nils-herrmann/clean_scirepeval huggingface dataset:
Life event dimensions of loss, humiliation, entrapment, and danger in the prediction of onsets of major depression and generalized anxiety.

Although substantial evidence suggests that stressful life events predispose to the onset of episodes of depression and anxiety, the essential features of these events that are depressogenic and anxiogenic remain uncertain.



High contextual threat stressful life events, assessed in 98 592 person-months from 7322 male and female adult twins ascertained from a population-based registry, were blindly rated on the dimensions of humiliation, entrapment, loss, and danger and their categories. Onsets of pure major depression (MD), pure generalized anxiety syndrome (GAS) (defined as generalized anxiety disorder with a 2-week minimum duration), and mixed MD-GAS episodes were examined using logistic regression.



Onsets of pure MD and mixed MD-GAS were predicted by higher ratings of loss and humiliation. Onsets of pure GAS were predicted by higher ratings of loss and danger. High ratings of entrapment predicted only onsets of mixed episodes. The loss categories of death and respondent-initiated separation predicted pure MD but not pure GAS episodes. Events with a combination of humiliation (especially other-initiated separation) and loss were more depressogenic than pure loss events, including death. No sex differences were seen in the prediction of episodes of illness by event categories.



In addition to loss, humiliating events that directly devalue an individual in a core role were strongly linked to risk for depressive episodes. Event dimensions and categories that predispose to pure MD vs pure GAS episodes can be distinguished with moderate specificity. The event dimensions that preceded mixed MD-GAS episodes were largely the sum of those that preceded pure MD and pure GAS episodes.
Coverage-directed differential testing of JVM implementations

Java virtual machine (JVM) is a core technology, whose reliability is critical. Testing JVM implementations requires painstaking effort in designing test classfiles (*.class) along with their test oracles. An alternative is to employ binary fuzzing to differentially test JVMs by blindly mutating seeding classfiles and then executing the resulting mutants on different JVM binaries for revealing inconsistent behaviors. However, this blind approach is not cost effective in practice because most of the mutants are invalid and redundant. This paper tackles this challenge by introducing classfuzz, a coverage-directed fuzzing approach that focuses on representative classfiles for differential testing of JVMs’ startup processes. Our core insight is to (1) mutate seeding classfiles using a set of predefined mutation operators (mutators) and employ Markov Chain Monte Carlo (MCMC) sampling to guide mutator selection, and (2) execute the mutants on a reference JVM implementation and use coverage uniqueness as a discipline for accepting representative ones. The accepted classfiles are used as inputs to differentially test different JVM implementations and find defects. We have implemented classfuzz and conducted an extensive evaluation of it against existing fuzz testing algorithms. Our evaluation results show that classfuzz can enhance the ratio of discrepancy-triggering classfiles from 1.7% to 11.9%. We have also reported 62 JVM discrepancies, along with the test classfiles, to JVM developers. Many of our reported issues have already been confirmed as JVM defects, and some even match recent clarifications and changes to the Java SE 8 edition of the JVM specification.
Best of Both Worlds: Making Word Sense Embeddings Interpretable

Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.
Predicting Stock Prices Using Technical Analysis and Machine Learning

Historical stock prices are used to predict the direction of future stock prices. The developed stock price prediction model uses a novel two-layer reasoning approach that employs domain knowledge from technical analysis in the first layer of reasoning to guide a second layer of reasoning based on machine learning. The model is supplemented by a money management strategy that use the historical success of predictions made by the model to determine the amount of capital to invest on future predictions. Based on a number of portfolio simulations with trade signals generated by the model, we conclude that the prediction model successfully outperforms the Oslo Benchmark Index (OSEBX).
Performance optimization for doubly-fed wind power generation systems

Significant variation of the resource kinetic energy, in the form of wind speed, results in substantially reduced energy capture in a fixed speed wind turbine. In order to increase the wind energy capture in the turbine, variable speed generation (VSG) strategies have been proposed and implemented. However, that requires an expensive AC/AC power converter which increases the capital investment significantly. Consequently doubly-fed systems have been proposed to reduce the size of the power converter and thereby the associated cost. Additionally, in doubly-fed systems, at a fixed operating point (power and speed), power flow can be regulated between the two winding systems on the machine. This feature can be utilized to essentially minimize losses in the machine associated with the given operating point or achieve other desired performance enhancements. In this paper, a brushless doubly-fed machine (BDFM) is utilized to develop a VSG wind power generator. The VSG controller employs a wind speed estimation based maximum power point tracker (MPPT) and a heuristic model based maximum efficiency point tracker (MEPT) to optimize the power output of the system. The controller has been verified for efficacy on a 1.5 kW laboratory VSG wind generator. The strategy is applicable to all doubly-fed configurations, including conventional wound rotor induction machines, Scherbius cascades, brushless doubly fed machines, and doubly-fed reluctance machines.
High-Performance Low-Cost Electric Motor for Electric Vehicles Using Ferrite Magnets

Permanent-magnet motors with rare-earth magnets are among the best candidates for high-performance applications such as automotive applications. However, due to their cost and risks relating to the security of supply, alternative solutions such as ferrite magnets have recently become popular. In this paper, the two major design challenges of using ferrite magnets for a high-torque-density and high-speed application, i.e., their low remanent flux density and low coercivity, are addressed. It is shown that a spoke-type design utilizing a distributed winding may overcome the torque density challenge due to a simultaneous flux concentration and a reluctance torque possibility. Furthermore, the demagnetization challenge can be overcome through the careful optimization of the rotor structure, with the inclusion of nonmagnetic voids on the top and bottom of the magnets. To meet the challenges of a high-speed operation, an extensive rotor structural analysis has been undertaken, during which electromagnetics and manufacturing tolerances are taken into account. Electromagnetic studies are validated through the testing of a prototype, which is custom built for static torque and demagnetization evaluation. The disclosed motor design surpasses the state-of-the-art performance and cost, merging the theories into a multidisciplinary product.
A Compact UWB Bandpass Filter With Embedded SIR as Band Notch Structure

In this letter, an ultrawideband (UWB) bandpass filter with a band notch is proposed. The UWB BPF (3.1-10.6 GHz) is realized by cascading a distributed high-pass filter and an elliptic low-pass filter with an embedded stepped impedance resonator (SIR) to achieve a band notch characteristic. The notch band is obtained at 5.22 GHz. It is shown that the notch frequency can be tuned by changing the impedance ratio of the embedded SIR. A fabricated prototype of the proposed UWB bandpass filter is developed. The inband and out-of-band performance obtained by measurement, EM simulation, and that with an equivalent circuit model are in good agreement.
Design aspects for 5G V2X physical layer

Next generation 5G communication systems will need to employ a range of diverse technologies in order to support vehicle-to-everything (V2X) use cases, ultimately leading to accident-free, cooperative autonomous vehicles that use the available roadway efficiently. V2X is considered as one of the most challenging applications of 5G, as it requires ultra-reliable and low-latency communication for safety-critical use cases and has to provide high data rates in many scenarios. This paper discusses design aspects for the radio access in 5G V2X. Selected key technologies and their integration towards future 5G V2X physical layer are addressed. We discuss channel modeling in the context of 5G V2X use cases and we present first results for frame structure and numerology design, coexistence with earlier systems, multi-link synchronization, and multi-antenna transmission techniques.
Phylogenetic convolutional neural networks in metagenomics

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.
SVELTE: Real-time intrusion detection in the Internet of Things

In the Internet of Things (IoT), resource-constrained things are connected to the unreliable and untrusted Internet via IPv6 and 6LoWPAN networks. Even when they are secured with encryption and authentication, these things are exposed both to wireless attacks from inside the 6LoWPAN network and from the Internet. Since these attacks may succeed, Intrusion Detection Systems (IDS) are necessary. Currently, there are no IDSs that meet the requirements of the IPv6-connected IoT since the available approaches are either customized for Wireless Sensor Networks (WSN) or for the conventional Internet. In this paper we design, implement, and evaluate a novel intrusion detection system for the IoT that we call SVELTE. In our implementation and evaluation we primarily target routing attacks such as spoofed or altered information, sinkhole, and selective-forwarding. However, our approach can be extended to detect other attacks. We implement SVELTE in the Contiki OS and thoroughly evaluate it. Our evaluation shows that in the simulated scenarios, SVELTE detects all malicious nodes that launch our implemented sinkhole and/or selective forwarding attacks. However, the true positive rate is not 100%, i.e., we have some false alarms during the detection of malicious nodes. Also, SVELTE’s overhead is small enough to deploy it on constrained nodes with limited energy and memory capacity.
HOMR: a hybrid approach to exploit maximum overlapping in MapReduce over high performance interconnects

Hadoop MapReduce is the most popular open-source parallel programming model extensively used in Big Data analytics. Although fault tolerance and platform independence make Hadoop MapReduce the most popular choice for many users, it still has huge performance improvement potentials. Recently, RDMA-based design of Hadoop MapReduce has alleviated major performance bottlenecks with the implementation of many novel design features such as in-memory merge, prefetching and caching of map outputs, and overlapping of merge and reduce phases. Although these features reduce the overall execution time for MapReduce jobs compared to the default framework, further improvement is possible if shuffle and merge phases can also be overlapped with the map phase during job execution. In this paper, we propose HOMR (a Hybrid approach to exploit maximum Overlapping in MapReduce), that incorporates not only the features implemented in RDMA-based design, but also exploits maximum possible overlapping among all different phases compared to current best approaches. Our solution introduces two key concepts: Greedy Shuffle Algorithm and On-demand Shuffle Adjustment, both of which are essential to achieve significant performance benefits over the default MapReduce framework. Architecture of HOMR is generalized enough to provide performance efficiency both over different Sockets interface as well as previous RDMA-based designs over InfiniBand. Performance evaluations show that HOMR with RDMA over InfiniBand can achieve performance benefits of 54% and 56% compared to default Hadoop over IPoIB (IP over InfiniBand) and 10GigE, respectively. Compared to the previous best RDMA-based designs, this benefit is 29%. HOMR over Sockets also achieves a maximum of 38-40% benefit compared to default Hadoop over Sockets interface. We also evaluate our design with real-world workloads like SWIM and PUMA, and observe benefits of up to 16% and 18%, respectively, over the previous best-case RDMA-based design. To the best of our knowledge, this is the first approach to achieve maximum possible overlapping for MapReduce framework.
Hierarchical Question Answering for Long Documents

Reading an article and answering questions about its content is a fundamental task for natural language understanding. While most successful neural approaches to this problem rely on recurrent neural networks (RNNs), training RNNs over long documents can be prohibitively slow. We present a novel framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance. Our approach combines a coarse, inexpensive model for selecting one or more relevant sentences and a more expensive RNN that produces the answer from those sentences. A central challenge is the lack of intermediate supervision for the coarse model, which we address using reinforcement learning. Experiments demonstrate state-of-the-art performance on a challenging subset of the WIKIREADING dataset (Hewlett et al., 2016) and on a newly-gathered dataset, while reducing the number of sequential RNN steps by 88% against a standard sequence to sequence model.
Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT

Reversing with a dolly steered trailer configuration is a hard task for any driver without extensive training. In this work we present a motion planning and control framework that can be used to automatically plan and execute complicated manoeuvres. The unstable dynamics of the reversing general 2-trailer configuration with off-axle hitching is first stabilised by an LQ-controller and then a pure pursuit path tracker is used on a higher level giving a cascaded controller that can track piecewise linear reference paths. This controller together with a kinematic model of the trailer configuration is then used for forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate motion plans that are not only kinematically feasible but also include the limitations of the controller's tracking performance when reversing. The approach is evaluated over a series of Monte Carlo simulations on three different scenarios and impressive success rates are achieved. Finally the approach is successfully tested on a small scale test platform where the motion plan is calculated and then sent to the platform for execution.
Overview of research on the educational use of video games

This paper overviews research on the educational use of video games by examining the viability of the different learning theories in the field, namely behaviorism, cognitivism, constructionism and the socio-cultural approach. In addition, five key tensions that emerge from the current research are examined: 1) Learning vs. playing, 2) freedom vs. control, 3) drill-and-practice games vs. microworlds, 4) transmission vs. construction, 5) teacher intervention vs. no teacher intervention. keywords education • video games • edutainment • learning • video games • overview • behaviorism • constructionism • cognitivism • socio-cultural • instructional technology • playing • teacher More than once we have heard that research on video games is an emerging field in which there has been no prior research, even though this is clearly not the case. Unfortunately, amnesia shackles too many researchers. In providing a comprehensive overview of the educational use of computer games, the paper contributes to the cure for this amnesia and highlights key tensions emerging from the current research that should be considered by practitioners and researchers alike. The method behind this overview The paper presents the most influential research on the subject based on an extensive search of the literature. The following database resources were used: Eric, Psych info, Medline, Ingenta, Emerald, ProQuest, Game Studies, Game-research.com, Simulation & Gaming and several others, as well as the most recent literature overviews, conference proceedings and websites of researchers. References found throughout these sources have been further expanded by examination of the references cited there. dk-2006-3.book Page 184 Wednesday, August 23, 2006 9:08
Why Do Social Media Users Share Misinformation?

