nils-herrmann/clean_scirepeval huggingface dataset:
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

C. F. Dormann (carsten.dormann@biom.uni-freiburg.de), B. Gruber and S. Lautenbach, Helmholtz Centre for Environmental Research-UFZ, Dept of Computational Landscape Ecology, Permoserstr. 15, DE-04318 Leipzig, Germany. CFD also at: Biometry and Environmental System Analysis, Tennenbacher Stra ß e 4, Univ. Freiburg, DE-79085 Freiburg, Germany. BG also at: Inst. for Applied Ecology, Faculty of Applied Sciences, Univ. of Canberra, ACT 2601, Australia. SL also at: Univ. of Bonn, Inst. of Geodesy and Geoinformation, Dept Urban Planning and Real Estate Management, Nussallee 1, DE-53115 Bonn, Germany. – J. Elith, School of Botany,  Dept of Biology, Unit of Ecology and Evolution, Chemin du Mus é e 10, CH-1700 Fribourg, Switzerland. – C. Buchmann, Univ. of Potsdam, Plant Ecology and Nature Conservation, Maulbeerallee 2, DE-14469 Potsdam, Germany. – G. Carl, Helmholtz Centre for Environmental Research-UFZ, Dept of Community Ecology, Th eodor-Lieser-Str. 4, DE-06120 Halle, Germany. – G. Carr é , Inra Paca, Domaine Saint-Paul, Site Agroparc, FR-84914 Avignon, France. – B. Lafourcade and T. M ü nkem ü ller, Laboratoire d’Ecologie Alpine, UMR-CNRS 5553, LOEWE Bik-F), Senckenberganlage 25, DE-60325 Frankfurt/Main, Germany. – P. J. Leit ã o, Geomatics Lab, Geography Dept, Humboldt-Univ. Berlin, Rudower Chaussee 16, DE-12489 BerlinAdlershof, Germany. PJL also at: Centre for Applied Ecology, Inst. of Agronomy,  PT-1349 -017 Lisboa, Portugal. – C. McClean, Environment Dept, Univ. of York, Heslington, York YO10 5DD, UK. – P. E. Osborne, Centre for Environmental Sciences, Faculty of Engineering and the Environment, Univ. of Southampton, Highfi eld, Southampton SO17 1BJ, UK. – B. Reineking, Biogeographical Modelling, BayCEER, Univ. of Bayreuth, Universit ä tsstr. 30, DE-95447 Bayreuth, Germany. – B. Schr ö der, Inst. of Earth and Environmental Sciences, Univ. of Potsdam, Karl-Liebknecht-Str. 24/25, DE-14476 Potsdam, Germany. BS also at: Landscape Ecology, Technische Univ. München, Emil-Ramann-Str. 6, DE-85354 Freising, Germany. – A. K. Skidmore, ITC, Univ. of Twente, PO Box 217, NL-7000 AE Enschede, the Netherlands. – D. Zurell, Univ. of Potsdam, Plant Ecology and Nature Conservation, Maulbeerallee 2, DE-14469 Potsdam, Germany. DZ also at: Inst. of Earth and Environmental Sciences, Univ. of Potsdam, Karl-Liebknecht-Str. 24/25, DE-14476 Potsdam, Germany.
Bit selection algorithm suitable for high-volume production of SRAM-PUF

Physically Unclonable Functions (PUFs) are impacted by environmental variations and aging which can reduce their acceptance in identification and authentication applications. Prior approaches to improve PUF reliability include bit analysis across environmental conditions, better design, and post-processing error correction, but these are of high cost in terms of test time and design overheads, making them unsuitable for high volume production. In this paper, we aim to address this issue for SRAM PUFs with novel bit analysis and bit selection algorithms. Our analysis of real SRAM PUFs reveals (i) critical conditions on which to select stable SRAM cells for PUF at low-cost (ii) unexplored spatial correlation between stable bits, i.e., cells that are the most stable tend to be surrounded by stable cells determined during enrollment. We develop a bit selection procedure around these observations that produces very stable bits for the PUF generated ID/key. Experimental data from real SRAM PUFs show that our approaches can effectively reduce number of errors in PUF IDs/keys with fewer enrollment steps.
Efficient 3-D placement of an aerial base station in next generation cellular networks

Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.
Automatic Generation of Smooth Paths Bounded by Polygonal Chains

