Reviewerly/exHarmony huggingface dataset:
Competitive Physical Human-Robot Game Play

While competitive games have been studied extensively in the AI community for benchmarking purposes, there has only been limited discussion of human interaction with embodied agents under competitive settings. In this work, we aim to motivate research in competitive human-robot interaction (competitive-HRI) by discussing how human users can benefit from robot competitors. We then examine the concepts from game AI that we can adopt for competitive-HRI. Based on these discussions, we propose a robotic system that is designed to support future competitive-HRI research. A human-robot fencing game is also proposed to evaluate a robot's capability in competitive-HRI scenarios. Finally, we present the initial experimental results and discuss possible future research directions.
CyberSecurity Challenges for Software Developer Awareness Training in Industrial Environments

Awareness of cybersecurity topics facilitates software developers to produce secure code. This awareness is especially important in industrial environments for the products and services in critical infrastructures. In this work, we address how to raise awareness of software developers on the topic of secure coding. We propose the “CyberSecurity Challenges”, a serious game designed to be used in an industrial environment and address software developers’ needs. Our work distills the experience gained in conducting these CyberSecurity Challenges in an industrial setting. The main contributions are the design of the CyberSecurity Challenges events, the analysis of the perceived benefits, and practical advice for practitioners who wish to design or refine these games.
Predicting syntactic choice in Mandarin Chinese: a corpus-based analysis of <i>ba</i> sentences and SVO sentences

Abstract This paper investigates the effects of 10 factors on the choice between alternative ba sentences and SVO sentences in Mandarin Chinese. These factors are givenness, definiteness, animacy and pronominality of NP2s, NP2 length, VP length, verb sense, syntactic parallelism, dependency distance, and surprisal. Using corpus data and mixed-effects logistic regression modeling, we find that on the one hand, givenness, syntactic parallelism, and the log-transformed ratio of NP2 length and VP length are significant predictors of the choice between ba sentences and SVO sentences. A new NP2, a large length ratio and a parallel construction predict an SVO sentence rather than a ba sentence. On the other hand, dependency distance and surprisal estimated by the trigram model are effective in predicting the choice between naturally occurring ba /SVO sentences and their alternatives. Naturally occurring sentences are more likely to have shorter dependency distances and smaller surprisal values than the converted sentences. The effects of these five factors on syntactic choice are congruent with results of previous studies, which suggests that some determinants of syntactic choice are shared among languages.
Application of grey wolf algorithm for solving engineering optimization problems

This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other methods.
Flexible Example-Based Image Enhancement with Task Adaptive Global Feature Self-guided Network

We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current state of the art in learning a single enhancement mapping, while having significantly fewer parameters than its competitors. Furthermore, the model achieves even higher performance on learning multiple mappings simultaneously, by taking advantage of shared representations. Our network is based on the recently proposed SGN architecture, with modifications targeted at incorporating global features and style adaption. Finally, we present an unpaired learning method for multitask image enhancement, that is based on generative adversarial networks (GANs).
Studying test-driven development and its retainment over a six-month time span

In this paper, we investigate the effect of TDD, as compared to a non-TDD approach, as well as its retainment (or retention) over a time span of (about) six months. To pursue these objectives, we conducted a (quantitative) longitudinal cohort study with 30 novice developers (i.e., third-year undergraduate students in Computer Science). We observed that TDD affects neither the external quality of software products nor developers' productivity. However, we observed that the participants applying TDD produced significantly more tests, with a higher fault-detection capability than those using a non-TDD approach. As for the retainment of TDD, we found that TDD is retained by novice developers for at least six months.
Arbitrary Configurable 20-Channel Coincidence Counting Unit for Multi-Qubit Quantum Experiment

This paper presents a 20-channel coincidence counting unit (CCU) using a low-end field-programmable gate array (FPGA). The architecture of the CCU can be configured arbitrarily to measure from twofold to twentyfold coincidence counts thanks to a multifold controllable architecture, which can be easily manipulated by a graphical user interface (GUI) program. In addition, it provides up to 20 of each input signal count simultaneously. The experimental results show twentyfold coincidence counts with the resolution occurring in a less than 0.5 ns coincidence window. This CCU has appropriate characteristics for various quantum optics experiments using multi-photon qubits.
Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions

Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspace modeled as a graph, A*-based approaches have been widely investigated due to their guarantees on completeness and solution optimality, and have demonstrated their efficiency in many scenarios. However, almost all of these A*-based methods assume that each agent executes an action concurrently in that all agents start and stop together. This article presents a natural generalization of MAPF with asynchronous actions (MAPF-AA) where agents do not necessarily start and stop concurrently. The main contribution of the work is a proposed approach called Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle asynchronous actions. We show LSS is complete and finds an optimal solution if one exists. We also combine LSS with other existing MAPF methods that aims to trade-off optimality for computational efficiency. Numerical results are presented to corroborate the performance of LSS and the applicability of the proposed method is verified in the Robotarium, a remotely accessible swarm robotics research platform.
A Sufficient Statistic for Influence in Structured Multiagent Environments

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods. A body of work in AI has tried to mitigate this problem by trying to distill interaction to its essence: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than the space of policies. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs). On the one hand, this generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex multiagent settings. On the other hand, since the presence of other agents can be seen as a generalization of single agent settings, our formulation of IBA also provides a sufficient statistic for decision making under abstraction for a single agent. We also give a detailed discussion of the relations to such previous works, identifying new insights and interpretations of these approaches. In these ways, this paper deepens our understanding of abstraction in a wide range of sequential decision making settings, providing the basis for new approaches and algorithms for a large class of problems.
Distance learning hybrid format for university students in post-pandemic perspective: Collaborative technologies aspect

The relevance of the research is determined by the key ideas of the hybrid format of students distance learning and collaborative technologies of its implementation in the post- pandemic perspective of the university educational process. The authors, taking into account the trends of transformations of the educational process of the university in the post-pandemic perspective, reveal the content of the key concepts of the hybrid format of distance learning for students. The priorities of collaborative technologies aimed at supporting the hybrid format of distance learning of university students are determined. Based on the results of the study, the authors of the article substantiate the productivity and prove the effectiveness of the implementation of platform models of the hybrid distance-learning format. The materials of the article are recommended to teachers and students of the university, methodologists, curators, tutors.&#x0D; &#x0D; Keywords: student's personality, post-pandemic perspective of university development, distance learning, hybrid format of distance learning, mixed learning, collaborative technologies, platform model, pedagogical design
Reflection as Component of Professional Pedagogical Culture

The paper examines the reflexive component of professional pedagogical culture. The article reveals the notions “professional pedagogical culture”, “components of professional pedagogical culture”, “reflection”, proposes a model of correlation between reflection and components of professional pedagogical culture, formulates a definition of the reflexive component of professional pedagogical culture. Scientific originality of the study lies in the fact that reflection is considered as a component of professional pedagogical culture. The conclusion is made that the reflexive component is a key one in the structure of professional pedagogical culture.
An Adaptive Visible Watermark Embedding Method based on Region Selection

Aiming at the problem that the robustness, visibility, and transparency of the existing visible watermarking technologies are difficult to achieve a balance, this paper proposes an adaptive embedding method for visible watermarking. Firstly, the salient region of the host image is detected based on superpixel detection. Secondly, the flat region with relatively low complexity is selected as the embedding region in the nonsalient region of the host image. Then, the watermarking strength is adaptively calculated by considering the gray distribution and image texture complexity of the embedding region. Finally, the visible watermark image is adaptively embedded into the host image with slight adjustment by just noticeable difference (JND) coefficient. The experimental results show that our proposed method improves the robustness of visible watermarking technology and greatly reduces the risk of malicious removal of visible watermark image. Meanwhile, a good balance between the visibility and transparency of the visible watermark image is achieved, which has the advantages of high security and ideal visual effect.
Lower and upper bounds for unilateral coherence and applying them to the entropic uncertainty relations

