Reviewerly/exHarmony huggingface dataset:
A paradigm of Sixth Sense: Finger Cursor

Sixth sense can be defined as a wearable gestural interface that augments our physical world with the digital information and also lets us communicate with this information using natural hand gestures. Without any mechanical means humans can interface with the machine (HMI) and interact with them. As we know today, human- machine interaction is very important aspect for user friendliness. Most of the gadgets today use touch screen technology, but this is too often nowadays. [3] Till now everybody is using mouse to operate computers, but surveys have found that using mouse continuously can cause various health issues like carpel tunnel syndromes, repeated motion injuries etc. Also due to the short wire the area of reach is limited. People with crippled hands or fingers are not able to use computers as they cannot hold the mouse or control a touch pad. Considering these problems we put forth the idea of “FINGER CURSOR: SIXTH SENSE TECHNOLOGY”, where you don't need to touch the screen or hold the mouse but can control the cursor by moving fingers in the atmosphere. Here we use a webcam as hardware and MATLAB as software. We implement our project using RGB (RED, GREEN, BLUE) caps. The computer interprets these caps and we can move the cursor accordingly. We capture real time videos which are converted into frames processed by extracting its RGB components, gray scale, binary and further evaluating its centroid. The mouse movements are carried out using the Robotic Class of Java. We also add features like screenshot and right click operation.
Detecting Spam Reviews for Improving House Sharing Recommendation

With the rapid development of technology, the business model of the tourism industry has changed. More and more people need to rent the houses, and it is easier for customers to use and get information on the Internet. In the era of Web 2.0, consumers can leave rating scores and write reviews on the online social platforms to share their experience with others. Nonetheless, there may exist some spam reviews. In this paper, we propose a novel approach to detect user profiles and spam reviews so as to generate a rental house recommendation. With this new mechanism, consumers can receive an appropriate recommendation from their own basic information, preference, and their close friends or family who are with powerful influence on them. Also, with the support of the proposed mechanism, less fake or useless reviews influence them.
Watson–Crick quantum finite automata

One-way quantum finite automata are reversible in nature, which greatly reduces its accepting property. In fact, the set of languages accepted by one-way quantum finite automata is a proper subset of regular languages. In this paper, we replace the tape head of one-way quantum finite automata with DNA double strand and name the model Watson–Crick quantum finite automata. The non-injective complementarity relation of Watson–Crick automata introduces non-determinism in the quantum model. We show that this introduction of non-determinism increases the computational power of one-way quantum finite automata significantly. Watson–Crick quantum finite automata can accept all regular languages and also accepts some languages which are not accepted by any multi-head deterministic finite automata. Exploiting the superposition property of quantum finite automata, we show that Watson–Crick quantum finite automata accept the language L = {ww|w ∈ {a, b}*}.
Three-Dimensional Block Matching Using Orthonormal Tree-Structured Haar Transform for Multichannel Images

Multichannel images, i.e., images of the same object or scene taken in different spectral bands or with different imaging modalities/settings, are common in many applications. For example, multispectral images contain several wavelength bands and hence, have richer information than color images. Multichannel magnetic resonance imaging and multichannel computed tomography images are common in medical imaging diagnostics, and multimodal images are also routinely used in art investigation. All the methods for grayscale images can be applied to multichannel images by processing each channel/band separately. However, it requires vast computational time, especially for the task of searching for overlapping patches similar to a given query patch. To address this problem, we propose a three-dimensional orthonormal tree-structured Haar transform (3D-OTSHT) targeting fast full search equivalent for three-dimensional block matching in multichannel images. The use of a three-dimensional integral image significantly saves time to obtain the 3D-OTSHT coefficients. We demonstrate superior performance of the proposed block matching.
Deep Learning of Web Page Verification Code-taking Project for Implementation of Remedial Instruction-Technology-Base Test Website as an Example

This study using Python integrates tools such as Selenium, Pillow and WebDriver for Chrome, automatically logs in to the web page -Project for implementation of remedial instruction-technology-based test website to capture graphic verification code image, and uploads to Google Cloud Vision API which provide pre-trained models. The deep learning model recognizes the content and reads the text in the picture. The result is more than 80% recognition rate successfully login to the website.
An 8-bit Bit-Slice TEA-Cryptographic Accelerator for 64-bit RSFQ Secure Coprocessors

