Reviewerly/exHarmony huggingface dataset:
Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET

Contemporary concerns over the governance of technological systems often run up against narratives about the technical infeasibility of designing mechanisms for accountability. While in recent AI ethics literature these concerns have been deliberated predominantly in relation to machine learning, other instances in the history of computing also presented circumstances in which computer scientists needed to un-muddle what it means to design accountable systems. One such compelling narrative can frequently be found in canonical histories of the Internet that highlight how its original designers’ commitment to the “End-to-End” architectural principle precluded other features from being implemented, resulting in the fast-growing, generative, but ultimately unaccountable network we have today. This paper offers a critique of such technologically essentialist notions of accountability and the characterization of the “unaccountable Internet” as an unintended consequence. It explores the changing meaning of accounting and its relationship to accountability in a selected corpus of requests for comments (RFCs) concerning the early Internet’s design from the 1970s and 80s. We characterize four ways of conceptualizing accounting: as billing, as measurement, as management, and as policy, and demonstrate how an understanding of accountability was constituted through these shifting meanings. We link together the administrative and technical mechanisms of accounting for shared resources in a distributed system and an emerging notion of accountability as a social, political, and technical category, arguing that the former is constitutive of the latter. Recovering this history is not only important for understanding the processes that shaped the Internet, but also serves as a starting point for unpacking the complicated political choices that are involved in designing accountability mechanisms for other technological systems today.
On abelian covers of the projective line with fixed gonality and many rational points

A smooth geometrically connected curve over the finite field [Formula: see text] with gonality [Formula: see text] has at most [Formula: see text] rational points. Faber and Grantham conjectured that there exist curves of every sufficiently large genus with gonality [Formula: see text] that achieve this bound. In this paper, we show that this bound can be achieved for an infinite sequence of genera using abelian covers of the projective line. We also argue that abelian covers will not suffice to prove the full conjecture.
Curvetime: A blockchain framework for Artificial Intelligence computation

Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment's state, whereas a new block is added and verified. Specifically, we design the block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a result, our method utilizes the determination of state transition and the randomness of action selection of a Markov decision process, as well as the computational complexity of a deep neural network, collectively to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain nodes are exploited to train the same deep neural network with different data samples (state-action pairs) in parallel, allowing the model to experience multiple episodes across computing nodes but at one time. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
Cost-effective estimation of single-mode thermal states by probabilistic quantum metrology

Abstract In probabilistic quantum metrology, one aims at finding measurements that concentrate the Fisher information on the resulting conditional quantum states, i.e. those states post-selected according to the outcome of the measurement. The quantum Cramér–Rao (QCR) bound itself cannot be surpassed this way, but it could be possible to improve the information-cost ratio (where cost refers to the experimental implementation), or even the total Fisher information. In this paper, we propose a post-selection protocol achieving this goal using single-photon subtraction and thermal states of radiation. In particular, we achieve a larger information-cost ratio for the estimation of temperature than the strategy attaining the QCR bound. Our scheme involves only classical states of radiation and this contrasts with (but does not contradict) recent results proving that for unitary quantum statistical models, post-selection strategies can outperform direct protocols only exploiting nonclassical states.
The sound of the udu

Abstract The udu is a traditional Nigerian percussion instrument that can be thought of as a Helmholtz resonator with two apertures. We use a simple two-particle mechanical analogue to study the behaviour of such a resonator and find that the model describes quite well the main features of udu acoustics.
What do developers consider magic literals? A smalltalk perspective

Literals are constant values (numbers, strings, etc.) used in the source code. Magic literals are such values used without an explicit explanation of their meaning. Such undocumented values may hinder source-code comprehension, negatively impacting maintenance. Relatively little literature can be found on the subject beyond the usual (and very old) recommendation of avoiding literals and preferring named constants. Yet, magic literals are still routinely found in source code. We studied literal values in source code to understand when they should be considered magic or not (i.e., acceptable). First, we perform a qualitative study of magic literals, to establish why and under which conditions they are considered harmful. We formalize hypotheses about the reasoning behind how literals are considered magic. Second, we perform a quantitative study on seven real systems ranging from small (a few classes) to large (thousands of classes). We report the literals’ types (number, string, Boolean, …), their grammatical function (e.g., argument in a call, operand in an expression, value assigned, …), or the purpose of the code in which they appear (test methods, regular code). Third, we report on another study involving 26 programmers who analyzed about 24,000 literals, to understand which ones they consider magic. Finally, we evaluate the hypotheses defining specific conditions under which literals are acceptable. We show that (1) literals still exist and are relatively frequent (found in close to 50% of the methods considered); (2) they are more frequent in test methods (in 80% of test methods); (3) to a large extent, they were considered acceptable (only 25% considered magic); and (4) the hypotheses concerning acceptable literals are valid to various degrees. We thus pave the way to future research on magic literals, for example, with tools that could help developers deciding if a literal is acceptable.
Autoregressive based Drift Detection Method

