nils-herrmann/clean_scirepeval huggingface dataset:
Spectrum Sensing in Cognitive Radio Networks

The rising number and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. In this approach, the major challenge in realizing the full potential of CR systems is to identify the spectrum opportunities in the wide band regime reliably and optimally. In the spectrum underlay approach, CR systems enable dynamic spectrum access by co-existing and transmitting simultaneously with licensed primary users without creating harmful interference to them. In this case, the challenge is to transmit with low power so as not to exceed the tolerable interference level to the primary users. Spectrum sensing and estimation is an integral part of the CR system, which is used to identify the spectrum opportunities in spectrum overlay and to identify the interference power to primary users in spectrum underlay approach. In this chapter, the authors present a comprehensive study of signal detection techniques for spectrum sensing proposed for CR systems. Specifically, they outline the state of the art research results, challenges, and future perspectives of spectrum sensing in CR systems, and also present a comparison of different methods. With this chapter, readers can have a comprehensive insight of signal processing methods of spectrum sensing for cognitive radio networks and the ongoing research and development in this area.
Consistent Linear Tracker With Converted Range, Bearing, and Range Rate Measurements

Active sonar and radar systems often include a measurement of range rate in addition to the position-based measurements of range and bearing. Due to the nonlinearity of the range rate measurement with respect to a target state in the Cartesian coordinate system, this measurement is not always fully exploited. The state of the art methods to utilize range rate include the extended Kalman filter and the unscented Kalman filter with sequential processing of the position based measurements and the range rate measurement. Common to these approaches is that the measurement prediction function remains nonlinear. The goal of this work is to develop a measurement conversion from range, bearing, and range rate to Cartesian position and velocity that is unbiased and consistent, with appropriate elimination of estimation bias. The converted measurement is then used with a linear Kalman filter. Performance of this new method is compared to state of the art techniques and shown to match or exceed that of existing techniques over a wide range of scenarios.
"Good job, you're so smart": The effects of inconsistency of praise type on young children's motivation.

Previous research has demonstrated that generic praise ("good drawer") is related to children giving up after failure because failure implies the lack of a critical trait (e.g., drawing ability). Conversely, nongeneric praise ("good job drawing") is related to mastery motivation because it implies that success is related to effort. Yet children may receive a mixture of these praise types (i.e., inconsistent praise), the effects of which are unclear. We tested how inconsistent praise influenced two components of motivation: self-evaluation and persistence. Kindergarteners (N=135) were randomly assigned to one of five conditions in which consistency of praise type was varied. After two failure scenarios, children reported self-evaluations and persistence. Results indicated that more nongeneric praise related linearly to greater motivation, yet self-evaluation and persistence were impacted differently by inconsistent praise types. Hearing even a small amount of generic praise reduced persistence, whereas hearing a small amount of nongeneric praise preserved self-evaluation.
Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness.

Women have consistently higher prevalence rates of anxiety disorders, but less is known about how gender affects age of onset, chronicity, comorbidity, and burden of illness. Gender differences in DSM-IV anxiety disorders were examined in a large sample of adults (N=20,013) in the United States using data from the Collaborative Psychiatric Epidemiology Studies (CPES). The lifetime and 12-month male:female prevalence ratios of any anxiety disorder were 1:1.7 and 1:1.79, respectively. Women had higher rates of lifetime diagnosis for each of the anxiety disorders examined, except for social anxiety disorder which showed no gender difference in prevalence. No gender differences were observed in the age of onset and chronicity of the illness. However, women with a lifetime diagnosis of an anxiety disorder were more likely than men to also be diagnosed with another anxiety disorder, bulimia nervosa, and major depressive disorder. Furthermore, anxiety disorders were associated with a greater illness burden in women than in men, particularly among European American women and to some extend also among Hispanic women. These results suggest that anxiety disorders are not only more prevalent but also more disabling in women than in men.
Calculating the similarity between words and sentences using a lexical database and corpus statistics

Calculating the semantic similarity between sentences is a long dealt problem in the area of natural language processing. The semantic analysis field has a crucial role to play in the research related to the text analytics. The semantic similarity differs as the domain of operation differs. In this paper, we present a methodology which deals with this issue by incorporating semantic similarity and corpus statistics. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. The methodology can be applied in a variety of domains. The methodology has been tested on both benchmark standards and mean human similarity dataset. When tested on these two datasets, it gives highest correlation value for both word and sentence similarity outperforming other similar models. For word similarity, we obtained Pearson correlation coefficient of 0.8753 and for sentence similarity, the correlation obtained is 0.8794.
Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

This paper represents energy efficient routing protocols in WSN. It is a collection of sensor nodes with a set of limited Processor and limited memory unit embedded in it. Reliable routing of packets from the sensor node to its base station is the most important task for the networks. The routing protocols applied for the other networks cannot be used here due to its battery powered nodes This paper gives an overview of the different routing strategies used in wireless sensor networks and gives a brief working model of energy efficient routing protocols in WSN. It also shows the comparison of these different routing protocols based on metrics such as mobility support, stability, issues and latency.
Sleep deprivation-induced reduction in cortical functional response to serial subtraction.