An 8-bit bit-slice TEA-cryptographic accelerator for 64-bit RSFQ secure coprocessors is proposed. The accelerator is based on Tiny Encryption Algorithm (TEA) and mainly consists of bit-slice adders and bit-slice shifters. Synchronous concurrent-flow clocking is used to design a fully pipelined RSFQ logic design. For verifying the algorithm and the logic design, the RSFQ logic circuits of the proposed accelerator have been simulated with a target operating frequency of 50 GHz. It consists of 21 stages. The throughput is 7.672 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> 64-bit TEA encryptions per second.
Image Segmentation: Automatic Cluster Number Generation in K-Means

Nowadays, image analysis has become popular in every field, and the process is different for the different field. This leads to a huge advancement in the area of image analysis. One of the major steps of image analysis is image segmentation that plays a vital role in extracting the information from an image. Although there are several segmentation algorithms present in literature, however, there is no standard algorithm which works equally in all fields. Clustering algorithms are a new way of identifying and classifying regions in an image using mathematical patterns. K-means is one of the most popular clustering algorithms because of its simplicity and efficiency. However, it has three main drawbacks: 1) cluster number, 2) initialization and 3) noise sensitivity. This paper focuses on the cluster number problem and proposes a novel threshold-based approach to find out the cluster numbers. Experimental results show the effectiveness of the proposed cluster number generation method.
Energy Efficiency Metrics for Safeguarding the Performance of Data Centre Communication Systems by Green Cloud Solutions

Cloud computing involves sharing of the reservoir containing resources of computation for data centres whenever demanded. Proficiency in a data centre, is a core requirement to be achieved. To attain the highest performance and efficiency levels the consumption of energy also increases simultaneously. This increased energy consumption is associated with untoward side-effects like heat & carbon emissions. Hence we have to proceed with an objective of highest performance and energy efficiency with least associated side-effects to avoid the environmental threat of global warming and raised carbon footprint levels. These parameters are achieved to a great extent by application of suitable metrics on the cloud computing platform. These metrics mainly focus on the objectives as to how to enhance the IT tools and systems power proficiency. The metrics discussed in this paper resolve this problem to a great extent. None of the metrics proposed is singularly capable of performance and energy enhancement. Therefore the metrics in consideration are collectively utilized for improving performance of communication systems of data-centres. These metrics are tested on four different architectures viz. DCell, BCube, Hypercube and Fat tree three-tier.
A Chinese Continuous Keystroke Authentication Method Using Cognitive Factors

Everyone has different biometrics, so personal biometrics can be applied to authentication. Moreover, the biological characteristics are not easy to be copied, and the user does not need to memorize and does not need to carry extra things. Biometrics can be divided into two types: physiological characteristics and behavioral characteristics. This study belongs to behavioral characteristics and adopts dynamic keystroke behavior to verify the identity. Many dynamic keystroke authentication methods have been proposed, but most of them are only suitable for English. Only very small numbers of keystroke authentication research are designed for other language (such as Chinese). This study focuses on how to use Chinese keystrokes for continuous authentication. Different persons have different vocabulary usages. The main influence comes from different exposure to different “word frequency” during the learning process. “Word frequency” is the frequency at which words appear in a person's life. Frequency varies due to the individual's life experience. Therefore, if the user frequently enters a word, the user will be more familiar with the input of the word, so the familiarity will be related to the past learning cognition. This study treats this familiarity as a cognitive factor, and explores the impact of cognitive factors on identity verification in Chinese language. This study compares the keystroke authentication with cognitive factors and the keystroke authentication without considering cognitive factors. It is found that the former is more accurate than the latter.
Discrete transfer function modeling of non-linear systems using neural networks

ANNs (Artificial Neural Networks) have been used successfully in various system identification applications. However, in general, the ANN-based identification is primarily nonparametric in nature, as the system information is hidden within the neural network architecture, which is not transparent enough to be modeled explicitly. In this paper, a series parallel neural network architecture has been proposed for parametric system identification, whose weights are optimized using adaptive learning through training data, collected from unknown nonlinear system. The optimized weights of the proposed ANN structure are then directly utilized to model the unknown system, in terms of an equivalent discrete transfer function model. The estimated model using proposed technique can be effectively utilized to analyze, test and control the unknown system dynamics. Simulation examples with measurement noise are used to test the effectiveness of proposed identification technique.
STAMP-Based Approach to Analyze Safety, Security and Data Privacy

