nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.
Model Reference Adaptive Control-Based Speed Control of Brushless DC Motors With Low-Resolution Hall-Effect Sensors

A control system with a novel speed estimation approach based on model reference adaptive control (MRAC) is presented for low cost brushless dc motor drives with low-resolution hall sensors. The back EMF is usually used to estimate speed. But the estimation result is not accurate enough at low speeds because of the divided voltage of stator resistors and too small back EMF. Moreover, the stator resistor is always varying with the motor's temperature. A speed estimation algorithm based on MRAC was proposed to correct the speed error estimated by using back EMF. The proposed algorithm's most innovative feature is its adaptability to the entire speed range including low speeds and high speeds and temperature and different motors do not affect the accuracy of the estimation result. The effectiveness of the algorithm was verified through simulations and experiments.
Four Assumptions Of Multiple Regression That Researchers Should Always Test

Most statistical tests rely upon certain assumptions about the variables used in the analysis. When these assumptions are not met the results may not be trustworthy, resulting in a Type I or Type II error, or overor under-estimation of significance or effect size(s). As Pedhazur (1997, p. 33) notes, "Knowledge and understanding of the situations when violations of assumptions lead to serious biases, and when they are of little consequence, are essential to meaningful data analysis". However, as Osborne, Christensen, and Gunter (2001) observe, few articles report having tested assumptions of the statistical tests they rely on for drawing their conclusions. This creates a situation where we have a rich literature in education and social science, but we are forced to call into question the validity of many of these results, conclusions, and assertions, as we have no idea whether the assumptions of the statistical tests were met. Our goal for this paper is to present a discussion of the assumptions of multiple regression tailored toward the practicing researcher.
Artificial Bee Colony algorithm for optimization of truss structures

The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design.
Fitts' law and expanding targets: Experimental studies and designs for user interfaces

Recently, there has been renewed interest in techniques for facilitating the selection of user interface widgets or other on-screen targets with a pointing device. We report research into using target expansion for facilitating selection. Widgets that expand or grow in response to the user's focus of attention allow for a reduced initial size which can help optimize screen space use and may be easier to select than targets that do not expand. However, selection performance could plausibly suffer from a decreased initial widget size. We describe an experiment in which users select a single, isolated target button that expands just before it is selected. Our results show that users benefit from target expansion even if the target only begins expanding after 90% of the distance to the target has been travelled. Furthermore, our results suggest that, for sufficiently large ID values, users are able to take approximately full advantage of the expanded target size. For interfaces with multiple expanding widgets, however, subtle problems arise due to the collisions or overlap that may occur between adjacent expanding widgets. We give a detailed examination of the issues involved in both untiled and tiled multiple expanding targets and present various design strategies for improving their performance.
Automatic Localization of Indoor Soccer Players from Multiple Cameras

Nowadays, there is an ever growing quest for finding sophisticated performance evaluation tools by team sports that could give them an additional inch or a quarter of a second of advantage in a competition. Using cameras to shoot the events of a game, for instance, the teams can analyze the performance of the athletes and even extrapolate the data to obtain semantical information about the behavior of the teams themselves at relatively low costs. In this context, this paper introduces a new approach for better estimating the positions of indoor soccer players using multiple cameras at all moments of a game. The setup consists of four stationary cameras set around the soccer court. Our solution relies on individual object detectors (one per camera) working in the image coordinates and a robust fusion approach working in the world coordinates in a plane that represents the soccer court. The fusion approach relies on a gradient ascent algorithm over a multimodal bidimensional mixture of Gaussians function representing all the players in the soccer court. In the experiments, we show that the proposed solution improves standard object detector approaches and greatly reduces the mean error rate of soccer player detection to a few centimeters with respect to the actual positions of the players.
Autonomous Positioning of Eye Surgical Robot Using the Tool Shadow and Kalman Filtering

Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon’s coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions—namely, a micropipette, its shadow, and the eye ground—using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.
The Life Satisfaction Advantage of Being Married and Gender Specialization

This investigation examined whether the life satisfaction advantage of married over unmarried persons decreased over the last three decades, and whether the changes in the contextual gender specialization explained this trend. The author used representative data from the World Values Survey–European Values Study (WVS–EVS)-integrated data set for 87 countries (N = 292,525) covering a period of 29 years. Results showed that the life satisfaction advantage of being married decreased among men but not among women. The analysis did not support the hypothesis that contextual gender specialization shaped the observed trend. Only in developed countries the declining contextual specialization correlated with smaller life satisfaction advantage of being married. This evidence suggests that the advantages of marriage are greater under conditions that support freedom of choice rather than economic necessity.
Corrosion Study and Intermetallics Formation in Gold and Copper Wire Bonding in Microelectronics Packaging

