nils-herrmann/clean_scirepeval huggingface dataset:
A NOVEL WIDEBAND CIRCULARLY POLARIZED ANTENNA FOR WORLDWIDE UHF BAND RFID READER APPLICATIONS

This paper presents the novel design of a wideband circularly polarized (CP) Radio Frequency Identification (RFID) reader microstrip patch antenna for worldwide Ultra High Frequency (UHF) band which covers 840–960 MHz. The proposed antenna, which consists of a microstrip patch with truncated corners and a cross slot, is placed on a foam substrate (εr = 1.06) above a ground plane and is fed through vias through ground plane holes that extend from the quadrature 3 dB branch line hybrid coupler placed below the ground plane. This helps to separate feed network radiation, from the patch antenna and keeping the CP purity. The prototype antenna was fabricated with a total size of 225 × 250 × 12.8 mm3 which shows a measured impedance matching band of 840–1150MHz (31.2%) as well as measured rotating linear based circularly polarized radiation patterns. The simulated and measured 3 dB Axial Ratio (AR) bandwidth is better than 23% from 840–1050 MHz meeting and exceeding the target worldwide RFID UHF band.
Breast cancer imaging at mm-waves: Feasibility study on the safety exposure limits

Breast cancer is a leading disease worldwide, and the success of medical therapies is heavily related to the availability of breast cancer imaging techniques. While current methods, mainly ultrasound, x-ray mammography, and magnetic resonance imaging, all exhibit some disadvantages, a possible alternative investigated in recent years is based on microwave and mm-wave imaging system. A key point for these systems is their reliability in terms of safety, in particular exposure limits. This paper presents a feasibility study for a mm-wave breast cancer imaging system, with the aim of ensuring safety and compliance with the widely adopted European ICNIRP recommendations. The study is based on finite element method models of human tissues, experimentally characterized by measures obtained at one of the most important European clinical center for cancer treatments. Results prove the feasibility of the system, which can meet the exposure limits while providing the required dynamic range to let the receiver detect the cancer anomaly. In addition, the dosimetric quantities used at the present and their maximum limits at mm-waves are taking into discussion and the possibility of needing moderns quantities and limitations is discussed.
CETAD: Detecting evil twin access point attacks in wireless hotspots

Wireless hotspots allow users to use Internet via Wi-Fi interface, and many shops, cafés, parks, and airports provide free wireless hotspot services to attract customers. However, there is no authentication mechanism of Wi-Fi access points (APs) available in such hotspots, which makes them vulnerable to evil twin AP attacks. Such attacks are harmful because they allow to steal sensitive data from users. Today, there is no client-side mechanism that can effectively detect an evil twin AP attack without additional infrastructure supports. In this paper, we propose a mechanism CETAD leveraging public servers to detect such attacks. CETAD only requires installing an app at the client device and does not require to change the hotspot APs. CETAD explores the similarities between the legitimate APs and discrepancies between evil twin APs, and legitimate ones to detect an evil twin AP attack. Through our implementation and evaluation, we show that CETAD can detect evil twin AP attacks in various scenarios effectively.
Tensor Sparse Coding for Region Covariances

Sparse representation of signals has been the focus of much research in the recent years. A vast majority of existing algorithms deal with vectors, and higher–order data like images are dealt with by vectorization. However, the structure of the data may be lost in the process, leading to a poorer representation and overall performance degradation. In this paper we propose a novel approach for sparse representation of positive definite matrices, where vectorization will destroy the inherent structure of the data. The sparse decomposition of a positive definite matrix is formulated as a convex optimization problem, which falls under the category of determinant maximization (MAXDET) problems [1], for which efficient interior point algorithms exist. Experimental results are shown with simulated examples as well as in real–world computer vision applications, demonstrating the suitability of the new model. This forms the first step toward extending the cornucopia of sparsity-based algorithms to positive definite matrices.
Concepts and concept formation.

