nils-herrmann/clean_scirepeval huggingface dataset:
An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions

Consider a set of <italic>S</italic> of <italic>n</italic> data points  in real <italic>d</italic>-dimensional space, R<supscrpt>d</supscrpt>, where distances are measured using any Minkowski metric. In nearest neighbor searching, we preprocess <italic>S</italic> into a data structure, so that given any query point <italic>q</italic><inline-equation> <f>∈</f></inline-equation> R<supscrpt>d</supscrpt>, is the closest point of S to <italic>q</italic> can be reported quickly. Given any positive real ε, data point <italic>p</italic> is a (1 +ε)-<italic>approximate nearest neighbor</italic> of <italic>q</italic> if its distance from <italic>q</italic> is within a factor of (1 + ε) of the distance to the true nearest neighbor. We show that it is possible to preprocess a    set of <italic>n</italic> points in     R<supscrpt>d</supscrpt> in <italic>O(dn</italic> log <italic>n</italic>) time and <italic>O(dn)</italic> space, so that given a query point <italic> q</italic> <inline-equation> <f>∈</f></inline-equation> R<supscrpt>d</supscrpt>, and ε > 0, a (1 + ε)-approximate nearest neighbor of <italic>q</italic> can be computed in <italic>O</italic>(<italic>c</italic><subscrpt><italic>d</italic>, ε</subscrpt> log <italic>n</italic>) time, where <italic>c<subscrpt>d,ε</subscrpt></italic>≤<italic>d</italic> <inline-equation> <f><fen lp="ceil">1 + 6d/<g>e</g><rp post="ceil"></fen></f></inline-equation>;<supscrpt>d</supscrpt> is a factor depending only on dimension and ε. In general, we show that given an integer <italic>k</italic> ≥ 1, (1 + ε)-approximations  to the  <italic>k</italic> nearest neighbors of <italic>q</italic> can  be computed in additional <italic>O(kd</italic> log <italic>n</italic>) time.
LR-CNN for fine-grained classification with varying resolution

In this work, we present an extended study of image representations for fine-grained classification with respect to image resolution. Understudied in literature, this parameter yet presents many practical and theoretical interests, e.g. in embedded systems where restricted computational resources prevent treating high-resolution images. It is thus interesting to figure out which representation provides the best results in this particular context. On this purpose, we evaluate Fisher Vectors and deep representations on two significant finegrained oriented datasets: FGVC Aircraft  and PPMI . We also introduce LR-CNN, a deep structure designed for classification of low-resolution images with strong semantic content. This net provides rich compact features and outperforms both pre-trained deep features and Fisher Vectors.
Fuzzy logic based emotion classification

Emotions affect many aspects of our daily lives including decision making, reasoning and physical wellbeing. Researchers have therefore addressed the detection of emotion from individuals' heart rate, skin conductance, pupil dilation, tone of voice, facial expression and electroencephalogram (EEG). This paper presents an algorithm for classifying positive and negative emotions from EEG. Unlike other algorithms that extract fuzzy rules from the data, the fuzzy rules used in this paper are obtained from emotion classification research reported in the literature and the classification output indicates both the type of emotion and its strength. The results show that the algorithm is more than 90 times faster than the widely used LIBSVM and the obtained average accuracy of 63.52 % is higher than previously reported using the same EEG dataset. This makes this algorithm attractive for real time emotion classification. In addition, the paper introduces a new oscillation feature computed from local minima and local maxima of the signal.
Classifying depression patients and normal subjects using machine learning techniques

Depression is one of the most common mental disorder that at its worst can lead to suicide. Diagnosing depression in the early curable stage is very important. In this paper we study performance of different classification techniques for classifying depression patients from normal subjects. For this aim, power spectrum of three frequency band (alpha, beta, theta) and the whole bands of EEG are used as features. We have shown that Support Vector Machine (SVM) classifier using Genetic algorithm for feature selection can achieve accuracy of 88.6% on classifying depression patients.
Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes

Cognitive control supports flexible behavior by selecting actions that are consistent with our goals and appropriate for our environment. The prefrontal cortex (PFC) has an established role in cognitive control, and research on the functional organization of PFC promises to contribute to our understanding of the architecture of control. A recently popular hypothesis is that the rostro-caudal axis of PFC supports a control hierarchy whereby posterior-to-anterior PFC mediates progressively abstract, higher-order control. This review discusses evidence for a rostro-caudal gradient of function in PFC and the theories proposed to account for these results, including domain generality in working memory, relational complexity, the temporal organization of behavior and abstract representational hierarchy. Distinctions among these frameworks are considered as a basis for future research.
A review: Can robots reshape K-12 STEM education?

Can robots in classroom reshape K-12 STEM education, and foster new ways of learning? To sketch an answer, this article reviews, side-by-side, existing literature on robot-based learning activities featuring mathematics and physics (purposefully putting aside the well-studied field of “robots to teach robotics”) and existing robot platforms and toolkits suited for classroom environment (in terms of cost, ease of use, orchestration load for the teacher, etc.). Our survey suggests that the use of robots in classroom has indeed moved from purely technology to education, to encompass new didactic fields. We however identified several shortcomings, in terms of robotic platforms and teaching environments, that contribute to the limited presence of robotics in existing curricula; the lack of specific teacher training being likely pivotal. Finally, we propose an educational framework merging the tangibility of robots with the advanced visibility of augmented reality.
A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation

The Perspective-Three-Point (P3P) problem aims at determining the position and orientation of the camera in the world reference frame from three 2D-3D point correspondences. This problem is known to provide up to four solutions that can then be disambiguated using a fourth point. All existing solutions attempt to first solve for the position of the points in the camera reference frame, and then compute the position and orientation of the camera in the world frame, which alignes the two point sets. In contrast, in this paper we propose a novel closed-form solution to the P3P problem, which computes the aligning transformation directly in a single stage, without the intermediate derivation of the points in the camera frame. This is made possible by introducing intermediate camera and world reference frames, and expressing their relative position and orientation using only two parameters. The projection of a world point into the parametrized camera pose then leads to two conditions and finally a quartic equation for finding up to four solutions for the parameter pair. A subsequent backsubstitution directly leads to the corresponding camera poses with respect to the world reference frame. We show that the proposed algorithm offers accuracy and precision comparable to a popular, standard, state-of-the-art approach but at much lower computational cost (15 times faster). Furthermore, it provides improved numerical stability and is less affected by degenerate configurations of the selected world points. The superior computational efficiency is particularly suitable for any RANSAC-outlier-rejection step, which is always recommended before applying PnP or non-linear optimization of the final solution.
Implementing a Wall-In Building Placement in StarCraft with Declarative Programming

In real-time strategy games like StarCraft, skilled players often block the entrance to their base with buildings to prevent the opponent’s units from getting inside. This technique, called “walling-in”, is a vital part of player’s skill set, allowing him to survive early aggression. However, current artificial players (bots) do not possess this skill, due to numerous inconveniences surfacing during its implementation in imperative languages like C++ or Java. In this text, written as a guide for bot programmers, we address the problem of finding an appropriate building placement that would block the entrance to player’s base, and present a ready to use declarative solution employing the paradigm of answer set programming (ASP). We also encourage the readers to experiment with different declarative approaches to this problem.
HeteRecom: a semantic-based recommendation systemin heterogeneous networks

Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.
Small-Size LTE/WWAN Tablet Device Antenna With Two Hybrid Feeds

