nils-herrmann/clean_scirepeval huggingface dataset:
Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the "best" knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.



The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.



We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.



The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.



The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.
PET-CT image registration in the chest using free-form deformations

We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.
High-Frequency Stock Trend Forecast Using LSTM Model

The prediction of price trend in stock market is a challenging task due to the inherent complexity and dynamics in price movement. Many machine learning algorithms, such as Support Vector Machine, Artificial Neural Network, and Hidden Markov Model, have been applied to it and achieved positive results. Long Short-Term Memory (LSTM), as a variant of RNN, can obtain hidden dependencies in data and has shown a significant performance in processing time series data. In this paper, we apply LSTM networks to predict the price movement of a short-term and test it by an experiment on some stocks randomly selected from CSI 300 constituent stocks. The experiment shows that the precision, recall rate and critical error of LSTM are all better than that of the random prediction. It indicates that LSTM can be used in the trend prediction of stock price. We also notice that many improvements need to be done in future.
Combining geometry and combinatorics: A unified approach to sparse signal recovery

There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach utilizes geometric properties of the measurement matrix Phi. A notable example is the Restricted Isometry Property, which states that the mapping Phi preserves the Euclidean norm of sparse signals; it is known that random dense matrices satisfy this constraint with high probability. On the other hand, the combinatorial approach utilizes sparse matrices, interpreted as adjacency matrices of sparse (possibly random) graphs, and uses combinatorial techniques to recover an approximation to the signal. In this paper we present a unification of these two approaches. To this end, we extend the notion of Restricted Isometry Property from the Euclidean lscr2 norm to the Manhattan lscr1 norm. Then we show that this new lscr1 -based property is essentially equivalent to the combinatorial notion of expansion of the sparse graph underlying the measurement matrix. At the same time we show that the new property suffices to guarantee correctness of both geometric and combinatorial recovery algorithms. As a result, we obtain new measurement matrix constructions and algorithms for signal recovery which, compared to previous algorithms, are superior in either the number of measurements or computational efficiency of decoders.
An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments

Grabbing and manipulating virtual objects is an important user interaction for immersive virtual environments. We present implementations and discussion of six techniques which allow manipulation of remote objects. A user study of these techniques was performed which revealed their characteristics and deficiencies, and led to the development of a new class of techniques. These hybrid techniques provide istinct advantages in terms of ease of use and efficiency because they consider the tasks of grabbing and manipulation separately. CR Categories and Subject Descr iptors: I.3.7 [Computer Graphics]:Three-Dimensional Graphics and Realism Virtual Reality; I.3.6 [Computer Graphics]:Methodology and Techniques Interaction Techniques.
Similarity Measures for Categorical Data: A Comparative Evaluation

Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated. In this paper we study the performance of a variety of similarity measures in the context of a specific data mining task: outlier detection. Results on a variety of data sets show that while no one measure dominates others for all types of problems, some measures are able to have consistently high performance.
Design for Privacy in Ubiquitous Computing Environments

Current developments in information technology are leading to increasing capture and storage of information about people and their activities. This raises serious issues about the preservation of privacy. In this paper we examine why these issues are particularly important in the introduction of ubiquitous computing technology into the working environment. Certain problems with privacy are closely related to the ways in which the technology attenuates natural mechanisms of feedback and control over information released. We describe a framework for design for privacy in ubiquitous computing environments and conclude with an example of its application.
Computational Thinking as a Computer Science Education Framework and the Related Effects on Gender Equity

I have recently completed my third year of study in the Educational Psychology and Educational Technology doctoral program at Michigan State University. I have successfully completed all of my coursework and comprehensive/qualifying exams. I will be proposing my dissertation study in the Fall semester of 2016 and expect to defend my dissertation in the Fall semester of 2017. My prior research has been focused on issues related to computational thinking, creativity, and computer science education. I am currently developing my literature review and honing my core research questions. I hope to subsequently develop my research methods and measures more fully, with plans to begin fieldwork in Fall of 2016.
Circularly-Polarized Reconfigurable Transmitarray in Ka-Band With Beam Scanning and Polarization Switching Capabilities

The design, realization, and experimental characterization of a 400-element electronically reconfigurable transmitarray operating in the Ka-band is presented. It is based on linearly polarized unit-cells with 180° phase-shifting capability. Several sequential rotation schemes have been compared numerically to generate a circularly polarized beam over a broad frequency band, and a random distribution has been selected to mitigate spurious cross-polarized side-lobes when scanning the main beam. The 2-D electronic beam-steering capabilities of ±60° have been verified experimentally. The prototype, illuminated by a horn antenna as a focal source, exhibits a broadside gain of 20.8 dBi at 29.0 GHz and a 3-dB bandwidth of 14.6% with radiation efficiency of 58%. The axial ratio remains below 2 dB within this bandwidth. Next, a planar substrate integrated waveguide focal source array was designed in order to reduce the focal distance by about 50% and thereby significantly improve the antenna compactness, and similar radiation performance is demonstrated numerically and experimentally.
Prediction of Web Users Browsing Behavior : A Review