In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution does not change over time (stationarity). However, in real-world scenarios, the data generation process changes over time and the model has to adapt to the new incoming data. This phenomenon is known as concept drift and leads to a decrease in the predictive model's performance. In this study, we propose a new concept drift detection method based on autoregressive models called ADDM. This method can be integrated into any machine learning algorithm from deep neural networks to simple linear regression model. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods, both on synthetic data sets and real-world data sets. Our approach is theoretically guaranteed as well as empirical and effective for the detection of various concept drifts. In addition to the drift detector, we proposed a new method of concept drift adaptation based on the severity of the drift.
The Independence of Distinguishability and the Dimension of the System

The are substantial studies on distinguishabilities, especially local distinguishability, of quantum states. It is shown that a necessary condition of a local distinguishable state set is the total Schmidt rank not larger than the system dimension. However, if we view states in a larger system, the restriction will be invalid. Hence, a nature problem is that can indistinguishable states become distinguishable by viewing them in a larger system without employing extra resources. In this paper, we consider this problem for (perfect or unambiguous) LOCC1, PPT and SEP distinguishabilities. We demonstrate that if a set of states is indistinguishable in $${\otimes }_{k=1}^{K} C^{d_{k}}$$ , then it is indistinguishable even being viewed in $$\otimes _{k=1}^{K} C^{d_{k}+h_{k}}$$ , where $$K, d_{k}\geqslant 2, h_{k}\geqslant 0$$ are integers. This shows that such distinguishabilities are properties of states themselves and independent of the dimension of quantum system. Our result gives the maximal numbers of LOCC1 distinguishable states and can be employed to construct a LOCC indistinguishable product basis in general systems. Our result is suitable for general states in general systems. For further discussions, we define the local-global indistinguishable property and present a conjecture.
Maximizing Cancer Data—The Future of Cancer Is Now

This Viewpoint discusses sharing of cancer data through a national cancer data ecosystem for the benefit of public health.
Based on the Exploration of VR in the Teaching and Training of Special Children

In this paper, virtual reality technology in the rehabilitation training and teaching of children with special needs as the starting point, through consulting the development of rehabilitation training for children with special needs in recent years and existing problems, combined with the charactcristics of virtual reality technology, in-depth discussion on how to explore the potential of virtual reality technology, to better serve the physical disability, visual disability and hearing impairment of three special children. On this basis, it is proposed to construct a "game-based" rehabilitation system for physical disorders , a visual disability learning system integrating "improved perceptual learning system" and "implanted sensory visual signal elements", and a virtual reality system for speech training for deaf children, which is characterized by "giving full play to the advantages of deaf children's visual ability and reasoning ability". This paper hopes to provide new methods and ideas for the development of special children with the help of the above training and rehabilitation system for special children.
Higher-order thinking word problem-solving errors made by low-performing pupils: Comparative case study across school types in Malaysia

The achievement gaps existed among the three types of Malaysian elementary schools with different instruction mediums. Thus, this study sought to compare the types of errors made by the low-performing students from different school types in solving word problems involving higher-order thinking skills by conducting a multiple case study. The study involved 18 low-performing students from a National Primary School (NPS), National-Type Chinese Primary School (NTCPS), and National-Type Tamil Primary School (NTTPS) in Malaysia. The students’ errors in solving eight word problems were identified qualitatively through conducting four sessions of diagnostic interviews, followed by the Newman’s Error Analysis. Then, a cross-case analysis was conducted. The findings indicated that the participants of the three school types made reading, comprehension, transformation, process skills and encoding errors. However, the NPS and NTTPS low-performing students made more process skills errors and encoding errors, as compared to the NTCPS low-performing students. The findings imply the need to enhance the procedural fluency of NPS and NTTPS low-performing students for reducing the achievement gaps among the school types.
PENGARUH EFIKASI DIRI DAN LINGKUNGAN BELAJAR TERHADAP HASIL BELAJAR EKONOMI DENGAN MOTIVASI BELAJAR SEBAGAI VARIABEL MEDIASI DI MASA PANDEMI COVID 19

