Reviewerly/exHarmony huggingface dataset:
Full-Text and URL Search Over Web Archives

Web archives are a historically valuable source of information. In some respects, web archives are the only record of the evolution of human society in the last two decades. They preserve a mix of personal and collective memories, the importance of which tends to grow as they age. However, the value of web archives depends on their users being able to search and access the information they require in efficient and effective ways. Without the possibility of exploring and exploiting the archived contents, web archives are useless. Web archive access functionalities range from basic browsing to advanced search and analytical services, accessed through user-friendly interfaces. Full-text and URL search have become the predominant and preferred forms of information discovery in web archives, fulfilling user needs and supporting search APIs that feed complex applications. Both full-text and URL search are based on the technology developed for modern web search engines, since the Web is the main resource targeted by both systems. However, while web search engines enable searching over the most recent web snapshot, web archives enable searching over multiple snapshots from the past. This means that web archives have to deal with a temporal dimension that is the cause of new challenges and opportunities, discussed throughout this chapter.
Technological Populism and Its Archetypes: Blockchain and Cryptocurrencies

Blockchain technology claims to disrupt the existing financial system, the way of doing business, and to empower ordinary citizens against an elitist economy through decentralization of the decision-making process. In the political arena, the disruptive ideology branded as ‘populism’ challenges the neo-liberal establishment. By appealing to peoples’ fears, frustrations, and dissatisfaction with the political elites, exploiting distrust in the so-called establishment, populism claims to deliver more power to the people.In this article, we draw a parallel between core foundations of political populism and those of blockchain and propose a theory of technological populism. Technological populism as reflected by blockchain platforms exploits the rhetoric of empowering the disenfranchised through decentralized decision-making process, enabling anonymity of transactions, dehumanizing trust (promoting trust in computation rather than trust in humans and institutions) as well as breaking the monopoly in the financial system and money supply.  The rhetoric of empowering the disenfranchised against financial elites is not only propaganda but also a method of accumulating wealth for technocratic elites.Ultimately, the blockchain and cryptocurrency world has perfected what political populists have pioneered — unrealistic promises, turning the citizen against “the elites” only so long as they are not the elites in charge.
Pedestrian attribute recognition using trainable Gabor wavelets

Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We test our method on publicly available challenging datasets and demonstrate considerable improvements over state of the art approaches.
Out-of-the-Lab Research in Usable Security and Privacy

The COVID pandemic made it challenging for usable security and privacy researchers around the globe to run experiments involving human subjects, specifically in cases where such experiments are conducted in controlled lab setting. Examples include but are not limited to (a) observing and collecting data on user behavior with the goal of (b) informing the design and (c) engineering novel concepts based on adaptation and personalization as well as (d) evaluating such concepts regarding user performance and robustness against different threat models. In this keynote I will set out with providing a brief introduction to and examples on our research on behavioral biometrics. I will then discuss how the current situation influences research requiring close work with human subjects in lab settings and outline approaches to address emerging issues. Finally, I will provide some examples of out-of-the-lab research and reflect on both challenges and opportunities of these approaches.
Electromagnetic Analysis of an Ultra-Lightweight Cipher: PRESENT

Side-channel attacks are an unpredictable risk factor in cryptography. Therefore, continuous observations of physical leakages are essential to minimise vulnerabilities associated with cryptographic functions. Lightweight cryptography is a novel approach in progress towards internet-of-things (IoT) security. Thus, it would provide sufficient data and privacy protection in such a constrained ecosystem. IoT devices are resource-limited in terms of data rates (in kbps), power maintainability (battery) as well as hardware and software footprints (physical size, internal memory, RAM/ROM). Due to the difficulty in handling conventional cryptographic algorithms, lightweight ciphers consist of small key sizes, block sizes and few operational rounds. Unlike in the past, affordability to perform side-channel attacks using inexpensive electronic circuitries is becoming a reality. Hence, cryptanalysis of physical leakage in these emerging ciphers is crucial. Among existing studies, power analysis seems to have enough attention in research, whereas other aspects such as electromagnetic, timing, cache and optical attacks continue to be appropriately evaluated to play a role in forensic analysis. As a result, we started analysing electromagnetic emission leakage of an ultra-lightweight block cipher, PRESENT. According to the literature, PRESENT promises to be adequate for IoT devices, and there still seems not to exist any work regarding correlation electromagnetic analysis (CEMA) of it. Firstly, we conducted simple electromagnetic analysis in both time and frequency domains and then proceeded towards CEMA attack modelling. This paper provides a summary of the related literature (IoT, lightweight cryptography, side-channel attacks and EMA), our methodology, current outcomes and future plans for the optimised results.
An Awareness Epistemic Framework for Belief, Argumentation and Their Dynamics