A small-size tablet device antenna formed by a simple folded metal plate excited by a low-band feed (direct feed with matching network) and a high-band feed (gap-coupled feed with matching network) for the LTE/WWAN operation in the 698-960 and 1710-2690 MHz bands is presented. The folded metal plate has a size of 3 × 5 × 30 mm3 and is disposed in a clearance area of 10 × 30 mm2 (300 mm2) above the top edge of the device ground plane. The matching network in the low-band feed compensates for additional capacitance for the folded metal plate with a resonant length much less than 0.25 wavelength in the lower band (698-960 MHz) and enhances the antenna's impedance matching to achieve a wide lower band. The matching network in the high-band feed also enhances the antenna's impedance matching to achieve a wide higher band (1710-2690 MHz). In addition, the coupling gap in the high-band feed can reject the low-band frequency. Also, the matching network in the low-band (high-band) feed includes a bandpass circuit which can help reject the high-band (low-band) frequency. Good isolation between the two hybrid feeds can, hence, be obtained, which makes the antenna with simple structure and small size have good radiation performances in both lower and higher bands. Details of the proposed antenna are presented.
The Rhetorical Parsing of Unrestricted Texts: A Surface-based Approach

Coherent texts are not just simple sequences of clauses and sentences, but rather complex artifacts that have highly elaborate rhetorical structure. This paper explores the extent to which well-formed rhetorical structures can be automatically derived by means of surface-form-based algorithms. These algorithms identify discourse usages of cue phrases and break sentences into clauses, hypothesize rhetorical relations that hold among textual units, and produce valid rhetorical structure trees for unrestricted natural language texts. The algorithms are empirically grounded in a corpus analysis of cue phrases and rely on a first-order formalization of rhetorical structure trees. The algorithms are evaluated both intrinsically and extrinsically. The intrinsic evaluation assesses the resemblance between automatically and manually constructed rhetorical structure trees. The extrinsic evaluation shows that automatically derived rhetorical structures can be successfully exploited in the context of text summarization.
Multivariate decision trees

Unlike a univariate decision tree, a multivariate decision tree is not restricted to splits of the instance space that are orthogonal to the features' axes. This article addresses several issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present several new methods for forming multivariate decision trees and compare them with several well-known methods. We compare the different methods across a variety of learning tasks, in order to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are in general more effective than others (in the context of our experimental assumptions). In addition, the experiments confirm that allowing multivariate tests generally improves the accuracy of the resulting decision tree over a univariate tree.
Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, OPEN ACCESS Remote Sensing 2010, 2 674 coefficient of determination (r), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r) value of 0.45 as compared to 0.31–0.37 for other index models. Log10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r value of 0.72 and standard error of prediction of 0.05 t/ha.
A Flow Model of Neural Networks

Based on a natural connection between ResNet and transport equation or its characteristic equation, we propose a continuous flow model for both ResNet and plain net. Through this continuous model, a ResNet can be explicitly constructed as a refinement of a plain net. The flow model provides an alternative perspective to understand phenomena in deep neural networks, such as why it is necessary and sufficient to use 2-layer blocks in ResNets, why deeper is better, and why ResNets are even deeper, and so on. It also opens a gate to bring in more tools from the huge area of differential equations.
Syntactically Based Sentence Processing Classes: Evidence from Event-Related Brain Potentials

Theoretical considerations and diverse empirical data from clinical, psycholinguistic, and developmental studies suggest that language comprehension processes are decomposable into separate subsystems, including distinct systems for semantic and grammatical processing. Here we report that event-related potentials (ERPs) to syntactically well-formed but semantically anomalous sentences produced a pattern of brain activity that is distinct in timing and distribution from the patterns elicited by syntactically deviant sentences, and further, that different types of syntactic deviance produced distinct ERP patterns. Forty right-handed young adults read sentences presented at 2 words/sec while ERPs were recorded from over several positions between and within the hemispheres. Half of the sentences were semantically and grammatically acceptable and were controls for the remainder, which contained sentence medial words that violated (1) semantic expectations, (2) phrase structure rules, or (3) WH-movement constraints on Specificity and (4) Subjacency. As in prior research, the semantic anomalies produced a negative potential, N400, that was bilaterally distributed and was largest over posterior regions. The phrase structure violations enhanced the N125 response over anterior regions of the left hemisphere, and elicited a negative response (300-500 msec) over temporal and parietal regions of the left hemisphere. Violations of Specificity constraints produced a slow negative potential, evident by 125 msec, that was also largest over anterior regions of the left hemisphere. Violations of Subjacency constraints elicited a broadly and symmetrically distributed positivity that onset around 200 msec. The distinct timing and distribution of these effects provide biological support for theories that distinguish between these types of grammatical rules and constraints and more generally for the proposal that semantic and grammatical processes are distinct subsystems within the language faculty.
ULTRA WIDEBAND POWER DIVIDER USING TAPERED LINE

