nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]

Traditionally most of the anomaly detection algorithms have been designed for 'static' datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in 'evolving' data. Over the last two decades, many algorithms have been proposed to detect anomalies in evolving data. Some of them consider scenarios where a sequence of objects (called data streams) with one or multiple features evolves over time. Whereas the others concentrate on more complex scenarios, where streaming objects with one or multiple features have causal/non-causal relationships with each other. The latter can be represented as evolving graphs. In this paper, we categorize existing strategies for detecting anomalies in both scenarios including the state-of-the-art techniques. Since label information is mostly unavailable in real-world applications when data evolves, we review the unsupervised approaches in this paper. We then present an interesting application example, i.e., forest re risk prediction, and conclude the paper with future research directions in this eld for researchers and industry.
Supporting Diverse Notations in MPS' Projectional Editor

To be able to build effective DSLs, these DSLs must not just use language concepts that are aligned with their respective domain, but also use notations that correspond closely to established domain notations – and those are often not purely textual or graphical. The underlying language workbench must support these notations, and combining different notations in a single editor must be supported as well in order to support the coherent definitions of systems that use several DSLs. In this paper we provide an overview over the notations supported by JetBrains MPS. MPS is a language workbench that uses a projectional editor, which, by its very nature, can deal with many different notational styles, including text, prose, math tables and graphics. The various supported notations are illustrated with examples from real-world systems.
Visual Memory for Robust Path Following

Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.
Text Mining on Player Personality for Game Recommendation

Recommender systems have emergedrecently in many aspects due to enormous amount of information or items existed in e-commerce or social network services. Users find it convenient if the service can recommend interesting items to them automatically. Past systems adopted users' preference or social connections for such recommendation. In this work, we will try to identify a game player's personality traits and use them to recommend games that may be interested by the player. The personality traits of a player is identified by applying a text mining process on textual contents related to the player. The same process is also applied on the comments of the games to identified the personality traits of games. Recommendation is then performed based on the similarity between the traits of the user and the game. We performed experiments on 63 players and 2050 games and obtained promising results which may demonstrate the plausibility of personality-based recommender systems.
Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research.

Progress and issues in the study of coping with stress during childhood and adolescence are reviewed. Definitions of coping are considered, and the relationship between coping and other aspects of responses to stress (e.g., temperament and stress reactivity) is described. Questionnaire, interview, and observation measures of child and adolescent coping are evaluated with regard to reliability and validity. Studies of the association of coping with symptoms of psychopathology and social and academic competence are reviewed. Initial progress has been made in the conceptualization and measurement of coping, and substantial evidence has accumulated on the association between coping and adjustment. Problems still remain in the conceptualization and measurement of coping in young people, however, and aspects of the development and correlates of coping remain to be identified. An agenda for future research on child-adolescent coping is outlined.
Hierarchical Peer-To-Peer Systems

Structured peer-to-peer (P2P) lookup services—such as Chord, CAN, Pastry and Tapestry—organize peers into a flat overlay network and offer distributed hash table (DHT) functionality. In these systems, data is associated with keys and each peer is responsible for a subset of the keys. We study hierarchical DHTs, in which peers are organized into groups, and each group has its autonomous intra-group overlay network and lookup service. The groups themselves are organized in a top-level overlay network. To find a peer that is responsible for a key, the top-level overlay first determines the group responsible for the key; the responsible group then uses its intra-group overlay to determine the specific peer that is responsible for the key. After providing a general framework for hierarchical P2P lookup, we consider the specific case of a two-tier hierarchy that uses Chord for the top level. Our analysis shows that by designating the most reliable peers in the groups as superpeers, the hierarchical design can significantly reduce the expected number of hops in Chord. We also propose a scalable design for managing the groups and the superpeers.
The varied nature of women's sexuality: Unresolved issues and a theoretical approach

During the 20th century there were clear indications that the socio-cultural suppression of women's sexuality had lessened, revealing a marked variability of women's sexual expression. In this article we review the recent literature to explore explanations for this variability. It is clear that we know little about the nature of sexual desire, and in particular, what it is that is desired. There is also now substantial evidence that vaginal response, as measured by vaginal pulse amplitude, is a relatively automatic response to perception of sexual stimuli, regardless of whether these stimuli are perceived positively or result in subjective arousal. This is considered as a possible mechanism that allows vaginal intercourse without pain, even when the woman is not sexually aroused. The roles of androgens and estrogen in women's sexuality remain uncertain. The evidence is, however, consistent with there being a testosterone-dependent component of women's sexuality that is more important for some women than others. Finally, a new theoretical model is presented that aims to resolve these uncertainties and that proposes different types of women's sexuality. Once we have a better understanding of "normal" female sexuality, in its various forms, our ability to develop effective treatments for women's sexual problems should improve.
Single and Multi Objective Genetic Programming for software development effort estimation

