nils-herrmann/clean_scirepeval huggingface dataset:
On Power Splitting Games in Distributed Computation: The Case of Bitcoin Pooled Mining

Several new services incentivize clients to compete in solving large computation tasks in exchange for financial rewards. This model of competitive distributed computation enables every user connected to the Internet to participate in a game in which he splits his computational power among a set of competing pools -- the game is called a computational power splitting game. We formally model this game and show its utility in analyzing the security of pool protocols that dictate how financial rewards are shared among the members of a pool. As a case study, we analyze the Bitcoin crypto currency which attracts computing power roughly equivalent to billions of desktop machines, over 70% of which is organized into public pools. We show that existing pool reward sharing protocols are insecure in our game-theoretic analysis under an attack strategy called the "block withholding attack". This attack is a topic of debate, initially thought to be ill-incentivized in today's pool protocols: i.e., causing a net loss to the attacker, and later argued to be always profitable. Our analysis shows that the attack is always well-incentivized in the long-run, but may not be so for a short duration. This implies that existing pool protocols are insecure, and if the attack is conducted systematically, Bitcoin pools could lose millions of dollars worth in months. The equilibrium state is a mixed strategy -- that is -- in equilibrium all clients are incentivized to probabilistically attack to maximize their payoffs rather than participate honestly. As a result, the Bitcoin network is incentivized to waste a part of its resources simply to compete.
Monocular Road Terrain Detection by Combining Visual and Spatial Information

For future driver assistance systems and autonomous vehicles, the road course, i.e., the width and shape of the driving path, is an important source of information. In this paper, we introduce a new hierarchical two-stage approach for learning the spatial layout of road scenes. In the first stage, base classifiers analyze the local visual properties of patches extracted from monocular camera images and provide metric confidence maps. We use classifiers for road appearance, boundary appearance, and lane-marking appearance. The core of the proposed approach is the computation of SPatial RAY (SPRAY) features from each metric confidence map in the second stage. A boosting classifier selecting discriminative SPRAY features can be trained for different types of road terrain and allows capturing the local visual properties together with their spatial layout in the scene. In this paper, the extraction of road area and ego-lane on inner-city video streams is demonstrated. In particular, the detection of the ego-lane is a challenging semantic segmentation task showing the power of SPRAY features, because on a local appearance level, the ego-lane is not distinguishable from other lanes. We have evaluated our approach operating at 20 Hz on a graphics processing unit on a publicly available data set, demonstrating the performance on a variety of road types and weather conditions.
Power of the Few vs . Wisdom of the Crowd : Wikipedia and the Rise of the Bourgeoisie

Wikipedia has been a resounding success story as a collaborative system with a low cost of online participation. However, it is an open question whether the success of Wikipedia results from a “wisdom of crowds” type of effect in which a large number of people each make a small number of edits, or whether it is driven by a core group of “elite” users who do the lion’s share of the work. In this study we examined how the influence of “elite” vs. “common” users changed over time in Wikipedia. The results suggest that although Wikipedia was driven by the influence of “elite” users early on, more recently there has been a dramatic shift in workload to the “common” user. We also show the same shift in del.icio.us, a very different type of social collaborative knowledge system. We discuss how these results mirror the dynamics found in more traditional social collectives, and how they can influence the design of new collaborative knowledge systems.
Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit

A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherent structures in the input signals, and they are chosen from a large, linearly dependent collection. The first part of this paper proposes a greedy pursuit algorithm, called Simultaneous Orthogonal Matching Pursuit, for simultaneous sparse approximation. Then it presents some numerical experiments that demonstrate how a sparse model for the input signals can be identified more reliably given several input signals. Afterward, the paper proves that the S-OMP algorithm can compute provably good solutions to several simultaneous sparse approximation problems. The second part of the paper develops another algorithmic approach called convex relaxation, and it provides theoretical results on the performance of convex relaxation for simultaneous sparse approximation.  2005.
An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle

In this paper, we present an onboard monocular vision system for autonomous takeoff, hovering and landing of a Micro Aerial Vehicle (MAV). Since pose information with metric scale is critical for autonomous flight of a MAV, we present a novel solution to six degrees of freedom (DOF) pose estimation. It is based on a single image of a typical landing pad which consists of the letter “H” surrounded by a circle. A vision algorithm for robust and real-time landing pad recognition is implemented. Then the 5 DOF pose is estimated from the elliptic projection of the circle by using projective geometry. The remaining geometric ambiguity is resolved by incorporating the gravity vector estimated by the inertial measurement unit (IMU). The last degree of freedom pose, yaw angle of the MAV, is estimated from the ellipse fitted from the letter “H”. The efficiency of the presented vision system is demonstrated comprehensively by comparing it to ground truth data provided by a tracking system and by using its pose estimates as control inputs to autonomous flights of a quadrotor.
Interlingual Machine Translation

