nils-herrmann/clean_scirepeval huggingface dataset:
Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire

Nomophobia is considered a modern age phobia introduced to our lives as a byproduct of the interaction between people and mobile information and communication technologies, especially smartphones. This study sought to contribute to the nomophobia research literature by identifying and describing the dimensions of nomophobia and developing a questionnaire to measure nomophobia. Consequently, this study adopted a two-phase, exploratory sequential mixed methods design. The first phase was a qualitative exploration of nomophobia through semi-structured interviews conducted with nine undergraduate students at a large Midwestern university in the U.S. As a result of the first phase, four dimensions of nomophobia were identified: not being able to communicate, losing connectedness, not being able to access information and giving up convenience. The qualitative findings from this initial exploration were then developed into a 20-item nomophobia questionnaire (NMP-Q). In the second phase, the NMP-Q was validated with a sample of 301 undergraduate students. Exploratory factor analysis revealed a four-factor structure for the NMP-Q, corresponding to the dimensions of nomophobia. The NMP-Q was shown to produce valid and reliable scores; and thus, can be used to assess the severity of nomophobia.
The general employee scheduling problem. An integration of MS and AI

The general employee scheduling problem extends the standard shift scheduling problem by discarding key limitations such as employee homogeneity and the absence of connections across time period blocks. The resulting increased generality yields a scheduling model that applies to real world problems confronted in a wide variety of areas. The price of the increased generality is a marked increase in size and complexity over related models reported in the literature. The integer programming formulation for the general employee scheduling problem, arising in typical real world settings, contains from one million to over four million zero~ne variables. By contrast, studies of special cases reported over the past decade have focused on problems involving between 100 and 500 variables. We characterize the relationship between the general employee scheduling problem and related problems, reporting computational results for a procedure that solves these more complex problems within 98-99 % optimality and runs on a microcomputer. We view our approach as an integration of management science and artificial intelligence techniques. The benefits of such an integration are suggested by the fact that other zero~ne scheduling implementations reported in the literature, including the one awarded the Lancaster Prize in 1984, have obtained comparable approximations of optimality only for problems from two to three orders of magnitude smaller, and then only by the use of large mainframe computers.
WaP: Indoor localization and tracking using WiFi-Assisted Particle filter

High accurate indoor localization and tracking of smart phones is critical to pervasive applications. Most radio-based solutions either exploit some error prone power-distance models or require some labor-intensive process of site survey to construct RSS fingerprint database. This study offers a new perspective to exploit RSS readings by their contrast relationship rather than absolute values, leading to three observations and functions called turn verifying, room distinguishing and entrance discovering. On this basis, we design WaP (WiFi-Assisted Particle filter), an indoor localization and tracking system exploiting particle filters to combine dead reckoning, RSS-based analyzing and knowledge of floor plan together. All the prerequisites of WaP are the floor plan and the coarse locations on which room the APs reside. WaP prototype is realized on off-the-shelf smartphones with limited particle number typically 400, and validated in a college building covering 1362m2. Experiment results show that WaP can achieve average localization error of 0.71m for 100 trajectories by 8 pedestrians.
Energy-Efficient Cooperative Relaying for Unmanned Aerial Vehicles

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate and network lifetime, especially in real-time applications. We propose an energy-efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is formulated to minimize the maximum (min-max) energy consumption under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient suboptimal algorithm to reduce the scheduling complexity, where energy balancing and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by orders of magnitude with marginal loss of the optimal network yield (throughput) and lifetime. The proposed suboptimal algorithm can also save energy by 50 percent, increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.
A case study of applying boosting naive Bayes to claim fraud diagnosis

We apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) to the problem of diagnosing insurance claim fraud. The method effectively combines the advantages of boosting and the explanatory power of the weight of evidence scoring framework. We present the results of an experimental evaluation with an emphasis on discriminatory power, ranking ability, and calibration of probability estimates. The data to which we apply the method consists of closed personal injury protection (PIP) automobile insurance claims from accidents that occurred in Massachusetts (USA) during 1993 and were previously investigated for suspicion of fraud by domain experts. The data mimic the most commonly occurring data configuration, that is, claim records consisting of information pertaining to several binary fraud indicators. The findings of the study reveal the method to be a valuable contribution to the design of intelligible, accountable, and efficient fraud detection support.
Discriminative Sparse Neighbor Approximation for Imbalanced Learning

Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue, conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that have a large degree of class overlap. In this paper, we propose a novel discriminative sparse neighbor approximation (DSNA) method to ameliorate the effect of class-imbalance during prediction. Specifically, given a test sample, we first traverse it through a cost-sensitive decision forest to collect a good subset of training examples in its local neighborhood. Then, we generate from this subset several class-discriminating but overlapping clusters and model each as an affine subspace. From these subspaces, the proposed DSNA iteratively seeks an optimal approximation of the test sample and outputs an unbiased prediction. We show that our method not only effectively mitigates the imbalance issue, but also allows the prediction to extrapolate to unseen data. The latter capability is crucial for achieving accurate prediction on small data set with limited samples. The proposed imbalanced learning method can be applied to both classification and regression tasks at a wide range of imbalance levels. It significantly outperforms the state-of-the-art methods that do not possess an imbalance handling mechanism, and is found to perform comparably or even better than recent deep learning methods by using hand-crafted features only.
Comparison of envelope extraction algorithms for cardiac sound signal segmentation

This paper describes a comparative study of the envelope extraction algorithms for the cardiac sound signal segmentation. In order to extract the envelope curves based on the time elapses of the first and the second heart sounds of cardiac sound signals, three representative algorithms such as the normalized average Shannon energy, the envelope information of Hilbert transform, and the cardiac sound characteristic waveform (CSCW) are introduced. Performance comparison of the envelope extraction algorithms, and the advantages and disadvantages of the methods are examined by some parameters.
Design high power and high efficiency inverter operating at 13.56MHz for wireless power transfer systems

This paper presents the proposed design of high power and high efficiency inverter for wireless power transfer systems operating at 13.56 MHz using multiphase resonant inverter and GaN HEMT devices. The high efficiency and the stable of inverter are the main targets of the design. The module design, the power loss analysis and the drive circuit design have been addressed. In experiment, a 3 kW inverter with the efficiency of 96.1% is achieved that significantly improves the efficiency of 13.56 MHz inverter. In near future, a 10 kW inverter with the efficiency of over 95% can be realizable by following this design concept.
COLABA : Arabic Dialect Annotation and Processing

In this paper, we describe COLABA, a large effort to create resources and processing tools for Dialectal Arabic Blogs. We describe the objectives of the project, the process flow and the interaction between the different components. We briefly describe the manual annotation effort and the resources created. Finally, we sketch how these resources and tools are put together to create DIRA, a termexpansion tool for information retrieval over dialectal Arabic collections using Modern Standard Arabic queries.
Extending the understanding of critical success factors for implementing business intelligence systems

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource-intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronios’s (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross-case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems.
Price discounts significantly enhance fruit and vegetable purchases when combined with nutrition education: a randomized controlled supermarket trial.

Reducing fruit and vegetable (F&V) prices is a frequently considered policy to improve dietary habits in the context of health promotion. However, evidence on the effectiveness of this intervention is limited.



The objective was to examine the effects of a 50% price discount on F&Vs or nutrition education or a combination of both on supermarket purchases.



A 6-mo randomized controlled trial within Dutch supermarkets was conducted. Regular supermarket shoppers were randomly assigned to 1 of 4 conditions: 50% price discounts on F&Vs, nutrition education, 50% price discounts plus nutrition education, or no intervention. A total of 199 participants provided baseline data; 151 (76%) were included in the final analysis. F&V purchases were measured by using supermarket register receipts at baseline, at 1 mo after the start of the intervention, at 3 mo, at 6 mo (end of the intervention period), and 3 mo after the intervention ended (9 mo).



