nils-herrmann/clean_scirepeval huggingface dataset:
Artificial Roughness Encoding with a Bio-inspired MEMS- based Tactile Sensor Array

A compliant 2×2 tactile sensor array was developed and investigated for roughness encoding. State of the art cross shape 3D MEMS sensors were integrated with polymeric packaging providing in total 16 sensitive elements to external mechanical stimuli in an area of about 20 mm(2), similarly to the SA1 innervation density in humans. Experimental analysis of the bio-inspired tactile sensor array was performed by using ridged surfaces, with spatial periods from 2.6 mm to 4.1 mm, which were indented with regulated 1N normal force and stroked at constant sliding velocity from 15 mm/s to 48 mm/s. A repeatable and expected frequency shift of the sensor outputs depending on the applied stimulus and on its scanning velocity was observed between 3.66 Hz and 18.46 Hz with an overall maximum error of 1.7%. The tactile sensor could also perform contact imaging during static stimulus indentation. The experiments demonstrated the suitability of this approach for the design of a roughness encoding tactile sensor for an artificial fingerpad.
Scalable Multiplex Network Embedding

Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multitype relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.
CyTIME: Cyber Threat Intelligence ManagEment framework for automatically generating security rules

It is becoming increasingly necessary for organizations to build a Cyber Threat Intelligence (CTI) platform to fight against sophisticated attacks. To reduce the risk of cyber attacks, security administrators and/or analysts can use a CTI platform to aggregate relevant threat information about adversaries, targets and vulnerabilities, analyze it and share key observations from the analysis with collaborators. In this paper, we introduce CyTIME (Cyber Threat Intelligence ManagEment framework) which is a framework for managing CTI data. CyTIME can periodically collect CTI data from external CTI data repositories via standard interfaces such as Trusted Automated Exchange of Indicator Information (TAXII). In addition, CyTIME is designed to automatically generate security rules without human intervention to mitigate discovered new cybersecurity threats in real time. To show the feasibility of CyTIME, we performed experiments to measure the time to complete the task of generating the security rule corresponding to a given CTI data. We used 1,000 different CTI files related to network attacks. Our experiment results demonstrate that CyTIME automatically generates security rules and store them into the internal database within 12.941 seconds on average (max = 13.952, standard deviation = 0.580).
An Overview of Hierarchical Topic Modeling

Topic correlations are common in real-world textual information. However, classic topic modeling isn't able to model the correlations among topics because of a single distribution over topics in each document. Therefore, hierarchical topic modeling is designed to relax this restriction. In this paper, hierarchical topic modeling is summarized by analysis of existing studies, especially, two important representatives of hierarchical topic models and their extension are focused on. To the best of our knowledge, this is the first effort to review the development of hierarchical topic modeling.
How the Dark Triad traits predict relationship choices

While previous studies have correlated the Dark Triad traits (i.e., narcissism, psychopathy, and Machiavellianism) with a preference for short-term relationships, little research has addressed possible correlations with short-term relationship sub-types. In this online study using Amazon’s Mechanical Turk system (N = 210) we investigated the manner in which scores on the Dark Triad relate to the selection of different mating environments using a budget-allocation task. Overall, the Dark Triad were positively correlated with preferences for short-term relationships and negatively correlated with preferences for a long-term relationship. Specifically, narcissism was uniquely correlated with preferences for one-night stands and friends-with-benefits and psychopathy was uniquely correlated with preferences for bootycall relationships. Both narcissism and psychopathy were negatively correlated with preferences for serious romantic relationships. In mediation analyses, psychopathy partially mediated the sex difference in preferences for booty-call relationships and narcissism partially mediated the sex difference in preferences for one-night stands. In addition, the sex difference in preference for serious romantic relationships was partially mediated by both narcissism and psychopathy. It appears the Dark Triad traits facilitate the adoption of specific mating environments providing fit with people’s personality traits.
Mesoscopic Simulation of Heat Transfer and Fluid Flow in Laser Powder Bed Additive Manufacturing

