nils-herrmann/clean_scirepeval huggingface dataset:
Smart Contract Templates: foundations, design landscape and research directions

In this position paper, we consider some foundational topics regarding smart contracts (such as terminology, automation, enforceability, and semantics) and define a smart contract as an agreement whose execution is both automatable and enforceable. We explore a simple semantic framework for smart contracts, covering both operational and non-operational aspects. We describe templates and agreements for legally-enforceable smart contracts, based on legal documents. Building upon the Ricardian Contract triple, we identify operational parameters in the legal documents and use these to connect legal agreements to standardised code. We also explore the design landscape, including increasing sophistication of parameters, increasing use of common standardised code, and long-term academic research. We conclude by identifying further work and sketching an initial set of requirements for a common language to support Smart Contract Templates.
Better Optical Triangulation Through Spacetime Analysis

The standard methods for extracting range data from optical triangulation scanners are accurate only for planar objects of uniform reflectance illuminated by an incoherent source. Using these methods, curved surfaces, discontinuous surfaces, and surfaces of varying reflectance cause systematic distortions of the range data. Coherent light sources such as lasers introduce speckle artifacts that further degrade the data. We present a new ranging method based on analyzing the time evolution of the structured light reflections. Using our spacetime analysis, we can correct for each of these artifacts, thereby attaining significantly higher accuracy using existing technology. We present results that demonstrate the validity of our method using a commercial laser stripe triangulation scanner.
Importance Weighted Autoencoders

The variational autoencoder (VAE; Kingma & Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to overly simplified representations which fail to use the network’s entire modeling capacity. We present the importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting. In the IWAE, the recognition network uses multiple samples to approximate the posterior, giving it increased flexibility to model complex posteriors which do not fit the VAE modeling assumptions. We show empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log-likelihood on density estimation benchmarks.
Experiences with an Interactive Museum Tour-Guide Robot

This article describes the software architecture of an auto nomous, interactive tour-guide robot. It presents a modular and distributed software archi te ture, which integrates localization, mapping, collision avoidance, planning, and vari ous modules concerned with user interaction and Web-based telepresence. At its heart, the s oftware approach relies on probabilistic computation, on-line learning, and any-time alg orithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot ’s peration. Special emphasis is placed on the design of interactive capabilities that appeal to people’s intuition. The interface provides new means for human-robot interaction w ith crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approac h, results are reported obtained in , when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate relia bl operation in public environments. The robot successfully raised the museum’s atten dance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much large r range of application domains for service robots.
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.
Memory Interference as a Determinant of Language Comprehension

The parameters of the human memory system constrain the operation of language comprehension processes. In the memory literature, both decay and interference have been proposed as causes of forgetting; however, while there is a long history of research establishing the nature of interference effects in memory, the effects of decay are much more poorly supported. Nevertheless, research investigating the limitations of the human sentence processing mechanism typically focus on decay-based explanations, emphasizing the role of capacity, while the role of interference has received comparatively little attention. This paper reviews both accounts of difficulty in language comprehension by drawing direct connections to research in the memory domain. Capacity-based accounts are found to be untenable, diverging substantially from what is known about the operation of the human memory system. In contrast, recent research investigating comprehension difficulty using a retrieval-interference paradigm is shown to be wholly consistent with both behavioral and neuropsychological memory phenomena. The implications of adopting a retrieval-interference approach to investigating individual variation in language comprehension are discussed.
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs

SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.
Using Multiple Barometers to Detect the Floor Location of Smart Phones with Built-in Barometric Sensors for Indoor Positioning

Following the popularity of smart phones and the development of mobile Internet, the demands for accurate indoor positioning have grown rapidly in recent years. Previous indoor positioning methods focused on plane locations on a floor and did not provide accurate floor positioning. In this paper, we propose a method that uses multiple barometers as references for the floor positioning of smart phones with built-in barometric sensors. Some related studies used barometric formula to investigate the altitude of mobile devices and compared the altitude with the height of the floors in a building to obtain the floor number. These studies assume that the accurate height of each floor is known, which is not always the case. They also did not consider the difference in the barometric-pressure pattern at different floors, which may lead to errors in the altitude computation. Our method does not require knowledge of the accurate heights of buildings and stories. It is robust and less sensitive to factors such as temperature and humidity and considers the difference in the barometric-pressure change trends at different floors. We performed a series of experiments to validate the effectiveness of this method. The results are encouraging.
Larger-Context Language Modelling with Recurrent Neural Network

In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained largercontext language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.
The concept of gender identity disorder in childhood and adolescence after 39 years.