The notion of argumentation and the one of belief stand in a problematic relation to one another. On the one hand, argumentation is crucial for belief formation: as the outcome of a process of arguing, an agent might come to (justifiably) believe that something is the case. On the other hand, beliefs are an input for argument evaluation: arguments with believed premisses are to be considered as strictly stronger by the agent to arguments whose premisses are not believed. An awareness epistemic logic that captures qualified versions of both principles was recently proposed in the literature. This paper extends that logic in three different directions. First, we try to improve its conceptual grounds, by depicting its philosophical foundations, critically discussing some of its design choices and exploring further possibilities. Second, we provide a (heretofore missing) completeness theorem for the basic fragment of the logic. Third, we study, using techniques from dynamic epistemic logic, how different forms of information change can be captured in the framework.
Online and Offline Hybrid Teaching in the Luminescent Materials and Applications Course

Due to the outbreak of COVID-19, colleges and universities have actively responded to the call of the state to carry out online teaching in completing their teaching tasks. After the epidemic, the online and offline hybrid teaching has become a novel mode of teaching which meets the requirements of teaching reform and the information society. This teaching method integrates both the online and offline teaching which plays an important role in enhancing teaching qualities and learning experiences. However, due to the lack of experience, there are some issues occurring in the teaching process of this method. The Luminescent Materials and Applications course is used as the subject in this article. The difficulties in traditional offline teaching as well as the advantages, detailed course construction process, and effectiveness evaluation of hybrid teaching are summarized and meticulously analyzed. In regard to that, the application of the online and offline hybrid teaching in the Luminescent Materials and Applications course is beneficial to the learning of professional knowledge and the cultivation of students’ scientific literacy. Therefore, it is an effective way to improve and enrich this course by using the online and offline hybrid teaching method.
Local available quantum correlations of X states: The symmetric and anti-symmetric cases

Local available quantum correlations (LAQC), as defined by Mundarain et al., are analyzed for two-qubit X states with local Bloch vectors of equal magnitude. Symmetric X-states are invariant under the exchange of subsystems, hence having the same Bloch vector. On the other hand, anti-symmetric X states have Bloch vectors with an equal magnitude but opposite direction. In both cases, we obtain exact analytical expressions for their LAQC quantifier. We present some examples and compare this quantum correlation to concurrence and quantum discord. We have also included Markovian decoherence, with Werner states under amplitude damping decoherence. As is the case for depolarization and phase damping, no sudden death behavior occurs for the LAQC of these states with this quantum channel.
Neural Network Compression Through Shunt Connections and Knowledge Distillation for Semantic Segmentation Problems

Employing convolutional neural network models for large scale datasets represents a big challenge. Especially embedded devices with limited resources cannot run most state-of-the-art model architectures in real-time, necessary for many applications. This paper proves the applicability of shunt connections on large scale datasets and narrows this computational gap. Shunt connections is a proposed method for MobileNet compression. We are the first to provide results of shunt connections for the MobileNetV3 model and for segmentation tasks on the Cityscapes dataset, using the DeeplabV3 architecture, on which we achieve compression by 28%, while observing a 3.52 drop in mIoU. The training of shunt-inserted models are optimized through knowledge distillation. The full code used for this work will be available online.
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks

Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis with graph neural networks. In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. An irredundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph-based tasks. We compare differences between concatenation methods on connecting embeddings between features and show that the simplest is the best. We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between structural features.
Team OmarRadi at the Mowjaz Multi-Topic Labelling Task

This paper describes the 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> winning system for the Mowjaz Multi-Topic Labelling Task [1]. The current research work discusses the models used for the experimentation that led to the proposed system and shows the models' performances on the task's dataset of Arabic Articles. The proposed system, which was a MLP system with a single hidden layer. It achieved an F1-Sample score of 0.8594, F1-Macro score of 0.8476, F1-Micro score of 0.8504 and an Accuracy score of 82.9 which published on the task's website which led to rank the model as 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> in the Mowjaz Multi-Topic Labelling Task [1].
An Improved Tracking Learning Detection Method Based on AKAZE Feature Points

In order to improve the robustness and tracking accuracy of the long-term target tracking learning detection (TLD) method, this paper proposes an improved TLD tracking algorithm based on AKAZE feature points. The idea is as follows. Firstly, a small number of uniform points and AKAZE feature points with scale invariance and rotation invariance are used to form a tracking point set instead of the uniform points in the TLD algorithm for tracking. Secondly, a positive and negative template deletion method is introduced. When a certain number of template libraries are accumulated, the similarity between the samples in the template library and the current tracking target is calculated, and a small number of templates with a small similarity are deleted after the similarity ranking. Finally, the effectiveness of the proposed method is illustrated by comparative experimental analysis. The experimental results show that the running time of the proposed method is similar to the original algorithm, the tracking accuracy is better, and the success rate is higher.
Metadata Made Easy: Develop and Use Domain‐Specific Metadata Schemes by following the dmdScheme approach

Abstract Metadata plays an essential role in the long‐term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain‐specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co‐development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet. We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain‐specific scheme. We describe how to create a domain‐specific scheme from this template, including the iterative co‐development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation. The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML. Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain‐specific schemes. A near‐future priority is to provide methods to interface domain‐specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.
Characterizing Resource Heterogeneity in Edge Devices for Deep Learning Inferences.

