nils-herrmann/clean_scirepeval huggingface dataset:
Twevent: segment-based event detection from tweets

Event detection from tweets is an important task to understand the current events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for well written documents (e.g. news articles) cannot be directly adopted. In this paper, we propose a segment-based event detection system for tweets, called Twevent. Twevent first detects bursty tweet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tweet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tweets containing the segment published within that time window. The similarity between a pair of bursty segments is computed using their associated tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events. We evaluate Twevent and compare it with the state-of-the-art method using 4.3 million tweets published by Singapore-based users in June 2010. In our experiments, Twevent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Twevent can be easily interpreted with little background knowledge because of the newsworthy segments. We also show that Twevent is efficient and scalable, leading to a desirable solution for event detection from tweets.
Automatic malware classification and new malware detection using machine learning

The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.
Data Mining Algorithms and Statistical Analysis for Sales Data Forecast

This paper develops and compares different models to forecast new product sales data with increasing sales trend and multiple predictor inputs. In order to analyze new product with increasing sales trend, we developed and evaluated multiple time series forecasting methods, including Exponential Smoothing model, Holt's Linear model, ARMA model, and ARMA wit linear trend models. Furthermore, we created multiple Causal Factor Forecasting models to incorporate various dependent input factors such as sale person's quotes, product pricing, product seasonality factors, to further reduce forecasting error. We analyzed original data regression model, trend and residual regression model, and ARMAV wit linear trend model to consider input factors. We discovered that ARMAV wit linear trend model gives best forecasting accuracy and lowest RSS (Residual Sum of Square). In conclusion, ARMAV with linear trend method is the best benchmark model to forecast sales data for new product with trend and with sales person's inputs.
Design and evaluation of decentralized online clustering

Ensuring the efficient and robust operation of distributed computational infrastructures is critical, given that their scale and overall complexity is growing at an alarming rate and that their management is rapidly exceeding human capability. Clustering analysis can be used to find patterns and trends in system operational data, as well as highlight deviations from these patterns. Such analysis can be essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require appropriate management actions.
 This work analyzes the automated application of clustering for online system management, from the point of view of the suitability of different clustering approaches for the online analysis of system data in a distributed environment, with minimal prior knowledge and within a timeframe that allows the timely interpretation of and response to clustering results. For this purpose, we evaluate DOC (Decentralized Online Clustering), a clustering algorithm designed to support data analysis for autonomic management, and compare it to existing and widely used clustering algorithms. The comparative evaluations will show that DOC achieves a good balance in the trade-offs inherent in the challenges for this type of online management.
The role of Certified Registered Nurse Anesthetists in the United States

Certified Registered Nurse Anesthetists (CRNAs) have been providing anesthesia care in the United States (US) for nearly 150 years. Historically, anesthesia care for surgical patients was mainly provided by trained nurses under the supervision of surgeons until the establishment of anesthesiology as a medical specialty in the US. Currently, all 50 US states utilize CRNAs to perform various kinds of anesthesia care, either under the medical supervision of anesthesiologists in most states, or independently without medical supervision in 16 states; the latter has become an on-going source of conflict between anesthesiologists and CRNAs. Understanding the history and current conditions of anesthesia practice in the US is crucial for countries in which the shortage of anesthesia care providers has become a national issue.
Serotonin, tryptophan metabolism and the brain-gut-microbiome axis

The brain-gut axis is a bidirectional communication system between the central nervous system and the gastrointestinal tract. Serotonin functions as a key neurotransmitter at both terminals of this network. Accumulating evidence points to a critical role for the gut microbiome in regulating normal functioning of this axis. In particular, it is becoming clear that the microbial influence on tryptophan metabolism and the serotonergic system may be an important node in such regulation. There is also substantial overlap between behaviours influenced by the gut microbiota and those which rely on intact serotonergic neurotransmission. The developing serotonergic system may be vulnerable to differential microbial colonisation patterns prior to the emergence of a stable adult-like gut microbiota. At the other extreme of life, the decreased diversity and stability of the gut microbiota may dictate serotonin-related health problems in the elderly. The mechanisms underpinning this crosstalk require further elaboration but may be related to the ability of the gut microbiota to control host tryptophan metabolism along the kynurenine pathway, thereby simultaneously reducing the fraction available for serotonin synthesis and increasing the production of neuroactive metabolites. The enzymes of this pathway are immune and stress-responsive, both systems which buttress the brain-gut axis. In addition, there are neural processes in the gastrointestinal tract which can be influenced by local alterations in serotonin concentrations with subsequent relay of signals along the scaffolding of the brain-gut axis to influence CNS neurotransmission. Therapeutic targeting of the gut microbiota might be a viable treatment strategy for serotonin-related brain-gut axis disorders.
On the Asymptotic Distribution of Zeros of Modular Forms

