Reviewerly/exHarmony huggingface dataset:
Dynamic classification approach using scalable ensemble of autoencoders to classify data with drift

Abstract The problem of classification under concept drift conditions is investigated. The importance of anomaly detection is emphasized as a key feature of successful approach to operate with adversarial attacks and data poisoning. An approach to classification in the context of both drift and anomalies is introduced. It is based on ensemble of one-class classifiers, implemented by neural network autoencoders. Numeric parameters and supplementary logic are also supposed to distinguish between different classification cases. The quality of classifiers is estimated by original characteristics (EDCA), which examine both training set area and the area around it. The proposed approach is evaluated on synthetic data to highlight its properties in various conditions including normal, drift, new class and anomaly cases.
Numerical Analysis of Voice Activity Detection and Interactive Cloud Computing System in Language Education Application

With the further development of computer network technology, cloud computing technology has gradually become popular, affecting all aspects of people's lives. Some advantages of cloud computing can well make up for the shortcomings and deficiencies of existing teaching methods in the current college English teaching, open a wider space for English teaching, and realize accompanying ubiquitous learning. Based on the speech activity detection algorithm and interactive cloud computing system, this paper studies the scalability and applicability of online language education, and obtains a numerical analysis of algorithms and cloud computing in language teaching.
VTON-HF: High Fidelity Virtual Try-on Network via Semantic Adaptation

The image-based virtual try-on network transfers the target garment item to the corresponding region of the human body. Due to its commercial value in online garment shopping, it has attracted extensive attention from researchers. However, the previous virtual try-on methods are interfered heavily by garments in reference images, so they have defects in maintaining details of human upper limbs, neck, and given garment. Therefore, a novel High Fidelity Virtual Try-on Network via Semantic Adaptation (VTON-HF) is proposed to generate a result with better details. The main processes are as follows: 1) Thin Plate Spline (TPS) warps the target garment coarsely, 2) parsing network generates a target semantic map with the coarse warped garment, 3) our novel Semantic Map-based Image Adjustment Network (SMIAN) generates components separately to avoid interference between image parts with different semantics, 4) SMIAN fuses all components to generate the final result. VTON-HF can retain the maximum amount of detail in the reference garment than previous methods. Our novel architecture generates desired results by fusing separately generated components (garment, upper limb, and neck) and unchanging parts of the reference image. Moreover, our SMIAN incorporates a priori multimodal information in the tail layer, which effectively improves the convergence efficiency of the network. Our method achieves state-of-the-art quantitative results on IS, SSIM, PSNR, and FID using the VITON dataset. (see Fig. 1).
EPSim-GS: Efficient and Privacy-Preserving Similarity Range Query over Genomic Sequences

Similarity query over genomic sequences has played a significant role in personalized medicine and has applications in various fields, including DNA alignment and genomic sequencing. Since handling genomic sequences requires massive storage and considerable computational capacity, service providers prefer to process similarity queries over genomic sequences on cloud servers rather than at the client side. Due to the sensitivity of genomic sequences, preserving the privacy of queries has attracted considerable attention, and as a result, genomic sequences are demanded to be outsourced in an encrypted form. Although many schemes have been proposed for similarity queries over encrypted genomic data, they are either inefficient or have limitations in supporting the dynamic update of the dataset. To address the challenges, we propose an efficient and privacy-preserving similarity range query scheme, namely EPSim-GS. First, we introduce how to build a hash table to index the dataset, and present a similarity range query algorithm based on the hash table. Then, we design two cloud-based privacy-preserving protocols based on the Paillier cryptosystem to support the similarity range query algorithm over the encrypted dataset. After that, we propose EPSim-GS by leveraging the two privacy-preserving protocols. We then analyze the security of EPSim-GS and prove that it is privacy-preserving. Finally, we perform experiments to evaluate the scheme’s performance, and the results indicate that it is computationally efficient.
Multi-angle Face Recognition Based on GMRF