Laser-powder bed fusion (L-PBF) additive manufacturing involves complex physics such as heat transfer and molten metal flow, which have a significant influence on the final build quality. In this study, transport phenomena based modeling is used to provide a quantitative understanding of complex molten pool transients. In particular, a three dimensional (3D), transient numerical model is developed for L-PBF additive manufacturing by solving the governing partial differential equations of mass, momentum and energy conservation. The individual powder particles are resolved using the volume of fluid method (VOF) method with a fine mesh size of 3 m (thus at meso-scale). The powder particle arrangement including particle size distribution and packing density are taken into account in placement of individual particles calculated using discrete element method. Moreover, the model considers Marangoni shear stress, an important driving force for molten metal flow. The numerical model is used to quantitatively study the effect of laser power, scanning speed, and powder size distribution on the bead geometry and formation of balling defect.
Big Data Challenges

Big Data, an umbrella term for the explosion in the quantity and diversity of high frequency digital data. The Big Data may be logs, mobile-banking transactions, online usergenerated content such as blog posts and Tweets, online searches, satellite images, etc.into actionable information requires using computational techniques to unveil trends and patterns within and between these extremely large socioeconomic datasets. These data hold the potential to allow decision makers to track development progress, improve social protection, and understand where existing policies and programmes require adjustment. This paper presents the novel challenges and opportunities associated with Big Data necessitate rethinking many aspects of these data management platforms, while retaining other desirable aspects.
The relative importance of task, citizenship, and counterproductive performance to global ratings of job performance: a policy-capturing approach.

A review of research on job performance suggests 3 broad components: task, citizenship, and counterproductive performance. This study examined the relative importance of each component to ratings of overall performance by using an experimental policy-capturing design. Managers in 5 jobs read hypothetical profiles describing employees' task, citizenship, and counterproductive performance and provided global ratings of performance. Within-subjects regression analyses indicated that the weights given to the 3 performance components varied across raters. Hierarchical cluster analyses indicated that raters' policies could be grouped into 3 homogeneous clusters: (a) task performance weighted highest, (b) counterproductive performance weighted highest, and (c) equal and large weights given to task and counterproductive performance. Hierarchical linear modeling indicated that demographic variables were not related to raters' weights.
Maximum power point tracking (MPPT) of sensorless PMSG wind power system

This paper investigates the modeling, simulation and implementation of sensorless maximum power point tracking (MPPT) of permanent magnet synchronous generator wind power system. A comprehensive portfolio of control schemes are discussed and verified by simulations and experiments. Particularly, a PMSG-based wind power emulation system has been developed based on two machine drive setups — one is controlled as wind energy source and operated in torque control mode while the other is controlled as a wind generator and operated in speed control mode to attain MPPT. Both simulation and experimental results demonstrate a robust sensorless MPPT operation in the customized PMSG wind power system.
A computer vision assisted system for autonomous forklift vehicles in real factory environment

Industry 4.0 is an important trend in factory automation nowadays. Among the Automated-Storage-and-Retrieval-System (ASRS) is one of the most important issues for industry. It is widely used in a variety of industries for a variety of storage applications in factories and warehouses. However, the cost of constructing an ASRS is so high that most small/medium enterprises cannot afford it. A forklift system is a cheaper alternative to a complicated ASRS. In this work, a new pallet detection method that uses an Adaptive Structure Feature (ASF) and Direction Weighted Overlapping (DWO) ratio to allow forklifts to pick up a pallet is proposed, using a monocular vision system on the forklift. Combining the ASF and DWO ratio for pallet detection, the proposed method removes most of the non-stationary (dynamic) background and significantly increases the processing efficiency. A Haar like-based Adaboost scheme uses an AS for pallets algorithm to detect pallets. It detects the pallet in a dark environment. Finally, by calculating the DWO ratio between the detected pallets and tracking records, it avoids erroneous candidates during object tracking. Therefore, this work improves the pallet detection to solve the problem with an effective design. As results show that the hybrid algorithms that are proposed in this work increase the average pallet detection rate by 95 %.
Compact High-Isolation Base-Station Duplexer Using Triple-Mode Ceramic Cavities

A microwave duplexer with high isolation is presented in this paper. The device is based on triple-mode filters that are built using silver-plated ceramic cuboids. To create a six-pole, six-transmission-zero filter in the DCS-1800 band, which is utilized in mobile communications, two cuboids are cascaded. To shift spurious harmonics, low dielectric caps are placed on the cuboid faces. These caps push the first cuboid spurious up in frequency by around 340 MHz compared to the uncapped cuboid, allowing a 700-MHz spurious free window. To verify the design, a DCS-1800 duplexer with 75-MHz widebands is built. It achieves around 1 dB of insertion loss for both the receive and transmit ports with around 70 dB of mutual isolation within only 20-MHz band separation, using a volume of only 30 cm3 .
Three-Dimensional CT Image Segmentation by Combining 2 D Fully Convolutional Network with 3 D Majority Voting

