nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis of Attacks on Blockchain Consensus

We present and validate a novel mathematical model of the blockchain mining process and use it to conduct an economic evaluation of the double-spend attack, which is fundamental to all blockchain systems. Our analysis focuses on the value of transactions that can be secured under a conventional double-spend attack, both with and without a concurrent eclipse attack. We account for an attacker capable of increasing profits by targeting multiple merchants simultaneously. Our model quantifies the importance of several factors that determine the attack’s success, including confirmation depth, attacker mining power, and a confirmation deadline set by the merchant. In general, the security of a transaction against a double-spend attack increases roughly logarithmically with the depth of the block, made easier by the increasing potential profits, but more difficult by the increasing proof of work required. We find that a merchant requiring a single confirmation is protected against attackers that possess as much as 10% of the mining power, but only provided that the total value of goods at risk for double-spend is less than 100 BTC. A merchant that requires a much longer 55 confirmations (≈9 hours) will prevent an attacker from breaking even unless he possesses more than 35% of the current mining power, or the value of goods at risk exceeds 1M BTC.
Deep Learning with Differential Privacy

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations

Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.
Factors influencing Chinese Consumer Online Group-Buying Purchase Intention : An Empirical Study

Because of the high-speed development of e-commerce, online group buying has become a new popular pattern of consumption for Chinese consumers. Previous research has studied online group-buying (OGB) purchase intention in some specific areas such as Taiwan, but in mainland China.  The purpose of this study is to contribute to the Technology Acceptance Model, incorporating other potential driving factors to address how they influence Chinese consumers' online group-buying purchase intentions.  The study uses two steps to achieve its purpose. The first step is that I use the focus group interview technique to collect primary data. The results combining the Technology Acceptance model help me propose hypotheses. The second step is that the questionnaire method is applied for empirical data collection. The constructs are validated with exploratory factor analysis and reliability analysis, and then the model is tested with Linear multiple regression.  The results have shown that the adapted research model has been successfully tested in this study. The seven factors (perceived usefulness, perceived ease of use, price, e-trust, Word of Mouth, website quality and perceived risk) have significant effects on Chinese consumers' online group-buying purchase intentions. This study suggests that managers of group-buying websites need to design easy-to-use platform for users. Moreover, group-buying website companies need to propose some rules or regulations to protect consumers' rights. When conflicts occur, evendors can follow these rules to provide solutions that are reasonable and satisfying for consumers.
Maximum Entropy Models for Named Entity Recognition

In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional nonannotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.
How intermittent breaks in interaction improve collective intelligence

People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects' best solutions so that they could be reloaded and possibly modified in subsequent rounds-a ubiquitous feature of personal productivity software-is similar to constant social influence: It increases mean performance but decreases exploration.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGBD images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose. Our code and video are available at https://sites.google.com/view/densefusion/.
clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs

Sparse matrix vector multiplication (SpMV) kernel is a key computation in linear algebra. Most iterative methods are composed of SpMV operations with BLAS1 updates. Therefore, researchers make extensive efforts to optimize the SpMV kernel in sparse linear algebra. With the appearance of OpenCL, a programming language that standardizes parallel programming across a wide variety of heterogeneous platforms, we are able to optimize the SpMV kernel on many different platforms. In this paper, we propose a new sparse matrix format, the Cocktail Format, to take advantage of the strengths of many different sparse matrix formats. Based on the Cocktail Format, we develop the clSpMV framework that is able to analyze all kinds of sparse matrices at runtime, and recommend the best representations of the given sparse matrices on different platforms. Although solutions that are portable across diverse platforms generally provide lower performance when compared to solutions that are specialized to particular platforms, our experimental results show that clSpMV can find the best representations of the input sparse matrices on both Nvidia and AMD platforms, and deliver 83% higher performance compared to the vendor optimized CUDA implementation of the proposed hybrid sparse format in , and 63.6% higher performance compared to the CUDA implementations of all sparse formats in .
A Right to Access Implies a Right to Know: An Open Online Platform for Research on the Readability of Law

The widespread availability of legal materials online has opened the law to a new and greatly expanded readership. These new readers need the law to be readable by them when they encounter it. However, the available empirical research supports a conclusion that legislation is difficult to read if not incomprehensible to most citizens. We review approaches that have been used to measure the readability of text including readability metrics, cloze testing and application of machine learning. We report the creation and testing of an open online platform for readability research. This platform is made available to researchers interested in undertaking research on the readability of legal materials. To demonstrate the capabilities of the platform, we report its initial application to a corpus of legislation. Linguistic characteristics are extracted using the platform and then used as input features for machine learning using the Weka package. Wide differences are found between sentences in a corpus of legislation and those in a corpus of graded reading material or in the Brown corpus (a balanced corpus of English written genres). Readability metrics are found to be of little value in classifying sentences by grade reading level (noting that such metrics were not designed to be used with isolated sentences).
Learning bilingual word embeddings with (almost) no bilingual data

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this work, we further reduce the need of bilingual resources using a very simple self-learning approach that can be combined with any dictionary-based mapping technique. Our method exploits the structural similarity of embedding spaces, and works with as little bilingual evidence as a 25 word dictionary or even an automatically generated list of numerals, obtaining results comparable to those of systems that use richer resources.
A Spreadsheet Auditing Tool Evaluated in an Industrial Context

