Reviewerly/exHarmony huggingface dataset:
A Novel CNN Training Framework: Loss Transferring

As one of the indispensable components in convolutional neural network (CNN), loss function assists in updating parameters of CNN models during the training phase. Generally, different loss functions can assist convolutional neural network (CNN) to learn different feature representations, and different feature representations can be treated as different knowledge learned from objects. In this paper we introduce a novel training framework, namely Loss Transferring (LT), to improve the generalization ability of CNN. LT contains multiple training phases, and each training phase uses a different loss function. Under this framework, CNN models can combine different knowledge of objects by transferring the knowledge learned via one loss function to another. LT contains two components, i.e., loss function set and training strategy. In order to build appropriate loss function set, we establish two basic guides. And according to these basic guides, we design a new loss function in the last layer of CNN models (layer before softmax operation), namely Near Classifier Hyper-Plane (N-CHP) loss, which makes the learned object features belonging to the same category have the minimum intra-class distance and be near the classifier hyper-plane. Based on the two loss function set (MSE, softmaxl and (N-CHP, softmaxl, we setup two specific training methods, LTMSE, softmax and LTN-CHP, softmax, which can be universally applied to different CNN models with low additional computation cost. Meanwhile, two training strategies, multi-phase strategy 1 and multi-phase strategy 2, are further proposed to improve the training efficiency of LT. Extensive experimental results on shallow, moderate and deep models with four benchmark datasets, including MNIST, SVHN, CIFAR-10 and CIFAR-100, demonstrate that CNN models can bring obvious performance improvements when working with LTMSE, softmax and LTN-CHP, softmax, which verifies the effectiveness of LT and the proposed two basic guides.
Live Beyond Fear: A Virtual Reality Serious Game Platform to Overcome Phobias

The thesis focuses in development of a Virtual Reality based Serious Game platform for overcoming phobia, with special focus on acrophobia i.e., fear of heights. Levels of acrophobic scenarios with gradual exposure to heights is developed. The prototype includes game elements such as motivation, target and set of goals to be achieved. The serious game platform reduces the level of fear by encouraging the individual to use it again since they are challenging and attractive. The degree of fear of heights in people is evaluated using questionnaire and since it showed a significant number of people experience acrophobia, this platform may be used in treatment to overcome the same. Future studies should include the deployment of serious game platform on patients with acrophobia to measure the effectiveness of this system.
Recognition and translation of code-switching speech utterances

Code-switching (CS), a hallmark of worldwide bilingual communities, refers to a strategy adopted by bilinguals (or multilinguals) who mix two or more languages in a discourse often with little change of interlocutor or topic. The units and the locations of the switches may vary widely from single-word switches to whole phrases (beyond the length of the loanword units). Such phenomena pose challenges for spoken language technologies, i.e., automatic speech recognition (ASR), since the systems need to be able to handle the input in a multilingual setting. Several works constructed a CS ASR on many different language pairs. But the common aim of developing a CS ASR is merely for transcribing CS-speech utterances into CS-text sentences within a single individual. In contrast, in this study, we address the situational context that happens during dialogs between CS and non-CS (monolingual) speakers and support monolingual speakers who want to understand CS speakers. We construct a system that recognizes and translates from codeswitching speech to monolingual text. We investigated several approaches, including a cascade of ASR and a neural machine translation (NMT), a cascade of ASR and a deep bidirectional language model (BERT), an ASR that directly outputs monolingual transcriptions from CS speech, and multi-task learning. Finally, we evaluate and discuss these four ways on a Japanese- English CS to English monolingual task.
A Candidate Aware Internet Voting System For Indian Scenario

India is the largest democratic nation in the world. Democracy is considered as one of the best forms of government that allow the citizens to cast a vote and elect a government of their choice. A vote can change the future of the country as well as the fate of the citizens. It gives the ultimate power to shape the destiny of the country. According to the Election Commission of India (ECI), the number of electors registered is 834 million and the highest ever turnout of the voters is about 66.40%. The main reason behind this low turnout was due to constraints in the current (traditional) voting system. This paper suggests a new online voting method for Indian government elections to increase the number of voter's participation in the election process. The proposed method employs two approaches 1) Novel candidate verifiability mechanism and 2) OTP (one-time-password) mechanism. The candidate verifiability allows the candidate to check the integrity of the vote and OTP acts as an additional authentication factor to identify the voter.
Web Portal for Effective Student Grievance Support System

