nils-herrmann/clean_scirepeval huggingface dataset:
Variational Knowledge Graph Reasoning

Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (DIVA) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, DIVA is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.
Employing Program Semantics for Malware Detection

In recent years, malware has emerged as a critical security threat. In addition, malware authors continue to embed numerous anti-detection features to evade the existing malware detection approaches. Against this advanced class of malicious programs, dynamic behavior-based malware detection approaches outperform the traditional signature-based approaches by neutralizing the effects of obfuscation and morphing techniques. The majority of dynamic behavior detectors rely on system-calls to model the infection and propagation dynamics of malware. However, these approaches do not account an important anti-detection feature of modern malware, i.e., systemcall injection attack. This attack allows the malicious binaries to inject irrelevant and independent system-calls during the program execution thus modifying the execution sequences defeating the existing system-call-based detection. To address this problem, we propose an evasion-proof solution that is not vulnerable to system-call injection attacks. Our proposed approach characterizes program semantics using asymptotic equipartition property (AEP) mainly applied in information theoretic domain. The AEP allows us to extract information-rich call sequences that are further quantified to detect the malicious binaries. Furthermore, the proposed detection model is less vulnerable to call-injection attacks as the discriminating components are not directly visible to malware authors. We run a thorough set of experiments to evaluate our solution and compare it with the existing system-call-based malware detection techniques. The results demonstrate that the proposed solution is effective in identifying real malware instances.
Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user’s needs, with good levels of quality and completeness as highlighted in the experimental results.
Three Spaces of Spatial Cognition

All our lives, we interact with the space around us, whether we are finding our way to a remote cabana in an exotic tropical isle or reaching for a ripe mango on the tree beside the cabana or finding a comfortable position in the hammock to snack after the journey. Each of these natural situations is experienced differently, and as a consequence, each is conceptualized differently. Our knowledge of space, unlike geometry or physical measurements of space, is constructed out of the things in space, not space itself. Mental spaces are schematized, eliminating detail and simplifying features around a framework consisting of elements and the relations among them. Our research suggests that which elements and spatial relations are included and how they are schematized varies with the space in ways that reflect our experience in the space. The space of navigation is too large to be seen from a single place (short of flying over it, but that is a different experience). To find our way in a large environment requires putting together information from different views or different sources. For the most part, the space of navigation is conceptualized as a two-dimensional plane, like a map. Maps, too, are schematized, yet they differ in significant ways from mental representations of space. The space around the body stands in contrast to the space of navigation. It can be seen from a single place, given rotation in place. It is the space of immediate action, our own or the things around us. It is also conceptualized schematically, but in three dimensions. Finally, there is the space of our own bodies. This space is the space of our own actions and our own sensations, experienced from the inside as well as the outside. It is schematized in terms of our limbs. Knowledge of these three spaces, that is, knowledge of the relative locations of the places in navigation space that are critical to our lives, knowledge of the space we are currently interacting with, and knowledge of the space of our bodies, is essential to finding our way in the world, to fulfilling our needs, and to avoiding danger, in short, necessary to survival.
Gray-Box Adversarial Training

Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbations can only be crafted using fast and simple methods (e.g., gradient ascent). However, it is shown that adversarial training converges to a degenerate minimum, where the model appears to be robust by generating weaker adversaries. As a result, the models are vulnerable to simple black-box attacks. In this paper we, (i) demonstrate the shortcomings of existing evaluation policy, (ii) introduce novel variants of white-box and black-box attacks, dubbed “gray-box adversarial attacks” based on which we propose novel evaluation method to assess the robustness of the learned models, and (iii) propose a novel variant of adversarial training, named “Graybox Adversarial Training” that uses intermediate versions of the models to seed the adversaries. Experimental evaluation demonstrates that the models trained using our method exhibit better robustness compared to both undefended and adversarially trained models.
3-D sound for virtual reality and multimedia

