Reviewerly/exHarmony huggingface dataset:
Research on the Application of Face Deep Learning Technology in University Management

Abstract In the current machine learning methods, deep learning is the focus of attention. Deep learning technology has achieved rapid development in various related fields, especially in the field of face recognition applications. Deep learning is to imitate the mechanism of the human neural perception system by layer-by-layer autonomous learning to obtain high-level abstract features, which can solve the distribution of facial changes, with fast learning speed and high recognition accuracy. This article introduces the advantages and core technologies of face deep learning, and studies and analyzes the application of face deep learning technology in university management.
FPGA Implementation of True Random Number Generator Architecture Using All Digital Phase-Locked Loop

This study is a unique approach for the design and implementation of True Random Number Generator (TRNG) using ADPLL, on Field-Programmable Gate Array (FPGA) board Artrix-7 (XC7A35T-CPG236-1) and the simulation was done on Vivado v.2015.2 design suite. TRNG is solely based on the different seeds of entropy like Jitter, and metastability was produced from Ring Oscillator, Flip Flop (FF) and other primitives. In this paper, we have realized and implemented two architectures based on the use of ADPLL. TRNG with single ADPLL is represented as Novel design-1 (ND-1) and TRNG with two ADPLL as Novel design-2 (ND-2) cascading with other primitive like ring Oscillator combined with FF. Different from other approaches, this proposed TRNG architecture has higher speed, consumes less power in spite of employing 2 Look-Up-Tables (LUTs) and 1 slice block without compromising the overall throughput producing at 680.7 Mbps for ND-1 (Single ADPLL) and 676 Mbps for ND-2 (Two ADPLL). Comparing with other existing designs in the Field of TRNG and found out to have higher throughput and less power consumption, less complexity by employing a reduced FPGA hardware resource. Digital storage oscilloscope (DSO) is used to capture output waveform and FFT waveform for both ND-1 (single ADPLL) and ND-2 (two ADPLL). The randomness of the generated bitstream output of the design architecture is validated by passing the NIST SP 800-22 test which evidences that the proposed ADPLL-based TRNG can be better suited for different industrial applications such as security Network system, cybersecurity, Banking security, IIOT, IOT.
FREEDOM OF EXPRESSION AND SOCIAL COERCION

ABSTRACT Much legal and philosophical work has been devoted to discussing the importance of protecting freedom of expression from legislative curtailment by the state. That state-centric focus has meant that the ways that wider social phenomena can stifle freedom of expression have, with a notable exception, escaped sustained philosophical attention. The paper reflects on the nature of socially coercive restrictions on free expression and offers an account of how it is appropriate to respond to such forms of social coercion. First, it considers a range of social costs pertaining to expression and argues that such costs can constitute meaningful restrictions on the freedom to express. Second, it reflects on the normative implications concerning that threat to free expression and defends two related moral duties citizens have to refrain from being complicit in unjustified social coercion—a duty of expressive toleration and a duty of respect for expressive agency.
A Heterogeneous Face Recognition Via Part Adaptive And Relation Attention Module

In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The illumination difference between NIR and visible-light (VIS) images causes a domain gap, and the variations in pose and emotion also make facial matching more difficult. Since heterogeneous face recognition (HFR) has difficulties in domain discrepancy, many studies have focused on extracting domain-invariant features, such as facial part relational information. However, when pose variation occurs, the facial component position changes and a different part relation is extracted. In this paper, we propose a part relation attention module that crops facial parts obtained through a semantic mask and performs relational modeling using each of these representative features. Furthermore, we suggest component adaptive triplet loss using adaptive weights for each part to reduce the intra-class distance regardless of the domain as well as pose. Finally, our method exhibits a performance improvement in the CASIA NIR-VIS 2.0 [1] and achieves superior results in the BUAA-VisNir [2] with large pose and emotion variations.
Multi-Camera Trajectory Forecasting with Trajectory Tensors