A Grievance is that the dispute that arises at any level of organization. In an education organization, a student's community is the most vulnerable entity. In many circumstances students fail to state their issues and sometimes fail to seek out proper support for the issue they are facing in an organization. On analyzing the above mentioned problems we designed Student Grievance support system to deal with the grievance and to seek the redressal. The web application builds a platform for the students to lodge the arising conflicts in their daily walk of lives. In the web application students can address their complaints which are forwarded to the Grievance Redressal Committee. The Committee will forward the valid complaints to the Institute or Department supporting the sensitivity of the matter. The Institute or Department will take action and update the status which can be viewed by the students. This project provides transparency to the students which can be incorporated to supply solutions to the students.
A robust fast LMB filter for superpositional sensors

This paper aims to deal with the problem of multitarget tracking based on superpositional sensors, while the measurement noise is unknown. To this end, the labeled multi-Bernoulli (LMB) filter is adopted in order to jointly estimate the number of existing targets and the state of each target recursively. Since the LMB filter admits no analytical solution, a Gaussian mixture (GM) approximated implementation is proposed. In order to deal with the unknown measurement noise, the single target state is augmented to incorporate the noise parameters, and the augmented state is modeled as the Gaussian and inverse Wishart mixture (GIWM) distribution. The posterior is approximately computed using the variational Bayesian (VB) estimation method so as to ensure that the updated distributions of augmented states belong to the same family of the predicted one, thus can be used as prior for the subsequent iterations. The performance of the proposed algorithm is assessed via simulation experiments.
An Optimized Zero-Watermarking Technique Based on SFL Algorithm

Zero-watermarking techniques are the most interesting types of digital watermarking, especially, in legal and medical images. These techniques are work on extracting the most important features from images for creating secret watermarks without any alteration in the original images. In order to extract the robust features which are able to resist various types of attacks, the optimization algorithms should be used. In this paper, appropriate locations for extracting the most important features from the singular-value decomposition (SVD) of a digital image are selected depending on Shuffled Frog Leaping (SFL) algorithm. The analysis of the implementation presents that the proposed technique is highly robust under various attacks, where the values of normalized cross-correlation (NCC) of the selected attacks are utilized a fitness function for the SFL algorithm.
Geo-Tagging Quality-of-Experience Self-Reporting on Twitter to Mobile Network Outage Events

Mobile wireless networks underpin digital economies and smart cities. Local and national scale network failures cause widespread social and economic impact. Self-reporting of consumer experience on social media platforms can inform operators. This paper investigates an innovative method to detect the consumer experience to outage events in both temporal and spatial domain using Twitter data. We use a variety of natural language processing (NLP) analysis to detect the consumer sentiment from a custom made dictionary and using naive Bayes classifier. We propose a hybrid geo-information extraction that sequentially extracts the geo-location from a priority list. A case study upon recent UK wide mobile network failure has been implemented in this paper. The results show that our proposed hybrid geo-information extraction system has been able to increase data size and accuracy of geo labelled Tweets. Also, our system can successfully detect this network issue in both time and location, which is validated by the national newspaper reports on this issue.
Visual Landmarks Recognition of Urban Structures using Convolutional Neural Network

The landmark Recognition of any urban structure is a frustrating task to accomplish. This paper presents a deep learning based approach for recognition of the urban structures. Urban structures are basically the usual offices, houses, apartments etc. A generalized word for all these buildings can be urban structures. This classification problem was identified by our team while making our way into the visual positioning system, we saw that the visual landmark recognition of urban structures was an unexplored domain by using deep learning approaches. We also Identified that unlike the landmarks such as the Eiffel Tower, Statue Of Liberty etc, urban structures were difficult to learn by a neural network, the data requirements were quite distinct compared to the ones mentioned for all these famous landmarks around the globe. Most of all the landmark database was created, augmented, labelled and preprocessed in lab. The transfer learning based approaches were not that useful since the overall size of the model would always be so enormous and integration of a transfer learning based model would be meaningless for any deployed web app or even for a local mobile based app. By collecting and engineering our own data using a data collection app, we have proposed a neural architecture which can be configured to learn multiple number of urban landmarks while retaining its overall size under 60MBs. This was achieved by a Convolutional Neural Network based binary classification model trained on landmarks existing in our university. Later this was deployed on a web based application and further on it is being made into an app.
The Multi-Layer Constrained Loss for Cross-Modality Person Re-Identification

