nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent Tutoring System-Bayesian Student Model

Nowadays different approaches are coming forth to tutor students using computers. In this paper, a computer based intelligent tutoring system (ITS) is presented. It projects out a new approach dealing with diagnosis in student modeling which emphasizes on Bayesian networks (for decision making) and item response theory (for adaptive question selection). The advantage of such an approach through Bayesian networks (formal framework of uncertainty) is that this structural model allows substantial simplification when specifying parameters (conditional probabilities) which measures student ability at different levels of granularity. In addition, the probabilistic student model is proved to be more quicker, accurate and efficient. Since most of the tutoring systems are static HTML web pages of class textbooks, our intelligent system can help a student navigate through online course materials and recommended learning goals.
Complications associated with injectable soft-tissue fillers: a 5-year retrospective review.

Even when administered by experienced hands, injectable soft-tissue fillers can cause various unintended reactions, ranging from minor and self-limited responses to severe complications requiring prompt treatment and close follow-up.



To review the complications associated with injectable soft-tissue filler treatments administered in the Williams Rejuva Center during a 5-year period and to discuss their management.



Retrospective medical record review in a private practice setting.



Patients receiving injectable soft-tissue fillers and having a treatment-related complication.



Injectable soft-tissue filler treatments.



A retrospective medical record review was conducted of patients undergoing treatment with injectable soft-tissue fillers between January 1, 2007, and December 31, 2011, and identified as having a treatment-related complication.



A total of 2089 injectable soft-tissue filler treatments were performed during the study period, including 1047 with hyaluronic acid, 811 with poly-L-lactic acid, and 231 with calcium hydroxylapatite. Fourteen complications were identified. The most common complication was nodule or granuloma formation. Treatment with calcium hydroxylapatite had the highest complication rate.



Complications are rare following treatment with injectable soft-tissue fillers. Nevertheless, it is important to be aware of the spectrum of potential adverse sequelae and to be comfortable with their proper management.
Theory and implementation of dielectric resonator antenna excited by a waveguide slot

Excitation of dielectric resonator antennas (DRAs) by waveguide slots is proposed as an alternative to traditionally used excitation mechanisms in order to enhance the frequency bandwidth of slotted waveguide radiators and to control the power coupled to the DRA. The analysis is based on the numerical solution of coupled integral equations discretized using the method of moments (MoM). The dielectric resonator (DR) is modeled as a body-of-revolution based on the integral equation formulation for the equivalent electric and magnetic surface current densities. The analysis of an infinite or a semi-infinite waveguide containing longitudinal or transverse narrow slots uses the appropriate dyadic Green's function resulting in closed-form analytical expressions of the MoM matrix. The scattering parameters for a slotted waveguide loaded with a dielectric resonator antenna disk are calculated and compared with finite-difference time-domain results. Bandwidth enhancement is achieved by the proper selection for the antenna parameters.
Casting Random Forests as Artificial Neural Networks (and Profiting from It)

While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the majority of the UCI datasets used for benchmarking.
Learning to Accept New Classes without Training

Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called openworld learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach 1.
Cyber Victimization : School Experience of Malaysian Cyberbullied Teenagers

Cyberbullying among schoolchildren and teenagers became a hot issue discussed by Malaysian society. Cyberbullying is a new age of bullying because it uses the modern digital technology intentionally to hurt and degrade someone in the cyber world. Cyberbullying is a problem affecting many teenagers as they embrace online communication and interaction whereby virtual world with no borders. By adopting a qualitative approach, this study has captured 8 cyberbullied victims’ school experience. Even years after leaving school, these 8 cyberbullied victims remember how it feels to be bullied in the cyber world. The principal investigator also tries to identify the possibility factors that contribute to cyberbullying among these 8 victims. The result shows that these victims were bullied differently in cyber world. This study not just primarily focuses on cyberbullying issues among schoolchildren and teenagers; it also addresses the motives and causes of cyberbullying. Lastly, this article will be served as guidance for school teachers, parents and teenagers to prepare to tackle cyberbullying together. Cyberbullying is no laughing matter in our community, and it is time to spread the seeds of peace inspires others to do the same. Cyberbullying, cyber victimization, internet, school experience, teenagers.
International physical activity questionnaire: 12-country reliability and validity.

Physical inactivity is a global concern, but diverse physical activity measures in use prevent international comparisons. The International Physical Activity Questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity.



Between 1997 and 1998, an International Consensus Group developed four long and four short forms of the IPAQ instruments (administered by telephone interview or self-administration, with two alternate reference periods, either the "last 7 d" or a "usual week" of recalled physical activity). During 2000, 14 centers from 12 countries collected reliability and/or validity data on at least two of the eight IPAQ instruments. Test-retest repeatability was assessed within the same week. Concurrent (inter-method) validity was assessed at the same administration, and criterion IPAQ validity was assessed against the CSA (now MTI) accelerometer. Spearman's correlation coefficients are reported, based on the total reported physical activity.



