nils-herrmann/clean_scirepeval huggingface dataset:
Incentivizing Double-Spend Collusion in Bitcoin

Bitcoin’s core innovation is its solution to double-spending, called Nakamoto consensus. This provides a probabilistic guarantee that transactions will not be reversed or redirected, provided that it is improbable for an attacker to obtain a majority of mining power in the network. While this may be true in the traditional sense, this assumption becomes tenuous when miners are assumed to be rational and hence venal. Accordingly, we present the whale attack, in which a minority attacker increases her chances of double-spending by incentivizing miners to subvert the consensus protocol and to collude via whale transactions, or transactions carrying anomalously large fees. We analyze the expected cost to carry out the attack, and simulate the attack under realistic system parameters. Our results show that double-spend attacks, conventionally thought to be impractical for minority attackers, can actually be financially feasible and worthwhile under the whale attack. Perhaps more importantly, this work demonstrates that rationality should not underestimated when evaluating Bitcoin’s security.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management

Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System

This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC [6] while being simpler. We show competitive results in word error rate on the Librispeech corpus [18] with MFCC features, and promising results from raw waveform.
OpenCog: A Software Framework for Integrative Artificial General Intelligence

The OpenCog software development framework, for advancement of the development and testing of powerful and responsible integrative AGI, is described. The OpenCog Framework (OCF) 1.0, to be released in 2008 under the GPLv2, is comprised of a collection of portable libraries for OpenCog applications, plus an initial collection of cognitive algorithms that operate within the OpenCog framework. The OCF libraries include a flexible knowledge representation embodied in a scalable knowledge store, a cognitive process scheduler, and a plug-in architecture for allowing interaction between cognitive, perceptual, and control algorithms.
Critical success factors for implementing knowledge management in small and medium enterprises

To date, critical success factors (CSFs) for implementing knowledge management (KM) in small and medium enterprises (SMEs) have not been systematically investigated. Existing studies have derived their CSFs from large companies’ perspectives and have not considered the needs of smaller businesses. This paper is aimed to bridge this gap.  Existing studies on CSFs were reviewed and their limitations were identified. By integrating insights drawn from these studies as well as adding some new factors, the author proposed a set of 11 CSFs which is believed to be more suitable for SMEs. The importance of the proposed CSFs was theoretically discussed and justified. In addition, an empirical assessment was conducted to evaluate the extent of success of this proposition.  The overall results from the empirical assessment were positive, thus reflecting the appropriateness of the proposed CSFs.  The set of CSFs can act as a list of items for SMEs to address when adopting KM. This helps to ensure that the essential issues and factors are covered during implementation. For academics, it provides a common language for them to discuss and study the factors crucial for the success of KM in SMEs.  This study is probably the first to provide an integrative perspective of CSFs for implementing KM in the SME sector. It gives valuable information, which hopefully will help this business sector to accomplish KM.
The results on vertex domination in Fuzzy graphs

We do fuzzification the concept of domination in crisp graph by using membership values of nodes, α-strong and arcs. In this paper, we introduce a new variation on the domination theme which we call vertex domination. We determine the vertex domination number γv for several classes of fuzzy graphs, specially complete fuzzy graph and complete bipartite fuzzy graphs. The bounds is obtained for the vertex domination number of fuzzy graphs. Also the relationship between M -strong arcs and α-strong is obtained. In fuzzy graphs, monotone decreasing property and monotone increasing property is introduced. We prove the vizing’s conjecture is monotone decreasing fuzzy graph property for vertex domination. we prove also the Grarier-Khelladi’s conjecture is monotone decreasing fuzzy graph property for it. We obtain Nordhaus-Gaddum (NG) type results for these parameters. The relationship between several classes of operations on fuzzy graphs with the vertex domination number of them is studied.
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings

The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any taskspecific feature engineering effort. Our experimental results on the task of opinion target identification show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based models. Our framework is flexible, allows us to incorporate other linguistic features, and achieves results that rival the top performing systems in SemEval-2014.
SemEval-2015 Task 10: Sentiment Analysis in Twitter

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.
Mate preferences do predict attraction and choices in the early stages of mate selection.