The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on the observation that the effort estimation problem can be formulated as an optimization problem. Indeed, among the possible models, we have to identify the one providing the most accurate estimates. To this end a suitable measure to evaluate and compare different models is needed. However, in the context of effort estimation there does not exist a unique measure that allows us to compare different models but several different criteria (e.g., MMRE, Pred(25), MdMRE) have been proposed. Aiming at getting an insight on the effects of using different measures as fitness function, in this paper we analyzed the performance of GP using each of the five most used evaluation criteria. Moreover, we designed a Multi-Objective Genetic Programming (MOGP) based on Pareto optimality to simultaneously optimize the five evaluation measures and analyzed whether MOGP is able to build estimation models more accurate than those obtained using GP. The results of the empirical analysis, carried out using three publicly available datasets, showed that the choice of the fitness function significantly affects the estimation accuracy of the models built with GP and the use of some fitness functions allowed GP to get estimation accuracy comparable with the ones provided by MOGP.
Justifications, Strategies, and Critical Success Factors in Successful ITIL Implementations in U.S. and Australian Companies: An Exploratory Study

A growing number of organizations are implementing the ITIL (IT Infrastructure Library) „best practice‟ framework in an attempt to improve their IT service management processes. However, not all ITIL implementations are successful and some companies have been disappointed with the outcomes. This exploratory research reports on four case studies of „successful‟ implementations of IT service management using the process-based ITIL V2 framework. Two companies are located in the U.S. and two in Australia. The cases demonstrate a mix of implementation justifications and strategies. Critical success factors (CSFs) suggested in the literature are compared against those attributed to these successful ITIL implementations. Some CSFs, including executive management support, interdepartmental communication and collaboration, use of consultants, training and careful software selection are confirmed. Three new CSFs are identified: creating an ITIL-friendly culture, process as a priority, and customerfocused metrics. Practitioner guidelines, to assist IT managers who are contemplating adopting ITIL for process improvement and organisational transformation, are also provided together with some challenges encountered and their associated resolutions.
ParCube: Sparse Parallelizable Tensor Decompositions

How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose ParCube, a new and highly parallelizable method for speeding up tensor decompositions that is well-suited to producing sparse approximations. Experiments with even moderately large data indicate over 90% sparser outputs and 14 times faster execution, with approximation error close to the current state of the art irrespective of computation and memory requirements. We provide theoretical guarantees for the algorithm’s correctness and we experimentally validate our claims through extensive experiments, including four different real world datasets (Enron, Lbnl, Facebook and Nell), demonstrating its effectiveness for data mining practitioners. In particular, we are the first to analyze the very large Nell dataset using a sparse tensor decomposition, demonstrating that ParCube enables us to handle effectively and efficiently very large datasets.
Tagging Patient Notes With ICD-9 Codes

There is substantial growth in the amount of medical data being generated in hospitals. With over 96% adoption rate , Electronic Medical/Health Records are used to store most of this medical data. If harnessed correctly, this medium provides a very convenient platform for secondary data analysis of these records to improve medical and patient care. One crucial feature of the information stored in these systems are ICD9-diagnosis codes, which are used for billing purposes and integration to other databases. These codes are assigned to medical text and require expert annotators with experience and training. In this paper we formulate this problem as a multi-label classification problem and propose a deep learning framework to classify the ICD-9 codes a patient is assigned at the end of a visit. We demonstrate that a simple LSTM model with a single layer of non-linearity can learn to classify patient notes with their corresponding ICD-9 labels moderately well.
ScienceDirect The 2 nd International Workshop on Communications and Sensor Networks ( ComSense-2014 ) A cloud-based healthcare framework for security and patients ' data privacy using wireless body area networks

