graph total variation minimization to reduce the complexity of the optimization algorithm. The resulting optimization problem is convex, scalable, and is shown to be robust against missing data and noise. The proposed framework is evaluated on both synthetic and real spatiotemporal urban traffic data and compared with baseline methods.
Recapitulation of Research in Artificial Intelligence: A Bibliometric Analysis

“Artificial intelligence” is the ability to understand and adjust technological advances; computer thinking theory and actions have become a vital task for successful operational advancement in the most modern organizational environment. Artificial intelligence technology provides a significant competitive advantage in the status of society. This article aims to analyze essential artificial intelligence work and discusses the concept of artificial intelligence by incorporating bibliometric research.
Cross-Modal Representation Reconstruction for Zero-Shot Classification

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.
Learning Support for Tactics Identification Skills in Team Sports by Gaze Awareness

In this study, we focus on basketball tactics identification skills and provide learning support. In previous research, we implemented and presented a system that automatically extracts scenes where predefined tactics are used in basketball games for understanding tactics and highlights them in animations. However, it is necessary for system users to properly recognize tactics and understand the situation, but it is difficult for the learners themselves to judge whether or not they have done them properly. Therefore, in this study, we developed a system that supports learning by using the gaze information of the learner as one of the objective indicator.
Social Media Behavioral Intelligence using Feature Extraction

Analysis of the behavioral pattern of a people using data of the social media became a trend in last couple of years. Among this popular network, Twitter, Facebook and the Instagram become more and more popular and that’s why these platforms attract the lots of researchers to predict the sentiment regarding major events like election, product brand, movie, stock market and recent trends are some of them. By identifying the attitude associated with the text in terms of positive, negative or the neutral we are able to analyze the opinion behind the content generated by the user and this opinions about the sentiment are very helpful to for the organization or the political parties or among other entities. The task of sentiment analysis is conducted using identifying the polarity associated with the word or document or we can say sentence. This paper consists research work which is designed to improve the accuracy of the model by improving the Naïve Bayes algorithm and I also worked to improve the 3-gram method during my research
Research on fine management of expressway construction schedule, quality and safety based on mobile terminal

Abstract In view of the management characteristics of highway engineering, it integrates BIM, mobile Internet, cloud computing, Internet of things and other technologies to study and establish a fine and collaborative management platform for project progress, quality and safety on the basis of mobile terminal application. This paper realize the dynamic control of project construction resources with progress as the main line and the collaborative management of projects with safety and quality as the core. It can meet the application analysis demands of all parties involved in the construction of highway for the whole service data, and give full play to the convenience of mobile terminals, and carry out task coordination and mobile operation of various management work in time through the mobile terminal anytime and anywhere. At the same time, the data of the mobile terminal is associated with the structure through BIM model, which can be statistically analyzed and visually displayed, and realize the collaborative association of various business data, thus greatly improving the refined management level of highway engineering construction.
Summary of Test Training Network

In the process of test and training, the effective acquisition, circulation and distribution of test and training data are mainly completed by the test and training network. This paper briefly introduces the characteristics, development status, technical system and typical application fields of test training network at home and abroad, deeply discusses the development trend and requirements of test training network, and puts forward the ideas and methods of constructing test training network in China.
Application: The Predicate ‘High Risk’

Abstract This chapter begins to flesh out the theory of de-anthropocentrized externalism, by considering what we should say about AI systems’ use of predicates, such as ‘is a benign lesion’ or ‘will default on a loan’. Introducing Kripke’s seminal causal theory of names, the chapter shows how (and why it’s necessary) to abstract from the theory’s anthropocentric features while still preserving its key features, such as the idea that use of a term should be anchored in a baptismal event and that the term’s reference is passed on from speaker to speaker.
Font Your Friends and Loved Ones: On the Utility of Ugly Interfaces

User interface design often focuses so heavily on clean and minimal interface aesthetics that any deviation is often rejected as “ugly”. This tendency towards abstraction in UI design can be contextualized as a removal of the “human” or “physical world” from the aesthetic choices and design considerations for the system. To resist this techno-deterministic eradication of the human presence from UI design, as well as radically inject the human presence back into user interfaces, we present typeFACE, a web interface and generative adversarial network designed to create fonts from human faces. We provide an implementation and applications for such a system, as well as contextualize and analyze the history of “ugliness” and the “uncanny” in UI design history. We also discuss implications of such a system within the domains of data ownership, identity, and HCI design research.
The Evolution of Hot Topics in the Field of Data Mining in China Based on the Visualization Analysis of CSSCI (2000–2019)