We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras. While multi-camera setups are widespread for applications such as surveillance and traffic monitoring, existing trajectory forecasting methods typically focus on single-camera trajectory forecasting (SCTF), limiting their use for such applications. Furthermore, using a single camera limits the field-of-view available, making long-term trajectory forecasting impossible. We address these shortcomings of SCTF by developing an MCTF framework that simultaneously uses all estimated relative object locations from several viewpoints and predicts the object's future location in all possible viewpoints. Our framework follows a Which-When-Where approach that predicts in which camera(s) the objects appear and when and where within the camera views they appear. To this end, we propose the concept of trajectory tensors: a new technique to encode trajectories across multiple camera views and the associated uncertainties. We develop several encoder-decoder MCTF models for trajectory tensors and present extensive experiments on our own database (comprising 600 hours of video data from 15 camera views) created particularly for the MCTF task. Results show that our trajectory tensor models outperform coordinate trajectory-based MCTF models and existing SCTF methods adapted for MCTF. Code is available from: https://github.com/olly-styles/Trajectory-Tensors.
“Please Connect Me to a Specialist”: Scrutinising ‘Recipient Design’ in Interaction with an Artificial Conversational Agent

This paper explores how callers formulate information enquiries for an artificial conversational agent in a call centre and compares it with the way enquiries are addressed to human operators of the same call centre. It includes 60 call recordings with human operators and 103 call recordings with the artificial conversational agent, transcribed and analysed using the method of Conversation Analysis. We show that people formulate and reformulate their enquiries differently to an artificial agent, even though the goal in both cases is to get an answer to the same enquiry. When talking to the artificial conversational agent, callers produce short enquiries, similar to web searches. When connected to human operators, callers formulate longer enquiries which include many details. By analysing the differences in the way callers formulate their enquiries to robots and human operators, we show what callers expect artificial conversational agents to process. These expectations affect the way the enquiry is formulated and, as a result, operators and artificial agents encounter different types of problems they have to repair to understand the question correctly and find an answer to it. Our findings have interesting implications for Human Computer Interaction both in terms of “robot-recipient design” and “user-recipient design”.
Construction of Two Way Interactive and Cooperative Teaching Mode between College English Teachers and Students under the Network Autonomous Learning Platform

With the development of Internet plus, the infiltration of Internet in education has had a great impact on the form and method of education. Classroom teaching from closed to open, from real-time to anytime, blended learning has become a new trend of learning mode in Colleges and universities. As a part of blended learning, network collaborative learning has also attracted the attention of scholars. In this paper, the English subject is tested and analyzed under the network cooperative learning based on genetic algorithm. The test shows that 29.3% of the speeches belong to shallow interaction, 46.6% belong to moderate interaction, and 24.1% belong to deep interaction.
Image restoration: Structured low rank matrix framework for piecewise smooth functions and beyond

Recently, mapping a signal/image into a low rank Hankel/Toeplitz matrix has become an emerging alternative to the traditional sparse regularization, due to its ability to alleviate the basis mismatch between the true support in the continuous domain and the discrete grid. In this paper, we introduce a novel structured low rank matrix framework to restore piecewise smooth functions. Inspired by the total generalized variation to use sparse higher order derivatives, we derive that the Fourier samples of higher order derivatives satisfy an annihilation relation, resulting in a low rank multi-fold Hankel matrix. We further observe that the SVD of a low rank Hankel matrix corresponds to a tight wavelet frame system which can represent the image with sparse coefficients. Based on this observation, we also propose a wavelet frame analysis approach based continuous domain regularization model for the piecewise smooth image restoration. Finally, numerical results on image restoration tasks are presented as a proof-of-concept study to demonstrate that the proposed approach is compared favorably against several popular discrete regularization approaches and structured low rank matrix approaches.
Data Selection in Neural Networks

In the era of big data, profitable opportunities are becoming available for many applications.As the amount of data keeps increasing, machine learning becomes an attractive tool to analyze the information acquired.However, harnessing meaningful data remains a challenge.The machine learning tools employed in many applications apply all training data without taking into consideration how relevant are some of them.In this paper, we propose a data selection strategy for the training step of Neural Networks to obtain the most significant data information and improve algorithm performance during training.The approach proposes a data-selection strategy applied to classification and regression problems leading to computational savings and classification error reduction.Based on open datasets, including a deep neural network case, the examples corroborate the effectiveness of the proposed approach.
Tracking Visual Object As An Extended Target

