nils-herrmann/clean_scirepeval huggingface dataset:
Synthetic Aperture Radar Ship Detection Using Haar-Like Features

The detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility, and large areas of concern. Synthetic aperture radar (SAR) imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard constant false alarm rate (FAR) prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using adaptive boosting training on the classifier with an accuracy of 89.38% and FAR of  $1.47 \times 10^{-8}$  across a large swath Sentinel-1 and RADARSAT-2 newly created SAR data set.
Stack-Pointer Networks for Dependency Parsing

We introduce a novel architecture for dependency parsing: stack-pointer networks (STACKPTR). Combining pointer networks (Vinyals et al., 2015) with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depthfirst search and the pointer networks select one child for the word at the top of the stack at each step. The STACKPTR parser benefits from the information of the whole sentence and all previously derived subtree structures, and removes the leftto-right restriction in classical transitionbased parsers. Yet, the number of steps for building any (including non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with O(n2) time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-theart performance on 21 of them.
Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory

With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is whether the machine can give good responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). In our paper, we propose a new model based on long short-term memory, which is used to achieve an encoder-decoder framework, and we address the emotional factor of conversation generation by changing the model’s input using a series of input transformations: a sequence without an emotional category, a sequence with an emotional category for the input sentence, and a sequence with an emotional category for the output responses. We perform a comparison between our work and related work and find that we can obtain slightly better results with respect to emotion consistency. Although in terms of content coherence our result is lower than those of related work, in the present stage of research, our method can generally generate emotional responses in order to control and improve the user’s emotion. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion.
Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Real-world relations among entities can oen be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item’s aributes, the user’s profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost. CCS CONCEPTS •Computingmethodologies→Machine learning; Supervised learning; Factorization methods;
Fringing Field Capacitance Sensor for Measuring the Moisture Content of Agricultural Commodities

A fringing field capacitive sensor is described for measuring the moisture content (MC) and temperature of agricultural commodities. Sensor performance was characterized by mounting the device on handheld probes and in acrylic canisters to determine the dielectric constant and MC of wheat and corn. The handheld probes demonstrated a promising capability to measure the MC of grain in hoppers, truck beds, and cargo holds. It is proposed that the sensors be supported on cables in grain silos and storage bins to acquire in situ data for grain storage management and control of aeration systems. The sensor is watertight and constructed with corrosion resistant materials which allow MC measurements to be made of industrial materials, chemicals, and fuels.
High-Speed Parallel CRC Implementation Based on Unfolding, Pipelining, and Retiming

This brief presents a high-speed parallel cyclic redundancy check (CRC) implementation based on unfolding, pipelining, and retiming algorithms. CRC architectures are first pipelined to reduce the iteration bound by using novel look-ahead pipelining methods and then unfolded and retimed to design high-speed parallel circuits. A comparison on commonly used generator polynomials between the proposed design and previously proposed parallel CRC algorithms shows that the proposed design can increase the speed by up to 25% and control or even reduce hardware cost
Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

0957-4174/$ see front matter 2013  E-mail addresses: t.c.li@mail.nwpu.edu.cn, lit3@lsbu.ac.uk (T. Li), sdsun@ nwpu.edu.cn (S. Sun), sattartp@lsbu.ac.uk (T.P. Sattar), corchado@usal.es (J.M. Corchado). Tiancheng Li a,⇑, Shudong Sun , Tariq Pervez Sattar , Juan Manuel Corchado c
Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques

Assessment as a dynamic process produces data that reasonable conclusions are derived by stakeholders for decision making that expectedly impact on students' learning outcomes. The data mining methodology while extracting useful, valid patterns from higher education database environment contribute to proactively ensuring students maximize their academic output. This paper develops a methodology by the derivation of performance prediction indicators to deploying a simple student performance assessment and monitoring system within a teaching and learning environment by mainly focusing on performance monitoring of students' continuous assessment (tests) and examination scores in order to predict their final achievement status upon graduation. Based on various data mining techniques (DMT) and the application of machine learning processes, rules are derived that enable the classification of students in their predicted classes. The deployment of the prototyped solution, integrates measuring, 'recycling' and reporting procedures in the new system to optimize prediction accuracy.
Accuracy and consensus in judgments of trustworthiness from faces: behavioral and neural correlates.