Although mate preference research has firmly established that men value physical attractiveness more than women do and women value social status more than men do, recent speed-dating studies have indicated mixed evidence (at best) for whether people's sex-differentiated mate preferences predict actual mate choices. According to an evolutionary, mate preference priority model (Li, Bailey, Kenrick, & Linsenmeier, 2002; Li & Kenrick, 2006; Li, Valentine, & Patel, 2011), the sexes are largely similar in what they ideally like, but for long-term mates, they should differ on what they most want to avoid in early selection contexts. Following this model, we conducted experiments using online messaging and modified speed-dating platforms. Results indicate that when a mating pool includes people at the low end of social status and physical attractiveness, mate choice criteria are sex-differentiated: Men, more than women, chose mates based on physical attractiveness, whereas women, more than men, chose mates based on social status. In addition, individuals who more greatly valued social status or physical attractiveness on paper valued these traits more in their actual choices. In particular, mate choices were sex-differentiated when considering long-term relationships but not short-term ones, where both sexes shunned partners with low physical attractiveness. The findings validate a large body of mate preferences research and an evolutionary perspective on mating, and they have implications for research using speed-dating and other interactive contexts.
Differentiable Physics-informed Graph Networks

While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behaviors when the knowledge is given less explicitly. In this work, we propose a novel architecture called Differentiable Physics-informed Graph Networks (DPGN) to incorporate implicit physics knowledge which is given from domain experts by informing it in latent space. Using the concept of DPGN, we demonstrate that climate prediction tasks are significantly improved. Besides the experiment results, we validate the effectiveness of the proposed module and provide further applications of DPGN, such as inductive learning and multistep predictions.
Analytics on Big Aviation Data: Turning Data into Insights

The business world is undergoing a revolution driven by the use of data and analytics to guide decision-making. While many forces are at work, a major reason for the business analytics revolution is the rapid proliferation of the amount of data available to be analysed. Recent days, big data is beginning to have a major impact on air travel with more data being created both through the plane sensors and the passengers on board; the opportunities to use this data will only increase. It provides innovative companies with the opportunity to improve major aspects of their business, from using data to improve customer retention through to making planes safer and more reliable. In this paper we discuss a big data concept, definitions, and further present some cases for aviation industry to analyse data from every conceivable channel, for instance, customer data to create a unique profile for each customer based on a wide range of demographic data, behaviours, and preferences.
Trajectory-Based Modeling of Human Actions with Motion Reference Points

Human action recognition in videos is a challenging problem with wide applications. State-of-the-art approaches often adopt the popular bag-of-features representation based on isolated local patches or temporal patch trajectories, where motion patterns like object relationships are mostly discarded. This paper proposes a simple representation specifically aimed at the modeling of such motion relationships. We adopt global and local reference points to characterize motion information, so that the final representation can be robust to camera movement. Our approach operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a necessary step to model object relationships. Through an extensive experimental evaluation, we show that the proposed representation offers very competitive performance on challenging benchmark datasets, and combining it with the bag-of-features representation leads to substantial improvement. On Hollywood2, Olympic Sports, and HMDB51 datasets, we obtain 59.5%, 80.6% and 40.7% respectively, which are the best reported results to date.
Augmented Reality Video Games : New Possibilities and Implications for Children and Adolescents

In recent years, the video game market has embraced augmented reality video games, a class of video games that is set to grow as gaming technologies develop. Given the widespread use of video games among children and adolescents, the health implications of augmented reality technology must be closely examined. Augmented reality technology shows a potential for the promotion of healthy behaviors and social interaction among children. However, the full immersion and physical movement required in augmented reality video games may also put users at risk for physical and mental harm. Our review article and commentary emphasizes both the benefits and dangers of augmented reality video games for children and adolescents.
Promotion and Its Influence on Destination Awareness and Loyalty in the Tourism Industry

Internet is commonly accepted as an important aspect in successful tourism promotion as well as destination marketing in this era. The main aim of this study is to explore how online promotion and its influence on destination awareness and loyalty in the tourism industry. This study proposes a structural model of the relationships among online promotion (OP), destination awareness (DA), tourist satisfaction (TS) and destination loyalty (DL). Randomly-selected respondents from the population of international tourists departing from Vietnamese international airports were selected as the questionnaire samples in the study. Initially, the exploratory factor analysis (EFA) was performed to test the validity of constructs, and the confirmatory factor analysis (CFA), using AMOS, was used to test the significance of the proposed hypothesizes model. The results show that the relationships among OP, DA, TS and DL appear significant in this study. The result also indicates that online promotion could improve the destination loyalty. Finally, the academic contribution, implications of the findings for tourism marketers and limitation are also discussed in this study.
Enabling Low-Latency Applications in Fog-Radio Access Networks

The ultra low-latency operations of communications and computing enable many potential IoT applications, and thus have gained widespread attention recently. Existing mobile devices and telecommunication systems may not be able to provide the highly desired low-latency computing and communications services. To meet the needs of those applications, we introduce the Fog-Radio Access Network (F-RAN) architecture, which brings the efficient computing capability of the cloud to the edge of the network. By distributing computing-intensive tasks to multiple F-RAN nodes, F-RAN has the potential to meet the requirements of those ultra low-latency applications. In this article, we first introduce the F-RAN and its rationale in serving ultra low-latency applications. Then we discuss the need for a service framework for F-RAN to cope with the complex tradeoff among performance, computing cost, and communication cost. Finally, we illustrate the mobile AR service as an exemplary scenario to provide insights for the design of the framework. Examples and numerical results show that ultra low-latency services can be achieved by the F-RAN by properly handling the tradeoff.
Monte Carlo convolution for learning on non-uniformly sampled point clouds

Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN.
Thermal therapy, part 2: hyperthermia techniques.

