Reviewerly/exHarmony huggingface dataset:
SinGAN-Based Asteroid Surface Image Generation

While it is risky considering spacecraft constraints and unknown environment on asteroid, surface sampling is an important technique for asteroid exploration. One of the sample return missions is to seek an optimal landing site, which may be in hazardous terrain. Since autonomous landing is particularly challenging, it is necessary to simulate the effectiveness of this process and prove the onboard optical hazard avoidance is robust to various uncertainties. This paper aims to generate realistic surface images of asteroids for simulations of asteroid exploration. A SinGAN-based method is proposed, which only needs a single input image for training a pyramid of multi-scale patch generators. Various images with high fidelity can be generated, and manipulations such as shape variation, illumination direction variation, super resolution generation are well achieved. The method's applicability is validated by extensive experimental results and evaluations. At last, the proposed method has been used to help set up a test environment for landing site selection simulation.
The Application of Machine Learning in Bitcoin Ransomware Family Prediction

In recent years, ransomware attacks have become increasingly rampant, resulting in many large companies or financial institutions suffering heavy losses from ransomware attacks. Bitcoin, is a means of payment demanded by the Ransomware Family. By comparing and analyzing the characteristics of bitcoin transactions, we can predict the types of Ransomware Family. Therefore, in this paper, the algorithm of machine learning is used to put forward the prediction method of Ransomware Family, so as to achieve the better effect of helping the attacked institutions to avoid being extorted effectively. In the traditional method, the judgment of Ransomware Family can only rely on human experience and subjective judgment, instead of accurate and batch analysis of Bitcoin transactions and prediction results. In this paper, a large number of known data sets of bitcoin's transaction features are used for analysis and modeling. First, we carried out descriptive statistical analysis to explore the differences between different Ransomware Families in bitcoin trading behavior. Next, we used a series of machine learning models to build the prediction model of Ransomware Family and conduct identification and classification, so as to help avoid financial losses from the Ransomware. Finally, we found that Ransomware family species were most significantly affected by year. In addition, it can be found that the accuracy of the Boosting model is the highest, and the test error is only about 3%.
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations

Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search.This requires strong locality properties from the representation space, e.g., close allocations of each small group of relevant texts, which are hard to generalize to domains without sufficient training data.In this paper, we aim to improve the generalization ability of DR models from source training domains with rich supervision signals to target domains without any relevance label, in the zero-shot setting.To achieve that, we propose Momentum adversarial Domain Invariant Representation learning (MoDIR), which introduces a momentum method to train a domain classifier that distinguishes source versus target domains, and then adversarially updates the DR encoder to learn domain invariant representations.Our experiments show that MoDIR robustly outperforms its baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setup, with more than 10% relative gains on datasets with enough sensitivity for DR models' evaluation.
The environmental geochemical baseline, background and sources of metal and metalloids present in urban, peri-urban and rural soils in the O´Higgins region, Chile

The importance of environmental geochemistry baseline in soils of O´Higgins Region, Chile, since it hosts in its eastern area one of the major Cu-Mo producing mines in the country, is to establish and explain relationships between the chemical compositions of the Earth's surface and potential contaminants sources such as mining industry, agriculture and urban activity. A total of 109 samples of urban, peri-urban and rural soils were analyzed with X-ray fluorescence to determine most of the elemental concentrations analyzed. The C and S analyses were performed with the high-temperature combustion method, and a MERCUR mercury analyzer was used for Hg. The study shows that the distribution patterns for most major elements and some trace elements are controlled by the lithologic substrate. This study identified areas with metals and metalloids in high concentrations, which are a risk to the environment and health according to established international regulations. Some of these components correspond to Cu (2500 ppm), Mo (26,5 ppm), As (134,6 ppm), Cr (206.6 ppm), Hg (0.2 ppm), Ni (26.4 ppm), Pb (61.7 ppm), V (227,2 ppm) and Zn (180.3 ppm). Through an elementary association analysis, most of these elements resulted from extractive activities of Cu, metal alloys and oil combustion. It was also possible to trace the use of fertilizers and pesticides in agricultural soils, as well as the combustion of oil related to vehicles in the study area. This information is relevant to implement environmental management strategies to control possible exposure to toxic compounds to human health.
Safer at any speed: automatic context-aware safety enhancement for Rust

