nils-herrmann/clean_scirepeval huggingface dataset:
Facets of visual aesthetics

Visual aesthetics has been shown to critically affect a variety of constructs such as perceived usability, satisfaction, and pleasure. Given the importance of visual aesthetics in humancomputer interaction, it is vital that it is adequately assessed. The present research aimed at providing a precise operational definition and to develop a new measure of perceived visual aesthetics of websites. Construction of the Visual Aesthetics of Website Inventory (VisAWI) was based on a comprehensive and broad definition of visual aesthetics so that the resulting instrument would completely describe the domain of interest. Four interrelated facets of perceived visual aesthetics of websites were identified and validated in a series of seven studies. Simplicity and Diversity have repeatedly been treated as formal parameters of aesthetic objects throughout the history of empirical aesthetics, Colors are a critical property of aesthetic objects, and Craftsmanship addresses the skillful and coherent integration of the relevant design dimensions. These four facets jointly represent perceived visual aesthetics, but are still distinguishable from each other and carry unique meaning. The subscales contained in the VisAWI demonstrate good internal consistencies. Evidence for the convergent, divergent, discriminative, and concurrent validity of the VisAWI is provided. Overall, the present research suggests that the VisAWI appears to be a sound measure of visual aesthetics of websites comprising facets of both practical and theoretical interest.
Anomaly detection and monitoring in Internet of Things communication

The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.
Performance of Store Brands: A Cross-Country Analysis of Consumer Store Brand Preferences, Perceptions, and Risk

This paper empirically studies consumer choice behavior in regard to store brands in the US, UK and Spain. Store brand market shares differ by country and they are usually much higher in Europe than in the US. However, there is surprisingly little work in marketing that empirically studies the reasons that underlie higher market shares associated with store brands in Europe over the US. In this paper, we empirically study the notion that the differential success of store brands in the US versus in Europe is the higher brand equity that store brands command in Europe over the US. We use a framework based on previous work to conduct our analysis: consumer brand choice under uncertainty, and brands as signals of product positions. More specifically, we examine whether uncertainty about quality (or, the positioning of the brand in the product space), perceived quality of store brands versus national brands, consistency in store brand offerings over time, as well as consumer attitudes towards risk, quality and price underlie the differential success of store brands at least partially in the US versus Europe. We propose and estimate a model that explicitly incorporates the impact of uncertainty on consumer behavior. We compare 1) levels of uncertainty associated with store brands versus national brands, 2) consistency in product positions over time for both national and store brands, 3) relative quality levels of national versus store brands, and 4) consumer sensitivity to price, quality and risk across the three countries we study. The model is estimated on scanner panel data on detergent in the US, UK and Spanish markets, and on toilet paper and margarine in the US and Spain. We find that consumer learning and perceived risk (and the associated brand equity), as well as consumer attitude towards risk, quality and price, play an important role in consumers’ store versus national brand choices and contribute to the differences in relative success of store brands across the countries we study.
Beam-Steerable Planar Antenna Using Circular Disc and Four PIN-Controlled Tapered Stubs for WiMAX and WLAN Applications

A planar beam-steerable antenna aimed at WiMAX and WLAN applications is presented. The design uses a single-layer structure, which includes a central circular disc surrounded by four PIN-controlled tapered microstrip stubs. Using the PIN diodes, the stubs change their status from grounded to open-ended mode to provide pattern reconfigurability in four directions. To support WiMAX and WLAN applications, the antenna is designed to operate at IEEE 802.11b/g standard 2.4 GHz with a bandwidth of 150 MHz in all the four states. A prototype on FR4 substrate with thickness of 6.4 mm and radius of 50 mm is designed and tested. The measured and simulated results of the antenna indicate that the direction of the main beam can be successfully controlled at four specific directions ( φ = 0°, 90 °, 180 °, 270 °), at a deflection angle of 35 ° from the boresight with a stable gain of more than 5 dBi and front-to-back ratio of more than 20 dB across the band 2.38-2.53 GHz.
The Additive Value of Multimodal Features for Predicting Engagement, Frustration, and Learning during Tutoring

Detecting learning-centered affective states is difficult, yet crucial for adapting most effectively to users. Within tutoring in particular, the combined context of student task actions and tutorial dialogue shape the student's affective experience. As we move toward detecting affect, we may also supplement the task and dialogue streams with rich sensor data. In a study of introductory computer programming tutoring, human tutors communicated with students through a text-based interface. Automated approaches were leveraged to annotate dialogue, task actions, facial movements, postural positions, and hand-to-face gestures. These dialogue, nonverbal behavior, and task action input streams were then used to predict retrospective student self-reports of engagement and frustration, as well as pretest/posttest learning gains. The results show that the combined set of multimodal features is most predictive, indicating an additive effect. Additionally, the findings demonstrate that the role of nonverbal behavior may depend on the dialogue and task context in which it occurs. This line of research identifies contextual and behavioral cues that may be leveraged in future adaptive multimodal systems.
SSNet: Scale Selection Network for Online 3D Action Prediction

