nils-herrmann/clean_scirepeval huggingface dataset:
Does Bidirectional Traffic Do More Harm Than Good in LoRaWAN Based LPWA Networks?

The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out 'what-if analyses' and predeployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.
Multiple components of developmental dyscalculia

Unresolved controversies regarding the functional impairments at the origin of dyscalculia, including working memory, approximate number system and attention have pervaded the field of mathematical learning disabilities. These controversies are fed by the tendency to focus on a single explanatory factor. We argue that we are in need of neurocognitive frameworks involving multiple functional components that contribute to inefficient numerical problem solving and dyscalculia. & 2013 Elsevier GmbH. All rights reserved.
Television violence and children's aggression: testing the priming, social script, and disinhibition predictions.

The effect of television violence on boys' aggression was investigated with consideration of teacher-rated characteristic aggressiveness, timing of frustration, and violence-related cues as moderators. Boys in Grades 2 and 3 (N = 396) watched violent or nonviolent TV in groups of 6, and half the groups were later exposed to a cue associated with the violent TV program. They were frustrated either before or after TV viewing. Aggression was measured by naturalistic observation during a game of floor hockey. Groups containing more characteristically high-aggressive boys showed higher aggression following violent TV plus the cue than following violent TV alone, which in turn produced more aggression than did the nonviolent TV condition. There was evidence that both the violent content and the cue may have suppressed aggression among groups composed primarily of boys low in characteristic aggressiveness. Results were interpreted in terms of current information-processing theories of media effects on aggression.
Linking Cybersecurity Knowledge : Cybersecurity Information Discovery Mechanism

To cope with increasing amount of cyber threats, organizations need to share cybersecurity information beyond the borders of organizations, countries, and even languages. Assorted organizations built repositories that store and provide XML-based cybersecurity information on the Internet. Among them are NVD , OSVDB [2], and JVN , and more cybersecurity information from various organizations from various countries will be available in the Internet. However, users are unaware of all of them. To advance information sharing, users need to be aware of them and be capable of identifying and locating cybersecurity information across such repositories by the parties who need that, and then obtaining the information over networks. This paper proposes a discovery mechanism, which identifies and locates sources and types of cybersecurity information and exchanges the information over networks. The mechanism uses the ontology of cybersecurity information [4] to incorporate assorted format of such information so that it can maintain future extensibility. It generates RDF-based metadata from XML-based cybersecurity information through the use of XSLT. This paper also introduces an implementation of the proposed mechanism and discusses extensibility and usability of the proposed mechanism.
The effect of online reviews on product sales: A joint sentiment-topic analysis

This research examines the business impact of online reviews. It empirically investigates the influence of numerical and textual reviews on product sales performance. We use a Joint Sentiment-Topic model to extract the topics and associated sentiments in review texts. We further propose that numerical rating mediates the effects of textual sentiments. Findings not only contribute to the knowledge of how eWOM impacts product sales, but also illustrate how numerical rating and textual reviews interplay while shaping product sales. In practice, the findings help online vendors strategize business analytics operations by focusing on more relevant aspects that ultimately drive sales.
A Theoretical Integration of User Satisfaction and Technology Acceptance

I general, perceptions of information systems (IS) success have been investigated within two primary research streams—the user satisfaction literature and the technology acceptance literature. These two approaches have been developed in parallel and have not been reconciled or integrated. This paper develops an integrated research model that distinguishes beliefs and attitudes about the system (i.e., object-based beliefs and attitudes) from beliefs and attitudes about using the system (i.e., behavioral beliefs and attitudes) to build the theoretical logic that links the user satisfaction and technology acceptance literature. The model is then tested using a sample of 465 users from seven different organizations who completed a survey regarding their use of data warehousing software. The proposed model was supported, providing preliminary evidence that the two perspectives can and should be integrated. The integrated model helps build the bridge from design and implementation decisions to system characteristics (a core strength of the user satisfaction literature) to the prediction of usage (a core strength of the technology acceptance literature).
Attention networks for image-to-text