Widespread misinformation on social media is a cause of concern. Currently, it is unclear what factors prompt regular social media users with no malicious intent to forward misinformation to their online networks. Using a questionnaire informed by the Uses and Gratifications theory and the literature on rumor research, this study asked university students in Singapore why they shared misinformation on social media. Gender differences were also tested. The study found that perceived information characteristics such as its ability to spark conversations and its catchiness were top factors. Self-expression and socializing motivations were also among the top reasons. Women reported a higher prevalence of misinformation sharing. The implications for the design of social media applications and information literacy training were discussed.
Linguistically Motivated Large-Scale NLP with C&C and Boxer

The statistical modelling of language, together with advances in wide-coverage grammar development, have led to high levels of robustness and efficiency in NLP systems and made linguistically motivated large-scale language processing a possibility (Matsuzaki et al., 2007; Kaplan et al., 2004). This paper describes an NLP system which is based on syntactic and semantic formalisms from theoretical linguistics, and which we have used to analyse the entire Gigaword corpus (1 billion words) in less than 5 days using only 18 processors. This combination of detail and speed of analysis represents a breakthrough in NLP technology. The system is built around a wide-coverage Combinatory Categorial Grammar (CCG) parser (Clark and Curran, 2004b). The parser not only recovers the local dependencies output by treebank parsers such as Collins (2003), but also the long-range depdendencies inherent in constructions such as extraction and coordination. CCG is a lexicalized grammar formalism, so that each word in a sentence is assigned an elementary syntactic structure, in CCG’s case a lexical category expressing subcategorisation information. Statistical tagging techniques can assign lexical categories with high accuracy and low ambiguity (Curran et al., 2006). The combination of finite-state supertagging and highly engineered C++ leads to a parser which can analyse up to 30 sentences per second on standard hardware (Clark and Curran, 2004a). The C&C tools also contain a number of Maximum Entropy taggers, including the CCG supertagger, a POS tagger (Curran and Clark, 2003a), chunker, and named entity recogniser (Curran and Clark, 2003b). The taggers are highly efficient, with processing speeds of over 100,000 words per second. Finally, the various components, including the morphological analyser morpha (Minnen et al., 2001), are combined into a single program. The output from this program — a CCG derivation, POS tags, lemmas, and named entity tags — is used by the module Boxer (Bos, 2005) to produce interpretable structure in the form of Discourse Representation Structures (DRSs).
Collective Matrix Factorization Hashing for Multimodal Data

Nearest neighbor search methods based on hashing have attracted considerable attention for effective and efficient large-scale similarity search in computer vision and information retrieval community. In this paper, we study the problems of learning hash functions in the context of multimodal data for cross-view similarity search. We put forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH). CMFH learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources. We also prove that CMFH, a similarity-preserving hashing learning method, has upper and lower boundaries. Extensive experiments verify that CMFH significantly outperforms several state-of-the-art methods on three different datasets.
Design of ultrastable and high resolution eddy-current displacement sensor system

This paper presents a comprehensive analysis and discussion of how to design a high resolution and low thermal drift eddy current sensor (ECS). A simple transformer based equivalent model was presented to explain the influence of target conductivity and working frequency to the ECS's sensitivity and stability. The relationships between the ECS's performances and target conditions were also obtained by finite element analysis (FEA). These theoretical and FEA results prove that ECS working at higher frequency and with better conductive target can achieve better performances. A new high performance bridge based synchronous demodulation circuit was designed to precisely measure tiny variations of the sensor coil's inductance and resistance, separately. An ECS prototype was manufactured and tested. The results shows that The total noise of this ECS from 0.1 Hz to 100 Hz was only 70 pmrms, and the temperature drift is less than 3 nm/°C by using both the inductance and resistance signal. This new ECS system with sub-nanometer resolution and ultrahigh stability could be widely used in all kinds of advanced applications, even in harsh environments.