nils-herrmann/clean_scirepeval huggingface dataset:
Why Are Adolescents Addicted to Online Gaming? An Interview Study in Taiwan

The purpose of this study was twofold: to investigate the conscious and unconscious psychological motivations of online game addicts, and to further discuss the relationship between surface and source motivations. Ten Taiwanese adolescents with online game addiction were selected for in-depth interviews. Through sentence completion test and semi-structured interviews, data were collected and analyzed from the following four realms: (1) surface motivations, (2) source motivations, (3) self-conception, and (4) interpersonal relationships in real life. After content analysis, five categories with distinct themes were formed: (1) addicts' psychological needs and motivations; (2) online games as the everyday focus of the addicts; (3) the interplay of real self and virtual self; (4) online games as the compensatory or extensive satisfaction for addicts' needs; and (5) addicts' self-reflections. The implications of the present study are discussed.
Hazard avoidance for high-speed mobile robots in rough terrain

Unmanned ground vehicles have important applications in high speed, rough terrain scenarios. In these scenarios unexpected and dangerous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform navigation and hazard avoidance calculations based on detailed vehicle and terrain models. This paper presents a method for high speed hazard avoidance based on the “trajectory space,” which is a compact model-based representation of a robot’s dynamic performance limits in rough, natural terrain. Simulation and experimental results on a small gasoline-powered unmanned ground vehicle demonstrate the method’s effectiveness on sloped and rough terrain.
Techniques for fast and scalable time series traffic generation

Many IoT applications ingest and process time series data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity). To design and test such systems, it is desirable to have a high-performance traffic generator specifically designed for time series data, preferably using archived data to create a truly realistic workload. However, most existing traffic generator tools either are designed for generic network applications, or only produce synthetic data based on certain time series models. In addition, few have raised their performance bar to millions-packets-per-second level with minimum time violations. In this paper, we design, implement and evaluate a highly efficient and scalable time series traffic generator for IoT applications. Our traffic generator stands out in the following four aspects: 1) it generates time-conforming packets based on high-fidelity reproduction of archived time series data; 2) it leverages an open-source Linux Exokernel middleware and a customized userspace network subsystem; 3) it includes a scalable 10G network card driver and uses "absolute" zero-copy in stack processing; and 4) it has an efficient and scalable application-level software architecture and threading model. We have conducted extensive experiments on both a quad-core Intel workstation and a 20-core Intel server equipped with Intel X540 10G network cards and Samsung's NVMe SSDs. Compared with a stock Linux baseline and a traditional mmap-based file I/O approach, we observe that our traffic generator significantly outperforms other alternatives in terms of throughput (10X), scalability (3.6X) and time violations (46.2X).
Development of low-loss broad-band planar baluns using multilayered organic thin films

This paper presents the design and development of a low-loss and wide-band width multilayered Marchand balun. The balun has been implemented using printed circuit board materials with integrated multilayered organic thin films. We have designed the top layer transmission lines on twin-thickness organic thin films to achieve low loss and wide bandwidth for the balun. The experimental results demonstrate that the balun achieves less than 0.5-0.7 dB insertion loss throughout the 4-20-GHz operation bandwidth. The phase and amplitude imbalances are less than 5/spl deg/ and 0.5 dB, respectively. Time-domain measurement results demonstrate that the balun has negligible dispersion.
Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.