The paper approaches the problem of imageto-text with attention-based encoder-decoder networks that are trained to handle sequences of characters rather than words. We experiment on lines of text from a popular handwriting database with different attention mechanisms for the decoder. The model trained with softmax attention achieves the lowest test error, outperforming several other RNN-based models. Our results show that softmax attention is able to learn a linear alignment whereas the alignment generated by sigmoid attention is linear but much less precise.
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture

In this paper, we propose an architecture model called Design Rule Space (DRSpace). We model the architecture of a software system as multiple overlapping DRSpaces, reflecting the fact that any complex software system must contain multiple aspects, features, patterns, etc. We show that this model provides new ways to analyze software quality. In particular, we introduce an Architecture Root detection algorithm that captures DRSpaces containing large numbers of a project’s bug-prone files, which are called Architecture Roots (ArchRoots). After investigating ArchRoots calculated from 15 open source projects, the following observations become clear: from 35% to 91% of a project’s most bug-prone files can be captured by just 5 ArchRoots, meaning that bug-prone files are likely to be architecturally connected. Furthermore, these ArchRoots tend to live in the system for significant periods of time, serving as the major source of bug-proneness and high maintainability costs. Moreover, each ArchRoot reveals multiple architectural flaws that propagate bugs among files and this will incur high maintenance costs over time. The implication of our study is that the quality, in terms of bug-proneness, of a large, complex software project cannot be fundamentally improved without first fixing its architectural flaws.
Reconfigurable Processor for Deep Learning in Autonomous Vehicles

The rapid growth of civilian vehicles has stimulated the development of advanced driver assistance systems (ADASs) to be equipped in-car. Real-time autonomous vision (RTAV) is an essential part of the overall system, and the emergence of deep learning methods has greatly improved the system quality, which also requires the processor to offer a computing speed of tera operations per second (TOPS) and a power consumption of no more than 30 W with programmability. This article gives an overview of the trends of RTAV algorithms and different hardware solutions, and proposes a development route for the reconfigurable RTAV accelerator. We propose our field programmable gate array (FPGA) based system Aristotle, together with an all-stack software-hardware co design workflow including compression, compilation, and customized hardware architecture. Evaluation shows that our FPGA system can realize real-time processing on modern RTAV algorithms with a higher efficiency than peer CPU and GPU platforms. Our outlook based on the ASIC-based system design and the ongoing implementation of next generation memory would target a 100 TOPS performance with around 20 W power.
Size-dependent cytotoxicity of gold nanoparticles.

Gold nanoparticles are widely used in biomedical imaging and diagnostic tests. Based on their established use in the laboratory and the chemical stability of Au(0), gold nanoparticles were expected to be safe. The recent literature, however, contains conflicting data regarding the cytotoxicity of gold nanoparticles. Against this background a systematic study of water-soluble gold nanoparticles stabilized by triphenylphosphine derivatives ranging in size from 0.8 to 15 nm is made. The cytotoxicity of these particles in four cell lines representing major functional cell types with barrier and phagocyte function are tested. Connective tissue fibroblasts, epithelial cells, macrophages, and melanoma cells prove most sensitive to gold particles 1.4 nm in size, which results in IC(50) values ranging from 30 to 56 microM depending on the particular 1.4-nm Au compound-cell line combination. In contrast, gold particles 15 nm in size and Tauredon (gold thiomalate) are nontoxic at up to 60-fold and 100-fold higher concentrations, respectively. The cellular response is size dependent, in that 1.4-nm particles cause predominantly rapid cell death by necrosis within 12 h while closely related particles 1.2 nm in diameter effect predominantly programmed cell death by apoptosis.
Large deep neural networks for MS lesion segmentation

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.
Treetopes and their Graphs