In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user’s browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user’s behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior.  WUM, classification, Browsing behavior, prediction, ARM, session
CCured in the real world

CCured is a program transformation system that adds memory safety guarantees to C programs by verifying statically that memory errors cannot occur and by inserting run-time checks where static verification is insufficient.This paper addresses major usability issues in a previous version of CCured, in which many type casts required the use of pointers whose representation was expensive and incompatible with precompiled libraries. We have extended the CCured type inference algorithm to recognize and verify statically a large number of type casts; this goal is achieved by using physical subtyping and pointers with run-time type information to allow parametric and subtype polymorphism. In addition, we present a new instrumentation scheme that splits CCured's metadata into a separate data structure whose shape mirrors that of the original user data. This scheme allows instrumented programs to invoke external functions directly on the program's data without the use of a wrapper function.With these extensions we were able to use CCured on real-world security-critical network daemons and to produce instrumented versions without memory-safety vulnerabilities.
Large Scale Image Deduplication

With the rise of internet and personal digital camera, it becomes easy for researchers to get image data in mass quantity. With these large amount of image data, it is impossible for humans to examine each image and insure the quality of the dataset. Therefore it is crucial to develop algorithms that can process large amount of data. This paper will focus on a particular problem related to image dataset, image deduplication. We propose an efficient and scalable method to find near-duplicate images in an image collection. Our method includes 3-steps, first, extract compact features from each image. Second, use a fast clustering algorithm to reduce possible image match. The clustering algorithm should be CPU/memory efficient and can scale through multiple machines easily. We will use the method proposed by  that uses map-reduce to implement approximate nearest neighbor. Finally, apply a more accurate method on the clustered images to find duplicate images in each group. We will use method proposal by .
Cues to deception.

Do people behave differently when they are lying compared with when they are telling the truth? The combined results of 1,338 estimates of 158 cues to deception are reported. Results show that in some ways, liars are less forthcoming than truth tellers, and they tell less compelling tales. They also make a more negative impression and are more tense. Their stories include fewer ordinary imperfections and unusual contents. However, many behaviors showed no discernible links, or only weak links, to deceit. Cues to deception were more pronounced when people were motivated to succeed, especially when the motivations were identity relevant rather than monetary or material. Cues to deception were also stronger when lies were about transgressions.
Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining

A common feature of many online review sites is the use of an overall rating that summarizes the opinions expressed in a review. Unfortunately, these document-level ratings do not provide any information about the opinions contained in the review that concern a specific aspect (e.g., cleanliness) of the product being reviewed (e.g., a hotel). In this paper we study the finer-grained problem of aspect-oriented opinion mining at the sentence level, which consists of predicting, for all sentences in the review, whether the sentence expresses a positive, neutral, or negative opinion (or no opinion at all) about a specific aspect of the product. For this task we propose a set of increasingly powerful models based on conditional random fields (CRFs), including a hierarchical multi-label CRFs scheme that jointly models the overall opinion expressed in the review and the set of aspect-specific opinions expressed in each of its sentences. We evaluate the proposed models against a dataset of hotel reviews (which we here make publicly available) in which the set of aspects and the opinions expressed concerning them are manually annotated at the sentence level. We find that both hierarchical and multi-label factors lead to improved predictions of aspect-oriented opinions.
Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it

Large scale corpora have benefited many areas of research in natural language processing, but until recently, resources for dialogue have lagged behind. Now, with the emergence of large scale social media websites incorporating a threaded dialogue structure, content feedback, and self-annotation (such as stance labeling), there are valuable new corpora available to researchers. In previous work, we released the INTERNET ARGUMENT CORPUS, one of the first larger scale resources available for opinion sharing dialogue. We now release the INTERNET ARGUMENT CORPUS 2.0 (IAC 2.0) in the hope that others will find it as useful as we have. The IAC 2.0 provides more data than IAC 1.0 and organizes it using an extensible, repurposable SQL schema. The database structure in conjunction with the associated code facilitates querying from and combining multiple dialogically structured data sources. The IAC 2.0 schema provides support for forum posts, quotations, markup (bold, italic, etc), and various annotations, including Stanford CoreNLP annotations. We demonstrate the generalizablity of the schema by providing code to import the ConVote corpus.
The Santa Claus problem

