nils-herrmann/clean_scirepeval huggingface dataset:
Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins.

Data on use and misuse of six classes of illicit substances by male twin pairs were used to examine whether genetic and shared environmental risk factors for substance use disorders are substance-specific or -nonspecific in their effect.



Lifetime history of use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates was assessed at personal interview in both members of 1,196 male-male twin pairs ascertained by the Virginia Twin Registry. Multivariate twin modeling of substance-nonspecific (common) and substance-specific genetic, shared environmental, and unique environmental risk factors was performed by using the program Mx.



High levels of comorbidity involving the different substance categories were observed for both use and abuse/dependence. One common genetic factor was found to have a strong influence on risk for illicit use and abuse/dependence for all six substance classes. A modest influence of substance-specific genetic factors was seen for use but not for abuse/dependence. Shared environmental factors were more important for use than for abuse/dependence and were mediated entirely through a single common factor.



In an adult population-based sample of male twins, both the genetic and the shared environmental effects on risk for the use and misuse of six classes of illicit substances were largely or entirely nonspecific in their effect. Environmental experiences unique to the person largely determine whether predisposed individuals will use or misuse one class of psychoactive substances rather than another.
Easily Add Significance Testing to your Market Basket Analysis in SAS ® Enterprise Miner TM

Market Basket Analysis is a popular data mining tool that can be used to search through data to find patterns of co-occurrence among objects. It is an algorithmic process that generates business rules and several metrics for each business rule such as support, confidence and lift that help researchers identify “interesting” patterns. Although useful, these popular metrics do not incorporate traditional significance testing. This paper describes how to easily add a well-known statistical significance test, the Pearson’s Chi Squared statistic, to the existing output generated by SAS® Enterprise Miner’s Association Node. The addition of this significance test enhances the ability of data analysts to make better decisions about which business rules are likely to be more useful.
A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics. Recommendation system; collaborative filtering; similarity method; data sparsity
Augmented Reality for the Study of Human Heart Anatomy

Augmented reality is increasingly applied in medical education mainly because educators can share knowledge through virtual objects. This research describes the development of a web application, which enhances users' medical knowledge with regards to the anatomy of the human heart by means of augmented reality. Evaluation is conducted in two different facets. In the first one, the authors of this paper evaluate the feasibility of a three-dimensional human heart module using one investigator under the supervision of an expert. In the second, evaluation aims at identifying usability issues by means of the cognitive walkthrough method. Three medical students (naive users) are called upon three target tasks in the web application. Task completion is appreciated in the light of the standard set of cognitive walkthrough questions. Augmented reality content miss hits are revealed by means of the first evaluation in an effort to enhance the educational utility of the three-dimensional human heart. Cognitive walkthrough provides further improvement points, which may further enhance usability in the next software release. The current piece of work constitutes the pre-pilot evaluation. Standardized methodologies are utilized in an effort to improve the application before its wider piloting to proper student populations. Such evaluations are considered important in experiential learning methods aiding online education of anatomy courses.
Affective Computing: A Review

Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.
An Overview of Smart Home Environments: Architectures, Technologies and Applications

The aim of this research survey note is to provide a comprehensive overview of Smart Home Environments with a focus on their architectures and application areas, as well as utilized technologies, infrastructures, and standards. The main source of information was provided by past and existing research and development projects carried out in this area, while the main result of our survey is a classification of Smart Home Environments, as revealed by this projects’ survey.
An ant colony algorithm for the multi-compartment vehicle routing problem

We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.
Deep into the Brain: Artificial Intelligence in Stroke Imaging

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.
Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets

Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: specifically, one can only quickly obtain plots such as histograms and heatmaps. In this paper, we contribute Gaussian Cubes, which significantly improves on state-of-the-art systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis (PCA). The fundamental insight in Gaussian Cubes is that instead of precomputing counts of many data subsets (as state-of-the-art systems do), Gaussian Cubes precomputes the best multivariate Gaussian for the respective data subsets. As an example, Gaussian Cubes can fit hundreds of models over millions of data points in well under a second, enabling novel types of visual exploration of such large datasets. We present three case studies that highlight the visualization and analysis capabilities in Gaussian Cubes, using earthquake safety simulations, astronomical catalogs, and transportation statistics. The dataset sizes range around one hundred million elements and 5 to 10 dimensions. We present extensive performance results, a discussion of the limitations in Gaussian Cubes, and future research directions.
Deep Learning for abnormality detection in Chest X-Ray images

