Reviewerly/exHarmony huggingface dataset:
Indoor Navigation in Stadium using Virtual Reality

Virtual Reality is a computer generated scenario that simulates a realistic experience. Current popular online maps majorly provide two-dimensional upper views of buildings. It becomes difficult to visualize the interiors of a place. Indoor navigation using VR technology addresses the problem of visualizing the interior infrastructure of huge complex buildings like stadium. This paper ‘Indoor Navigation in Stadium using Virtual Reality’ aims to provide highly realistic and immersive experience by incorporating an app that uses 360 degree images to serve the purpose. The paper explains how WebVR technology can be used effectively in order to provide a three dimensional view of the stadium with navigation functionalities to the users.
PURSUING FUTURE FOREIGN LANGUAGE TEACHERS’ PROFESSIONAL DEVELOPMENT

The study was aimed to determine the importance of pursuing professional development of foreign language teachers. Three approaches to professional development such as theory-to-practice, coaching/mentoring and reflection are identified. The scientists’ outlooks on the given issue are analyzed. Some recommendations for using mobile technologies in professional development are highlighted. Characteristics of effective professional development are substantiated in the study. Teachers’ effective professional development activities are outlined.
Design and Development of Solar Powered Intelligent Irrigation &amp; Water Management System using Data Science

India is an agricultural country. Agriculture and its allied activities act as main source of livelihood for more than 80 percent population of rural India. Available irrigation systems are not efficient and lead to wastage of water. Literature revels that there is need to develop automated irrigation system with latest technology. In this paper we are proposing a system which will detect the moisture percentage of the respective farmland and compare it with the provided set point. Based on the difference our machine learning algorithms will decide how long the water pump will remain switched on, it will then close the pump after that particular time and give out final moisture percentage reading and final water level of the water storage tank. Also, there will be a website provided which will continuously show current moisture percentage, amount of water present in the tank, how long water in the tank will last, the usage statistics and predict preferred and non- preferred crop to grow in that particular season. Plus a manual override will be provided for all systems.Usage statistics will consist of a graph showing water usage vs day. System display will show amount of water used vs date and below that the median and mode of the outcomes will also be indicated. Proposed in-house designed system has potential to provide the list of preferred and non-preferred crops.
User Demand Research Based on Internet Information

With the rapid development of the Internet, network interactions become more frequent. How to use network big data to obtain user needs in a timely, fast, and accurate manner is the only way to improve the competitiveness of enterprises in the market. This article focuses on product-related online review texts, and proposes a new method for obtaining user needs based on Internet information, which is, using the TF-IDF function to extract product feature words, then using a method of similarity forest calculation based on path and depth to classify the review text, and using the sentiment dictionary to calculate the sentiment tendency of the review text under each type of feature set. So, a product feature evaluation model based on text mining can be constructed. Then, through the automatically generated survey questionnaire to simulate the shopping decision behavior of the user, a large amount of real user demand information is quickly obtained. This method also provides convenience for users' shopping decisions and has verified its feasibility and effectiveness by taking Jingdong Mall's top ten mobile phone brands as an example.
Philosophical Approaches to Smart Education and Smart Cities

The impact of technology affects the educational field in an extraordinary way. The effect of technology must be treated from the educational method itself as well as its horizon in the new paradigm of citizen in this smart environment. This work proposes a revision of the immediate future of education and citizenship, as determined by the exponential impact of technology, based on relevant issues of classic philosophy and specially along the history and didactic program of the Trivium (Grammar, Rhetoric and Logic). The paper analyses firstly the perspective of the transformation of current education towards a smart education. This transition is determined by the development of Artificial Intelligence (AI), and the reflection on the competences of the 21st century, as something intrinsically related to the issues of citizenship as well as smart cities; secondly, we review the strategic and methodological proposals in accordance with this transformation, based on the theory of generative learning and on computational and algorithmic thinking; thirdly, from the point of view of contents, we analyse the importance of digital skills and, as a fundamental element of these, programming skills. Our proposal is to recover and update the contents of the Trivium, as a renewing and revitalizing element of the methodology and contents of education in the 21st century. This proposal is based on the assumption of the epistemological unity of these disciplines and on the integral anthropological vision that supports them. Both ideas acquire special relevance in the current context marked by the impact of technology as a determining element of the medium (smart education), of the methodology (generative learning, computational thinking) and of the contents (digital skills and programming).
Image Retrieval Algorithm Based on Deep Learning

