nils-herrmann/clean_scirepeval huggingface dataset:
On Feature Matching and Image Registration for Two-dimensional Forward-scan Sonar Imaging

The computer processing of forward-look sonar video imagery enables significant capabilities in a wide variety of underwater operations within turbid environments. Accurate automated registration of sonar video images to complement measurements from traditional positioning devices can be instrumental in the detection, localization, and tracking of distinct scene targets, building feature maps, change detection, as well as improving precision in the positioning of unmanned submarines. This work offers a novel solution for the registration of two-dimensional (2-D) forward-look sonar images recorded from a mobile platform, by optimization over the sonar 3-D motion parameters. It incorporates the detection of key features and landmarks, and effectively represents them with Gaussian maps. Improved performance is demonstrated with respect to the state-of-the-art approach utilizing 2-D similarity transformation, based on experiments with real data.
MODIFIED BROADBAND SCHIFFMAN PHASE SHIFTER USING DENTATE MICROSTRIP AND PATTERNED GROUND PLANE

In this letter, a modified broadband 90◦ phase shifter is proposed. By using a dentate microstrip and a patterned ground plane, an extremely tight coupling can be obtained, and consequently a constant phase shift over a wide bandwidth can be achieved. To verify the proposed idea, a topology is implemented, the measured results of a phase difference of 90 ± 5◦ in 79.5% bandwidth, better than 10 dB return loss across the whole operating band, are also given. The measurement results agree well with the full-wave electromagnetic simulated responses.
Natural signal statistics and sensory gain control

We describe a form of nonlinear decomposition that is well-suited for efficient encoding of natural signals. Signals are initially decomposed using a bank of linear filters. Each filter response is then rectified and divided by a weighted sum of rectified responses of neighboring filters. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, provides a good characterization of the nonlinear response properties of typical neurons in primary visual cortex or auditory nerve, respectively. These results suggest that nonlinear response properties of sensory neurons are not an accident of biological implementation, but have an important functional role.
Scanpath comparison revisited

The scanpath comparison framework based on string editing is revisited. The previous method of clustering based on k-means "preevaluation" is replaced by the mean shift algorithm followed by elliptical modeling via Principal Components Analysis. Ellipse intersection determines cluster overlap, with fast nearest-neighbor search provided by the kd-tree. Subsequent construction of Y - matrices and parsing diagrams is fully automated, obviating prior interactive steps. Empirical validation is performed via analysis of eye movements collected during a variant of the Trail Making Test, where participants were asked to visually connect alphanumeric targets (letters and numbers). The observed repetitive position similarity index matches previously published results, providing ongoing support for the scanpath theory (at least in this situation). Task dependence of eye movements may be indicated by the global position index, which differs considerably from past results based on free viewing.
Virtual experiment system for electrician training based on Kinect and Unity3D

In order to establish low-cost and strongly-immersive desktop virtual experiment system, a solution based on Kinect and Unity3D engine technology was herein proposed, with a view to applying Kinect gesture recognition and triggering more spontaneous human-computer interactions in three-dimensional virtual environment. A kind of algorithm tailored to the detection of concave-convex points of fingers is put forward to identify various gestures and interaction semantics. In the context of Unity3D, Finite-State Machine (FSM) programming was applied in intelligent management for experimental logic tasks. A “Virtual Experiment System for Electrician Training” was designed and put into practice by these methods. The applications of “Lighting Circuit” module prove that these methods can be satisfyingly helpful to complete virtual experimental tasks and improve user experience. Compared with traditional WIMP interaction, Kinect somatosensory interaction is combined with Unity3D so that three-dimensional virtual system with strong immersion can be established.
Emotional Regulation and Emotional Development

Current neofunctionalist views of emotion underscore the biologically adaptive and psychologically constructive contributions of emotion to organized behavior, but little is known of the development of the emotional regulatory processes by which this is fostered. Emotional regulation refers to the extrinsic and intrinsic processes responsible for monitoring, evaluating, and modifying emotional reactions. This review provides a developmental outline of emotional regulation and its relation to emotional development throughout the life-span. The biological foundations of emotional self-regulation and individual differences in regulatory tendencies are summarized. Extrinsic influences on the early regulation of a child's emotion and their long-term significance are then discussed, including a parent's direct intervention strategies, selective reinforcement and modeling processes, affective induction, and the caregiver's ecological control of opportunity for heightened emotion and its management. Intrinsic contributors to the growth of emotional self-regulatory capacities include the emergence of language and cognitive skills, the child's growing emotional and self-understanding (and cognized strategies of emotional self-control), and the emergence of a "theory of personal emotion" in adolescence.
Learning to "Read Between the Lines" using Bayesian Logic Programs

Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires “reading between the lines”. Human readers naturally use common sense knowledge to infer such implicit information from the explicitly stated facts. We propose an approach that uses Bayesian Logic Programs (BLPs), a statistical relational model combining firstorder logic and Bayesian networks, to infer additional implicit information from extracted facts. It involves learning uncertain commonsense knowledge (in the form of probabilistic first-order rules) from natural language text by mining a large corpus of automatically extracted facts. These rules are then used to derive additional facts from extracted information using BLP inference. Experimental evaluation on a benchmark data set for machine reading demonstrates the efficacy of our approach.
Text Classification for Data Loss Prevention

Businesses, governments, and individuals leak confidential information, both accidentally and maliciously, at tremendous cost in money, privacy, national security, and reputation. Several security software vendors now offer “data loss prevention” (DLP) solutions that use simple algorithms, such as keyword lists and hashing, which are too coarse to capture the features what makes sensitive documents secret. In this paper, we present automatic text classification algorithms for classifying enterprise documents as either sensitive or non-sensitive. We also introduce a novel training strategy, supplement and adjust, to create a classifier that has a low false discovery rate, even when presented with documents unrelated to the enterprise. We evaluated our algorithm on several corpora that we assembled from confidential documents published on WikiLeaks and other archives. Our classifier had a false negative rate of less than 3.0% and a false discovery rate of less than 1.0% on all our tests (i.e, in a real deployment, the classifier can identify more than 97% of information leaks while raising at most 1 false alarm every 100 time).
Reviewing the Design of DAML+OIL: An Ontology Language for the Semantic Web

In the current "Syntactic Web", uninterpreted syntactic constructs are given meaning only by private off-line agreements that are inaccessible to computers. In the Semantic Web vision, this is replaced by a web where both data and its semantic definition are accessible and manipulable by computer software. DAML+OIL is an ontology language specifically designed for this use in the Web; it exploits existing Web standards (XML and RDF), adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The definition of DAML+OIL is now over a year old, and the language has been in fairly widespread use. In this paper, we review DAML+OIL's relation with its key ingredients (XML, RDF, OIL, DAML-ONT, Description Logics), we discuss the design decisions and trade-offs that were the basis for the language definition, and identify a number of implementation challenges posed by the current language. These issues are important for designers of other representation languages for the Semantic Web, be they competitors or successors of DAML+OIL, such as the language currently under definition by W3C.
Speed Sensorless Control of PMSM using Model Reference Adaptive System and RBFN

In the speed sensorless vector control system, the amended method of estimating the rotor speed about model reference adaptive system (MRAS) based on radial basis function neural network (RBFN) for PMSM sensorless vector control system was presented. Based on the PI regulator, the radial basis function neural network which is more prominent learning efficiency and performance is combined with MRAS. The reference model and the adjust model are the PMSM itself and the PMSM current, respectively. The proposed scheme only needs the error signal between q axis estimated current and q axis actual current. Then estimated speed is gained by using RBFN regulator which adjusted error signal. Comparing study of simulation and experimental results between this novel sensorless scheme and the scheme in reference literature, the results show that this novel method is capable of precise estimating the rotor position and speed under the condition of high or low speed. It also possesses good performance of static and dynamic.
A look into Twitter hashtag discovery and generation

Twitter is a micro-blogging site that allows users and companies to post brief pieces of information called Tweets . Some of the tweets contain keywords such as Hashtags denoted with a # , essentially one word summaries of either the topic or emotion of the tweet. The goal of this paper is to examine an approach to perform hashtag discovery on Twitter posts that do not contain user labeled hashtags. The process described in this paper is geared to be as automatic as possible, taking advantage of web information, sentiment analysis, geographic location, basic filtering and classification processes, to generate hashtags for tweets. Hashtags provide users and search queries a fast and simple basis to filter and find information that they are interested in.
Stereotype Threat Effects on Black and White Athletic Performance

