nils-herrmann/clean_scirepeval huggingface dataset:
Non-linear Feedback Neural Network for Solution of Quadratic Programming Problems

This paper presents a recurrent neural circuit for solving quadratic programming problems. The objective is tominimize a quadratic cost function subject to linearconstraints. The proposed circuit employs non-linearfeedback, in the form of unipolar comparators, to introducetranscendental terms in the energy function ensuring fastconvergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit comparesfavorably with other proposed circuits for the same task. PSPICE simulation results arepresented for a chosen optimization problem and are foundto agree with the algebraic solution.
Making USB Great Again with USBFILTER

USB provides ubiquitous plug-and-play connectivity for a wide range of devices. However, the complex nature of USB obscures the true functionality of devices from the user, and operating systems blindly trust any physically-attached device. This has led to a number of attacks, ranging from hidden keyboards to network adapters, that rely on the user being unable to identify all of the functions attached to the host. In this paper, we present USBFILTER, which provides the first packet-level access control for USB and can prevent unauthorized interfaces from successfully connecting to the host operating system. USBFILTER can trace individual USB packets back to their respective processes and block unauthorized access to any device. By instrumenting the host’s USB stack between the device drivers and the USB controller, our system is able to filter packets at a granularity that previous works cannot — at the lowest possible level in the operating system. USBFILTER is not only able to block or permit specific device interfaces; it can also restrict interfaces to a particular application (e.g., only Skype can access my webcam). Furthermore, our experimental analysis shows that USBFILTER introduces a negligible (3-10μs) increase in latency while providing mediation of all USB packets on the host. Our system provides a level of granularity and extensibility that reduces the uncertainty of USB connectivity and ensures unauthorized devices are unable to communicate with the host.
A deep learning approach for on-site plant leaf detection

Plant diseases are the major problem in the worldwide agriculture sector. Therefore, the early detection is essential for reducing economic losses and mitigating the seriousness of the global food problem. Some fast and accurate computer-based methods have been applied to detect plant diseases. However, as far as our best knowledge, all those methodologies only accept a narrow range image, typically one or limited number of target(s) are in the image frame as their input. Thus, they are time-consuming and difficult to be applied for on-site wide range images (e.g. images or videos from stationary surveillance camera). In this paper, we propose leaf localization method from on-site wide-angle images with a deep learning approach. Our method achieves a detection performance of 78.0% in F1-measure at 2.0 fps.
Comparing Twitter Summarization Algorithms for Multiple Post Summaries

Due to the sheer volume of text generated by a micro log site like Twitter, it is often difficult to fully understand what is being said about various topics. In an attempt to understand micro logs better, this paper compares algorithms for extractive summarization of micro log posts. We present two algorithms that produce summaries by selecting several posts from a given set. We evaluate the generated summaries by comparing them to both manually produced summaries and summaries produced by several leading traditional summarization systems. In order to shed light on the special nature of Twitter posts, we include extensive analysis of our results, some of which are unexpected.
Statistical language learning

The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface:
Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network

J. Xiao, S. Baker, I. Matthews, T. Kanade, “Real-Time Combined 2D + 3D Active Appearance Models,” CVPR, pp.535-542, 2004.  N. Gourier, D. Hall, J. L. Crowley, “Estimating Face Orientation from Robust Detection of Salient Facial Features”,  2004.  G. Fanelli, M. Dantone, J. Gall, A. Jssati, L. Van Gool, “Random Forests for Real Time 3D Face Analysis,” IJCV, pp.437-458, 2013.  Y. LeCun, R. Bottou, Y. Bengio, P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proc.  1998.  A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NIPS, pp.1106-1114, 2012. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2010-0028680). Proposed Method  Conventional Head Pose Estimation Approaches
Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs) to represent target appearances, where multiple CNNs collaborate to estimate target states and determine the desirable paths for online model updates in the tree. By maintaining multiple CNNs in diverse branches of tree structure, it is convenient to deal with multi-modality in target appearances and preserve model reliability through smooth updates along tree paths. Since multiple CNNs share all parameters in convolutional layers, it takes advantage of multiple models with little extra cost by saving memory space and avoiding redundant network evaluations. The final target state is estimated by sampling target candidates around the state in the previous frame and identifying the best sample in terms of a weighted average score from a set of active CNNs. Our algorithm illustrates outstanding performance compared to the state-of-the-art techniques in challenging datasets such as online tracking benchmark and visual object tracking challenge.
Missing data management and statistical measurement of socio-economic status: application of big data