Most visual object tracking (VOT) algorithms treat the object as a single point in the output score map, and the bounding box is estimated by a multi-scale search. Further, most of them are based on the concept of tracking-by-detection, which is focused on the detection step and ignores the tracking step and object’s dynamics. In this paper, we address these limitations by developing a new VOT framework. Instead of a point object, we mathematically model the shape of the extended visual object as an ellipse. We allow multiple detections in the score map, and derive an elliptical gating method to discard possible clutters. We apply a sophisticated extended target tracking algorithm to track the object’s kinematic state and shape simultaneously. Experiment results are provided to show that the proposed algorithm outperforms several state-of-the-art methods.
Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.
Number Theoretic Transform Architecture suitable to Lattice-based Fully-Homomorphic Encryption

The Number Theoretic Transform (NTT) plays a central role for supporting high-performance polynomial multiplication on Post-Quantum Cryptography (PQC) and Fully-Homomorphic Encryption (FHE). This paper proposes a novel Montgomery-based butterfly to efficiently implement NTTs on FPGAs. This proposal is supported on prime moduli suitable for FHE, which minimizes the requirements to allow the speedup of the computation of the butterfly. A search algorithm is presented to select these moduli, while flexibility is a target in all parameters making the proposed architectures well-suited for FHE and PQC schemes. We experimentally evaluate the effectiveness of the novel butterfly-core on a Xilinx Virtex-7 device. The results show reductions up to 19%, 41%, 37%, and 67% in the number of lookup tables, slices, flip-flops, and Digital Signal Processors (DSPs), respectively, in comparison to the related state of the art. By integrating the proposed butterflies in a complete NTT accelerator, a speedup of up to 1.42 is achieved, while less than half of the number of DSPs are required, when compared to the other proposals. Moreover, the integration of the proposed accelerators to design FHE-based processors is discussed.
Optical Flow Estimation Via Motion Feature Recovery

Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning. We call this phenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local motion tends to be highly consistent within a short temporal window, we propose a novel iterative Motion Feature Recovery (MFR) method to address the vanishing cost volume via modeling motion consistency across multiple frames. In each MFR iteration, invalid entries from original motion features are first determined based on the current flow. Then, an efficient network is designed to adaptively learn the motion correlation to recover invalid features for lost-information restoration. The final optical flow is then decoded from the recovered motion features. Experimental results on Sintel and KITTI show that our method achieves state-of-the-art performances. In fact, MFR currently ranks second on Sintel public website.
Marker-Less Augmented Reality System for Home Interior and Designing

This paper inspects importance of augmented reality (AR) technology and 3D interior models for designing purpose. We are launching augmented reality technology for interior and exterior design. Architectural projects also require virtual information. This paper represents the use of AR technology for interior and exterior designing. AR helps in various fields like digital architectural design and construction of buildings. Finally, the purpose of this technology is to apply new way of AR to the work in interior designing, where user can observe virtual design and transform a 3D virtual environment design using information, with a active and supple user interface Can interact easily. To solve this issue, we have evolved a technique that automatically computes all the appropriate point to increase the understanding of the location design and allow the user to easily understand to this approach. This paper helps to improve and evaluate the design project; a complex decoration modeling structure is created to ensure that the user can connect with every points of the project design to achieve a perfect coordination between structure and Function. Extended materials and receptive info of adorning materials-designing-tools for simple designing be modeled using special stereoscopic tools as prototypes for the AR3D Interior design in the ARID (augmented reality interior design) technique to increase customer conception and involvement in a personalized interior and exterior design project. Hence this Technique helps to understand the use of new appearance of virtual reality (VR) application called Augmented Reality.
A Rule-Based Bengali Grammar Checker

Natural language processing or computational linguistics is a subfield of Artificial intelligence that is growing very fast. Unfortunately, there has not been much work done in terms of the Bengali language in this sector. There have been works done on spell checking, Text to Speech (TTS), and Optical Character Recognition (OCR) for Bengali. Since there has not been any significant work on Bengali grammar checking, we have built a system for checking the grammar of Bengali text. To check the grammar of Bengali sentences, we have written a few grammar rules considering the fundamental dependencies of different parts of speech on each other. We ran our grammar program on the sentences split into words and tagged using pre-trained parts of speech (POS) tagger. This paper describes the grammatical rule checking and POS tagging to identify grammatical errors and pointing out misplaced words in Bengali sentences.
Hardware-Based Document Image Thresholding Techniques Using DSP Builder and Simulink

