nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-wideband Vivaldi arrays for see-through-wall imaging radar applications

Two Vivaldi antenna arrays have been presented. First is an 8-element tapered slot array covering 1.2 to 4 GHz band for STW applications for brick/concrete wall imaging. Second is a 16-element antipodal array operating at 8 to 10.6 GHz for high resolution imaging when penetrating through dry wall. Based on the two designs, and utilizing a smooth wide band slot to microstrip transition to feed the Vivaldi antenna array, a 1–10 GHz frequency band can be covered. Alternatively, the design can be used in a reconfigurable structure to cover either a 1–3 GHz or 8–10 GHz band. Experimental and measured results have been completed and will be discussed in detail. The designs will significantly impact the development of compact reconfigurable and portable systems.
Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.
Seven Pernicious Kingdoms: A Taxonomy of Software Security Errors

We want to help developers and security practitioners understand common types of coding errors that lead to vulnerabilities. By organizing these errors into a simple taxonomy, we can teach developers to recognize categories of problems that lead to vulnerabilities and identify existing errors as they build software. The information contained in our taxonomy is most effectively enforced via a tool. In fact, all of the errors included in our taxonomy are amenable to automatic identification using static source code analysis techniques. We demonstrate why our taxonomy is not only simpler, but also more comprehensive than other modern taxonomy proposals and vulnerability lists. We provide an in-depth explanation and one or more code-level examples for each of the errors on a companion web site: http://vulncat.fortifysoftware.com.
Optimized rate matching architecture for a LTE-Advanced FPGA-based PHY

In this paper we present an optimized rate matching architecture for a LTE-Advanced FPGA-based physical layer. Since LTE-Advanced can reach up to rates of 1 Gbps in downlink, and since rate matching is in that critical path, it is very important that the design of the hardware architecture be efficient enough to allow this high data rate with little resources as possible. If not well planned, implementations on FPGAs can be quite challenging, limiting the choices of speed grades and FPGAs sizes capable of supporting such requirements. We propose efficient hardware architecture for the LTE-Advanced rate matching generic procedure; occupying only 218 slices and 9 block RAMs and performing in frequencies greater than 400 MHz in a FPGA-based solution.
Statistical Schema Induction

While the realization of the Semantic Web as once envisioned by Tim Berners-Lee remains in a distant future, the Web of Data has already become a reality. Billions of RDF statements on the Internet, facts about a variety of different domains, are ready to be used by semantic applications. Some of these applications, however, crucially hinge on the availability of expressive schemas suitable for logical inference that yields non-trivial conclusions. In this paper, we present a statistical approach to the induction of expressive schemas from large RDF repositories. We describe in detail the implementation of this approach and report on an evaluation that we conducted using several data sets including DBpedia.
At the end of the 14C time scale--the Middle to Upper Paleolithic record of western Eurasia.

The dynamics of change underlying the demographic processes that led to the replacement of Neandertals by Anatomically Modern Humans (AMH) and the emergence of what are recognized as Upper Paleolithic technologies and behavior can only be understood with reference to the underlying chronological framework. This paper examines the European chronometric (mainly radiocarbon-based) record for the period between ca. 40 and 30 ka 14C BP and proposes a relatively rapid transition within some 2,500 years. This can be summarized in the following falsifiable hypotheses: (1) final Middle Paleolithic (FMP) "transitional" industries (Uluzzian, Chatelperronian, leaf-point industries) were made by Neandertals and date predominantly to between ca. 41 and 38 ka 14C BP, but not younger than 35/34 ka 14C BP; (2) initial (IUP) and early (EUP) Upper Paleolithic "transitional" industries (Bachokirian, Bohunician, Protoaurignacian, Kostenki 14) will date to between ca. 39/38 and 35 ka 14C BP and document the appearance of AMH in Europe; (3) the earliest Aurignacian (I) appears throughout Europe quasi simultaneously at ca. 35 ka 14C BP. The earliest appearance of figurative art is documented only for a later phase ca. 33.0/32.5-29.2 ka 14C BP. Taken together, the Middle to Upper Paleolithic transition appears to be a cumulative process involving the acquisition of different elements of "behavioral modernity" through several "stages of innovation."
Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor

Brain is the most important and vital organ of the human body. The control and coordination of all the other vital structures is carried out by the brain. The tumor is formed by the uncontrolled multiplication of cell division. Numerous techniques were developed to detect and segment the brain tumor. Using thresholding and morphological operations efficient brain tumor segmentation is carried out. This is the efficient algorithm where segmentation of tumor is carried out and its features such as centroid, perimeter and area are calculated from the segmented tumor. To detect the brain tumor, scanned MRI images are given as the input. The work involved here helps in medical field to detect tumor and its features helps in giving the treatment plan to the patient. The entire paper is divided in to seven sections which are described in detailed in the following sections.
Text Clustering for Topic Detection