1.1. Our purpose in this note is to study the limiting distribution of zeros of modular forms. We review some definitions: A modular form of weight k for SL2(Z) is a holomorphic function on the upper half-plane H, transforming as f( cz+d ) = (cz + d)f(z), for all ( a b c d ) ∈ SL2(Z) (this forces k to be even), and “holomorphic at the cusp” (see § 2.1). A form is cuspidal if it vanishes at the cusp. For a modular form of weight k, let ν(f) be the number of inequivalent zeros of f in H, with the convention that a zero at z is counted with weight w(z) inverse to the number of elements of SL2(Z)/{±I} fixing z. Then ν(f) ≤ k/12. For a sequence of modular forms, where we assume that the number of inequivalent zeros tends to infinity, we would like to examine the manner in which the resulting configuration of zeros is distributed in the modular domain SL2(Z)\H.
Parsing Tweets into Universal Dependencies

We study the problem of analyzing tweets with Universal Dependencies (UD; Nivre et al., 2016). We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-ofspeech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (TWEEBANK V2) that is four times larger than the (unlabeled) TWEEBANK V1 introduced by Kong et al. (2014). We characterize the disagreements between our annotators and show that it is challenging to deliver consistent annotation due to ambiguity in understanding and explaining tweets. Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD. To overcome annotation noise without sacrificing computational efficiency, we propose a new method to distill an ensemble of 20 transition-based parsers into a single one. Our parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and outperforms parsers that are state-ofthe-art on other treebanks in both accuracy and speed.
Semantic Role Labeling with Neural Network Factors

We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task. We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset.
Web ontology language (OWL) and semantic web

Ontology is a sub-field of Philosophy. It is the study of the nature of existence and a branch of metaphysics concerned with identifying the kinds of things that actually exists and how to describe them. It describes formally a domain of discourse. Ontology is used to capture knowledge about some domain of interest and to describe the concepts in the domain and also to express the relationships that hold between those concepts. Ontology consists of finite list of terms (or important concepts) and the relationships among the terms (or Classes of Objects). Relationships typically include hierarchies of classes. It is an explicit formal specification of conceptualization and the science of describing the kind of entities in the world and how they are related (W3C). Web Ontology Language (OWL) is a language for defining and instantiating web ontologies (a W3C Recommendation). OWL ontology includes description of classes, properties and their instances. OWL is used to explicitly represent the meaning of terms in vocabularies and the relationships between those terms. Such representation of terms and their interrelationships is called ontology. OWL has facilities for expressing meaning and semantics and the ability to represent machine interpretable content on the Web. OWL is designed for use by applications that need to process the content of information instead of just presenting information to humans. This is used for knowledge representation and also is useful to derive logical consequences from OWL formal semantics.
PrologCheck - Property-Based Testing in Prolog

We present PrologCheck, an automatic tool for propertybased testing of programs in the logic programming language Prolog with randomised test data generation. The tool is inspired by the well known QuickCheck, originally designed for the functional programming language Haskell. It includes features that deal with specific characteristics of Prolog such as its relational nature (as opposed to Haskell) and the absence of a strong type discipline. PrologCheck expressiveness stems from describing properties as Prolog goals. It enables the definition of custom test data generators for random testing tailored for the property to be tested. Further, it allows the use of a predicate specification language that supports types, modes and constraints on the number of successful computations. We evaluate our tool on a number of examples and apply it successfully to debug a Prolog library for AVL search trees.
Modeling and Application of Customer Lifetime Value in Online Retail

This article provides an empirical statistical analysis and discussion of the predictive abilities of selected customer lifetime value (CLV) models that could be used in online shopping within e-commerce business settings. The comparison of CLV predictive abilities, using selected evaluation metrics, is made on selected CLV models: Extended Pareto/NBD model (EP/NBD), Markov chain model and Status Quo model. The article uses six online store datasets with annual revenues in the order of tens of millions of euros for the comparison. The EP/NBD model has outperformed other selected models in a majority of evaluation metrics and can be considered good and stable for non-contractual relations in online shopping. The implications for the deployment of selected CLV models in practice, as well as suggestions for future research, are also discussed.
Fog Computing and Smart Gateway Based Communication for Cloud of Things

With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) are gaining importance. In IoTs, literally anything can be part of it, whether it is sensor nodes or dumb objects, so very diverse types of services can be produced. In this regard, resource management, service creation, service management, service discovery, data storage, and power management would require much better infrastructure and sophisticated mechanism. The amount of data IoTs are going to generate would not be possible for standalone power-constrained IoTs to handle. Cloud computing comes into play here. Integration of IoTs with cloud computing, termed as Cloud of Things (CoT) can help achieve the goals of envisioned IoT and future Internet. This IoT-Cloud computing integration is not straight-forward. It involves many challenges. One of those challenges is data trimming. Because unnecessary communication not only burdens the core network, but also the data center in the cloud. For this purpose, data can be preprocessed and trimmed before sending to the cloud. This can be done through a Smart Gateway, accompanied with a Smart Network or Fog Computing. In this paper, we have discussed this concept in detail and present the architecture of Smart Gateway with Fog Computing. We have tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data Upload Delay, and Bulk-data Synchronization Delay.
International consensus guidelines for management of intraductal papillary mucinous neoplasms and mucinous cystic neoplasms of the pancreas.

