nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Summarization of Open-Domain Multiparty Dialogues in Diverse Genres

Automatic summarization of open-domain spoken dialogues is a relatively new research area. This article introduces the task and the challenges involved and motivates and presents an approach for obtaining automatic-extract summaries for human transcripts of multiparty dialogues of four different genres, without any restriction on domain. We address the following issues, which are intrinsic to spoken-dialogue summarization and typically can be ignored when summarizing written text such as news wire data: (1) detection and removal of speech disfluencies; (2) detection and insertion of sentence boundaries; and (3) detection and linking of cross-speaker information units (question-answer pairs). A system evaluation is performed using a corpus of 23 dialogue excerpts with an average duration of about 10 minutes, comprising 80 topical segments and about 47,000 words total. The corpus was manually annotated for relevant text spans by six human annotators. The global evaluation shows that for the two more informal genres, our summarization system using dialogue-specific components significantly outperforms two baselines: (1) a maximum-marginal-relevance ranking algorithm using TFIDF term weighting, and (2) a LEAD baseline that extracts the first n words from a text.
Child pornography offenses are a valid diagnostic indicator of pedophilia.

This study investigated whether being charged with a child pornography offense is a valid diagnostic indicator of pedophilia, as represented by an index of phallometrically assessed sexual arousal to children. The sample of 685 male patients was referred between 1995 and 2004 for a sexological assessment of their sexual interests and behavior. As a group, child pornography offenders showed greater sexual arousal to children than to adults and differed from groups of sex offenders against children, sex offenders against adults, and general sexology patients. The results suggest child pornography offending is a stronger diagnostic indicator of pedophilia than is sexually offending against child victims. Theoretical and clinical implications are discussed.
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.  Shallow network, Deep networks, Intrusion detection, False positive alarm rates and True positive alarm rates 1.0 INTRODUCTION Computer networks have developed rapidly over the years contributing significantly to social and economic development. International trade, healthcare systems and military capabilities are examples of human activity that increasingly rely on networks. This has led to an increasing interest in the security of networks by industry and researchers. The importance of Intrusion Detection Systems (IDS) is critical as networks can become vulnerable to attacks from both internal and external intruders , . An IDS is a detection system put in place to monitor computer networks. These have been in use since the 1980’s . By analysing patterns of captured data from a network, IDS help to detect threats [4]. These threats can be devastating, for example, Denial of service (DoS) denies or prevents legitimate users resource on a network by introducing unwanted traffic . Malware is another example, where attackers use malicious software to disrupt systems .
Measuring Semantic Relatedness Across Languages

Measures of Semantic Relatedness determine the degree of relatedness between two words. Most of these measures work only between pairs of words in a single language. We propose a novel method of measuring semantic relatedness between pairs of words in two different languages. This method does not use a parallel corpus but is rather seeded with a set of known translations. For evaluation we construct a cross-language dataset of French-English word pairs with similarity scores. Our new cross-language measure correlates more closely with averaged human scores than our unilingual baselines. 1. Distributional Semantics “You shall know a word by the company it keeps” – Firth (1957) •Construct a word-context matrix • Corpora: French and English Wikipedias • Used POS-tagged words as contexts • Re-weight matrix – Pointwise Mutual Information (PMI) •Cosine similarity •Evaluate correlation on Rubenstein and Goodenough (1965) style dataset
Euler Spiral for Shape Completion

In this paper we address the curve completion problem, e.g., the geometric continuation of boundaries of objects which are temporarily interrupted by occlusion. Also known as the gap completion or shape completion problem, this problem is a significant element of perceptual grouping of edge elements and has been approached by using cubic splines or biarcs which minimize total curvature squared (elastica), as motivated by a physical analogy. Our approach is motivated by railroad design methods of the early 1900's which connect two rail segments by “transition curves”, and by the work of Knuth on mathematical typography. We propose that in using an energy minimizing solution completion curves should not penalize curvature as in elastica but curvature variation. The minimization of total curvature variation leads to an Euler Spiral solution, a curve whose curvature varies linearly with arclength. We reduce the construction of this curve from a pair of points and tangents at these points to solving a nonlinear system of equations involving Fresnel Integrals, whose solution relies on optimization from a suitable initial condition constrained to satisfy given boundary conditions. Since the choice of an appropriate initial curve is critical in this optimization, we analytically derive an optimal solution in the class of biarc curves, which is then used as the initial curve. The resulting interpolations yield intuitive interpolation across gaps and occlusions, and are extensible, in contrast to the scale-invariant version of elastica. In addition, Euler Spiral segments can be used in other applications of curve completions, e.g., modeling boundary segments between curvature extrema or modeling skeletal branch geometry.
Deep Reinforcement Learning of Region Proposal Networks for Object Detection

