Reviewerly/exHarmony huggingface dataset:
Jacobi-Style Iteration for Distributed Submodular Maximization

This article presents a novel Jacobi-style iteration algorithm for solving the problem of distributed submodular maximization, in which multiple agents determine their strategies from the private sets so that a global, nonseparable submodular objective function is jointly maximized. Building on the multilinear extension of the submodular function, we expect to achieve the solution from a probabilistic, rather than deterministic, perspective, and thus, transfer the considered problem from the discrete domain into the continuous domain. Since it is observed that an unbiased estimation of the gradient of multilinear extension function can be obtained by sampling the agents’ local strategies, a projected stochastic gradient algorithm is proposed to solve the problem. Our algorithm enables simultaneous updates among all individual agents and guarantees to converge asymptotically to a desirable equilibrium solution. Such an equilibrium solution is shown to be at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/2$</tex-math></inline-formula> suboptimal, which is comparable to the state-of-art in the literature. The convergence rate, which is characterized by the running average of gradient mapping, is proved to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(1/T)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the number of iterations. Moreover, we further enhance the proposed algorithm by handling the scenario in which agents’ communication delays are present. The enhanced algorithm admits a more realistic distributed implementation of our approach. Finally, a movie recommendation task is conducted on a real-world movie rating dataset to validate the numerical performance of our algorithms.
Speculative Energy Futures

Our technological landscape is becoming increasingly populated with systems characterized by their distributed intelligence and agency, as well as their networked capabilities. Systems such as these are especially critical in the domain of renewable energy infrastructures. User's access to energy will fluctuate according to the availability of renewable energy sources at that moment, for example: is it sunny or windy, is this a period of peak demand? These distributed systems present critical challenges to existing human-technology practice, expectations, relations, and dynamics. Similarly, they challenge human-centric design paradigms. To address these challenges and leverage the opportunities for designers, we see the need to build competences and methodologies in HCI and design research that extend past human-centered design approaches. In this workshop we will employ speculative design strategies to envision energy infrastructures that are not human-centric. Together we will identify the critical questions and design considerations necessary for facilitating the transition to renewable energies.
How can governments nudge students to become ebook readers? Evidence from Indonesia

Purpose The purpose of this study is to investigate if and how government intervention can nudge students to become ebook readers. Design/methodology/approach A cross-sectional survey research design was adopted for this study. A total of 1,144 students from four middle and high schools in urban and rural areas of Indonesia participated in this study. The results from statistical analyses were further discussed through the lens of the nudge theory. Findings This paper founds evidence that government intervention in the form of the Buku Sekolah Elektronik (BSE) policy that has been providing free electronic textbooks for more than a decade can help nudge students to become ebook readers. After controlling for student’s demographic information, this paper founds that their awareness of such a policy is significantly associated with a stronger preference toward ebooks while having no significant effect on their preferences toward printed book format. This paper also founds that mobile device adoption plays an important role where early adopters tend to prefer ebook format, whereas laggards are more associated with printed book format. Originality/value Many have studied the benefits of using ebooks in learning, but the literature also shows that most students still prefer reading printed books over ebooks. This is true not only in developing countries where problems with infrastructures can hamper the adoption of ebooks in general but also in developed countries where ebooks are much more prevalent, even among the general population. This paper showed how government interventions have the potency to help tip the scales and nudge students to become ebook readers.
Detecting objects in drone imagery: a brief overview of recent progress

Detecting objects on unmanned aerial vehicle (Drones) imagery is a challenging task and an underresearched problem that lately receives more and more attention in the research community. When shooting with a drone, not only the weather and the light conditions change, but the shooting height and angle change as well because the position of the camera is not fixed during shooting. The paper aims to describe the possibility of using drones in search and rescue operations and to provide a comprehensive overview of the area related to the detection of persons in drone imagery. The paper includes a description of publicly available datasets and a comparison of the state-of-the-art models for person detection in drone recordings and ends with a proposal for future research.
A Gamma Filter for Positive Parameter Estimation

