nils-herrmann/clean_scirepeval huggingface dataset:
Deep Exploration via Bootstrapped DQN

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as -greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.
Switching control strategy for full ZVS soft-switching operation of a Dual Active Bridge AC/DC converter

A switching control strategy to enable Zero-Voltage-Switching (ZVS) over the entire input-voltage interval and the full power range of a single-stage Dual Active Bridge (DAB) AC/DC converter is proposed. The converter topology consists of a DAB DC/DC converter, receiving a rectified AC line voltage via a synchronous rectifier. The DAB comprises primary and secondary side full bridges, linked by a high-frequency isolation transformer and inductor. Using conventional control strategies, the soft-switching boundary conditions are exceeded at the higher voltage conversion ratios of the AC input interval. Recently we presented a novel pulse-width-modulation strategy to fully eliminate these boundaries, using a half bridge - full bridge DAB configuration. In this papers the analysis is extended towards a full bridge - full bridge DAB setup, providing more flexibility to minimize the component RMS currents and allowing increased performance (in terms of efficiency and volume). Experimental results are given to validate the theoretical analysis and practical feasibility of the proposed strategy.
Yes, but are they happy? Effects of trait self-control on affective well-being and life satisfaction.

Does trait self-control (TSC) predict affective well-being and life satisfaction--positively, negatively, or not? We conducted three studies (Study 1: N = 414, 64% female, Mage = 35.0 years; Study 2: N = 208, 66% female, Mage = 25.24 years; Study 3: N = 234, 61% female, Mage = 34.53 years). The key predictor was TSC, with affective well-being and life satisfaction ratings as key outcomes. Potential explanatory constructs including goal conflict, goal balancing, and emotional distress also were investigated. TSC is positively related to affective well-being and life satisfaction, and managing goal conflict is a key as to why. All studies, moreover, showed that the effect of TSC on life satisfaction is at least partially mediated by affect. Study 1's correlational study established the effect. Study 2's experience sampling approach demonstrated that compared to those low in TSC, those high in TSC experience higher levels of momentary affect even as they experience desire, an effect partially mediated through experiencing lower conflict and emotional distress. Study 3 found evidence for the proposed mechanism--that TSC may boost well-being by helping people avoid frequent conflict and balance vice-virtue conflicts by favoring virtues. Self-control positively contributes to happiness through avoiding and dealing with motivational conflict.
Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net

Parsing human regions into semantic parts, e.g., body, head and arms etc., from a random natural image is challenging while fundamental for computer vision and widely applicable in industry. One major difficulty to handle such a problem is the high flexibility of scale and location of a human instance and its corresponding parts, making the parsing task either lack of boundary details or suffer from local confusions. To tackle such problems, in this work, we propose the “Auto-Zoom Net” (AZN) for human part parsing, which is a unified fully convolutional neural network structure that: (1) parses each human instance into detailed parts. (2) predicts the locations and scales of human instances and their corresponding parts. In our unified network, the two tasks are mutually beneficial. The score maps obtained for parsing help estimate the locations and scales for human instances and their parts. With the predicted locations and scales, our model “zooms” the region into a right scale to further refine the parsing. In practice, we perform the two tasks iteratively so that detailed human parts are gradually recovered. We conduct extensive experiments over the challenging PASCAL-Person-Part segmentation, and show our approach significantly outperforms the state-of-art parsing techniques especially for instances and parts at small scale. In addition, we perform experiments for horse and cow segmentation and also obtain results which are considerably better than state-of-the-art methods (by over 5%)., which is contribued by the proposed iterative zooming process.
Taking Advantage of Sparsity in Multi-Task Learning

We study the problem of estimating multiple linear regressi on equations for the purpose of both prediction and variable selection. Following recen t work on multi-task learning Argyriou et al. [2008], we assume that the regression vector s share the same sparsity pattern. This means that the set of relevant predictor variable s is the same across the different equations. This assumption leads us to consider the Group La sso s a candidate estimation method. We show that this estimator enjoys nice sparsit y oracle inequalities and variable selection properties. The results hold under a certain restricted eigenvalue condition and a coherence condition on the design matrix, which natura lly extend recent work in Bickel et al. [2007], Lounici [2008]. In particular, in the m ulti-task learning scenario, in which the number of tasks can grow, we are able to remove compl etely the effect of the number of predictor variables in the bounds. Finally, we sho w w our results can be extended to more general noise distributions, of which we only require the variance to be finite.
Variational Autoencoder for Semi-Supervised Text Classification

Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder’s capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two specific decoder structures are investigated and both of them are verified to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG’s News corpus show that the proposed approach significantly improves the classification accuracy compared with pure-supervised classifiers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.
IntelliDroid: A Targeted Input Generator for the Dynamic Analysis of Android Malware