Socio-economic status measurement is an ongoing problem where different suggested measurements are given by researchers. This work investigates a socio-economic status measurement derived from natural correlations of variables which can better and meaningfully cluster African countries for the level of status. The researcher used 48 African countries socio-economic yearly time series data from 1993 to 2013 of IMF 2013 data set for data management (i.e, 2737 variables for 21 years), however, the analysis is reasonably done based on recent 14 years time series data. In data management, missing values are treated (imputed) by using regression estimates, Lagrange interpolation, linear interpolation and linear spline interpolation based on the appropriate method which best fits for the trend of data with minimum error at each time level. From principal component and factor analysis of average time series data, 7 principal factors contributed by 84 variables which explain $$70\%$$ 70 % of the variation in the data set are suggested as a socio-economic status measuring components and as a result the considered clustering methods (K-mean Method, Average linkage method, Ward’s method and Bootstrap Ward’s method) are agreed on six clusters of countries, those are statistically significant at $$95\%$$ 95 % , where as three countries each where suggested as outlier-countries made an individual cluster.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept–reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.
Knowledge-based end-to-end memory networks

End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction. Retrieval and Generative methods have been explored in this area with mixed results. A key element that is missing so far, is the incorporation of a-priori knowledge about the task at hand. This knowledge may exist in the form of structured or unstructured information. As a first step towards this direction, we present a novel approach, Knowledge based end-to-end memory networks (KB-memN2N), which allows special handling of named entities for goaloriented dialog tasks. We present results on two datasets, DSTC6 challenge dataset and dialog bAbI tasks.
Transition-based Knowledge Graph Embedding with Relational Mapping Properties

Many knowledge repositories nowadays contain billions of triplets, i.e. (head-entity, relationship, tail-entity), as relation instances. These triplets form a directed graph with entities as nodes and relationships as edges. However, this kind of symbolic and discrete storage structure makes it difficult for us to exploit the knowledge to enhance other intelligenceacquired applications (e.g. the QuestionAnswering System), as many AI-related algorithms prefer conducting computation on continuous data. Therefore, a series of emerging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with fewer parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flexible enough to tackle well with the various mapping properties of triplets, even though its authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the distinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet depending on its own weight. We carry out extensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application scenarios on several benchmark datasets. Results show that the model we proposed significantly outperforms the former ones with lower parameter complexity as TransE.
Quantifying the evolutionary dynamics of language

Human language is based on grammatical rules. Cultural evolution allows these rules to change over time. Rules compete with each other: as new rules rise to prominence, old ones die away. To quantify the dynamics of language evolution, we studied the regularization of English verbs over the past 1,200 years. Although an elaborate system of productive conjugations existed in English’s proto-Germanic ancestor, Modern English uses the dental suffix, ‘-ed’, to signify past tense. Here we describe the emergence of this linguistic rule amidst the evolutionary decay of its exceptions, known to us as irregular verbs. We have generated a data set of verbs whose conjugations have been evolving for more than a millennium, tracking inflectional changes to 177 Old-English irregular verbs. Of these irregular verbs, 145 remained irregular in Middle English and 98 are still irregular today. We study how the rate of regularization depends on the frequency of word usage. The half-life of an irregular verb scales as the square root of its usage frequency: a verb that is 100 times less frequent regularizes 10 times as fast. Our study provides a quantitative analysis of the regularization process by which ancestral forms gradually yield to an emerging linguistic rule.
Revising the Newman-Girvan Algorithm

One of the common approaches for the community detection in complex networks is the Girvan-Newman algorithm  which is based on repeated deletion of edges having the maximum edge betweenness centrality. Although widely used, it may result in different dendrograms of hierarchies of communities if there are several edges eligible for removal (see ) thus leaving an ambiguous formation of network subgroups. We will present possible ways to overcome these issues using, instead of edge betweenness computation for single edges, the group edge betweenness for subsets of edges to be subjects of removal.
Integrity and Consistency for Untrusted Services - (Extended Abstract)