Multimedia für Insider von Ron Wodaski Übersetzung: Judith Muhr Das Buch vermittelt Ihnen einen anschaulichen und direkten Einblick in die Multimedia-Welt. Sie erleben an vorderster Front neue Produkte, mit denen Sie Ihren PC auf innovative und aufregende Weise ganz neu einsetzen können. Sie entdecken, um was es bei Grafiken, Sound, MIDI und Multmedia-Präsentationen wirklich geht. Lernen Sie mit diesem Buch die neuesten Technologien im Bereich Multimedia kennen, einschließlich Video für Windows und Quicktime für Windows. Mit CD. 1995, 699 Seiten, geb., ‘Sams’, öS 585,--
Combining static and dynamic analysis for the detection of malicious documents

The widespread adoption of the PDF format for document exchange has given rise to the use of PDF files as a prime vector for malware propagation. As vulnerabilities in the major PDF viewers keep surfacing, effective detection of malicious PDF documents remains an important issue. In this paper we present MDScan, a standalone malicious document scanner that combines static document analysis and dynamic code execution to detect previously unknown PDF threats. Our evaluation shows that MDScan can detect a broad range of malicious PDF documents, even when they have been extensively obfuscated.
In Teacher We Trust : Deep Network Compression for Pedestrian Detection

Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process, we introduce a higher-dimensional hint layer to increase information flow. We also estimate the uncertainty in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally, we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection. For only a 2.8% increase in miss rate, we have succeeded in training a student network that is 8 times faster and 21 times smaller than the teacher network.
A BL-CSC Converter-Fed BLDC Motor Drive With Power Factor Correction

This paper presents a power factor correction (PFC)-based bridgeless canonical switching cell (BL-CSC) converter-fed brushless dc (BLDC) motor drive. The proposed BL-CSC converter operating in a discontinuous inductor current mode is used to achieve a unity power factor at the ac mains using a single voltage sensor. The speed of the BLDC motor is controlled by varying the dc bus voltage of the voltage source inverter (VSI) feeding the BLDC motor via a PFC converter. Therefore, the BLDC motor is electronically commutated such that the VSI operates in fundamental frequency switching for reduced switching losses. Moreover, the bridgeless configuration of the CSC converter offers low conduction losses due to partial elimination of diode bridge rectifier at the front end. The proposed configuration shows a considerable increase in efficiency as compared with the conventional scheme. The performance of the proposed drive is validated through experimental results obtained on a developed prototype. Improved power quality is achieved at the ac mains for a wide range of control speeds and supply voltages. The obtained power quality indices are within the acceptable limits of IEC 61000-3-2.
The relationship between parental substance abuse and child maltreatment: findings from the Ontario Health Supplement.

This study examined the relationship between reported exposure to child abuse and a history of parental substance abuse (alcohol and drugs) in a community sample in Ontario, Canada.



The sample consisted of 8472 respondents to the Ontario Mental Health Supplement (OHSUP), a comprehensive population survey of mental health. The association of self-reported retrospective childhood physical and sexual abuse and parental histories of drug or alcohol abuse was examined.



Rates of physical and sexual abuse were significantly higher, with a more than twofold increased risk among those reporting parental substance abuse histories. The rates were not significantly different between type or severity of abuse. Successively increasing rates of abuse were found for those respondents who reported that their fathers, mothers or both parents had substance abuse problems; this risk was significantly elevated for both parents compared to father only with substance abuse problem.



Parental substance abuse is associated with a more than twofold increase in the risk of exposure to both childhood physical and sexual abuse. While the mechanism for this association remains unclear, agencies involved in child protection or in treatment of parents with substance abuse problems must be cognizant of this relationship and focus on the development of interventions to serve these families.
Takagi-Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory to model and predict skin permeability. Using a previously published database containing 140 compounds, a rule-based Takagi–Sugeno fuzzy model is shown to predict skin permeability of compounds using octanol-water partition coefficient, molecular weight, and temperature as inputs. Model performance was estimated using a cross-validation approach. In addition, 10 data points were removed prior to model development for additional testing with new data. The fuzzy model is compared to a regression model for the same inputs using both R2 and root mean square error measures. The quality of the fuzzy model is also compared with previously published models. The statistical analysis demonstrates that the fuzzy model performs better than the regression model with identical data and validation protocols. The prediction quality for this model is similar to others that were published. The fuzzy model provides insights on the relationships between lipophilicity, molecular weight, and temperature on percutaneous penetration. This model can be used as a tool for rapid determination of initial estimates of skin permeability.
Word Sense Disambiguation with Neural Language Models