We propose a novel approach for automatic segmentation of anatomical structures on 3D CT images by voting from a fully convolutional network (FCN), which accomplishes an end-to-end, voxel-wise multiple-class classification to map each voxel in a CT image directly to an anatomical label. The proposed method simplifies the segmentation of the anatomical structures (including multiple organs) in a CT image (generally in 3D) to majority voting for the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. An FCN consisting of “convolution” and “de-convolution” parts is trained and re-used for the 2D semantic image segmentation of different slices of CT scans. All of the procedures are integrated into a simple and compact all-in-one network, which can segment complicated structures on differently sized CT images that cover arbitrary CT scan regions without any adjustment. We applied the proposed method to segment a wide range of anatomical structures that consisted of 19 types of targets in the human torso, including all the major organs. A database consisting of 240 3D CT scans and a humanly annotated ground truth was used for training and testing. The results showed that the target regions for the entire set of CT test scans were segmented with acceptable accuracies (89 % of total voxels were labeled correctly) against the human annotations. The experimental results showed better efficiency, generality, and flexibility of this end-to-end learning approach on CT image segmentations comparing to conventional methods guided by human expertise.
Population-based linkage analysis of schizophrenia and bipolar case–control cohorts identifies a potential susceptibility locus on 19q13

Population-based linkage analysis is a new method for analysing genomewide single nucleotide polymorphism (SNP) genotype data in case–control samples, which does not assume a common disease, common variant model. The genome is scanned for extended segments that show increased identity-by-descent sharing within case–case pairs, relative to case–control or control–control pairs. The method is robust to allelic heterogeneity and is suited to mapping genes which contain multiple, rare susceptibility variants of relatively high penetrance. We analysed genomewide SNP datasets for two schizophrenia case–control cohorts, collected in Aberdeen (461 cases, 459 controls) and Munich (429 cases, 428 controls). Population-based linkage testing must be performed within homogeneous samples and it was therefore necessary to analyse the cohorts separately. Each cohort was first subjected to several procedures to improve genetic homogeneity, including identity-by-state outlier detection and multidimensional scaling analysis. When testing only cases who reported a positive family history of major psychiatric disease, consistent with a model of strongly penetrant susceptibility alleles, we saw a distinct peak on chromosome 19q in both cohorts that appeared in meta-analysis (P=0.000016) to surpass the traditional level for genomewide significance for complex trait linkage. The linkage signal was also present in a third case–control sample for familial bipolar disorder, such that meta-analysing all three datasets together yielded a linkage P=0.0000026. A model of rare but highly penetrant disease alleles may be more applicable to some instances of major psychiatric diseases than the common disease common variant model, and we therefore suggest that other genome scan datasets are analysed with this new, complementary method.
MIRROR NEURONS: OUR CURRENT UNDERSTANDING

Mirror neurons are a relatively new phenomena, first observed in the premotor cortex of macaque monkeys when a number of neurons were observed to respond both when a monkey performed a goal orientated task, and when the monkey watched another (human or monkey) perform that task. A number of researchers have suggested that mirror neurons also exist in humans. It is proposed that a human mirror neuron system may contribute to a number of cognitive functions such as action understanding; ‘theory of mind’, humans’ abilities to infer another’s mental state through experiences or others’ behaviour; emotion understanding; imitation; and speech perception. Faulty human mirror neurons have even been suggested to underpin social impairments such as those characteristic of Autistic Spectrum Disorder (ASD). However, there has been much debate regarding the existence and functional roles of mirror neurons in humans. While there is much literature regarding human mirror neurons, the majority consist of reviews while few concern empirical experiments. Additionally concern has been expressed for some of the experimental methods used in empirical studies. A recent experiment from Mukamel et al. (2010) is the first of its kind to directly gather evidence for the existence of mirror neurons in humans and for their function subserving action understanding. The present review critically outlines the growth in this controversial field of research, taking into account the recent direct recording of human mirror neurons, and what implications this may have on our understanding of social cognition.
Development and preliminary evaluation of the transsexual voice questionnaire for male-to-female transsexuals.