With the rapid development of cloud computing, Internet of Things and social media technologies, big data mining and analysis have become an important means of knowledge discovery in the future, and it is also an area of rapid development of academic research. How to objectively and comprehensively reveal the research status of the field of data mining, how to efficiently identify potential frontier hot topics from the massive literature, track its evolution path and provide visualization has become the biggest challenge facing scholars in this field. Using 2924 papers indexed by CSSCI with the theme of “data mining” published from 2000 to 2019 as the research object, and uses visualization software such as CiteSpace, Sati, Ucinet to draw authors, institution cooperation maps, keyword co-occurrence maps, etc. The study analyzes the hot topics and development trends in the field of data mining, and also point out the future development direction for data mining research.
Recognition of Signal Modulation Mode Based on Gaussian Filter and Deep Learning

In recent years, some studies have shown that the deep learning method can be used to recognize the modulation mode of signal quickly and effectively, but the modulation recognition effect of low signal to noise rate (SNR) signal is obviously poor. This paper presented a signal modulation recognition method based on filtering technique and Convolution Neural Network (CNN) plus Long-Short Term Memory (LSTM) networks. First of all, signals are divided into high SNR signals and low SNR signals by second-order statistics of modulated signals, and Gaussian filtering is carried out for low SNR signals, so as to achieve the effect of noise reduction. Then, modulation recognition is carried out for all signals. Experimental results show that this method can recognize 11 types of modulation which including digital signals and analog signals, and the average recognition accuracy is 71.33%. When the SNR is greater than 0 dB, the average recognition accuracy can reach 96.53%. When the SNR is at -20 dB ∼ -8 dB, the recognition accuracy is also improved compared with the CNN method.
Immersive Pedigree Graph Visualisations

We describe a study of six visualisation methods for hierarchical pedigree data in virtual reality. We model six different pedigree graph layouts: planar, cylinder, floor, sphere, cone (force directed) and vase layouts. Measurements of task accuracy and task completion time showed a statistically significant pairwise comparison on two task completion times between the sphere (better) and floor (worse) conditions. Likert ratings of user sentiments showed a statistically significant main effect of graph condition ratings of the understandability of the data with the sphere and vase graph layouts to be generally higher and the floor layout to be generally lower.
Coordination Analysis and Term Correction for Statutory Sentences Using Machine Learning

Statutes form an essential infrastructure that sustains and improves human society. They are continuously updated as society changes; that is, legislation (interpreting, drafting, and managing statutes) is an endless work. In Japan, statutes are written following a number of rules regarding document structures, orthography, and phraseology. Therefore, we need to pay attention to the formalism in addition to the substantial contents in drafting a statute. Another issue in Japanese statutes is complex hierarchical coordination that impedes smooth comprehension. In this chapter, machine-learning-aided methodologies for interpreting support and drafting support of Japanese statutory sentences are discussed. As for interpreting support, a coordination analysis methodology that utilizes neural language models is established. By transforming the whole sentences into vectors, the proposed methodology takes broader contexts into account in judgment. As for drafting support, a legal term correction methodology that finds misused legal terms and offers their correction ideas is established. Classifiers for legal term correction are utilized by regarding this task as a kind of sentence completion test with choices. Legislation itself can be regarded as a real-world data circulation of statutes from the viewpoint that legislation is to recognize problems in the real world and to solve them by updating statuses. Both coordination analysis and legal term correction support this data circulation by accelerating interpretation and drafting of statutes, respectively. Furthermore, each technology constructs another data circulation centered on its analysis results.
"Through the Solar System" ——XR science education system based on multiple monitors

This paper presents a multi-user extended reality (XR) science education system based on multiple monitors. It effectively uses existing equipment and space, allowing teachers and students to share immersive virtual scenes, increasing the possibility of collaboration, and attracting students through interactive interfaces and plot designs. Actively learn scientific knowledge, face-to-face communication, and interaction, thereby enhancing the effect of science education.
Counting distinct functional graphs from linear finite dynamical systems

Let Fq be the finite field with q elements and, for each positive integer n, let Aq(n) be the number of non isomorphic functional graphs arising from Fq-linear maps T:Fqn→Fqn. In 2013, Bach and Bridy proved that, for every prime power q, we have that 12≤liminfn→+∞log⁡log⁡Aq(n)log⁡n≤limsupn→+∞log⁡log⁡Aq(n)log⁡n≤1. By combining some ideas from linear algebra, combinatorics and number theory, in this paper we provide sharper estimates on the function Aq(n) and, in particular, we prove that limn→+∞⁡log⁡log⁡Aq(n)log⁡n=1 for every prime power q.
An Algorithm for Optimizing Small-Large Outer Join in Cloud Computing Environment

Join operation has always been a topic of interest in scientific research that is commonly used in most applications. Given that a massive amount of information is generated daily, one of the problems and bottlenecks in Join operations is the execution time and the complexity of parallelization. Between all the various join types, the left outer join is the most common whereas little work has been done to optimize this operation. A common type of outer join is Left outer join between small and large tables, and the optimal execution of this operation can have a major impact on the overall performance of programs. In this paper, we present an optimal algorithm that performs left outer join on small-large tables in parallel. We will also discuss all the challenges of parallel join and explain how to implement the algorithm in detail. We perform several experiments in the cloud computing environment using the Spark framework. The results show that the proposed algorithm is scalable and has better performance than existing algorithms.
Data-In-Image: a novel concept for data manipulation and encryption implementation