Thirteen normal volunteers were studied with fMRI during arithmetic performance after a normal night of sleep and following sleep deprivation (SD). Aims included determining whether the prefrontal cortex (PFC) and the parietal lobe arithmetic areas are vulnerable to the effects of SD. After a normal night of sleep, activation localized to the bilateral PFC, parietal lobes and premotor areas. Following SD, activity in these regions decreased markedly, especially in the PFC. Performance also dropped. Data from the serial subtraction task are consistent with Horne's PFC vulnerability hypothesis but, based on this and other studies, we suggest the localized, functional effects of SD in the brain may vary, in part, with the specific cognitive task.
A Survey on Automatically Mining Facets for Web Queries

2017 In this paper, a detailed survey on different facet mining techniques, their advantages and disadvantages is carried out. Facets are any word or phrase which summarize an important aspect about the web query. Researchers proposed different efficient techniques which improves the user’s web query search experiences magnificently. Users are happy when they find the relevant information to their query in the top results. The objectives of their research are: (1) To present automated solution to derive the query facets by analyzing the text query; (2) To create taxonomy of query refinement strategies for efficient results; and (3) To personalize search according to user interest.
A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: results of a pilot study.

Informal caregivers often experience psychological distress due to the changing functioning of the person with dementia they care for. Improved understanding of the person with dementia reduces psychological distress. To enhance understanding and empathy in caregivers, an innovative technology virtual reality intervention Through the D'mentia Lens (TDL) was developed to experience dementia, consisting of a virtual reality simulation movie and e-course. A pilot study of TDL was conducted.



A pre-test-post-test design was used. Informal caregivers filled out questionnaires assessing person-centeredness, empathy, perceived pressure from informal care, perceived competence and quality of the relationship. At post-test, additional questions about TDL's feasibility were asked.



Thirty-five caregivers completed the pre-test and post-test. Most participants were satisfied with TDL and stated that TDL gave more insight in the perception of the person with dementia. The simulation movie was graded 8.03 out of 10 and the e-course 7.66. Participants significantly improved in empathy, confidence in caring for the person with dementia, and positive interactions with the person with dementia.



TDL is feasible for informal caregivers and seems to lead to understanding of and insight in the experience of people with dementia. Therefore, TDL could support informal caregivers in their caregiving role.
A Subthreshold to Above-Threshold Level Shifter Comprising a Wilson Current Mirror

In this brief, we propose a novel level shifter circuit that is capable of converting subthreshold to above-threshold signal levels. In contrast to other existing implementations, it does not require a static current flow and can therefore offer considerable static power savings. The circuit has been optimized and simulated in a 90-nm process technology. It operates correctly across process corners for supply voltages from 100 mV to 1 V on the low-voltage side. At the target design voltage of 200 mV, the level shifter has a propagation delay of 18.4 ns and a static power dissipation of 6.6 nW. For a 1-MHz input signal, the total energy per transition is 93.9 fJ. Simulation results are compared to an existing subthreshold to above-threshold level shifter implementation from the paper of Chen et al.
A system for generating and injecting indistinguishable network decoys

We propose a novel trap-based architecture for detecting passive, “silent”, attackers who are eavesdropping on enterprise networks. Motivated by the increasing number of incidents where attackers sniff the local network for interesting information, such as credit card numbers, account credentials, and passwords, we introduce a methodology for building a trap-based network that is designed to maximize the realism of bait-laced traffic. Our proposal relies on a “record, modify, replay” paradigm that can be easily adapted to different networked environments. The primary contributions of our architecture are the ease of automatically injecting large amounts of believable bait, and the integration of different detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. Our network traps consist of several types of monitored passwords, authentication cookies, credit cards, and documents containing beacons to alarm when opened. The efficacy of our decoys against a model attack program is also discussed, along with results obtained from experiments in the field. In addition, we present a user study that demonstrates the believability of our decoy traffic, and finally, we provide experimental results to show that our solution causes only negligible interference to ordinary users.
Effective application of process improvement patterns to business processes

Improving the operational effectiveness and efficiency of processes is a fundamental task of business process management (BPM). There exist many proposals of process improvement patterns (PIPs) as practices that aim at supporting this goal. Selecting and implementing relevant PIPs are therefore an important prerequisite for establishing process-aware information systems in enterprises. Nevertheless, there is still a gap regarding the validation of PIPs with respect to their actual business value for a specific application scenario before implementation investments are incurred. Based on empirical research as well as experiences from BPM projects, this paper proposes a method to tackle this challenge. Our approach toward the assessment of process improvement patterns considers real-world constraints such as the role of senior stakeholders or the cost of adapting available IT systems. In addition, it outlines process improvement potentials that arise from the information technology infrastructure available to organizations, particularly regarding the combination of enterprise resource planning with business process intelligence. Our approach is illustrated along a real-world business process from human resource management. The latter covers a transactional volume of about 29,000 process instances over a period of 1 year. Overall, our approach enables both practitioners and researchers to reasonably assess PIPs before taking any process implementation decision.
Ethics , Performance Measure Choice , and Accounting Manipulation