A group of mutually trusting clients outsources an arbitrary computation service to a remote provider, which they do not fully trust and that may be subject to attacks. The clients do not communicate with each other and would like to verify the integrity of the stored data, the correctness of the remote computation process, and the consistency of the provider’s responses. We present a novel protocol that guarantees atomic operations to all clients when the provider is correct and fork-linearizable semantics when it is faulty; this means that all clients which observe each other’s operations are consistent, in the sense that their own operations, plus those operations whose effects they see, have occurred atomically in same sequence. This protocol generalizes previous approaches that provided such guarantees only for outsourced storage services.
Event-Centric Clustering of News Articles

Entertainity AB plans to build a news service to provide news to end-users in an innovative way. The service must include a way to automatically group series of news from different sources and publications, based on the stories they are covering. This thesis include three contributions: a survey of known clustering methods, an evaluation of human versus human results when grouping news articles in an event-centric manner, and last an evaluation of an incremental clustering algorithm to see if it is possible to consider a reduced input size and still get a sufficient result. The conclusions are that the result of the human evaluation indicates that users are different enough to warrant a need to take that into account when evaluating algorithms. It is also important that this difference is considered when conducting cluster analysis to avoid overfitting. The evaluation of an incremental event-centric algorithm shows it is desirable to adjust the similarity threshold, depending on what result one want. When running tests with different input sizes, the result implies that a short summary of a news article is a natural feature selection when performing cluster analysis.
Relations among positive parenting, children's effortful control, and externalizing problems: a three-wave longitudinal study.

In a 3-wave longitudinal study (with assessments 2 years apart) involving 186 early adolescents (M ages of approximately 9.3, 11.4, and 13.4), the hypothesis that parental warmth/positive expressivity predicts children's effortful control (EC) (a temperamental characteristic contributing to emotion regulation) 2 years later, which in turn predicts low levels of externalizing problems another 2 years later, was examined. The hypothesis that children's EC predicts parenting over time was also examined. Parents were observed interacting with their children; parents and teachers reported children's EC and externalizing problems; and children's persistence was assessed behaviorally. Children's EC mediated the relation between positive parenting and low levels of externalizing problems (whereas there was no evidence that children's EC predicted parenting).
An improved NSGA-III algorithm for feature selection used in intrusion detection

Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few effort s have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity.
Strategic Assessment of Risk and Risk Tolerance (StARRT) framework for return-to-play decision-making.

The sport medicine clinician is faced with return-to-play (RTP) decisions for every patient who wants to return to activity. The complex interaction of factors related to history, physical examination, testing, activity and baseline characteristics can make RTP decision-making challenging. Further, when reasoning is not explicit, unnecessary conflict can arise among clinicians themselves, or among clinicians and patients. This conflict can have negative health consequences for the patient. In 2010, a transparent framework for RTP decisions was proposed. However, some have identified limitations to the framework and found difficulties in its implementation. This paper presents a revised framework that addresses the limitations, and provides concrete examples of how to apply it in simple and complex cases.
Visual imagery can impede reasoning.

Although it is natural to suppose that visual mental imagery is important in human deductive reasoning, the evidence is equivocal. This article argues that reasoning studies have not distinguished between ease of visualization and ease of constructing spatial models. Rating studies show that these factors can be separated. Their results yielded four sorts of relations: (1) visuospatial relations that are easy to envisage visually and spatially, (2) visual relations that are easy to envisage visually but hard to envisage spatially, (3) spatial relations that are hard to envisage visually but easy to envisage spatially, and (4) control relations that are hard to envisage both visually and spatially. Three experiments showed that visual relations slow down the process of reasoning in comparison with control relations, whereas visuospatial and spatial relations yield inferences comparable with those of control relations. We conclude that irrelevant visual detail can be a nuisance in reasoning and can impede the process.
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms

With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes.
Frequency-minimal moving target defense using software-defined networking

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.