Reviewerly/exHarmony huggingface dataset:
Lightweight Access Control Scheme for Attribute Change Based on Blockchain

Cryptographic policy attribute based encryption (CP-ABE) has been extensively researched and employed in access control systems to facilitate secure data sharing. However, this scheme requires very high computing resources for decryption, and users' attributes change frequently, which limits the use of CP-ABE in the Internet of Things where computing resources are insufficient. Many solutions adopt cloud proxy outsourcing decryption to solve the problem of insufficient computing power, but there are problems of distrust in outsourcing decryption or additional verification costs. User attribute updating is also more limited to attribute revocation and requires updating ciphertext at the same time to resist user collusion attacks. To address these issues, a blockchain-based access control scheme with Attribute change capability is proposed. The blockchain is leveraged to securely outsource complex computational tasks, while user attribute tokens are updated in conjunction with the blockchain to update user attributes. Finally, we conduct a performance analysis and comparison of this scheme.
A Survey of Database Knob Tuning with Machine Learning

As the scale of modern database management systems (DBMSs) becomes larger and larger, the configuration knobs have reached hundreds, which makes the traditional knob tuning methods can’t meet the demand. It is urgent to use the latest technology to study more intelligent and perfect knob tuning technology to ensure the high-performance operation of DBMS. In this paper, we first analyze the limitations of the traditional knob tuning methods. After that, we discuss the research status and challenges of Bayesian Optimization (BO), Reinforcement Learning (RL) and Deep Learning (DL) applied to database knob tuning problems.
Key-count differential-based proactive key relay algorithm for scalable quantum-secured networking

By overcoming the distance limit in quantum key distribution (QKD) technology, key relaying over trusted nodes is regarded as an essential function for practical quantum-secured networking. The key relay function consumes a series of costly quantum key resources to establish an end-to-end key in QKD networks (QKDNs); thus, an efficient key relay algorithm is required. To investigate this problem, we developed an integer linear programming (ILP) formulation that maximizes a balanced summation between max-min fairness and efficiency in the key relay. Inspired by the ILP model, this study developed a key-count differential-based proactive key relay (DPKR) heuristic algorithm to provide a scalable solution for key relays in QKDNs. The proposed DPKR algorithm iteratively selects a set of nodes by considering the key-count differential between nodes and establishes an end-to-end key by relaying it over the selected nodes. Owing to the proactive key relay model, an effective key relay route in the key management layer in the QKDN can be calculated solely by the key-count status in the key management layer. The proposed DPKR algorithm reduces manifold order-of-magnitude in the algorithm computation time at the cost of an acceptable loss in the average and minimum numbers of keys from those of the ILP optimization model. The computation time evaluation clearly manifests a scalability of the DPKR algorithm in quantum-secured networking.
Adaptive Collaborative Soft Label Learning for Unsupervised Multi-View Feature Selection

Unsupervised multi-view feature selection aims to select informative features with multi-view features and unsupervised learning. It is a challenging problem due to the absence of explicit semantic supervision. Recently, graph theory and hard pseudo-label learning have been adopted to solve multi-view feature selection problems under the unsupervised learning paradigm. However, graph-based methods are difficult to support large-scale real scenarios due to the high computational complexity of graph construction. Moreover, existing methods based on hard pseudo-label learning generally result in significant information loss. In this article, we propose an Adaptive Collaborative Soft Label Learning (ACSLL) model for unsupervised multi-view feature selection. In this model, collaborative soft label learning and multi-view feature selection are integrated into a unified framework. Specifically, we learn the pseudo soft labels from each view feature by a simple and efficient method and fuse them with an adaptive weighting strategy into a joint soft label matrix. This matrix is further used for guiding the feature selection process to identify valuable features. An effective optimization strategy guaranteed with proven convergence is derived to iteratively solve this problem. Experiments demonstrate the superiority of the proposed method in both feature selection accuracy and efficiency.
Machine-learning-based device-independent certification of quantum networks