We examine whether embedding ethical values within t e ‘fiber’ of the firm influences performance measure choices and whether accounting manipulation is more or less severe depending on these ethical values. Based on a sampl e of 550 managers our findings show that ethical values prevalent in a work unit affect perf ormance measure choice, namely ethical values that “focus on self” increase the use of costly agg re ated performance measures that capture the joint performance of multiple work units to promote b tween-unit cooperation. We estimate that the effect of ethical values on the use of aggregat e measures is almost twice as large as the effect o f within-firm interdependencies, which have been cons idered the main determinant of their use in prior literature. We also demonstrate that an ethic al work climate with a lower “focus on self” reduces the incidence of accounting manipulation.
A Single-Handed Partial Zooming Technique for Touch-Screen Mobile Devices

Despite its ubiquitous use, the pinch zooming technique is not effective for one-handed interaction. We propose ContextZoom, a novel technique for single-handed zooming on touch-screen mobile devices. It allows users to specify any place on a device screen as the zooming center to ensure that the intended zooming target is always visible on the screen after zooming. ContextZoom supports zooming in/out a portion of a viewport, and provides a quick switch between the partial and whole viewports. We conducted an empirical evaluation of ContextZoom through a controlled lab experiment to compare ContextZoom and the Google maps’ single-handed zooming technique. Results show that ContextZoom outperforms the latter in task completion time and the number of discrete actions taken. Participants also reported higher levels of perceived effectiveness and overall satisfaction with ContextZoom than with the Google maps’ single-handed zooming technique, as well as a similar level of perceived ease of use.
Patching. Restitching business portfolios in dynamic markets.

In turbulent markets, businesses and opportunities are constantly falling out of alignment. New technologies and emerging markets create fresh opportunities. Converging markets produce more. And of course, some markets fade. In this landscape of continuous flux, it's more important to build corporate-level strategic processes that enable dynamic repositioning than it is to build any particular defensible position. That's why smart corporate strategists use patching, a process of mapping and remapping business units to create a shifting mix of highly focused, tightly aligned businesses that can respond to changing market opportunities. Patching is not just another name for reorganizing; patchers have a distinctive mindset. Traditional managers see structure as stable; patching managers believe structure is inherently temporary. Traditional managers set corporate strategy first, but patching managers keep the organization focused on the right set of business opportunities and let strategy emerge from individual businesses. Although the focus of patching is flexibility, the process itself follows a pattern. Patching changes are usually small in scale and made frequently. Patching should be done quickly; the emphasis is on getting the patch about right and fixing problems later. Patches should have a test drive before they're formalized but then be tightly scripted after they've been announced. And patching won't work without the right infrastructure: modular business units, fine-grained and complete unit-level metrics, and companywide compensation parity. The authors illustrate how patching works and point out some common stumbling blocks.
Compact printed dipole antenna with folding element for 2.4 Ghz WLAN communications

A novel printed dipole antenna suitable for 2.4 GHz WLAN application is presented and investigated in this paper. A folding structure in the end of dipole antenna is designed so that the antenna length has a reduction of 46.43 % compared to traditional printed dipole antenna. The effects of some important parameters on the return loss of the proposed antenna have been investigated. In order to operate as WLAN device, the printed dipole antenna should be characterized by a low profile and cover the operation bandwidth of the 2.400-2.484 GHz. The radiation pattern is similar to those conventional dipole antennas and the measured gain variation is less than 0.3dBi.
Control of hair growth and follicle size by VEGF-mediated angiogenesis.

The murine hair follicle undergoes pronounced cyclic expansion and regression, leading to rapidly changing demands for its vascular support. Our study aimed to quantify the cyclic changes of perifollicular vascularization and to characterize the biological role of VEGF for hair growth, angiogenesis, and follicle cycling. We found a significant increase in perifollicular vascularization during the growth phase (anagen) of the hair cycle, followed by regression of angiogenic blood vessels during the involution (catagen) and the resting (telogen) phase. Perifollicular angiogenesis was temporally and spatially correlated with upregulation of VEGF mRNA expression by follicular keratinocytes of the outer root sheath, but not by dermal papilla cells. Transgenic overexpression of VEGF in outer root sheath keratinocytes of hair follicles strongly induced perifollicular vascularization, resulting in accelerated hair regrowth after depilation and in increased size of hair follicles and hair shafts. Conversely, systemic treatment with a neutralizing anti-VEGF antibody led to hair growth retardation and reduced hair follicle size. No effects of VEGF treatment or VEGF blockade were observed in mouse vibrissa organ cultures, which lack a functional vascular system. These results identify VEGF as a major mediator of hair follicle growth and cycling and provide the first direct evidence that improved follicle vascularization promotes hair growth and increases hair follicle and hair size.
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems

Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keywordbased queries. We propose a reinforcementlearning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.
Fixed-class Classifier Personalized Classifier User-specific incremental update

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often have content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user’s domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.
The Beta Generalized Exponential Distribution

We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions.  Beta exponential distribution, Information matrix, Generalized exponential distribution, Maximum likelihood estimation.