In action prediction (early action recognition), the goal is to predict the class label of an ongoing action using its observed part so far. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the time axis. As there are significant temporal scale variations of the observed part of the ongoing action at different progress levels, we propose a novel window scale selection scheme to make our network focus on the performed part of the ongoing action and try to suppress the noise from the previous actions at each time step. Furthermore, an activation sharing scheme is proposed to deal with the overlapping computations among the adjacent steps, which allows our model to run more efficiently. The extensive experiments on two challenging datasets show the effectiveness of the proposed action prediction framework.
A privacy paradox: Social networking in the United States

Teenagers will freely give up personal information to join social networks on the Internet. Afterwards, they are surprised when their parents read their journals. Communities are outraged by the personal information posted by young people online and colleges keep track of student activities on and off campus. The posting of personal information by teens and students has consequences. This article will discuss the uproar over privacy issues in social networks by describing a privacy paradox; private versus public space; and, social networking privacy issues. It will finally discuss proposed privacy solutions and steps that can be taken to help resolve the privacy paradox.
Adaptive game AI with dynamic scripting

Online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played. As such it provides a mechanism to deal with weaknesses in the game AI, and to respond to changes in human player tactics. We argue that online learning of game AI should meet four computational and four functional requirements. The computational requirements are speed, effectiveness, robustness and efficiency. The functional requirements are clarity, variety, consistency and scalability. This paper investigates a novel online learning technique for game AI called ‘dynamic scripting’, that uses an adaptive rulebase for the generation of game AI on the fly. The performance of dynamic scripting is evaluated in experiments in which adaptive agents are pitted against a collection of manually-designed tactics in a simulated computer roleplaying game. Experimental results indicate that dynamic scripting succeeds in endowing computer-controlled opponents with adaptive performance. To further improve the dynamic-scripting technique, an enhancement is investigated that allows scaling of the difficulty level of the game AI to the human player’s skill level. With the enhancement, dynamic scripting meets all computational and functional requirements. The applicability of dynamic scripting in state-of-the-art commercial games is demonstrated by implementing the technique in the game Neverwinter Nights. We conclude that dynamic scripting can be successfully applied to the online adaptation of game AI in commercial computer games.
A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application

Soft sensors have been widely used in industrial processes. The core issue of data-driven soft sensors is building soft sensor models with excellent performance and robustness. This paper introduces deep learning to soft sensor modeling and proposes a novel soft sensor modeling method based on a deep learning network that integrates denoising autoencoders with a neural network (DAE-NN). An improved gradient descent is employed to update the model parameters. The proposed modeling method is able to capture the essential information of input data through deep architecture, building soft sensors with excellent performance. The DAE-NN-based soft sensor is applied in practical applications to estimate the oxygen content in flue gasses in 1000-MW ultrasuperficial units. Comparing conventional soft sensor modeling methods, i.e., shallow learning methods, DAE-NN-based soft sensor significantly improves the performance and generalization of data-driven soft sensors. Deep learning provides a very effective and promising method for soft sensor modeling.
BuildingRules: a trigger-action based system to manage complex commercial buildings

Modern Building Management Systems (BMSs) provide limited amount of control to its occupants, and typically allow only the facility manager to set the building policies. BuildingRules let occupants to customise their office spaces using trigger-action programming. In order to accomplish this task, BuildingRules automatically detects conflicts among the policies expressed by the users using a SMT based logic. We tested our system with 23 users across 17 days in a virtual office building, and evaluate the effectiveness and scalability of the system.
A Time Series Classification Method for Behaviour-Based Dropout Prediction

Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.
A Zero Voltage Switching SVM (ZVS–SVM) Controlled Three-Phase Boost Rectifier

A zero voltage switching space vector modulation (ZVS-SVM) controlled three-phase boost rectifier is proposed. A branch composed of one active switch, one resonant inductor and one clamping capacitor is added to the six-switch boost rectifier. In addition, a ZVS-SVM for the rectifier is proposed. It can effectively suppress the reverse recovery process of the bridge switch anti-parallel diodes. All the switches are turned on under zero voltage condition, thus the switching loss is reduced, and electromagnetic inference can be lowered. The switching frequency of the rectifier is fixed. The auxiliary switch works at the same frequency with other switch. All the switches in the rectifier have lower voltage stress. A digital signal processor controlled 4-kW prototype is built to verify the theory
Ring Resonator Modulators in Silicon for Interchip Photonic Links

