nils-herrmann/clean_scirepeval huggingface dataset:
Deep Domain Confusion: Maximizing for Domain Invariance

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.
Software Architecture Evaluation: A Systematic Mapping Study

Software architecture provides the big picture of software systems, hence the need to evaluate its quality before further software engineering work. In this paper, a systematic mapping study was performed to summarize the existing software architecture evaluation approaches in literature and to organize the selected studies according to six classification criteria: research types, empirical types, contribution types, software quality models, quality attributes and software architecture models. Publication channels and trends were also identified. 60 studies were selected from digital libraries.
Design of a Direct-Driven Linear Actuator for a High-Speed Quadruped Robot, Cheetaroid-I

Several legged robots have been developed in recent years and proved that they are an effective transportation system on an uneven terrain. In particular, quadruped robots are regarded as a new trend in robotics due to their superior gait stability and robustness to disturbances. More recently, many robotics researchers are making their best efforts to improve the locomotion speed, as well as the stability and robustness, of quadruped robots. The high-speed locomotion creates various challenges in the development of actuators, mechanical design, and control algorithms of the robot. In this paper, a linear actuation system for the high-speed locomotion of a quadruped robot is introduced. The proposed actuator is designed based on the principle of brushed direct-current electric motor systems. For the minimal impedance and improved power capacity, the actuator is designed with dual layers of cores, which are aligned parallel to permanent magnets. The mechanical and electrical properties of the actuation system, such as back-drivability, controllability, and response time, are verified by experimental results. A robotic leg, which is the rear leg of a Cheetah-like robot, is designed with the proposed actuator, and experimental results for trajectory tracking performance are presented.
Automatic question answering using the web: Beyond the Factoid

In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.
Similarity Search in High Dimensions via Hashing

The nearest or near neighbor query problems arise in a large variety of database applications usually in the context of similarity searching Of late there has been increasing interest in build ing search index structures for performing simi larity search over high dimensional data e g im age databases document collections time series databases and genome databases Unfortunately all known techniques for solving this problem fall prey to the curse of dimensionality That is the data structures scale poorly with data dimen sionality in fact if the number of dimensions exceeds to searching in k d trees and re lated structures involves the inspection of a large fraction of the database thereby doing no better than brute force linear search It has been sug gested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic determining an approximate nearest neighbor should su ce for most practi cal purposes In this paper we examine a novel scheme for approximate similarity search based on hashing The basic idea is to hash the points Supported by NAVY N grant and NSF Grant IIS Supported by Stanford Graduate Fellowship and NSF NYI Award CCR Supported by ARO MURI Grant DAAH NSF Grant IIS and NSF Young Investigator Award CCR with matching funds from IBM Mitsubishi Schlum berger Foundation Shell Foundation and Xerox Corporation Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage the VLDB copyright notice and the title of the publication and its date appear and notice is given that copying is by permission of the Very Large Data Base Endowment To copy otherwise or to republish requires a fee and or special permission from the Endowment Proceedings of the th VLDB Conference Edinburgh Scotland from the database so as to ensure that the prob ability of collision is much higher for objects that are close to each other than for those that are far apart We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in high dimensional spaces based on hierarchical tree de composition Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions more than
Rough sets theory for multicriteria decision analysis

The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identi®ed with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.
A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers

It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object’s residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.
Solving the Multiple Instance Problem with Axis-Parallel Rectangles

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.
View Morphing

Mittels Image Morphing ist es m ̈ oglich überzeugendëUbergänge zwischen 2-dimensionalen Objekten zu erzeugen. Ver ̈ andern der Objektposition oder des Beobachtungspunktes f ̈ uhren jedoch zu unnat ̈ urlichen Verzerrungen, welche nur schwierig zu vermeiden sind. Das Paper von Seitz und Dyer stellt eine Methode vor, welche es mittels einfachen geometrischen Projektionen erm ̈ oglichtÜbergänge zu erzeugen, bei welchen sich die Kamerasicht oder die Objektpositionierungen ändern. Diese Technik wird View Morphing genannt. Dabei werden die Bilder zuerst transformiert. Dann werden die Zwischenbilder f ̈ ur denÜbergang erzeugt, welche dann in einem letzten Schritt wieder zur ̈ ucktransformiert werden. Diese Methode kann auf jegliches Bildmaterial angewendet werden, da keine 3D Informationüber den Bildinhalt ben ̈ otigt wird. Weil es möglich ist sowohlÄnderungen in der Kamerasicht als auch in den Objektpositionierungen durchzuf ̈ u ren können mittels View Morphing viele interessante 3-dimensionale Effekte anhand einfacher Bildtransformationen berechnet werden.
Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae.