The traditional hashing method of manual feature extraction uses image tags as the supervision information to obtain the loss function, and the retrieval accuracy is low and the effect is not good. This paper proposes a new deep learning image retrieval algorithm based on the traditional supervised hash algorithm. The algorithm integrates feature learning and hash code learning in an end-to-end framework, and converts multi-labels of images into binary paired labels. Based on the AlexNet framework, a feature learning module is established, and a pair of loss function and a balanced hash code loss function are combined to generate a loss function for network training. After the experimental test of the CIFAR-10 data set, the method of this paper greatly improves the average accuracy of image retrieval.
A Report on Behavior-Based Implicit Continuous Biometric Authentication for Smart Phone

Continuous authentication mechanism is gaining popularity in the recent days as the conventional mechanism of authentication is difficult for the user to memorize, such as the passcode and pattern, and for the device and data, it is vulnerable to different types of attacks. The continuous authentication mechanism has the advantage that, at any point of time, without the user’s permission the device can check the genuineness of the user credentials, thereby ensuring that the registered owner is using the device and the stored data. This technique uses the behavioral biometric data which is difficult for an impersonator to mimic and ensure the security of the data as well as to the device. This paper proposes a method based on continuous authentication by exploring the possibilities of the signals generated from micro-electro-mechanical systems (MEMS) that are inbuilt in the smartphone. Signals collected which are generated by the movement of the hand, orientation of the device, and the holding behavior of the user are analyzed to get a unique parameter, which will be used for the authentication purposes.
Protecting financial transactions through networks and point of sales

Purchasing items on the Internet with a credit card can be extremely risky. To mitigate this risk, we propose a new Multi-Layer Defense (MLD) scheme which uses two-factor authentication by instituting a random number generator that is only valid for a preset amount of time. For two-factor authentication, we use an eight-digit code that serves as a redundant private key to authenticate the purchases. This can be utilized to identify users of credit cards and establish a more secure way of purchasing items via various marketplaces on the Internet. Specifically, the MLD scheme uses electronic devices to log into the credit card accounts via an application to view a randomly generated code. This is then inputted on an Online Retailer’s website to authorize the use of the credit card. This ensures that a legitimate user is attempting to make a purchase and is not fraudulent. This redundancy, if used, can mitigate the circumstance of lost or stolen credit card information. The use of our proposed MLD scheme, specifically randomly generated code, when making a credit card purchase online could drastically cut down on fraudulent credit card purchases.
Explainable and Transferrable Text Categorization

Automated argument stance (pro/contra) detection is a challenging text categorization problem, especially if said arguments are to be detected for new topics. In previous research, we designed and evaluated an explainable machine learning based classifier. It was capable to achieve 96% F1 for argument stance recognition within the same topic and 60% F1 for previously unseen topics, which informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features. An advantage of the described system is its quick transferability to new problems. Besides providing further details about the developed C3 TFIDF-SVM classifier, we investigate the classifiers effectiveness for different text categorization problems spanning two natural languages. Besides the quick transferability, the generation of human readable explanations about why specific results were achieved is a key feature of the described approach. We further investigate the generated explanation understandability and conduct a survey about how understandable the classifier’s explanations are.
Depicting the Nature of Nature

The term “visual data” is often connected with strategies related to developing graphs or other types of quantitative visual data arrays. Visual data provide quantitative statistical information about our natural and constructed worlds. There is great power in the graphical visual data; they can provide portraits of our world that might escape our notice. This chapter focuses on portraying certain qualitative aspects of visual data that allow for a sense of the deeper nature (essence) of selected data sets. This approach can be termed aesthetically enhanced visual data. The data drive image making, while emphasis is given to aesthetic concerns. The goal is to identify and portray the nature of the nature of the aesthetic visual data. In this chapter the artist-author presents aesthetic visual data focused on describing certain aspects of the world. Artworks created through the aesthetic data visualization are presented along with clarifying notations.
Implementation Of Triangle Chain Cipher Algorithm in Security Message of Social Media

Abstract Messages sent on social media can be seen or used and obtained by others. Given the current many types of text messages that must not be known by others, or secret messages. Because social media is free, users cannot request security facilities for their messages. The triangle chain cipher algorithm is one of the encryption algorithms that operates based on classical encryption (cryptography), especially in character substitution techniques. Each character will be substituted based on the key and multiplier that has been determined based on the formula that applies in this algorithm. This algorithm encodes text messages twice and always depends on the results of the previous process. This is what underlies the complexity of solving this encryption coding algorithm. Encoding of messages especially in text messages is one of the most important things to do in improving the security of text messages from various levels of crime committed by people who are not entitled and not responsible. The implementation of the triangle chain cipher algorithm in encrypting text messages can make it difficult for anyone who is able to access and obtain text messages to understand and understand, damage, distribute, steal text messages or other actions that can harm the parties to the text message.
The Design and Development of Marathi Speech Corpus from Native and Non-native Speakers