Adjusted multilevel models showed significantly higher F&V purchases (per household/2 wk) as a result of the price discount (+3.9 kg; 95% CI: 1.5, 6.3 kg) and the discount plus education intervention (+5.6 kg; 95% CI: 3.2, 7.9 kg) at 6 mo compared with control. Moreover, the percentage of participants who consumed recommended amounts of F&Vs (≥400 g/d) increased from 42.5% at baseline to 61.3% at 6 mo in both discount groups (P = 0.03). Education alone had no significant effect.



Discounting F&Vs is a promising intervention strategy because it resulted in substantially higher F&V purchases, and no adverse effects were observed. Therefore, pricing strategies form an important focus for future interventions or policy. However, the long-term effects and the ultimate health outcomes require further investigation.
A practical approach to Sentiment Analysis of hindi tweets

Sentiment Analysis(SA) is a combination of emotions, opinions and subjectivity of text. Today, social networking sites like Twitter are tremendously used in expressing the opinions about a particular entity in the form of tweets which are limited to 140 characters. Reviews and opinions play a very important role in understanding peoples satisfaction regarding a particular entity. Such opinions have high potential for knowledge discovery. The main target of SA is to find opinions from tweets, extract sentiments from them and then define their polarity, i.e, positive, negative or neutral. Most of the work in this domain has been done for English Language. In this paper, we discuss and propose sentiment analysis using Hindi language. We will discuss an unsupervised lexicon method for classification.
Complex event extraction at PubMed scale

There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources. The event model allows detailed representation of complex natural language statements and can support a number of advanced text mining applications ranging from semantic search to pathway extraction. A recent collaborative evaluation demonstrated the potential of event extraction systems, yet there have so far been no studies of the generalization ability of the systems nor the feasibility of large-scale extraction.



This study considers event-based IE at PubMed scale. We introduce a system combining publicly available, state-of-the-art methods for domain parsing, named entity recognition and event extraction, and test the system on a representative 1% sample of all PubMed citations. We present the first evaluation of the generalization performance of event extraction systems to this scale and show that despite its computational complexity, event extraction from the entire PubMed is feasible. We further illustrate the value of the extraction approach through a number of analyses of the extracted information.


AVAILABILITY
The event detection system and extracted data are open source licensed and available at http://bionlp.utu.fi/.
Hand Data Glove : A Wearable Real-Time Device for Human-Computer Interaction

In this paper, a real-time Human-Computer Interaction (HCI) based on the hand data glove and K-NN classifier for gesture recognition is proposed. HCI is moving more and more natural and intuitive way to be used. One of the important parts of our body is our hand which is most frequently used for the Interaction in Digital Environment and thus complexity and flexibility of motion of hands are the research topics. To recognize these hand gestures more accurately and successfully data glove is used. Here, gloves are used to capture current position of the hand and the angles between the joints and then these features are used to classify the gestures using K-NN classifier. The gestures classified are categorized as clicking, rotating, dragging, pointing and ideal position. Recognizing these gestures relevant actions are taken, such as air writing and 3D sketching by tracking the path helpful in virtual augmented reality (VAR). The results show that glove used for interaction is better than normal static keyboard and mouse as the interaction process is more accurate and natural in dynamic environment with no distance limitations. Also it enhances the user’s interaction and immersion feeling.
The brain as a dream state generator: an activation-synthesis hypothesis of the dream process.

Recent research in the neurobiology of dreaming sleep provides new evidence for possible structural and functional substrates of formal aspects of the dream process. The data suggest that dreaming sleep is physiologically determined and shaped by a brain stem neuronal mechanism that can be modeled physiologically and mathematically. Formal features of the generator processes with strong implications for dream theory include periodicity and automaticity of forebrain activation, suggesting a preprogrammed neural basis for dream mentation in sleep; intense and sporadic activation of brain stem sensorimotor circuits including reticular, oculomotor, and vestibular neurons, possibly determining spatiotemporal aspects of dream imagery; and shifts in transmitter ratios, possibly accounting for dream amnesia. The authors suggest that the automatically activated forebrain synthesizes the dream by comparing information generated in specific brain stem circuits with information stored in memory.
Issues in wearable computing for medical monitoring applications: a case study of a wearable ECG monitoring device

