Reviewerly/exHarmony huggingface dataset:
Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.
Evading Capital Controls via Cryptocurrencies: Evidence from the Blockchain

How pervasive is the use of cryptocurrencies to evade capital controls? We develop a new method that exploits blockchain data to identify cross-border capital flows that circumvent controls via cryptocurrencies. Applying the method to China, we find that the aggregate volume of capital flight is over a quarter of Chinese Bitcoin exchange volume. Capital flight from China is positively associated with Chinese economic policy uncertainty and the Bitcoin premium in Chinese Yuan, inconsistent with triangular arbitrage. Individuals engaging in capital flight are less likely to use Bitcoin to trade illegal goods or services, suggesting that capital flight has different motivations to other criminal activity. Our findings suggest that cryptocurrencies indeed provide a new way of circumventing capital controls.
Predicting Change Prone Classes in Open Source Software

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.
EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks

Recent work has shown how easily white-box adversarial attacks can be applied to state-of-the-art image classifiers. However, real-life scenarios resemble more the black-box adversarial conditions, lacking transparency and usually imposing natural, hard constraints on the query budget.
Politics of Inviting: Co-Articulations of Issues in Designerly Public Engagement

A crucial but little-explored practice in design anthropology is the crafting of invitations: invitations to user studies, to interviews, to ethnographic fieldwork projects, to co-design workshops, to prototyping and to public engagement events. This chapter engages with the politics of inviting by proposing a shift in what we invite to, and when. Rather than inviting stakeholders to participate in design projects before use, it argues for the value of inviting participants to take part in co-articulations of issues that arise in the course of the ongoing living with technologies. The chapter also argues that the issues and potentialities that emerge as things are used can never be fully predicted in a design project, whether by designers or potential users. It describes this practice of inviting to collaborative formations of issues connected to the ongoing living with technologies designerly public engagements.
On the Possibilities of Evaluating Properties of Scientific Documents on the Basis of their Citations Count (or again: What Property is Reflected by Citations Count par excellence, after all?). Part 2: Quality?

The objective is consideration of the recent works on the problem of the phenomenon reflected by citations count of scientific documents with a slant to the papers analyzing the citations count possibilities to reflect quality of cited research. The method: authoritative definitions of quality were under analysis and interpretation of corresponding research publications in the context of the results the mentioned analysis was undertaken. The results: citations count does not reflect quality of cited research as there are not causal relations between quality and citedness. The conclusions: since in practice the results of the citations count for documents value assessment and of peer assessment of their quality may mostly coincide, this makes it possible still to use citations count as a proxy indicator of quality. As for the "negative" citations, they seem to refer to cited items of high value, but of low quality.
Intelligent Expert Decision Support Systems

In this chapter, the authors present Intelligent Expert Decision Support Systems (IEDSSs) technology and conceptual models of Expert systems(ES) for Human-Operator (H-O) of different areas and Air Navigation System (ANS) too. The authors demonstrate some interesting applications of IEDSS. Intelligent Expert Decision Support Systems technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology .Researchers have been used the AI concepts and theories to develop a robust generation of IEDSSs. Moreover, the convergence of AI technologies and web technologies (WT) is enabling the creation of a new generation of web-based IEDSSs for all domains and tasks. This chapter discusses the AI methodologies and techniques for developing the IEDSSs. Two most popular paradigms are discussed namely; case-based reasoning and ontological engineering. Moreover, the chapter addresses the challenges faced by the application developers and knowledge engineers in developing and deploying AI-based expert decision support systems. In addition, the chapter presents some examples of ES by the author and colleagues at National Aviation University, Ukraine and some cases of IEDSSs developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.
Introduction to Human-Computer Interaction

The objective of this course is to provide newcomers to Human-Computer Interaction (HCI) with an introduction and overview of the field. Attendees often include practitioners without a formal education in HCI, and those teaching HCI for the first time. This course includes content on theory, cognition, design, evaluation, and user diversity.
Revisiting Anonymity and Privacy of Bitcoin

Since its introduction, Bitcoin has received extensive attentions. With features like privacy and anonymity, Bitcoin has been widely used. However, the mainstream finance and business worlds have not fully embraced Bitcoin yet. This study analyzes the assumption of anonymity and privacy of Bitcoin, give a threat model to Bitcoin privacy. Then present a comprehensive overview on practical attacks on anonymity and privacy of Bitcoin and proposals to improve anonymity of Bitcoin. Compare the state of-the-art proposals that address the privacy threats and enables strong privacy in Bitcoin. Finally, future research directions on privacy of Bitcoin are discussed.
Multi-target Joint Tracking and Classification Using the Trajectory PHD Filter