In many data fusion applications, the parameter of interest only takes positive values. For example, it might be the goal to estimate a distance or to count instances of certain items. Optimal data fusion then should model the system state as a positive random variable, which has a probability density function that is restricted to the positive real axis. However, classical approaches based on normal densities fail here, in particular whenever the variance of the likelihood is rather large compared to the mean. In this paper, it is considered to model such random parameters with a Gamma distribution, since its support is positive and it is the maximum entropy distribution for such variables. For a Bayesian recursion, an approximative moment matching approach is proposed. An example within the framework of an autonomous simulation and further numerical considerations demonstrate the feasibility of the approach.
Contribution of Improved Character Embedding and Latent Posting Styles to Authorship Attribution of Short Texts

Text contents generated by social networking platforms tend to be short. The problem of authorship attribution on short texts is to determine the author of a given collection of short posts, which is more challenging than that on long texts. Considering the textual characteristics of sparsity and using informal terms, we propose a method of learning text representations using a mixture of words and character n-grams, as input to the architecture of deep neural networks. In this way we make full use of user mentions and topic mentions in posts. We also focus on the textual implicit characteristics and incorporate ten latent posting styles into the models. Our experimental evaluations on tweets show a significant improvement over baselines. We achieve a best accuracy of 83.6%, which is 7.5% improvement over the state-of-the-art. Further experiments with increasing number of authors also demonstrate the superiority of our models.
Evaluation Criteria for Visual Cryptography Schemes via Neural Networks

Visual cryptography schemes are developed for image security, where encryption is realized by distributing a secret image into shares and decryption is done only by stacking the shares. Human eyesight is usually used to evaluate the security and performance of visual cryptography schemes. However, objective criteria for visual cryptography schemes are not yet established. In this paper, by the aid of neural networks, we propose two criteria called encryption-inconsistency and decryption-consistency for evaluating the shares and the recovered images, respectively. We also implemented the experiments for two representatives of visual cryptography schemes by applying three popular convolutional neural networks (CNN) to adopt our proposed criteria.
Communication-Efficient and Byzantine-Robust Distributed Learning

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorithm that performs a simple thresholding based on gradient norms to mitigate Byzantine failures. We show the (statistical) error-rate of our algorithm matches that of [YCKB18], which uses more complicated schemes (like coordinate-wise median or trimmed mean) and thus optimal. Furthermore, for communication efficiency, we consider a generic class of δ-approximate compressors from [KRSJ19] that encompasses sign-based compressors and top-k sparsification. Our algorithm uses compressed gradients and gradient norms for aggregation and Byzantine removal respectively. We establish the statistical error rate of the algorithm for arbitrary (convex or non-convex) smooth loss function. We show that, in the regime when the compression factor δ is constant and the dimension of the parameter space is fixed, the rate of convergence is not affected by the compression operation, and hence we effectively get the compression for free. Moreover, we extend the compressed gradient descent algorithm with error feedback proposed in [KRSJ19] for the distributed setting. We have experimentally validated our results and shown good performance in convergence for convex (least-square regression) and non-convex (neural network training) problems.
A Call for Auditable Virtual, Augmented and Mixed Reality

XR (Virtual, Augmented and Mixed Reality) technologies are growing in prominence. However, they are increasingly being used in sectors and in situations that can result in harms. As such, this paper argues the need for auditability to become a key consideration of XR systems. Auditability entails capturing information of a system’s operation to enable oversight, inspection or investigation. Things can and will go wrong, and information that helps unpack situations of failure or harm, and that enables accountability and recourse, will be crucial to XR’s adoption and acceptance. In drawing attention to the urgent need for auditability, we illustrate some risks associated with XR technology and their audit implications, and present some initial findings from a survey with developers indicating the current ‘haphazard’ approach towards such concerns. We also highlight some challenges and considerations of XR audit in practice, as well as areas of future work for taking this important area of research forward.
Identifying pathfinder elements for gold in bulk-rock geochemical data from the Cripple Creek Au–Te deposit: a statistical approach