Overall, the IPAQ questionnaires produced repeatable data (Spearman's rho clustered around 0.8), with comparable data from short and long forms. Criterion validity had a median rho of about 0.30, which was comparable to most other self-report validation studies. The "usual week" and "last 7 d" reference periods performed similarly, and the reliability of telephone administration was similar to the self-administered mode.



The IPAQ instruments have acceptable measurement properties, at least as good as other established self-reports. Considering the diverse samples in this study, IPAQ has reasonable measurement properties for monitoring population levels of physical activity among 18- to 65-yr-old adults in diverse settings. The short IPAQ form "last 7 d recall" is recommended for national monitoring and the long form for research requiring more detailed assessment.
Resource Allocation in Hybrid Macro/Femto Networks

Femtocell has been considered by the wireless industry as a promising solution not only to improve indoor coverage, but also to unload traffic from overburdened macrocell networks. In hybrid macro/femto networks, macrocells may have to share the same spectrum with femtocells, due to spectrum availability and network infrastructure considerations, in spite of potentially excessive interference caused by densely deployed femtocells. In this paper, we propose a decentralized resource allocation scheme for the orthogonal frequency division multiple access (OFDMA) downlink of a shared-spectrum macro/femto network, where each femtocell randomly selects a subset of available OFDMA resources for transmission, in order to achieve decentralized inter-cell interference avoidance. The performance of the decentralized resource allocation scheme is evaluated by simulations based on per-cell or network-wide performance metrics. Simulation results provide insights on how the resource allocation in a shared-spectrum hybrid macro/femto network should take into account the spatial density of femtocells and indoor/outdoor radio propagation channel conditions.
Agile Model-Driven Modernization to the Service Cloud

Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advant ges, including reduced costs, automation of migration ac tivities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus , a methodology for seamless execution of different mig ration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a pa rticular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile tech niques to address different challenging issues. The extended agile methodology could be used by organizations which ha ve already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service
The RB and p 53 pathways in cancer

The life history of cancer cells encompasses a series of genetic missteps in which normal cells are progressively transformed into tumor cells that invade surrounding tissues and become malignant. Most prominent among the regulators disrupted in cancer cells are two tumor suppressors, the retinoblastoma protein (RB) and the p53 transcription factor. Here, we discuss interconnecting signaling pathways controlled by RB and p53, attempting to explain their potentially universal involvement in the etiology of cancer. Pinpointing the various ways by which the functions of RB and p53 are subverted in individual tumors should provide a rational basis for developing more refined tumor-specific therapies.
Machine learning plug-ins for GNU Radio Companion

This paper gives an insight about how to create classifier plug-ins (signal processing blocks) using hard-code input for GNU Radio Companion (GRC). GNU Radio Companion is an open source Visual programming language for any real time signal processing applications. At present there is no classifier block available inside this GRC tool. Here we are introducing a low cost classifier which utilizes the basic machine learning algorithms:linear regression and logistic regression. The creation of classifier plug-ins in an open source software enables easy manipulation of real time classification problems during the transmission and reception of signals in Software Defined Radios. So this workdescribes the development of signal processing block that can be done by changing the Python code and C++ codes of the `gr-modtool' package. It is highly cost effective and with great potential since GNU Radio software is open source and free.
Applications of RFID in Pharmaceutical Industry

Security and safety are two important features desired in pharmaceutical supply chain and achieving the same is a challenging task. The need to secure and authenticate pharmaceutical products has increased tremendously with the emerging counterfeit product market. The motivation to introduce counterfeit pharmaceutical products in the supply chain could be to gain rapid economic benefits or affecting the reputation of strong brand in the pharmaceutical industry. RFID technology can be used to deter counterfeiting attempts. It can also be used in various other domains in the pharmaceutical industry. The main aim of this paper is to outline all the applications of RFID in the pharmaceutical industry. After explaining the main applications, we discuss how information hiding techniques could be used with RFID to offer efficient expiry date management, pharmaceutical tamper detection, and fraud detection and prevention.
A 2D broadband, printed Luneburg lens antenna

This paper details the design of a printed-circuit-board implementation of a 2D Luneburg lens. The refractive index of the lens is controlled through a combination of meandering crossed microstrip lines and varying their widths. The 12.4⋋￮ diameter lens is designed to operate in the Transverse Electromagnetic (TEM) mode at 13 GHz. The lens was designed, fabricated, and measured. The measured half power beamwidth of the experimental lens is 5.26°.
Pointwise Prediction and Sequence-Based Reranking for Adaptable Part-of-Speech Tagging