categories. That is, since cue validity is the probability of being in some category given some property, this probability will increase (or at worst not decrease) as the size of the category increases (e.g. the probability of being an animal given the property of flying is greater than the probability of bird given flying, since there must be more animals that fly than birds that fly).6 The idea that cohesive categories maximize the probability of particular properties given the category fares no better. In this case, the most specific categories will always be picked out. Medin (1982) has analyzed a variety of formal measures of category cohe­ siveness and pointed out problems with all of them. For example, one possible principle is to have concepts such that they minimize the similarity between contrasting categories; but minimizing between-category similarity will always lead one to sort a set of n objects into exactly two categories. Similarly, functions based on maximizing within-category similarity while minimizing between-category similarity lead to a variety of problems and counterintuitive expectations about when to accept new members into existent categories versus when to set up new categories. At a less formal but still abstract level, Sternberg (1982) has tried to translate some of Goodman's (e.g. 1983) ideas about induction into possible constraints on natural concepts. Sternberg suggests that the apparent naturalness of a concept increases with the familiarity of the concept (where familiarity is related to Goodman's notion of entrenchment), and decreases with the number of transformations specified in the concept (e.g. aging specifies certain trans­
Quantified fault tree techniques for calculating hardware fault metrics according to ISO 26262

Since its introduction in 2011, the ISO 26262 standard has provided the state-of-the-art methodology for achieving functional safety of automotive electrical and electronic systems. Among other requirements, the standard requires estimation of quantified metrics such as the Probabilistic Metric for Hardware Failure (PMHF) using quantitative failure analysis techniques. While the standard provides some brief guidance, a complete methodology to calculate the PMHF in detail has not been well described in literature. This paper will draw out several key frameworks for successfully calculating the probabilistic metric for hardware failure using Fault Tree Analysis (FTA). At the top levels of the analysis, methods drawn from previous literature can be used to organize potential failures within a complex multifunctional system. At the lower levels of the FTA, the effects of all fault categories, including dual-point latent and detected faults, can be accounted for using appropriate diagnostic coverage and proof-test interval times. A simple example is developed throughout the paper to demonstrate the methods. Some simplifications are proposed to estimate an upper bound on the PMHF. Conclusions are drawn related to the steps and methods employed, and the nature of PMHF calculation in practical real-world systems.
Operating systems transactions

Applications must be able to synchronize accesses to operating system resources in order to ensure correctness in the face of concurrency and system failures. System transactions allow the programmer to specify updates to heterogeneous system resources with the OS guaranteeing atomicity, consistency, isolation, and durability (ACID). System transactions efficiently and cleanly solve persistent concurrency problems that are difficult to address with other techniques. For example, system transactions eliminate security vulnerabilities in the file system that are caused by time-of-check-to-time-of-use (TOCTTOU) race conditions. System transactions enable an unsuccessful software installation to roll back without disturbing concurrent, independent updates to the file system.
 This paper describes TxOS, a variant of Linux 2.6.22 that implements system transactions. TxOS uses new implementation techniques to provide fast, serializable transactions with strong isolation and fairness between system transactions and non-transactional activity. The prototype demonstrates that a mature OS running on commodity hardware can provide system transactions at a reasonable performance cost. For instance, a transactional installation of OpenSSH incurs only 10% overhead, and a non-transactional compilation of Linux incurs negligible overhead on TxOS. By making transactions a central OS abstraction, TxOS enables new transactional services. For example, one developer prototyped a transactional ext3 file system in less than one month.
A novel mm-Wave phased array antenna with 360° coverage for 5G smartphone applications

In this paper, a novel capacitive coupled patch antenna array capable of providing 360° coverage is proposed. The design and performance of the antenna element is discussed. The proposed antenna design is positioned in the mobile phone chassis as a set of 4 sub-arrays each with 12 antenna elements to provide high gain around 27 dBi with each sub-array providing 90° coverage. The antenna array covers the frequency range of 24–28 GHz which is a promising band for future 5G based smartphone services. The antenna array performance is evaluated. The proposed antenna exhibits a uniform radiation pattern with a stable gain when integrated with the mobile phone chassis.
Highly-Efficient Self-Compact Monopulse Antenna System With Integrated Comparator Network for RF Industrial Applications