The general face recognition methods mostly use positive face images or with a small angle of deflection. Such databases are well established. But in real life, face recognition problems often do not happen to be positive face state, especially in monitoring and public security monitoring. The existing effect of multi-angle face recognition is not ideal, and this paper proposes a face recognition method based on Gaussian Markov Random Fields (GMRF). GMRF model is a statistical probability model that can effectively extract image texture information, which first divides the face image into several sub-blocks in different chunks, and then, for each sub-block under the block mode, extracts the GMRF feature after wavelet transformation; finally combines the GMRF features of different blocking methods, and then classifies the SVM of the Gaussian nuclear function. Experiments were carried out on the self-built data set, and the proposed method reached 98.83% for face recognition.
Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Over the last two decades, we have witnessed a fundamental transformation of the advertising industry, which has been steadily moving away from the traditional advertising mediums, such as television or direct marketing, towards digital-centric and internet-based platforms. Unfortunately, due to its large-scale adoption and significant revenue potential, digital advertising has become a very attractive and frequent target for numerous cybercriminal groups. The goal of this study is to provide a consolidated view of different categories of threats in the online advertising ecosystems. We begin by introducing the main elements of an online ad platform and its different architecture and revenue models. We then review different categories of ad fraud and present a taxonomy of known attacks on an online advertising system. Finally, we provide a comprehensive overview of methods and techniques for the detection and prevention of fraudulent practices within those system—both from the scientific as well as the industry perspective. The main novelty of our work lies in the development of an innovative taxonomy of different types of digital advertising fraud based on their actual executors and victims. We have placed different advertising fraud scenarios into real-world context and provided illustrative examples thereby offering an important practical perspective that is very much missing in the current literature.
Quantum uncertainty relations of Tsallis relative α entropy coherence based on MUBs

Abstract In this paper, we discuss quantum uncertainty relations of Tsallis relative α entropy coherence for a single qubit system based on three mutually unbiased bases. For <?CDATA $\alpha \in \left[\tfrac{1}{2},1\right)\cup (1,2]$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> <mml:mo>∈</mml:mo> <mml:mfenced close=")" open="["> <mml:mrow> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>∪</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">]</mml:mo> </mml:math> , the upper and lower bounds of sums of coherence are obtained. However, the above results cannot be verified directly for any <?CDATA $\alpha \in \left(0,\tfrac{1}{2}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> <mml:mo>∈</mml:mo> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> </mml:mrow> </mml:mfenced> </mml:math> . Hence, we only consider the special case of <?CDATA $\alpha =\tfrac{1}{n+1}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> </mml:math> , where n is a positive integer, and we obtain the upper and lower bounds. By comparing the upper and lower bounds, we find that the upper bound is equal to the lower bound for the special <?CDATA $\alpha =\tfrac{1}{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> </mml:math> , and the differences between the upper and the lower bounds will increase as α increases. Furthermore, we discuss the tendency of the sum of coherence, and find that it has the same tendency with respect to the different θ or φ , which is opposite to the uncertainty relations based on the Rényi entropy and Tsallis entropy.
Enhanced Single Shot Multiple Detection for Real-Time Object Detection in Multiple Scenes

CNN-based object detection architectures have achieved great performances in recent times using SSD, YOLO, and R-CNN. However, using these algorithms for real-time detection suffer from low FPS and accuracy. In this paper, we enhanced the conventional SSD as research has shown that it has higher FPS and accuracy compared to others making it more suitable for real-time object detection. However, this conventional SSD suffers computational complexity and low accuracy for small objects detection. We proposed an enhanced SSD for real-time object detection to improve the accuracy of conventional SSD and reduce its computational complexity with a higher FPS. Our main contribution is at the level of the multi-scale object detection, where we implemented PIV layers for enhanced localization and detection of objects in the feature layers. Furthermore, we introduced extended dilated convolutions with different dilation operations thereby increasing the receptive field and improved the detection of objects. To demonstrate the effectiveness of our proposed method, we first carried out experiments on PASCAL VOC 2007 and PASCAL VOC 2012 and achieved improved performances in mAP of 82.0 and mAP of 85.6 on PASCAL VOC 2007 and PASCAL VOC 2012 respectively at 63 FPS, with input size of 300x300 for a batch size of 8. Using the same experimental approach, we further demonstrated the versatility of the proposed method on the underwater image dataset where we achieved also improved performance in mAP of 79.1. Our experimental results have shown to be an effective alternative for real-time objection detection to the conventional SSD and other state-of-the-art architectures.
Multi Features-based Baseband Modulation Classification using Support Vector Machine