Two experiments showed that framing an athletic task as diagnostic of negative racial stereotypes about Black or White athletes can impede their performance in sports. In Experiment 1, Black participants performed significantly worse than did control participants when performance on a golf task was framed as diagnostic of "sports intelligence." In comparison, White participants performed worse than did control participants when the golf task was framed as diagnostic of "natural athletic ability." Experiment 2 observed the effect of stereotype threat on the athletic performance of White participants for whom performance in sports represented a significant measure of their self-worth. The implications of the findings for the theory of stereotype threat (C. M. Steele, 1997) and for participation in sports are discussed.
Phone Synchronous Decoding with CTC Lattice

Connectionist Temporal Classification (CTC) has recently shown improved efficiency in LVCSR decoding. One popular implementation is to use a CTC model to predict the phone posteriors at each frame which are then used for Viterbi beam search on a modified WFST network. This is still within the traditional frame synchronous decoding framework. In this paper, the peaky posterior property of a CTC model is carefully investigated and it is found that ignoring blank frames will not introduce additional search errors. Based on this phenomenon, a novel phone synchronous decoding framework is proposed. Here, a phone-level CTC lattice is constructed purely using the CTC acoustic model. The resultant CTC lattice is highly compact and removes tremendous search redundancy due to blank frames. Then, the CTC lattice can be composed with the standard WFST to yield the final decoding result. The proposed approach effectively separates the acoustic evidence calculation and the search operation. This not only significantly improves online search efficiency, but also allows flexible acoustic/linguistic resources to be used. Experiments on LVCSR tasks show that phone synchronous decoding can yield an extra 2-3 times speed up compared to the traditional frame synchronous CTC decoding implementation.
Categories and particulars: prototype effects in estimating spatial location.

A model of category effects on reports from memory is presented. The model holds that stimuli are represented at 2 levels of detail: a fine-grain value and a category. When memory is inexact but people must report an exact value, they use estimation processes that combine the remembered stimulus value with category information. The proposed estimation processes include truncation at category boundaries and weighting with a central (prototypic) category value. These processes introduce bias in reporting even when memory is unbiased, but nevertheless may improve overall accuracy (by decreasing the variability of reports). Four experiments are presented in which people report the location of a dot in a circle. Subjects spontaneously impose horizontal and vertical boundaries that divide the circle into quadrants. They misplace dots toward a central (prototypic) location in each quadrant, as predicted by the model. The proposed model has broad implications; notably, it has the potential to explain biases of the sort described in psychophysics (contraction bias and the bias captured by Weber's law) as well as symmetries in similarity judgments, without positing distorted representations of physical scales.
3D Reconstruction from Projection Matrices in a C-Arm Based 3D-Angiography System

3D reconstruction of arterial vessels from planar radiographs obtained at several angles around the object has gained increasing interest. The motivating application has been interventional angiography. In order to obtain a three-dimensional reconstruction from a C-arm mounted X-Ray Image Intensifier (XRII) traditionally the trajectory of the source and the detector system is characterized and the pixel size is estimated. The main use of the imaging geometry characterization is to provide a correct 3D-2D mapping between the 3D voxels to be reconstructed and the 2D pixels on the radiographic images. We propose using projection matrices directly in a voxel driven backprojection for the reconstruction as opposed to that of computing all the geometrical parameters, including the imaging parameters. We discuss the simplicity of the entire calibration-reconstruction process, and the fact that it makes the computation of the pixel size, source to detector distance, and other explicit imaging parameters unnecessary. A usual step in the reconstruction is sinogram weighting, in which the projections containing corresponding data from opposing directions have to be weighted before they are filtered and backprojected into the object space. The rotation angle of the C-arm is used in the sinogram weighting. This means that the C-arm motion parameters must be computed from projection matrices. The numerical instability associated with the decomposition of the projection matrices into intrinsic and extrinsic parameters is discussed in the context. The paper then describes our method of computing motion parameters without matrix decomposition. Examples of the calibration results and the associated volume reconstruction are also shown. 1 Background and Justification Interventional angiography has mot iva ted m a n y research and development work on 3D reconstruct ion of arterial vessels f rom planar radiographs obta ined at several angles around the subject . The endovascular therapy of subarachnoid aneurysms using detachable Guglielmi coils is an applicat ion where an imaging
DebugAdvisor: a recommender system for debugging