This paper proposes an accurate method for partof-speech (POS) tagging that is highly domain-adaptable. The method is based on an assumption that the POS transition tendencies do not depend on domains, and has the following three characteristics: 1) it is trainable from partially annotated data, 2) it uses efficiently trainable pointwise POS taggers to allow for active learning, and 3) is more accurate than the pointwise or sequence-based POS taggers. The proposed method estimates POS tags by stacking pointwise and sequence-based predictors. In the experiments we deal with the joint problem of word segmentation and POS tagging in Japanese. We show that our proposed stacking process improves over pointwise and sequence-based methods (hidden Markov models and conditional random fields) both in the general domain and the target domain. In addition we show the learning curve in a domain adaptation scenario. The result shows that our method outperforms state-of-the-art methods in the same domain as the training data and is better than them in domain adaptation situations as well. Active learning; Reranking; Word segmentation; Part-of-speech tagging; Pointwise prediction;
A conceptual framework for integrated information privacy protection

Successful organizations strive to achieve a high degree of corporate governance, effective techniques for risk management, and an assurance regarding the fulfilment of compliance requirements. This effort bears the Governance, Risk and Compliance (GRC) label, which entails integrating these three disparate disciplines to achieve effectiveness and efficiency in meeting the organization's strategic objectives. An interesting development has been the integration of privacy within a GRC context. Privacy has a number of elements, including governance, management, legal, technical aspects, compliance, risk management, information security, business processes and organizational issues which fall into the GRC processes. A large number of privacy breaches and a growing number of privacy regulations will steer organizations in the realm of managing privacy protection within a GRC context. There are a number of privacy facets but the focus of this paper is specifically on information privacy protection. This paper seeks to develop a formalized and repeatable conceptual framework to address information privacy protection within a GRC frame of reference.
Analysis of a ferrofluid core LVDT displacement sensor

In this work, we present the analysis of a Linear Variable Differential Transformer (LVDT), which movable core is an encapsulated ferrofluid. A ferrofluid is a ferromagnetic nanoparticles composed fluid, which physical properties, including geometry, vary when it is under the influence magnetic field. Our proposed analysis is on the performance of a ferrofluid core LVDT, based in the fact that as the ferrofluid core moves between the magnetic fields generated in the coils of the LVDT, the core geometry varies. A sensibility and linearity comparison between a conventional LVDT and the ferrofluid core LVDT is presented, along with three different quantities of encapsulated ferrofluid inside the ferrofluid cores. The results of this work will be further used in order to obtain industrial grade sensors and actuators.
Proxemic Transitions: Designing Shape-Changing Furniture for Informal Meetings

The field of Shape-Changing Interfaces explores the qualities of physically dynamic artifacts. At furniture-scale, such artifacts have the potential of changing the ways we collaborate and engage with interiors and physical spaces. Informed by theories of proxemics, empirical studies of informal meetings and design work with shape-changing furniture, we develop the notion of proxemic transitions. We present three design aspects of proxemic transitions: transition speed, stepwise reconfiguration, and radical shifts. The design aspects focus on how to balance between physical and digital transformations in designing for proxemic transitions. Our contribution is three-fold: 1) the notion of proxemic transitions, 2) three design aspects to consider in designing for proxemic transitions, and 3) initial exploration of how these design aspects might generate designs of dynamic furniture. These contributions outline important aspects to consider when designing shape-changing furniture for informal workplace meetings.
Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory [26], where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.
Statistics and Social Network of YouTube Videos

YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.
Value Priorities and Behavior : Applying a Theory of Integrated Value Systems

A major goal of research on values has been to relate individual differences in value priorities to differences in attitudes, behavior and background variables. Past research most commonly adopted one of two approaches. Much research has selected a few single target values whose priorities were postulated to associate with the attitude, behavior and background variable of interest and then examined empirical relationships. Other research has been more exploratory.It has related lists of values to various other variables and then discussed the significant associations that emerge. The focus on relationships with single values make both these approaches insatisfying. My work has sought to overcome those approaches.It has derived what may be a nearly comprehensive set of different motivational types of values, recognized across cultures. Each value type is represented by a number of single values that are combined to form relatively reliable indexes of values priorities. Value systems can be treated as integrated wholes in their relations with behaviors and, thereby, encourages researches to abandom the prevailing single-values approaches.
Text Segmentation with Topic Modeling and Entity Coherence

This paper describes a system which uses entity and topic coherence for improved Text Segmentation (TS) accuracy. First, Linear Dirichlet Allocation (LDA) algorithm was used to obtain topics for sentences in the document. We then performed entity mapping across a window in order to discover the transition of entities within sentences. We used the information obtained to support our LDA-based boundary detection for proper boundary adjustment. We report the significance of the entity coherence approach as well as the superiority of our algorithm over existing works.