This research discusses the implementation of machine learning for modulation classification purpose. In order to proof the concept, 6 types of modulation have been selected, i.e., BPSK, QPSK, 8-PSK, 16-QAM, BFSK, and 8-PAM. Machine learning algorithm that was used in this research is support vector machine (SVM) and implemented using MATLAB’s classification learner. Data sets were generated using an ADALM-PLUTO SDR, and processed at baseband frequency range. Regarding the input predictors to the SVM algorithm, this research proposes multi classification features, such as wavelet transform-based, spectral-based, and higher-order statistical-based features. SVM algorithm obtained a classification-rule model with 91.4% of accuracy without any optimization applied.
Analysis of MIA GMDH as a self-organizing deep neural network

In this paper, most popular deep feed-forward deterministic supervised neural networks are considered and the multilayered iterative GMDH algorithm as a self-organizing deep neural network is analyzed. Brief comparison of main features of the GMDH neural network and some other deep feed-forward neural networks are also given.
Custom Deep Learning Face Recognition based on Tensorflow Model for Attendance Management System

Face recognition technology has many implementation roles in the attendance management system. Attendance systems need proper solutions to detect a face in real-time situations using a particular purpose device. Face recognition systems can differentiate human faces based on face features trained in the deep learning model. Although significant advances in face recognition can increase the variety of face conditions in detection, some challenges still exist in face recognition that makes the existing model need to be taken apart and reparametrized. Challenge comes from lighting condition, blurred condition, also face tilt position. This research design a superficial layer of convolutional neural network using a built-in Tensorflow sequential model library. Generally, a transfer learning mechanism is used in object detection, especially in face recognition. This research doesn't use transfer learning because the accuracy of an existing model like the InceptionV1 model gives good accuracy in cross-validation training but gives a significant error in testing the trained face. The attendance management system was built in Flask Web Framework because developed in a Python language environment. The accuracy of the custom model has an average of 88.23%, which is tested with 16 different students, with each student having 48 pictures.
Fine-grained recognition based on improved EfficientNet model

Due to subtle differences between fine-grained images, ordinary CNN models cannot extract key features well when directly applied to fine-grained image classification, leading low recognition accuracy. To solve this problem, based on the EfficientNet-B3 model proposed by Google, we add a spatial attention module before the GAP (Global average pooling) layer and use transfer learning to build the improved recognition model taking CUB-200-2011 datasets as the validation object. The test results show that the recognition accuracy of our improved model is about 84.5%, which is 2.9% higher than the original network. Moreover, compared with the VGG16 model and the Resnet50 model, our improved model has lower complexity and better recognition accuracy.
Families’ influence on romantic relationship and its reconstruction

Families’ influence on romantic relationship is investigated from theoretical and practical perspectives. Theoretically, an ordinary differential equation based model is proposed to describe the romantic relationship between two partners, where the influence received by each individual from its family is taken into account. The introduction of the families’ opinions lead to rich and interesting structure in the dynamical process. With the decreasing response of a partner to its own family, two bifurcations occur, separating the dynamical behavior into three types, i.e., the transition from damping oscillation to one of four stable states, two of stable states, and limit cycles. These findings are explained with the stability analysis of equilibrium points. Practically, for each individual the opinion from its partner’s family is an interesting but hidden variable. The reservoir computing is adopted to discover the hidden variable from the activities of the individual, its family, and its partner. The model and the discovering method can be extended easily to investigate the relationship between two social groups such as the lateral negotiation, where the two representatives play game under the guidance from their own groups each.
Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion in advance. Looking into the problem definition, we propose an Encoder-Decoder based Long-Short Term Memory (LSTM) model to more accurately capture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct input to predict the future user movement. Then, this prediction model is combined with a rate adaptation approach that assigns the bitrates to various tiles for 360-degree video frames under a given network capacity. Hence, our proposed work aims to facilitate improved system performance when QoE parameters are jointly optimized. Some experiments were carried out and compared with existing work to prove the performance of the proposed model. Last but not least, the experiments implementation of our proposed work provides high user's QoE than its competitors.
Designing a virtual reality programming environment for quantum computers

