nils-herrmann/clean_scirepeval huggingface dataset:
Salamandra Robotica II: An Amphibious Robot to Study Salamander-Like Swimming and Walking Gaits

In this paper, we present Salamandra robotica II: an amphibious salamander robot that is able to walk and swim. The robot has four legs and an actuated spine that allow it to perform anguilliform swimming in water and walking on the ground. The paper first presents the new robot hardware design, which is an improved version of Salamandra robotica I. We then address several questions related to body–limb coordination in robots and animals that have a sprawling posture like salamanders and lizards, as opposed to the erect posture of mammals (e.g., in cats and dogs). In particular, we investigate how the speed of locomotion and curvature of turning motions depend on various gait parameters such as the body–limb coordination, the type of body undulation (offset, amplitude, and phase lag of body oscillations), and the frequency. Comparisons with animal data are presented, and our results show striking similarities with the gaits observed with real salamanders, in particular concerning the timing of the body’s and limbs’ movements and the relative speed of locomotion.
AutoMoDe: A novel approach to the automatic design of control software for robot swarms

We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias–variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules—the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.
THE DEMOCRATIZATION OF PERSONAL CONSUMER LOANS? DETERMINANTS OF SUCCESS IN ONLINE PEER-TO-PEER LENDING COMMUNITIES

Online peer-to-peer (P2P) lending communities enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand loan listing-related determinants of funding success in an online P2P lending community by conceptualizing loan decision variables (loan amount, interest rate offered, duration of loan listing) as mediators between borrower attributes such as demographic characteristics, financial strength, and effort prior to making the request, and the likelihood of funding success. Borrower attributes are also treated as moderators of the effects of loan decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan transactions, provide support for the proposed conceptual framework. Although demographic attributes such as race and gender do affect likelihood of funding success, their effects are very small in comparison to effects of borrowers’ financial strength and their effort when listing and publicizing the loan. These results are substantially different from the documented discriminatory practices of US financial institutions, suggesting that individual lenders lend more fairly when their own investment money is at stake in P2P loans. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P lending communities, and a discussion of future research opportunities.
Move Evaluation in Go Using Deep Convolutional Neural Networks

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional-search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates two million positions per move.
Discovering Texture Regularity as a Higher-Order Correspondence Problem

Understanding texture regularity in real images is a challenging computer vision task. We propose a higher-order feature matching algorithm to discover the lattices of near-regular textures in real images. The underlying lattice of a near-regular texture identifies all of the texels as well as the global topology among the texels. A key contribution of this paper is to formulate lattice-finding as a correspondence problem. The algorithm finds a plausible lattice by iteratively proposing texels and assigning neighbors between the texels. Our matching algorithm seeks assignments that maximize both pair-wise visual similarity and higher-order geometric consistency. We approximate the optimal assignment using a recently developed spectral method. We successfully discover the lattices of a diverse set of unsegmented, real-world textures with significant geometric warping and large appearance variation among
Asking "Good" Questions: Questionnaire Design and Analysis in Interactive Information Retrieval Research

Asking questions is a central element of Interactive Information Retrieval (IIR) studies, yet the challenges associated with asking "good" questions is are rarely discussed. This perspectives paper examines the development, evaluation, and application of questionnaires in IIR research. The authors draw upon prior literature and their own research to explore how to best construct and extract meaning from questionnaires. Since building IIR knowledge and theories requires the employment of valid, reliable, dimensional, and sensitive self-report measures, this paper's contribution is an overview of best practices for developing, administering, and analyzing questionaires.
Greed is good: algorithmic results for sparse approximation

This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho's basis pursuit (BP) paradigm can recover the optimal representation of an exactly sparse signal. It leverages this theory to show that both OMP and BP succeed for every sparse input signal from a wide class of dictionaries. These quasi-incoherent dictionaries offer a natural generalization of incoherent dictionaries, and the cumulative coherence function is introduced to quantify the level of incoherence. This analysis unifies all the recent results on BP and extends them to OMP. Furthermore, the paper develops a sufficient condition under which OMP can identify atoms from an optimal approximation of a nonsparse signal. From there, it argues that OMP is an approximation algorithm for the sparse problem over a quasi-incoherent dictionary. That is, for every input signal, OMP calculates a sparse approximant whose error is only a small factor worse than the minimal error that can be attained with the same number of terms.
A printed planar helix antenna