The world wide web represents vast stores of information. However, the sheer amount of such information makes it practically impossible for any human user to be aware of much of it. Therefore, it would be very helpful to have a system that automatically discovers relevant, yet previously unknown information, and reports it to users in human-readable form. As the first attempt to accomplish such a goal, we proposed a new clustering algorithm and compared it with existing clustering algorithms. The proposed method is motivated by constructive and competitive learning from neural network research. In the construction phase, it tries to find the optimal number of clusters by adding a new cluster when the intrinsic difference between the instance presented and the existing clusters is detected. Each cluster then moves toward the optimal cluster center according to the learning rate by adjusting its weight vector. From the experimental results on the three different real world data sets, the proposed method shows an even trend of performance across the different domains, while the performance of our algorithm on text domains was better than that reported in previous research.
Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation

Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagation in an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this problem, this paper associates each possible label with a “teacher”, and proposes a “Multi-Label Teaching-to-Learn and Learning-toTeach” (ML-TLLT) algorithm, so that the entire propagation process is guided by the teachers and manipulated from simple examples to more difficult ones. In the teaching-to-learn step, the teachers select the simplest examples for the current propagation by investigating both the definitiveness of each possible label of the unlabeled examples, and the dependencies between labels revealed by the labeled examples. In the learning-to-teach step, the teachers reversely learn from the learner’s feedback to properly select the simplest examples for the next propagation. Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets.
A survey on ontology mapping

Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology research. This paper presents ontology mapping categories, describes the characteristics of each category, compares these characteristics, and surveys tools, systems, and related work based on each category of ontology mapping. We believe this paper provides readers with a comprehensive understanding of ontology mapping and points to various research topics about the specific roles of ontology mapping.
FPGA Vendor Agnostic True Random Number Generator

This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phase-locked loop) or external analog components are required. Our solution is based on a framework for a provable secure true random number generator recently proposed by Sunar, Martin and Stinson. It uses a large amount of ring oscillators with identical ring lengths as a fast noise source - but with some deterministic bits - and eliminates the non-random samples by appropriate post-processing based on resilient functions. This results in a slower bit stream with high entropy. Our FPGA implementation achieves a random bit throughput of more than 2 Mbps, remains fairly compact (needing minimally 110 ring oscillators of 3 inverters) and is highly portable
Whole-function vectorization

Data-parallel programming languages are an important component in today's parallel computing landscape. Among those are domain-specific languages like shading languages in graphics (HLSL, GLSL, RenderMan, etc.) and "general-purpose" languages like CUDA or OpenCL. Current implementations of those languages on CPUs solely rely on multi-threading to implement parallelism and ignore the additional intra-core parallelism provided by the SIMD instruction set of those processors (like Intel's SSE and the upcoming AVX or Larrabee instruction sets). In this paper, we discuss several aspects of implementing dataparallel languages on machines with SIMD instruction sets. Our main contribution is a language- and platform-independent code transformation that performs whole-function vectorization on low-level intermediate code given by a control flow graph in SSA form. We evaluate our technique in two scenarios: First, incorporated in a compiler for a domain-specific language used in realtime ray tracing. Second, in a stand-alone OpenCL driver. We observe average speedup factors of 3.9 for the ray tracer and factors between 0.6 and 5.2 for different OpenCL kernels.
SwiPIN: Fast and Secure PIN-Entry on Smartphones

In this paper, we present SwiPIN, a novel authentication system that allows input of traditional PINs using simple touch gestures like up or down and makes it secure against human observers. We present two user studies which evaluated different designs of SwiPIN and compared it against traditional PIN. The results show that SwiPIN performs adequately fast (3.7 s) to serve as an alternative input method for risky situations. Furthermore, SwiPIN is easy to use, significantly more secure against shoulder surfing attacks and switching between PIN and SwiPIN feels natural.
Machine learning applications in genetics and genomics

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
Local Indicators of Spatial Association

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the "spatial" aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G; statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify "outliers," as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
Learning Semantic Relatedness in Community Question Answering Using Neural Models

Community Question Answering forums, such as Quora and Stackoverflow contain millions of questions and answers. Automatically finding the relevant questions from the existing questions and finding the relevant answers to a new question are Natural Language Processing tasks. In this paper, we aim to address these tasks, which we refer to as similar-Question Retrieval and Answer Selection. We present a neural-based model with stacked bidirectional LSTMs and MLP to address these tasks. The model generates the vector representations of the question-question or question-answer pairs and computes their semantic similarity scores, which are then employed to rank and predict relevancies. Extensive experiments demonstrate our results outperform the baselines.
A broadband Wilkinson balun using microstrip metamaterial lines