In large software development projects, when a programmer is assigned a bug to fix, she typically spends a lot of time searching (in an ad-hoc manner) for instances from the past where similar bugs have been debugged, analyzed and resolved. Systematic search tools that allow the programmer to express the context of the current bug, and search through diverse data repositories associated with large projects can greatly improve the productivity of debugging This paper presents the design, implementation and experience from such a search tool called DebugAdvisor.
 The context of a bug includes all the information a programmer has about the bug, including natural language text, textual rendering of core dumps, debugger output etc. Our key insight is to allow the programmer to collate this entire context as a query to search for related information. Thus, DebugAdvisor allows the programmer to search using a fat query, which could be kilobytes of structured and unstructured data describing the contextual information for the current bug. Information retrieval in the presence of fat queries and variegated data repositories, all of which contain a mix of structured and unstructured data is a challenging problem. We present novel ideas to solve this problem.
 We have deployed DebugAdvisor to over 100 users inside Microsoft. In addition to standard metrics such as precision and recall, we present extensive qualitative and quantitative feedback from our users.
FPGA based implementation of deep neural networks using on-chip memory only

Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM. The execution time and energy consumption of the developed system is compared with a GPU based implementation. Since the capacity of memory in FPGA is limited, only 3-bit weights are used for this implementation, and training based fixed-point weight optimization is employed. The implementation using Xilinx XC7Z045 is tested for the MNIST handwritten digit recognition benchmark and a phoneme recognition task on TIMIT corpus. The obtained speed is about one quarter of a GPU based implementation and much better than that of a PC based one. The power consumption is less than 5 Watt at the full speed operation resulting in much higher efficiency compared to GPU based systems.
Scalpel: A Frugal, High Performance File Carver

File carving is an important technique for digital forensics investigation and for simple data recovery. By using a database of headers and footers (essentially, strings of bytes at predictable offsets) for specific file types, file carvers can retrieve files from raw disk images, regardless of the type of filesystem on the disk image. Perhaps more importantly, file carving is possible even if the filesystem metadata has been destroyed. This paper presents some requirements for high performance file carving, derived during design and implementation of Scalpel, a new open source file carving application. Scalpel runs on machines with only modest resources and performs carving operations very rapidly, outperforming most, perhaps all, of the current generation of carving tools. The results of a number of experiments are presented to support this assertion.
Real-Time Synchronization on Multiprocessors: To Block or Not to Block, to Suspend or Spin?

In the domain of multiprocessor real-time systems, there has been a wealth of recent work on scheduling, but relatively little work on the equally-important topic of synchronization. When synchronizing accesses to shared resources, four basic options exist: lock-free execution, wait-free execution, spin- based locking, and suspension-based locking. To our knowledge, no empirical multiprocessor-based evaluation of these basic techniques that focuses on real-time systems has ever been conducted before. In this paper, we present such an evaluation and report on our efforts to incorporate synchronization support in the testbed used in this effort.
A One-Pass Algorithm for Accurately Estimating Quantiles for Disk-Resident Data

The cpquantile of an ordered sequence of data values is the element with rank ‘pn, where n is the total number of values. Accurate estimates of quantiles are required for the solution of many practical problems. In this paper, we present a new algorithm for estimating the quantile values for disk-resident data. Our algorithm has the following characteristics: (1) It requires only one pass over the data; (2) It is deterministic; (3) It produces good lower and upper bounds of the true values of the quantiles; (4) It requires no a priori knowledge of the distribution of the data set; (5) It has a scalable parallel formulation; (6) Extra time and memory for computing additional quantiles (beyond the first one) are constant per quantile. We present experimental results on the IBM SP-2. The experimental results show that the algorithm is indeed robust and does not depend on the distribution of the data sets.
Completely Lazy Learning

Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not completely lazy because the neighborhood size k (or other locality parameter) is usually chosen by cross validation on the training set, which can require significant preprocessing and risks overfitting. We propose a simple alternative to cross validation of the neighborhood size that requires no preprocessing: instead of committing to one neighborhood size, average the discriminants for multiple neighborhoods. We show that this forms an expected estimated posterior that minimizes the expected Bregman loss with respect to the uncertainty about the neighborhood choice. We analyze this approach for six standard and state-of-the-art local classifiers, including discriminative adaptive metric kNN (DANN), a local support vector machine (SVM-KNN), hyperplane distance nearest neighbor (HKNN), and a new local Bayesian quadratic discriminant analysis (local BDA). The empirical effectiveness of this technique versus cross validation is confirmed with experiments on seven benchmark data sets, showing that similar classification performance can be attained without any training.