nils-herrmann/clean_scirepeval huggingface dataset:
ACCURACY ANALYSIS FOR DEM AND ORTHOIMAGES DERIVED FROM SPOT HRS STEREO DATA WITHOUT USING GCP

ISPRS and CNES announced the HRS (High Resolution Stereo)  9 test areas throughout the world have been selected for this program. One of the test sites is located in Bavaria, Germany, for which the PI comes from DLR. For a second region, which is situated in Catalonia – Barcelona and surroundings – DLR has the role of a Co-Investigator. The goal is to derive a DEM from the along-track stereo data of the SPOT HRS sensor and to assess the accuracy by comparison with ground control points and DEM data of superior quality. For the derivation of the DEM, the stereo processing software, developed at DLR for the MOMS-2P three line stereo camera is used. As a first step, the interior and exterior orientation of the camera, delivered as ancillary data (DORIS and ULS) are extracted. According to CNES these data should lead to an absolute orientation accuracy of about 30 m. No bundle block adjustment with ground control is used in the first step of the photogrammetric evaluation. A dense image matching, using very dense positions as kernel centers provides the parallaxes. The quality of the matching is controlled by forward and backward matching of the two stereo partners using the local least squares matching method. Forward intersection leads to points in object space which are then interpolated to a DEM of the region in a regular grid. Additionally, orthoimages are generated from the images of the two looking directions. The orthoimage and DEM accuracy is determined by using the ground control points and the available DEM data of superior accuracy (DEM derived from laser data and/or classical airborne photogrammetry). DEM filtering methods are applied and a comparison to SRTM-DEMs is performed. It is shown that a fusion of the DEMs derived from optical and radar data leads to higher accuracies. In the second step ground control points are used for bundle adjustment to improve the exterior orientation and the absolute accuracy of the SPOT-DEM.
Digital Forensics as a Big Data Challenge

Digital Forensics, as a science and part of the forensic sciences, is facing new challenges that may well render established models and practices obsolete. The dimensions of potential digital evidence supports has grown exponentially, be it hard disks in desktop and laptops or solid state memories in mobile devices like smartphones and tablets, even while latency times lag behind. Cloud services are now sources of potential evidence in a vast range of investigations and network traffic also follows a growing trend and in cyber security the necessity of sifting through vast amount of data quickly is now paramount. On a higher level investigations -and intelligence analysiscan profit from sophisticated analysis of such datasets as social network structures, corpora of text to be analysed for authorship and attribution. All of the above highlights the convergence between so-called data science and digital forensics, to tack the fundamental challenge of analyse vast amount of data ("big data") in actionable time while at the same time preserving forensic principles in order for the results to be presented in a court of law. The paper, after introducing digital forensics and data science, explores the challenges above and proceed to propose how techniques and algorithms used in big data analysis can be adapted to the unique context of digital forensics, ranging from the managing of evidence via Map-Reduce to machine learning techniques for triage and analysis of big forensic disk images and network traffic dumps. In the conclusion the paper proposes a model to integrate this new paradigm into established forensic standards and best practices and tries to foresee future trends.
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

We present a robust and real-time monocular six degree of freedom relocalization system. Our system trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking 5ms per frame to compute. It obtains approximately 2m and 3 degrees accuracy for large scale outdoor scenes and 0.5m and 5 degrees accuracy indoors. This is achieved using an efficient 23 layer deep convnet, demonstrating that convnets can be used to solve complicated out of image plane regression problems. This was made possible by leveraging transfer learning from large scale classification data. We show that the PoseNet localizes from high level features and is robust to difficult lighting, motion blur and different camera intrinsics where point based SIFT registration fails. Furthermore we show how the pose feature that is produced generalizes to other scenes allowing us to regress pose with only a few dozen training examples.
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System

In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence tagging model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL2003 benchmark datasets, to demonstrate the new system’s outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9 % and that on CoNLL-2003 dataset by 1.4 %.
Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction

Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results.
Medicinal Plants Used in the Treatment of Human Immunodeficiency Virus