Significant advances in hardware capabilities and the availability of enormous data sets have led to the rise and penetration of artificial intelligence (AI) and deep learning (DL) in various domains. Considerable efforts have been put forth in academia and industry to make these computationally demanding DL tasks work on resource-constrained edge devices. However, performing DL tasks on edge devices is still challenging due to the diversity of DNN (Deep Neural Networks) architectures and heterogeneity of edge devices. This study evaluates and characterizes the performance and resource heterogeneity in various edge devices for performing DL tasks. We benchmark various DNN models for image classification on a set of edge devices ranging from the widely popular and relatively less powerful Raspberry Pi to GPU-equipped high-performance edge devices like Jetson Xavier NX. We also compare and contrast the performance of three widely-used DL frameworks when used in these edge devices. We report DL inference throughput, CPU and memory usage, power consumption, and frameworks' initialization overhead, which are the most critical factors for characterizing DL tasks on edge devices. Additionally, we provide our insights and findings, which will provide a better idea of how compatible or feasible edge devices are for running DL applications.
Simply Adjusting for Schedulers’ Bias in Estimated Case Durations Can Accomplish the Same Objectives of Improving Predictions as Use of Machine Learning—Reply

In Reply We appreciate Dexter and Epstein for their interest in our article. Prior to developing the randomized clinical trial,1 we explored different modeling approaches and data inputs. We selected the random forest model because it provided the best results on a testing set.
Fast Generation of RSA Keys Using Smooth Integers

Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30% in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems.
General Automation in Coq through Modular Transformations

Whereas proof assistants based on Higher-Order Logic benefit from external solvers' automation, those based on Type Theory resist automation and thus require more expertise. Indeed, the latter use a more expressive logic which is further away from first-order logic, the logic of most automatic theorem provers. In this article, we develop a methodology to transform a subset of Coq goals into first-order statements that can be automatically discharged by automatic provers. The general idea is to write modular, pairwise independent transformations and combine them. Each of these eliminates a specific aspect of Coq logic towards first-order logic. As a proof of concept, we apply this methodology to a set of simple but crucial transformations which extend the local context with proven first-order assertions that make Coq definitions and algebraic types explicit. They allow users of Coq to solve non-trivial goals automatically. This methodology paves the way towards the definition and combination of more complex transformations, making Coq more accessible.
Symmetrized persistency of Bell correlations for Dicke states and GHZ-based mixtures

Abstract Quantum correlations, in particular those, which enable to violate a Bell inequality, open a way to advantage in certain communication tasks. However, the main difficulty in harnessing quantumness is its fragility to, e.g, noise or loss of particles. We study the persistency of Bell correlations of GHZ based mixtures and Dicke states. For the former, we consider quantum communication complexity reduction (QCCR) scheme, and propose new Bell inequalities (BIs), which can be used in that scheme for higher persistency in the limit of large number of particles N . In case of Dicke states, we show that persistency can reach 0.482 N , significantly more than reported in previous studies.
A Preliminary Study on the Effect of Somatosensory Games upon Children’s Activity Space and Bodily Movements

With the progress of game technology, the interactions between people and input devices have become more diverse and versatile. Meanwhile, with the introduction of somatosensory games, the mode of interaction is no longer restricted to manual operation. Players can input operational instructions into the game through gestures or bodily movements; in addition, the demand for activity space is different from that in the past. When operating game consoles, players not only pay attention to the distance between their eyes and monitors but also need a proper space to move around. Therefore, besides exploring the correlation between bodily activity space and virtual space, this study probes the influence of somatosensory dance games on children's bodily movements. The findings show that by performing dances with the help of somatosensory games, children can understand the dance movements in the games and improve their spatial presence. Moreover, the ages of children have a close relationship with the execution of basic bodily movements. Lastly, children in the youngest group mainly employ the upper body while dancing. The findings can be used as a reference for game development.
Graph Adversarial Learning for Noisy Skeleton-based Action Recognition

Skeleton based action recognition is playing a critical role in computer vision research, its applications have been widely deployed in many areas. Currently, benefiting from the graph convolutional networks (GCN), the performance of this task is dramatically improved due to the powerful ability of GCN for modeling the Non-Euclidean data. However, most of these works are designed for the clean skeleton data while one unavoidable drawback is such data is usually noisy in reality, since most of such data is obtained using depth camera or even estimated from RGB camera, rather than recorded by the high quality but extremely costly Motion Capture (MoCap) [1] system. Under this circumstance, we propose a novel GCN framework with adversarial training to deal with the noisy skeleton data. With the guiding of the clean data in the semantic level, a reliable graph embedding can be extracted for noisy skeleton data. Besides, a discriminator is introduced such that the feature representation could further improved since it is learned with an adversarial learning fashion. We empirically demonstrate the proposed framework based on two current largest scale skeleton-based action recognition datasets. Comparison results show the superiority of our method when compared to the state-of-the-art methods under the noisy settings.