We define treetopes, a generalization of the threedimensional roofless polyhedra (Halin graphs) to arbitrary dimensions. Like roofless polyhedra, treetopes have a designated base facet such that every face of dimension greater than one intersects the base in more than one point. We prove an equivalent characterization of the 4-treetopes using the concept of clustered planarity from graph drawing, and we use this characterization to recognize the graphs of 4-treetopes in polynomial time. This result provides one of the first classes of 4-polytopes, other than pyramids and stacked polytopes, that can be recognized efficiently from their graphs.
Experimenting N-Grams in Text Categorization

This paper deals with automatic supervised classification of documents. The approach suggested is based on a vector representation of the documents centred not on the words but on the n-grams of characters for varying n. The effects of this method are examined in several experiments using the multivariate chi-square to reduce the dimensionality, the cosine and Kullback&Liebler distances, and two benchmark corpuses the reuters-21578 newswire articles and the 20 newsgroups data for evaluation. The evaluation was done, by using the macroaveraged F1 function. The results show the effectiveness of this approach compared to the Bag-OfWord and stem representations.
Agile Project Management

• Use small, cross-functional teams managing smaller, prioritized tasks • Frequently test incremental project progress against user stories to ensure a match between final product and customer expectation • Utilize the best mix of agile, traditional, and hybrid techniques to meet specific project requirements, recognize and avoid pitfalls, and improve quality • Differentiate between frameworks such as Scrum, Extreme Programming (XP), and Lean, and select the most suitable for the specific domain and project
Smart Tourism Destinations

The rapid development of technologies introduces smartness to all organisations and communities. The Smart Tourism Destinations (STD) concept emerges from the development of Smart Cities. With technology being embedded on all organisations and entities, destinations will exploit synergies between ubiquitous sensing technology and their social components to support the enrichment of tourist experiences. By applying smartness concept to address travellers’ needs before, during and after their trip, destinations could increase their competitiveness level. This paper aims to take advantage from the development of Smart Cities by conceptualising framework for Smart Tourism Destinations through exploring tourism applications in destination and addressing both opportunities and challenges it possessed.
The networked cultural diffusion of Korean wave

The purpose of this paper is to examine the diffusion of a popular Korean music video on the video-sharing web site YouTube. It applies a webometric approach in the diffusion of innovations framework to study three elements of diffusion in a Web 2.0 environment: users, user-to-user relationship and user-generated comment.  The webometric approach combines profile analyses, social network analyses, semantic and sentiment analyses.  The results show that male users in the US played a dominant role in the early-stage diffusion. The dominant users represented the innovators and early adopters in the evaluation stage of the diffusion, and they engaged in continuous discussions about the cultural origin of the video and expressed criticisms. Overall, the discussion between users varied according to their gender, age, and cultural background. Specifically, male users were more interactive than female users, and users in countries culturally similar to Korea were more likely to express favourable attitudes toward the video.  The study provides a webometric approach to examine the Web 2.0-based social system in the early-stage global diffusion of cultural offerings. This approach connects the diffusion of innovations framework to the new context of Web 2.0-based diffusion.
Freeway Travel Time Prediction Using Takagi-Sugeno-Kang Fuzzy Neural Network

This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno– Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US-290 in Houston, Texas are used to train and validate the network. The prediction performance of the TSKFNN is investigated with different combinations of traffic count, occupancy, and speed as input options. The comparison between online TSKFNN, offline TSKFNN, the back propagation neural network (BPNN) and the time series model (ARIMA) is made to evaluate the performance of TSKFNN. The results show that using count, speed, and occupancy together as input produces the best TSKFNN predictions. The online TSKFNN outperforms other commonly used models and is a promising tool for reliable travel time prediction on
Convergent and Divergent Operations in Cognitive Search