A comparison study on the reliability of gold (Au) and copper (Cu) wire bonding is conducted to determine their corrosion and oxidation behavior in different environmental conditions. The corrosion and oxidation behaviors of Au and Cu wire bonding are determined through soaking in sodium chloride (NaCl) solution and high temperature storage (HTS) at 175 °C, 200 °C and 225 °C. Galvanic corrosion is more intense in Cu wire bonding as compared to Au wire bonding in NaCl solution due to the minimal formation of intermetallics in the former. At all three HTS annealing temperatures, the rate of Cu-Al intermetallic formation is found to be three to five times slower than Au-Al intermetallics. The faster intermetallic growth rate and lower activation energy found in this work for both Au/Al and Cu/Al as compared to literature could be due to the thicker Al pad metallization which removed the rate-determining step in previous studies due to deficit in Al material.
HMM-KNN word recognition engine for bank cheque processing

This article describes the mixed HMM-KNN word recognition module of a bank cheque processing system developed at CENPARMI. It uses a combination of 2 segmentation free word recognition schemes. The first scheme uses a set of global features associated to a modified K Nearest Neighbour classifier; while the second one uses a set of directional contour features as input to an HMM. The system has been designed to be modular and independent of specific languages as in Canada one has to deal with at least 2 languages, namely English and French. It can be easily adapted to read other European languages based on the Roman alphabet. The system is continuously tested on data from the local phone company, and we report here the results on a database of approximately 4,500 cheques.
Multi-Task Learning for Mental Health using Social Media Text

We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multitask learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.
Major components of the gravity recommendation system

The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his/her and other users' previous ratings. The Netflix Prize dataset is much larger than former benchmark datasets, therefore the scalability of the algorithms is a must. This paper describes the major components of our blending based solution, called the Gravity Recommendation System (GRS). In the Netflix Prize contest, it attained RMSE 0.8743 as of November 2007. We now compare the effectiveness of some selected individual and combined approaches on a particular subset of the Prize dataset, and discuss their important features and drawbacks.
The cortical language circuit: from auditory perception to sentence comprehension

Over the years, a large body of work on the brain basis of language comprehension has accumulated, paving the way for the formulation of a comprehensive model. The model proposed here describes the functional neuroanatomy of the different processing steps from auditory perception to comprehension as located in different gray matter brain regions. It also specifies the information flow between these regions, taking into account white matter fiber tract connections. Bottom-up, input-driven processes proceeding from the auditory cortex to the anterior superior temporal cortex and from there to the prefrontal cortex, as well as top-down, controlled and predictive processes from the prefrontal cortex back to the temporal cortex are proposed to constitute the cortical language circuit.
Design and VLSI implementation of high performance DUC and DDC for software defined radio applications

Paper deals with the implementation of high performance digital up converter and digital down converter for software defined radio application. Digital up converters are used at the transmitter side to up convert the baseband signal to intermediate frequency signal. Digital down converters are used at receiver side to down convert intermediate frequency signal to baseband signal. DUC and DDC modules are designed using System generator Xilinx block set and generated verilog code for the same. Simulation is performed using Isim and synthesis is carried out using Xilinx ISE 13.2. Design has implemented on vertex-4 FPGA board. The power consumed in this design is 52.6mWatts and the maximum operating frequency is up to 336.1MHz and total time taken for output is 2.93ns.
CertLedger: A New PKI Model with Certificate Transparency Based on Blockchain

In conventional PKI, CAs are assumed to be fully trusted. However, in practice, CAs’ absolute responsibility for providing trustworthiness caused major security and privacy issues. To prevent such issues, Google introduced the concept of Certificate Transparency (CT) in 2013. Later, several new PKI models (e.g., AKI, ARPKI, and DTKI) are proposed to reduce the level of trust to the CAs. However, all of these proposals are still vulnerable to split-world attacks if the adversary is capable of showing different views of the log to the targeted victims. In this paper, we propose a new PKI architecture with certificate transparency based on blockchain, what we called CertLedger, to eliminate the split-world attacks and to provide certificate/revocation transparency. All TLS certificates’ validation, storage, and entire revocation process are conducted in CertLedger as well as Trusted CA certificate management. During a TLS connection, TLS clients get an efficient proof of existence of the certificate directly from its domain owners. Hence, privacy is now perfectly preserved by eliminating the traceability issue of OCSP servers. It also provides a unique, efficient, and trustworthy certificate validation process eliminating the conventional inadequate and incompatible certificate validation processes implemented by different software vendors. TLS clients in CertLedger also do not require to make certificate validation and store the trusted CA certificates anymore. We analyze the security and performance of CertLedger and provide a comparison with the previous proposals.
Place recognition using keypoint voting in large 3D lidar datasets