This paper presents design, fabrication and measurement results of a novel broadband, circularly polarized, planar helix antenna operating at X-band that is fabricated using printed circuit techniques. The conventional circular helix antenna is modified into a planar helix antenna, and the effect of various parameters like pitch angle, dimensions, feed and substrate, etc. on the impedance and radiation characteristics is studied. The performance parameters under investigation are S11, directivity, side lobe level, and axial ratio. These parameters are obtained using CST Microwave Studio. For a design with 5 turns of the planar helix, the results of the simulations are as follows: over a frequency range of 8.3 - 11.7 GHz, S11 is less than -10 dB, directivity ranges from 11.7 - 12.6 dBi, and the axial ratio ranges from 0.68 - 3.3 dB. The corresponding design is fabricated using RO4003C dielectric substrates. The measurement results match the simulation results reasonably well.
An advanced p-channel LDMOS FET with HTRB tolerability of high-voltage pulse transmitter ICs for ultrasound applications

Novel +/-100-V p-channel LDMOS FET technology were developed for a pulse transmitter IC for an ultrasound application. With a design of steep-profile in drift region for a higher RESURF effect, area efficiency of the output performance can be improved by 20 % of Ron, sp. By an optimization of gate poly-Si structure for reducing electric field and higher tolerability against electron trapping in LOCOS, stable performance against high-temperature reverse bias (HTRB) can be obtained.  V/3079 ohm·mm2 of p-channel LDMOS and well matched ID-VDS curve traces between n-channel and p-channel LDMOS FETs enabled the suitable bipolar pulse mirror symmetry with less than -40 dBc of second harmonic distortion in the IC with long-term stability.
Wearable Electronics and Smart Textiles: A Critical Review

Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.
ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction

We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of an ITS into a single component to allow customization of teaching goals and to simplify improving the ITS. The first agent is responsible for learning a model of how students perform using the tutor in a variety of contexts. The second agent is provided this model of student behavior and a goal specifying the desired educational objective. Reinforcement learning is used by this agent to derive a teaching policy that meets the specified educational goal. Component evaluation studies show each agent performs adequately in isolation. We have also conducted an evaluation with actual students of the complete architecture. Results show ADVISOR was successful in learning a teaching policy that met the educational objective provided. Although this set of machine learning agents has been integrated with a specific intelligent tutor, the general technique could be applied to a broad class of ITS.
Multi-image matching using multi-scale oriented patches

This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.
High Quality Uniform Random Number Generation Using LUT Optimised State-transition Matrices

This paper presents a family of uniform random number generators designed for efficient implementation in Lookup table (LUT) based FPGA architectures. A generator with a period of 2j1 can be implemented using k flip-flops and k LUTs, and provides k random output bits each cycle. Each generator is based on a binary linear recurrence, with a state-transition matrix designed to make best use of all available LUT inputs in a given FPGA architecture, and to ensure that the critical path between all registers is a single LUT. This class of generator provides a higher sample rate per area than LFSR and Combined Tausworthe generators, and operates at similar or higher clock-rates. The statistical quality of the generators increases with k, and can be used to pass all common empirical tests such as Diehard, Crush and the NIST cryptographic test suite. Theoretical properties such as global equidistribution can also be calculated, and best and average case statistics shown. Due to the large number of random bits generated per cycle these generators can be used as a basis for generators with even higher statistical quality, and an example involving combination through addition is demonstrated.
Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding

There has been significant recent interest towards achievin g highly efficient deep neural network architectures. A promising paradigm for ach ieving this is the concept ofevolutionary deep intelligence , which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural n etworks over successive generations. An important aspect of evolutionary deep inte lligence is the genetic encoding scheme used to mimic heredity, which can have a sign ificant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genet ic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of ima ge classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art perf ormance while having network architectures that are not only significantly more e ffici nt (with a∼125fold decrease in synapses for MNIST) compared to the origina l a cestor network, but also tailored for GPU-accelerated machine learning app lications.
Mitigation of Denial of Service Attack with Hardware Trojans in NoC Architectures