A highly-efficient monopulse antenna system is proposed for radar tracking system application. In this study, a novel integrated front-end and back-end complicated three-dimensional (3-D) system is realized practically to achieve high-level of self-compactness. A wideband and compact monopulse comparator network is developed and integrated as the back-end circuit in the system. Performance of the complete monopulse system is verified together with the front-end antenna array. To ensure the system's electrical efficiency and mechanical strength, a 3-D metal-direct-printing technique is utilized to fabricate the complicated structure, avoiding drawbacks from conventional machining methods and assembly processes. Experimental results show the monopulse system can achieve a bandwidth of 12.9% with VSWR less than 1.5 in the Ku-band, and isolation is better than 30 dB. More than 31.5 dBi gain can be maintained in the sum-patterns of wide bandwidth. The amplitude imbalance is less than 0.2 dB and null-depths are lower than -30 dB in the difference-patterns. In particular, with the help of the metal-printing technique, more than 90% efficiency can be retained in the monopulse system. It is a great improvement compared with that obtained from traditional machining approaches, indicating that this technique is promising for realizing high-performance RF intricate systems electrically and mechanically.
A Video Fingerprint Based on Visual Digest and Local Fingerprints

A fingerprinting design extracts discriminating features, called fingerprints. The extracted features are unique and specific to each image/video. The visual hash is usually a global fingerprinting technique with crypto-system constraints. In this paper, we propose an innovative video content identification process which combines a visual hash function and a local fingerprinting. Thanks to a visual hash function, we observe the video content variation and we detect key frames. A local image fingerprint technique characterizes the detected key frames. The set of local fingerprints for the whole video summarizes the video or fragments of the video. The video fingerprinting algorithm identifies an unknown video or a fragment of video within a video fingerprint database. It compares the local fingerprints of the candidate video with all local fingerprints of a database even if strong distortions are applied to an original content.
Latency-aware elastic scaling for distributed data stream processing systems

Elastic scaling allows a data stream processing system to react to a dynamically changing query or event workload by automatically scaling in or out. Thereby, both unpredictable load peaks as well as underload situations can be handled. However, each scaling decision comes with a latency penalty due to the required operator movements. Therefore, in practice an elastic system might be able to improve the system utilization, however it is not able to provide latency guarantees defined by a service level agreement (SLA).
 In this paper we introduce an elastic scaling system, which optimizes the utilization under certain latency constraints defined by a SLA. Specifically, we present a model, which estimates the latency spike created by a set of operator movements. We use this model to built a latency-aware elastic operator placement algorithm, which minimizes the number of latency violations. We show that our solution is able to reduce the 90th percentile of the end to end latency by up to 30% and reduce the number of latency violations by 50%. The achieved system utilization for our approach is comparable to a scaling strategy, which does not use latency as optimization target.
Multilingual Hierarchical Attention Networks for Document Classification

Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer.
Cloud Migration Research: A Systematic Review

By leveraging cloud services, organizations can deploy their software systems over a pool of resources. However, organizations heavily depend on their business-critical systems, which have been developed over long periods. These legacy applications are usually deployed on-premise. In recent years, research in cloud migration has been carried out. However, there is no secondary study to consolidate this research. This paper aims to identify, taxonomically classify, and systematically compare existing research on cloud migration. We conducted a systematic literature review (SLR) of 23 selected studies, published from 2010 to 2013. We classified and compared the selected studies based on a characterization framework that we also introduce in this paper. The research synthesis results in a knowledge base of current solutions for legacy-to-cloud migration. This review also identifies research gaps and directions for future research. This review reveals that cloud migration research is still in early stages of maturity, but is advancing. It identifies the needs for a migration framework to help improving the maturity level and consequently trust into cloud migration. This review shows a lack of tool support to automate migration tasks. This study also identifies needs for architectural adaptation and self-adaptive cloud-enabled systems.
A survey of document image word spotting techniques

Vast collections of documents available in image format need to be indexed for information retrieval purposes. In this framework, word spotting is an alternative solution to optical character recognition (OCR), which is rather inefficient for recognizing text of degraded quality and unknown fonts usually appearing in printed text, or writing style variations in handwritten documents. Over the past decade there has been a growing interest in addressing document indexing using word spotting which is reflected by the continuously increasing number of approaches. However, there exist very few comprehensive studies which analyze the various aspects of a word spotting system. This work aims to review the recent approaches as well as fill the gaps in several topics with respect to the related works. The nature of texts and inherent challenges addressed by word spotting methods are thoroughly examined. After presenting the core steps which compose a word spotting system, we investigate the use of retrieval enhancement techniques based on relevance feedback which improve the retrieved results. Finally, we present the datasets which are widely used for word spotting, we describe the evaluation standards and measures applied for performance assessment and discuss the results achieved by the state of the art.
Directional Cell Discovery in Millimeter Wave Cellular Networks