This study, in an attempt to rise above the intricacy of 'being informed on the verge of globalization,' is founded on the premise that Machine Translation (MT) applications searching for an ideal key to find a universal foundation for all natural languages have a restricted say over the translation process at various discourse levels. Our paper favors not judging against the superiority of human translation vs. machine translation or automated translation in non-English speaking settings, but rather referring to the inadequacies and adequacies of MT at certain pragmatic levels, lacking the right sense and dynamic equivalence, but producing syntactically well-formed or meaning-extractable outputs in restricted settings. Reasoning in this way, the present study supports MT before, during, and after translation. It aims at making translators understand that they could cooperate with the software to obtain a synergistic effect. In other words, they could have a say and have an essential part to play in a semi-automated translation process (Rodrigo, 2001). In this respect, semi-automated translation or MT courses should be included in the curricula of translation departments worldwide to keep track of the state of the art as well as make potential translators aware of future trends.
Honeywords: making password-cracking detectable

We propose a simple method for improving the security of hashed passwords: the maintenance of additional ``honeywords'' (false passwords) associated with each user's account. An adversary who steals a file of hashed passwords and inverts the hash function cannot tell if he has found the password or a honeyword. The attempted use of a honeyword for login sets off an alarm. An auxiliary server (the ``honeychecker'') can distinguish the user password from honeywords for the login routine, and will set off an alarm if a honeyword is submitted.
A PWM and PFM Hybrid Modulated Three-Port Converter for a Standalone PV/Battery Power System

A pulsewidth modulation (PWM) and pulse frequency modulation (PFM) hybrid modulated three-port converter (TPC) interfacing a photovoltaic (PV) source, a storage battery, and a load is proposed for a standalone PV/battery power system. The TPC is derived by integrating a two-phase interleaved boost circuit and a full-bridge LLC resonant circuit. Hence, it features a reduced number of switches, lower cost, and single-stage power conversion between any two of the three ports. With the PWM and PFM hybrid modulation strategy, the dc voltage gain from the PV to the load is wide, the input current ripple is small, and flexible power management among three ports can be easily achieved. Moreover, all primary switches turn ON with zero-voltage switching (ZVS), while all secondary diodes operate with zero-current switching over full operating range, which is beneficial for reducing switching losses, switch voltage stress, and electromagnetic interference. The topology derivation and power transfer analysis are presented. Depending on the resonant states, two different operation modes are identified and explored. Then, main characteristics, including the gain, input current ripple, and ZVS, are analyzed and compared. Furthermore, guidelines for parameter design and optimization are given as well. Finally, a 500-W laboratory prototype is built and tested to verify the effectiveness and advantages of all proposals.
An empirical investigation of online consumer purchasing behavior

This article is focused on examining the factors and relationships that influence the browsing and buying behavior of individuals when they shop online. Specifically, we are interested in individual buyers using business-to-consumer sites. We are also interested in examining shopping preferences based on various demographic categories that might exhibit distinct purchasing attitudes and behaviors for certain categories of products and services. We examine these behaviors in the context of both products and services. After a period of decline in recent months, online shopping is on the rise again. By some estimates, total U.S. spending on online sales increased to $5.7 billion in December 2001 from $3.2 billion in June of 2001 . By these same estimates, the number of households shopping online increased to 18.7 million in December 2001 from 13.1 million in June 2001. Consumers spent an average of $304 per person in December 2001, compared with $247 in June 2001. According to an analyst at Forrester: “The fact that online retail remained stable during ... such social and economic instability speaks volumes about how well eCommerce is positioned to stand up to a poor economy” . What do consumers utilize the Internet for? Nie and Erbring suggest that 52% of the consumers use the Internet for product information, 42% for travel information, and 24% for buying . Recent online consumer behavior-related research refers to any Internet-related activity associated with the consumption of goods, services, and information . In the definition of Internet consumption, Goldsmith and Bridges include “gathering information passively via exposure to advertising; shopping, which includes both browsing and deliberate information search, and the selection and buying of specific goods, services, and information” . For the purposes of this study, we focus on all aspects of this consumption. We include all of them because information gathering aspects of e-commerce serve to educate the consumer, which is ulti-
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called “health factors,” or quantitative indicators, of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management, and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance tradeoffs in terms of frequency of unexpected breaks and unexploited lifetime, and then employing this information in an operating cost-based maintenance decision system to minimize expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.
Blockchain applications in microgrids an overview of current projects and concepts

Since the release of Bitcoins as crypto currency, Bitcoin has played a prominent part in the media. However, not Bitcoin but the underlying technology blockchain offers the possibility to innovatively change industries. The decentralized structure of the blockchain is particularly suitable for implementing control and business processes in microgrids, using smart contracts and decentralized applications. This paper provides a state of the art survey overview of current blockchain technology based projects with the potential to revolutionize microgrids and provides a first attempt to technically characterize different start-up approaches. The most promising use case from the microgrid perspective is peer-to-peer trading, where energy is exchanged and traded locally between consumers and prosumers. An application concept for distributed PV generation is provided in this promising area.
Defeating IMSI Catchers