To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter. The JTC-TPHD filter classifies different targets based on their motion models and each target is assigned with multiple class hypotheses. By using this strategy, we can not only obtain the category information of the targets, but also a more accurate trajectory estimation than the traditional TPHD filter. The JTC-TPHD filter is derived by finding the best Poisson posterior approximation over trajectories on an augmented state space using the Kullback-Leibler divergence (KLD) minimization. The Gaussian mixture is adopted for the implementation, which is referred to as the GMJTC-TPHD filter. The L-scan approximation is also presented for the GM-JTC-TPHD filter, which possesses lower computational burden. Simulation results show that the GM-JTC-TPHD filter can classify targets correctly and obtain accurate trajectory estimation.
Dissecting Representations

Choosing effective representations for a problem and for the person solving it has benefits, including the ability or inability to solve it. We previously devised a novel framework consisting of a language to describe representations and computational methods to analyse them in terms of their formal and cognitive properties. In this paper we demonstrate the application of this framework to a variety of notations including natural languages, formal languages, and diagrams. We show how our framework, and the analysis of representations that it enables, gives us insight into how and why we can select representations which are appropriate for both the task and the user.
Estimating Image Quality for Person Re-Identification

Task-based image quality is an important component in designing a real-life analytics system, and has been studied in many fields such as biometric recognition and pedestrian detection. Person re-identification (re-id) is an application in surveillance where one attempts to identify a person after they leave the surveillance system and then re-enter it. As a newer field in recognition applications, re-id lacks image quality-related research. In this paper, we propose an unsupervised and automated quality measure for the query images used in re-id, which we call "identifiability". Images that are less identifiable are more challenging for any re-identification system, creating output results that would likely be less reliable. Our proposed method measures feature consistency in the presence of perturbations as an indicator of identifiability. We then introduce two evaluation protocols for such a quality measure. We demonstrate our proposed quality measure is effective at ranking an image’s usefulness to a recognition system, and at identifying an image’s robustness against further compression.
Spam Detection on Social Media Using Semantic Convolutional Neural Network

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.
A Survey of Research on Image Style Transfer Based on Deep Learning

The explosive growth of graphics card computing power is accompanied by the rise of deep learning, and the development of style transfer has also ushered in a new stage. And this technology is widely used in all aspects of life. This paper firstly introduces the traditional image style transfer method, and then focuses on the image style transfer method based on deep learning that has emerged in recent years. These image style transfer methods, based on convolutional neural network and generative adversarial network, are introduced respectively. Finally, some problems that exist in the current image style transfer and future development trends are discussed.
A Blockchain Scheme Based on DAG Structure Security Solution for IIoT

In recent years, with the increase of industrial volume, problems such as difficult management and low efficiency have become obstacles to the development of industry. Many studies have shown that industry can complete the automation of industry by combining with Internet of Things, that is, Industrial Internet of Things (IIoT), which will contribute to the secondary development of industry. However, in IIoT, there are also malicious attacks against industrial devices or sensors, such as DDoS, Sybil, etc., which will cause great security risks to the system. As a popular security trust framework, blockchain technology will face the dilemma of low throughput and limited energy if it is directly applied in the IoT devices. Based on this, in view of the existing IIoT security problems and the difficulties of traditional blockchain based on chain application, this paper proposes a blockchain security solution based on Directed Acyclic Graph(DAG) structure for IIoT, and combines differential privacy technology to further ensure the privacy and integrity of data. In order to ensure the stability and anti-interference ability of the network, this paper also proposes a load balancing algorithm, which effectively balances the relationship between node power consumption and network lifetime. Finally, the simulation results show that the proposed scheme is superior to the chain-blockchain scheme in network performance and power consumption. The average network delay is reduced by 24%, and the average network lifetime is increased by 48%.
Beyond Homeostasis: A Novel Approach for Assessing the Stability and Coherence of Self-Adaptive Systems

The increasing complexity and coupling of technical devices and systems leads to challenges in controllability and administrability. Self-adaptive and self-organising solutions attempt to address these challenges by autonomously altering the system's behaviour at runtime, with a focus on the interaction of (partially) autonomous, decentralised units. To achieve the most efficient overall system control, we need a suitable quantification of the observed behaviour. In this paper, we present a contribution to an integrated measurement framework that measures the coherence of configurations. We investigate the behaviour of the metrics in example applications and compare it with alternative methods such as the homeostasis metric.
WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.
Research on Autonomous Obstacle Avoidance and Target Tracking of UAV Based on Improved Dueling DQN Algorithm