In developing autonomous solutions for mapping and localization, one problem that often needs to be dealt with is determining when an area is revisited despite having poor or no prior information on the relative alignment error. There are well-formulated approaches for recognizing such matches using the rich information in camera data; however, it is a much more challenging problem using lidar sensors alone. Most existing approaches employ a pairwise place comparison of place descriptors and thus finding matches requires linear time per place. We instead propose the use of a keypoint voting approach to achieve sub-linear matching times. A constant number of nearest neighbor votes per keypoint are queried from a database of local descriptors and aggregated to determine likely place matches. It becomes critical to analyze the distributions of vote scores such that a suitable threshold for matching scores can be determined a priori, so that the system is not overwhelmed by false positives nor starved for true matches. We have empirically determined that the vote scores follow a log-normal distribution, and we are able to fit a parametric model of its hyper-parameters based on the number of neighbors, the number of keypoints in a place, and the total number of keypoints in the database. We demonstrate the performance of our system in a variety of large scale 3D lidar datasets using data collected from a continually scanning handheld lidar sensor, and also on two publicly available lidar datasets.
Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection of h times h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach.
Security and Privacy in Cloud Computing: Vision, Trends, and Challenges

Cloud computing offers cost-effective solutions via a variety of flexible services. However, security concerns related to managing data, applications, and interactions hamper the rapid deployment of cloud-based services on a large scale. Although many solutions exist, efficiency, scalability, and provable security still have issues that need to be properly addressed. This article explores the various challenges, existing solutions, and limitations of cloud security, with a focus on data utilization management aspects, including data storage, data analytics, and access control. The article concludes with a discussion on future research directions that might lead to more trustworthy cloud security and privacy.
Affordances as a Framework for Robot Control

The concept of affordances, with its emphasis to the interactions between the robot and the environment, is highly relevant for epigenetic robotics. However, the use of the concept in robotics has been rather rudimentary, mostly confined to an inspiration source. In this paper, we present a new formalization of the concept, based partially on the recent formalizations proposed in Ecological Psychology and Linguistics, which provide a framework for robot control, learning and planning. We argue that affordances, as relations within the robotenvironment system, can be seen from three different perspectives; namely agent, observer and environmental. We argue that affordance relations can be learned from the interactions of the robot within its environment. The formalism argues that the interactions of the robot, can be represented as a nested triple of the form (effect, (entity, behavior)) indicating that the behavior applied in an environment perceived as the entity, would produce a perceivable effect. It is suggested that these nested triples of raw sensorymotor data obtained from different interactions can be used to form four different equivalence classes towards the formation of affordance relations. We present three studies implementing certain aspects of the formalism on a mobile robot moving in an environment filled with different types of objects. Specifically, we show that, (1) the formation of the entity equivalence classes corresponds to the perceptual learning of affordances, (2) the formation of effect equivalence classes, followed by the formation of entity equivalence classes can lead to the development of goal-directed behaviors from a set of primitive ones, and (3) the formed equivalence classes and relations provide support for planning and deliberation.
Fermented foods, neuroticism, and social anxiety: An interaction model

Animal models and clinical trials in humans suggest that probiotics can have an anxiolytic effect. However, no studies have examined the relationship between probiotics and social anxiety. Here we employ a cross-sectional approach to determine whether consumption of fermented foods likely to contain probiotics interacts with neuroticism to predict social anxiety symptoms. A sample of young adults (N=710, 445 female) completed self-report measures of fermented food consumption, neuroticism, and social anxiety. An interaction model, controlling for demographics, general consumption of healthful foods, and exercise frequency, showed that exercise frequency, neuroticism, and fermented food consumption significantly and independently predicted social anxiety. Moreover, fermented food consumption also interacted with neuroticism in predicting social anxiety. Specifically, for those high in neuroticism, higher frequency of fermented food consumption was associated with fewer symptoms of social anxiety. Taken together with previous studies, the results suggest that fermented foods that contain probiotics may have a protective effect against social anxiety symptoms for those at higher genetic risk, as indexed by trait neuroticism. While additional research is necessary to determine the direction of causality, these results suggest that consumption of fermented foods that contain probiotics may serve as a low-risk intervention for reducing social anxiety.