The Cripple Creek alkaline igneous rock-related, low-sulfidation epithermal gold telluride deposit, Colorado, is hosted in the 10 km wide Oligocene alkaline volcanic Cripple Creek diatreme in Proterozoic rocks. Gold occurs as native gold, Au-tellurides, and in the structure of arsenian pyrite, in potassically altered high-grade veins, and as disseminations in the host rocks. Correlation coefficients, principal component analysis, hierarchical cluster analysis and random forests were used to analyse major and trace element compositions of 995 rock samples primarily from low-grade gold mineralization in drill core from three currently operating pits (Wild Horse Extension, Globe Hill and Schist Island) in the northwestern part of the Cripple Creek diatreme. These methods suggest that Ag, As, Bi, Te and W are the best pathfinders to gold mineralization in low-grade disseminated ore. Although Mo correlates with gold in other studies and is spatially related to gold veins, molybdenite post-dated the formation of gold and is likely related to a late-stage porphyry overprint. These elements, in conjunction with mineralogical studies, indicate that tellurides, fluorite, quartz, carbonates, roscoelite, tennantite-tetrahedrite, pyrite, sphalerite, muscovite, monazite, bastnäsite and hübnerite serve as exploration guides to ore.
E-ABS

Conditional generative models, such as Schott et al.'s Analysis-by-Synthesis (ABS), have state-of-the-art robustness on MNIST, but fail in more challenging datasets. In this paper, we present E-ABS, an improvement on ABS that achieves state-of-the-art robustness on SVHN. E-ABS gives more reliable class-conditional likelihood estimations on both in-distribution and out-of-distribution samples than ABS. Theoretically, E-ABS preserves ABS's key features for robustness; thus, we show that E-ABS has similar certified robustness as ABS. Empirically, E-ABS outperforms both ABS and adversarial training on SVHN and a traffic sign dataset, achieving state-of-the-art robustness on these two real-world tasks. Our work shows a connection between ABS-like models and some recent advances on generative models, suggesting that ABS-like models are a promising direction for defending adversarial examples.
Duration modelling and evaluation for Arabic statistical parametric speech synthesis

Sound duration is responsible for rhythm and speech rate. Furthermore, in some languages phoneme length is an important phonetic and prosodic factor. For example, in Arabic, gemination and vowel quantity are two important characteristics of the language. Therefore, accurate duration modelling is crucial for Arabic TTS systems. This paper is interested in improving the modelling of phone duration for Arabic statistical parametric speech synthesis using DNN-based models. In fact, since a few years, DNN have been frequently used for parametric speech synthesis, instead of HMM. Therefore, several variants of DNN-based duration models for Arabic are investigated. The novelty consists in training a specific DNN model for each class of sounds, i.e. short vowels, long vowels, simple consonants and geminated consonants. The main idea behind this choice is the improvement that we already achieved in the quality of Arabic parametric speech synthesis by the introduction of two specific features of Arabic, i.e. gemination and vowel quantity into the standard HTS feature set. Both objective and subjective evaluations show that using a specific model for each class of sounds leads to a more accurate modelling of the phone duration in Arabic parametric speech synthesis, outperforming the state-of-the-art duration modelling systems.
Borno: Bangla Handwritten Character Recognition Using a Multiclass Convolutional Neural Network

Handwriting recognition is still not a solved problem. With the advancements in artificial intelligence and machine learning, the construction of Optical Character Recognition systems (OCRs) has become more effective. However, there is still no serious commercially available OCRs for many low-resource languages, such as Bangla. Bangla presents additional challenges, since oftentimes, the vowels and consonants in the middle of the words are abbreviated and replaced with notations called diacritics, and multiple letters can be combined to build shorthand representations, called compound characters. Furthermore, the compound characters can have diacritics as well, making the recognition task extremely complex. This means that a successful commercial OCR should not only model individual characters but also model these diacritics and combined characters, leading us to propose a grapheme-based holistic recognition approach. Borno is the first multiclass convolutional neural network-based deep learning model that can recognize Bangla handwritten characters with graphemes. The proposed model has been trained on a dataset of 1,069,132 images, with 50 basic characters, 10 numerals, 146 compound characters, 10 modifiers, and 6 consonant diacritics classes. The trained Borno model achieves a 92.61% average character recognition accuracy in the validation set.
Augmented Reality Application in Vocational Education: A Case of Welding Training