Witnessing nonclassical behavior is a crucial ingredient in quantum information processing. For that, one has to optimize the quantum features a given physical setup can give rise to, which is a hard computational task currently tackled with semidefinite programming, a method limited to linear objective functions and that becomes prohibitive as the complexity of the system grows. Here, we propose an alternative strategy, which exploits a feedforward artificial neural network to optimize the correlations compatible with arbitrary quantum networks. A remarkable step forward with respect to existing methods is that it deals with nonlinear optimization constraints and objective functions, being applicable to scenarios featuring independent sources and nonlinear entanglement witnesses. Furthermore, it offers a significant speedup in comparison with other approaches, thus allowing to explore previously inaccessible regimes. We also extend the use of the neural network to the experimental realm, a situation in which the statistics are unavoidably affected by imperfections, retrieving device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. In this way, this work paves the way for the certification of quantum resources in networks of growing size and complexity.
EFL Teachers’ Experiences and Attitudes to Emergency Remote Teaching at Japanese Universities

In 2020 teachers worldwide faced an unprecedented situation in the form of the COVID-19 pandemic. In Japan, this forced educators to quickly adapt face-to-face programs to online ones as the new academic year started in April 2020. Although Japan is a technology rich society, the use of technology in education had been limited up until
ScriptButler serves an Empirical Study of PuzzleScript

Automated Game Design (AGD) empowers game designers with languages and tools that automate game design processes. Domain-Specific Languages (DSLs) promise to deliver an expressive means for rapidly prototyping and fine-tuning interaction mechanisms that support rich emergent player experiences. However, despite the growing number of studies that center around languages for games and play, few prototypes are ever thoroughly validated and evaluated in practice. As a result, it is not yet well understood what the costs, benefits and limitations of DSL formalisms are.
Multilayer Map Generation Using Attribute Loss Functions

Procedural Content Generation via Machine Learning (PCGML) has been studied to generate terrain maps, but many studies focus on height maps and lack human control. We propose a method based on Generative Adversarial Networks (GANs) to generate multilayer maps of terrain with statistical attributes as inputs to introduce more human control. Since the discriminators used in GANs are difficult to evaluate and lack transparency, we propose attribute loss functions, which work as a supervised approach to evaluate the statistical attributes of generated maps directly using differentiable functions for backpropagation. We tested combinations of two model architectures and different conditional normalisation methods and analysed their characteristics. We found that CGAN architecture with batch normalisation worked well in general, while SPADE block introduced more fragments, and channel-wise normalisation satisfied input conditions better but lost distribution diversity and inter-layer relationships.
Towards a Unified Language for Card Game Design

Card game creation is a powerful tool for game design. Using playing cards, game designers can rapidly prototype and iteratively playtest a game's core mechanisms to explore alternatives and improve the gameplay. However, this process is time-consuming, imprecise and challenging to steer and focus. We aim to empower designers with solutions that automate game design processes. In particular, we study to what extent a unified game design language can offer theoretical foundations, systematic techniques and practical solutions.
Introducing QRogue: Teaching Quantum Computing Using a Rogue-like Game Concept

Recent years have shown that we are steadily getting closer to industrial applications of quantum computing. As such it is important to teach quantum computing concepts to users to allow them to incorporate quantum computing into their toolbox. As educational research has shown the potential of game-based learning in the past years, we are thus proposing QRogue, an educational game with Rogue-like elements targeted at computer science students. The game's goal is to teach the math behind quantum computing in a playful environment with analogies to this technology's counter-intuitive fundamentals. To gather first feedback and stir the further development, we conducted a user study – involving playtesting and a post-experience survey – with eight students showing that the game was positively received but requires further tuning of the onboarding process and of the in-game feedback provided to players.
Sailing through Signs: Attention Guidance towards Texts in Virtual Reality

Virtual reality (VR) devices have technically matured during the last few years. Apart from offering better resolutions and a wider field of view, these devices provide novel interaction forms that resemble real-world interactions. Players can explore the virtual environment and manipulate objects to reach game goals. Frequently, players need to be informed about what to do next or how a particular objective could be achieved. This is often done via texts placed in the virtual environment. However, these texts are frequently overlooked or ignored, resulting in player frustration due to a lack of guidance. This paper investigates various design strategies to guide the player's attention to pay attention to the text in a virtual environment. To achieve this, we set up a comparative study to evaluate four different types of attention guidance. Results revealed that players prefer some guidance forms in particular.
“Generator’s Haunted”: A Brief, Spooky Account of Hauntological Effects in the Player Experience of Procedural Generation