Abstract Quantum computation based on quantum physics is spreading rapidly with many interdisciplinary studies from machine learning to genetics. After the unveiling of real quantum computers, this process has been rising almost exponentially. We are about to experience an effect with quantum computers similar to the effect created by the process from the first electromechanical computers to very large‐scale integrated circuit computers. In the programming of quantum computers, we are in a similar place to programming classical computers at an early stage. There are various models in quantum computing, and the most widely used model is quantum gates. Therefore, an easy and practical understanding of the behavior of the gates used in the programming of quantum computers is of great importance. To realize this process, a virtual reality (VR)‐based teaching platform of quantum computer programming called QU‐VR Education Tool was designed by using Unity 3D VR. In the creation of this design, IBM's so‐called Quantum Composer platform and Microsoft Quantum Katas were taken as a reference. In this study, the design process and usage of a novel VR‐based platform are presented in detail.
STIP: A new model of trusted network

We present STIP, a new model for Scalable Trusted IP networks, that are secure and resilient to cyber-attacks without impairing reliability. STIP addresses managed network use-cases including enterprise network authentication and authorization, and ISP use-cases, including trust based routing, and application aware networking. It can provide an enabling infrastructure that improves resilience to the painful BGP hijacking and distributed denial of service attacks.At the data plane, STIP consists of a trusted forwarding engine, that uses authenticated trust extensions to process traffic reliably. At the control and management plane STIP divides the network into trust domains that evaluate trustworthiness of devices in the domain, and distribute it securely using transitive trust. Our vision is Internet-wide STIP deployment . We present a migration process based on trust domains that can be used to gradually upgrade current IP networks to STIP.
ROS-Based Global Path Planning for Autonomous Ground Robot Using the Pre-Built Map of the Environment

Optimal global pathway planning has been a great area of research for many years and continues to develop new approaches. Without a suitable global path, a robot can get stuck in local minima. For path planning, a map is required which has been developed implying different sensors by moving the robot manually in an environment that is troublesome, costly, time taken to process, and lacks accuracy. This paper suggests a way to utilize pre-built maps of the environment. All the environments are built according to the structural map, we aim to use the pre-built map for path planning which is faraway accurate and detailed even to the extent of millimeters. We are introducing an approach that converts the pre-built map into a robot understandable format through image processing. The superiority and effectiveness of our approach have been verified using Robot Operating System (ROS) packages. ROS-supported path planning algorithms are applied on pre-built maps and results are compared with the result of the same approaches on sensor maps which produce better results in terms of pathway length and time taken.
Automatic Text Summarization using Word Embeddings

As the amount of textual data on the internet continues to grow at a breathtaking pace, the need for a means to condense this data into smaller packets of more readily analysable/consumable information is obvious. To spend man-hours on manually performing this task would be counter-intuitive. A means of automatically producing summaries of texts using word embeddings is explored here. Word embedding refers to a set of techniques which map words from a textual corpus onto a numeric vector space, as per their context, thereby capturing an abstract sense of ‘word meaning’ in the vector space. The goal of this project is to usefully apply the concept of Word Embeddings to the task of Automatic Text Summarization, and to review existing techniques.
Mapping Solar Energy Potential Zones using Remote Sensing based Solar Data

The cause of this thesis is to analyze and meet Indian Army requirements in power consumption in the pursuit of improved renewable power technology and throughout Indian Army installations in Leh Ladakh. This would facilitate in making the military installations energy independent from power distribution companies (DISCOMS) and to meet operational requirements with efficiency without any disruptions from fossil fuels-based power plants. Use of Remote Sensing and GIS to evaluate solar irradiation and economic viability of the project with the supervision of Solar Energy Corporation of India (SECI) will achieve a long-term objective of Energy Security of various Military installations in remote areas and under adverse climatic conditions in Off Grid/ Grid connected solar power projects.Firstly, we decide installation-specific power consumption, useful resource requirements, and modern renewable electricityera projects as subsidized by the SECI, Government of India. Second, we analyze Indian Army set up in Energy portfolios in Leh Ladakh in view of abrogation of article 370 and increased ease and availability of land acquisition towards infrastructuredevelopment especially in view of Works of Defense Act (1903).