Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Authentication Method with One-Time Password

Trust mechanisms based on trusted authorities are used to establish trust between two unfamiliar entities in conventional authentication, which cannot meet the higher security requirements of emerging technologies for authentication. Blockchain achieves a decentralized trust mechanism between a group of mutually distrusted nodes without a trusted authority by means of data encryption, timestamp and consensus mechanisms. In this work, we therefore propose an innovative blockchain-based authentication method with one-time password (OTP). Specifically, we propose an OTP scheme and apply it to the blockchain, where blockchain serves as an OTP verifier. We then develop a list of security criteria to analyze the security of the proposed authentication protocol. This paper shows that our scheme can resist replay attacks, brute force attacks, OTP forgery attacks and so on. We finally compare the proposed OTP scheme with the representative OTP schemes according to the designed evaluation criteria and conduct a series of comparative experiments on Hyperledger Fabric. The results show that the proposed scheme is superior in terms of performance and security.
Educational trends in software engineering: a systematic review study

Although there is an updated knowledge's body in software engineering, even so the curricular guides must be updated periodically. This article presents a systematic review study, which addresses the need to provide suggestions regarding the issues that should be emphasized in the area of software engineering in the systems and computer engineering program at the Catholic University of Colombia. 20 themes were identified, presented in 855 selected studies and which are studied in 50 articles, at a minimum. The most studied topic is related to software design (91.5%), followed by software development in (58%), testing and software quality in (55.5%), open source software in (33.5%), software architecture (23%), web technologies (22 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), agile methodologies (18.5%), software project management (16%), software development processes (12%)), C & C ++ programming language (11%), programming languages, object-oriented programming and graphical user interface (10%), Java programming language (9%), prototype-based software development in (8.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), requirements, standards and computer-aided software engineering in (8%), mobile software and cloud services in a (7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). The results indicate the top 20 of the essential topics in a curriculum of the software engineering area.
Perceptual MR Space: Interactive Toolkit for Efficient Environment Reconstruction in Mobile Mixed Reality

With the rapid development of relevant hardware and software, mixed reality has been widely applied in gaming, industrial production, tourism et al.. However, the environment understanding cannot fulfill the need of a mobile mixed reality application in some occasions. In this paper, we propose Perceptual MR Space, a semi-automatic environment understanding technique for complete, consistent and efficient environment understanding in mobile mixed reality applications. We use several environment reconstruction tasks to evaluate the effectiveness of the technique. A mixed reality game "Run!Tom" is developed to show the application and advantage of the technique.
Doing business and constructing identities through small talk in workplace instant messaging

Abstract This paper describes how bilingual colleagues living in Hong Kong make small talk in instant messaging to achieve various business-oriented goals and construct multiple identities in the discursive process. Guided by James Paul Gee’s revised framework of discourse analysis, the analyses evidenced that, overall, colleagues use small talk in instant messages to maintain minimal ties with distant partners, fill in silence during computer work, affect informal decision-making at work, and to diffuse useful surrounding information into business talk. These instances interplay with different affordances provided by the gadgets in the instant messenger interfaces. Such creative usage, together with the perceived nature of online interaction and instant messaging, results in multiple and turbulent identities circulating in the broader context of workplace discourse. The article concludes by arguing that computer-mediated communication has offered participants an emerging modus of interacting socially, beyond the physical and psychological constraints of time and space.
Deep Learning Approach Based Dominant Age Group Based Classification for Social Network

In the current era of technology, the social network is the most important tool of data science. Social network have a largest dataset of people profile, comments and their day to events with photographs. Some people hide the personal data such as age, gender and other demographics. The sentimental analysis is used for the business, political reviews and social opinion about the events. For the sentimental analysis and opinion mining the age and gender are the dominant features. The social nature of the people has been changed as per the age group and gender. This research provides the one of the most relevant parameter of user profile in account creation strategy such as age group. The behavior of the same age group persons are similar and the behavior is homogeneous if discussion of the same topic. The dataset for this experiment has been collected from the Facebook, Twitter. The dataset was 20,000 sample sentences. The experiment was tested over the collected sample sentence dataset. The detail analysis has been done with use of punctuation, number of character, topic used in the sentence. The above characteristics are used for the age group classification. This dataset has been tested deep convolution neural network algorithm for classification of age group as a two class such as teenager and adult age group. Deep Convolution Neural Network (DCNN) had the best performance, reaching a precision of 0.89 in the validation tests. For the validation of the performance of proposed model for age group classification enhanced Sentiment Metric (eSM) is used. In the performance validation, subjective tests are performed and the eSM with the proposed model reached a Root Mean Square Error and a Pearson Correlation Coefficient of 0.32 and 0.89, respectively outclassing the eSM metric.
Stit Semantics for Epistemic Notions Based on Information Disclosure in Interactive Settings