&amp;lt;p&amp;gt;Conventionally, the way of storing and exchange numerical data depends mainly on binary data files in compressible form. In this era of the Big Data and machine learning systems and with the accumulation of data with different forms and types, it is important to find an alternative way for handling the data. The binary data are software dependent which does not exhibit its content and type without accessing the data by the proper software. In addition, it does not have any encryption ability. To solve this issue, we propose a new concept to handle the digital data in a descriptive, encrypted, compressed form, and able to be previewed. The idea is to pack the binary bits into a bitmap image with specific coding scheme. This approach employs the Steim scheme as a primary compression tool with a 128-bit encryption method then packs the encrypted codes into a WebP image file. The WebP image is featured by being an independent, web friendly, and highly compressed file. In order to make the file describing its contents, we reserved some pixels as coded descriptive pixels. By this way, the now packed data exhibits its contents and type during image preview.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;It is proven that the Data-In-Image format, regardless of being encrypted, occupies the least amount of storage space among other image formats that can be easily handled, stored, and shared through clouds and devices safely with a lower cost. For seismic data, &amp;amp;#160;the size of the WebP image comprises ~20% of the corresponding binary size with a bit-rate of ~5.6 b/s which is smaller than that of the Steim form, 27% and 8.9 b/s, respectively. Regarding the compression speed, it is found that the code compresses data with a rate of ~11,118 samples/s or&amp;amp;#8201;~&amp;amp;#8201;44 Kbytes/s in average.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition, the data image is able to be digitally scanned and with some modifications can be remotely accessed like the quick response code, the thing that is not possible in the binary form. Moreover, the descriptive pixels in the image allow the implementations of smart tools to archive and classify data by machine learning and recognition algorithms.&amp;lt;/p&amp;gt;
Automatic Detection of Stationary Waves in the Venus Atmosphere Using Deep Generative Models

Various anomaly detection methods utilizing different types of images have recently been proposed. However, anomaly detection in the field of planetary science is still done predominantly by the human eye because explainability is crucial in the physical sciences and most of today's anomaly detection methods based on deep learning cannot offer enough. Moreover, preparing a large number of images required for fully utilizing anomaly detection is not always feasible. In this work, we propose a new framework that automatically detects large bow-shaped structures (stationary waves) appearing on the surface of the Venus clouds by applying a variational auto-encoder (VAE) and attention maps to anomaly detection. We also discuss the advantages of using image augmentation. Experiments show that our approach can achieve higher accuracy than the state-of-the-art methods even when the anomaly images are scarce. On the basis of this finding, we discuss anomaly detection frameworks particularly suited to physical science domains.
[DC] Eye Fixation Forecasting in Task-Oriented Virtual Reality

In immersive virtual reality (VR), users' visual attention is crucial for many important applications, including VR content design, gaze-based interaction, and gaze-contingent rendering. Especially, information on users' future eye fixations is key for intelligent user interfaces and has significant relevance for many areas, such as visual attention enhancement, dynamic event triggering, and human-computer interaction. However, previous works typically focused on free-viewing conditions and paid less attention to task-oriented attention. This paper aims at forecasting users' eye fixations in task-oriented virtual reality. To this end, a VR eye tracking dataset that corresponds to different users performing a visual search task in immersive virtual environments is built. A comprehensive analysis of users' eye fixations is performed based on the collected data. The analysis reveals that eye fixations are correlated with users' historical gaze positions, task-related objects, saliency information of the VR content, and head rotation velocities. Based on this analysis, a novel learning-based model is proposed to forecast users' eye fixations in the near future in immersive virtual environments.
Graph-Based M-tortuosity Estimation

The sinuosity of a porous microstructure may be quantified by geometric tortuosity characterization, namely the ratio of geodesic and euclidean distances. The assessment of geometric tortuosity, among other descriptors, is of importance for rigorous characterization of complex materials. This paper proposes a new way of calculation, based on a graph structure, of the topological descriptor M-tortuosity introduced in []. The original M-tortuosity descriptor is based on a geodesic distance computation algorithm. A pore network partition [] method is used to extract pores and construct a graph from the void of a porous microstructure. Through this scheme, pores are the nodes, distances between pores are the arcs between nodes and the goal boils down to the determination of the shortest paths between nodes. Solving this on a graph requires a tree search formulation of the problem. Our results have shown a drastic time complexity decrease while preserving good agreement with the original results. The added value of our method consists in its simplicity of implementation and its reduced execution time.