We consider the problem of planning smooth paths for a vehicle in a region bounded by polygonal chains. The paths are represented as B-spline functions. A path is found by solving an optimization problem using a cost function designed to care for both the smoothness of the path and the safety of the vehicle. Smoothness is defined as small magnitude of the derivative of curvature and safety is defined as the degree of centering of the path between the polygonal chains. The polygonal chains are preprocessed in order to remove excess parts and introduce safety margins for the vehicle. The method has been implemented for use with a standard solver and tests have been made on application data provided by the Swedish mining company LKAB.
A Comparison of Message Passing and Shared Memory Architectures for Data Parallel Programs

Shared memory and message passing are two opposing communication models for parallel multicomputer architectures. Comparing such architectures has been difficult, because applications must be hand-crafted for each architecture, often resulting in radically different sources for comparison. While it is clear that shared memory machines are currently easier to program, in the future, programs will be written in high-level languages and compiled to the specific parallel target, thus eliminating this difference.In this paper, we evaluate several parallel architecture alternatives --- message passing, NUMA, and cachecoherent shared memory --- for a collection of scientific benchmarks written in C*, a data-parallel language. Using a single suite of C* source programs, we compile each benchmark and simulate the interconnect for the alternative models. Our objective is to examine underlying, technology-independent costs inherent in each alternative. Our results show the relative work required to execute these data parallel programs on the different architectures, and point out where some models have inherent advantages for particular data-parallel program styles.
Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, this paper presents an image formation method that formulates the SAR imaging problem as a sparse signal representation problem. For problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since features of the SAR reflectivity magnitude are usually of interest, the approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimisation problem over the representation of magnitude and phase of the underlying field reflectivities. The authors develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimisation problem. The experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high-quality SAR images and exhibiting robustness to uncertain or limited data.
Big Data, Simulations and HPC Convergence

Two major trends in computing systems are the growth in high performance computing (HPC) with in particular an international exascale initiative, and big data with an accompanying cloud infrastructure of dramatic and increasing size and sophistication. In this paper, we study an approach to convergence for software and applications/algorithms and show what hardware architectures it suggests. We start by dividing applications into data plus model components and classifying each component (whether from Big Data or Big Compute) in the same way. This leads to 64 properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution Features (Micro patterns);  and finally the Processing (runtime) View. We discuss convergence software built around HPC-ABDS (High Performance Computing enhanced Apache Big Data Stack) and show how one can merge Big Data and HPC (Big Simulation) concepts into a single stack and discuss appropriate hardware.
Tilt & touch: mobile phone for 3D interaction

Mobile phones are becoming de facto pervasive devices for people's daily use. This demonstration illustrates a new interaction, Tilt & Touch, to enable a smart phone to be a 3D controller. It exploits capacitive touchscreen and built-in MEMS motion sensors. When people want to navigate in a virtual reality environment on a large display, they can tilt the phone for viewpoint transforming, touch the phone screen for avatar moving, and pinch screen for viewing camera zooming. The virtual objects in the virtual reality environment can be rotated accordingly by tilting the phone.
A NOVEL HYBRID APPROACH TO ESTIMATING MISSING VALUES IN DATABASES USING K-NEAREST NEIGHBORS AND NEURAL NETWORKS

Missing values in datasets and databases can be estimated via statistics, machine learning and artificial intelligence methods. This paper uses a novel hybrid neural network and weighted nearest neighbors to estimate missing values and provides good results with high performance. In this work, four different characteristic datasets were used and missing values were estimated. Error ratio, correlation coefficient, prediction accuracy were calculated between actual and estimated values and the results were compared with basic neural network-genetic algorithm estimation method.
A Neural Conversational Model for Chatbots

One of the fundamental objectives of Computer Science is to reduce the menial, repetitive and mundane tasks. It might be arithmetic calculations or maintaining huge amount of data. With the advent of Artificial Intelligence and Machine Learning, we are a step ahead. We not only make the machine perform these tasks but also those which would require the intelligence and the reasoning of a human brain. This involves decision making, interpretation and prediction. The project makes an attempt to minimize human intervention in a wide range of domains. The tool that is built is capable of having human-like conversations with a person. Currently, the conversational model built is capable of having casual conversations in a variety of topics which involve no domain specific knowledge. The same can be replicated in a domain specific environment by including the previous conversations or data of that specific domain. The concepts of Machine Learning and Artificial Intelligence i.e., Recurrent Neural Networks along with LSTMs (Long Short Term Memory) are used to train the neural network. Recurrent Neural Networks, Chatbot, Machine Consciousness.
Structure Learning in Power Distribution Networks