This paper embodies the need, design and evaluation of an AR welding mobile application highlighting the potential of mobile AR interactions.
Runtime-Safety-Guided Policy Repair

We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-based safety controller. The safety controller is endowed with the abilities to predict whether the trained policy will lead the system to an unsafe state, and take over control when necessary. While this architecture can provide added safety assurances, intermittent and frequent switching between the trained policy and the safety controller can result in undesirable behaviors and reduced performance. We propose to reduce or even eliminate control switching by 'repairing' the trained policy based on runtime data produced by the safety controller in a way that deviates minimally from the original policy. The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints. Experimental results demonstrate that our approach is effective even when the system model in the safety controller is unknown and only approximated.
X-Men: A Mutation-Based Approach for the Formal Analysis of Security Ceremonies

There is an increasing number of cyber-systems (e.g., payment, transportation, voting, critical-infrastructure systems) whose security depends intrinsically on human users. A security ceremony expands a security protocol with everything that is considered out-of-band to it, including, in particular, the mistakes that human users might make when participating actively in the security ceremony. In this paper, we introduce a novel approach for the formal analysis of security ceremonies. Our approach defines mutation rules that model possible behaviors of a human user, and automatically generates mutations in the behavior of the other agents of the ceremony to match the human-induced mutations. This allows for the analysis of the original ceremony specification and its possible mutations, which may include the way in which the ceremony has actually been implemented. To automate our approach, we have developed the tool X-Men, which is a prototype that extends Tamarin, one of the most common tools for the automatic unbounded verification of security protocols. As a proof of concept, we have applied our approach to two real-life case studies, uncovering a number of concrete vulnerabilities.
DeePar-SCA: Breaking Parallel Architectures of Lattice Cryptography via Learning Based Side-Channel Attacks

This paper proposes the first deep-learning based side-channel attacks on post-quantum key-exchange protocols. We target hardware implementations of two lattice-based key-exchange protocols—Frodo and NewHope—and analyze power side-channels of the security-critical arithmetic functions. The challenge in applying side-channel attacks stems from the single-trace nature of the protocols: each new execution will use a fresh and unique key, limiting the adversary to a single power measurement. Although such single-trace attacks are known, they have been so far constrained to sequentialized designs running on simple micro-controllers. By using deep-learning and data augmentation techniques, we extend those attacks to break parallelized hardware designs, and we quantify the attack’s limitations. Specifically, we demonstrate single-trace deep-learning based attacks that outperform traditional attacks such as horizontal differential power analysis and template attacks by up to 900% and 25%, respectively. The developed attacks can therefore break implementations that are otherwise secure, motivating active countermeasures even on parallel architectures for key-exchange protocols.
Unsupervised Derivation of Keyword Summary for Short Texts

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named Frequent Closed Wordsets Ranking (FCWRank) to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.
Truesight Battle Grid - Enhancing the Game Experience of Tabletop Role-Playing through Tangible Data Visualization

Data visualization is often used in computer gaming to provide feedback to players. However, little research has investigated its potential in hybrid physical/digital tabletop games, or for tabletop role-playing games. Building upon the benefits of tangible interfaces we introduce and report on the design of the Truesight Battle Grid, a modular, tangible battle grid for the role-playing game Dungeons & Dragons. Through visualizing game-play possibilities with lights, it aims to provide players with information about the fantasy world without being distracting. We report on a preliminary evaluation with two focus groups - showing that the tool was helpful and is able to contribute to an engaged audience - and reflect on possibilities for future work.