We characterize four types of agentive knowledge using a stit semantics over branching discrete-time structures. These are ex ante knowledge, ex interim knowledge, ex post knowledge, and know-how. The first three are notions that arose from game-theoretical analyses on the stages of information disclosure across the decision making process, and the fourth has gained prominence both in logics of action and in deontic logic as a means to formalize ability. In recent years, logicians in AI have argued that any comprehensive study of responsibility attribution and blameworthiness should include proper treatment of these kinds of knowledge. This paper intends to clarify previous attempts to formalize them in stit logic and to propose alternative interpretations that in our opinion are more akin to the study of responsibility in the stit tradition. The logic we present uses an extension with knowledge operators of the Xstit language, and formulas are evaluated with respect to branching discrete-time models. We also present an axiomatic system for this logic, and address its soundness and completeness.
Indoor Scene Reconstruction: From Panorama Images to CAD Models

This paper presents a novel method of reconstructing indoor scenes, both structures and objects, by a single panorama photo. The method combines room structure estimation, furniture detection, models selection, as well as 3D positions reasoning. Compare with others, our preliminary results show this method could get almost the same performance with a simpler procedure.
Classification of Abusive Comments Using Various Machine Learning Algorithms

In the past decade, we have seen an increasing surge in the popularity of social networking sites, with Twitter and Facebook being some of the most popular ones. These sites allow you to share your expressions and views. However, as of now, there is no particular restriction applied by them to control the kind of content that is being uploaded. These uploaded contents may have obnoxious words, explicit images which may be unsuitable for social platforms. There is no predefined method for restricting unpleasant texts from publishing on social sites. To solve this problem, we propose a method that can aid human moderators as well as work independently. In this approach, logistic regression, multinomial Naïve Bayes, and random forest techniques are used to extract features like term frequency–inverse document frequency features, text features, and frequency features of the comments, respectively, to obtain a weak prediction model. Gradient boosting is applied to this model to obtain the final prediction model. We also applied a neural network using bidirectional long short-term memory and compared the accuracy rate of the two models. We believe that these models can help human moderators on various online platforms to filter out abusive comments.
Assigning users to domains of interest based on content and network similarity with champion instances

In this paper, we propose two approaches to the problem of finding similar users to a set of champions representing domains of interest on social media. The first approach is based on the content shared by the users, while the second one relies on the social network connections (following, followers, and mentions). Given a small set of champion accounts, we construct a centroid and we rank candidates by computing their distance from the centroid. Experiments show that social network features provide better performance, but they are computationally much more intensive. This approach can be used for providing highly reliable recommendations of the top-k instances which are most similar to a given target, specified through examples rather than through specific properties.
Towards Scalable Semantic Service Composition

State-of-the-art approaches in web services research have used semantics of web services for different purposes, for service discovery, composition, selection and execution. In this paper, our main focus is on semantics driven Quality of Service (QoS) aware service composition. Most of the contemporary approaches on service composition have used the semantic information to combine the services appropriately to generate the composition solution. However, in this paper, our objective is to use the semantic information to expedite the service composition algorithm. Here, we present a service composition framework that uses semantic information of a web service to generate different clusters, where the services are semantically related within a cluster. Our final aim is to construct a composition solution using these clusters that can efficiently scale to large service spaces, while ensuring solution quality. Experimental results show the efficiency of our proposed method.
Logics of Synonymy

Abstract We investigate synonymy in the strong sense of content identity (and not just meaning similarity). This notion is central in the philosophy of language and in applications of logic. We motivate, uniformly axiomatize, and characterize several “benchmark” notions of synonymy in the messy class of all possible notions of synonymy. This class is divided by two intuitive principles that are governed by a no-go result. We use the notion of a scenario to get a logic of synonymy ( S F ) which is the canonical representative of one division. In the other division, the so-called conceptivist logics, we find, e.g., the well-known system of analytic containment ( A C ). We axiomatize four logics of synonymy extending A C , relate them semantically and proof-theoretically to S F , and characterize them in terms of weak/strong subject matter preservation and weak/strong logical equivalence. This yields ways out of the no-go result and novel arguments—independent of a particular semantic framework—for each notion of synonymy discussed (using, e.g., Hurford disjunctions or homotopy theory). This points to pluralism about meaning and a certain non-compositionality of truth in logic programs and neural networks. And it unveils an impossibility for synonymy: if it is to preserve subject matter, then either conjunction and disjunction lose an essential property or a very weak absorption law is violated.
A Study of Efficacy of Cross-lingual Word Embeddings for Indian Languages