Traditional power distribution networks suffer from a lack of real-time observability. This complicates development and implementation of new smart-grid technologies, such as those related to demand response, outage detection and management, and improved load monitoring. In this paper, inspired by proliferation of metering technology, we discuss topology estimation problems in structurally loopy but operationally radial distribution grids from measurements, for example, voltage data, which are either already available or can be made available with a relatively minor investment. The primary objective of this paper is to learn the operational layout of the grid. Further, the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The algorithms are computationally efficient—polynomial in time—which is proven theoretically and illustrated in numerical experiments on a number of test cases. The techniques developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.
Data security and privacy in wireless body area networks

The wireless body area network has emerged as a new technology for e-healthcare that allows the data of a patient's vital body parameters and movements to be collected by small wearable or implantable sensors and communicated using short-range wireless communication techniques. WBAN has shown great potential in improving healthcare quality, and thus has found a wide range of applications from ubiquitous health monitoring and computer assisted rehabilitation to emergency medical response systems. The security and privacy protection of the data collected from a WBAN, either while stored inside the WBAN or during their transmission outside of the WBAN, is a major unsolved concern, with challenges coming from stringent resource constraints of WBAN devices, and the high demand for both security/privacy and practicality/usability. In this article we look into two important data security issues: secure and dependable distributed data storage, and fine-grained distributed data access control for sensitive and private patient medical data. We discuss various practical issues that need to be taken into account while fulfilling the security and privacy requirements. Relevant solutions in sensor networks and WBANs are surveyed, and their applicability is analyzed.
Data-Driven Dialogue Systems for Social Agents

In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data-driven approach that includes insight into human conversational “chit-chat”, and which incorporates different natural language processing modules. Our strategy is to analyze and index large corpora of social media data, including Twitter conversations, online debates, dialogues between friends, and blog posts, and then to couple this data retrieval with modules that perform tasks such as sentiment and style analysis, topic modeling, and summarization. We aim for personal assistants that can learn more nuanced human language, and to grow from task-oriented agents to more personable social bots. 1 From Task-Oriented Agents to Social Bots Devices like the Amazon Echo and Google Home have entered our homes to perform task-oriented functions, such as looking up today’s headlines and setting reminders [1, 2]. As these devices evolve, we have begun to expect social conversation, where the device must learn to personalize and produce natural language style. Social conversation is not explicitly goal-driven in the same way as task-oriented dialogue. Many dialogue systems in both the written and spoken medium have been developed for task-oriented agents with an explicit goal of restaurant information retrieval, booking a flight, diagnosing an IT issue, or providing automotive customer support [6, 16, 23, 24, 4, 21]. These tasks often revolve around question answering, with little “chit-chat”. Templates are often used for generation and state tracking, but since they are optimized for the task at hand, the conversation can either become stale, or maintaining a conversation requires the intractable task of manually authoring many different social interactions that can be used in a particular context. We argue that a social agent should be spontaneous, and allow for human-friendly conversations that do not follow a perfectly-defined trajectory. In order to build such a conversational dialogue system, we exploit the abundance of human-human social
A Frequency-Reconfigurable Quasi-Yagi Dipole Antenna

A new frequency-reconfigurable quasi-Yagi dipole antenna is presented. It consists of a driven dipole element with two varactors in two arms, a director with an additional varactor, a truncated ground plane reflector, a microstrip-to-coplanar-stripline (CPS) transition, and a novel biasing circuit. The effective electrical length of the director element and that of the driven arms are adjusted together by changing the biasing voltages. A 35% continuously frequency-tuning bandwidth, from 1.80 to 2.45 GHz, is achieved. This covers a number of wireless communication systems, including 3G UMTS, US WCS, and WLAN. The length-adjustable director allows the endfire pattern with relatively high gain to be maintained over the entire tuning bandwidth. Measured results show that the gain varies from 5.6 to 7.6 dBi and the front-to-back ratio is better than 10 dB. The H-plane cross polarization is below -15 dB, and that in the E-plane is below -20 dB.
Applying Clustering and Ensemble Clustering Approaches to phishing Profiling

This paper describes a novel approach to profiling phishing emails based on the combination of multiple independent clusterings of the email documents. Each clustering is motivated by a natural representation of the emails. A data set of 2048 phishing emails provided by a major Australian financial institution was pre-processed to extract features describing the textual content, hyperlinks and orthographic structure of the emails. Independent clusterings using different techniques were performed on each representation, and these clusterings were then ensembled using a variety of consensus functions. This paper concentrates on using several clustering approaches to determine the most likely number of phishing groups and explores ways in which individual and combined results relate. The approach suggests a number of phishing groups and the structure of the approach can aid the development of profiles based on the individual clusters. The actual profiling is not carried out in this paper.
Promise and perils of Dynamic Sensitivity control in IEEE 802.11ax WLANs

Dynamic sensitivity control (DSC) is being discussed within the new IEEE 802.11ax task group as one of the potential techniques to improve the system performance for next generation Wi-Fi in high capacity and dense deployment environments, e.g. stadiums, conference venues, shopping malls, etc. However, there appears to be lack of consensus regarding the adoption of DSC within the group. This paper reports on investigations into the performance of the baseline DSC technique proposed in the IEEE 802.11ax task group under realistic scenarios defined by the task group. Simulations were carried out and the results suggest that compared with the default case (no DSC), the use of DSC may lead to mixed results in terms of throughput and fairness with the gain varying depending on factors like inter-AP distance, node distribution, node density and the DSC margin value. Further, we also highlight avenues for mitigating the shortcomings of DSC found in this study.
Smartphone gaming and frequent use pattern associated with smartphone addiction

The aim of this study was to investigate the risk factors of smartphone addiction in high school students.A total of 880 adolescents were recruited from a vocational high school in Taiwan in January 2014 to complete a set of questionnaires, including the 10-item Smartphone Addiction Inventory, Chen Internet Addiction Scale, and a survey of content and patterns of personal smartphone use. Of those recruited, 689 students (646 male) aged 14 to 21 and who owned a smartphone completed the questionnaire. Multiple linear regression models were used to determine the variables associated with smartphone addiction.Smartphone gaming and frequent smartphone use were associated with smartphone addiction. Furthermore, both the smartphone gaming-predominant and gaming with multiple-applications groups showed a similar association with smartphone addiction. Gender, duration of owning a smartphone, and substance use were not associated with smartphone addiction.Our findings suggest that smartphone use patterns should be part of specific measures to prevent and intervene in cases of excessive smartphone use.
A Comparative Review and Evaluation of Approximate Adders

As an important arithmetic module, the adder plays a key role in determining the speed and power consumption of a digital signal processing (DSP) system. The demands of high speed and power efficiency as well as the fault tolerance nature of some applications have promoted the development of approximate adders. This paper reviews current approximate adder designs and provides a comparative evaluation in terms of both error and circuit characteristics. Simulation results show that the equal segmentation adder (ESA) is the most hardware-efficient design, but it has the lowest accuracy in terms of error rate (ER) and mean relative error distance (MRED). The error-tolerant adder type II (ETAII), the speculative carry select adder (SCSA) and the accuracy-configurable approximate adder (ACAA) are equally accurate (provided that the same parameters are used), however ETATII incurs the lowest power-delay-product (PDP) among them. The almost correct adder (ACA) is the most power consuming scheme with a moderate accuracy. The lower-part-OR adder (LOA) is the slowest, but it is highly efficient in power dissipation.
Mobile Banking and Payment in China

The number of Internet users in China was more than 590 million at the end of June 2013. Among these users, 460 million accessed the Web through mobile devices such as cellphones and tablets. Many Chinese are now using cellphones to perform a wide range of transactions, such as transferring money, paying bills, booking hotels, buying tickets, and shopping in online stores such as Taobao, Paipai, Dangdang, and Yoybuy. An investigation of the development of mobile banking and mobile payments in China, as well as opportunities and barriers, is therefore important. This article is part of a special issue on mobile commerce.
Exploring ROI size in deep learning based lipreading

Automatic speechreading systems have increasingly exploited deep learning advances, resulting in dramatic gains over traditional methods. State-of-the-art systems typically employ convolutional neural networks (CNNs), operating on a video region-of-interest (ROI) that contains the speaker’s mouth. However, little or no attention has been paid to the effects of ROI physical coverage and resolution on the resulting recognition performance within the deep learning framework. In this paper, we investigate such choices for a visual-only speech recognition system based on CNNs and long short-term memory models that we present in detail. Further, we employ a separate CNN to perform face detection and facial landmark localization, driving the ROI extraction process. We conduct experiments on a multi-speaker corpus of connected digits utterances, recorded in ideal visual conditions. Our results show that ROI design choices affect automatic speechreading performance significantly: the best visual-only word error rate (5.07%) corresponds to a ROI that contains a large part of the lower face, in addition to just the mouth, and at a relatively high resolution. Noticeably, the result represents a 27% relative error reduction compared to employing the entire lower face as the ROI.