Reviewerly/exHarmony huggingface dataset:
Gesture-Controlled System Using Convolutional Neural Network

Gesture recognition is a hot topic in computer science which deals with the detection, classification, and interpretation of human gestures via mathematical/machine learning models. Humans use bodily gestures to convey various emotions or messages. These gestures mainly originate from face and hands. Therefore, there arises an idea that if it is possible to interact or control the devices without physically touching them but by just using our gestures. Many approaches have been made using computer vision algorithms to interpret sign language. Gesture recognition can be reckoned as a groundbreaking method for computers to comprehend human body language, thus building a richer bridge between machines and humans than primitive interfaces, which limits the majority of input to input devices. Using the concept of gesture recognition, it is possible to control devices effortlessly. This project aims to explore the use of touchless interaction within a device, allowing applications to be controlled and manipulated without contact, through the use of camera-based gesture recognition technology. In particular, the project seeks to understand the challenges of these environments for the design and deployment of such systems, as well as articulate how these technologies may alter the multimedia system. While our primary concerns here are effortless control of the devices, the use of these touchless gesture-based technologies offers other potential uses as well.
The Co-ChiLeRFE: Couple LBP and LTP Methods of Children-Learning Readiness Using Facial Expression

Children’s emotions can affect the learning process, especially positive emotions, making them more focused on learning. In addition, in terms of identifying someone’s emotions, we can represent them through facial expressions by combining the local binary pattern (LBP) and the local ternary patterns (LTP) method, known as Co-ChiLeRFE. The reasons for combining the two methods are that the LBP has proven to be very good at performing feature extraction, especially in describing textures. At the same time, LTP is adept at dealing with uniform motifs such as those on the face area. Subsequently, in this study, we used the NIMH child emotional faces picture set (NIMH-ChEFS), which has five-class expressions: sad, neutral, happy, angry, and afraid. To achieve optimal results in the Co-ChiLeRFE method, we set the LBP parameter as P = 8, R = 8, and the LTP parameter threshold value of one. The results we got from this experiment achieved a system performance superior accuracy of 92.51%.
Empirical Study of Data-Free Iterative Knowledge Distillation

Iterative Knowledge Distillation (IKD) [] is an iterative variant of Hinton’s knowledge distillation framework for deep neural network compression. IKD has shown promising model compression results for image classification tasks where a large amount of training data is available for training the teacher and student models. In this paper, we consider problems where training data is not available, making it impractical to use the usual IKD approach. We propose a variant of the IKD framework, called Data-Free IKD (or DF-IKD), that adopts recent results from data-free learning of deep models []. This exploits generative adversarial networks (GANs), in which a readily available pre-trained teacher model is regarded as a fixed discriminator, and a generator (a deep network) is used to generate training samples. The goal of the generator is to generate samples that can obtain a maximum predictive response from the discriminator. In DF-IKD, the student model at every IKD iteration is a compressed version of the original discriminator (‘teacher’). Our experiments suggest: (a) DF-IKD results in a student model that is significantly smaller in size than the original parent model; (b) the predictive performance of the compressed student model is comparable to that of the parent model.
A Framework for Modeling Critical Success Factors in the Selection of Machine Learning Algorithms for Breast Cancer Recognition

Analysis of critical success factors allows software development organizations to focus on the factors to be successful. Selecting and implementing an algorithm for bosom cancer recognition could be hard. In this paper, a framework for modeling and analysis of success factors for the selection of Machine Learning methods used for the recognition of bosom cancer is presented. The objective is to analyze critical success factors in Machine Learning techniques selection for bosom cancer recognition built on Fuzzy Mental Maps. A group of common ML algorithms is presented in conjunction with the success factors. An analysis through measures calculation is presented in a case study. It was concluded that relevant factors for the selection of ML algorithms in the recognition of bosom cancer are: Selection of an ML algorithm according to the results, the study of ML algorithms tested in bosom cancer, obtaining and analyzing algorithm results.
Improved unsupervised domain adaptation network based on category attention

Abstract Domain adaptation method can significantly reduce the distribution difference between images in variant domains, which plays an important role in unsupervised medical image segmentation. In this paper, an improved unsupervised domain adaptation framework is proposed based on category attention mechanism. The framework considers both image-level and feature-level alignment, and realizes semantic segmentation in different domains through a shared encoder-decoder. A category attention based classifier is proposed to compute the category attention feature and refine the semantic segmentation prediction. Sufficient experiments on MMWHS-2017 dataset indicate that the proposed method achieves the best segmentation performance among all comparasion algorithms.
Infrared and optical image matching method based on illumination robust SIFT