Cross-lingual word embeddings have become ubiquitous for various NLP tasks. Existing literature primarily evaluate the quality of cross-lingual word embeddings on the task of Bilingual Lexicon Induction. They report very high accuracies for European languages. In this paper, we report the accuracy of Bilingual Lexicon Induction (BLI) task for cross-lingual word embeddings generated using two mapping based unsupervised approaches: VecMap and MUSE for Indian languages on a dataset created using linked Indian Wordnet. We also show the comparison of these approaches with a simple baseline where the embeddings for all languages are trained using fast-text on the combined corpora of 11 Indian languages. Our experiments show that existing cross-lingual word embedding approaches give low accuracy on bilingual lexicon induction for cognate words. Given the high cognate overlap of several Indian languages, this is a serious limitation of existing approaches.
Recognizing Text with a CNN

We seek to detect text in images using multiple techniques and recognize characters using a Convolutional Neural Network (CNN). Individual characters are combined to form words, which can then be used in a variety of applications, such as automated translation. Text recognition is difficult when different types of text formats and conditions are involved, such as fonts, orientation, color, complex backgrounds, and low-quality images. Our contribution is a novel combination of techniques to perform text detection and a CNN model to classify text characters. Experiments show that both the detection algorithm and machine learning model generally succeed with clear text. The system has more difficulty detecting text from complex and low-resolution images, as well as parsing words whose characters are connected together, since this causes segmentation issues.
Augmented Robotics for Learners: A Case Study on Optics

In recent years, robots have been surfing on a trendy wave as standard devices for teaching programming. The tangibility of robotics platforms allows for collaborative and interactive learning. Moreover, with these robot platforms, we also observe the occurrence of a shift of visual attention from the screen (on which the programming is done) to the physical environments (i.e. the robot). In this paper, we describe an experiment aiming at studying the effect of using augmented reality (AR) representations of sensor data in a robotic learning activity. We designed an AR system able to display in real-time the data of the Infra-Red sensors of the Thymio robot. In order to evaluate the impact of AR on the learner's understanding on how these sensors worked, we designed a pedagogical lesson that can run with or without the AR rendering. Two different age groups of students participated in this between-subject experiment, counting a total of 74 children. The tests were the same for the experimental (AR) and control group (no AR). The exercises differed only through the use of AR. Our results show that AR was worth being used for younger groups dealing with difficult concepts. We discuss our findings and propose future works to establish guidelines for designing AR robotic learning sessions.
A supervised learning approach for heading detection

Abstract As the popularity of the portable document format (PDF) file format increases, research that facilitates PDF text analysis or extraction is necessary. Heading detection is a crucial component of PDF‐based text classification processes. This research involves training a supervised learning model to detect headings by systematically testing and selecting classifier features using recursive feature elimination . Results indicate that decision tree is the best classifier with an accuracy of 95.83%, sensitivity of 0.981, and a specificity of 0.946. This research into heading detection contributes to the field of PDF‐based text extraction and can be applied to the automation of large scale PDF text analysis in a variety of professional and policy‐based contexts.
Genetic Algorithms: A Mature Bio-inspired Optimization Technique for Difficult Problems

This chapter is dedicated to the method of genetic algorithms. Aiming at offering the essentials and at encouraging prospective users, it includes: (a) Description of the basic idea and the respective terminology, (b) Presentation of the basic genetic operators (selection, crossover, mutation), together with some additional ones, (c) Investigation of the values of basic parameters (crossover and mutation probability), (d) Outline of techniques for handling constraints and of conditions for the termination of the optimization process, and (e) Discussion on advantages and disadvantages of genetic algorithms. Moreover, the relationship between overall accuracy and optimization process accuracy is discussed, and some hints regarding teaching course modules on genetic algorithms are presented.
Elderly Health Care System Based on High Precision Vibration Sensor