As Multiprocessor System-on-Chips (MPSoCs) continue to scale, security for Network-on-Chips (NoCs) is a growing concern as rogue agents threaten to infringe on the hardware's trust and maliciously implant Hardware Trojans (HTs) to undermine their reliability. The trustworthiness of MPSoCs will rely on our ability to detect Denial-of-Service (DoS) threats posed by the HTs and mitigate HTs in a compromised NoC to permit graceful network degradation. In this paper, we propose a new light-weight target-activated sequential payload (TASP) HT model that performs packet inspection and injects faults to create a new type of DoS attack. Faults injected are used to trigger a response from error correction code (ECC) schemes and cause repeated retransmission to starve network resources and create deadlocks capable of rendering single-application to full chip failures. To circumvent the threat of HTs, we propose a heuristic threat detection model to classify faults and discover HTs within compromised links. To prevent further disruption, we propose several switch-to-switch link obfuscation methods to avoid triggering of HTs in an effort to continue using links instead of rerouting packets with minimal overhead (1-3 cycles). Our proposed modifications complement existing fault detection and obfuscation methods and only adds 2% in area overhead and 6% in excess power consumption in the NoC micro-architecture.
PRACTICAL HYPERPARAMETER OPTIMIZATION

Recently, the bandit-based strategy Hyperband (HB) was shown to yield good hyperparameter settings of deep neural networks faster than vanilla Bayesian optimization (BO). However, for larger budgets, HB is limited by its random search component, and BO works better. We propose to combine the benefits of both approaches to obtain a new practical state-of-the-art hyperparameter optimization method, which we show to consistently outperform both HB and BO on a range of problem types, including feed-forward neural networks, Bayesian neural networks, and deep reinforcement learning. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.
Competitive contagion in networks

We develop a game-theoretic framework for the study of competition between firms who have budgets to "seed" the initial adoption of their products by consumers located in a social network. The payoffs to the firms are the eventual number of adoptions of their product through a competitive stochastic diffusion process in the network. This framework yields a rich class of competitive strategies, which depend in subtle ways on the stochastic dynamics of adoption, the relative budgets of the players, and the underlying structure of the social network.
 We identify a general property of the adoption dynamics --- namely, decreasing returns to local adoption --- for which the inefficiency of resource use at equilibrium (the Price of Anarchy) is uniformly bounded above, across all networks. We also show that if this property is violated the Price of Anarchy can be unbounded, thus yielding sharp threshold behavior for a broad class of dynamics.
 We also introduce a new notion, the Budget Multiplier, that measures the extent that imbalances in player budgets can be amplified at equilibrium. We again identify a general property of the adoption dynamics --- namely, proportional local adoption between competitors --- for which the (pure strategy) Budget Multiplier is uniformly bounded above, across all networks. We show that a violation of this property can lead to unbounded Budget Multiplier, again yielding sharp threshold behavior for a broad class of dynamics.
Computational Creativity

Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords.
A novel binary particle swarm optimization

Particle swarm optimization (PSO) as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the velocity vector of binary PSO. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
Reading senseless sentences: brain potentials reflect semantic incongruity.

In a sentence reading task, words that occurred out of context were associated with specific types of event-related brain potentials. Words that were physically aberrant (larger than normal) elecited a late positive series of potentials, whereas semantically inappropriate words elicited a late negative wave (N400). The N400 wave may be an electrophysiological sign of the "reprocessing" of semantically anomalous information.
A Graph Coloring Algorithm for Large Scheduling Problems *

A ne w graph coloring algorit hm is prese nt ed and compared to a wide variety of known algorit hms. The algorithm is shown to exhibit O(n 2) time be havior for most s parse graphs and thus is found to be parti cularl y well suited for use wi th large-scale sc heduling problems. In addition, a procedure for ge nerating large random test graphs with known chromatic number is present ed and is used to e valuate he uri sti call y the capabiliti es of the a lgorithms discussed.
A design methodology for the realization of multi-decade baluns at microwave frequencies

A new methodology is presented for designing baluns exhibiting multi-decade bandwidths at microwave frequencies. Simulations show that resistors terminating the outer transmission line suppress the half-wavelength resonance and greatly extend the bandwidth. Using linear measurements at microwave frequencies, ferrite beads have been shown to behave as resistors with a small reactance, suitable for terminating the outer transmission line. This paper shows that ferrite beads can perform the dual role of improving magnetic coupling at low frequency and suppressing resonance at higher frequencies. The design methodology was applied to produce a balun that operates between 30MHz and 6GHz, displays less than 1dB of power loss up to 4.4GHz, and delivers an impedance transformation of 2∶1.