nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Detection and Classification of Brain Hemorrhages

Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing images of different parts of the human body for a quick and accurate diagnosis. In this paper, the aforementioned approach is followed to detect whether a brain hemorrhage exists or not in a Computed Topography (CT) scans of the brain. Moreover, the type of the hemorrhage is identified. The implemented system consists of several stages that include image preprocessing, image segmentation, feature extraction, and classification. The results of the conducted experiments are very promising. A recognition rate of 100% is attained for detecting whether a brain hemorrhage exists or not. For the hemorrhage type classification, more than 92% accuracy is achieved. brain hemorrhage, brain ct scans, machine learning, image processing, image segmentation
Affordances and Limitations of Immersive Participatory Augmented Reality Simulations for Teaching and Learning

The purpose of this study was to document how teachers and students describe and comprehend the ways in which participating in an augmented reality (AR) simulation aids or hinders teaching and learning. Like the multi-user virtual environment (MUVE) interface that underlies Internet games, AR is a good medium for immersive collaborative simulation, but has different strengths and limitations than MUVEs. Within a designbased research project, the researchers conducted multiple qualitative case studies across two middle schools (6th and 7th grade) and one high school (10th grade) in the northeastern United States to document the affordances and limitations of AR simulations from the student and teacher perspective. The researchers collected data through formal and informal interviews, direct observations, web site posts, and site documents. Teachers and students reported that the technology-mediated narrative and the interactive, situated, collaborative problem solving affordances of the AR simulation were highly engaging, especially among students who had previously presented behavioral and academic challenges for the teachers. However, while the AR simulation provided potentially transformative added value, it simultaneously presented unique technological, managerial, and cognitive challenges to teaching and learning.
Learning Fair Classifiers

Automated data-driven decision systems are ubiquitous across a wide variety of online services, from online social networking and ecommerce to e-government. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead to user discrimination, even in the absence of intent, leading to a lack of fairness, i.e., their outcomes have a disproportionally large adverse impact on particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers in a principled manner, by leveraging a novel intuitive measure of decision boundary (un)fairness. Then, we instantiate this mechanism on two well-known classifiers: logistic regression and support vector machines. Experiments on both synthetic and real-world data show that our mechanism allows for a finegrained control of the level of fairness, often at a minimal cost in terms of accuracy, and it provides more flexibility than alternatives.
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries – there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser improves over strong baselines.
Phialemoniopsis endophytica sp. nov., a new species of endophytic fungi from Luffa cylindrica in Henan, China

During a survey of endophytic fungi in the cucurbit plants collected from Henan, China, a new species, Phialemoniopsis endophytica was isolated from the lower stem of Luffa cylindrica. It differs from other Phialemoniopsis species by its cylindrical to flask-shaped phialides, falcate conidia with blunt ends, ostiolate pycnidium-like conidiomata without marginal setae and ellipsoidal chlamydospores. Multi-locus (ITS, LSU, ACT, and TUB) phylogenetic analysis confirmed that P. endophytica is distinct from other species. A synopsis of the morphological characters of the new species is provided.
Dynamic program slicing methods

A dynamic program slice is that part of a program that ‘‘affects’’ the computation of a variable of interest during program execution on a specific program input. Dynamic program slicing refers to a collection of program slicing methods that are based on program execution and may significantly reduce the size of a program slice because run-time information, collected during program execution, is used to compute program slices. Dynamic program slicing was originally proposed only for program debugging, but its application has been extended to program comprehension, software testing, and software maintenance. Different types of dynamic program slices, together with algorithms to compute them, have been proposed in the literature. In this paper we present a classification of existing dynamic slicing methods and discuss the algorithms to compute dynamic slices. In the second part of the paper, we compare the existing methods of dynamic slice computation.  1998 Elsevier Science B.V. All rights reserved.
Load balancing and OpenMP implementation of nested parallelism

Many problems have multiple layers of parallelism. The outer-level may consist of few and coarse-grained tasks. Next, each of these tasks may also be rich in parallelism, and be split into a number of fine-grained tasks, which again may consist of even finer subtasks, and so on. Here we argue and demonstrate by examples that utilizing multiple layers of parallelism may give much better scaling than if one restricts oneself to only one level of parallelism. Two non-trivial issues for multi-level parallelism are load balancing and implementation. In this paper we provide an algorithm for finding good distributions of threads to tasks and discuss how to implement nested parallelism in OpenMP.
Deep neural network based spectral feature mapping for robust speech recognition

Automatic speech recognition (ASR) systems suffer from performance degradation under noisy and reverberant conditions. In this work, we explore a deep neural network (DNN) based approach for spectral feature mapping from corrupted speech to clean speech. The DNN based mapping substantially reduces interference and produces estimated clean spectral features for ASR training and decoding. We experiment with several different feature mapping approaches and demonstrate that a DNN trained to predict clean log filterbank coefficients from noisy spectrogram directly can be extremely effective. The experiments show that the ASR systems with these cleaned features perform well under joint noisy and reverberant conditions, and achieve the state-of-the-art results on the CHiME-2 corpus with stereo (corrupted and clean) data.
Shape Based Image Retrieval : A Review