IMSI catching is a problem on all generations of mobile telecommunication networks, i.e., 2G (GSM, GPRS), 3G (HDSPA, EDGE, UMTS) and 4G (LTE, LTE+). Currently, the SIM card of a mobile phone has to reveal its identity over an insecure plaintext transmission, before encryption is enabled. This identifier (the IMSI) can be intercepted by adversaries that mount a passive or active attack. Such identity exposure attacks are commonly referred to as 'IMSI catching'. Since the IMSI is uniquely identifying, unauthorized exposure can lead to various location privacy attacks. We propose a solution, which essentially replaces the IMSIs with changing pseudonyms that are only identifiable by the home network of the SIM's own network provider. Consequently, these pseudonyms are unlinkable by intermediate network providers and malicious adversaries, and therefore mitigate both passive and active attacks, which we also formally verified using ProVerif. Our solution is compatible with the current specifications of the mobile standards and therefore requires no change in the infrastructure or any of the already massively deployed network equipment. The proposed method only requires limited changes to the SIM and the authentication server, both of which are under control of the user's network provider. Therefore, any individual (virtual) provider that distributes SIM cards and controls its own authentication server can deploy a more privacy friendly mobile network that is resilient against IMSI catching attacks.
On compressive sensing applied to radar

Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signals with a reduced number of samples. Today, modern radar systems operate with high bandwidths—demanding high sample rates according to the Shannon–Nyquist theorem—and a huge number of single elements for phased array consumption and costs of radar systems. There is only a small number of publications addressing the application of CS to radar, leaving several open questions. This paper addresses some aspects as a further step to CS-radar by presenting generic system architectures and implementation considerations. It is not the aim of this paper to investigate numerically efficient algorithms but to point to promising applications as well as arising problems. Three possible applications are considered: pulse compression, radar imaging, and air space surveillance with array antennas. Some simulation results are presented and enriched by the evaluation of real data acquired by an experimental radar system of Fraunhofer FHR. & 2009 Elsevier B.V. All rights reserved.
Motivation and emotion as mediators in multimedia learning

Against the background of Moreno’s “cognitive-affective theory of learning with media” (CATLM) (Moreno, 2006), three papers on cognitive and affective processes in learning with multimedia are discussed in this commentary. The papers provide valuable insights in how cognitive processing and learning results can be affected by constructs such as “situational interest”, “positive emotions”, or “confusion”, and they suggest questions for further research in this field.
Functional Dependencies for Graphs

We propose a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities, and subsume conditional functional dependencies (CFDs) as a special case. We show that the satisfiability and implication problems for GFDs are coNP-complete and NP-complete, respectively, no worse than their CFD counterparts. We also show that the validation problem for GFDs is coNP-complete. Despite the intractability, we develop parallel scalable algorithms for catching violations of GFDs in large-scale graphs. Using real-life and synthetic data, we experimentally verify that GFDs provide an effective approach to detecting inconsistencies in knowledge and social graphs.
Compiling Quantum Circuits to Realistic Hardware Architectures using Temporal Planners

To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.
Incremental Joint Extraction of Entity Mentions and Relations

We present an incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search. A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations. Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.
Friends with benefits: on the positive consequences of pet ownership.

Social support is critical for psychological and physical well-being, reflecting the centrality of belongingness in our lives. Human interactions often provide people with considerable social support, but can pets also fulfill one's social needs? Although there is correlational evidence that pets may help individuals facing significant life stressors, little is known about the well-being benefits of pets for everyday people. Study 1 found in a community sample that pet owners fared better on several well-being (e.g., greater self-esteem, more exercise) and individual-difference (e.g., greater conscientiousness, less fearful attachment) measures. Study 2 assessed a different community sample and found that owners enjoyed better well-being when their pets fulfilled social needs better, and the support that pets provided complemented rather than competed with human sources. Finally, Study 3 brought pet owners into the laboratory and experimentally demonstrated the ability of pets to stave off negativity caused by social rejection. In summary, pets can serve as important sources of social support, providing many positive psychological and physical benefits for their owners.
Ringing out fault tolerance. A new ring network for superior low-cost dependability

Dependability properties of bi-directional and braided rings are well recognized in improving communication availability. However, current ring-based topologies have no mechanisms for extreme integrity and have not been considered for emerging high-dependability markets where cost is a significant driver, such as the automotive "by-wire" applications. This paper introduces a braided-ring architecture with superior guardian functionality and complete Byzantine fault tolerance while simultaneously reducing cost. This paper reviews anticipated requirements for high-dependability low-cost applications and emphasizes the need for regular safe testing of core coverage functions. The paper describes the ring's main mechanisms for achieving integrity and availability levels similar to SAFEbus/spl reg/ but at low automotive costs. The paper also presents a mechanism to achieve self-stabilizing TDMA-based communication and design methods for fault-tolerant protocols on a network of simplex nodes. The paper also introduces a new self-checking pair concept that leverages braided-ring properties. This novel message-based self-checking-pair concept allows high-integrity source data at extremely low cost.