We consider the following problem: The Santa Claus has n presents that he wants to distribute among m kids. Each kid has an arbitrary value for each present. Let p<sub>ij</sub> be the value that kid i has for present j. The Santa's goal is to distribute presents in such a way that the least lucky kid is as happy as possible, i.e he tries to maximize min<sub>i=1,...,m</sub> sum<sub>j ∈ S<sub>i</sub></sub> p<sub>ij</sub> where S<sub>i</sub> is a set of presents received by the i-th kid.Our main result is an O(log log m/log log log m) approximation algorithm for the restricted assignment case of the problem when p<sub>ij</sub> ∈ p<sub>j</sub>,0 (i.e. when present j has either value p<sub>j</sub> or 0 for each kid). Our algorithm is based on rounding a certain natural exponentially large linear programming relaxation usually referred to as the configuration LP. We also show that the configuration LP has an integrality gap of Ω(m<sup>1/2</sup>) in the general case, when p<sub>ij</sub> can be arbitrary.
Modeling consumer loan default prediction using ensemble neural networks

In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network. The neural networks are trained using real world credit application cases from a German bank datasets which has 1000 cases; each case with 24 numerical attributes; upon, which the decision is based. The aim of this paper was to compare between the resulting models produced from using different training algorithms, scaled conjugate gradient backpropagation, Levenberg-Marquardt algorithm, One-step secant backpropagation (SCG, LM and OSS) and an ensemble of SCG, LM and OSS. Empirical results indicate that training algorithms improve the design of a loan default prediction model and ensemble model works better than the individual models.
ACCURATE VISUAL LOCALIZATION IN OUTDOOR AND INDOOR ENVIRONMENTS EXPLOITING 3D IMAGE SPACES AS SPATIAL REFERENCE

In this paper, we present a method for visual localization and pose estimation based on 3D image spaces. The method works in indoor and outdoor environments and does not require the presence of control points or markers. The method is evaluated with different sensors in an outdoor and an indoor test field. The results of our research show the viability of single image localization with absolute position accuracies at the decimetre level for outdoor environments and 5 cm or better for indoor environments. However, the evaluation also revealed a number of limitations of single image visual localization in real-world environments. Some of them could be addressed by an alternative AR-based localization approach, which we also present and compare in this paper. We then discuss the strengths and weaknesses of the two approaches and show possibilities for combining them to obtain accurate and robust visual localization in an absolute coordinate frame.
Temporal perception deficits in schizophrenia: integration is the problem, not deployment of attentions

Patients with schizophrenia are known to have impairments in sensory processing. In order to understand the specific temporal perception deficits of schizophrenia, we investigated and determined to what extent impairments in temporal integration can be dissociated from attention deployment using Attentional Blink (AB). Our findings showed that there was no evident deficit in the deployment of attention in patients with schizophrenia. However, patients showed an increased temporal integration deficit within a hundred-millisecond timescale. The degree of such integration dysfunction was correlated with the clinical manifestations of schizophrenia. There was no difference between individuals with/without schizotypal personality disorder in temporal integration. Differently from previous studies using the AB, we did not find a significant impairment in deployment of attention in schizophrenia. Instead, we used both theoretical and empirical approaches to show that previous findings (using the suppression ratio to correct for the baseline difference) produced a systematic exaggeration of the attention deficits. Instead, we modulated the perceptual difficulty of the task to bring the baseline levels of target detection between the groups into closer alignment. We found that the integration dysfunction rather than deployment of attention is clinically relevant, and thus should be an additional focus of research in schizophrenia.
Design considerations for a contactless electric vehicle battery charger

This paper overviews theoretical and practical design issues related to inductive power transfer systems and verifies the developed theory using a practical electric vehicle battery charger. The design focuses on the necessary approaches to ensure power transfer over the complete operating range of the system. As such, a new approach to the design of the primary resonant circuit is proposed, whereby deviations from design expectations due to phase or frequency shift are minimized. Of particular interest are systems that are neither loosely nor tightly coupled. The developed solution depends on the selected primary and secondary resonant topologies, the magnetic coupling coefficient, and the secondary quality factor.
Relations among children's social goals, implicit personality theories, and responses to social failure.

Two studies examined children's thought patterns in relation to their responses to social challenge. In Study 1, 4th and 5th graders tried out for a pen pal club under either a performance goal (stressing the evaluative nature of the tryout) or a learning goal (emphasizing the potential learning opportunities). In their behavior and attributions following rejection, children who were focused on a performance goal reacted with more helplessness, whereas children given a learning goal displayed a more mastery-oriented response. Study 2 found that in response to hypothetical socially challenging situations, 4th, 5th, and 6th graders who believed personality was nonmalleable (entity theorists) vs. malleable (incremental theorists) were more likely to endorse performance goals. Together, these studies indicate that children's goals in social situations are associated with their responses to social failure and are predicted by their implicit theories about their personality.