Content Based Image Retrieval (CBIR) system is an approach to search images and retrieve relevant images from image databases using visual content (information) of an image. In today’s scenario, CBIR receives a query object as input and retrieves similar objects as output from an image database according to a similarity/distance measure. Generally, color, texture, shapes or any combinations of them could be used as visual contents (feature) for image retrieval. Among them, shape is one of the most important features used in CBIR. While performing Shape based retrieval, it involves three primary issues in retrieving images namely Shape Representation, Shape Matching (Similarity Measures) and Shape Indexing. Also, it is important that the descriptors need to extract the characteristics of objects being robust to image rotation, scale changes, illumination variation, occlusion, noises and change of views etc. Motivated by the above factors, this paper gives a short review involving Geometrical and Structural Shape Representation of descriptors and a few Shape Matching algorithms. Keywords-shape descriptors; similarity measures; geometric matching; structural matching
Building an Ontology of Cyber Security

Situation awareness depends on a reliable perception of the environment and comprehension of its semantic structures. In this respect, cyberspace presents a unique challenge to the situation awareness of users and analysts, since it is a unique combination of human and machine elements, whose complex interactions occur in a global communication network. Accordingly, we outline the underpinnings of an ontology of secure operations in cyberspace, presenting the ontology framework and providing two modeling examples. We make the case for adopting a rigorous semantic model of cyber security to overcome the current limits of the state of the art.  cyber security, ontology, situation awareness,
Application of TextRank Algorithm for Credibility Assessment

In this article we examine the use of Text Rank algorithm for identifying web content credibility. Text Rank has come to be a widely applied method for automated text summarization. In our research we apply it to see how well does it fare in recognizing credible statements from a given corpus. So far, research into use of NLP algorithms in credibility assessment was focused more on extracting the most informative statements, or dealing with recognizing the relation between claims within a document. In our paper, we use a collection of 100 websites reviewed by human subjects in regard to their credibility, therefore allowing us to check the algorithm's performance in this task. The data collected showed that the Text Rank algorithm can be used for recognizing credibility on the level of aggregated statement credibility.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion

We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences. It then recovers the missing scenes from the clean parts using the proposed "TECROMAC" (TEmporally Contiguous RObust MAtrix Completion) objective. The objective function balances temporal smoothness with a low rank solution while staying close to the original observations. The matrix whose the rows are pixels and columns are days corresponding to the image, has low-rank because the pixels reflect land-types such as vegetation, roads and lakes and there are relatively few variations as a result. We provide efficient optimization algorithms for TECROMAC, so we can exploit images containing millions of pixels. Empirical results on real satellite image sequences, as well as simulated data, demonstrate that our approach is able to recover underlying images from heavily cloud-contaminated observations.
Automated and safe vulnerability assessment

As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%
Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.
Digital photography with flash and no-flash image pairs

Digital photography has made it possible to quickly and easily take a pair of images of low-light environments: one with flash to capture detail and one without flash to capture ambient illumination. We present a variety of applications that analyze and combine the strengths of such flash/no-flash image pairs. Our applications include denoising and detail transfer (to merge the ambient qualities of the no-flash image with the high-frequency flash detail), white-balancing (to change the color tone of the ambient image), continuous flash (to interactively adjust flash intensity), and red-eye removal (to repair artifacts in the flash image). We demonstrate how these applications can synthesize new images that are of higher quality than either of the originals.
Exploring the Role of Prior Beliefs for Argument Persuasion

Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one’s stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader-level factors: political and religious ideology. We find that prior beliefs affected by these reader-level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.
A fast and efficient sift detector using the mobile GPU

Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.
Android Code Protection via Obfuscation Techniques: Past, Present and Future Directions

Mobile devices have become ubiquitous due to centralization of private user information, contacts, messages and multiple sensors. Google Android, an open-source mobile Operating System (OS), is currently the market leader. Android popularity has motivated the malware authors to employ set of cyber attacks leveraging code obfuscation techniques. Obfuscation is an action that modifies an application (app) code, preserving the original semantics and functionality to evade anti-malware. Code obfuscation is a contentious issue. Theoretical code analysis techniques indicate that, attaining a verifiable and secure obfuscation is impossible. However, obfuscation tools and techniques are popular both among malware developers (to evade anti-malware) and commercial software developers (protect intellectual rights). We conducted a survey to uncover answers to concrete and relevant questions concerning Android code obfuscation and protection techniques. The purpose of this paper is to review code obfuscation and code protection practices, and evaluate efficacy of existing code de-obfuscation tools. In particular, we discuss Android code obfuscation methods, custom app protection techniques, and various de-obfuscation methods. Furthermore, we review and analyze the obfuscation techniques used by malware authors to evade analysis efforts. We believe that, there is a need to investigate efficiency of the defense techniques used for code protection. This survey would be beneficial to the researchers and practitioners, to understand obfuscation and de-obfuscation techniques to propose novel solutions on Android.
A multiscale retinex for bridging the gap between color images and the human observation of scenes

Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field. We analyze the effective receptive field in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to suggestions for ways to address its tendency to be too small.
Visual pattern discovery for architecture image classification and product image search

Many objects have repetitive elements, and finding repetitive patterns facilitates object recognition and numerous applications. We devise a representation to describe configurations of repetitive elements. By modeling spatial configurations, visual patterns are more discriminative than local features, and are able to tackle with object scaling, rotation, and deformation. We transfer the pattern discovery problem into finding frequent subgraphs from a graph, and exploit a graph mining algorithm to solve this problem. Visual patterns are then exploited in architecture image classification and product image retrieval, based on the idea that visual pattern can describe elements conveying architecture styles and emblematic motifs of brands. Experimental results show that our pattern discovery approach has promising performance and is superior to the conventional bag-of-words approach.
Learning to Forget: Continual Prediction with LSTM

Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive forget gate that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.