Type-safe languages improve application safety by eliminating whole classes of vulnerabilities–such as buffer overflows–by construction. However, this safety sometimes comes with a performance cost. As a result, many modern type-safe languages provide escape hatches that allow developers to manually bypass them. The relative value of performance to safety and the degree of performance obtained depends upon the application context, including user goals and the hardware upon which the application is to be executed. Since libraries may be used in many different contexts, library developers cannot make safety-performance trade-off decisions appropriate for all cases. Application developers can tune libraries themselves to increase safety or performance, but this requires extra effort and makes libraries less reusable. To address this problem, we present NADER, a Rust development tool that makes applications safer by automatically transforming unsafe code into equivalent safe code according to developer preferences and application context. In end-to-end system evaluations in a given context, NADER automatically reintroduces numerous library bounds checks, in many cases making application code that uses popular Rust libraries safer with no corresponding loss in performance.
Infrastructuring ESTS

In our inaugural editorial, we, the incoming Editorial Collective (EC) of Engaging Science, Technology, and Society (ESTS), describe the digital and social infrastructural work that we have undertaken since assuming editorship of the journal. We also note some of the changes we have introduced in terms of the journal’s content and policies. A key argument is that even though publishing infrastructures shape the form and movement of scholarly content in crucial ways, they often remain black-boxed, rendering invisible the time, labor, and skill in developing and sustaining them.
Improving Chinese Character Representation with Formation Graph Attention Network

Chinese characters are often composed of subcharacter components which are also semantically informative, and the component-level internal semantic features of a Chinese character inherently bring with additional information that benefits the semantic representation of the character. Therefore, there have been several studies that utilized subcharacter component information (e.g. radical, fine-grained components and stroke n-grams) to improve Chinese character representation.
Characterization of photovoltaic solar energy systems in a Colombian region

Objective: To establish a baseline about the solar photovoltaic energy systems installed in the department of Caldas, Colombia. Methodology: The solar photovoltaic installations were identified from secondary sources of information, followed by field visits to collect information on the characteristics of these systems. An evaluation instrument was designed consisting of 6 categories which was subsequently evaluated by expert peers. It was applied accompanied by interviews with owners of the identified systems. Results: 41 solar photovoltaic systems were identified and installed in 11 municipalities of Caldas. Data was collected from 28 installations with different characteristics. 50% of the solar photovoltaic systems are in the Central-South region of Caldas. 71% of the systems are mainly installed in the urban area; 64% correspond to small-scale isolated solar photovoltaic energy generation systems, with a maximum power of less than 600W. These installations have become a viable energy alternative for the region and are being used especially for lighting and the operation of household appliances in residences, hotels, restaurants, and educational institutions. Conclusions: Although regulations on solar energy installations had not been established, these systems have already been implemented in the department in a successful, but moderate, manner.
Subtle CSCW Traits: Tensions Around Identity Formation and Online Activism in the Asian Diaspora

The COVID-19 pandemic has been uniquely challenging for the Asian diaspora. The virus has directly devastated Asian communities around the world, most notably across India. Its indirect effects have also been crushing: violent hate crimes against elders, the dissolution of once-thriving businesses, and the trauma of pandemic-enforced disconnect from transnational family networks have all weighed heavily on Asian people. Publicly grappling with these difficulties, through hashtags and GoFundMes across social media, has raised awareness of the issues that Asian people have dealt with long before COVID. But doing so amidst isolation has illuminated a need for space to build relationships, confront intra- and inter-community biases, and envision a more hopeful future. This workshop looks to create that space. By convening social computing researchers with ties to Asian diaspora identities, we aim to foster discussion of how social platforms enable identity formation and online activism unique to the Asian diasporic experience. We will consider what it means to be an Asian diaspora researcher, challenge CSCW's notion of what it means to be Asian, and explore how Asianness can work in alliance with other marginalized identities to ultimately concretize a research agenda for CSCW to more meaningfully engage with Asian diaspora experiences.
Research on the method of underwater image enhancement