Person Re-identification (Re-ID) has great potential in video surveillance and security. At present, Re-ID in the visible light field has reached a relatively high precision, but the related research on infrared-visible cross-modality ReID problem for night monitoring still has plenty of room for improvement. Current methods mainly focus on extracting the features of thermal(infrared) and visible person images using two models, and then classifying and measuring learning through partial sharing weights and the same loss regression layer. We found that the main difference between infrared image and visible light lies in color and edge sharpening. In this paper, we proposed a simple and effective end-to-end crossmodality Re-ID model named cmPCB by using a Part-based Convolution-al Baseline (PCB) model for feature learning. We use Resnet network to extract network features, and we proposed a Multi-Layer Constrained (MLC) Loss, and extend the distance between near infrared and visible modes. Experimental results demonstrate that our model has superi-or performance compared to the state-of-the-arts, i.e. on SYSU- MM01 dataset(All-search multi-shot), Rank1 is improved from 43.86% to 65.83%, mAP is improved from 30.48% to 53.34%; on RegDB dataset, Rank1 is improved from 50.85% to 81.02%, mAP is improved from 48.67% to 78.73%.
A Survey on Data Integrity Checking and Enhancing Security for Cloud to Fog Computing

Cloud computing plays an important role in the computing world as it provides a cost-effective way of delivering services to customers through the internet. The Cloud Service Providers (CSPs) offer an enormous amount of storage space with low cost and eliminates the data management burden of the customers. As the operational details are not transparent, the CSPs may be untrusted. They may steal the user data, tamper the data, remove the rarely accessed data or hide the data loss by duplicating the data. In such cases, data integrity fails and needs to be addressed. The emerging fog computing paradigm extends the cloud platform to provide services for IoT applications. So the data integrity verification techniques are not only to be incorporated over the cloud but also to be extended for fog computing. This survey discusses the various data integrity checking methodologies for both cloud and fog environment.
Agent Applications In E-Learning Systems And Current Development And Challenges Of Adaptive E-Learning Systems

This research paper aims at closely reviewing what are the various applications in e-learning systems. In order to achieve this, it must first define e-learning systems, look at the various components, approaches, and examples of e-learning systems. This paper is divided into two parts; section II and section III. In the first part, the paper tackles the various agent applications in e-learning systems. While In the second section, the research paper looks at the current development, or evolution, of adaptive e-learning systems and the challenges this system faces. In the second section, a review of different forms of adaptive e-learning systems have been made and comparisons made between them.
Blockchain Technology in Data Management

The introduction of cryptocurrency based decentralized applications and data storage has been illustrating the importance of blockchain technology to the industries. Blockchain technology as a platform allows creating a distributed and replicated ledger of transactions. Blockchain provides a guarantee of tamper resistance for the data generated from various processes to compile massive datasets. Since the data is huge in size and grows exponentially with time, the data management should be efficient to store or process it. We survey the different standards of data processing works in several research directions for bringing the blockchain execution closer to the various domains of databases and its operations. Furthermore, the proposed research work also discusses about the performance, challenges, and future research directions.
An Efficient SecureU Application to Detect Malicious Applications in Social Media Networks

Utilizing interpersonal interaction locales online has become a significant piece of our lives. It encourages us to associate with our friends and family. We can likewise utilize these stages for various motivations behind media sharing, for example, music, recordings, and so forth. Also, these destinations are turning out to be considerably more mainstream nowadays because of the uses of outsiders that work on these sites. In any case, the guarantee of these applications has been found by gatecrashers and utilized as a device for spam clients. As a rule, these projects are malevolent as per a study led. A spammer can exploit these applications in different ways, for example, arriving at an enormous number of clients, extricating individual data from the client, and furthermore spamming numerous different clients with the guide of a solitary client. As the examination proceeds, inquire about gatherings have focused on distinguishing bogus or spam-like pernicious URLs and online social battles. In our everyday lives, the presence of Facebook, Twitter or some other Online Social Networking Sites (OSNs) is an absolute necessity. As it is an approach to remain connected and associate with our shut and removed ones. The locales are utilized for different purposes, for example, music, pictures, transferring recordings, posting distinctive photographs, data, and so forth. These OSNs ' addictiveness and rage has expanded because of the nearness of outsider applications. The following state is that of a reflection of a capacity that tests the highlights characterized by the SecureU demand. Here we utilize the SVM Algorithm as a classifier which comprises of various parameters characterized to group the noxious or kind gadget, message, picture, and so forth.
Internet of Things-based Smart Facilities Management Services Successful Implementation Instrument Development, Validity, and Reliability