Since the beginning of the epidemic, human immunodeficiency virus (HIV) has infected around 70 million people worldwide, most of whom reside is sub-Saharan Africa. There have been very promising developments in the treatment of HIV with anti-retroviral drug cocktails. However, drug resistance to anti-HIV drugs is emerging, and many people infected with HIV have adverse reactions or do not have ready access to currently available HIV chemotherapies. Thus, there is a need to discover new anti-HIV agents to supplement our current arsenal of anti-HIV drugs and to provide therapeutic options for populations with limited resources or access to currently efficacious chemotherapies. Plant-derived natural products continue to serve as a reservoir for the discovery of new medicines, including anti-HIV agents. This review presents a survey of plants that have shown anti-HIV activity, both in vitro and in vivo.
Cleverhans V0.1: an Adversarial Machine Learning Library

cleverhans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models’ performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the cleverhans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.
From TreeBank to PropBank

This paper describes our approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank. Our primary goal is the labeling of syntactic nodes with specific argument labels that preserve the similarity of roles such as the window in John broke the window and the window broke. After motivating the need for explicit predicate argument structure labels, we briefly discuss the theoretical considerations of predicate argument structure and the need to maintain consistency across syntactic alternations. The issues of consistency of argument structure across both polysemous and synonymous verbs are also discussed and we present our actual guidelines for these types of phenomena, along with numerous examples of tagged sentences and verb frames. Metaframes are introduced as a technique for handling similar frames among near− synonymous verbs. We conclude with a summary of the current status of annotation process.
An End-to-End Text-Independent Speaker Identification System on Short Utterances

In the field of speaker recognition, text-independent speaker identification on short utterances is still a challenging task, since it is rather tough to extract a robust and dicriminative speaker feature in short duration condition. This paper explores an endto-end speaker identification system, which maps utterances to a speaker identity subspace where the similarity of speakers can be measured by Euclidean distance. To be specific, we apply stacked gated recurrent unit (GRU) architectures to extract utterance-level feature. Then it is assumed that one’s various utterances can be viewed as transformations of a single object in an ideal speaker identity subspace. Based on this assumption, the residual convolution neural network (ResCNN) architecture is utilized to model the transformation, and the whole system is jointly optimized by speaker identity subspace loss. Experimental results demonstrate the effectiveness of our proposed system and superiroity over pervious methods. For example, the GRU learned feature reduces the equal error rate by 27.53% relatively and the speaker identity subspace loss further brings 7.22% relative reduction compared to softmax loss.
CAT: Credibility Analysis of Arabic Content on Twitter

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author’s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9, 000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average Fmeasure. We also conducted experiments to highlight the importance of the userbased features as opposed to the contentbased features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.
Using ICT with people with special education needs: what the literature tells us

To provide a review of the past studies on use of information and communications technology (ICT) for people with special education needs (SEN) to inform a major research project on using ICT to facilitate self-advocacy and learning for SEN learners.  Literature review, encompassing academic journals indexed in education, information science and social sciences databases, books, grey literature (including much internet-based material), and government reports. Information was gathered on the perceived benefits of ICT in SEN, and the use of some specific applications with people having various conditions. A number of usability studies, mainly Internet and web technologies, are also outlined.  Although the literature shows a great number of ICT initiatives for people with all kinds of disabilities, there has been a surprising lack of research into the usability of the various applications developed, and even less concerning those with learning difficulties. The review of existing literature indicates a lack of attention to the application of ICT for people with SEN, compared to the other groups of disabled people such as visually impaired.  Findings highlight the need for more research on usability aspects of current and potential applications of ICT for people with SEN.
OpenSGX: An Open Platform for SGX Research

Hardware technologies for trusted computing, or trusted execution environments (TEEs), have rapidly matured over the last decade. In fact, TEEs are at the brink of widespread commoditization with the recent introduction of Intel Software Guard Extensions (Intel SGX). Despite such rapid development of TEE, software technologies for TEE significantly lag behind their hardware counterpart, and currently only a select group of researchers have the privilege of accessing this technology. To address this problem, we develop an open source platform, called OpenSGX, that emulates Intel SGX hardware components at the instruction level and provides new system software components necessarily required for full TEE exploration. We expect that the OpenSGX framework can serve as an open platform for SGX research, with the following contributions. First, we develop a fully functional, instruction-compatible emulator of Intel SGX for enabling the exploration of software/hardware design space, and development of enclave programs. OpenSGX provides a platform for SGX development, meaning that it provides not just emulation but also operating system components, an enclave program loader/packager, an OpenSGX user library, debugging, and performance monitoring. Second, to show OpenSGX’s use cases, we applied OpenSGX to protect sensitive information (e.g., directory) of Tor nodes and evaluated their potential performance impacts. Therefore, we believe OpenSGX has great potential for broader communities to spark new research on soon-to-becommodity Intel SGX.
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs

We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task.
Empathy in Traditional and Cyber Bullying / Victimization Involvement From Early to Middle Adolescence : A Cross Sectional Study

The purpose of this study was to investigate the relationship of affective and cognitive empathy with traditional and cyber bullying/victimization experiences among Greek participants who attended the fifth and sixth grade of elementary school (n=126), junior high school (n=140), and senior high school (n=157). Overall, results indicated negative correlations of empathy (especially affective) with traditional and cyber bullying/victimization. Negative correlations were particularly observed among elementary school participants, while affective empathy was found to be negatively related to bullying involvement, especially among girls. Overall, empathy (especially affective) may not be sufficiently developed among elementary school students, thus precluding them from understanding and caring about others' emotional state. The negative correlation of empathy and cyber-bullying among junior high school participants might be related to the characteristics of computer mediated communication. Implications of the findings are discussed.
Probabilistic Latent-Factor Database Models

We describe a general framework for modelling probabilistic databases using factorization approaches. The framework includes tensor-based approaches which have been very successful in modelling triple-oriented databases and also includes recently developed neural network models. We consider the case that the target variable models the existence of a tuple, a continuous quantity associated with a tuple, multiclass variables or count variables. We discuss appropriate cost functions with different parameterizations and optimization approaches. We argue that, in general, some combination of models leads to best predictive results. We present experimental results on the modelling of existential variables and count variables.
Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, Decision tree, Naïve Bayes and Artificial Neural Network to massive volume of healthcare data. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. For data preprocessing and effective decision making One Dependency Augmented Naïve Bayes classifier (ODANB) and naive credal classifier 2 (NCC2) are used. This is an extension of naive Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Discovery of hidden patterns and relationships often goes unexploited. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established.
Analyzing Regression Test Selection Techniques

Regression testing is a necessary but expensive maintenance activity aimed at showing that code has not been adversely affected by changes. Regression test selection techniques reuse tests from an existing test suite to test a modified program. Many regression test selection techniques have been proposed; however, it is difficult to compare and evaluate these techniques because they have different goals. This paper outlines the issues relevant to regression test selection techniques, and uses these issues as the basis for a framework within which to evaluate the techniques. We illustrate the application of our framework by using it to evaluate existing regression test selection techniques. The evaluation reveals the strengths and weaknesses of existing techniques, and highlights some problems that future work in this area should address.
Universal Artificial Intelligence

The dream of creating artificial devices that reach or outperform human intelligence is many centuries old. In this talk I present an elegant parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment that possesses essentially all aspects of rational intelligence. The theory reduces all conceptual AI problems to pure computational questions. The necessary and sufficient ingredients are Bayesian probability theory; algorithmic information theory; universal Turing machines; the agent framework; sequential decision theory; and reinforcement learning, which are all important subjects in their own right. I also present some recent approximations, implementations, and applications of this modern top-down approach to AI. Marcus Hutter 3 Universal Artificial Intelligence Overview Goal: Construct a single universal agent that learns to act optimally in any environment. State of the art: Formal (mathematical, non-comp.) definition of such an agent.  Well-defines AI. Formalizes rational intelligence. Formal “solution” of the AI problem in the sense of ... =⇒ Reduces the conceptional AI problem to a (pure) computational problem.  Mathematical optimality proofs and some experimental results. Marcus Hutter 4 Universal Artificial Intelligence
Deriving a representative vector for ontology classes with instance word vector embeddings

Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected. Ontology classes are sets of homogeneous instance objects that can be converted to a vector space by word vector embeddings. This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space. We start by deriving five candidate vectors which are then used to train a machine learning model that would calculate a representative vector for the class. We show that our methodology out-performs the traditional mean and median vector representations.