Abstract In this paper, a method is provided for obtaining the entropic uncertainty relations in the presence of a quantum memory by using quantum coherence. In the method, firstly, one can use the quantum relative entropy of quantum coherence to obtain the uncertainty relations. Secondly, these relations are applied to obtain the entropic uncertainty relations in the presence of a quantum memory. In comparison with other methods this approach is much simpler. Also, for a given state, the upper bounds on the sum of the relative entropies of unilateral coherences are provided, and it is shown which one is tighter. In addition, using the upper bound obtained for unilateral coherence, the nontrivial upper bound on the sum of the entropies for different observables is derived in the presence of a quantum memory.
Experimental Analysis of Locality Sensitive Hashing Techniques for High-Dimensional Approximate Nearest Neighbor Searches

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many multimedia retrieval applications. Exact tree-based indexing approaches are known to suffer from the notorious curse of dimensionality for high-dimensional data. Approximate searching techniques sacrifice some accuracy while returning good enough results for faster performance. Locality Sensitive Hashing (LSH) is a very popular technique for finding approximate nearest neighbors in high-dimensional spaces. Apart from providing theoretical guarantees on the query results, one of the main benefits of LSH techniques is their good scalability to large datasets because they are external memory based. The most dominant costs for existing LSH techniques are the algorithm time and the index I/Os required to find candidate points. Existing works do not compare both of these dominant costs in their evaluation. In this experimental survey paper, we show the impact of both these costs on the overall performance of the LSH technique. We compare three state-of-the-art techniques on four real-world datasets, and show that, in contrast to recent works, C2LSH is still the state-of-the-art algorithm in terms of performance while achieving similar accuracy as its recent competitors.
An Instrument for Measuring Perception about Social and Human Factors that Influence Software Development Productivity

In terms of productivity in software development, there is specific interest in identifying its influencing factors. For this purpose, several classification approaches have been previously used, which have already recognized technical factors, organizational factors, product factors, project factors, and personal factors. However, these approaches often focus on technical factors over social and human factors (SHFs). Nevertheless, in addition to the obvious technical aspects, the software development process involves problem-solving skills and cognitive aspects and social interaction. In this sense, determining SHFs can lead to software organizations designing strategies for improving team productivity. In this study, we first conducted a preliminary classification of the SHFs identified in the literature. Because this study seeks to assess the factors from the standpoint of software development professionals, we developed and validated an instrument to measure the perception of software development team members about SHFs that may be affecting their productivity. For this purpose, the first four stages of survey-based research were followed: objective definition, survey design, instrument construction, instrument validity, and reliability assessment. The instrument included 79 items assessing 13 different SHFs. After assessing both their validity and reliability, the results demonstrated that the instrument is a valid and reliable tool for measuring SHFs perception among software development team members.
Waveguide-lattice-based architecture for multichannel optical transformations

We consider waveguide lattices as the architecture to implement a wide range of multiport transformations. In this architecture, a particular transfer matrix is obtained by setting step-wise profiles of propagation constants experienced by a field evolving in a lattice. To investigate the capabilities of this architecture, we numerically study the implementation of random transfer matrices as well as several notable cases, such as the discrete Fourier transform, the Hadamard, and permutation matrices. We show that waveguide lattice schemes are more compact than their traditional lumped-parameter counterparts, thus the proposed architecture may be beneficial for photonic information processing systems of the future.
Synthesizing Context-free Grammars from Recurrent Neural Networks

Abstract We present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework, pattern rule sets (PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the $$L^{*}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>L</mml:mi><mml:mrow><mml:mrow /><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:math> algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned language. Extracting the learned language of an RNN is important to facilitate understanding of the RNN and to verify its correctness. Furthermore, the extracted CFG can augment the RNN in classifying correct sentences, as the RNN’s predictive accuracy decreases when the recursion depth and distance between matching delimiters of its input sequences increases.
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