While dynamic malware analysis methods generally provide better precision than purely static methods, they have the key drawback that they can only detect malicious behavior if it is executed during analysis. This requires inputs that trigger the malicious behavior to be applied during execution. All current methods, such as hard-coded tests, random fuzzing and concolic testing, can provide good coverage but are inefficient because they are unaware of the specific capabilities of the dynamic analysis tool. In this work, we introduce IntelliDroid, a generic Android input generator that can be configured to produce inputs specific to a dynamic analysis tool, for the analysis of any Android application. Furthermore, IntelliDroid is capable of determining the precise order that the inputs must be injected, and injects them at what we call the device-framework interface such that system fidelity is preserved. This enables it to be paired with full-system dynamic analysis tools such as TaintDroid. Our experiments demonstrate that IntelliDroid requires an average of 72 inputs and only needs to execute an average of 5% of the application to detect malicious behavior. When evaluated on 75 instances of malicious behavior, IntelliDroid successfully identifies the behavior, extracts path constraints, and executes the malicious code in all but 5 cases. On average, IntelliDroid performs these tasks in 138.4 seconds per application.
Wikipedia-based Semantic Interpretation for Natural Language Processing

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
Short text classification using very few words

We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text. It first selects the most representative and topical-indicative words from a given short text as query words, and then searches for a small set of labeled short texts best matching the query words. The predicted category label is the majority vote of the search results. Evaluated on a collection of more than 12K Web snippets, the proposed approach achieves comparable classification accuracy with the baseline Maximum Entropy classifier using as few as 3 query words and top-5 best matching search hits. Among the four query word selection schemes proposed and evaluated in our experiments, term frequency together with clarity gives the best classification accuracy.
Design and Manufacturing of a Mobile Rescue Robot

This paper presents design and manufacturing procedure of a tele-operative rescue robot. First, the general task to be performed by such a robot is defined, and variant kinematic mechanisms to form the basic structure of the robot are discussed. Choosing an appropriate mechanism, geometric dimensions, and mass properties are detailed to develop a dynamics model for the system. Next, the strength of each component is analyzed to finalize its shape. To complete the design procedure, Patran/Nastran was used to apply the finite element method for strength analysis of complicated parts. Also, ADAMS was used to model the mechanisms, where 3D sketch of each component of the robot was generated by means of Solidworks, and several sets of equations governing the dimensions of system were solved using Matlab. Finally, the components are fabricated and assembled together with controlling hardware. Two main processors are used within the control system of the robot. The operator's PC as the master processor and the laptop installed on the robot as the slave processor. The performance of the system was demonstrated in Rescue robot league of  2005 in Osaka (Japan) and achieved the 2nd best design award
VisualBackProp: visualizing CNNs for autonomous driving

This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data and reveals that it compares favorably to the LRP approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.
Multilevel minimum cross entropy threshold selection based on particle swarm optimization

Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications.
Ontologies for semantically interoperable systems

In this paper, we discuss the use of ontologies for semantic interoperability and integration. We argue that information technology has evolved into a world of largely loosely coupled systems and as such, needs increasingly more explicit, machine-interpretable semantics. Ontologies in the form of logical domain theories and their knowledge bases offer the richest representations of machine-interpretable semantics for systems and databases in the loosely coupled world, thus ensuring greater semantic interoperability and integration. Finally, we discuss how ontologies support semantic interoperability in the real, commercial and governmental world.
An overview of today's high-level synthesis tools

High-level synthesis (HLS) is an increasingly popular approach in electronic design automation (EDA) that raises the abstraction level for designing digital circuits. With the increasing complexity of embedded systems, these tools are particularly relevant in embedded systems design. In this paper, we present our evaluation of a broad selection of recent HLS tools in terms of capabilities, usability and quality of results. Even though HLS tools are still lacking some maturity, they are constantly improving and the industry is now starting to adopt them into their design flows.
Stray Current Corrosion and Mitigation: A synopsis of the technical methods used in dc transit systems.

Stray-current corrosion has been a source of concern for the transit authorities and utility companies since the inception of the electrified rail transit system. The corrosion problem caused by stray current was noticed within ten years of the first dc-powered rail line in the United States in 1888  in Richmond, Virginia, and ever since, the control of stray current has been a critical issue. Similarly, the effects of rail and utility-pipe corrosion caused by stray current had been observed in Europe.
A Grammatical Approach to Automatic Improvisation

We describe an approach to the automatic generation of convincing jazz melodies using probabilistic grammars. Uses of this approach include a software tool for assisting a soloist in the creation of a jazz solo over chord progressions. The method also shows promise as a means of automatically improvising complete solos in real-time. Our approach has been implemented and demonstrated in a free software tool.  jazz, improvisation, educational software, probabilistic context-free grammar, melody generator.
Tree Kernels for Semantic Role Labeling