Theories of the poetics of procedural generation attempt to explain the player experience of interacting with generators by describing the aesthetic or experiential qualities that generators can afford when they are deployed in particular ways. We propose that an underinvestigated aspect of procgen poetics—the experiential effects of the sequencing of generated artifacts—can be understood in terms of hauntology, a theory of textual interpretation that aims to account for the lingering effects of past texts (and their implied futures) on present ones. We briefly introduce hauntology, discuss a few examples of hauntological effects in player experiences of procgen, and gesture at implications for future technical work.
Re-trainable Procedural Level Generation via Machine Learning (RT-PLGML) as Game Mechanic

We present re-trainable procedural level generation via machine learning (RT-PLGML), a game mechanic of providing in-game training examples for a PLGML system. We discuss opportunities and challenges, along with concept RT-PLGML games.
Automated Testing in Super Metroid with Abstraction-Guided Exploration

Machine playtesting systems often aim to demonstrate how to reach certain moments of play. To provide design feedback in a timely manner, they often internally rely on heuristic-guided search. However, there are many types of videogames for which sufficiently accurate heuristics are not available. We use an imperfect abstraction of an underlying game to define progress scores, and show that combining these scores yields a highly effective cell selection heuristic for use in the Go-Explore algorithm. We demonstrate the impact of this approach in automated gameplay for Super Metroid (involving mandatory item collection, destructible blocks, and backtracking) using a tile-based abstraction of the game that only models a small subset of the game's mechanics. Surprisingly, our abstraction guidance mechanism is able to explore this complex game several orders of magnitude more efficiently than past work with similar exploration methods in Montezuma's Revenge.
Hierarchical Semantic Wave Function Collapse

There are few proposals to improve the interactivity and control of wave function collapse (WFC) in a mixed-initiative setting. Moreover, most WFC algorithm variants operate on an simple, unstructured set of tiles. This limitation on the level of control provided to designers hampers their creative work in various ways. We propose Hierarchical Semantic WFC, a generalized approach to WFC that organizes its tile-set into a hierarchy akin to a taxonomy induced by the relation 'consists-of'. In such a hierarchical structure, abstract tiles (i.e. non-leaf nodes) can represent the first sketchy intentions of a designer (e.g. forest, urban, desert,...) This allows a designer to interactively collapse a given area into abstract tiles, while subsequently, (and repeatedly, if desired) WFC can resolve each area into a variety of particular instances, by further collapsing it into (a valid combination of) its children tiles (whether leaves or not). We identify how this subtle tile-set change affects the whole WFC algorithm, describe a number of novel exploratory and interactive functions that this enables, and showcase these with a variety of examples generated with our prototype implementation. We conclude that these new mixed-initiative content generation methods can considerably reduce design iteration times and improve the assistance given to designers in expressing their creative intent.
Conceptual Art Made Real: Why Procedural Content Generation is Impossible

Procedural content generation is impossible: insofar as it is popularly understood as the generation of artifacts that can give us the same experience as if a human had crafted them by hand, it involves an intrinsic contradiction. If a thing has been generated once, it can be generated again. Kate Compton has introduced a term for this unending content: liquid art. Compton's category of the "Bach faucet" describes the way that the endless supply of generativity destroys rarity. Conceptual art provides some examples of navigating this paradox. The PCG community is uniquely positioned to provide direction because of its existing understanding of the properties of generativity as an art form.
Computer Aided Content Generation – A Gloomhaven Case Study

We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.
Privacy-preserving data analytics for smart decision-making energy systems in sustainable smart community

Nowadays, smart community becomes as a key component of Internet of Things (IoT). The fast booming of it significantly enhances the application of smart cities. To achieve data analytics and decision making for energy systems in smart cities, we propose use fog computing to establish a smart community due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging in this scheme because continuous attacks put sensitive information under great threats. Besides, the loss of sensitive information degrade the quality of data analytics and decision making. Motivated by this, we propose a differentially private smart community model with game theory-based personalized privacy protection (GPDP). Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Then we use game theory to model the confrontation and further derive optimized trade-off, which is denoted by the Nash Equilibrium. In addition, we develop a modified reinforcement learning algorithm to achieve fast convergence. The advanced evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility, which improves data analytics and decision-making performances for energy management in smart cities.
Exploring the Path of Autonomous Development: the Development Dilemma and Coping Strategies of Sub-Saharan Africa in the Post-epidemic Era