Security has been of paramount importance to organizations since its lack can result in financial and reputational losses. Privacy is gaining attention because of the increasing legal protection of the right to data privacy. Due to their complexity, in terms of technology, sociology and law, assuring both security and privacy is a major challenge in the development of cyber-physical systems. In general, both security and privacy concerns are addressed by security countermeasures. There is no approach that employs the systems theory model to jointly identify and analyze security and privacy issues. STAMP is a causation model, based on systems theory, that allows analyzing emergent properties in the concept stage of system development. STPA is the tool based on STAMP to analyze safety. STPA has been employed to analyze more recently security. In this work, we propose an approach based on STAMP to analyze safety, security and privacy concerns jointly for cyber-physical systems. The approach uses attributes and threats of security and privacy to identify losses and hazards. We employ the approach in an example of electronic voting system development and we show that the approach is effective in identifying hazardous control actions.
Implementation of SHA-256 and AES-256 for Securing Digital Al Quran Verification System

Almazrooie verified digital Quran using 3 methods (SHA 256, RIPEMD 160, hexadecimal compression) which produced 3 database tables. However, there is no concern about security database. Hash value on database that is still stored plaintext can be changed by non-users. The vulnerability in the database can be solved by encrypting hash value using AES-256. In this study an Quran verification application was built using the SHA-256 and AES-256 hash values. Application development uses a research experiment Design Research Methodology with WebML modeling. The results of the development of the system were obtained by the digital mushaf reconciliation system which had passed the web testing from Kundu (2012), user acceptance tests, and collision value tests.
Safe Compilation for Hidden Deterministic Hardware Aliasing

Hardware aliasing occurs when the same logical address can access different physical memory locations. This is a problem for software on some embedded systems and more generally when hardware becomes faulty in irretrievable locations, such as on a Mars Lander. We show how to work around the hardware problem with software logic, compiling code so it works on any platform with hardware aliasing with hidden determinism. That means: (i) a copy of an address accesses the same location, and (ii) repeating an address calculation exactly will repeat the same access again. Stuck bits can mean that even adding zero to an address can make a difference in that environment so nothing but a systematic approach could work.
Improving Irregular Text Recognition by Integrating Gabor Convolutional Network

Scene text, especially irregular text, is difficult to recognize due to the arbitrary-oriented characters and irregular arrangement. Most existing methods address the irregular text by rectifying it into a regular one, which achieve good performance. However, these methods are possible to remove character information in some curved texts. To overcome this issue, we focus on extracting features that are robust to orientation changes instead of rectifying. In this work, we propose an end-to-end trainable model that combines a Gabor Convolutional Network (GCN) and a Sequence Recognition Network (SRN). The GCN is capable of extracting more robust features against the orientation, which is produced by incorporating Gabor filters of different orientations into Convolutional Neural Network (CNN). The SRN is an attention-based sequence-to-sequence model that sequentially outputs characters from the robust features. We evaluate the recognition accuracy of the proposed method on various benchmark datasets of scene text, including both regular and irregular texts. The extensive experimental results show that our proposed method achieves the state-of-the-art recognition performance on most of the irregular benchmarks as well as a regular benchmark.
Friendly and Progressive Visual Secret Sharing with Lossless Reconstruction

This paper presents a new simple and efficient technique for secret sharing. This technique inherits the effectiveness of Progressive Visual Secret Sharing (PVSS), in which the quality of recovered secret image is improved if more shared images are stacked together using the eXclusive-OR (XOR) operation. The proposed method also extends the usability of PVSS approach into the Friendly Visual Secret Sharing (PVSS). The content of shared image is converted into more friendly appearances rather than in the noise-like shared image. The proposed method offers promising result for grayscale and color image. It also produces recovered secret image in the lossless condition. The proposed method can be regarded as a good candidate for secret sharing.
Blur Detection in Identity Images Using Convolutional Neural Network

Today, the digital photos especially identity images are used in almost every form filling application. As user is uploading his/her photographs, the image quality issues will come up. Especially, if some automatic processing like face recognition is happening at back end. The image quality parameters include resolution/dimensions, size and blur. Apart form blur, rest can be checked by simple conditions. But, blur detection cannot be solved in a trivial way. Here we are proposing a deep learning based approach for detecting the blur in an image. We have prepared data comprising 250,000 identity images. Our aim is to provide a comprehensive list of clear and blur images before learning happens. Apart from naturally blurred images, we have added five types of artificial blur into the images for better generalization. We have done comparative analysis of our approach to statistical feature extractor i.e. BRISQUE which was trained on SVM. We have empirically selected the convolutional network with 4 layers. Performance evaluation shows the proposed approach is giving 98.05% on 113,000 images, and thus outperforming BRISQUE using SVM.
Smart Optimization of Energy Consumption using IoT

