nils-herrmann/clean_scirepeval huggingface dataset:
Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.
Personal Learning Environments: Challenging the Dominant Design of Educational Systems

Current systems used in education follow a consistent design pattern, one that is not supportive of lifelong learning or personalization, is asymmetric in terms of user capability, and which is disconnected from the global ecology of Internet services. In this paper we propose an alternative design pattern for educational systems that emphasizes symmetric connections with a range of services both in formal and informal learning, work, and leisure, and identify strategies for implementation and experimentation.  Challenging the dominant design of educational systems Journal of e-Learning and Knowledge Society — Vol. 3, n. 2, june 2007 (pp. 27-38 ) Je-LKS Methodologies and scenarios Scott Wilson, Prof. Oleg Liber, Mark Johnson, Phil Beauvoir, Paul Sharples & Colin Milligan University of Bolton, UK; Scott.Bradley.Wilson@gmail. com, o.liber@bolton.ac.uk, m.w.Johnson@bolton.ac.uk, p.beauvoir@bolton.ac.uk, p.sharples@bolton.ac.uk, colin.
Visualization-Based Active Learning for Video Annotation

Video annotation is an effective way to facilitate content-based analysis for videos. Automatic machine learning methods are commonly used to accomplish this task. Among these, active learning is one of the most effective methods, especially when the training data cost a great deal to obtain. One of the most challenging problems in active learning is the sample selection. Various sampling strategies can be used, such as uncertainty, density, and diversity, but it is difficult to strike a balance among them. In this paper, we provide a visualization-based batch mode sampling method to handle such a problem. An iso-contour-based scatterplot is used to provide intuitive clues for the representativeness and informativeness of samples and assist users in sample selection. A semisupervised metric learning method is incorporated to help generate an effective scatterplot reflecting the high-level semantic similarity for visual sample selection. Moreover, both quantitative and qualitative evaluations are provided to show that the visualization-based method can effectively enhance sample selection in active learning.
Automatic keyphrase extraction: An overview of the state of the art

Keyphrases are useful for a variety of tasks in information retrieval systems and natural language processing, such as text summarization, automatic indexing, clustering/classification, ontology learning and building and conceptualizing particular knowledge domains, etc. However, assigning these keyphrases manually is time consuming and expensive in term of human resources. Therefore, there is a need to automate the task of extracting keyphrases. A wide range of techniques of keyphrase extraction have been proposed, but they are still suffering from the low accuracy rate and poor performance. This paper presents a state of the art of automatic keyphrase extraction approaches to identify their strengths and weaknesses. We also discuss why some techniques perform better than others and how can we improve the task of automatic keyphrase extraction.
Modeling and Management of Firewall Policies

Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires thorough analysis of the relationship between this rule and other rules in order to determine the proper order of this rule and commit the updates. In this paper we present a set of techniques and algorithms that provide automatic discovery of firewall policy anomalies to reveal rule conflicts and potential problems in legacy firewalls, and anomaly-free policy editing for rule insertion, removal, and modification. This is implemented in a user-friendly tool called ¿Firewall Policy Advisor. The Firewall Policy Advisor significantly simplifies the management of any generic firewall policy written as filtering rules, while minimizing network vulnerability due to firewall rule misconfiguration.
Detecting hip fractures with radiologist-level performance using deep neural networks

We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of clinical x-rays (≈53,000 studies) and can be applied to clinical data, automatically excluding inappropriate and technically unsatisfactory studies. We demonstrate diagnostic performance equivalent to a human radiologist and an area under the ROC curve of 0.994. Translated to clinical practice, such a system has the potential to increase the efficiency of diagnosis, reduce the need for expensive additional testing, expand access to “expert level” medical image interpretation, and improve overall patient outcomes.
Multipath Issues in Unicast and Multicast Next-Hop Selection

Status of this Memo This memo provides information for the Internet community. It does not specify an Internet standard of any kind. Distribution of this memo is unlimited.  Various routing protocols, including Open Shortest Path First (OSPF) and Intermediate System to Intermediate System (ISIS), explicitly allow "Equal-Cost Multipath" (ECMP) routing. Some router implementations also allow equal-cost multipath usage with RIP and other routing protocols. The effect of multipath routing on a forwarder is that the forwarder potentially has several next-hops for any given destination and must use some method to choose which next-hop should be used for a given data packet.
Hypertableau Reasoning for Description Logics

We present a novel reasoning calculus for the Description Logic SHOIQ—a knowledge representation formalism with applications in areas such as the Semantic Web. In order to reduce the nondeterminism due to general inclusion axioms, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to ensure termination. To prevent the calculus from generating large models, we introduce anywhere pairwise blocking. We also present a new way of ensuring termination while allowing for nomials, inverse roles, and number restrictions—a combination of DL constructs that has proven notoriously difficult to handle. Our preliminary implementation shows significant performance improvements on several well-known ontologies. To the best of our knowledge, our reasoner is currently the only one that can classify the original version of the GALEN terminology.
Security in mobile ad hoc networks: challenges and solutions

Security has become a primary concern in order to provide protected communication between mobile nodes in a hostile environment. Unlike the wireline networks, the unique characteristics of mobile ad hoc networks pose a number of nontrivial challenges to security design, such as open peer-to-peer network architecture, shared wireless medium, stringent resource constraints, and highly dynamic network topology. These challenges clearly make a case for building multifence security solutions that achieve both broad protection and desirable network performance. In this article we focus on the fundamental security problem of protecting the multihop network connectivity between mobile nodes in a MANET. We identify the security issues related to this problem, discuss the challenges to security design, and review the state-of-the-art security proposals that protect the MANET link- and network-layer operations of delivering packets over the multihop wireless channel. The complete security solution should span both layers, and encompass all three security components of prevention, detection, and reaction.
Deep Reinforcement Learning in Pac-man

In recent years, major advances in Artificial Intelligence have been made. Developments in the field of parallelism and distributed computing have led to significant results in artificial intelligence. At the same time, progress in Reinforcement Learning has brought Artificial Intelligence a step closer to general intelligence. In this paper, a model capable of learning to play the game Pac-man is proposed. The technique of Deep Q-learning is applied, whereby Reinforcement Learning methods are combined with Deep Learning. The model finds an action-selection policy, on the basis of observations of the game grid and game score, without additional knowledge of the inner workings of the game. The study strengthens the idea that Deep Reinforcement Learning is a valuable method for finding an action-selection policy for dynamic stochastic systems.
Market Time Series Prediction with Recurrent Neural Networks

We used echo state networks, a subclass of recurrent neural networks, to predict stock prices of the S&P 500. Our network outperformed a Kalman filter, predicting more of the higher frequency fluctuations in stock price. The Challenge of Time Series Prediction Learning from past history is a fudamentality ill-posed. A model may fit past data well but not perform well when presented with new inputs. With recurrent neural networks (RNNs), we leverage the modeling abilities of neural networks (NNs) for time series forecastings. Feedforward NNs have done well in classification tasks such as handwriting recognition, however in dynamical environments, we need techniques that account for history. In RNNs, signals passing through recurrent connections constitute an effective memory for the network, which can then use information in memory to better predict future time series values. Unfortunately, RNNs are difficult to train. Traditional techniques used with feedforward NNs such as backpropagation fail to yield acceptable performance. However, subsets of RNNs that are more amenable to training have been developed in the emerging field known as reservoir computing. In reservoir computing, the recurrent connections of the network are viewed as a fixed reservoir used to map inputs into a high dimensional, dynamical space–a similar idea to the support vector machine. With a sufficiently high dimensional space, a simple linear decode can be used to approximate any function varying with time. Two reservoir networks known as Echo State Networks (ESNs) and Liquid State Machines (LSMs) have met with success in modeling nonlinear dynamical systems [2, 4]. We focus on the former, ESN, in this project and use it to predict stock prices and compare its performance to a Kalman filter. In an ESN, only the output weights are trained . Echo State Network Implementation The state vector, x(t), of the network is governed by x(t+ 1) = f ( W u(t) +Wx(t) +W y(t) ) , (1) where f(·) = tanh(·), W in describes the weights connecting the inputs to the network, u(t) is the input vector, W describes the recurrent weights, W fb describes the feedback weights connecting the outputs back to the network, and y(t) are the outputs. The output y(t) is governed by y(t) = W z(t), where z(t) = [x(t),u(t)] is the extended state. By including the input vector, the extended state allows the network to use a linear combination of the inputs in addition to the state to form the output. ESN creation follows the procedure outlined in . Briefly, 1. Initialize network of N reservoir units with random W , W , and W .
Revealing stealthy attacks in control systems

In this paper the problem of revealing stealthy data-injection attacks on control systems is addressed. In particular we consider the scenario where the attacker performs zero-dynamics attacks on the system. First, we characterize and analyze the stealthiness properties of these attacks for linear time-invariant systems. Then we tackle the problem of detecting such attacks by modifying the system's structure. Our results provide necessary and sufficient conditions that the modifications should satisfy in order to detect the zero-dynamics attacks. The results and proposed detection methods are illustrated through numerical examples.
Development of miniaturized light endoscope-holder robot for laparoscopic surgery

We have conducted experiments with an innovatively designed robot endoscope holder for laparoscopic surgery that is small and low cost.



A compact light endoscope robot (LER) that is placed on the patient's skin and can be used with the patient in the lateral or dorsal supine position was tested on cadavers and laboratory pigs in order to allow successive modifications. The current control system is based on voice recognition. The range of vision is 360 degrees with an angle of 160 degrees . Twenty-three procedures were performed.