Heart and lung failure account for more than 500,000 deaths annually in the United States and are most commonly screened for using plain film chest x-rays(CXR). The time constraints imposed on radiologists by their massive workload severely impede communication with direct-care physicians and result in unnecessarily long and deleterious time-to-treatment periods for patients. A computer-aided triage system would mitigate these issues by communicating directly to the primary care physician when more tests are necessary, but the development of such a system has been hindered by immense variance of CXR images and lack of labeled data. Deep learning, a technique that has seen considerable growth in recent years, allows for classification of highly heterogeneous images given a sufficiently large dataset. The Stanford Normal Radiology Diagnostic Dataset (NeRDD) provides us with more than 400,000 CXR cases that have been expertly labeled as either normal, non-normal, or extremely high-risk(emergent). To apply deep learning to the novel CXR dataset, we will 1. develop a simple preprocessing pipeline using digital image processing techniques and expert radiologist advice 2. create a pipeline that can apply three neural network architectures that have proven successful in classification tasks, GoogLeNet, InceptionNet, and ResNet, on our cohort of CXR images 3. use neural network visualization techniques to understand what type of features our model weights most heavily. With a computer aided triaging system that is able to visualize its results, we will not only be able to streamline higher risk patients to get the immediate help they need, but also communicate how the network works to clinicians, ultimately improving the national standard of care.
KBQA: An Online Template Based Question Answering System over Freebase

Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. QA systems over knowledge bases produce accurate and concise answers. The key of QA over knowledge bases is to map the question to a certain substructure in the knowledge base. To do this, KBQA (Question Answering over Knowledge Bases) uses a new kind of question representation: templates, learned from a million scale QA corpora. For example, for questions about a city’s population, KBQA learns templates such as What’s the population of $city?, How many people are there in $city?. It learns overall 1171303 templates for 4690 relations. Based on these templates, KBQA effectively and efficiently supports binary factoid questions or complex questions.
A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.
A study of factors that affect user intentions toward email service switching

Internet portals have long been using email services to attract new members and retain existing customers. Recent announcements by major Internet portals of increases in email storage capacity demonstrate this strategy. It is, however, uncertain how people react to such announcements. We wished to understand the mechanisms associated with users’ intention to switch their current email service provider. Key variables pertaining to customer satisfaction with email service, attractive alternatives, and switching cost were examined to explain the dynamics of switching. An empirical study based on 1408 survey responses confirmed the importance of user satisfaction for service continuation. The variables attractive alternatives and continuity cost also demonstrated strong association with intention to switch. We found that the variable attractive alternatives moderated the relationship between customer satisfaction and intention to switch, but a moderating effect of switching cost was not found. Our investigation indicated that the findings of extant studies on the dynamics of service switching or re-purchase also apply to email services.
Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies: A Longitudinal Test of an Evolutionary Model.

The National Longitudinal Study of Adolescent Health data were used to test predictions from life history theory. We hypothesized that (1) in young adulthood an emerging life history strategy would exist as a common factor underlying many life history traits (e.g., health, relationship stability, economic success), (2) both environmental harshness and unpredictability would account for unique variance in expression of adolescent and young adult life history strategies, and (3) adolescent life history traits would predict young adult life history strategy. These predictions were supported. The current findings suggest that the environmental parameters of harshness and unpredictability have concurrent effects on life history development in adolescence, as well as longitudinal effects into young adulthood. In addition, life history traits appear to be stable across developmental time from adolescence into young adulthood.
Multiple Post Microblog Summarization

The use of microblogs such as Twitter has increased incredibly over the past few years. Because of the public nature and sheer volume of text from these constantly changing microblogs, it is often difficult to fully understand what is being said about various topics. A method for summarizing popular topics of microblogs has been proposed but its summaries are only one sentence or phrase in length. Therefore, this work focuses on extending microblog summarization by producing multiple post summaries. Two main summarization algorithms are explored: a clustering based algorithm and a threshold based Hybrid TF-IDF algorithm. The results will be evaluated by comparing the generated summaries with manually generated summaries. For purposes of comparison, the results are also compared to MEAD, LexRank and TextRank—some leading traditional multidocument summarization systems.
Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems

In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.
Gossip along the way : Order-Optimal Consensus through Randomized Path Averaging

Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges very slowly. A recently proposed algorithm called geographic gossip improves gossip efficiency by a p n/ log n factor for random geometric graphs, by exploiting geographic information of node locations. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional p n/ log n factor and is order optimal for grids and random geometric graphs. Our analysis provides some general techniques and can be used to provide bounds on the performance of randomized message passing algorithms operating over various graph topologies.
Single Image Shadow Removal via Neighbor-Based Region Relighting

In this paper we present a novel method for shadow removal in single images. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Then, we adjust the CIELAB a and b channels of the shadow region by adding constant offsets based on the difference of the median shadow and lit pixel values. We demonstrate that our approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.
Real Time Vehicle Diagnostics Using Head Mounted Displays

This thesis evaluates how a head mounted display (HMD) can be used to increase usability compared to existing computer programs that are used during maintenance work on vehicles. Problems identified during a case study in a vehicle workshop are first described. As an attempt to solve some of the identified problems a prototype application using a HMD was developed. The prototype application aids the user during troubleshooting of systems on the vehicle by leading the mechanic with textual information and augmented reality (AR). Assessment of the prototype application was done by comparing it to the existing computer program and measuring error rate and time to completion for a predefined task. Usability was also measured using the System Usability Scale. The assessment showed that HMDs can provide higher usability in terms of efficiency and satisfaction. Furthermore, the thesis describes and discusses other possibilities and limitations that usage of HMDs and AR can lead to that were identified both from theory and during implementation.
Deep Underwater Image Enhancement

In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network.