The availability of large scale data sets of manually annotated predicate-argument structures has recently favored the use of machine learning approaches to the design of automated semantic role labeling (SRL) systems. The main research in this area relates to the design choices for feature representation and for effective decompositions of the task in different learning models. Regarding the former choice, structural properties of full syntactic parses are largely employed as they represent ways to encode different principles suggested by the linking theory between syntax and semantics. The latter choice relates to several learning schemes over global views of the parses. For example, re-ranking stages operating over alternative predicate-argument sequences of the same sentence have shown to be very effective. In this article, we propose several kernel functions to model parse tree properties in kernel-based machines, for example, perceptrons or support vector machines. In particular, we define different kinds of tree kernels as general approaches to feature engineering in SRL. Moreover, we extensively experiment with such kernels to investigate their contribution to individual stages of an SRL architecture both in isolation and in combination with other traditional manually coded features. The results for boundary recognition, classification, and re-ranking stages provide systematic evidence about the significant impact of tree kernels on the overall accuracy, especially when the amount of training data is small. As a conclusive result, tree kernels allow for a general and easily portable feature engineering method which is applicable to a large family of natural language processing tasks.
Visualizing and Understanding Deep Texture Representations

A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations. Nevertheless, it is still unclear how these models represent texture and invariances to categorical variations. This work conducts a systematic evaluation of recent CNN-based texture descriptors for recognition and attempts to understand the nature of invariances captured by these representations. First we show that the recently proposed bilinear CNN model  is an excellent generalpurpose texture descriptor and compares favorably to other CNN-based descriptors on various texture and scene recognition benchmarks. The model is translationally invariant and obtains better accuracy on the ImageNet dataset without requiring spatial jittering of data compared to corresponding models trained with spatial jittering. Based on recent work [13, 28] we propose a technique to visualize pre-images, providing a means for understanding categorical properties that are captured by these representations. Finally, we show preliminary results on how a unified parametric model of texture analysis and synthesis can be used for attribute-based image manipulation, e.g. to make an image more swirly, honeycombed, or knitted. The source code and additional visualizations are available at http://vis-www.cs.umass.edu/texture.
Visual Analytics

DEFINITION Visual analytics is the science of analytical reasoning supported by interactive visual interfaces according to . Over the last decades data was produced at an incredible rate. However, the ability to collect and store this data is increasing at a faster rate than the ability to analyze it. While purely automatic or purely visual analysis methods were developed in the last decades, the complex nature of many problems makes it indispensable to include humans at an early stage in the data analysis process. Visual analytics methods allow decision makers to combine their flexibility, creativity, and background knowledge with the enormous storage and processing capacities of today’s computers to gain insight into complex problems. The goal of visual analytics research is thus to turn the information overload into an opportunity by enabling decision-makers to examine this massive information stream to take effective actions in real-time situations.
Identifying Users Across Social Tagging Systems

How much do tagging activities tell about a user? Is it possible to identify people in Delicious based on the tags, which they use in Flickr? In this paper we study those questions and investigate whether users can be identified across social tagging systems. We combine two kinds of information: their user ids and their tags. We introduce and compare a variety of approaches to measure the distance between user profiles for identification. With the best performing combination we achieve, depending on the actual settings, accuracies of between 60% and 80%, which demonstrates that the traces of Web 2.0 users can reveal quite much about their identity.
Checking More and Alerting Less: Detecting Privacy Leakages via Enhanced Data-flow Analysis and Peer Voting

Serious concerns have been raised about stealthy disclosures of private user data in smartphone apps, and recent research efforts in mobile security have studied various mechanisms to detect privacy disclosures. However, existing approaches are not effective in informing users and security analysts about potential privacy leakage threats. This is because these methods largely fail to 1) provide highly accurate and inclusive detection of privacy disclosures, and 2) filter out legitimate privacy disclosures that usually dominate detection results and in turn obscure true threats. In this paper, we propose AAPL, an automated system that detects privacy leaks (i.e., truly suspicious privacy disclosures) in Android apps. AAPL is based on multiple special static analysis techniques that we’ve developed for Android apps, including conditional flow identification and joint flow tracking. Furthermore, AAPL employs a new approach called peer voting to filter out most of the legitimate privacy disclosures from the results, purifying the detection results for automatic and easy interpretation. We implemented AAPL and evaluated it over 40, 456 apps. The results indicate that, on average, AAPL achieves an accuracy of 88.7%. For particular disclosures (e.g., contacts), the accuracy is up to 94.6%. Using AAPL, we successfully revealed a collection of unknown privacy leaks. The throughput of our privacy disclosure analysis module is 4.5 apps per minute on a threemachine cluster.