Determining the intended sense of words in text – word sense disambiguation (WSD) – is a long-standing problem in natural language processing. In this paper, we present WSD algorithms which use neural network language models to achieve state-of-the-art precision. Each of these methods learns to disambiguate word senses using only a set of word senses, a few example sentences for each sense taken from a licensed lexicon, and a large unlabeled text corpus. We classify based on cosine similarity of vectors derived from the contexts in unlabeled query and labeled example sentences. We demonstrate state-of-the-art results when using the WordNet sense inventory, and significantly better than baseline performance using the New Oxford American Dictionary inventory. The best performance was achieved by combining an LSTM language model with graph label propagation.
The Fast Downward Planning System

Fast Downward is a classical planning system based on heuris tic search. It can deal with general deterministic planning problems encoded in the propos itional fragment of PDDL2.2, including advanced features like ADL conditions and effects and deriv ed predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a pro gression planner, searching the space of world states of a planning task in the forward direct ion. However, unlike other PDDL planning systems, Fast Downward does not use the propositional P DDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks , which makes many of the implicit constraints of a propositi nal planning task explicit. Exploiting this alternative representatio n, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic fun ction, called thecausal graph heuristic , which is very different from traditional HSP-like heuristi cs based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s app roach to solving multi-valued planning tasks. We extend our earlier discussion of the caus al graph heuristic to tasks involving axioms and conditional effects and present some novel techn iques for search control that are used within Fast Downward’s best-first search algorithm: preferred operatorstransfer the idea of helpful actions from local search to global best-first search, deferred evaluationof heuristic functions mitigates the negative effect of large branching factors on earch performance, and multi-heuristic best-first searchcombines several heuristic evaluation functions within a s ingle search algorithm in an orthogonal way. We also describe efficient data structu es for fast state expansion ( successor generatorsandaxiom evaluators ) and present a new non-heuristic search algorithm called focused iterative-broadening search , which utilizes the information encoded in causal graphs in a ovel way. Fast Downward has proven remarkably successful: It won the “ classical” (i. e., propositional, non-optimising) track of the 4th International Planning Co mpetition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions a d provide some insights about the usefulness of the new search enhancements.
Performance analysis of the ROCOF and Vector Shift methods using a dynamic protection modelling approach

Due to the continually increasing levels of penetration of distributed generation the correct operation of Loss-Of-Mains protection is of prime importance. Many UK utilities report persistent problems relating to incorrect operation of the ROCOF and Vector Shift methods which are currently the most commonly applied methods for Loss-Of-Mains (LOM) detection. The main focus of this paper is to demonstrate the problems associated with these methods through detailed dynamic modelling of existing available relays. The ability to investigate the transient response of the LOM protection to various system events highlights the main weaknesses of the existing methods, and more importantly, provides the means of quantitative analysis and better understanding of these weaknesses. Consequently, the dynamic analysis of the protective algorithms supports the identification of best compromise settings and gives insight to the future areas requiring improvement.
Detecting Cancer Metastases on Gigapixel Pathology Images

Each year, the treatment decisions for more than 230, 000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100×100 pixels in gigapixel microscopy images sized 100, 000×100, 000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.
Efficient Neural Architecture Search via Parameter Sharing

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. ENAS constructs a large computational graph, where each subgraph represents a neural network architecture, hence forcing all architectures to share their parameters. A controller is trained with policy gradient to search for a subgraph that maximizes the expected reward on a validation set. Meanwhile a model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Sharing parameters among child models allows ENAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On Penn Treebank, ENAS discovers a novel architecture that achieves a test perplexity of 56.3, on par with the existing state-of-the-art among all methods without post-training processing. On CIFAR-10, ENAS finds a novel architecture that achieves 2.89% test error, which is on par with the 2.65% test error of NASNet (Zoph et al., 2018).
Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.
Two-Stream Convolutional Networks for Action Recognition in Videos

We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to incorporate into the network design aspects of the best performing hand-crafted features. Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multitask learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it matches the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification.