nils-herrmann/clean_scirepeval huggingface dataset:
Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes

With the wide availability of GPS trajectory data, sustainable development on understanding travel behaviors has been achieved in recent years. But relatively less attention has been paid to uncovering the trip purposes, i.e., why people make the trips. Unlike to the GPS trajectory data, the trip purposes cannot be easily and directly collected on a large scale, which necessitates the inference of trip purposes automatically. To this end, in this paper, we propose a device-free and novel model called Trip2Vec, which consists of three components. In the first component, it augments the context on trip origins and destinations, respectively, by extracting the information about the nearby point of interest configurations and human activity popularity at particular time periods (i.e., activity period popularity) from two crowdsourced datasets. Such context is well-recognized as the clear clue of trip purposes. In the second component, on the top of the augmented context, a deep embedding approach is developed to get a more semantical and discriminative context representation in the latent space. In the third component, we simply adopt the common clustering algorithm (i.e., K-means) to aggregate trips with similar latent representation, then conduct trip purpose interpretation based on the clustering results, followed by understanding the time-evolving tendency of trip purpose patterns (i.e., profiling) in the city-wide level. Finally, we present extensive experiment results with real-world taxi trajectory and Foursquare check-in data generated in New York City (NYC) to demonstrate the effectiveness of the proposed model, and moreover, the obtained city-wide trip purpose patterns are quite consistent with real situations.
Visual Text Analytics: Techniques for Linguistic Information Visualization

Visual Text Analytics has been an active area of interdisciplinary research (http://textvis.lnu.se/). This interactive tutorial is designed to give attendees an introduction to the area of information visualization, with a focus on linguistic visualization. After an introduction to the basic principles of information visualization and visual analytics, this tutorial will give an overview of the broad spectrum of linguistic and text visualization techniques, as well as their application areas . This will be followed by a hands-on session that will allow participants to design their own visualizations using tools (e.g., Tableau), libraries (e.g., d3.js), or applying sketching techniques [4]. Some sample datasets will be provided by the instructor. Besides general techniques, special access will be provided to use the VisArgue framework  for the analysis of selected datasets.
Changes in materialism , changes in psychological well-being : Evidence from three longitudinal studies and an intervention experiment

Few studies have examined how changes in materialism relate to changes in well-being; fewer have experimentally manipulated materialism to change wellbeing. Studies 1, 2, and 3 examined how changes in materialistic aspirations related to changes in well-being, using varying time frames (12 years, 2 years, and 6 months), samples (US young adults and Icelandic adults), and measures of materialism and well-being. Across all three studies, results supported the hypothesis that people’s well-being improves as they place relatively less importance on materialistic goals and values, whereas orienting toward materialistic goals relatively more is associated with decreases in well-being over time. Study 2 additionally demonstrated that this association was mediated by changes in psychological need satisfaction. A fourth, experimental study showed that highly materialistic US adolescents who received an intervention that decreased materialism also experienced increases in self-esteem over the next several months, relative to a control group. Thus, well-being changes as people change their relative focus on materialistic goals.
Effects of Unaugmented Periphery and Vibrotactile Feedback on Proxemics with Virtual Humans in AR

In this paper, we investigate factors and issues related to human locomotion behavior and proxemics in the presence of a real or virtual human in augmented reality (AR). First, we discuss a unique issue with current-state optical see-through head-mounted displays, namely the mismatch between a small augmented visual field and a large unaugmented periphery, and its potential impact on locomotion behavior in close proximity of virtual content. We discuss a potential simple solution based on restricting the field of view to the central region, and we present the results of a controlled human-subject study. The study results show objective benefits for this approach in producing behaviors that more closely match those that occur when seeing a real human, but also some drawbacks in overall acceptance of the restricted field of view. Second, we discuss the limited multimodal feedback provided by virtual humans in AR, present a potential improvement based on vibrotactile feedback induced via the floor to compensate for the limited augmented visual field, and report results showing that benefits of such vibrations are less visible in objective locomotion behavior than in subjective estimates of co-presence. Third, we investigate and document significant differences in the effects that real and virtual humans have on locomotion behavior in AR with respect to clearance distances, walking speed, and head motions. We discuss potential explanations for these effects related to social expectations, and analyze effects of different types of behaviors including idle standing, jumping, and walking that such real or virtual humans may exhibit in the presence of an observer.
Ano/microphthalmia, ocular cysts, central nervous system malformations, and neuropsychological delay. Diagnostic considerations on 2 Brazilian patients

We report on two unrelated Brazilian patients both presenting a very unusual association of ano/microphthalmia, cystic orbital anomaly, atypical clefting, and facial appendages in one patient. Clinical manifestations presented by these patients represent a MCA/MR syndrome of unknown etiology. Considerations about syndromic delineation, genetic aspects, and differential diagnosis are discussed.
H-PARAFAC: Hierarchical Parallel Factor Analysis of Multidimensional Big Data

It has long been an important issue in various disciplines to examine massive multidimensional data superimposed by a high level of noises and interferences by extracting the embedded multi-way factors. With the quick increases of data scales and dimensions in the big data era, research challenges arise in order to (1) reflect the dynamics of large tensors while introducing no significant distortions in the factorization procedure and (2) handle influences of the noises in sophisticated applications. A hierarchical parallel processing framework over a GPU cluster, namely H-PARAFAC, has been developed to enable scalable factorization of large tensors upon a “divide-and-conquer” theory for Parallel Factor Analysis (PARAFAC). The H-PARAFAC framework incorporates a coarse-grained model for coordinating the processing of sub-tensors and a fine-grained parallel model for computing each sub-tensor and fusing sub-factors. Experimental results indicate that (1) the proposed method breaks the limitation on the scale of multidimensional data to be factorized and dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., the runtime increases in the order of <inline-formula> <tex-math notation="LaTeX">$n^2$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq1-2613054.gif"/> </alternatives></inline-formula> when the data volume increases in the order of <inline-formula> <tex-math notation="LaTeX">$n^3$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq2-2613054.gif"/> </alternatives></inline-formula>, (2) H-PARAFAC has potentials in refraining the influences of significant noises, and (3) H-PARAFAC is far superior to the conventional window-based counterparts in preserving the features of multiple modes of large tensors.
ON THE EVOLUTION OF RANDOM GRAPHS by

(n) k edges have equal probabilities to be chosen as the next one . We shall 2 study the "evolution" of such a random graph if N is increased . In this investigation we endeavour to find what is the "typical" structure at a given stage of evolution (i . e . if N is equal, or asymptotically equal, to a given function N(n) of n) . By a "typical" structure we mean such a structure the probability of which tends to 1 if n -* + when N = N(n) . If A is such a property that lim Pn,N,(n ) ( A) = 1, we shall say that „almost all" graphs Gn,N(n) n--possess this property .
Junction Temperature Measurement of IGBTs Using Short-Circuit Current as a Temperature-Sensitive Electrical Parameter for Converter Prototype Evaluation

This paper proposes a method to measure the junction temperatures of insulated-gate bipolar transistors (IGBTs) during the converter operation for prototype evaluation. The IGBT short-circuit current is employed as the temperature-sensitive electrical parameter (TSEP). The calibration experiments show that the short-circuit current has an adequate temperature sensitivity of 0.35 A/°C. The parameter also has good selectivity and linearity, which makes it suitable to be used as a TSEP. Test circuit and hardware design are proposed for the IGBT junction temperature measurement in various power electronics dc-dc and ac-dc converter applications. By connecting a temperature measurement unit to the converter and giving a short-circuit pulse during the converter operation, the short-circuit current is measured, and the IGBT junction temperature can be derived from the calibration curve. The proposed temperature measurement method is a valuable tool for prototype evaluation and avoids the unnecessary safety margin regarding device operating temperatures, which is significant particularly for high-temperature/high-density converter applications.
Implicit Heterogeneous Features Embedding in Deep Knowledge Tracing

Deep recurrent neural networks have been successfully applied to knowledge tracing, namely, deep knowledge tracing (DKT), which aims to automatically trace students’ knowledge states by mining their exercise performance data. Two main issues exist in the current DKT models: First, the complexity of the DKT models increases the tension of psychological interpretation. Second, the input of existing DKT models is only the exercise tags representing via one-hot encoding. The correlation between the hidden knowledge components and students’ responses to the exercises heavily relies on training the DKT models. The existing rich and informative features are excluded in the training, which may yield sub-optimal performance. To utilize the information embedded in these features, researchers have proposed a manual method to pre-process the features, i.e., discretizing them based on the inner characteristics of individual features. However, the proposed method requires many feature engineering efforts and is infeasible when the selected features are huge. To tackle the above issues, we design an automatic system to embed the heterogeneous features implicitly and effectively into the original DKT model. More specifically, we apply tree-based classifiers to predict whether the student can correctly answer the exercise given the heterogeneous features, an effective way to capture how the student deviates from others in the exercise. The predicted response and the true response are then encoded into a 4-bit one-hot encoding and concatenated with the original one-hot encoding features on the exercise tags to train a long short-term memory (LSTM) model, which can output the probability that a student will answer the exercise correctly on the corresponding exercise. We conduct a thorough evaluation on two educational datasets and demonstrate the merits and observations of our proposal.
Applying Design Patterns in Game Programming

This paper discussed an object-oriented design for general game using C# and XNA using design pattern. We presented application of structural patterns, creat ional pattern and behavioral pattern to create game sprite, manag e game state and game sprites, different collision and rewards a mong sprites or between sprites and map; we also discussed how t o apply design patterns to handle communications between sp rites and NPC by using observer pattern and mediator patterns . Although lots of design patterns are discussed, other design patterns might suitable as well because game programming are so co mplicated to separate each pattern independently. Game, Programming, Design Patterns, UML, XNA,
Live demonstration: A scaled-down version of the BrainScaleS wafer-scale neuromorphic system

This demonstration is based on the wafer-scale neuromophic system presented in the previous papers by Schemmel et. al. (20120), Scholze et. al. (2011) and Millner et. al. (2010). The demonstration setup will allow the visitors to monitor and partially manipulate the neural events at every level. They will get an insight into the complex interplay between packet-based and realtime communication necessary to combine continuous-time mixed-signal neural networks with a packet-based transport network. Several network experiments implemented on the setup will be accessible for user interaction.
Extending the Limits of Feature-Based SLAM With B-Splines

This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments.
Dietary n-6 and n-3 polyunsaturated fatty acids: from biochemistry to clinical implications in cardiovascular prevention.

Linoleic acid (LA) and alpha linolenic acid (ALA) belong to the n-6 (omega-6) and n-3 (omega-3) series of polyunsaturated fatty acids (PUFA), respectively. They are defined "essential" fatty acids since they are not synthesized in the human body and are mostly obtained from the diet. Food sources of ALA and LA are most vegetable oils, cereals and walnuts. This review critically revises the most significant epidemiological and interventional studies on the cardioprotective activity of PUFAs, linking their biological functions to biochemistry and metabolism. In fact, a complex series of desaturation and elongation reactions acting in concert transform LA and ALA to their higher unsaturated derivatives: arachidonic acid (AA) from LA, eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) from ALA. EPA and DHA are abundantly present in fish and fish oil. AA and EPA are precursors of different classes of pro-inflammatory or anti-inflammatory eicosanoids, respectively, whose biological activities have been evoked to justify risks and benefits of PUFA consumption. The controversial origin and clinical role of the n-6/n-3 ratio as a potential risk factor in cardiovascular diseases is also examined. This review highlights the important cardioprotective effect of n-3 in the secondary prevention of sudden cardiac death due to arrhythmias, but suggests caution to recommend dietary supplementation of PUFAs to the general population, without considering, at the individual level, the intake of total energy and fats.
An IDS framework for internet of things empowered by 6LoWPAN

The Internet of Things (IoT) is an emerging paradigm where smart objects are seamlessly connected to the overall Internet and can potentially cooperate to achieve common objectives such as supporting innovative home automation services. With reference to such a scenario, this paper presents an Intrusion Detection System (IDS) framework for IoT empowered by IPv6 over low-power personal area network (6LoWPAN) devices. In fact, 6LoWPAN is an interesting protocol supporting the realization of IoT in a resource constrained environment. 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. The proposed IDS framework which includes a monitoring system and a detection engine has been integrated into the network framework developed within the EU FP7 project `ebbits'. A penetration testing (PenTest) system had been used to evaluate the performance of the implemented IDS framework. Preliminary tests revealed that the proposed framework represents a promising solution for ensuring better security in 6LoWPANs.
Answering Tree Pattern Queries Using Views

We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.
What is the expectation maximization algorithm?

897 don't know the coin used for each set of tosses. However, if we had some way of completing the data (in our case, guessing correctly which coin was used in each of the five sets), then we could reduce parameter estimation for this problem with incomplete data to maximum likelihood estimation with complete data. One iterative scheme for obtaining completions could work as follows: starting from some initial parameters, θ ˆ ˆ ˆ = θ Α ,θ Β (t) (t) (t) (), determine for each of the five sets whether coin A or coin B was more likely to have generated the observed flips (using the current parameter estimates). Then, assume these completions (that is, guessed coin assignments) to be correct, and apply the regular maximum likelihood estimation procedure to get θ ˆ(t+1). Finally, repeat these two steps until convergence. As the estimated model improves, so too will the quality of the resulting completions. The expectation maximization algorithm is a refinement on this basic idea. Rather than picking the single most likely completion of the missing coin assignments on each iteration, the expectation maximization algorithm computes probabilities for each possible completion of the missing data, using the current parameters θ ˆ(t). These probabilities are used to create a weighted training set consisting of all possible completions of the data. Finally, a modified version of maximum likelihood estimation that deals with weighted training examples provides new parameter estimates, θ ˆ(t+1). By using weighted training examples rather than choosing the single best completion, the expectation maximization algorithm accounts for the confidence of the model in each completion of the data . In summary, the expectation maximiza-tion algorithm alternates between the steps z = (z 1 , z 2 ,…, z 5), where x i ∈ {0,1,…,10} is the number of heads observed during the ith set of tosses, and z i ∈ {A,B} is the identity of the coin used during the ith set of tosses. Parameter estimation in this setting is known as the complete data case in that the values of all relevant random variables in our model (that is, the result of each coin flip and the type of coin used for each flip) are known. Here, a simple way to estimate θ A and θ B is to return the observed proportions of heads for each coin: (1) θ Α ˆ = # of heads using …
Simulation on a Fuzzy-PID Position Controller of the CNC Servo System

Variation of the system parameters and external disturbances always happen in the CNC servo system. With a traditional PID controller, it will cause large overshoot or poor stability. In this paper, a fuzzy-PID controller is proposed in order to improve the performance of the servo system. The proposed controller incorporates the advantages of PID control which can eliminate the steady-state error, and the advantages of fuzzy logic such as simple design, no need of an accurate mathematical model and some adaptability to nonlinearity and time-variation. The fuzzy-PID controller accepts the error (e) and error change(ec) as inputs ,while the parameters kp, ki, kd as outputs. Control rules of the controller are established based on experience so that self-regulation of the values of PID parameters is achieved. A simulation model of position servo system is constructed in Matlab/Simulink module based on a high-speed milling machine researched in our institute. By comparing the traditional PID controller and the fuzzy-PID controller, the simulation results show that the system has stronger robustness and disturbance rejection capability with the latter controller which can meet the performance requirements of the CNC position servo system better
Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on userspecified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.
Drug addiction and the memory systems of the brain.

We review drug addiction from the perspective of the hypothesis that drugs of abuse interact with distinct brain memory systems. We focus on emotional and procedural forms of memory, encompassing Pavlovian and instrumental conditioning, both for action-outcome and for stimulus-response associations. Neural structures encompassed by these systems include the amygdala, hippocampus, nucleus accumbens, and dorsal striatum. Additional influences emanate from the anterior cingulate and prefrontal cortex, which are implicated in the encoding and retrieval of drug-related memories that lead to drug craving and drug use. Finally, we consider the ancillary point that chronic abuse of many drugs may impact directly on neural memory systems via neuroadaptive and neurotoxic effects that lead to cognitive impairments in which memory dysfunction is prominent.