During the Covid 19 pandemic, the learning process is limited online and offline so it has less time allocation than before. It was found that there were some students who cheated while doing assignments, daily tests and there were students who were less active in the class in the learning process. Quantitative research is correlational with the aim of knowing the effect of self-efficacy and learning environment with learning motivation as a mediating variable on economic learning outcomes. The sample of this research is the students of class X E5 and X E8 of SMAN 3 Sidoarjo with a total of 73 people. Primary data by distributing questionnaires while secondary data using the Final Semester Assessment. Data analysis using Structural Equation Modeling using WarPLS type 7.0 tool. The results of the discussion of observations show that self-efficacy directly affects learning motivation, self-efficacy has no effect on learning outcomes, learning environment affects learning motivation and learning outcomes, learning motivation affects learning outcomes. However, indirectly self-efficacy and learning environment cannot affect learning outcomes through learning motivation. Keywords : Self-Efficacy, Learning Environment, Learning Motivation
Path Planning for Mine Rescue Robots Based on Improved Ant Colony Algorithm

An improved ant colony algorithm is proposed in order to improve the convergence speed and search efficiency of the traditional ant colony algorithm, as well as to reduce the influence of redundant fold points on the robot's movement mode. A 16-directional 24-neighbourhood ant search approach is adopted to expand the ant search range; the initial pheromone differential distribution is adopted to avoid the blindness of early search and improve the search efficiency of early ants; an obstacle avoidance strategy is introduced to greatly reduce the probability of ant deadlock, and a search direction angle is introduced to improve the heuristic function to accelerate the convergence speed; a random state transfer mechanism is adopted to improve the global search capability; secondly, we introduce an ant retreat strategy to ensure that each ant can escape from the trap, which solves the U-shaped of obstacle trap problem and improves the search efficiency. Finally, the effectiveness of the proposed algorithm is verified by experimental simulation.
How Have Astronomers Cited Other Fields in the Last Decade?

Abstract We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade (2010–2020). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field by astronomers, a trend highly correlated with the discovery of gravitational waves. Second, the fast growth of referenced papers in computer science and statistics, the first with a notable 15 fold increase since 2015. Such findings confirm the critical role of interdisciplinary efforts involving astronomy, statistics, and computer science in recent astronomical research.
Would You Ask it that Way?

Knowledge graph question answering (KGQA) facilitates information access by leveraging structured data without requiring formal query language expertise from the user. Instead, users can express their information needs by simply asking their questions in natural language (NL). Datasets used to train KGQA models that would provide such a service are expensive to construct, both in terms of expert and crowdsourced labor. Typically, crowdsourced labor is used to improve template-based pseudo-natural questions generated from formal queries. However, the resulting datasets often fall short of representing genuinely natural and fluent language. In the present work, we investigate ways to characterize and remedy these shortcomings. We create the IQN-KGQA test collection by sampling questions from existing KGQA datasets and evaluating them with regards to five different aspects of naturalness. Then, the questions are rewritten to improve their fluency. Finally, the performance of existing KGQA models is compared on the original and rewritten versions of the NL questions. We find that some KGQA systems fare worse when presented with more realistic formulations of NL questions. The IQN-KGQA test collection is a resource to help evaluate KGQA systems in a more realistic setting. The construction of this test collection also sheds light on the challenges of constructing large-scale KGQA datasets with genuinely NL questions.
Ransomware and the Robin Hood effect?: Experimental evidence on Americans’ willingness to support cyber-extortion