In the process of matching optical and infrared images, the gray scale correlation of the two images is low, and the gradient direction of the same key point neighborhood is also different, resulting in the failure of the point feature description and matching. Therefore, a new illumination robust SIFT model is proposed. In the feature detection stage, the CSS-DoG detector is used to extract CSS corner points on the edge by use of the similar features of the two image edges, which is an effective supplement to the DoG detector. In the feature description, the gradient direction correction function is defined to enhance the correlation between the two image gradients. Experiment results show that the illumination robust SIFT model is suitable for multi-mode image matching of optical and infrared images.
"Frontline Mediators" an ethnographic study of online welfare applications at the public library

This paper examines the work of "frontline mediators," a term used by public librarians to describe the practice of facilitating the use of online welfare applications. As welfare agencies closed their offices and required clients to access services online, public librarians operating in low-income communities adopted new practices to facilitate their clients’ use of web applications for welfare and other relevant public services. Pulling from interviews and observations from a multi-year ethnographic study, I discuss the emergence of frontline mediators, and examine the key practices and attributes involved with their work. Frontline mediators care for their clients and their clients’ information needs. Thus, I argue the idea of caring relationships should be at the center of public service design strategy. The frontline mediator case study offers relevant lessons for human-computer interaction and design researchers interested in broader questions of democracy, civic engagement, and public service.
Fight Detection in Images Using Postural Analysis

This paper defines the research process that has been carried out to develop a system for detecting fights in images. The system takes input frames and evaluates the probability that the frame contains one or more people fighting. Input frames containing images are initially processed using a well-known neural architecture, called OpenPose, which extracts pose information out of images that contain human postures. Human posture data is then processed by heuristics to extract both: angles for arms and legs for each person in the image. Angles are then used to feed an additional neural network that has been trained to make probability predictions of people being potentially involved in fights. This paper describes the full pipeline regarding techniques, tools and assessment required to create a camera based violence detection system.
Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

Data reduction and image compression techniques in the present Internet and multi-media age are essential to increase image and video capacity in relation to memory, network bandwidth use and safe data transmission. There have been a different variety of image compression models with varying compression efficiency and visual image quality in the literature. Vector Quantization (VQ) is a widely used image coding scheme that is designed to generate an efficient coding book that includes a list of codewords that assign the input image vector to a minimum distance of Euclidea. The Linde–Buzo–Gray (LBG) historically widely used model produces the local optimal codebook. The LBG model's codebook architecture is seen as an optimization challenge that can be resolved using metaheuristic algorithms. In this perspective, this paper introduces a new DPIO algorithm for codebook generation in VQ. The model presented uses LBG to initialise the DPIO algorithm to construct the VQ technique and is called the DPIO-LBG process. The performance of the DPIO-LBG model is validated with benchmark data and the effects of different performance aspects are investigated. The simulation values showed the DPIO-LBG model to be efficient interfaces of compression efficiency and image quality reconstructed. The presented model produces an efficient codebook with minimum computational time and a better signal to noise ratio (PSNR).
Performance Evaluation of Hyperledger Fabric Retrofitted by SM2/3/4

The underlying cryptography suite of Hyperledger Fabric only supports international cryptographic algorithm, and does not support Chinese commercial cryptographic algorithm SM2/3/4, which prevents the promoting of Hyperledger Fabric in China. To further promote the application of Hyperledger Fabric in China, we retrofit the underlying cryptography suite of Fabric v1.4.3 by Chinese commercial cryptographic algorithm SM2/3/4, called Fabric-SM, and then evaluate the performance of Fabric v1.4.3 and Fabric-SM in various aspects, such as transaction throughput, execution time and transaction latency. The experimental results show that the performance of Fabric-SM is close to that of Fabric v1.4.3. Without any degradation in performance, Fabric-SM fully meets the requirements of Chinese government on the legality of cryptography. In a word, developers can develop various applications of Permissioned Blockchain based on Fabric-SM under the condition of safety and legality.
Adversarial Command Detection Using Parallel Speech Recognition Systems