The acute disparity between increasing bandwidth demand and available spectrum has brought millimeter wave (mmWave) bands to the forefront of candidate solutions for the next-generation cellular networks. Highly directional transmissions are essential for cellular communication in these frequencies to compensate for higher isotropic path loss. This reliance on directional beamforming, however, complicates initial cell search since mobiles and base stations must jointly search over a potentially large angular directional space to locate a suitable path to initiate communication. To address this problem, this paper proposes a directional cell discovery procedure where base stations periodically transmit synchronization signals, potentially in time-varying random directions, to scan the angular space. Detectors for these signals are derived based on a Generalized Likelihood Ratio Test (GLRT) under various signal and receiver assumptions. The detectors are then simulated under realistic design parameters and channels based on actual experimental measurements at 28 GHz in New York City. The study reveals two key findings: 1) digital beamforming can significantly outperform analog beamforming even when digital beamforming uses very low quantization to compensate for the additional power requirements and 2) omnidirectional transmissions of the synchronization signals from the base station generally outperform random directional scanning.
Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database

We have acquired a set of audio-visual recordings of induced emotions. A collage of comedy clips and clips of disgusting content were shown to a number of participants, who displayed mostly expressions of disgust, happiness, and surprise in response. While displays of induced emotions may differ from those shown in everyday life in aspects such as the frequency with which they occur, they are regarded as highly naturalistic and spontaneous. We recorded 25 participants for approximately 5 minutes each. This collection of recordings has been added to the MMI Facial Expression Database, an online accessible, easily searchable resource that is freely available to the scientific community.
A rule-based CBR approach for expert finding and problem diagnosis

It is important to find the person with right expertise and the appropriate solutions in the specific field to solve a critical situation in a large complex system such as an enterprise level application. In this paper, we apply the experts’ knowledge to construct a solution retrieval system for expert finding and problem diagnosis. Firstly, we aim to utilize the experts’ problem diagnosis knowledge which can identify the error type of problem to suggest the corresponding expert and retrieve the solution for specific error type. Therefore, how to find an efficient way to use domain knowledge and the corresponding experts has become an important issue. To transform experts’ knowledge into the knowledge base of a solution retrieval system, the idea of developing a solution retrieval system based on hybrid approach using RBR (rule-based reasoning) and CBR (case-based reasoning), RCBR (rule-based CBR), is proposed in this research. Furthermore, we incorporate domain expertise into our methodology with role-based access control model to suggest appropriate expert for problem solving, and build a prototype system with expert finding and problem diagnosis for the complex system. The experimental results show that RCBR (rule-based CBR) can improve accuracy of retrieval cases and reduce retrieval time prominently.
Deep neural networks for improved, impromptu trajectory tracking of quadrotors

Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive “fly-as-you-draw” application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method's potential in real-world applications. Tracking errors are reduced by around 40–50% for both training and testing trajectories from users, highlighting the DNNs' capability of generalizing knowledge.
Learning small-size DNN with output-distribution-based criteria

Deep neural network (DNN) obtains significant accuracy improvements on many speech recognition tasks and its power comes from the deep and wide network structure with a very large number of parameters. It becomes challenging when we deploy DNN on devices which have limited computational and storage resources. The common practice is to train a DNN with a small number of hidden nodes and a small senone set using the standard training process, leading to significant accuracy loss. In this study, we propose to better address these issues by utilizing the DNN output distribution. To learn a DNN with small number of hidden nodes, we minimize the Kullback–Leibler divergence between the output distributions of the small-size DNN and a standard large-size DNN by utilizing a large number of un-transcribed data. For better senone set generation, we cluster the senones in the large set into a small one by directly relating the clustering process to DNN parameters, as opposed to decoupling the senone generation and DNN training process in the standard training. Evaluated on a short message dictation task, the proposed two methods get 5.08% and 1.33% relative word error rate reduction from the standard training method, respectively.