In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).
Estimating Just-Noticeable Distortion for Video

Just-noticeable distortion (JND), which refers to the maximum distortion that the human visual system (HVS) cannot perceive, plays an important role in perceptual image and video processing. In comparison with JND estimation for images, estimation of the JND profile for video needs to take into account the temporal HVS properties in addition to the spatial properties. In this paper, we develop a spatio-temporal model estimating JND in the discrete cosine transform domain. The proposed model incorporates the spatio-temporal contrast sensitivity function, the influence of eye movements, luminance adaptation, and contrast masking to be more consistent with human perception. It is capable of yielding JNDs for both still images and video with significant motion. The experiments conducted in this study have demonstrated that the JND values estimated for video sequences with moving objects by the model are in line with the HVS perception. The accurate JND estimation of the video towards the actual visibility bounds can be translated into resource savings (e.g., for bandwidth/storage or computation) and performance improvement in video coding and other visual processing tasks (such as perceptual quality evaluation, visual signal restoration/enhancement, watermarking, authentication, and error protection)
A Review of Adaptive Line Enhancers for Noise Cancellation

This paper provides a literature review on Adaptive Line Enhancer (ALE) methods based on adaptive noise cancellation systems. Such methods have been used in various applications, including communication systems, biomedical engineering, and industrial applications. Developments in ALE in noise cancellation are reviewed, including the principles, adaptive algorithms, and recent modifications on the filter design proposed to increase the convergence rate and reduce the computational complexity for future implementation. The advantages and drawbacks of various adaptive algorithms, such as the Least Mean Square, Recursive Least Square, Affine Projection Algorithm, and their variants, are discussed in this review. Design modifications of filter structures used in ALE are also evaluated. Such filters include Finite Impulse Response, Infinite Impulse Response, lattice, and nonlinear adaptive filters. These structural modifications aim to achieve better adaptive filter performance in ALE systems. Finally, a perspective of future research on ALE systems is presented for further consideration.
Throughput and delay analysis of IEEE 802.11 protocol

Wireless technologies in the LAN environment are becoming increasingly important. The IEEE 802.11 standard is the most mature technology for wireless local area networks (WLANs). The performance of the medium access control (MAC) layer, which consists of distributed coordination function (DCF) and point coordination function (PCF), has been examined over the past years. We present an analytical model to compute the saturated throughput of 802.11 protocol in the absence of hidden stations and transmission errors. A throughput analysis is carried out in order to study the performance of 802.11 DCF. Using the analytical model, we develop a frame delay analysis under traffic conditions that correspond to the maximum load that the network can support in stable conditions. The behaviour of the exponential backoff algorithm used in 802.11 is also examined.
Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality

This study aims to look beyond the quantitative summary to provide a more comprehensive view of online user-generated content. We obtain a unique and extensive dataset of online user reviews for hotels across various review sites and over a long time periods. We use the sentiment analysis technique to decompose user reviews into five dimensions to measure hotel service quality. Those dimensions are then incorporated into econometrics models to examine their effect in shaping users' overall evaluation and content generating behavior. The results suggest that different dimensions of user reviews have significantly differential impact in forming user evaluation and driving content generation.
Broadband planar wide-scan array employing tightly coupled elements and integrated balun

In this paper we present the design, fabrication and demonstration of an X-band phased array capable of wide-angle scanning. A new non-symmetric element for wideband tightly coupled dipole arrays is integrated with a low-profile microstrip balun printed on the array ground plane. The feed connects to the array aperture with vertical twin-wire transmission lines that concurrently perform impedance matching. The proposed element arms are identical near the center feed portion but dissimilar towards the ends, forming a ball-and-cup. A 64 element array prototype is verified experimentally and compared to numerical simulation. The array aperture is placed λ/7 (at 8 GHz) above a ground plane and shown to maintain a VSWR < 2 from 8–12.5 GHz while scanning up to 75° and 60° in E and H-plane, respectively.
A Survey of Phishing Email Filtering Techniques

Phishing email is one of the major problems of today's Internet, resulting in financial losses for organizations and annoying individual users. Numerous approaches have been developed to filter phishing emails, yet the problem still lacks a complete solution. In this paper, we present a survey of the state of the art research on such attacks. This is the first comprehensive survey to discuss methods of protection against phishing email attacks in detail. We present an overview of the various techniques presently used to detect phishing email, at the different stages of attack, mostly focusing on machine-learning techniques. A comparative study and evaluation of these filtering methods is carried out. This provides an understanding of the problem, its current solution space, and the future research directions anticipated.
The five-step lower blepharoplasty: blending the eyelid-cheek junction.