Commonsense AI has long been seen as a near impossible goal -- until recently. Now, research interest has sharply increased with an influx of new benchmarks and models. We propose two new ways to evaluate commonsense models, emphasizing their generality on new tasks and building on diverse, recently introduced benchmarks. First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets. Second, we propose a novel evaluation, the cost equivalent curve, that sheds new insight on how the choice of source datasets, pretrained language models, and transfer learning methods impacts performance and data efficiency. We perform extensive experiments -- over 200 experiments encompassing 4800 models -- and report multiple valuable and sometimes surprising findings, e.g., that transfer almost always leads to better or equivalent performance if following a particular recipe, that QA-based commonsense datasets transfer well with each other, while commonsense knowledge graphs do not, and that perhaps counter-intuitively, larger models benefit more from transfer than smaller ones. Last but not least, we introduce a new universal commonsense reasoning model, UNICORN, that establishes new state-of-the-art performance across 8 popular commonsense benchmarks, aNLI (87.3%), CosmosQA (91.8%), HellaSWAG (93.9%), PIQA (90.1%), SocialIQa (83.2%), WinoGrande (86.6%), CycIC (94.0%) and CommonsenseQA (79.3%).
The challenges and opportunities of Web 2.0 elections: the case of Zimbabwe

This study analyses different perspectives of the challenges and opportunities of using Web 2.0 technology with specific reference to Zimbabwe’s 2018 general elections. It discusses digital tools and resources such as social networking sites (SNSs) and biometric voter registration (BVR) for the management of the voters’ roll. The study includes in­depth interviews with several politicians and ZEC officials to discuss the challenges and opportunities of Web 2.0 in Zimbabwe’s elections. Informed by theoretical concepts on technology and politics, the study establishes that technology is not a panacea but can be used as an apparatus. This study concludes that political institutions in Zimbabwe should reach consensus that the country will not conduct another election until electoral reforms are implemented because technology alone cannot overcome political challenges. Thus, the election monitoring body should be sufficiently credible to ensure a free and fair election.
Efficient entanglement distillation for quantum channels with polarization mode dispersion

Quantum entanglement shared by remote network nodes serves as a valuable resource for promising applications in distributed computing, cryptography, and sensing. However, distributing high-quality entanglement via fiber-optic routes could be challenging due to the various decoherence mechanisms in fibers. In particular, one of the primary polarization decoherence mechanisms in optical fibers is polarization mode dispersion (PMD), which is the distortion of optical pulses by randomly varying birefringences. To mitigate the effect of decoherence in entangled particles, quantum entanglement distillation (QED) algorithms have been proposed. One particular class, the recurrence QED algorithms, stands out because it has relatively relaxed requirements both on the size of the quantum circuits involved and on the initial quality of entanglement between particles. However, because the number of required particles grows exponentially with the number of distillation rounds, an efficient recurrence algorithm needs to converge quickly. We present a recurrence QED algorithm designed for photonic qubit pairs affected by PMD-degraded channels. Our proposed algorithm achieves the optimal fidelity as well as the optimal success probability (conditioned on the fact that optimal fidelity is achieved) in every round of distillation. The attainment of the maximal fidelity improves the convergence speed of fidelity with respect to the number of distillation rounds from linear to quadratic and, hence, significantly reduces the number of rounds. Combined with the fact that the optimal success probability is achieved, the proposed algorithm provides an efficient method to distribute entangled states with a high fidelity via optical fibers.
Detecting ‘Dirt’ and ‘Toxicity’: Rethinking Content Moderation as Pollution Behaviour

"Dirt" and "toxicity" have become established ways of understanding and describing harmful content online. While the concepts have becoming naturalised as a way of expressing harm, its uses are also mobilised to very different ends. We wish to use this article to explore the role the notions of "toxic" and "dirt" have come to play in online content moderation, and what the politics of this discourse implies. Our overarching aim is to ask how content moderation infrastructures define toxic and dirty content and what the politics of these definitions are. We argue that content moderation’s historical reliance on binary categories – and those categories’ ongoing entanglements with social systems of racism and patriarchy – embeds the infrastructures in structures that risk reproducing inequalities.