The recent developments in remote healthcare systems have witnessed significant interests from IT industry (Microsoft, Google, VMware etc) that provide ubiquitous and easily deployable healthcare systems. These systems provide a platform to share medical information, applications, and infrastructure in a ubiquitous and fully automated manner. Communication security and patients' data privacy are the aspects that would increase the confidence of users in such remote healthcare systems. This paper presents a secure cloud-based mobile healthcare framework using wireless body area networks (WBANs). The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records (EMRs) are securely stored in the hospital community cloud and privacy of the patients' data is preserved. The evaluation and analysis shows that the proposed multi-biometric based mechanism provides significant security measures due to its highly efficient key generation mechanism.
Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis and Enhancement

) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is Distributed Coordination Function(DCF), which is a Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA) with binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Comparing with other models, this model is shown to be able to predict the behaviors of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks

One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical bidirectional recurrent neural network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract “temporal features” based on facial landmarks (geometry information) from consecutive frames. Meanwhile, in order to complement the still appearance information, a multi-signal convolutional neural network (MSCNN) is proposed to extract “spatial features” from still frames. We use both recognition and verification signals as supervision to calculate different loss functions, which are helpful to increase the variations of different expressions and reduce the differences among identical expressions. This deep evolutional spatial-temporal network (composed of PHRNN and MSCNN) extracts the partial-whole, geometry-appearance, and dynamic-still information, effectively boosting the performance of facial expression recognition. Experimental results show that this method largely outperforms the state-of-the-art ones. On three widely used facial expression databases (CK+, Oulu-CASIA, and MMI), our method reduces the error rates of the previous best ones by 45.5%, 25.8%, and 24.4%, respectively.
Comparison of classification techniques for intrusion detection dataset using WEKA

As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design “Intrusion Detection Models” which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance.  Forest classification approach outperformed the rest.
Action Design Research and Visualization Design

In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.
The iSLIP scheduling algorithm for input-queued switches

h increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known that if simple first in fh-st out (FIFO) input queues are used to hold packets then, even under benign conditions, head-of-line (HOL) blocking limits the achievable bandwidth to approximately 58.6% of the maximum. HOL blocking can be overcome by the use of virtual output queueing, which is described in this paper. A scheduling algorithm is used to configure the crossbar switch, deciding the order in which packets will be served. Recent results have shown that with a suitable scheduling algorithm, 100% throughput can he achieved. In this paper, we present a scheduling algorithm called iSLIP. An iterative, round-robin algorithm, iSLIP can achieve 100% throughput for uniform traffic, yet is simple to implement in hardware. Iterative and noniterative versions of the algorithms are presented, along with modified versions for prioritized traffic. Simulation results are presented to indicate the performance of iSLIP under benign and bursty traffic conditions. Prototype and commercial implementations of SLIP exist in systems with aggregate bandwidths ranging from 50 to SO0 Gb/s. When the traffic is nonuniform, SLIP quickly adapts to a fair scheduling policy that is guaranteed never to starve an input queue. Finally, we describe the implementation complexity of SLIP. Based on a two-dimensional (2-D) array of priority encoders, single-chip schedulers have been built supporting up to 32 ports, and making approximately 100 million scheduling decisions per second. I~l&x TermsATM switch, crossbar switch, input-queueing, IP router, scheduling.
A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons.

The role of back-propagating dendritic action potentials in the induction of long-term potentiation (LTP) was investigated in CA1 neurons by means of dendritic patch recordings and simultaneous calcium imaging. Pairing of subthreshold excitatory postsynaptic potentials (EPSPs) with back-propagating action potentials resulted in an amplification of dendritic action potentials and evoked calcium influx near the site of synaptic input. This pairing also induced a robust LTP, which was reduced when EPSPs were paired with non-back-propagating action potentials or when stimuli were unpaired. Action potentials thus provide a synaptically controlled, associative signal to the dendrites for Hebbian modifications of synaptic strength.
Why-type Question Classification in Question Answering System

The fundamental requisite to acquire information on any topic has become increasingly important. The need for Question Answering Systems (QAS) prevalent nowadays, replacing the traditional search engines stems from the user requirement for the most accurate answer to any question or query. Thus, interpreting the information need of the users is quite crucial for designing and developing a question answering system. Question classification is an important component in question answering systems that helps to determine the type of question and its corresponding type of answer. In this paper, we present a new way of classifying Why-type questions, aimed at understanding a questioner’s intent. Our taxonomy classifies Why-type questions into four separate categories. In addition, to automatically detect the categories of these questions by a parser, we differentiate them at lexical level.
Generalizing and Improving Weight Initialization

We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity’s influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.