AlignACE is a Gibbs sampling algorithm for identifying motifs that are over-represented in a set of DNA sequences. When used to search upstream of apparently coregulated genes, AlignACE finds motifs that often correspond to the DNA binding preferences of transcription factors. We previously used AlignACE to analyze whole genome mRNA expression data. Here, we present a more detailed study of its effectiveness as applied to a variety of groups of genes in the Saccharomyces cerevisiae genome. Published functional catalogs of genes and sets of genes grouped by common name provided 248 groups, resulting in 3311 motifs. In conjunction with this analysis, we present measures for gauging the tendency of a motif to target a given set of genes relative to all other genes in the genome and for gauging the degree to which a motif is preferentially located in a certain distance range upstream of translational start sites. We demonstrate improved methods for comparing and clustering sequence motifs. Many previously identified cis-regulatory elements were found. We also describe previously unidentified motifs, one of which has been verified by experiments in our laboratory. An extensive set of AlignACE runs on randomly selected sets of genes and on sets of genes whose upstream regions contain known transcription factor binding sites serve as controls.
Analysis on 2-element insertion sort algorithm

This paper proposes a 2-element insertion sort algorithm, an improved algorithm on the direct insertion sort, gives the algorithm design idea and the description in C. After analyzes comparatively the time complexity and space complexity of the algorithms, the paper summarizes the pros and cons of the 2-element insertion sort algorithm compared with the original one, so as to giving a theoretical basis for the direct insertion sort algorithm optimization, and playing a guiding role in teaching the relevant chapters in “Data Structure” curriculum.
An IQ-modulator based heterodyne 77-GHz FMCW radar

In this paper a method to realize a heterodyne frequency-modulated continuous-wave (FMCW) radar is presented. The system operates at a frequency of 77 GHz and is based on an in-phase quadrature-phase (IQ) modulator in the transmit (TX)-path of the system. This IQ modulator is used as a single-sideband mixer, which allows the realization of an offset frequency between the radar's TX and receive signal. In this way it is possible to shift the frequency band containing the target information away from dc, which simplifies FMCW range measurement of targets close to the radar.
Spatial Correlation and Mobility-Aware Traffic Modeling for Wireless Sensor Networks

Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with spatial correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and spatial correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and spatial correlation can lead to bursty traffic. More specifically, a high mobility variance and small spatial correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autocorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.
An approach to interactive deep reinforcement learning for serious games

Serious games receive increasing interest in the area of e-learning. Their development, however, is often still a demanding, specialized and arduous process, especially when regarding reasonable non-player character behaviour. Reinforcement learning and, since recently, also deep reinforcement learning have proven to automatically generate successful AI behaviour to a certain degree. These methods are computationally expensive and hardly scalable to various complex serious game scenarios. For this reason, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts' knowledge to guide the learning process. Thereby, we aim to create a synergistic combination of experts and emergent cognitive systems. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a framework.
Synthetic aperture beamformer for imaging through a dielectric wall

A coarray-based aperture synthesis scheme using subarrays and postdata acquisition beamforming is presented for through-the-wall wideband microwave imaging applications. The wall causes wave refraction and a change in the propagation speed, both effects alter the travel time between the transmitter, the target, and the receiver. Coherent combining of the pulse waveforms emitted by the different transmitters and incident at the receivers through reflections from targets and clutter requires incorporation of wall effects into the beamformer design. Simulation results verifying the proposed synthetic aperture technique for a through-the-wall imaging (TWI) system are presented. The impact of the wall ambiguities or incorrect estimates of the wall parameters, such as thickness and dielectric constant, on performance is considered.
Electrick: Low-Cost Touch Sensing Using Electric Field Tomography

Current touch input technologies are best suited for small and flat applications, such as smartphones, tablets and kiosks. In general, they are too expensive to scale to large surfaces, such as walls and furniture, and cannot provide input on objects having irregular and complex geometries, such as tools and toys. We introduce Electrick, a low-cost and versatile sensing technique that enables touch input on a wide variety of objects and surfaces, whether small or large, flat or irregular. This is achieved by using electric field tomography in concert with an electrically conductive material, which can be easily and cheaply added to objects and surfaces. We show that our technique is compatible with commonplace manufacturing methods, such as spray/brush coating, vacuum forming, and casting/molding enabling a wide range of possible uses and outputs. Our technique can also bring touch interactivity to rapidly fabricated objects, including those that are laser cut or 3D printed. Through a series of studies and illustrative example uses, we show that Electrick can enable new interactive opportunities on a diverse set of objects and surfaces that were previously static.
Mining heterogeneous information networks: a structural analysis approach

Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.
Automation of Attendances in Classrooms using RFID

This paper automates the design and implementation of students’ attendance management system, taken into consideration easy access and time saving. The system will be used in faculty of computing and information system (FCIT) at King Abdulaziz University (KAU). Radio Frequency Identification (RFID) and wireless technology are two technologies which will be applied as infrastructure in the indoor environment. University Based Services (UBS) and tag IDs of the RFID are being used in this paper, in order to determine the attendance list for academic staff. Once the academic staff enters a classroom, he will be able to register student’s presence. Therefore, an academic system for identifying and tracking attendance at computing college at KAU will be described, and hybrid RFID and Wireless LAN (WLAN) technologies will be implemented in FCIT academic environment.
Modeling Review Argumentation for Robust Sentiment Analysis

Most text classification approaches model text at the lexical and syntactic level only, lacking domain robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results. Here, we apply this idea to the supervised prediction of sentiment scores for reviews. We combine existing approaches from sentiment analysis with novel features that compare the overall argumentation structure of the given review text to a learned set of common sentiment flow patterns. Our evaluation in two domains demonstrates the benefit of modeling argumentation for text classification in terms of effectiveness and robustness.