nils-herrmann/clean_scirepeval huggingface dataset:
Probabilistic fibre tract analysis of cytoarchitectonically defined human inferior parietal lobule areas reveals similarities to macaques

The human inferior parietal lobule (IPL) is a multimodal brain region, subdivided in several cytoarchitectonic areas which are involved in neural networks related to spatial attention, language, and higher motor processing. Tracer studies in macaques revealed differential connectivity patterns of IPL areas as the respective structural basis. Evidence for comparable differential fibre tracts of human IPL is lacking. Here, anatomical connectivity of five cytoarchitectonic human IPL areas to 64 cortical targets was investigated using probabilistic tractography. Connection likelihood was assessed by evaluating the number of traces between seed and target against the distribution of traces from that seed to voxels in the same distance as the target. The main fibre tract pattern shifted gradually from rostral to caudal IPL: Rostral areas were predominantly connected to somatosensory and superior parietal areas while caudal areas more strongly connected with auditory, anterior temporal and higher visual cortices. All IPL areas were strongly connected with inferior frontal, insular and posterior temporal areas. These results showed striking similarities with connectivity patterns in macaques, providing further evidence for possible homologies between these two species. This shift in fibre tract pattern supports a differential functional involvement of rostral (higher motor functions) and caudal IPL (spatial attention), with probable overlapping language involvement. The differential functional involvement of IPL areas was further supported by hemispheric asymmetries of connection patterns which showed left-right differences especially with regard to connections to sensorimotor, inferior frontal and temporal areas.
A Finger-Vein Based Cancellable Bio-cryptosystem

Irrevocability is one major issue in existing bio-cryptosystems. In this paper, we proposed a cancellable bio-cryptosystem by taking the full advantage of cancellable and non-invertible properties of bio-hashing biometrics. Specifically, two transformed templates are generated by using the bio-hashing algorithm and applied into two different secure sketches, fuzzy commitment sketch and fuzzy vault sketch, respectively. These two secure sketches can be fused in two different ways: AND fusion and OR fusion, so as to emphasis either on the recognition accuracy or the security level of the system. Experimental results and security analysis show the validity of the proposed scheme.
AI-assisted analysis of player strategy across level progressions in a puzzle game

Presenting levels commensurate with players' current understanding of game mechanics and level design is a significant challenge in designing games. Often game designers create levels by hand intending for the levels to increase in difficulty over the the course of the game while relying on their intuition or extensive user feedback, reiteration, and testing. Instead, this study starts from a number of procedurally generated levels originally generated by parameters expected to encourage a good difficulty progression and then presented to players during playtests. A number of AI-bots with different characteristics were then designed to assess the difficulty of each level. These findings are then compared with player data. Our findings show that bots encapsulating idealized player strategies can help us create a richer model of level difficulty that then reveals useful information about player struggles and learning across level progressions.
Development of a one-axis actively regulated bearingless motor with a repulsive type passive magnetic bearing

This paper describes a novel one-axis actively regulated bearingless motor with a spinning top-shaped rotor, which is intended for a use as a cooling fan. This motor requires only one set of three-phase winding and thus one three-phase inverter to realize both rotation and magnetic suspension, and thus it is dubbed a single-drive bearingless motor. An axial position of the rotor is actively regulated and radial and tilting motions are passively stabilized by the passive magnetic bearing. Torque and axial suspension force are regulated by q-axis and d-axis currents, respectively. In this paper, the structure and principle, design, and test results of the proposed bearingless motor are presented.
Character-based Neural Machine Translation

We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.
Individual Differences in Visual Working Memory Capacity : Contributions of Attentional Control to Storage

The amount of visual information an individual can actively represent is severely limited; this limitation is known as the capacity of visual working memory (VWM). To estimate an individual’s VWM capacity, we present an observer with a memory array containing multiple visual stimuli to remember over a relatively short delay interval, after which we test their memory (Figure 1(a)). When we examine memory performance as a function of the number of stimuli to remember (i.e., set size), we find that healthy young adults can easily retain up to three simple objects (Figure 1(b)), with their performance declining thereafter as the set size increases. Further, when memory performance for a set size larger than three is transformed to an estimate of the number of stimuli retained in VWM, the average VWM capacity estimate (K) is consistently computed as around three objects across a wide range of paradigms (Cowan, 2001;  1997; Zhang & Luck, 2008). Based on such robust findings, the average VWM capacity for healthy young adults is thought to be around three simple objects. To derive a single metric of VWM capacity, we often average the K estimate across set sizes above four guided by the logic that across these large set sizes individuals’ VWM stores are equally filled to capacity (e.g., Fukuda & Vogel, 2009; Fukuda, Vogel, Mayr, & Awh, 2010; Vogel & Machizawa, 2004). In this chapter, we call this estimate Kave for the sake of clarity.
What video games have to teach us about learning and literacy

Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.
Human performance limitations (communication, stress, prospective memory and fatigue).