We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN automatically decides when to stop the search process and has the benefit of being able to jointly learn the parameters of the policy and the detector, both represented as deep networks. Our model can further learn to search over a wide range of exploration-accuracy trade-offs making it possible to specify or adapt the exploration extent at test time. The resulting search trajectories are image- and category-dependent, yet rely only on a single policy over all object categories. Results on the MS COCO and PASCAL VOC challenges show that our approach outperforms established, typical state-of-the-art object detection pipelines.
Guided GUI testing of android apps with minimal restart and approximate learning

Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called Swift-Hand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L*-based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L*-based testing.
A new sentence similarity measure and sentence based extractive technique for automatic text summarization

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. In our study we focus on sentence based extractive document summarization. We propose the generic document summarization method which is based on sentence clustering. The proposed approach is a continue sentence-clustering based extractive summarization methods, proposed in Alguliev [Alguliev, R. M., Aliguliyev, R. M., Bagirov, A. M. (2005). Global optimization in the summarization of text documents. Automatic Control and Computer Sciences 39, 42–47], Aliguliyev [Aliguliyev, R. M. (2006). A novel partitioning-based clustering method and generic document summarization. In  (WI–IATW’06), 18–22 December (pp. 626–629) Hong Kong, China], Alguliev and Alyguliev [Alguliev, R. M., Alyguliev, R. M. (2007). Summarization of text-based documents with a determination of latent topical sections and information-rich sentences. Automatic Control and Computer Sciences 41, 132–140] Aliguliyev, [Aliguliyev, R. M. (2007). Automatic document summarization by sentence extraction. Journal of Computational Technologies 12, 5–15.]. The purpose of present paper to show, that summarization result not only depends on optimized function, and also depends on a similarity measure. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.
Curie: A method for protecting SVM Classifier from Poisoning Attack

Machine learning is used in a number of security related applications such as biometric user authentication, speaker identification etc. A type of causative integrity attack against machine le arning called Poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier. In the context of the biometric authentication, this means that more intruders will be classified as valid user, and in case of speaker identification system, user A will be classified user B. In this paper, we examine poisoning attack against SVM and introduce Curie a method to protect the SVM classifier from the poisoning attack. The basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out. Our method is light weight and can be easily integrated into existing systems. Experimental results show that it works very well in filtering out the poisoned data.
Predicting solar generation from weather forecasts using machine learning

A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.
Consistent Multilabel Classification

Multilabel classification is rapidly developing as an important aspect of modern predictive modeling, motivating study of its theoretical aspects. To this end, we propose a framework for constructing and analyzing multilabel classification metrics which reveals novel results on a parametric form for population optimal classifiers, and additional insight into the role of label correlations. In particular, we show that for multilabel metrics constructed as instance-, microand macroaverages, the population optimal classifier can be decomposed into binary classifiers based on the marginal instance-conditional distribution of each label, with a weak association between labels via the threshold. Thus, our analysis extends the state of the art from a few known multilabel classification metrics such as Hamming loss, to a general framework applicable to many of the classification metrics in common use. Based on the population-optimal classifier, we propose a computationally efficient and general-purpose plug-in classification algorithm, and prove its consistency with respect to the metric of interest. Empirical results on synthetic and benchmark datasets are supportive of our theoretical findings.
BeamSpy: Enabling Robust 60 GHz Links Under Blockage