In recent years, the aging population has become a major social problem. We hope to achieve health-care system for older persons through technical means. In this study, we developed an elderly health care system based on vibration sensors. By analyzing the vibrations of behavior such as walking and falling, the system can determine the current state of the elderly and send it to the robot. Experiments show that our system can estimate the behavior of the elderly with an accuracy of 89%, in which the accuracy of fall detection is 96%.
3D driver pose estimation based on joint 2D–3D network

Three‐dimensional (3D) driver pose estimation is a promising and challenging problem for computer–human interaction. Recently convolutional neural networks have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this study, the proposed method is based on two types of inputs, infrared image and point cloud obtained from time‐of‐flight camera. The authors propose a joint 2D–3D network incorporating image‐based and point‐based feature to promote the performance of 3D human pose estimation and run on a high speed. For point cloud with invalid points, the authors first do preprocess and then design a denoising module to handle this problem. Experiments on private driver data set and public Invariant‐Top View data set show that the proposed method achieves efficient and competitive performance on 3D human pose estimation.
An Oculomotor Sensing Technique for Saccade Isolation of Eye Movements using OpenBCI

Oculomotor sensing is used in clinical diagnosing of medical disorders, human computer/robot interaction systems and surgical planning software. Various devices are used for oculomotor sensing, namely electrooculography (EOG), video oculography (VOG), infrared oculography (IROG) and sclera coil (SC). EOG is inferior to VOG and IROG in spatial and temporal resolution but its superior in linearity, non-invasiveness, no sight disturbance, low cost and ability of recording closed eye movements. Accurate saccade isolation is important to implement reliable EOG based techniques. This work presents an inexpensive and accurate signal processing technique to extract saccade information. Signal filtering, isolation and calibration are implemented to extract saccade information from the raw signal. The proposed technique showed a significant improvement with respect to other approaches (used for noise filtering) found in literature.
Multiplier-Less Architecture for 4-Tap Daubechies Wavelet Filters Using Algebraic Integers

This paper presents a 1-D Discrete Wavelet Transform architecture for Daubechies 4-tap filter bank. The coefficients of the filter banks are mostly irrational numbers and may not be represented properly using integers without injecting some error into the computation. The proposed architecture minimizes the use of such irrational numbers. They are represented using algebraic integers only in the last stage of the wavelet decomposition process. This way, the error does not propagate throughout the wavelet decomposition stages. Further, the architecture is very simple to understand and easy to realize in the hardware. The results depict that the average PSNR of the transformed images is as high as 64 dB. The architecture has been implemented on Xilinx Virtex-6 xc6vcx240t-1ff1156. The results have been compared with the convolution-based architecture. The proposed architecture is multiplier-less and saves approximately 63% of the area as compared to the convolution-based architecture. A comparison is also made with the existing methods and it is clear that the proposed architecture reduces the hardware cost considerably.
SQL2SPARQL4RDF

With the progress of RDF data storage technologies and the associated query capabilities using SPARQL, there is a want to permit SQL customers benefit from such competencies for interoperability goals and with none conversion of the RDF facts into relational data.
The Relevance of Visual Cues in Immersive Environments: Does Pictorial Realism Matter?

This paper aims at contributing to the debate on the extent to which cues of visual perception and pictorial realism in virtual reality (VR) environments are relevant in the context of construction practices. The discussion is conducted with the support of literature on human visual perception in physical and virtual environments. The article provides and develops on the argument that for various current VR applications in the construction industry VR environments may not need to resemble the physical world, especially in the design phase. Understanding the intricate relationship between our visual perception processes and information demands in the various construction activities could lead to the identification of visual aspects that are actually relevant for effective VR experiences. The discussion is merged into a summary on the power, limitations, and suitability of VR technology in building construction, followed by final considerations.
Neural embedding features for point-of-interest recommendation

The focus of point-of-interest recommendation techniques is to suggest a venue to a given user that would match the users' interests and is likely to be adopted by the user. Given the multitude of venues and the sparsity of user check-ins, the problem of recommending venues has shown to be a difficult task. Existing literature has already explored various types of features such as geographical distribution, social structure and temporal behavioral patterns to make a recommendation. In this paper, we propose a new set of features derived based on the neural embeddings of venues and users. We show how the neural embeddings for users and venues can be jointly learnt based on the prior check-in sequence of users and then be used to define three types of features, namely user, venue, and user-venue interaction features. These features are integrated into a feature-based matrix factorization model. Our experiments show that the features defined over the user and venue embeddings are effective for venue recommendation.