Ransomware attacks have become a critical security threat worldwide. However, existing research on ransomware has largely ignored public opinion. This initial study identifies patterns in the American public’s support for the use of ransomware, specifically when it is framed to provide benefits to others (i.e., in-group members). Drawing on the Robin Hood decision-making literature and Moral Foundations Theory, we offer theoretical predictions regarding ransomware support. In a survey of 1013 Americans, we embedded a split-ballot experiment in which respondents were randomly assigned to indicate their level of support or opposition to one of two sets of six ransomware scenarios. We manipulated the nationality, authority level, and political affiliation of the actors. We find that people are more supportive of ransomware use when the actors are from their own in-group, and the outcomes benefit their in-group members. Also, the more strongly participants endorsed the moral foundations of authority and harm/care, the more supportive they were of the use of ransomware that may benefit others from their in-group. These findings suggest political actors may be able to generate public support for morally questionable actions by emphasizing in-group benefits and the Robin Hood nature of an attack (e.g., outcome-based morality).
COMPO*SED: Composite Parallel Coordinates for Co-Dependent Multi-Attribute Choices

We propose Composite Parallel Coordinates, a novel parallel coordinates technique to effectively represent the interplay of component alternatives in a system. It builds upon a dedicated data model that formally describes the interaction of components. Parallel coordinates can help decision-makers identify the most preferred solution among a number of alternatives. Multi-component systems require one such multi-attribute choice for each component. Each of these choices might have side effects on the system&#x0027;s operability and performance, making them co-dependent. Common approaches employ complex multi-component models or involve back-and-forth iterations between single components until an acceptable compromise is reached. A simultaneous visual exploration across independently modeled but connected components is needed to make system design more efficient. Using dedicated layout and interaction strategies, our Composite Parallel Coordinates allow analysts to explore both individual properties of components as well as their interoperability and joint performance. We showcase the effectiveness of Composite Parallel Coordinates for co-dependent multi-attribute choices by means of three real-world scenarios from distinct application areas. In addition to the case studies, we reflect on observing two domain experts collaboratively working with the proposed technique and communicating along the way.
Lightweight Stream Ciphers based on Chaos for Time and Energy Constrained IoT Applications

The design of efficient and secure cryptographic algorithms is a fundamental problem of cryptography. Due to the tight cost and constrained resources devices such as Radio-Frequency IDentification (RFID), wireless sensors, smart cards, health-care devices, lightweight cryptography has received a great deal of attention. Recent research mainly focused on designing optimized cryptographic algorithms which trade offs between security performance, time consuming, energy consumption and cost. In this paper, we present two chaotic stream ciphers based on chaos and we report the results of a comparative performance evaluation study. Compared to other crypto-systems of the literature, we demonstrate that our designed stream ciphers are suitable for practical secure applications of the Internet of Things (IoT) in a constrained resource environment.
Harris Hawk Optimization Combined with Differential Evolution for the Estimation of Solar Cell Parameters

In a dynamic shift, lowering reliance on fossil fuels and greenhouse gas emissions is now a top goal. This is accomplished through expanding the usage of renewable energy. Solar photovoltaic (PV) energy is now more than ever at the heart of many cities’ policies. Improving the efficiency of PV systems is a current research goal. The key challenge in rectifying complex systems is to establish a model that correctly reproduces the system’s dynamic behaviour. The goal function and optimization method utilised are indicative of the model parameters’ correctness. This paper presents a mix of differential evolution (DE) and Harris hawk optimisation (HHO). The suggested technique estimates the parameter vector that minimises the objective function to the greatest extent possible. This is for the many diode models. The procedure is validated using experimental data acquired at a known temperature and irradiance. The root mean square error (RMSE) is used to assess the method’s effectiveness. A comparison is made between the objective function of the hybrid approach presented in this publication and previously authorised methods. The strategy utilised is as straightforward as many others stated in our predecessors’ publications, and this applies to both models analysed. When compared to the simple version of the Harris hawk optimizer, this approach allows for more experimentation.
Towards user-centered explainable energy demand forecasting systems

In recent years, eXplainable Artificial Intelligence (XAI) has received huge attention in the area of explaining the decision-making processes of machine learning models. The aim is to increase the acceptance, trust, and transparency of AI models by providing explanations about the models' decisions. But most of the prior works on XAI are focused to support AI practitioners and developers in understanding and debugging. In this paper, we propose a user-centered explainable energy demand prediction and forecasting system that aims to provide explanations to end-users in the smart home. In doing so, we present an overview of the explainable system and propose a method combining Deep Learning Important FeaTures (DeepLIFT) and Shapley Additive Explanations (SHAP) to explain the prediction of an LSTM-based energy forecasting model.