Due to high directionality and small wavelengths, 60 GHz links are highly vulnerable to human blockage. To overcome blockage, 60 GHz radios can use a phased-array antenna to search for and switch to unblocked beam directions. However, these techniques are reactive, and only trigger after the blockage has occurred, and hence, they take time to recover the link. In this paper, we propose BeamSpy, that can instantaneously predict the quality of 60 GHz beams, even under blockage, without the costly beam searching. BeamSpy captures unique spatial and blockage-invariant correlation among beams through a novel prediction model, exploiting which we can immediately select the best alternative beam direction whenever the current beam’s quality degrades. We apply BeamSpy to a run-time fast beam adaptation protocol, and a blockage-risk assessment scheme that can guide blockage-resilient link deployment. Our experiments on a reconfigurable 60 GHz platform demonstrate the effectiveness of BeamSpy’s prediction framework, and its usefulness in enabling robust 60 GHz links.
Indexing by Latent Semantic Analysis

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
Predicting Cyber-Events by Leveraging Hacker Sentiment

Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups’ collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major business organizations. We consider 3 types of events: malicious software installation, malicious destination visits, and malicious emails that surpassed the target organizations’ defenses. We construct predictive signals by applying sentiment analysis on hacker forum posts to better understand hacker behavior. We analyze over 400K posts generated between January 2016 and January 2018 on over 100 hacking forums both on surface and Dark Web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can outperform state-of-the-art deep learning and time-series models on forecasting cyber attacks weeks ahead of the events.
System Level Evaluation of LTE-V2V Mode 4 Communications and Its Distributed Scheduling

The 3GPP has recently published the first version of the Release 14 standard that includes support for V2V communications using LTE sidelink communications (referred to as LTE-V, LTE-V2X, LTE-V2V or Cellular V2X). The standard includes a mode (mode 4) where vehicles autonomously select and manage the radio resources without any cellular infrastructure support. This is highly relevant since V2V safety applications cannot depend on the availability of infrastructure-based cellular coverage, and transforms LTE-V into a possible (or complimentary) alternative to 802.11p. The performance of LTE-V in mode 4 is highly dependent on its distributed scheduling protocol (sensing-based Semi-Persistent Scheduling) that is used by vehicles to reserve resources for their transmissions. This paper presents the first evaluation of the performance and operation of this protocol under realistic traffic conditions in urban scenarios. The evaluation demonstrates that further enhancements should be investigated to reduce packet collisions.
A method for classification of network traffic based on C5.0 Machine Learning Algorithm

Monitoring of the network performance in highspeed Internet infrastructure is a challenging task, as the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis in Multi-hop Networks requires therefore knowledge about types of applications forming current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown to have ability to distinguish between 7 different applications in test set of 76,632-1,622,710 unknown cases with average accuracy of 99.3-99.9%. This high accuracy was achieved by using high quality training data collected by our system, a unique set of parameters used for both training and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options. This paper shows how we collected accurate traffic data, presents arguments used in classification process, introduces the C5.0 classifier and its options, and finally evaluates and compares the obtained results.
Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem

Supplier selection is nowadays one of the critical topics in supply chain management. This paper presents a new decision making approach for group multi-criteria supplier selection problem, which clubs supplier selection process with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner similar to the decision makers who have gathered considerable knowledge and expertise in procurement domain. Nevertheless, under many conditions, exact data are inadequate to model real-life situation and fuzzy logic can be incorporated to handle the vagueness of the decision makers. As per this concept, fuzzy-AHP method is used first for supplier selection through four classes ( Performance strategy, CLASS II: Quality of service,  Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using simulation based fuzzy TOPSIS technique, the criteria application is quantitatively evaluated for order allocation among the selected suppliers. As a result, the approach generates decision-making knowledge, and thereafter, the developed combination of rules order allocation can easily be interpreted, adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the results analyzed. & 2011 Elsevier Ltd. All rights reserved.
Analyzing the Performance of an Anycast CDN

Content delivery networks must balance a number of trade-offs when deciding how to direct a client to a CDN server. Whereas DNS-based redirection requires a complex global traffic manager, anycast depends on BGP to direct a client to a CDN front-end. Anycast is simple to operate, scalable, and naturally resilient to DDoS attacks. This simplicity, however, comes at the cost of precise control of client redirection. We examine the performance implications of using anycast in a global, latency-sensitive, CDN. We analyze millions of client-side measurements from the Bing search service to capture anycast versus unicast performance to nearby front-ends. We find that anycast usually performs well despite the lack of precise control but that it directs roughly 20% of clients to a suboptimal front-end. We also show that the performance of these clients can be improved through a simple history-based prediction scheme.