Abstract A multi-angle image enhancement method based on underwater imaging was proposed to solve the problems of color bias, geometric distortion and low contrast of the observed object in the complex and changeable underwater environment. Firstly, the gray world algorithm is used to preprocess the underwater image. Then an image optimization denoising algorithm based on K-SVD dictionary learning is used to keep the structure and texture information of the original image. Finally, combining with the underwater optical imaging model, the blue-green channel enhancement algorithm is used to realize the underwater imaging defog processing, which makes the restored image closer to the reality. The results show that this method can solve the problems of noise interference, fuzzy underwater image and low contrast.
Effects of best-practice based materials in receptive language learning behaviours in improving receptive language skills

This is a part of the research results entitled “The Effects of Best-Practice based Materials in Language Learning Behaviors in Improving Language Skills”. Best practice can generate good theory. In the field of language learning, many experts who have developed language learning theory based on best practices. Theory developed based on best practice has been used for complementary learning materials in the course Belajar dan Pembelajaran (Learning and Teaching) in English Department in a state university as an experimental study. This study aims to find out the effects of Best Practice–based materials in language learning behaviors in improving the subjects’ receptive language skills (Reading, Listening Skills). To find out the effects, researchers conducted qualitative experiment by giving open-ended questionnaires as the pretest and posttest, and treatment. The researchers chose 20 students with the lowest grade points. The differences of subjects’ answers in pretest and posttest indicate the effects of treatment with best practice-learning materials. Data analysis involves observation, open coding, and axial coding. The results reveal the effects on learning strategies to improve the for language skills used by students with the lowest grade point average.
The Overview of Decentralized Systems Scaling Methods

Decentralized systems and protocols are becoming more popular every year. The main limitations of public permissionless blockchains are low bandwidth and high fees. There are various ways to reduce the load on the network to address these drawbacks, the majority of them were developed for Ethereum but stay valid for many other chains. The paper considers the following options for scaling decentralized systems: consensus modification and sharding that are embedded in the core of a blockchain, second layer solutions and sidechains that can be built on top of a network.
Manikin Characters: User Characters in Human Computer Modelling

Design methods that support the development of a good understanding of user requirements are essential for product development. Traditional methods involve direct interaction with users but a product development team can still gain great benefit from applying what might otherwise be considered second best methods. Among the range of methods and tools available are User Characters and Human Computer Modelling. Why not combine them? This paper reviews the two approaches in the context of research conducted in user diversity considerations, and then discusses opportunities for integration of the two methods into a new approach that might be called Manikin Characters.
Neural Network Explainable AI Based on Paraconsistent Analysis: An Extension

This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural networks and whether it is worth further develop the subject in future research. The answers to these two questions are affirmative. Paraconsistent analysis provides insightful prediction visualisation through a mature formal framework that provides proper support for reasoning. The significant potential envisioned is the that paraconsistent analysis will be used for guiding neural network development projects, despite the performance issues. This paper provides two explorations. The first was a baseline experiment based on MNIST for establishing the link between paraconsistency and neural networks. The second experiment aimed to detect violence in audio files to verify whether the paraconsistent framework scales to industry level problems. The conclusion shown by this early assessment is that further research on this subject is worthful, and may eventually result in a significant contribution to the field.
Co-designing engaging and accessible data visualisations: a case study of the IPCC reports