This paper describes development process of a creation of speech database from native and non-native speakers of Marathi language. Reason for choosing this topic is people may feel hesitation in speaking language other than their mother tongue. So, for working on native and non-native Marathi speech recognition, it is the basic and essential step to develop speech database. It has been used to capture various variations of the visiting non-native speakers having the non-Marathi mother tongue. A total of 50 native and 50 non-native speakers have been tested to collect the sample data. In this analysis each speaker is supposed to repeat the text corpus approximately three times, which acquires 112 isolated words and has maximum capacity of 33,000 words.
Computation of continuous sequential reference paths from discrete optimal paths for mobile robots

This paper presents an new general solution for sequential calculation of continuous reference paths for path tracking of whiled mobile robots. The intelligent computational algorithms to find and optimize the reference paths results in discrete paths defined by knots, simple geometric prototypes, etc. This kind of path can not be directly used in the control design, because of it’s discrete nature and because it usually consists of a large number of knot points in the operation space of mobile robot. The resulting discrete path is therefore smoothed sequentially, going from the starting knot point to the end, taking into account only small number of discrete path point knots. The resulting spline curve is smooth and predictable without unintended overshooting or loops. Since the path is calculated algebraically, the computational complexity of the algorithm is predictable. The presented example is from a real-world solution for a whiled mobile robot used in rehabilitation.
Image Segmentation Based on Weakly Supervised MKL on Mixed Visual Features

Weakly supervised learning has outstanding ability to solve classification tasks, and multiformity middle-level visual features provide more abundant discriminant information for meaningful regions. In this paper, we study the integration of the middle-level visual features including homogeneity of superpixels, region objectness and texture map for segmentation. Then, three kernels are exploited to map visual features to high-dimensional space. A few labeled pixels are chosen for training support vector machines(SVMs) in a single image with hybrid kernels. On this basis, the remaining pixels are labeled with classified results of SVMs and refined the segmentation results by merging pre-segments of mean-shift. We perform sufficient experiments on Berkeley datasets and compared them with several excellent segmentation algorithms. Extensive experimental results of the proposed method show superior segmentation performance and expanded tests on PASCAL VOC datasets further validate the effectiveness of the algorithm.
Information and Digital Competencies of Teachers in the Function of Preventing Online Violence

Aggressive violent behavior today, especially peer and online violence, is a common phenomenon in educational institutions.Based on a review of the most relevant literature and research, information and digital literacy, phenomenon and occurrence of online violence as a specific form and type of violence are presented in this paper.The fundamental goal of this research was to provide outcomes on how to raise awareness and levels of knowledge and competencies of educators and children on how virtual reality can affect them and to which extent they can engage in the digital world, provided it is safe and without consequences.For this purpose, in educational institutions in Kosovo and Metohija research has been conducted in order to identify the impact of teachers' information and digital literacy in the function of preventing online violence.The results of this study will elaborate the following: information and digital literacy of teachers as well as the teachers' insight into the activities of students; teachers' control and the identification of the causes of online violence; and ultimately, to provide answers on how students can be protected from online violence.
Using Alternate Representations of Text for Natural Language Understanding

One of the core components of voice assistants is the Natural Language Understanding (NLU) model. Its ability to accurately classify the user’s request (or “intent”) and recognize named entities in an utterance is pivotal to the success of these assistants. NLU models can be challenged in some languages by code-switching or morphological and orthographic variations. This work explores the possibility of improving the accuracy of NLU models for Indic languages via the use of alternate representations of input text for NLU, specifically ISO-15919 and IndicSOUNDEX, a custom SOUNDEX designed to work for Indic languages. We used a deep neural network based model to incorporate the information from alternate representations into the NLU model. We show that using alternate representations significantly improves the overall performance of NLU models when training data is limited.
DegradeTimer: Mitigating Dedicated Thread Timer based Microarchitectural Timing Channels