Human goal-directed action emerges from the interaction between stimulus-driven sensorimotor online systems and slower-working control systems that relate highly processed perceptual information to the construction of goal-related action plans. This distribution of labor requires the acquisition of enduring action representations; that is, of memory traces which capture the main characteristics of successful actions and their consequences. It is argued here that these traces provide the building blocks for off-line prospective action planning, which renders the search through stored action representations an essential part of action control. Hence, action planning requires cognitive search (through possible options) and might have led to the evolution of cognitive search routines that humans have learned to employ for other purposes as well, such as searching for perceptual events and through memory. Thus, what is commonly considered to represent different types of search operations may all have evolved from action planning and share the same characteristics. Evidence is discussed which suggests that all types of cognitive search—be it in searching for perceptual events, for suitable actions, or through memory—share the characteristic of following a fi xed sequence of cognitive operations: divergent search followed by convergent search.
Tall Buildings and Urban Habitat of the 21 st Century : A Global Perspective

The tall building is the most dominating symbol of the cities and a human-made marvel that defies gravity by reaching to the clouds. It embodies unrelenting human aspirations to build even higher. It conjures a number of valid questions in our minds. The foremost and fundamental question that is often asked: Why tall buildings? This review paper seeks to answer the question by laying out arguments against and for tall buildings. Then, it provides a brief account of the historic and recent developments of tall buildings including their status during the current economic recession. The paper argues that as cities continue to expand horizontally, to safeguard against their reaching an eventual breaking point, the tall building as a building type is a possible solution by way of conquering vertical space through agglomeration and densification. Case studies of some recently built tall buildings are discussed to illustrate the nature of tall building development in their respective cities. The paper attempts to dispel any discernment about tall buildings as mere pieces of art and architecture by emphasizing their truly speculative, technological, sustainable, and evolving nature. It concludes by projecting a vision of tall buildings and their integration into the cities of the 21st century.
The Internet Blockchain: A Distributed, Tamper-Resistant Transaction Framework for the Internet

Existing security mechanisms for managing the Internet infrastructural resources like IP addresses, AS numbers, BGP advertisements and DNS mappings rely on a Public Key Infrastructure (PKI) that can be potentially compromised by state actors and Advanced Persistent Threats (APTs). Ideally the Internet infrastructure needs a distributed and tamper-resistant resource management framework which cannot be subverted by any single entity. A secure, distributed ledger enables such a mechanism and the blockchain is the best known example of distributed ledgers.
 In this paper, we propose the use of a blockchain based mechanism to secure the Internet BGP and DNS infrastructure. While the blockchain has scaling issues to be overcome, the key advantages of such an approach include the elimination of any PKI-like root of trust, a verifiable and distributed transaction history log, multi-signature based authorizations for enhanced security, easy extensibility and scriptable programmability to secure new types of Internet resources and potential for a built in cryptocurrency. A tamper resistant DNS infrastructure also ensures that it is not possible for the application level PKI to spoof HTTPS traffic.
Predicting Perceived Brand Personality with Social Media

Brand personality has been shown to affect a variety of user behaviors such as individual preferences and social interactions. Despite intensive research efforts in human personality assessment, little is known about brand personality and its relationship with social media. Leveraging the theory in marketing, we analyze how brand personality associates with its contributing factors embodied in social media. Based on the analysis of over 10K survey responses and a large corpus of social media data from 219 brands, we quantify the relative importance of factors driving brand personality. The brand personality model developed with social media data achieves predicted R values as high as 0.67. We conclude by illustrating how modeling brand personality can help users find brands suiting their personal characteristics and help companies manage brand perceptions.
Social network analysis : A methodological introduction

Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individual-level covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions.
Visualization of intrinsic harmonic distortion for closed-loop Class D amplifier

This paper presents an intuitive approach for analyzing the intrinsic harmonic distortion of a closed-loop Class D amplifier. The cubic spline interpolation was deployed for time domain waveform presentation of the phase and duty cycle errors, to provide an insight view of the distortion that are contained in the output pulse width modulation signal. Furthermore, the relationship between the phase and duty cycle errors and the distortion on the demodulated output signal has been shaped and verified through simulation and comparison with our previous analytical results, to further strengthen our conclusion on their effects on the 3rd-order harmonic distortion.