A metamaterial balun that converts a single-ended input to a differential output over a large bandwidth is presented. The device also exhibits excellent return loss, isolation, and through characteristics over the same frequency band. The balun comprises a Wilkinson divider, followed by a +90/spl deg/ negative-refractive-index (NRI) metamaterial (MM) phase-shifting line along the top branch, and a -90/spl deg/ MM phase-shifting line along the bottom branch. Utilizing MM lines for both the +90/spl deg/ and -90/spl deg/ branches allows the slopes of their phase responses to be matched, resulting in a broadband differential output signal. The theoretical performance of the balun is verified through circuit simulations and measurements of a fabricated prototype at 1.5 GHz. The MM balun exhibits a measured differential output phase bandwidth (180/spl deg//spl plusmn/10/spl deg/) of 1.16 GHz (77%), from 1.17 to 2.33 GHz. The measured isolation and return loss for all three ports remain below -10 dB over a bandwidth in excess of 2 GHz, while the output quantities |S/sub 21/| and |S/sub 31/| remain above -4 dB from 0.5 to 2.5 GHz.
Graph Embedding Problem Settings Graph Embedding Input Graph Embedding Output Homogeneous Graph Heterogeneous Graph Graph with Auxiliary Information Graph Constructed from Non-relational Data Node Embedding Edge Embedding Hybrid Embedding Whole-Graph Embedding Graph Embedding Techniques Matrix Facto

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.
Decentralized Consistent Updates in SDN

We present ez-Segway, a decentralized mechanism to consistently and quickly update the network state while preventing forwarding anomalies (loops and black-holes) and avoiding link congestion. In our design, the centralized SDN controller only pre-computes information needed by the switches during the update execution. This information is distributed to the switches, which use partial knowledge and direct message passing to efficiently realize the update. This separation of concerns has the key benefit of improving update performance as the communication and computation bottlenecks at the controller are removed. Our evaluations via network emulations and large-scale simulations demonstrate the efficiency of ez-Segway, which compared to a centralized approach, improves network update times by up to 45% and 57% at the median and the 99th percentile, respectively. A deployment of a system prototype in a real OpenFlow switch and an implementation in P4 demonstrate the feasibility and low overhead of implementing simple network update functionality within switches.
LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization

AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ability to characterize the appropriate category. Using Bag-Of-Words for text representation dramatically increases the computational time of AdaBoost.MH learning, especially for large-scale datasets. In this paper we demonstrate how to improve the efficiency and effectiveness of AdaBoost.MH using latent topics, rather than words. A well-known probabilistic topic modelling method, Latent Dirichlet Allocation, is used to estimate the latent topics in the corpus as features for AdaBoost.MH. To evaluate LDA-AdaBoost.MH, the following four datasets have been used: Reuters-21578-ModApte, WebKB, 20-Newsgroups and a collection of Arabic news. The experimental results confirmed that representing the texts as a small number of latent topics, rather than a large number of words, significantly decreased the computational time of AdaBoost.MH learning and improved its performance for text categorization.
Smarter City : Smart Energy Grid based on Blockchain Technology

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger.  Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.
Small planar monopole antenna with a shorted parasitic inverted-L wire for wireless communications in the 2.4-, 5.2-, and 5.8-GHz bands

A low-profile planar monopole antenna with a shorted parasitic inverted-L wire fed using a microstrip feedline for wireless communications in the wireless local-area network (WLAN) bands is studied. The driven monopole element and shorted parasitic wire can separately control the operating frequencies of two excited resonant modes, which cover the 2.4-, 5.2-, and 5.8-GHz WLAN bands. This antenna design is not only suitable as a monopole antenna but also as a diversity antenna for 2.4-, 5.2-, and 5.8-GHz band operations. The lower mode of the proposed antenna has an impedance bandwidth (2:1 VSWR) of about 188 MHz (2313-2501 MHz), which covers the required bandwidth for 2.4 GHz WLAN band (2400-2484 MHz); on the other band, the upper mode has a bandwidth of about 2843 MHz (3930-6773 MHz) covering the HIPERLAN band (5150-5350 MHz) and 5.8-GHz WLAN band (5725-5852 MHz). For frequencies across the three operating bands, the proposed antenna shows similar monopole-like radiation patterns, and good antenna gain across the operating bands is obtained. Details of the design considerations for the proposed antenna are described, and the experimental results of the antenna performances obtained are presented and discussed.