The purpose of Internet of Things-based Smart Facilities Management Services (IoTbSFMS) is to increase the efficiency and effectiveness of facilities management services through improvement and innovation. Despite a common understanding that individual technology readiness and overall quality is an essential element in IoT-based technology success, there is a dearth of theoretical and empirical research on these elements as a facilitator of successful IoTbSFMS implementation. This study develops the IoTbSFMS validated instrument and proposes an integrated approach of instrument development through a multi-stage technique and rigorous statistical testing. Thirteen IoT experts had evaluated the content validity where two measurement items were excluded as per expert review's suggestion, which remaining 11 constructs and 58 measurement items. The process was followed by a pre-test assessment to determine the effectiveness of the measurement items. Finally, a pilot study assessment was conducted among 33 respondents. The collected data were analysed using SPSS25, Smart-PLS, and JASP software. As a result, the Content Validity Index (CVI) for the final IoTbSFMS constructs and items was deemed acceptable (CVI =0.82). The internal consistency reliability of the measurement instruments showed that the Cronbach's alpha and McDonald's omega for independent variables ranged from 0.682 to 0.989 and 0.685 to 0.989. These values suggest that all the constructs had acceptable validity and reliability. This paper contributes in encouraging researchers to look beyond the traditional approach in measuring the internal consistency reliability of the measurement instruments.
A New Way to Store Simple Text Files

In the era of ubiquitous digitization, the Internet of Things (IoT), information plays a vital role. All types of data are collected, and some of this data are stored as text files. An important aspect—regardless of the type of data—is related to file storage, especially the amount of disk space that is required. The less space is used on storing data sets, the lower is the cost of this service. Another important aspect of storing data warehouses in the form of files is the cost of data transmission needed for file transfer and its processing. Moreover, the data that are stored should be minimally protected against access and reading by other entities. The aspects mentioned above are particularly important for large data sets like Big Data. Considering the above criteria, i.e., minimizing storage space, data transfer, ensuring minimum security, the main goal of the article was to show the new way of storing text files. This article presents a method that converts data from text files like txt, json, html, py to images (image files) in png format. Taking into account such criteria as the output size of the file, the results obtained for the test files confirm that presented method enables to reduce the need for disk space, as well as to hide data in an image file. The described method can be used for texts saved in extended ASCII and UTF-8 coding.
Quantum Antenna Theory for Secure Wireless Communications

We provide a very broad outline for a new research area within the domain for Future Antennas, namely quantum antenna (q-antenna) theory and their applications to building secure digital communication lines. The proposed quantum antenna theory purports to presenting a natural extension of RF antennas in classical wireless to the now established field of quantum communications. The paper provides a bird's eye view on the subject, highlighting the main themes and the expected results and benefits of such research domain.
Hindi Verse Class Predictor using Concept Learning Algorithms

Lazy classifiers give predictions that are more accurate. Implementing lazy classifiers is significant in the domain like recommender systems, where data is continuously updated. Classification of Hindi text using lazy classifiers is not explored much in the literature. Classifying and predictions on Hindi text is challenging but benefit many applications like organizing a large Hindi corpus, so on. The process of classification cuts off time and efforts required in manual scanning. In this paper, 565 Hindi poems are classified based on four topics using lazy machine-learning algorithms which are K-nearest neighbours and regression. K nearset neighbours performs better than Linear regression.
Generalize Robot Learning From Demonstration to Variant Scenarios With Evolutionary Policy Gradient

There has been substantial growth in research on the robot automation, which aims to make robots capable of directly interacting with the world or human. Robot learning for automation from human demonstration is central to such situation. However, the dependence of demonstration restricts robot to a fixed scenario, without the ability to explore in variant situations to accomplish the same task as in demonstration. Deep reinforcement learning methods may be a good method to make robot learning beyond human demonstration and fulfilling the task in unknown situations. The exploration is the core of such generalization to different environments. While the exploration in reinforcement learning may be ineffective and suffer from the problem of low sample efficiency. In this paper, we present Evolutionary Policy Gradient (EPG) to make robot learn from demonstration and perform goal oriented exploration efficiently. Through goal oriented exploration, our method can generalize robot learned skill to environments with different parameters. Our Evolutionary Policy Gradient combines parameter perturbation with policy gradient method in the framework of Evolutionary Algorithms (EAs) and can fuse the benefits of both, achieving effective and efficient exploration. With demonstration guiding the evolutionary process, robot can accelerate the goal oriented exploration to generalize its capability to variant scenarios. The experiments, carried out in robot control tasks in OpenAI Gym with dense and sparse rewards, show that our EPG is able to provide competitive performance over the original policy gradient methods and EAs. In the manipulator task, our robot can learn to open the door with vision in environments which are different from where the demonstrations are provided.