Perceivers' inferences about individuals based on their faces often show high interrater consensus and can even accurately predict behavior in some domains. Here we investigated the consensus and accuracy of judgments of trustworthiness. In Study 1, we showed that the type of photo judged makes a significant difference for whether an individual is judged as trustworthy. In Study 2, we found that inferences of trustworthiness made from the faces of corporate criminals did not differ from inferences made from the faces of noncriminal executives. In Study 3, we found that judgments of trustworthiness did not differ between the faces of military criminals and the faces of military heroes. In Study 4, we tempted undergraduates to cheat on a test. Although we found that judgments of intelligence from the students' faces were related to students' scores on the test and that judgments of students' extraversion were correlated with self-reported extraversion, there was no relationship between judgments of trustworthiness from the students' faces and students' cheating behavior. Finally, in Study 5, we examined the neural correlates of the accuracy of judgments of trustworthiness from faces. Replicating previous research, we found that perceptions of trustworthiness from the faces in Study 4 corresponded to participants' amygdala response. However, we found no relationship between the amygdala response and the targets' actual cheating behavior. These data suggest that judgments of trustworthiness may not be accurate but, rather, reflect subjective impressions for which people show high agreement.
Agile User Experience Development in a Large Software Organization: Good Expertise but Limited Impact

While Agile methods were originally introduced for small, tightly coupled teams, leaner ways of working are becoming a practical method to run entire enterprises. As the emphasis of user experience work has inherently been on the early phases before starting the development, it also needs to be adapted to the Agile way of working. To improve the current practices in Agile user experience work, we determined the present state of a multi-continental software development organization that already had a functioning user experience team. In this paper, we describe the most prevalent issues regarding the interaction of user experience design and software development activities, and suggest improvements to fix those. Most of the observed problems were related to communication issues and to the service mode of the user experience team. The user experience team was operating between management and development organizations trying to adapt to the dissimilar practices of both the disciplines.
Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload

Powered wheelchair users often struggle to drive safely and effectively and, in more critical cases, can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists users as and when they require help. The system uses a multiple-hypothesis method to predict the driver's intentions and, if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance but also, perhaps more importantly, characterize the user performance in an experiment that combines eye tracking with a secondary task. Without assistance, participants experienced multiple collisions while driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely but also they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain-machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input.
Circuit stickers: peel-and-stick construction of interactive electronic prototypes

We present a novel approach to the construction of electronic prototypes which can support a variety of interactive devices. Our technique, which we call circuit stickers, involves adhering physical interface elements such as LEDs, sounders, buttons and sensors onto a cheap and easy-to-make substrate which provides electrical connectivity. This assembly may include control electronics and a battery for standalone operation, or it can be interfaced to a microcontroller or PC. In this paper we illustrate different points in the design space and demonstrate the technical feasibility of our approach. We have found circuit stickers to be versatile and low-cost, supporting quick and easy construction of physically flexible interactive prototypes. Building extra copies of a device is straightforward. We believe this technology has potential for design exploration, research proto-typing, education and for hobbyist projects.
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

Summarising a high dimensional data set with a low dimension al embedding is a standard approach for exploring its structure. In this paper we provide an over view of some existing techniques for discovering such embeddings. We then introduce a novel prob abilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PC A (DPPCA). The DPPCA model has the additional advantage that the linear mappings from the e mbedded space can easily be nonlinearised through Gaussian processes. We refer to this mod el as a Gaussian process latent variable model (GP-LVM). Through analysis of the GP-LVM objective fu nction, we relate the model to popular spectral techniques such as kernel PCA and multidim ensional scaling. We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We demonstrat e the model on a range of real-world and artificially generated data sets.
Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis

In this paper, we consider the joint blind source separation (JBSS) problem and introduce a number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) framework. Source separation of multiple datasets simultaneously is possible when the sources within each and every dataset are independent of one another and each source is dependent on at most one source within each of the other datasets. In addition to source separation, the IVA framework solves an essential problem of JBSS, namely the identification of the dependent sources across the datasets. We propose to use the multivariate Gaussian source prior to achieve JBSS of sources that are linearly dependent across datasets. Analysis within the paper yields the local stability conditions, nonidentifiability conditions, and induced Cramér-Rao lower bound on the achievable interference to source ratio for IVA with multivariate Gaussian source priors. Additionally, by exploiting a novel nonorthogonal decoupling of the IVA cost function we introduce both Newton and quasi-Newton optimization algorithms for the general IVA framework.
Short-Step Chebyshev Impedance Transformers

Absfract—Impedance transforming networks are described which consist of short lengths of relatively high impedance transmission line alternating with short lengths of relatively low impedance line. The sections of transmission line are all exactly the same length (except for corrections for fringing capacitances), and the lengths of the line sections are typically short compared to a quarter wavelength throughout the operating band of the transformer. Tables of designs are presented which give exactly Chebyshev transmission characteristics between resistive terminations having ratios ranging from 1.5 to 10, and for fractional bandwidths ranging from 0.10 to 1.20. These impedance-transforming networks should have application where very compact transmission-line or dielectric-layer impedance transformers are desired.
High-Frequency High-Efficiency GaN-Based Interleaved CRM Bidirectional Buck/Boost Converter with Inverse Coupled Inductor

This paper presents a high-frequency high-efficiency GaN device-based interleaved critical current mode (CRM) bidirectional buck/boost converter with an inverse coupled inductor. The switching frequency is continually driven to megahertz range with GaN devices due to their small switching loss and driving loss, which greatly reduces the size of the passive components. The coupled inductor further reduces the core volume due to certain dc flux reductions. The equivalent inductance and the impact of the inverse coupled inductor on the CRM buck-boost converter are analyzed in detail. The resonant period in CRM is less with an inverse coupled inductor than with a noncoupled inductor, which is beneficial for the high-frequency operation. The soft-switching range and the circulating energy are both improved using an inverse coupled inductor in CRM. Experimental results validate the theoretical analysis, and the coupled inductor prototype efficiency is 98.5% at 1 MHz, which is 0.3% higher than a prototype with a noncoupled inductor.
SPIFFY: Inducing Cost-Detectability Tradeoffs for Persistent Link-Flooding Attacks

We have recently witnessed the real life demonstration of link-flooding attacks—DDoS attacks that target the core of the Internet that can cause significant damage while remaining undetected. Because these attacks use traffic patterns that are indistinguishable from legitimate TCP-like flows, they can be persistent and cause long-term traffic disruption. Existing DDoS defenses that rely on detecting flow deviations from normal TCP traffic patterns cannot work in this case. Given the low cost of launching such attacks and their indistinguishability, we argue that any countermeasure must fundamentally tackle the root cause of the problem: either force attackers to increase their costs, or barring that, force attack traffic to become distinguishable from legitimate traffic. Our key insight is that to tackle this root cause it is sufficient to perform a rate change test, where we temporarily increase the effective bandwidth of the bottlenecked core link and observe the response. Attacks by cost-sensitive adversaries who try to fully utilize the bots’ upstream bandwidth will be detected since they will be unable to demonstrably increase throughput after bandwidth expansion. Alternatively, adversaries are forced to increase costs by having to mimic legitimate clients’ traffic patterns to avoid detection. We design a software-defined network (SDN) based system called SPIFFY that addresses key practical challenges in turning this high-level idea into a concrete defense mechanism, and provide a practical solution to force a tradeoff between cost vs. detectability for linkflooding attacks. We develop fast traffic-engineering algorithms to achieve effective bandwidth expansion and suggest scalable monitoring algorithms for tracking the change in traffic-source behaviors. We demonstrate the effectiveness of SPIFFY using a real SDN testbed and large-scale packet-level and flow-level simulations.
A 60 GHz Horizontally Polarized Magnetoelectric Dipole Antenna Array With 2-D Multibeam Endfire Radiation