A power divider with ultra-wideband (UWB) performance has been designed. The quarter-wave transformer in the conventional Wilkinson power divider is replaced by an exponentially tapered microstrip line. Since the tapered line provides a consistent impedance transformation across all frequencies, very low amplitude ripple of 0.2 dB peak-to-peak in the transmission coefficient and superior input return loss better than 15 dB are achieved over an ultra-wide bandwidth. Two additional resistors are added along the tapered line to improve the output return loss and isolation. Simulation performed using CST Microwave Studio and measured results confirm the good performance of the proposed circuit. The return loss and the isolation between the output ports are better than 15 dB across the band 2– 10.2GHz. Standard off-the-shelf resistance values can be selected by optimizing the physical locations to mount the resistors. Better performance can be achieved with more isolation resistors added. Hence, the number of isolation resistors to be used may be selected based on the desired bandwidth and level of isolation and return loss specifications.
A Context-aware Natural Language Generator for Dialogue Systems

We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users’ way of speaking, providing contextually appropriate responses. The generator is based on recurrent neural networks and the sequence-to-sequence approach. It is fully trainable from data which include preceding context along with responses to be generated. We show that the context-aware generator yields significant improvements over the baseline in both automatic metrics and a human pairwise preference test.
Drowsiness Detection based on EEG Signal analysis using EMD and trained Neural Network

Detection of drowsiness based on extraction of IMF’s from EEG signal using EMD process and characterizing the features using trained Artificial Neural Network (ANN) is introduced in this paper. Our subjects are 8 volunteers who have not slept for last 24 hour due to travelling. EEG signal was recorded when the subject is sitting on a chair facing video camera and are obliged to see camera only. ANN is trained using a utility made in Matlab to mark the EEG data for drowsy state and awaked state and then extract IMF’s of marked data using EMD to prepare feature inputs for Neural Network. Once the neural network is trained, IMFs of New subjects EEG Signals is given as input and ANN will give output in two different states i.e. ‘drowsy’ or ‘awake’. The system is tested on 8 different subjects and it provided good results with more than 84.8% of correct detection of drowsy states.
Design space exploration of FPGA accelerators for convolutional neural networks

The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), makes the hardware accelerator approach very compelling. However the question of how to best design an accelerator for a given CNN has not been answered yet, even on a very fundamental level. This paper addresses that challenge, by providing a novel framework that can universally and accurately evaluate and explore various architectural choices for CNN accelerators on FPGAs. Our exploration framework is more extensive than that of any previous work in terms of the design space, and takes into account various FPGA resources to maximize performance including DSP resources, on-chip memory, and off-chip memory bandwidth. Our experimental results using some of the largest CNN models including one that has 16 convolutional layers demonstrate the efficacy of our framework, as well as the need for such a high-level architecture exploration approach to find the best architecture for a CNN model.
Deinterleaving for radar warning receivers with missed pulse consideration

Radar warning receivers (RWRs) classify the intercepted pulses into clusters utilizing multiple parameter deinterleaving. In order to make classification more elaborate time-of-arrival (TOA) deinterleaving should be performed for each cluster. In addition, identification of the classified pulse sequences has been exercised at last. It is essential to identify the classified sequences with a minimum number of pulses. This paper presents a method for deinterleaving of intercepted signals having small number of pulses that belong to stable or jitter pulse repetition interval (PRI) types in the presence of missed pulses. It is necessary for both stable and jitter PRI TOA deinterleaving algorithms to utilize predefined PRI range. However, jitter PRI TOA deinterleaving also requires variation about mean PRI value of emitter of interest as a priori.