Non-inflammatory cystic lesions of the pancreas are increasingly recognized. Two distinct entities have been defined, i.e., intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN). Ovarian-type stroma has been proposed as a requisite to distinguish MCN from IPMN. Some other distinct features to characterize IPMN and MCN have been identified, but there remain ambiguities between the two diseases. In view of the increasing frequency with which these neoplasms are being diagnosed worldwide, it would be helpful for physicians managing patients with cystic neoplasms of the pancreas to have guidelines for the diagnosis and treatment of IPMN and MCN. The proposed guidelines represent a consensus of the working group of the International Association of Pancreatology.
Malware Image Classification using Machine Learning with Local Binary Pattern

Malware classification is a critical part of the cybersecurity. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which are mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelope, and the LBP demonstrate that our proposed approach outperforms others.
Regulation of fracture repair by growth factors.

Fractured bones heal by a cascade of cellular events in which mesenchymal cells respond to unknown regulators by proliferating, differentiating, and synthesizing extracellular matrix. Current concepts suggest that growth factors may regulate different steps in this cascade (10). Recent studies suggest regulatory roles for PDGF, aFGF, bFGF, and TGF-beta in the initiation and the development of the fracture callus. Fracture healing begins immediately following injury, when growth factors, including TGF-beta 1 and PDGF, are released into the fracture hematoma by platelets and inflammatory cells. TGF-beta 1 and FGF are synthesized by osteoblasts and chondrocytes throughout the healing process. TGF-beta 1 and PDGF appear to have an influence on the initiation of fracture repair and the formation of cartilage and intramembranous bone in the initiation of callus formation. Acidic FGF is synthesized by chondrocytes, chondrocyte precursors, and macrophages. It appears to stimulate the proliferation of immature chondrocytes or precursors, and indirectly regulates chondrocyte maturation and the expression of the cartilage matrix. Presumably, growth factors in the callus at later times regulate additional steps in repair of the bone after fracture. These studies suggest that growth factors are central regulators of cellular proliferation, differentiation, and extracellular matrix synthesis during fracture repair. Abnormal growth factor expression has been implicated as causing impaired or abnormal healing in other tissues, suggesting that altered growth factor expression also may be responsible for abnormal or delayed fracture repair. As a complete understanding of fracture-healing regulation evolves, we expect new insights into the etiology of abnormal or delayed fracture healing, and possibly new therapies for these difficult clinical problems.
Bifurcation tracking by Harmonic Balance Method for performance tuning of nonlinear dynamical systems

The aim of this paper is to provide an efficient frequency-domain method for bifurcation analysis of nonlinear dynamical systems. The proposed method consists in directly tracking the bifurcation points when a system parameter such as the excitation or nonlinearity level is varied. To this end, a so-called extended system comprising the equation of motion and an additional equation characterizing the bifurcation of interest is solved by means of the Harmonic Balance Method coupled with an arc-length continuation technique. In particular, an original extended system for the detection and tracking of Neimark-Sacker (secondary Hopf) bifurcations is introduced. By applying the methodology to a nonlinear energy sink and to a rotor-stator rubbing system, it is shown that the bifurcation tracking can be used to efficiently compute the boundaries of stability and/or dynamical regimes, i.e., safe operating zones.
The Tradeoffs Between Open and Traditional Relation Extraction

Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relationindependent extraction paradigm that is tailored to massive and heterogeneous corpora such as the Web. An Open IE system extracts a diverse set of relational tuples from text without any relation-specific input.  We analyze a sample of English sentences to demonstrate that numerous relationships are expressed using a compact set of relation-independent lexico-syntactic patterns, which can be learned by an Open IE system. What are the tradeoffs between Open IE and traditional IE? We consider this question in the context of two tasks. First, when the number of relations is massive, and the relations themselves are not pre-specified, we argue that Open IE is necessary. We then present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than the model employed by TEXTRUNNER, the previous stateof-the-art Open IE system. Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction system, though at substantially lower recall. Finally, we show how to combine the two types of systems into a hybrid that achieves higher precision than a traditional extractor, with comparable recall.
Secure and Reliable Internet of Things Systems for Healthcare

Proposals and some implementations of Internet of Things (IoT) systems for healthcare are described. Implications of current European Union legislation, the new General Data Protection Regulation, for the security and reliability of healthcare IoT systems and for the privacy of users of these systems are presented. Analytics of healthcare IoT data for the requirements of evidence based healthcare is outlined. Threats to the security and reliability of healthcare IoT systems and to the privacy of the users of these systems, security and reliability requirements, and solutions for security and enhanced reliability are described. Visions for future healthcare IoT are presented and some future research directions are proposed.