Silicon photonics is the most promising pathway to achieve >10 Tb/s off-chip I/O bandwidth required by next-generation high-performance computing and switching systems. Ring resonator modulators offer the advantages of small footprint, low power, high efficiency, low loss, high speed, and CMOS compatibility for silicon photonic links. This paper presents an in-depth discussion of practical microring modulators in silicon, covering their performance metrics, design tradeoffs, optimization, p-n junction geometries, complex ring configurations, and tuning solutions. Various demonstrated Si ring modulators are reviewed and potential future developments are briefly discussed.
Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua

In recent years many knowledge bases (KBs) have been constructed, yet there is not yet a verb resource that maps to these growing KB resources. A resource that maps verbs in different languages to KB relations would be useful for extracting facts from text into the KBs, and to aid alignment and integration of knowledge across different KBs and languages. Such a multi-lingual verb resource would also be useful for tasks such as machine translation and machine reading. In this paper, we present a scalable approach to automatically construct such a verb resource using a very large web text corpus as a kind of interlingua to relate verb phrases to KB relations. Given a text corpus in any language and any KB, it can produce a mapping of that language’s verb phrases to the KB relations. Experiments with the English NELL KB and ClueWeb corpus show that the learned English verb-to-relation mapping is effective for extracting relation instances from English text. When applied to a Portuguese NELL KB and a Portuguese text corpus, the same method automatically constructs a verb resource in Portuguese that is effective for extracting relation instances from Portuguese text.
Maximum Principle Based Algorithms for Deep Learning

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin’s maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.
Interactive Attention Networks for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.

Morphological assessment of the degree of differentiation has been shown in numerous studies to provide useful prognostic information in breast cancer, but until recently histological grading has not been accepted as a routine procedure, mainly because of perceived problems with reproducibility and consistency. In the Nottingham/Tenovus Primary Breast Cancer Study the most commonly used method, described by Bloom & Richardson, has been modified in order to make the criteria more objective. The revised technique involves semiquantitative evaluation of three morphological features--the percentage of tubule formation, the degree of nuclear pleomorphism and an accurate mitotic count using a defined field area. A numerical scoring system is used and the overall grade is derived from a summation of individual scores for the three variables: three grades of differentiation are used. Since 1973, over 2200 patients with primary operable breast cancer have been entered into a study of multiple prognostic factors. Histological grade, assessed in 1831 patients, shows a very strong correlation with prognosis; patients with grade I tumours have a significantly better survival than those with grade II and III tumours (P less than 0.0001). These results demonstrate that this method for histological grading provides important prognostic information and, if the grading protocol is followed consistently, reproducible results can be obtained. Histological grade forms part of the multifactorial Nottingham prognostic index, together with tumour size and lymph node stage, which is used to stratify individual patients for appropriate therapy.
Building upon RouteFlow : a SDN development experience

RouteFlow is a platform for providing virtual IP routing services in OpenFlow networks. During the first year of development, we came across some use cases that might be interesting pursuing in addition to a number of lessons learned worth sharing. In this paper, we will discuss identified requirements and architectural and implementation changes made to shape RouteFlow into a more robust solution for Software Defined networking (SDN). This paper addresses topics of interest to the SDN community, such as development issues involving layered applications on top of network controllers, ease of configuration, and network visualization. In addition, we will present the first publicly known use case with multiple, heterogeneous OpenFlow controllers to implement a centralized routing control function, demonstrating how IP routing as a service can be provided for different network domains under a single central control. Finally, performance comparisons and a real testbed were used as means of validating the implementation.
LibD: Scalable and Precise Third-Party Library Detection in Android Markets

With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.
Electronic Game Play and School Performance of Adolescents in Southern Thailand

Increasing exposure of children and adolescents to electronic media is a worldwide phenomenon, including in Thailand. To date, few studies examine electronic game play in Thai adolescents. Our research describes the prevalence of electronic game play and examines associations between the time spent engaged in electronic game play and school performance of adolescents in Hat-Yai municipality. A cross-sectional study was conducted among 1,492 adolescents from four secondary schools and one commercial college from January through March 2007, using questionnaires for collecting information about demographic data, school grades, and electronic game play behaviors. The prevalence of electronic game play was 75% in boys and 59% in girls. Twenty-two percent of boys and 8.7% of girls played electronic games every day with more than 2 hours per session. The two most common places of game play were at game shops (71%), followed by at their own home (70%). Using linear regression analysis, the "low user or less than 2 hours per session" game players and females were less likely to have school grades below 3.00 with adjusted odds ratios of 0.44 (95% CI 0.25-0.80, p = 0.004) and 0.49 (95% CI 0.30-0.76, p = 0.005) respectively. This study finds that excessive playing of electronic games is associated with school grades below 3.00.