The tests made it possible to advance the prototype on a variety of aspects, including reliability, steadiness, ergonomics, and dimensions. The ease of installation of the robot, which takes only 5 minutes, and the easy handling made it possible for 21 of the 23 procedures to be performed without an assistant.



The LER is a camera holder guided by the surgeon's voice that can eliminate the need for an assistant during laparoscopic surgery. The ease of installation and manufacture should make it an effective and inexpensive system for use on patients in the lateral and dorsal supine positions. Randomized clinical trials will soon validate a new version of this robot prior to marketing.
Text classification with kernels on the multinomial manifold

Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.
LaNCoA: A Python toolkit for Language Networks Construction and Analysis

In this paper we describe LaNCoA, Language Networks Construction and Analysis toolkit implemented in Python. The toolkit provides various procedures for network construction from the text: on the word-level (co-occurrence networks, syntactic networks, shuffled networks), and on the subword-level (syllable networks, grapheme networks). Furthermore, we implement functions for the language networks analysis on the global and local level. The toolkit is organized in several modules that enable various aspects of language analysis: analysis of global network measures for different co-occurrence window, comparison of networks based on original and shuffled texts, comparison of networks constructed on different language levels, etc. Text manipulation methods, like corpora cleaning, lemmatization and stopwords removal, are also implemented. For the basic network representation we use available  However, language network analysis is specific and it requires implementation of additional functions and methods. That was the main motivation for this research.
A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors

As a mobile phone has various advanced functionalities or features, usability issues are increasingly challenging. Due to the particular characteristics of a mobile phone, typical usability evaluation methods and heuristics, most of which are relevant to a software system, might not effectively be applied to a mobile phone. Another point to consider is that usability evaluation activities should help designers find usability problems easily and produce better design solutions. To support usability practitioners of the mobile phone industry, we propose a framework for evaluating the usability of a mobile phone, based on a multilevel, hierarchical model of usability factors, in an analytic way. The model was developed on the basis of a set of collected usability problems and our previous study on a conceptual framework for identifying usability impact factors. It has multi-abstraction levels, each of which considers the usability of a mobile phone from a particular perspective. As there are goal-means relationships between adjacent levels, a range of usability issues can be interpreted in a holistic as well as diagnostic way. Another advantage is that it supports two different types of evaluation approaches: task-based and interface-based. To support both evaluation approaches, we developed four sets of checklists, each of which is concerned, respectively, with task-based evaluation and three different interface types: Logical User Interface (LUI), Physical User Interface (PUI) and Graphical User Interface (GUI). The proposed framework specifies an approach to quantifying usability so that several usability aspects are collectively measured to give a single score with the use of the checklists. A small case study was conducted in order to examine the applicability of the framework and to identify the aspects of the framework to be improved. It showed that it could be a useful tool for evaluating the usability of a mobile phone. Based on the case study, we improved the framework in order that usability practitioners can use it more easily and consistently.
Good enough practices in scientific computing

Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.
Facilitating Cognitive Presence in Online Learning : Interaction Is Not Enough

This study assessed the depth of online learning, with a focus on the nature of online interaction in four distance education course designs. The Study Process Questionnaire was used to measure the shift in students’ approach to learning from the beginning to the end of the courses. Design had a significant impact on the nature of the interaction and whether students approached learning in a deep and meaningful manner. Structure and leadership were found to be crucial for online learners to take a deep and meaningful approach to learning.
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations

The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for time periods 1 through T, can we predict the links in time period T +1? Specifically, we look at bipartite graphs changing over time and consider matrix- and tensor-based methods for predicting links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem.
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing

Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint localization and instance segmentation. The empirical results substantiate the utility of our deep supervision scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less overfitting compared to standard end-to-end training.
P3: Toward Privacy-Preserving Photo Sharing

With increasing use of mobile devices, photo sharing services are experiencing greater popularity. Aside from providing storage, photo sharing services enable bandwidth-efficient downloads to mobile devices by performing server-side image transformations (resizing, cropping). On the flip side, photo sharing services have raised privacy concerns such as leakage of photos to unauthorized viewers and the use of algorithmic recognition technologies by providers. To address these concerns, we propose a privacy-preserving photo encoding algorithm that extracts and encrypts a small, but significant, component of the photo, while preserving the remainder in a public, standards-compatible, part. These two components can be separately stored. This technique significantly reduces the accuracy of automated detection and recognition on the public part, while preserving the ability of the provider to perform server-side transformations to conserve download bandwidth usage. Our prototype privacy-preserving photo sharing system, P3, works with Facebook, and can be extended to other services as well. P3 requires no changes to existing services or mobile application software, and adds minimal photo storage overhead.