Lower lid blepharoplasty has potential for significant long-lasting complications and marginal aesthetic outcomes if not performed correctly, or if one disregards the anatomical aspects of the orbicularis oculi muscle. This has detracted surgeons from performing the technical maneuvers necessary for optimal periorbital rejuvenation. A simplified, "five-step" clinical approach based on sound anatomical principles is presented.



A review of 50 lower lid blepharoplasty patients (each bilateral) using the five-step technique was conducted to delineate the efficacy in improving lower eyelid aesthetics. Digital images from 50 consecutive primary lower blepharoplasty patients (100 lower lids: 37 women and 13 men) were measured using a computer program with standardized data points that were later converted to ratios.



Of the 100 lower eyelid five-step blepharoplasties analyzed, complication rates were low and data points measured demonstrated improvements in all aesthetic parameters. The width and position of the tear trough, position of the lower lid relative to the pupil, and the intercanthal angle were all improved. There were no cases of lower lid malposition.



Aesthetic outcomes in lower lid blepharoplasty can be improved using a five-step technical sequence that addresses all of the anatomical findings. Lower lid blepharoplasty results are improved when (1) the supportive deep malar fat compartment is augmented; (2) lower lid orbicularis oculi muscle is preserved with minimal fat removal (if at all); (3) the main retaining structure (orbicularis retaining ligament) is selectively released; (4) lateral canthal support is established or strengthened (lateral retinacular suspension); and (5) minimal skin is removed.



Therapeutic, IV.
Online Defect Prediction for Imbalanced Data

Many defect prediction techniques are proposed to improve software reliability. Change classification predicts defects at the change level, where a change is the modifications to one file in a commit. In this paper, we conduct the first study of applying change classification in practice.
 We identify two issues in the prediction process, both of which contribute to the low prediction performance. First, the data are imbalanced---there are much fewer buggy changes than clean changes. Second, the commonly used cross-validation approach is inappropriate for evaluating the performance of change classification. To address these challenges, we apply and adapt online change classification, resampling, and updatable classification techniques to improve the classification performance.
 We perform the improved change classification techniques on one proprietary and six open source projects. Our results show that these techniques improve the precision of change classification by 12.2-89.5% or 6.4--34.8 percentage points (pp.) on the seven projects. In addition, we integrate change classification in the development process of the proprietary project. We have learned the following lessons: 1) new solutions are needed to convince developers to use and believe prediction results, and prediction results need to be actionable, 2) new and improved classification algorithms are needed to explain the prediction results, and insensible and unactionable explanations need to be filtered or refined, and 3) new techniques are needed to improve the relatively low precision.
Mobile telephones, distracted attention, and pedestrian safety.

Driver distraction is a major cause of traffic accidents, with mobile telephones as a key source of distraction. In two studies, we examined distraction of pedestrians associated with mobile phone use. The first had 60 participants walk along a prescribed route, with half of them conversing on a mobile phone, and the other half holding the phone awaiting a potential call, which never came. Comparison of the performance of the groups in recalling objects planted along the route revealed that pedestrians conversing recalled fewer objects than did those not conversing. The second study had three observers record pedestrian behavior of mobile phone users, i-pod users, and pedestrians with neither one at three crosswalks. Mobile phone users crossed unsafely into oncoming traffic significantly more than did either of the other groups. For pedestrians as with drivers, cognitive distraction from mobile phone use reduces situation awareness, increases unsafe behavior, putting pedestrians at greater risk for accidents, and crime victimization.
When Work Matters: Transforming Classical Network Structures to Graph CNN