In this paper we discuss issues surrounding wearable computers used as intelligent health monitors. Unlike existing health monitors (for example, ECG and EEG holters), that are used mainly for data acquisition, the devices we discuss provide real-time feedback to the patient, either as a warning of impending medical emergency or as a monitoring aid during exercise. These medical applications are to be distinguished from applications of wearable computing for medical personnel, e.g. doctors, nurses, and emergency medical technicians. Medical monitoring applications differ from other wearable applications in their I/O requirements, sensors, reliability, privacy issues, and user interface. The paper describes a prototype wearable ECG monitor based upon a high-performance, low-power digital signal processor and the development environment for its design.
Intent Classification of Short-Text on Social Media

Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis events. Our novel method exploits a hybrid feature representation created by combining top-down processing using knowledge-guided patterns with bottom-up processing using a bag-of-tokens model. We employ pattern-set creation from a variety of knowledge sources including psycholinguistics to tackle the ambiguity challenge, social behavior about conversations to enrich context, and contrast patterns to tackle the sparsity challenge. Our results show a significant absolute gain up to 7% in the F1 score relative to a baseline using bottom-up processing alone, within the popular multiclass frameworks of One-vs-One and One-vs-All. Intent mining can help design efficient cooperative information systems between citizens and organizations for serving organizational information needs.
Nimble cybersecurity incident management through visualization and defensible recommendations

Analysts engaged in real-time monitoring of cybersecurity incidents must quickly and accurately respond to alerts generated by intrusion detection systems. We investigated two complementary approaches to improving analyst performance on this vigilance task: a graph-based visualization of correlated IDS output and defensible recommendations based on machine learning from historical analyst behavior. We tested our approach with 18 professional cybersecurity analysts using a prototype environment in which we compared the visualization with a conventional tabular display, and the defensible recommendations with limited or no recommendations. Quantitative results showed improved analyst accuracy with the visual display and the defensible recommendations. Additional qualitative data from a "talk aloud" protocol illustrated the role of displays and recommendations in analysts' decision-making process. Implications for the design of future online analysis environments are discussed.
Double-sided printed bow-tie antenna for UWB communications

This letter proposes a double-sided printed bow-tie antenna for ultra wide band (UWB) applications. The frequency band considered is 3.1-10.6 GHz, which has been approved by the Federal Communications Commission as a commercial UWB band. The proposed antenna has a return loss less than 10 dB, phase linearity, and gain flatness over the above frequency band.
How marketers can use celebrities to sell more effectively

The first part of this paper summarises the key points about the use of celebrities in advertising, sets this particular creative technique in context and demonstrates how significant its return on investment can be. In the second part the paper goes on to report a more detailed analysis of the ‘celebrity’ case histories among the winners in the IPA Effectiveness Awards, and how practitioners have applied celebrity use to brands to make exceptional impacts on profitability. DEFINITIONS ’Advertising’ Throughout this paper the word ‘advertising’ has the sense that the general public gives it, that is ‘anything that has a name on it is advertising’. This consumer definition results from extensive qualitative research (Ford-Hutchinson and Rothwell, 2002) conducted in 2002 by the Advertising Standards Authority (ASA), the UK advertising self-regulatory body. Its simplicity and directness reminds one that, while the industry sees itself promoting brands in a whole host of different ways, it is all ‘advertising’ from the customer’s point of view. Within the ‘marcoms’ industry, practitioners tend to segment these activities into particular niches and refer to the agencies that specialise in them as being in the creative, media, direct marketing, self-promotion, public relations, sponsorship, digital, new media and outdoor sectors, to name just a few. It would be very long-winded to list all these specialisms every time, and so the word ‘advertising’ will be used instead. Occasionally, and for variety, the words ‘marcoms’, ‘marketing communications’ or ‘commercial communications’ are employed instead of ‘advertising’. These terms are used interchangeably to signify all the means by which brands are promoted by Hamish Pringle Director General, IPA, 44 Belgrave Square, London, SW1X 8QS Tel: 020 7201 8201;  269778 (m) e-mail: hamish@ ipa.co.uk