In order to overcome the shortcomings of slow convergence, low success rate and sub-optimal tracking path of deep reinforcement learning algorithms in solving the problems of unmanned aerial vehicle (UAV) autonomous obstacle avoidance and target tracking. This paper adopts Dueling Deep Q-network (Dueling DQN) algorithm with improved network structure based on the Deep Q-network (DQN) algorithm, combined with ε-inspire exploration strategy and experience replay buffer idea, a Multi Pool Dueling Deep Q-network (MP-Dueling DQN) algorithm is proposed to optimize the success rate and path of UAV target tracking. Furthermore, UAV is endowed with the ability of environment perception, and the environmental generalization ability of UAV is improved by designing a continuous reward function. The simulation results show that MP-Dueling DQN algorithm has shorter motion path, stronger environmental adaptability and better performance compared with DQN, Double Deep Q-network (DDQN) and Dueling DQN.
Machine Learning

The field of machine learning is introduced at a conceptual level. Ideas such as supervised and unsupervised as well as regression and classification are explained. The tradeoff between bias, variance, and model complexity is discussed as a central guiding idea of learning. Various types of model that machine learning can produce are introduced such as the neural network (feed-forward and recurrent), support vector machine, random forest, self-organizing map, and Bayesian network. Training a model is discussed next with its main ideas of splitting a dataset into training, testing, and validation sets as well as performing cross-validation. Assessing the goodness of the model is treated next alongside the essential role of the domain expert in keeping the project real. The chapter concludes with some practical advice on how to perform a machine learning project.
Avoiding Overfitting of Decision Trees

This chapter begins by examining techniques for dealing with clashes (i.e. inconsistent instances) in a training set. This leads to a discussion of methods for avoiding or reducing overfitting of a decision tree to training data. Overfitting arises when a decision tree is excessively dependent on irrelevant features of the training data with the result that its predictive power for unseen instances is reduced. Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff. A method of post-pruning a decision tree based on comparing the static and backed-up estimated error rates at each node is also described.
Scallywag Pedagogy

This chapter explores the dynamic between truth and deceit in twenty-first-century transnational capitalism, emerging neo-fascist movements, and post-truth media landscapes marked by the Covid-19 pandemic and the anthropogenic bioinformational challenge. It establishes the centrality of the concept of truth in revolutionary critical pedagogy and underscores the importance of linking true words with true actions in the formation of critical praxis. Revolutionary praxis consists of the dialectical process of self and social formation, while critical educators are situated as protagonistic agents who work in and through history. Truth is therefore not about a timeless or objective state we name history. Action creates history, humans are historical beings, and truth is firmly situated within the dialectic of history. Using the work of Walter Benjamin, we show that aesthetic entertainment provided by fascist national leaders such as Donald Trump distracts the oppressed from the economic and social forms of oppression, supporting asymmetrical relations of power and privilege that repurposes the dominion of the ruling class. In response, we develop the concept of scallywag pedagogy: postdigital, ontological, epistemological, historical, and revolutionary praxis aimed at ambushing oppressive power relations and transforming the world in the interests of social justice through speaking a true word.
Recognizing Camera Wearer from Hand Gestures in Egocentric Videos

Wearable egocentric cameras are typically harnessed to a wearer's head, giving them the unique advantage of capturing their points of view. Hoshen and Peleg have shown that egocentric cameras indirectly capture the wearer's gait, which can be used to identify a wearer based on their egocentric videos. The authors have shown a wearer recognition accuracy of up to 77% over 32 subjects. However, an important limitation of their work is that such gait features can be extracted only from walking sequences of a wearer. In this work, we take the privacy threat a notch higher and show that even the wearer's hand gestures, as seen through an egocentric video, leak wearer's identity. We have designed a model to extract and match hand gesture signatures from egocentric videos. We demonstrate the threat on the EPIC kitchen dataset containing 55 hours of the egocentric videos acquired from 32 subjects doing various activities. We show that: (1) Our model can recognize a wearer with an accuracy of up to 73% based on the same activity, i.e., the model has seen 'cut' activity by a wearer in the train set, and recognizes the wearer based on another 'cut' activity by him/her while testing. (2) The hand gesture signatures transfer across activities, i.e., even if our model does not see 'cut' activity of a wearer at the train time, but sees other activities such as 'wash', 'mix' etc., the model can still recognize a wearer with an accuracy of up to 60%, by matching hand gesture signatures of 'cut' at test time with train time signatures of 'wash' or 'mix'. (3) The hand gesture features even transfer across subjects, i.e., even if the model has not seen any activity by some subject, one can still verify a wearer (open-set), and predict that the same wearer has performed both activities with an Equal Error Rate of 15.21%. The code, trained models are available at https://egocentricbiometric.github.io/