Personal Voice Assistants (PVAs) such as Apple’s Siri, Amazon’s Alexa and Google Home are now commonplace. PVAs are susceptible to adversarial commands; an attacker is able to modify an audio signal such that humans do not notice this modification but the Speech Recognition (SR) will recognise a command of the attacker’s choice. In this paper we describe a defence method against such adversarial commands. By using a second SR in parallel to the main SR of the PVA it is possible to detect adversarial commands. It is difficult for an attacker to craft an adversarial command that is able to force two different SR into recognising the adversarial command while ensuring inaudibility. We demonstrate the feasibility of this defence mechanism for practical setups. For instance, our evaluation shows that such system can be tuned to detect 50% of adversarial commands while not impacting on normal PVA use.
Facial Expression Transfer from Video Via Deep Learning

The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck musculoskeletal complex. Specifically, leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activations for the biomechanical model. Through biomechanical simulation, the activations deform the face, thereby transferring the expression to the model. The success of our approach is demonstrated through experiments involving the transfer of a range of expressive facial images and videos onto our biomechanical face-head-neck complex.
Refining the Semantics of Epistemic Specifications

Answer set programming (ASP) is an efficient problem-solving approach, which has been strongly supported both scientifically and technologically by several solvers, ongoing active research, and implementations in many different fields. However, although researchers acknowledged long ago the necessity of epistemic operators in the language of ASP for better introspective reasoning, this research venue did not attract much attention until recently. Moreover, the existing epistemic extensions of ASP in the literature are not widely approved either, due to the fact that some propose unintended results even for some simple acyclic epistemic programs, new unexpected results may possibly be found, and more importantly, researchers have different reasonings for some critical programs. To that end, Cabalar et al. have recently identified some structural properties of epistemic programs to formally support a possible semantics proposal of such programs and standardise their results. Nonetheless, the soundness of these properties is still under debate, and they are not widely accepted either by the ASP community. Thus, it seems that there is still time to really understand the paradigm, have a mature formalism, and determine the principles providing formal justification of their understandable models. In this paper, we mainly focus on the existing semantics approaches, the criteria that a satisfactory semantics is supposed to satisfy, and the ways to improve them. We also extend some well-known propositions of here-and-there logic (HT) into epistemic HT so as to reveal the real behaviour of programs. Finally, we propose a slightly novel semantics for epistemic ASP, which can be considered as a reflexive extension of Cabalar et al.'s recent formalism called autoepistemic ASP.
Rethinking Adversarial Examples Exploiting Frequency-Based Analysis

Deep neural networks (DNNs) have been recently found vulnerable to adversarial examples. Several previous works attempt to relate the low-frequency or high-frequency parts of adversarial inputs with the robustness of models. However, these studies lack comprehensive experiments and thorough analyses and even yield contradictory results. This work comprehensively explores the connection between the robustness of models and properties of adversarial perturbations in the frequency domain using six classic attack methods and three representative datasets. We visualize the distribution of successful adversarial perturbations using Discrete Fourier Transform and test the effectiveness of different frequency bands of perturbations on reducing the accuracy of classifiers through a proposed quantitative analysis. Experimental results show that the characteristics of successful adversarial perturbations in the frequency domain can vary from dataset to dataset, while their intensities are greater in the effective frequency bands. We analyze the obtained phenomena by combining principles of attacks and properties of datasets and offer a complete view of adversarial examples from the frequency domain perspective, which helps to explain the contradictory parts of previous works and provides insights for future research.
A High-Precision Lens Distortion Correction Method for Vision Measurement

The deviation caused by lens manufacturing and assembling will inevitably lead to imaging distortion of the vision system. For high-precision vision measurement tasks, the lens distortion must be accurately corrected. Aiming at the application of planar object geometry measurement, an efficient lens distortion correction method is proposed, it can obtain the distortion parameters of each pixel and improve the correction accuracy through multiple measurements. A chessboard plate is placed on the measurement plane and captured, the map from pixel coordinates to chessboard coordinates is calculated for each image. Using affine model, chessboard coordinates under several random movements are warped to the original coordinate system, and then amortization error method is applied on these frames of images to achieve higher correction accuracy.
DualLink: Dual Domain Adaptation for User Identity Linkage Across Social Networks

User Identity Linkage (UIL) across social networks can be used to identify the accounts belonging to the same individual in multiple social networks, which is of great significance for user behavior analysis and network security supervision. By integrating deep learning into the process of UIL, the original intrinsic features of users can be retained more completely. However, the data distribution among different networks is different, and the existing UIL methods often ignore these differences, or only adjust a single step by domain adaptation mechanism, so it is difficult to ensure the accuracy of UIL. In this paper, we propose a new UIL model (called DualLink) which uses a dual domain adaptation mechanism to solve the problem of inconsistent data distribution. On one hand, a node embedding method based on adversarial domain adaptation is proposed to learn the node representation by considering the attributes, the topological structure and the difference between domains. On the other hand, a node matching method based on back-propagation domain adaptation is proposed to learn the suitable matching function by using back propagation neural network (BPNN). The feasibility and effectiveness of the key technology proposed in this paper are verified by experiments.
Fundamental Concepts and Applications of Blockchain Technology

