nils-herrmann/clean_scirepeval huggingface dataset:
Dual-Band and Dual-Polarized SIW-Fed Microstrip Patch Antenna

A new dual-band and dual-polarized antenna is investigated in this letter. Two longitudinal and transverse slots on the broad wall of the substrate integrated waveguide (SIW) are responsible for creating two different frequency bands with distinct polarization. A frequency selective surface (FSS) is placed on top of the microstrip patches to reduce the cross-polarization level of the antenna. The resonance frequencies of the patch and slot are set near to each other to get a wider impedance bandwidth. Bandwidth of more than 7% is achieved in each band. SIW feeding network increases the efficiency of the antenna. The simulated radiation efficiency of the antenna is better than 87% over the impedance bandwidth .
Senior Citizens' Acceptance of Information Systems: A Study in the Context of e-Government Services

The turn of the century has seen a rapidly graying population concurrently with increasingly ubiquitous information technology (IT). The elderly population being less familiar with IT implies that issues pertaining to their acceptance of information systems (IS) deserve special attention. However, the IS and gerontology literatures seldom explore IS acceptance among the aged. Attempting to address this gap, a research model is developed by integrating relevant antecedents from previous literature and empirically tested in the context of an e-Government service tailored for senior citizens. Our findings reveal that consistent with previous technology acceptance studies, senior citizens' use intention is driven by their perceived usefulness and perceived ease of use of the service. Additionally, their Internet safety perception impacts the acceptance of this online financial service. Furthermore, resource savings and self-actualization determine senior citizens' usefulness perception, whereas computer anxiety and computing support are antecedents of perceived ease of use. Of these antecedents, self actualization and computer anxiety appear particularly relevant for the elderly as compared to younger working professionals in most previous literature for whom these antecedents have not been important. The study contributes by providing an integrative model of IS acceptance among the aged, which may facilitate theory development and practice
Digital Citizenship with Social Media: Participatory Practices of Teaching and Learning in Secondary Education

This article explores how social media use in formal and informal learning spaces can support the development of digital citizenship for secondary school students. As students increasingly spend large amounts of time online (e.g., an average of six hours of screen time per day, excluding school and homework), it is critical that they are developing skills enabling them to find, evaluate, and share information responsibly, engage in constructive conversation with others from diverse backgrounds, and to ensure their online participation is safe, ethical, and legal. And, yet, in spite of the importance of students learning these skills, opportunities for digital citizenship in formal and informal learning spaces have lagged behind our ideals. The article provides a conceptual analysis of civic engagement as digital citizenship and considers how digital media applications can support citizenship education in middleand high-school grades. Then, empirical research is provided that demonstrates how high school students develop digital citizenship practices through out-of-school practices. Finally, this article suggests that both dimensions of digital citizenship (i.e., in-school, traditional citizenship education and out-of-school activities aimed at civic engagement) can be integrated through a social media-facilitated curriculum. Finally, recommendations for teaching and learning through social media are offered to educators, community members, practitioners, parents, and others.
A miniaturized wideband dual-polarized linear array with balanced antipodal Vivaldi antenna

Previous studies on Vivaldi wideband phased arrays are mainly focused on Two-dimensional scanning phased arrays. A miniaturized balanced antipodal Vivaldi antenna (BAVA) is presented in this paper. A novel vertical parasitic metal strip loading is employed in the dual-polarized linear phased arrays, it to make a compact structure. With the arc-shaped slots and metal strip loads, the radiation performance in lower operating band can be greatly enhanced. The proposed antenna is simulated in the infinite condition through periodic boundary with the size of 100mm (length) *100mm (width) *125mm (depth). The antenna achieves an impedance bandwidth when scanning to ±50° for VSWR≤3 achieves 4:1 (0.5GHz-2GHz) bandwidth and 5:1 (0.4GHz-2GHz) bandwidth in the vertical polarization and horizontal polarization, respectively, and the isolation is less than -18dB over the operating frequency range.
Uplift Modeling with Multiple Treatments and General Response Types

Randomized experiments have been used to assist decisionmaking in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.  2017 SIAM International Conference on Data
From Young Adulthood Through the Middle Ages Ravenna Helson