Abstract Creating scientifically rigorous and user-friendly data visualisations can play a critical role in making complex information more accessible to wider audiences and supporting informed decision-making. ‘Co-design’ encapsulates a way of approaching data visualisation that ensures a deep and shared understanding between those creating the visuals (e.g. information designers, content experts, cognitive scientists) and the audience/users. This essay describes co-designing data visualisations with the Intergovernmental Panel on Climate Change (IPCC). A multidisciplinary design team made up of information designers and cognitive and social scientists worked closely with IPCC authors and staff to develop data visualisations for the Summary for Policymakers (SPM) of the Special Report on Global Warming of 1.5 °C and the Special Report on Climate Change and Land. In this essay, the authors consider the three crucial elements that underpin a successful co-design process—practical tools and a flexible method; cognitive science and psychology to better understand the needs of users; and the importance of trust and leadership. The authors reflect on the application of the co-design approach in an IPCC context, noting specific challenges and including recommendations for future IPCC reports. The mutual learning experience of the special reports indicates a shift towards a design culture within parts of the IPCC that recognises the value of telling a compelling visual story while retaining scientific integrity—an approach that has been retained for the Working Group I contribution to the Sixth Assessment Report.
Comparative Study for Anomaly Detection in Crowded Scenes

Nowadays, video analysis is an important research area especially from a security point of view. The discovery of unusual activities is important because it is a difficult task for humans especially with increasing number of surveillance cameras in all crowded places. That is because it requires a lot of human effort, and these activities happen rarely. Also the definition of anomaly events is different based on the location of the event. For example running in the park is a normal event but running in a restaurant is an abnormal event. The event is the same but the place was the factor of making it normal or not. The main objective of this paper is to compile what has been achieved in the field of anomaly detection and compare them, and to look at the different datasets used in the recent period. We will show how to detect and identify anomalies in videos, approaches for video anomaly detection and also what are the latest learning frameworks.
Multi-Agent Advisor Q-Learning

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online suboptimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
Elastic Hyperparameter Tuning on the Cloud

Hyperparameter tuning is a necessary step in training and deploying machine learning models. Most prior work on hyperparameter tuning has studied methods for maximizing model accuracy under a time constraint, assuming a fixed cluster size. While this is appropriate in data center environments, the increased deployment of machine learning workloads in cloud settings necessitates studying hyperparameter tuning with an elastic cluster size and time and monetary budgets. While recent work has leveraged the elasticity of the cloud to minimize the execution cost of a pre-determined hyperparameter tuning job originally designed for fixed-cluster sizes, they do not aim to maximize accuracy.
Selfish Mining Attacks Exacerbated by Elastic Hash Supply

Several attacks have been proposed against Proof-of-Work blockchains, which may increase the attacker’s share of mining rewards (e.g., selfish mining, block withholding). A further impact of such attacks, which has not been considered in prior work, is that decreasing the profitability of mining for honest nodes incentivizes them to stop mining or to leave the attacked chain for a more profitable one. The departure of honest nodes exacerbates the attack and may further decrease profitability and incentivize more honest nodes to leave. In this paper, we first present an empirical analysis showing that there is a statistically significant correlation between the profitability of mining and the total hash rate, confirming that miners indeed respond to changing profitability. Second, we present a theoretical analysis showing that selfish mining under such elastic hash supply leads either to the collapse of a chain, i.e., all honest nodes leaving, or to a stable equilibrium depending on the attacker’s initial share.
Voice Conversion Can Improve ASR in Very Low-Resource Settings

Voice conversion (VC) has been proposed to improve speech recognition systems in low-resource languages by using it to augment limited training data. But until recently, practical issues such as compute speed have limited the use of VC for this purpose. Moreover, it is still unclear whether a VC model trained on one well-resourced language can be applied to speech from another low-resource language for the purpose of data augmentation. In this work we assess whether a VC system can be used cross-lingually to improve low-resource speech recognition. Concretely, we combine several recent techniques to design and train a practical VC system in English, and then use this system to augment data for training a speech recognition model in several low-resource languages. We find that when using a sensible amount of augmented data, speech recognition performance is improved in all four low-resource languages considered.
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space

Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competition (DLC), a novel reinforcement learning algorithm that learns competitive visual control policies through self-play in imagination. The DLC agent imagines multi-agent interaction sequences in the compact latent space of a learned world model that combines a joint transition function with opponent viewpoint prediction. Imagined self-play reduces costly sample generation in the real world, while the latent representation enables planning to scale gracefully with observation dimensionality. We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations. Code and videos available at this https URL.