This study reports on the preliminary psychometric evaluation of the Transsexual Voice Questionnaire for Male-to-Female Transsexuals (MtF)-TVQ(MtF), a tool designed to measure the perceptions of MtF transsexuals regarding their voice. The TVQ(MtF) evolved from an extensive review of the existing Transgender Self-evaluation Questionnaire (TSEQ).



This study was conducted in two phases. In phase I, the TSEQ was reviewed. In phase II, classical test theory was applied to examine two psychometric properties (internal consistency and test-retest reliability) of the TVQ(MtF).



In phase I, two speech pathologists and two MtF individuals reviewed the TSEQ, and a comparison was made with the results of a thematic analysis of 137 voice-related extracts from the transcripts of in-depth interviews with 14 MtF individuals. In phase II, 29 Australian and six Canadian MtF transsexuals completed the new tool, the TVQ(MtF) twice with an interim period of 4-6 weeks to enable psychometric evaluation.



Phase I led to substantial changes to the content and structure of the original measure, the TSEQ, and the subsequent development of the TVQ(MtF). In phase II, internal consistency of the TVQ(MtF) was found to be high (time 1 Cronbach's α=.964; time 2 α=.974). Excellent test-retest reliability was demonstrated (intraclass correlation coefficient=0.979; 95% confidence interval=0.968-0.988).



This study provides psychometric evidence of the reliability of the TVQ(MtF). The MtF participants in this study reported a wide range of vocal functioning and voice-related difficulties. Voice pitch was reported as the most problematic aspect of vocal functioning, and psychosocial issues were the most frequently reported voice-related impacts.
The History and State of General Systems Theory

In a certain sense it can be said that the notion of system is as old as European philosophy. If we try to define the central motif in the birth of philosophical-scientific thinking with the Ionian pre-Socratics of the sixth century B.C., one way to spell it out would be as follows. Man in early culture, and even primitives of today, experience themselves as being "thrown" into a hostile world, governed by chaotic and incomprehensible demonic forces which, at best, may be propitiated or influenced by way of magical practices. Philosophy and its descendant, science, was born when the early Greeks learned to consider or find, in the experienced world, an order or kosmos which was intelligible and, hence, controllable by thought and rational action.
A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for a design of TRFN by either neural network or genetic algorithms depending on the learning environment. Set forth first is a recurrent fuzzy network which develops from a series of recurrent fuzzy if–then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule’s consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, TRFN-S has the admirable property of small network size and high learning accuracy. As to the problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, also is characterized by a high learning accuracy property. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified.
A 0.01%THD, 70dB PSRR Single Ended Class D using variable hysteresis control for headphone amplifiers

Switching audio amplifiers are widely used in HBridge topology thanks to their high efficiency; however low audio performances in single ended power stage topology is a strong weakness leading to not be used for headset applications. This paper explains the importance of efficient error correction in Single Ended Class-D audio amplifier. A hysteresis control for Class-D amplifier with a variable window is also presented. The analyses are verified by simulations and measurements. The proposed solution was fabricated in 0.13µm CMOS technology with an active area of 0.2mm2. It could be used in single ended output configuration fully compatible with common headset connectors. The proposed Class-D amplifier achieves a harmonic distortion of 0.01% and a power supply rejection of 70dB with a quite low static current consumption.
A 15–50-GHz Quasi-Optical Scalar Network Analyzer Scalable to Terahertz Frequencies

A 15-50-GHz two-port quasi-optical scalar network analyzer consisting of a transmitter and receiver built in a planar technology is presented. The network analyzer is based on a Schottky-diode multiplier and mixer integrated inside a planar antenna and fed differentially by a coplanar waveguide transmission line. The antenna is placed on an extended hemispherical high-resistivity silicon substrate lens. The local oscillator signal is swept from 3 to 5 GHz and high-order harmonic mixing in both the up- and down-conversion mode is used to realize the RF bandwidth. The network analyzer has a dynamic range of >;50 dB in a 1-kHz bandwidth, and was successfully used to measure frequency-selective surfaces with f0=20, 30, and 40 GHz and a second-order bandpass response. Furthermore, the system was built with circuits and components for easy scaling to millimeter-wave frequencies, which is the primary motivation for this work.