nils-herrmann/clean_scirepeval huggingface dataset:
Transductive Event Classification through Heterogeneous Networks

Events can be defined as "something that occurs at specific place and time associated with some specific actions". In general, events extracted from news articles and social networks are used to map the information from web to the various phenomena that occur in our physical world. One of the main steps to perform this relationship is the use of machine learning algorithms for event classification, which has received great attention in the web document engineering field in recent years. Traditional machine learning algorithms are based on vector space model representations and supervised classification. However, events are composed of multiple representations such as textual data, temporal information, geographic location and other types of metadata. All these representations are poorly represented together in a vector space model. Moreover, supervised classification requires the labeling of a significant sample of events to construct a training set for learning process, thereby hampering the practical application of event classification. In this paper, we propose a method called TECHN (Transductive Event Classification through Heterogeneous Networks), which considers event metadata as different objects in an heterogeneous network. Besides, the TECHN method has the ability to automatically learn which types of network objects (event metadata) are most efficient in the classification task. In addition, our TECHN method is based on a transductive classification that considers both labeled events and a vast amount of unlabeled events. The experimental results show that TECHN method obtains promising results, especially when we consider different weights of importance for each type of event metadata and a small set of labeled events.
The SLAM project: debugging system software via static analysis

The goal of the SLAM project is to check whether or not a program obeys "API usage rules" that specify what it means to be a good client of an API. The SLAM toolkit statically analyzes a C program to determine whether or not it violates given usage rules. The toolkit has two unique aspects: it does not require the programmer to annotate the source program (invariants are inferred); it minimizes noise (false error messages) through a process known as "counterexample-driven refinement". SLAM exploits and extends results from program analysis, model checking and automated deduction. We have successfully applied the SLAM toolkit to Windows XP device drivers, to both validate behavior and find defects in their usage of kernel APIs.
Genomic expression programs in the response of yeast cells to environmental changes.

We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
Transcription Methods for Consistency, Volume and Efficiency

This paper describes recent efforts at Linguistic Data Consortium at the University of Pennsylvania to create manual transcripts as a shared resource for human language technology research and evaluation. Speech recognition and related technologies in particular call for substantial volumes of transcribed speech for use in system development, and for human gold standard references for evaluating performance over time. Over the past several years LDC has developed a number of transcription approaches to support the varied goals of speech technology evaluation programs in multiple languages and genres. We describe each transcription method in detail, and report on the results of a comparative analysis of transcriber consistency and efficiency, for two transcription methods in three languages and five genres. Our findings suggest that transcripts for planned speech are generally more consistent than those for spontaneous speech, and that careful transcription methods result in higher rates of agreement when compared to quick transcription methods. We conclude with a general discussion of factors contributing to transcription quality, efficiency and consistency.
Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning

We introduce a framework for learning regrasping behaviors based on tactile data. First, we present a grasp stability predictor that uses spatio-temporal tactile features collected from the early-object-lifting phase to predict the grasp outcome with a high accuracy. Next, the trained predictor is used to supervise and provide feedback to a reinforcement learning algorithm that learns the required grasp adjustments based on tactile feedback. Our results gathered over more than 50 hours of real robot experiments indicate that the robot is able to predict the grasp outcome with 93% accuracy. In addition, the robot is able to improve the grasp success rate from 42% when randomly grasping an object to up to 97% when allowed to regrasp the object in case of a predicted failure.
Voiced-Unvoiced Classification of Speech Using a Neural Network Trained with LPC Coefficients

Voiced-Unvoiced classification (V-UV) is a well understood but still not perfectly solved problem. It tackles the problem of determining whether a signal frame contains harmonic content or not. This paper presents a new approach to this problem using a conventional multi-layer perceptron neural network trained with linear predictive coding (LPC) coefficients. LPC is a method that results in a number of coefficients that can be transformed to the envelope of the spectrum of the input frame. As a spectrum is suitable for determining the harmonic content, so are the LPC-coefficients. The proposed neural network works reasonably well compared to other approaches and has been evaluated on a small dataset of 4 different speakers.
Violent Scenes Detection Using Mid-Level Violence Clustering