In any large educational organization, the energy devices in the classrooms and laboratories are manually operated and remain ON throughout the day. These rooms have predefined schedule and when the venues are sparingly used a lot of energy is wasted if the energy devices remain ON throughout the day. A study on the estimates of energy consumption in a laboratory using the prevailing system was carried out. The proposed approach exhibited significant reduction in the energy consumption by automatically switching the energy devices ON and OFF of each venue based on the time table. Such optimization of load scheduling with the timetable of the venue intends to conserve the energy consumption. Moreover, the status of the energy devices could be transmitted over the internet using IoT and remotely controlled by developing an App which provided with a mechanism for smart monitoring of energy devices. Furthermore, this system can be used for smart estimation of power consumption.
Multi-scale Generative Adversarial Learning for Facial Attribute Transfer

Generative Adversarial Network (GAN) has shown its impressive ability on facial attribute transfer. One crucial part in facial attribute transfer is to retain the identity. To achieve this, most of existing approaches employ the L1 norm to maintain the cycle consistency, which tends to cause blurry results due to the weakness of the L1 loss function. To address this problem, we introduce the Structural Similarity Index (SSIM) in our GAN training objective as the measurement between input images and reconstructed images. Furthermore, we also incorporate a multi-scale feature fusion structure into the generator to facilitate feature learning and encourage long-term correlation. Qualitative and quantitative experiments show that our method has achieved better visual quality and fidelity than the baseline on facial attribute transfer.
A Subspace Hierarchical Clustering Algorithm for Categorical Data

In this paper, we propose a soft subspace hierarchical clustering for dealing with categorical data. The proposed algorithm extends the traditional agglomerative hierarchical clustering approach for identifying clusters of categorical data in subspaces. The algorithm adopts a correlation-based approach for measuring the relevance of each categorical attribute during the clustering process. We performed experiments on six well-known datasets, comparing the performance of our algorithms with the original agglomerative algorithm for hierarchical clustering and other five partitional subspace clustering algorithms, using two well-known evaluation metrics: accuracy and f-measure. According to the experiments, the proposed algorithm outperforms the original one. Besides that, the proposed algorithm outperforms most of the partitional algorithms, while provides additional advantages.
Image Processing based Traffic Convex Mirror Detection

On the road, there is plenty of valuable information available which assists the driver to maneuver the vehicle by prioritising his safety. One amongst them is Traffic Convex Mirror. Traffic Convex Mirror are frequently found in sharp turnings, T-junctions and parking areas where there is a need for more visibility. Detection of such mirror and notifying the driver in prior will play a vital role in Advance Driver Assistance System(ADAS). As of today most of the driver assistance systems don't have such a system for their semi-automated or automated cars. That is to say that with the available detection systems it is not possible to extract information which is readily available for the driver. In this paper, a computer vision based solution is provided to address this concern. With the front ADAS camera it is possible to detect the convex mirror which is available on the roadside. This convex mirror provides information about incoming object or obstacles from another direction which is not in the direct field of view.
Design of Graph Filter Using Spectral Transformation and Window Method

In this paper, the design of graph filter using spectral transformation and window method is presented. First, the specification of graph filter is converted to the one of digital filter using spectral transformation. Then, the window method is employed to design the digital filter with converted specification. Next, the binomial series expansion is used to obtain the coefficients of the graph filter from those of digital filter. Finally, the numerical design examples are demonstrated to show the effectiveness of the proposed graph filter design method.
Augmented Intelligence

Smart systems make decisions incorporating data available from different sensing in a way to control and make smart actions. In this context, smart actions consist in augmenting user's actions and/or decisions by using devices or additional information. Those actions could and should be different from user to user, depending on its characteristics and needs. To obtain smart actions adapted to the users, it is necessary to detect the user's individualities on-the-fly. This chapter focuses on how augmented intelligence can leverage smart systems, addressing: (a) the definitions and relations of artificial intelligence, augmented intelligence, and smart systems, namely the state of the art on how to extract human features that can be used to develop augmented intelligent systems (using only computer vision methods); (b) a brief explanation of a “describing people integrated framework”, a framework to extract user information automatically without any user intervention; and (c) a description of several implemented smart systems, including a future work perspective.