A novel substrate integrated waveguide (SIW) fed horizontally polarized endfire magnetoelectric (ME) dipole antenna composed of an open-ended SIW with broad walls vertical to substrates and a pair of electric dipoles realized by four metallic patches is proposed. Simple configuration and excellent performance including an impedance bandwidth of 46.5%, stable gain of around 6 dBi, and symmetrical cardioid radiation patterns with low backward radiation and low cross polarizations are achieved. An SIW 90° twist integrated in three-layered substrate is implemented in order to connect the ME-dipole antenna conveniently to the SIW beam-forming network with broad walls parallel to substrates. A <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> SIW Butler matrix with a three-layered zigzag topology is then designed, which enables a size reduction of 45% for the matrix compared with conventional single-layered configuration but not affecting its operating characteristics. By employing a <inline-formula> <tex-math notation="LaTeX">$2 \times 4$ </tex-math></inline-formula> ME-dipole array with 90° twists, two-folded Butler matrices and four SIW 3 dB E-plane couplers, a multibeam endfire array that can radiate eight beams scanning in two dimensions is designed at the 60 GHz band. The fabricated prototype verifies that a wide impedance bandwidth of 22.1%, gain varying from 10 to 13 dBi and stable radiation beams can be obtained. Due to good performance and the compact structure with low fabrication costs, the proposed design would be attractive for future millimeter-wave wireless applications including 5G communications and the WiGig system.
Android based malware detection using a multifeature collaborative decision fusion approach

Smart mobile device usage has expanded at a very high rate all over the world. Since the mobile devices nowadays are used for a wide variety of application areas like personal communication, data storage and entertainment, security threats emerge, comparable to those which a conventional PC is exposed to. Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. In this work, we have considered Android based malware for analysis and a scalable detection mechanism is designed using multifeature collaborative decision fusion (MCDF). The different features of a malicious file like the permission based features and the API call based features are considered in order to provide a better detection by training an ensemble of classifiers and combining their decisions using collaborative approach based on probability theory. The performance of the proposed model is evaluated on a collection of Android based malware comprising of different malware families and the results show that our approach give a better performance than state-of-the-art ensemble schemes available. & 2014 Elsevier B.V. All rights reserved.
Dual-Band and Triple-Band Substrate Integrated Waveguide Filters With Chebyshev and Quasi-Elliptic Responses

In this paper, synthesis and design techniques of dual- and triple-passband filters with Chebyshev and quasi-elliptic symmetric frequency responses are proposed and demonstrated for the first time on the basis of substrate integrated waveguide technology. The inverter coupled resonator section is first investigated, and then a dual-passband Chebyshev filter, a triple-passband Chebyshev filter, and a dual-passband quasi-elliptic filter, which consist of the inverter coupled resonator sections, are synthesized from the generalized low-pass prototypes having Chebyshev or quasi-elliptic responses, respectively. Subsequently, theses filters with a symmetric response are designed and implemented using the substrate integrated waveguide scheme over the -band frequency range. The inverter coupled resonator sections composed of side-by-side horizontally oriented substrate integrated waveguide cavities are coupled, in turn, by post-wall irises. 50-Omega microstrip lines are used to directly excite the filters. Measured results are presented and compared to those simulated by Ansoft's High Frequency Structure Simulator (HFSS) software package. A good agreement between the simulated and measured results is observed, which has also validated the proposed concept of design and synthesis with the substrate integration technology.