Based on the multidisciplinary research perspectives of economics, politics, and international relations, this paper starts with the analysis of the main characteristics of sub-Saharan African countries, analyzes their economic development difficulties and new challenges in the post-epidemic era, discusses the necessity for sub-Saharan African countries to explore the path of autonomous development at the level of economic structural reform and development model, and proposes coping strategies. Sub-Saharan African countries should seize the opportunity to accelerate economic restructuring under the favorable situation of the formal launch of the African Continental Free Trade Area (AfCFTA); take measures to develop agriculture and food production, manufacturing, digital economy, and the AfCFTA construction; and actively cooperate with China and other emerging countries to enhance their capacity for independent development.
Rail anomalies detection: A comparative analysis of three self-supervised models on real data

Monitoring the state of railways infrastructure is crucial for travel safety. These inspections are mostly accomplished by means of dedicated and expensive vehicles, which cause significant disruption to the normal operativity of a line. A recent emerging solution is to equip train vehicles with low cost sensors (mostly accelerometers) able to scan tracks, with the goal to detect anomalies as the train passes. This strategy has shown to be a powerful and effective tool to monitor the conditions of the infrastructure. In this work, we investigate the implications of processing time series collected using these accelerometers on train vehicles axes, by leveraging self-supervised anomaly detection techniques. This is motivated by the high economic value of the approach, with respect to other alternatives. Our work has the advantage of being performed on scarcely available real data annotated with track features, namely weldings positions. Specifically, we explore the performance of three different anomaly detection approaches in identifying the position of rail weldings in an accelerometric signal obtained from a real vehicle. By comparing the performance of three models and considering their features and differences, we provide insight about the anomaly detection problem in a real scenario concerning aspects of the problem that influence model selection, also considering the computational effort needed to train each considered model.
Runtime Variation in Big Data Analytics

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and adjust for them is crucial to cloud service providers to design reliable data processing pipelines, provision and allocate resources, adjust pricing services, meet SLOs and debug performance hazards. In this paper, we analyze the runtime variation of millions of production Scope jobs on Cosmos, an exabyte-scale internal analytics platform at Microsoft. We propose an innovative 2-step approach to predict job runtime distribution by characterizing typical distribution shapes combined with a classification model with an average accuracy of &gt;96%, using an innovative interpretable machine-learning algorithm out-performing traditional regression models and better capturing long tails. We examine factors such as job plan characteristics and inputs, resource allocation, physical cluster heterogeneity and utilization, and scheduling policies. To the best of our knowledge, this is the first study on predicting categories of runtime distributions for enterprise analytics workloads at scale. Furthermore, we examine how our methods can be used to analyze what-if scenarios, focusing on the impact of resource allocation, scheduling, and physical cluster provisioning decisions on a job's runtime consistency and predictability.
Toward resilient autonomous driving—An experience report on integrating resilience mechanisms into the Apollo autonomous driving software stack

Autonomous driver assistance systems (ADAS) have been progressively pushed to extremes. Today, increasingly sophisticated algorithms, such as deep neural networks, assume responsibility for critical driving functionality, including operating the vehicle at various levels of autonomy. Elaborate obstacle detection, classification, and prediction algorithms, mostly vision-based, trajectory planning, and smooth control algorithms, take over what humans learn until they are permitted to control vehicles and beyond. And even if humans remain in the loop (e.g., to intervene in case of error, as required by autonomy levels 3 and 4), it remains questionable whether distracted human drivers will react appropriately, given the high speed at which vehicles drive and the complex traffic situations they have to cope with. A further pitfall is trusting the whole autonomous driving stack not to fail due to accidental causes and to be robust against cyberattacks of increasing sophistication. In this experience report, we share our findings in retrofitting application-agnostic resilience mechanisms into an existing hardware-/software-stack for autonomous driving—Apollo—as well as where application knowledge helps improve existing resilience algorithms. Our goal is to ultimately decrease the vulnerability of autonomously driving vehicles to accidental faults and attacks, allowing them to absorb and tolerate both, as well as to come out of them at least as secure as before the attack has happened. We demonstrate replication and rejuvenation on the driving stack's Control module and indicate how this resilience can be extended both downwards to the hardware level, as well as upwards to the prediction and planning modules.