The term "gender role" appeared in print first in 1955. The term "gender identity" was used in a press release, November 21, 1966, to announce the new clinic for transsexuals at The Johns Hopkins Hospital. It was disseminated in the media worldwide, and soon entered the vernacular. The definitions of gender and gender identity vary on a doctrinal basis. In popularized and scientifically debased usage, sex is what you are biologically; gender is what you become socially; gender identity is your own sense or conviction of maleness or femaleness; and gender role is the cultural stereotype of what is masculine and feminine. Causality with respect to gender identity disorder is subdivisible into genetic, prenatal hormonal, postnatal social, and postpubertal hormonal determinants, but there is, as yet, no comprehensive and detailed theory of causality. Gender coding in the brain is bipolar. In gender identity disorder, there is discordancy between the natal sex of one's external genitalia and the brain coding of one's gender as masculine or feminine.
CogNet: a cognitive complete knowledge network system

The benefits of using cognitive information at the physical layer, as in cognitive radios, are many. In this article, we propose CogNet -- a cognitive complete knowledge network system -- which makes use of a large amount of information that can be gained from the experience of each node to improve the overall network and user performance. CogNet gathers, processes, analyzes, and stores information available through a variety of devices and protocols to build an omnipresent, distributed repository that holds the spatiotemporal, network-experience information. The inexpensiveness and plentifulness of storage resources and increasing processing power in handheld devices help accelerate the development of CogNet-like systems. Our contribution in this article is the proposal of the architecture and of the communication elements, as well as a transport layer application of CogNet as a proof of concept for possible application scenarios of such a system. We also present performance evaluation of CogTCP, the CogNetenabled TCP, which exploits the transport layer experience of other nodes for improved performance. From our experiments, we found that the use of cognitive information is very useful for networking.
Notions of Video Game Addiction and Their Relation to Self-Reported Addiction Among Players of World of Warcraft

In this study, 438 players of the online video game, World of Warcraft, completed a survey about video game addiction and answered an open-ended question about behaviors they considered characteristic of video game addiction. Responses were coded and correlated with players’ self-reports of being addicted to games and scores on a modified video game addiction scale. The behaviors most frequently mentioned as characteristic of addiction included playing a lot and games’ interfering with other activities, especially socializing or work. Few players mentioned such signs of addiction as withdrawal symptoms or tolerance, and some thought it was not possible to become addicted to video games. Self-reported addiction to video games correlated positively with perceptions that video game addiction involved playing a lot or playing to escape problems, and correlated negatively with perceptions that addiction involved games’ interfering with other activities or not being able to stop play. Implications for assessment are discussed.
Toward Predictable Performance in Software Packet-Processing Platforms

To become a credible alternative to specialized hardware, general-purpose networking needs to offer not only flexibility, but also predictable performance. Recent projects have demonstrated that general-purpose multicore hardware is capable of high-performance packet processing, but under a crucial simplifying assumption of uniformity: all processing cores see the same type/amount of traffic and run identical code, while all packets incur the same type of conventional processing (e.g., IP forwarding). Instead, we present a general-purpose packetprocessing system that combines ease of programmability with predictable performance, while running a diverse set of applications and serving multiple clients with different needs. Offering predictability in this context is considered a hard problem because software processes contend for shared hardware resources—caches, memory controllers, buses—in unpredictable ways. Still, we show that, in our system, (a) the way in which resource contention affects performance is predictable and (b) the overall performance depends little on how different processes are scheduled on different cores. To the best of our knowledge, our results constitute the first evidence that, when designing software network equipment, flexibility and predictability are not mutually exclusive goals.
Solving Constraint Satisfaction Problems through Belief Propagation-guided decimation