This paper presents the basic thresholding techniques using Simulink and DSP builder tool for removing the show-through noise from Document images using Altera Cyclone® V FPGA kit. Initially, show-through noise from the document images was removed using the thresholding method in the OpenCV environment and compared the results with the FPGA results. Then, we created a library file for Cyclone® V 5CSEMA5F31C6N series, as board block in DSP builder. We have designed, developed and implemented four methods of thresholding techniques using Simulink and DSP builder. The OpenCV software and Altera DE1-SoC board hardware results were compared qualitatively and quantitatively. DSP builder and Simulink make complex tasks like image processing and reduce the complications in designing the structure of algorithm, which pave the way for hardware co-simulation.
Detection and classification of phishing websites

In the direct simulation Monte Carlo method and multi-phase flow calculations, structured and unstructured grid systems for complex geometries necessitate the time-consuming process of searching a large number of cells to position target particles.
Blockchain Applications and Peer-To-Peer Tradings

Blockchain can be considered as a very successful technique that can simplify the metering and billing systems for Peer-to-Peer (P2P) energy trading. Blockchain decentralised ledger technology’s promising features indicate its feasibility to meet the prevailing challenges to current power structures, as we review in the Open Digital Innovation Hub (ODIH). This study examines the Blockchain technology, smart contracts, the application of P2P energy trading from hardware, software, regulatory perspectives within ODIH, a self-sustaining smart home project. We show promising results in terms of the future potential of decentralised structures offered by Blockchain technology, which includes a reliable and transparent trading infrastructure for P2P energy trade. In ODIH, we presented our proposed business architecture design and implementation results for Smart Home System based on the Ethereum Blockchain. We implemented a smart contract using Solidity language to handle and record the transactions between the homeowners and Ethereum miners. ODIH shows that the integration of Blockchain technology in the hybrid P2P electricity market has a positive impact.
Video Summarization With Frame Index Vision Transformer

In this paper, we propose a novel frame index vision transformer for video summarization. Given training frames, we linearly project the content of the frames to obtain frame embedding. By incorporating the frame embedding with the index embedding and class embedding, the proposed frame index vision transformer can be efficiently and effectively applied to learn the importance of the input frames. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning methods including recurrent neural network (RNN) and convolutional neural network (CNN) based methods in both of the SumMe and TVSum datasets. In addition, our method can achieve real-time computational efficiency during testing.
EDNets: Deep Feature Learning for Document Image Classification Based on Multi-view Encoder-Decoder Neural Networks

In document analysis, text document images classification is a challenging task in several fields of application, such as archiving old documents, administrative procedures, or security. In this context, visual appearance has been widely used for document classification and considered as a useful and relevant features for the classification. However, visual information is insufficient to achieve higher classification rates, where relevant additional features, including textual features can be leveraged to improve classification results. In this paper, we propose a multi-view deep representation learning which allows combining textual and visual-based information respectively measured through the text and visual document images. The multi-view deep representation learning is designed to find a deeply shared representation between textual and visual features by fusing them into a joint latent space where a classifier model is trained to classify the document images. Our experimental results demonstrate the ability of the proposed model to outperform competitive approaches and to produce promising results.
Image-to-image translation for improvement of synthetic thermal infrared training data using generative adversarial networks

Training data is an essential ingredient within supervised learning, but time consuming, expensive and for some applications impossible to acquire. A possible solution is to use synthetic training data. However, the domain shift of synthetic data makes it challenging to obtain good results when used as training data for deep learning models. It is therefore of interest to refine synthetic data, e.g. using image-to-image translation, to improve results. The aim of this work is to compare different methods to do image-to-image translation of synthetic training data of thermal IR-images using generative adversarial networks (GANs). Translation is done both using synthetic thermal IR-images alone, as well as including pixelwise depth and/or semantic information. To evaluate, we propose a new measure based on the Frechet Inception Distance, adapted to work for thermal IR-images. We show that by adapting a GAN model to also include corresponding pixelwise depth data to each synthetic IR-image, the performance is improved compared to using only IR-images.