Hyperthermia, the procedure of raising the temperature of a part of or the whole body above normal for a defined period of time, is applied alone or as an adjunctive with various established cancer treatment modalities such as radiotherapy and chemotherapy. Clinical hyperthermia falls into three broad categories, namely, (1) localized hyperthermia, (2) regional hyperthermia, and (3) whole-body hyperthermia (WBH). Because of the various problems associated with each type of treatment, different heating techniques have evolved. In this article, background information on the biological rationale and current status of technologies concerning heating equipment for the application of hyperthermia to human cancer treatment are provided. The results of combinations of other modalities such as radiotherapy or chemotherapy with hyperthermia as a new treatment strategy are summarized. The article concludes with a discussion of challenges and opportunities for the future.
The Reactive Tabu Search

We propose an algorithm for combinatorial optimization where an explicit check for the repetition of configurations is added to the basic scheme of Tabu search. In our Tabu scheme the appropriate size of the list is learned in an automated way by reacting to the occurrence of cycles. In addition, if the search appears to be repeating an excessive number of solutions excessively often, then the search is diversified by making a number of random moves proportional to a moving average of the cycle length. The reactive scheme is compared to a ”strict” Tabu scheme, that forbids the repetition of configurations and to schemes with a fixed or randomly varying list size. From the implementation point of view we show that the Hashing or Digital Tree techniques can be used in order to search for repetitions in a time that is approximately constant. We present the results obtained for a series of computational tests on a benchmark function, on the 0-1 Knapsack Problem, and on the Quadratic Assignment Problem.
Sparse learning via Boolean relaxations

We introduce novel relaxations for cardinality-constrained learning problems, including least-squares regression as a special but important case. Our approach is based on reformulating a cardinality-constrained problem exactly as a Boolean program, to which standard convex relaxations such as the Lasserre and Sherali-Adams hierarchies can be applied. We analyze the first-order relaxation in detail, deriving necessary and sufficient conditions for exactness in a unified manner. In the special case of least-squares regression, we show that these conditions are satisfied with high probability for random ensembles satisfying suitable incoherence conditions, similar to results on 1-relaxations. In contrast to known methods, our relaxations yield lower bounds on the objective, and it can be verified whether or not the relaxation is exact. If it is not, we show that randomization based on the relaxed solution offers a principled way to generate provably good feasible solutions. This property enables us to obtain high quality estimates even if incoherence conditions are not met, as might be expected in real datasets. We numerically illustrate the performance of the relaxationrandomization strategy in both synthetic and real high-dimensional datasets, revealing substantial improvements relative to 1-based methods and greedy selection heuristics. B Laurent El Ghaoui elghaoui@berkeley.edu Mert Pilanci mert@berkeley.edu Martin J. Wainwright wainwrig@berkeley.edu 1 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA 2 Department of Electrical Engineering and Computer Sciences and Department of Statistics, University of California, Berkeley, CA, USA
Factors influencing the alignment of accounting information systems in small and medium sized Malaysian manufacturing firms

The concept of alignment or fit between information technology (IT) and business strategy has been discussed for many years, and strategic alignment is deemed crucial in increasing firm performance. Yet few attempts have been made to investigate the factors that influence alignment, especially in the context of small and medium sized firms (SMEs). This issue is important because results from previous studies suggest that many firms struggle to achieve alignment. Therefore, this study sought to identify different levels of alignment and then investigated the factors that influence alignment. In particular, it focused on the alignment between the requirements for accounting information (AIS requirements) and the capacity of accounting systems (AIS capacity) to generate the information, in the specific context of manufacturing SMEs in Malaysia. Using a mail questionnaire, data from 214 firms was collected on nineteen accounting information characteristics for both requirements and capacity. The fit between these two sets was explored using the moderation approach and evidence was gained that AIS alignment in some firms was high. Cluster analysis was used to find two sets of groups which could be considered more aligned and less aligned. The study then investigated some factors that might be associated with a small firm’s level of AIS alignment. Findings from the study suggest that AIS alignment was related to the firm’s: level of IT maturity; level of owner/manager’s accounting and IT knowledge; use of expertise from government agencies and accounting firms; and existence of internal IT staff.