A key role in anaesthetic practice is gathering and assimilating information from a variety of sources to construct and maintain an accurate mental model of what is happening to the patient, a model that will influence subsequent decisions made by the anaesthetist on the patient's behalf, as part of a larger team. Effective performance of this role requires a set of mental functions that place great demands upon the physiology and psychology of anaesthetists, functions that are vulnerable to a wide range of factors including those affecting team performance and those affecting the anaesthetist specifically. The number of tasks, their complexity, the physical and mental demands of the job, the underlying health and well-being of the anaesthetist and the environment and context within which the team attempt to meet the demands placed on them will influence the outcome of patient care.
MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS

The concepts of covering and matching in fuzzy graphs using strong arcs are introduced and obtained the relationship between them analogous to Gallai’s results in graphs. The notion of paired domination in fuzzy graphs using strong arcs is also studied. The strong paired domination number γspr of complete fuzzy graph and complete bipartite fuzzy graph is determined and obtained bounds for the strong paired domination number of fuzzy graphs. An upper bound for the strong paired domination number of fuzzy graphs in terms of strong independence number is also obtained.
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing

While Deep Neural Networks (DNNs) have established the fundamentals of DNN-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To resolve the safety issues of autonomous driving systems, a recent set of testing techniques have been designed to automatically generate test cases, e.g., new input images transformed from the original ones. Unfortunately, many such generated input images often render inferior authenticity, lacking accurate semantic information of the driving scenes and hence compromising the resulting efficacy and reliability. In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes. In particular, DeepRoad delivers driving scenes with various weather conditions (including those with rather extreme conditions) by applying the Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Moreover, we have implemented DeepRoad to test three well-recognized DNN-based autonomous driving systems. Experimental results demonstrate that DeepRoad can detect thousands of behavioral inconsistencies for these systems.
A deep learning-based method for relative location prediction in CT scan images

Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the study’s proposed method using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with the state-of-the-art techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm.
Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging.

A method for motion correction, involving both data collection and reconstruction, is presented. The PROPELLER MRI method collects data in concentric rectangular strips rotated about the k-space origin. The central region of k-space is sampled for every strip, which (a) allows one to correct spatial inconsistencies in position, rotation, and phase between strips, (b) allows one to reject data based on a correlation measure indicating through-plane motion, and (c) further decreases motion artifacts through an averaging effect for low spatial frequencies. Results are shown in which PROPELLER MRI is used to correct for bulk motion in head images and respiratory motion in nongated cardiac images.  1999.
System Reliability Theory: Models, Statistical Methods, and Applications

A thoroughly updated and revised look at system reliability theory Since the first edition of this popular text was published nearly a decade ago, new standards have changed the focus of reliability engineering and introduced new concepts and terminology not previously addressed in the engineering literature. Consequently, the Second Edition of System Reliability Theory: Models, Statistical Methods, and Applications has been thoroughly rewritten and updated to meet current standards. To maximize its value as a pedagogical tool, the Second Edition features:-Additional chapters on reliability of maintained systems and reliability assessment of safety-critical systems-Discussion of basic assessment methods for operational availability and production regularity-New concepts and terminology not covered in the first edition-Revised sequencing of chapters for better pedagogical structure-New problems, examples, and cases for a more applied focus-An accompanying Web site with solutions, overheads, and supplementary information With its updated practical focus, incorporation of industry feedback, and many new examples based on real industry problems and data, the Second Edition of this important text should prove to be more useful than ever for students, instructors, and researchers alike.
Blind detection of SCMA for uplink grant-free multiple-access

Sparse code multiple access (SCMA) is a new frequency domain non-orthogonal multiple-access technique which can enable massive connectivity and grant-free transmission in wireless radio access. With SCMA, different incoming data streams are directly mapped to codewords of different multi-dimensional cookbooks, where each codeword represents a spread transmission layer. Multiple SCMA layers share the same time-frequency resources of OFDMA. The sparsity of codewords allows low complexity of multi-layer detection for excessive codeword overloading which is the key feature to enable massive connectivity. In this paper, a blind detection solution is introduced and analyzed to support massive connectivity in a SCMA-based UL grant-free multiple access. The proposed solution is based on two major components: i) blind detection of active pilots/users with reasonable complexity, and ii) blind decoding of active users' data with no knowledge of active codebook set. Different activity detection algorithms and schemes are proposed, described, and analyzed. Simulation results are provided to evaluate the performance of the proposed schemes in various scenarios. Our analysis and performance evaluation confirm the proposed SCMA-based blind reception solution is a promising technology to enable massive connectivity for grant-free multiple-access transmission mode in future wireless networks.
Divertible Protocols and Atomic Proxy Cryptography