Periods of life have underpinnings in the universals of biology and social life, but they are conceptualized differently in different cultures and historical periods (Menon, 2001 ; Shweder, 1998). Middle age is not recognized at all in some societies, but in ours estimates often stretch from age 35 or 40 to age 60 or 65-a span of 20 to 30 years. The question of whether or not personality should be expected to change or develop within this period has received little attention. This chapter begins with a brief and selective review of two background topics: (a) relations between age, roles, status, and personality, and (b) personality change in adulthood. Then after a brief consideration of middle age as a whole, the issue of making differentiations within this long expanse is discussed. Three phases of middle age are proposed, then longitudinal evidence supporting the conception along with divergent findings are presented. Finally, there are suggestions for further research.
Variable step size P&O MPPT algorithm for PV systems

Maximum power point tracking (MPPT) techniques are employed in photovoltaic (PV) systems to maximize the PV array output power which depends on solar irradiance and temperature. Among all the MPPT strategies, The P&O Maximum Power Point Tracking algorithm is mostly used, due to its ease of implementation. On the other hand, its main drawbacks are the waste of energy in steady state conditions, when the working point moves across the MPP and the poor dynamic performances exhibited when a step change in solar irradiance or in temperature occurs. In this paper, a modified variable step size P&O MPPT algorithm is proposed, the step size is automatically tuned according to the operating point. Compared with the conventional fixed step size method, the proposed approach can effectively improve the MPPT speed and efficiency simultaneously. A theoretical analysis and the design principle of the proposed algorithm are provided and its feasibility is also verified by simulation results.
Grammar Inference Using Recurrent Neural Networks

This paper compares the performance of two recurrent neural network models learning the grammar of simple English sentences and introduces a method for creating useful negative training examples from a set of grammatical sentences. The learning task is cast as a classification problem as opposed to a prediction problem; networks are taught to classify a given sequence as grammatical or ungrammatical. The two neural network models compared are the simple architecture proposed by Elman in , and the Long Short-Term Memory (LSTM) network from [4]. For comparison, a Naive Bayes (NB) classifier is also trained with bigrams to accomplish this task. Only the Elman network learns the training data better than NB, and neither network generalizes better than NB on sentences longer than the training sentences. The LSTM network does not generalize as well as the Elman network on a simple regular grammar, but shows comparable ability to generalize knowledge of English Grammar.
Pareto Optimal Security Resource Allocation for Internet of Things

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.
An approach to Korean license plate recognition based on vertical edge matching

License plate recognition (LPR) has many applications in traffic monitoring systems. In this paper, a vertical edge matching based algorithm to recognize Korean license plate from input gray-scale image is proposed. The algorithm is able to recognize license plates in normal shape, as well as plates that are out of shape due to the angle of view. The proposed algorithm is fast enough, the recognition unit of a LPR system can be implemented only in software so that the cost of the system is reduced.
ASSESSMENT OF SLOPE STABILITY IN EMBANKMENT DAMS USING ARTIFICIAL NEURAL NETWORK

A slope formed when there is a height difference between two points of the ground surface. Limited or ambiguous data defined in the studying of the slip surface have always been posed as a challenge to engineers and geologists; however, previous studies presented the useful strategies for resolving this challenge. In general, Multi Layer Perceptrons (MLPs) (with three input, output and hidden layers) artificial neural networks (ANNs), as well as the transfer function and sufficient number of neurons in hidden layer are able to estimate any nonlinear function with any degree of complexity. In the present study, the Zonouz embankment dam at Iran was firstly modeled by using the classic limit equilibrium method (LEM) then, obtained results were used for training the ANN. Then the effect of various parameters such as logarithmic data and training epoch number on the network and sensitivity analysis on the input parameters were discussed. Finally the best network was selected in training step, and obtained results were verified. The results show that the ANN can be used as a suitable alternative technique instead of LEM for probing the slope stability in embankment dams.
Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.
Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.
Integrated microstrip and rectangular waveguide in planar form

Usually transitions from microstrip line to rectangular waveguide are made with three-dimensional complex mounting structures. In this paper, a new planar platform is developed in which the microstrip line and rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple taper. Our experiments at 28 GHz show that an effective bandwidth of 12% at 20 dB return loss is obtained with an in-band insertion loss better than 0.3 dB. The new transition allows a complete integration of waveguide components on substrate with MICs and MMICs.
Monday mornings are my fave : ) #not Exploring the Automatic Recognition of Irony in English tweets

Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).
Brain Tumor Detection using Curvelet Transform and Support Vector Machine

The prevalent cause of death in human being is brain tumor. A brain tumor is a mass or growth of anomalous cells in brain. The detection of brain tumor is difficult task. Image processing provides relevant techniques for efficient detection. In the proposed technique, first the features of MRI (Magnetic Resonance Imaging) images are extracted with curvelet transform, and then these features are applied to the support vector machine for successful identification. This proposed methodology gives efficient results.  Brain Tumor; Curvelet Transform; Support Vector Machine
Falsification Attacks against WPA-TKIP in a Realistic Environment

In this paper, we propose two new falsification attacks against Wi-Fi Protected Access Temporal Key Integrity Protocol (WPATKIP). A previous realistic attack succeeds only for a network that supports IEEE 802.11e QoS features by both an access point (AP) and a client, and it has an execution time of 12–15 min, in which it recovers a message integrity code (MIC) key from an ARP packet. Our first attack reduces the execution time for recovering a MIC key. It can recover the MIC key within 7–8 min. Our second attack expands its targets that can be attacked. This attack focuses on a new vulnerability of QoS packet processing, and this vulnerability can remove the condition that the AP supports  In addition, we discovered another vulnerability by which our attack succeeds under the condition that the chipset of the client supports IEEE 802.11e even if the client disables this standard through the OS. We demonstrate that chipsets developed by several kinds of vendors have the same vulnerability. key words: wireless LAN network, WPA-TKIP, falsification attack, QoS, vulnerability
Priming of semantic autobiographical knowledge: A case study of retrograde amnesia

The case of a 36-year-old man who suffers dense retrograde and anterograde amnesia as a result of closed-head injury that caused extensive damage to his left frontal-parietal and right parieto-occipital lobes is described. Patient K.C. has normal intelligence and relatively well-preserved perceptual, linguistic, short-term memory, and reasoning abilities. He possesses some fragmentary general knowledge about his autobiographical past, but he does not remember a single personal event or happening from any time of his life. He has some preserved expert knowledge related to the work he did for 3 years before the onset of amnesia, although he has no personal recollections from that period. Some features of K.C.'s retrograde amnesia can be interpreted in terms of the distinction between episodic and semantic memory, and in terms of the distinction between episodic and semantic autobiographical knowledge. K.C.'s semantic knowledge, but not his episodic knowledge, showed progressive improvement, or priming, in the course of the investigation.
Forecasting Nike's sales using Facebook data

This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.
Considering service strategy in ITIL V3 as a framework for IT Governance

IT governance is the process by which decisions are made around IT investments. A well-matured IT governance framework is based on three major elements: structure, process and communication. In addition, there are four objectives that drives IT governance and must be covered in IT governance processes: IT value and alignment, accountability, performance measurement, and risk management. As we know Information Technology Infrastructure Library (ITIL) is a public framework that describes Best Practice in IT service management. Although some researchers have considered ITIL V2 as a part of their IT Governance framework, this paper claims that ITIL V3 (new publication of ITIL in 2007) and “Service Strategy” as the core of this publication, can provide a well-matured framework for the governance of IT by covering all requirement of IT Governance.
Characterization of Traffic Analysis based video stream source identification

This paper presents the concept and characterization of Traffic Analysis (TA) for identifying sources of tunneled video streaming traffic. Such identification can be used in enterprise firewalls for blocking unauthorized viewing of tunneled video. We attempt to characterize and evaluate the impacts of the primary TA-influencing factors, namely, streaming protocol, codec, and the actual video content. A test environment is built to study the influence of those factors while Packet Size Distribution is used as the classification feature during Traffic Analysis. Analysis done on data obtained from the test environment has shown that the streaming protocols provide the most dominant source identification distinction. Also, while the codecs provide some weak distinctions, the influence of video content is marginal. In addition to in-laboratory experiments, a real-world verification for corroborating those observations is also made with commercial streaming service providers. Such long-haul experiments indicate that the end-to-end network conditions between the streaming server and video client can act as an additional influencing factor for traffic analysis towards video stream source identification. Overall, the results suggest the feasibility of TA for unknown video stream source identification with sufficiently diverse video examples.