Blockchain is the most influential technology when the Internet was invented. In the due course of time, the researcher’s focus shifted to explore the hallucinations of technology and it has a wide range of impacts on many applications including finance, medicine, logistics, supply chain, currencies, education, music, voting, and identity management. Blockchain, a digital database, is a combination of cryptography and peer-to-peer networking and can be identified as distributive ledger technology. The key concept of blockchain technology is to protect the distributed ledger. The major benefits of this technology are to reduce the complexity of time, space, and cost. Blockchain is the most prominent technology for efficient financial, consistent trading process and it also improves the authorization control, since the transactions are immutable, secured, and updated immediately within the peers in the network. Over a period blockchain is gaining momentum in the Internet systems to address global business challenges and to sustain environmental advances. Privacy and security factors play an important part in the execution of blockchain technology. Privacy Leakage, Scalability, and Selfish Mining are some of the major challenges in blockchain and have been the emphasis of both industry experts and academic researchers. In this chapter, we present a complete perspective about blockchain technology, security risks, and challenges that prevail due to the vulnerability of being a popular technology. We summarize the empowering technologies for being scalable, privacy-free, and honest mining blockchain systems. Finally, we discuss various applications, where blockchain technology is genuinely used.
Multi-scale Feature Fusion Network for Blind Deblurring

Blind deblurring is a challenging problem as the blur kernel is unknown. In recent years, most blind deblurring methods are based on deep neural networks, but their models cannot effectively utilize the information of multi-scale features. To solve these problems, we propose a new deblurring model. The main structure of this model is an encoder-decoder network, which produces feature maps with multiple spatial scales through several down-sampling and up-sampling operations. Generally speaking, feature maps with different sizes have different receptive fields, and they contain different information. In order to make use of the information in these features, we propose a cross-scale feature fusion module (CSFFM) to fuse feature maps of different spatial sizes. Besides that, we also propose a multiscale convolution block (MSCB), which aggregates multi-scale features at the same spatial size. These two modules can effectively learn the features of different receptive fields and fuse the information of multi-scale features. Moreover, we design a multiscale reconstruction decoder (MSR-Decoder) to restore the blurred image from coarse to fine. Our method was evaluated quantitatively and qualitatively with other methods on the GOPRO dataset. The proposed method has higher values of metrics (PSNR, SSIM) and better perceptual quality.
Integrating Guided Anchoring into One-stage Object Detectors

In this paper, we investigate how to effectively integrate Guided Anchoring into one-stage object detectors. Integrating directly guided anchoring scheme may lead to two immediate difficulties, inconsistency between feature map and shape of anchors, and more serious objective imbalance due to introducing more objectives at this time. To mitigate these issues, we propose an adaptive one-stage detector (AOSD) based on guided anchoring with two novel components: shaping convolutional neural network (SCN) and bent L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss. The former component focuses on adapting the original feature map by generated various anchors. The latter component can bend gradients of inliers to re-balance involved objectives. Benefited from the two proposed components, AOSD keeps the advantage of being good at large object detecting, meanwhile especially enhances small object detection and accurate localization. Our evaluations show that the proposed AOSD may perform more accurately and more efficiently than the corresponding strong baselines, achieving 37.4 average precision (AP) points with a small feature extractor ResNet50.
Hilbert-Peano and Zigzag: Two Approaches Mapping Pattern of Digital Watermarking for Text Images Authentication

The wide increment of information and communication technology nowadays in line with the usage of digital documents, the user from different organizations such as education, military, medical, business, and others tend to transfer any official file through various digital platforms. Thus, to secure this confidential data, a digital watermarking technique was chosen. This paper proposed an improved mapping pattern method of a fragile watermarking authentication algorithm for text images. There are various methods for watermark embedding, which mapping pattern is one of them. The aim is to validate and compare the SCAN pattern for digital watermarking in order to produce a fast and efficient authentication algorithm. We proposed a Zigzag SCAN pattern algorithm and compared it with the proposed Hilbert-Peano scheme. The result from this paper shows that Zigzag SCAN algorithm contributed to a superior performance in regard to processing time, while PSNR and MSE are similar.