Amongst the large number of write-and-throw-away-spreadsheets developed for one-time use there is a rather neglected proportion of spreadsheets that are huge, periodically used, and submitted to regular update-cycles like any conventionally evolving valuable legacy application software. However, due to the very nature of spreadsheets, their evolution is particularly tricky and therefore error-prone. In our strive to develop tools and methodologies to improve spreadsheet quality, we analysed consolidation spreadsheets of an internationally operating company for the errors they contain. The paper presents the results of the field audit, involving 78 spreadsheets with 60,446 non-empty cells. As a by-product, the study performed was also to validate our analysis tools in an industrial context. The evaluated auditing tool offers the auditor a new view on the formula structure of the spreadsheet by grouping similar formulas into equivalence classes. Our auditing approach defines three similarity criteria between formulae, namely copy, logical and structural equivalence. To improve the visualization of large spreadsheets, equivalences and data dependencies are displayed in separated windows that are interlinked with the spreadsheet. The auditing approach helps to find irregularities in the geometrical pattern of similar formulas.
GLAD: Group Anomaly Detection in Social Media Analysis

Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore, it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this article, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pairwise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.
Underwater Optical Wireless Communications, Networking, and Localization: A Survey

Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: 1) Potential network architectures; 2) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques 3) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; 4) Network layer topics containing relaying techniques and potential routing algorithms; 5) Transport layer subjects such as connectivity, reliability, flow and congestion control; 6) Application layer goals and state-of-the-art UOWN applications, and 7) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the future directions for underwater optical wireless communications, networking, and localization research.
Multi-stage Contextual Deep Learning for Pedestrian Detection

Cascaded classifiers have been widely used in pedestrian detection and achieved great success. These classifiers are trained sequentially without joint optimization. In this paper, we propose a new deep model that can jointly train multi-stage classifiers through several stages of back propagation. It keeps the score map output by a classifier within a local region and uses it as contextual information to support the decision at the next stage. Through a specific design of the training strategy, this deep architecture is able to simulate the cascaded classifiers by mining hard samples to train the network stage-by-stage. Each classifier handles samples at a different difficulty level. Unsupervised pre-training and specifically designed stage-wise supervised training are used to regularize the optimization problem. Both theoretical analysis and experimental results show that the training strategy helps to avoid over fitting. Experimental results on three datasets (Caltech, ETH and TUD-Brussels) show that our approach outperforms the state-of-the-art approaches.
Reducing loneliness amongst older people: a systematic search and narrative review.

To investigate the effectiveness of recent social therapeutic interventions to reduce loneliness in older people.



To examine this matter, a literature review, using seven databases, was undertaken using search terms relating to the themes of ageing, loneliness and social support. A total of 17 relevant studies relating to loneliness interventions were analysed.



Three studies reporting on new technologies and one on a group work intervention identified significant reductions in loneliness.



Further research into interventions using new technologies to reduce loneliness in older people is recommended.
Adaptive Skills, Adaptive Partitions (ASAP)

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up to lifelong learning agents. The ASAP framework can also solve related new tasks simply by adapting where it applies its existing learned skills. We prove that ASAP converges to a local optimum under natural conditions. Finally, our experimental results, which include a RoboCup domain, demonstrate the ability of ASAP to learn where to reuse skills as well as solve multiple tasks with considerably less experience than solving each task from scratch.
A Comprehensive Survey on Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into different categories. With a focus on graph convolutional networks, we review alternative architectures that have recently been developed; these learning paradigms include graph attention networks, graph autoencoders, graph generative networks, and graph spatial-temporal networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes and benchmarks of the existing algorithms on different learning tasks. Finally, we propose potential research directions in this
Activity, dietary intake, and weight changes in a longitudinal study of preadolescent and adolescent boys and girls.

To examine the role of physical activity, inactivity, and dietary patterns on annual weight changes among preadolescents and adolescents, taking growth and development into account.



We studied a cohort of 6149 girls and 4620 boys from all over the United States who were 9 to 14 years old in 1996. All returned questionnaires in the fall of 1996 and a year later in 1997. Each child provided his or her current height and weight and a detailed assessment of typical past-year dietary intakes, physical activities, and recreational inactivities (TV, videos/VCR, and video/computer games).



Our hypotheses were that physical activity and dietary fiber intake are negatively correlated with annual changes in adiposity and that recreational inactivity (TV/videos/games), caloric intake, and dietary fat intake are positively correlated with annual changes in adiposity. Separately for boys and girls, we performed regression analysis of 1-year change in body mass index (BMI; kg/m(2)). All hypothesized factors were in the model simultaneously with several adjustment factors.



Larger increases in BMI from 1996 to 1997 were among girls who reported higher caloric intakes (.0061 +/-.0026 kg/m(2) per 100 kcal/day; beta +/- standard error), less physical activity (-.0284 +/-.0142 kg/m(2)/hour/day) and more time with TV/videos/games (.0372 +/-.0106 kg/m(2)/hour/day) during the year between the 2 BMI assessments. Larger BMI increases were among boys who reported more time with TV/videos/games (.0384 +/-.0101) during the year. For both boys and girls, a larger rise in caloric intake from 1996 to 1997 predicted larger BMI increases (girls:.0059 +/-.0027 kg/m(2) per increase of 100 kcal/day; boys:.0082 +/-.0030). No significant associations were noted for energy-adjusted dietary fat or fiber.



For both boys and girls, a 1-year increase in BMI was larger in those who reported more time with TV/videos/games during the year between the 2 BMI measurements, and in those who reported that their caloric intakes increased more from 1 year to the next. Larger year-to-year increases in BMI were also seen among girls who reported higher caloric intakes and less physical activity during the year between the 2 BMI measurements. Although the magnitudes of these estimated effects were small, their cumulative effects, year after year during adolescence, would produce substantial gains in body weight. Strategies to prevent excessive caloric intakes, to decrease time with TV/videos/games, and to increase physical activity would be promising as a means to prevent obesity.