Message passing algorithms have proved surprisingly successful in solving hard constraint satisfaction problems on sparse random graphs. In such applications, variables are fixed sequentially to satisfy the constraints. Message passing is run after each step. Its outcome provides an heuristic to make choices at next step. This approach has been referred to as ‘decimation,’ with reference to analogous procedures in statistical physics. The behavior of decimation procedures is poorly understood. Here we consider a simple randomized decimation algorithm based on belief propagation (BP), and analyze its behavior on random k-satisfiability formulae. In particular, we propose a tree model for its analysis and we conjecture that it provides asymptotically exact predictions in the limit of large instances. This conjecture is confirmed by numerical simulations.
Self-Tuning Spectral Clustering

We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. We first propose that a ‘local’ scale should be used to compute the affinity between each pair of points. This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. This leads to a new algorithm in which the final randomly initialized k-means stage is eliminated.
Mid-term electricity price forecasting using SVM

In the modern electricity market, it is very significant to have a precise electricity price forecasting in medium term time horizon. There are few studies done concentrating on the medium term forecasting of the electricity price. Medium term time horizon of electricity price forecasting is very useful and of great importance. It has many applications such as future maintenance scheduling of the power plants, risk management, plan future contracts, purchasing raw materials and determine the market pricing. To forecast the electricity price, some factors are very important, such as choosing the most useful price features that influence the market price, and choosing the proper prediction model that is able to predict the price variations behavior using the historical data. The proposed SVM method and the other employed methods are evaluated using the data from the New England ISO which are published at their official website.
Image Sensor Based Visible Light Communication and Its Application to Pose, Position, and Range Estimations

This study introduces an image sensor based visible light communication (VLC) and its application to pose, position, and range estimations. There are two types of visible-light receiver: a photodiode and an image sensor. A photodiode is usually used as a reception device of VLC, and an image sensor consisting of a large number of pixels can also be used as a VLC reception device. A photodiode detects the signal intensity of incoming light, while an image sensor not only detects the incoming signal intensity but also an accurate angle of arrival of light emitted from a visible light transmitter such as a white LED light. After angles of arrival of light are detected by an image sensor, positioning and data reception can be performed. The ability of an image sensor to detect an accurate angle of arrival will provide attractive applications of VLC such as pose, position calculation, and range estimation. Furthermore, because the image sensor has the ability to spatially separate sources, outdoor positioning even with strong sunlight is possible by discarding the associated pixels of noise sources. key words: visible light communication, image sensor, pose estimation, positioning, range estimation
Political Polarization on Twitter

In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between leftand right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.
Building a better NetFlow

Network operators need to determine the composition of the traffic mix on links when looking for dominant applications, users, or estimating traffic matrices. Cisco's NetFlow has evolved into a solution that satisfies this need by reporting flow records that summarize a sample of the traffic traversing the link. But sampled NetFlow has shortcomings that hinder the collection and analysis of traffic data. First, during flooding attacks router memory and network bandwidth consumed by flow records can increase beyond what is available; second, selecting the right static sampling rate is difficult because no single rate gives the right tradeoff of memory use versus accuracy for all traffic mixes; third, the heuristics routers use to decide when a flow is reported are a poor match to most applications that work with time bins; finally, it is impossible to estimate without bias the number of active flows for aggregates with non-TCP traffic.In this paper we propose Adaptive NetFlow, deployable through an update to router software, which addresses many shortcomings of NetFlow by dynamically adapting the sampling rate to achieve robustness without sacrificing accuracy. To enable counting of non-TCP flows, we propose an optional Flow Counting Extension that requires augmenting existing hardware at routers. Both our proposed solutions readily provide descriptions of the traffic of progressively smaller sizes. Transmitting these at progressively higher levels of reliability allows graceful degradation of the accuracy of traffic reports in response to network congestion on the reporting path.
Pattern Recognition and Machine Learning

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A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

Physiological computing represents a mode of human–computer interaction where the computer monitors, analyzes and responds to the user’s psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subject’s psychological state. 2012 British Informatics Society Limited. All rights reserved.