First, we introduce the notion of divertibility as a protocol property as opposed to the existing notion as a language property (see Okamoto, Ohta [OO90]). We give a definition of protocol divertibility that applies to arbitrary 2-party protocols and is compatible with Okamoto and Ohta’s definition in the case of interactive zero-knowledge proofs. Other important examples falling under the new definition are blind signature protocols. We propose a sufficiency criterion for divertibility that is satisfied by many existing protocols and which, surprisingly, generalizes to cover several protocols not normally associated with divertibility (e.g., Diffie-Hellman key exchange). Next, we introduce atomic proxy cryptography, in which an atomic proxy function, in conjunction with a public proxy key, converts ciphertexts (messages or signatures) for one key into ciphertexts for another. Proxy keys, once generated, may be made public and proxy functions applied in untrusted environments. We present atomic proxy functions for discrete-log-based encryption, identification, and signature schemes. It is not clear whether atomic proxy functions exist in general for all public-key cryptosystems. Finally, we discuss the relationship between divertibility and proxy cryptography.
More than fun and money. Worker Motivation in Crowdsourcing - A Study on Mechanical Turk

The payment in paid crowdsourcing markets like Amazon Mechanical Turk is very low, and still collected demographic data shows that the participants are a very diverse group including highly skilled full time workers. Many existing studies on their motivation are rudimental and not grounded on established motivation theory. Therefore, we adapt different models from classic motivation theory, work motivation theory and Open Source Software Development to crowdsourcing markets. The model is tested with a survey of 431 workers on Mechanical Turk. We find that the extrinsic motivational categories (immediate payoffs, delayed payoffs, social motivation) have a strong effect on the time spent on the platform. For many workers, however, intrinsic motivation aspects are more important, especially the different facets of enjoyment based motivation like “task autonomy” and “skill variety”. Our contribution is a preliminary model based on established theory intended for the comparison of different crowdsourcing platforms.
Very low-voltage digital-audio Î”Î£ modulator with 88-dB dynamic range using local switch bootstrapping

A 1-V 1-mW 14-bit modulator in a standard CMOS 0.35m technology is presented. Special attention has been given to device reliability and power consumption in a switched-capacitor implementation. A locally bootstrapped symmetrical switch that avoids gate dielectric overstress is used in order to allow rail-to-rail signal switching. The switch constant overdrive also enhances considerably circuit linearity. Modulator coefficients of a single-loop third-order topology have been optimized for low power. Further reduction in the power consumption is obtained through a modified two-stage opamp. Measurement results show that for an oversampling ratio of 100, the modulator achieves a dynamic range of 88 dB, a peak signal-to-noise ratio of 87 dB and a peak signal-to-noise-plus-distortion ratio of 85 dB in a signal bandwidth of 25 kHz.
Neem and other Botanical insecticides: Barriers to commercialization

In spite of the wide recognition that many plants possess insecticidal properties, only a handful of pest control products directly obtained from plants,i. e., botanical insecticides, are in use in developed countries. The demonstrated efficacy of the botanical neem (based on seed kernel extracts ofAzadirachta indica), and its recent approval for use in the United States, has stimulated research and development of other botanical insecticides. However, the commercialization of new botanical insecticides can be hindered by a number of issues. The principal barriers to commercialization of new botanicals are (i) scarcity of the natural resource; (ii) standardization and quality control; and (iii) registration. These issues are no problem (i) or considerably less of a problem (ii, iii) with conventional insecticides. In this review I discuss these issues and suggest how the problems may be overcome in the future.
Articulated Billboards for Video-based Rendering

We present a novel representation and rendering method for free-viewpoint video of human characters based on multiple input video streams. The basic idea is to approximate the articulated 3D shape of the human body using a subdivision into textured billboards along the skeleton structure. Billboards are clustered to fans such that each skeleton bone contains one billboard per source camera. We call this representation articulated billboards. In the paper we describe a semi-automatic, data-driven algorithm to construct and render this representation, which robustly handles even challenging acquisition scenarios characterized by sparse camera positioning, inaccurate camera calibration, low video resolution, or occlusions in the scene. First, for each input view, a 2D pose estimation based on image silhouettes, motion capture data, and temporal video coherence is used to create a segmentation mask for each body part. Then, from the 2D poses and the segmentation, the actual articulated billboard model is constructed by a 3D joint optimization and compensation for camera calibration errors. The rendering method includes a novel way of blending the textural contributions of each billboard and features an adaptive seam correction to eliminate visible discontinuities between adjacent billboards textures. Our articulated billboards do not only minimize ghosting artifacts known from conventional billboard rendering, but also alleviate restrictions to the setup and sensitivities to errors of more complex 3D representations and multiview reconstruction techniques. Our results demonstrate the flexibility and the robustness of our approach with high quality free-viewpoint video generated from broadcast footage of challenging, uncontrolled environments.
System supporting money laundering detection

Criminal analysis is a complex process involving information gathered from di erent sources, mainly of quantitative character, such as billings or bank account transactions, but also of qualitative character such as eyewitnesses' testimonies. Due to the massive nature of this information, operational or investigation activities can be vastly improved when supported by dedicated techniques and tools. The system supporting the police analyst in the process of detecting money laundering is presented in this paper. The main parts of the system are data importer and analyzing algorithms, such as transaction mining algorithm and frequent pattern mining algorithms. The results obtained with the use of these algorithms can be visualized, so that they can be easily explored by the police analyst. The transactions found can be treated as suspected operations. The frequent patterns found are mainly used to identify the roles of suspected entities. For the transaction mining algorithm a performance study is also presented.