This work proposes a novel system for Violent Scenes Detection, which is based on the combination of visual and audio features with machine learning at segment-level. Multiple Kernel Learning is applied so that multimodality of videos can be maximized. In particular, Mid-level Violence Clustering is proposed in order for mid-level concepts to be implicitly learned, without using manually tagged annotations. Finally a violence-score for each shot is calculated. The whole system is trained ona dataset from MediaEval 2013 Affect Task and evaluated by its official metric. The obtained results outperformed its best score.
Schema Extraction and Structural Outlier Detection for JSON-based NoSQL Data Stores

Although most NoSQL Data Stores are schema-less, information on the structural properties of the persisted data is nevertheless essential during application development. Otherwise, accessing the data becomes simply impractical. In this paper, we introduce an algorithm for schema extraction that is operating outside of the NoSQL data store. Our method is specifically targeted at semi-structured data persisted in NoSQL stores, e.g., in JSON format. Rather than designing the schema up front, extracting a schema in hindsight can be seen as a reverse-engineering step. Based on the extracted schema information, we propose set of similarity measures that capture the degree of heterogeneity of JSON data and which reveal structural outliers in the data. We evaluate our implementation on two real-life datasets: a database from the Wendelstein 7-X project and Web Performance Data.
Field-Testing of High-Level Decentralized Controllers for a Multi-Function Drone Swarm

This paper describes the results of initial field testing of a high-level decentralized controller for a swarm of multi-rotor drones. This controller allows the intuitive implementation of a wide variety of swarm behaviors. The parameters of this controller were mapped to two possible missions. The proposed controller structure is parameterized to enable the evolution of swarm behaviors in future work. In this paper, a set of hand coded controllers, previously evaluated in simulator were field tested. The simulator used considers each UAV as a simple point-mass model of each agent. As such, the ability to transfer the controller structure from simulated UAVs to real UAVs is investigated. The experiments use a fleet of enhanced networking-capable commercial-of-the-shelf drones that were especially developed to support and demonstrate the proposed high-level controller in the decentralized swarm environment. The paper describes hardware and controller implementation and then proves feasibility and great potential of the proposed approach in a series of flight tests conducted by a swarm of four identical drones.
Exploring Application, Attitudes and Integration of Video Games: MinecraftEdu in Middle School

The aim of this study is to assess the use of MinecraftEdu in classroom practice analyzing the outcomes and attitudes of all members of the educational community through a quasi-experimental approach. The research presents three dimensions oriented to assessing the use of this application in a didactic unit “History and Architecture” compared through statistical inference (t-student) to a control group that develops the same unit with slides and traditional expositional methods. The second dimension values the attitudes of teachers, students and parents regarding the implementation of video games in formal education using descriptive analysis and nonparametric statistical inference through the Jonckheere-Terpstra test and the Kruskal-Wallis test, which allows each group ranks to be compared. The third dimension analyzes interactions in a virtual learning environment related to the implementation of MinecraftEdu. Although there are no significant improvements regarding academic outcomes and some parents hold negative attitudes, it is noteworthy that the majority of the sample considered that MinecraftEdu is fun, enhances creativity, develops discovery and is a good application for creating and exploring immersive historical environments.
"My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security

Many people use the Internet every day yet know little about how it really works. Prior literature diverges on how people’s Internet knowledge affects their privacy and security decisions. We undertook a qualitative study to understand what people do and do not know about the Internet and how that knowledge affects their responses to privacy and security risks. Lay people, as compared to those with computer science or related backgrounds, had simpler mental models that omitted Internet levels, organizations, and entities. People with more articulated technical models perceived more privacy threats, possibly driven by their more accurate understanding of where specific risks could occur in the network. Despite these differences, we did not find a direct relationship between people’s technical background and the actions they took to control their privacy or increase their security online. Consistent with other work on user knowledge and experience, our study suggests a greater emphasis on policies and systems that protect privacy and security without relying too much on users’ security practices.
Microcontroller Based Fan Speed Regulator with Continuous Monitoring using LCD Display

Design and implementation of Microcontroller based automatic Fan speed regulator using temperature sensor is represented here. Most of the available Fans today are controlled manually by voltage regulators which have different stages of speed. During summer nights, especially the room temperature is initially quite high, as time passes, the temperature starts dropping. Also, after a person falls asleep, the metabolic rate of one’s body decreases, and one is expected to wake up from time to time to adjust the speed of the Fan. Many people who are disabled / physically challenged persons are affected the most because of the inconveniences associated in changing the Fan speed level manually when the room temperature changes. So, an efficient automatic Fan speed control system that automatically changes the speed level according to the change in environment / room temperature which is more comfortable than manual system. This project has been designed in such a way that the required components are available in local market. Therefore an indigenous low cost control scheme has been developed which can be used in real life application.
Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts

We present a novel method to reconstruct the 3D shape of a scene from several calibrated images. Our motivation is that most existing multi-view stereovision approaches require some knowledge of the scene extent and often even of its approximate geometry (e.g. visual hull). This makes these approaches mainly suited to compact objects admitting a tight enclosing box, imaged on a simple or a known background. In contrast, our approach focuses on large-scale cluttered scenes under uncontrolled imaging conditions. It first generates a quasi-dense 3D point cloud of the scene by matching keypoints across images in a lenient manner, thus possibly retaining many false matches. Then it builds an adaptive tetrahedral decomposition of space by computing the 3D Delaunay triangulation of the 3D point set. Finally, it reconstructs the scene by labeling Delaunay tetrahedra as empty or occupied, thus generating a triangular mesh of the scene. A globally optimal label assignment, as regards photo-consistency of the output mesh and compatibility with the visibility of keypoints in input images, is efficiently found as a minimum cut solution in a graph.
Geometric Scene Parsing with Hierarchical LSTM

This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-theart methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.
Safety Verification of Deep Neural Networks

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on image manipulations, such as scratches or changes to camera angle or lighting conditions, and define safety for an image classification decision in terms of invariance of the classification with respect to manipulations of the original image within a region of images that are close to it. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.
Logical Parsing from Natural Language Based on a Neural Translation Model

Semantic parsing has emerged as a significant and powerful paradigm for natural language interface and question answering systems. Traditional methods of building a semantic parser rely on high-quality lexicons, hand-crafted grammars and linguistic features which are limited by applied domain or representation. In this paper, we propose a general approach to learn from denotations based on Seq2Seq model augmented with attention mechanism. We encode input sequence into vectors and use dynamic programming to infer candidate logical forms. We utilize the fact that similar utterances should have similar logical forms to help reduce the searching space. Under our learning policy, the Seq2Seq model can learn mappings gradually with noises. Curriculum learning is adopted to make the learning smoother. We test our method on the arithmetic domain which shows our model can successfully infer the correct logical forms and learn the word meanings, compositionality and operation orders simultaneously.
Supervised Representation Learning for Audio Scene Classification

This paper investigates the use of supervised feature learning approaches for extracting relevant and discriminative features from acoustic scene recordings. Owing to the recent release of open datasets for acoustic scene classification problems, representation learning techniques can now be envisioned for solving the problem of feature extraction. This paper makes a step toward this goal by first introducing a supervised nonnegative matrix factorization SNMF. Our goal through this SNMF is to induce the matrix decomposition to carry out discriminative information in addition to the usual generative ones. We achieve this objective by augmenting the nonnegative matrix factorization optimization problem with a novel loss function related to class labels of each column of the matrix to decompose. While the scale of the datasets available is still small compared to those available in computer vision, we have studied models based on convolutional neural networks. We have analyzed the performances of these models on the DCASE-16 dataset and a corrected version of the LITIS Rouen one. Our experiments show that despite the small-scale setting, supervised feature learning is favorably competitive compared to the current state-of-the-art features. We also point out that for smaller scale dataset, SNMF is indeed slightly less prone to overfitting than convolutional neural networks. While the performances of these learned features are interesting per se, a deeper analysis of their behavior in the acoustic scene problem context raises open and difficult questions that we believe, need to be addressed for further performance breakthroughs.
Evolution of Communication Technologies for Smart Grid applications

The idea of Smart Grid has started to evolve more rapidly with the enhancement in Communication Technologies. Two way communication is a key aspect in realizing Smart Grids and is easily possible with the help of modern day advancements in both wired and wireless communication technologies. This paper discusses some of the major communication technologies which include IEEE specified ZigBee, WiMAX and Wireless LAN (Wi-Fi) technologies, GSM 3G/4G Cellular, DASH 7 and PLC (Power Line Communications), with special focus on their applications in Smart Grids. The Smart Grid environments and domains such as Home Area Automation, Substation Automation, Automated Metering Infrastructure, Vehicle-to-Grid Communications, etc. are considered as priority areas for developing smarter grids. The advancements, challenges and the opportunities present in these priority areas are discussed in this paper.