Numerous pattern recognition applications can be formed as learning from graph-structured data, including social network, protein-interaction network, the world wide web data, knowledge graph, etc. While convolutional neural network (CNN) facilitates great advances in gridded image/video understanding tasks, very limited attention has been devoted to transform these successful network structures (including Inception net, Residual net, Dense net, etc.) to establish convolutional networks on graph, due to its irregularity and complexity geometric topologies (unordered vertices, unfixed number of adjacent edges/vertices). In this paper, we aim to give a comprehensive analysis of when work matters by transforming different classical network structures to graph CNN, particularly in the basic graph recognition problem. Specifically, we firstly review the general graph CNN methods, especially in its spectral filtering operation on the irregular graph data. We then introduce the basic structures of ResNet, Inception and DenseNet into graph CNN and construct these network structures on graph, named as G ResNet, G Inception, G DenseNet. In particular, it seeks to help graph CNNs by shedding light on how these classical network structures work and providing guidelines for choosing appropriate graph network frameworks. Finally, we comprehensively evaluate the performance of these different network structures on several public graph datasets (including social networks and bioinformatic datasets), and demonstrate how different network structures work on graph CNN in the graph recognition task.
RHSBoost: Improving classification performance in imbalance data

Imbalance data are defined as a dataset whose proportion of classes is severely skewed. Classification performance of existing models tends to deteriorate due to class distribution imbalance. In addition, over-representation by majority classes prevents a classifier from paying attention tominority classes, which are generally more interesting. An effective ensemble classificationmethod called RHSBoost has been proposed to address the imbalance classification problem. This classification rule uses random undersampling and ROSE sampling under a boosting scheme. According to the experimental results, RHSBoost appears to be an attractive classification model for imbalance data.
Web-Application Development Using the Model/View/Controller Design Pattern

The ModelNiew/Controller design pattern is very useful for architecting interactive software systems. This design pattern is partition-independent, because it is expressed in terms of an interactive application running in a single address space. Applying the ModelNiew/Controller design pattern to web-applications is therefore complicated by the fact that current technologies encourage developers to partition the application as early as in the design phase. Subsequent changes to that partitioning require considerable changes to the application's implementation despite the fact that the application logic has not changed. This paper introduces the concept of Flexible Web-Application Partitioning, a programming model and implementation infrastructure, that allows developers to apply the ModeWViewKontroller design pattern in a partition-independent manner: Applications are developed and tested in a single address-space; they can then be deployed to various clientherver architectures without changing the application's source code. In addition, partitioning decisions can be changed without modifying the application.
A Complex Network Approach to Stylometry

Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.
An ISU Dialogue System Exhibiting Reinforcement Learning of Dialogue Policies: Generic Slot-Filling in the TALK In-car System

We demonstrate a multimodal dialogue system using reinforcement learning for in-car scenarios, developed at Edinburgh University and Cambridge University for the TALK project1. This prototype is the first “Information State Update” (ISU) dialogue system to exhibit reinforcement learning of dialogue strategies, and also has a fragmentary clarification feature. This paper describes the main components and functionality of the system, as well as the purposes and future use of the system, and surveys the research issues involved in its construction. Evaluation of this system (i.e. comparing the baseline system with handcoded vs. learnt dialogue policies) is ongoing, and the demonstration will show both.
Frontal EEG asymmetry as a moderator and mediator of emotion

Frontal EEG asymmetry appears to serve as (1) an individual difference variable related to emotional responding and emotional disorders, and (2) a state-dependent concomitant of emotional responding. Such findings, highlighted in this review, suggest that frontal EEG asymmetry may serve as both a moderator and a mediator of emotion- and motivation-related constructs. Unequivocal evidence supporting frontal EEG asymmetry as a moderator and/or mediator of emotion is lacking, as insufficient attention has been given to analyzing the frontal EEG asymmetries in terms of moderators and mediators. The present report reviews the frontal EEG asymmetry literature from the framework of moderators and mediators, and overviews data analytic strategies that would support claims of moderation and mediation.