Microarchitectural timing channels, e.g., timing-based side channels or covert channels, endanger victims' data confidentiality by accurately measuring the time difference of accessing shared microarchitectural resources (e.g., cache and DRAM). Lowering the accuracy of such timers is a feasible and widely discussed direction for mitigating such channels. However, few solutions have paid attentions to the special dedicated thread timer, which measures the time by utilizing a dedicated thread to increment a time counter in an endless loop.In this paper, we present a novel approach, DegradeTimer, to degrade the dedicated thread timer. It first eliminates local cache sharing between the attacker thread and the timer thread in case the CPU's hyper-threading feature is enabled, by modifying Linux kernel's thread scheduling policy to enforce that any two threads sharing writable memory are dispatched to different physical cores. Then, it applies two novel cache coherence protocols, M-MESI and M-MOSEI, to randomly delay responses to remote cache requests. In this way, the attacker thread and the timer thread are dispatched to different cores and provided with fuzzy cache time transmission. We have implemented a prototype of DegradeTimer for both x86 and ARM architecture using the Linux kernel and the gem5 full system simulator. The evaluation results show that DegradeTimer provides a strong security guarantee against microarchitectural timing channels. Furthermore, the performance overhead introduced by DegradeTimer is less than 6%.
Combination of Cryptography Algorithm Knapsack and Run Length Enconding (RLE) Compression in Treatment of Text File

Abstract The Knapsack algorithm cryptographic method is asymmetric cryptography in which the encryption key is different from the decryption key. In addition to text file security issues, the issue of the size of a text file is also a consideration. Large text files can be compressed by doing the compression process. Run Length Encoding (RLE) algorithm is an algorithm that reduces the size of a text file, if the text has a lot of repetition of characters. The combination of Knapsack and RLE algorithms can guarantee Text files cannot be seen by unauthorized users and can guarantee text files can be stored in low capacity media files. In this study, the authors made a combination of knapsack and RLE algorithm in text files. In the Knapsack algorithm there will be an increase in the size of the text file, this can be seen in the example of a case where the size of the plaintext (the original message) is 9 bytes, then after the encryption process the text file size becomes 7 bytes. Because of that the use of a combination of encryption and data compression is better because the file becomes smaller than the combination of data compression and encryption. Plaintext that has a lot of repetition of characters will be well compressed.
Trainable monotone combiner

We consider a binary classification problem in which the class label is given in the form of a discriminant function that satisfies a monotone constraint. That is, the degree of confidence that an object belongs to a class can not decrease as one of the input features increases. This manuscript examines how such a discriminant function can be trained on the basis of a labeled data set. Two alternative quality measures are considered. One of them is the AUC, which is based on the ROC analysis. The second is encouraged by the Neyman-Pearson lemma, which aims to maximize the ratio of correctly classified to misclassified examples. We propose an approach in which feature space is partitioned into quality layers that can then effectively compute the discriminant function. We prove that the resulting discriminant function is optimal with respect to the two quality measures mentioned, which indicates, among other things, the equivalence of these two quality measures. We also show that the associated optimal partitioning of feature space is unique, and provide a polynomial training algorithm for generating this partitioning.
Federated Quantile Regression over Networks

In order to solve the issue of isolated data islands and data security and personal privacy in the development of artificial intelligence, federated machine learning effectively solves the problem of sharing knowledge while protecting user privacy and data security. In a wireless sensor network, a secure learning framework is particularly needed, so that each sensor node can jointly learn knowledge without leaking local node data. Compared to traditional regression analysis algorithms, quantile regression can more fully describe the relationship between response values and its covariates by estimating conditional quantile sequences rather than a single value (such as the mean). In this paper, we propose a quantile regression federated learning framework that applies quantile regression with federated learning frameworks to wireless sensor networks, studies the performance of the algorithms, and the effectiveness of the algorithms is verified by simulations.
Building Better India

We can name many industries that are still based on the same working practices and business models that they have had for a long time – maybe since they started. Despite the wealth of modern technology now available, public infrastructure, a critical component for the well-being of the society, is still an industry based on the paperwork, letters, emails, manual approvals, and a large amount of guess work. It involves a lot of manual effort and is also error prone. It is really very hard for the stakeholders and end users to get an update on the progress of the project, which impacts them directly or indirectly. The authors intend to develop a groundbreaking blockchain platform that can meet the needs of all the different stakeholders involved in creating and providing a better infrastructure. They plan to automate the entire process by using smart contracts to minimize paperwork for the government officials. This will not only eliminate the errors that can happen during manual execution but will also provide a real-time update to all the stakeholders in making the process more transparent.