nils-herrmann/clean_scirepeval huggingface dataset:
Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams

Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Most existing data stream classification techniques address only the first two challenges, and ignore the latter two. In this paper, we propose an ensemble classification framework, where each classifier is equipped with a novel class detector, to address concept-drift and concept-evolution. To address feature-evolution, we propose a feature set homogenization technique. We also enhance the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.
Friend or Foe: Cyberbullying in Social Network Sites

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.
Neural Variational Inference For Embedding Knowledge Graphs

Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound. The generative frameworks are flexible enough to allow training under any prior distribution that permits a reparametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters. Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations. Code publicly available on Github additionally allows learning Hyperspherical representations under a von-Mises Fisher prior distribution.
The Smart Home meets the Web of Things

In recent years, the merging of computing with physical things, enabled the transformation of everyday objects into information appliances. We propose to reuse the central principles of the modern Web architecture to fully integrate physical objects to the Web and build an interoperable Smart Home. We present an application framework that offers support for multiple home residents concurrently. We show that by using the Web as application layer we can build flexible applications on top of heterogeneous embedded devices with only a few lines of code, transforming home automation into a trivial task. We address many issues related to Web-enabling these devices, from their discovery and service description to the uniform interaction with them. Our evaluation efforts indicate that our framework offers acceptable performance and reliability.
Terpene synthases of oregano (Origanum vulgare L.) and their roles in the pathway and regulation of terpene biosynthesis

The aroma, flavor and pharmaceutical value of cultivated oregano (Origanum vulgare L.) is a consequence of its essential oil which consists mostly of monoterpenes and sesquiterpenes. To investigate the biosynthetic pathway to oregano terpenes and its regulation, we identified and characterized seven terpene synthases, key enzymes of terpene biosynthesis, from two cultivars of O. vulgare. Heterologous expression of these enzymes showed that each forms multiple mono- or sesquiterpene products and together they are responsible for the direct production of almost all terpenes found in O. vulgare essential oil. The correlation of essential oil composition with relative and absolute terpene synthase transcript concentrations in different lines of O. vulgare demonstrated that monoterpene synthase activity is predominantly regulated on the level of transcription and that the phenolic monoterpene alcohol thymol is derived from γ-terpinene, a product of a single monoterpene synthase. The combination of heterologously-expressed terpene synthases for in vitro assays resulted in blends of mono- and sesquiterpene products that strongly resemble those found in vivo, indicating that terpene synthase expression levels directly control the composition of the essential oil. These results will facilitate metabolic engineering and directed breeding of O. vulgare cultivars with higher quantity of essential oil and improved oil composition.
Boosting item keyword search with spreading activation

Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.
HALUX: projection-based interactive skin for digital sports

Entertainment contents employing users' whole-body action is now becoming popular, along with the prevalence of low-cost whole-body motion capture systems. To add haptic modality to this context, latency becomes a critical issue because it leads to spatial disparity between the assumed contact location and tactile stimulation position. To cope with this issue, we propose to project drive signal in advance so as to eliminate latency derived from communication. We do not explicitly control each vibrator, but we project "position-dependent, vibration strength distribution" image. Furthermore, the system becomes highly scalable, enabling simultaneous drive of hundreds of units attached to the body.
A simple and efficient eye detection method in color images

In this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these face regions. Finally, using the anthropological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast since it needs no template matching step for face verification. It is robust because it can deal with rotation. Experimental results clearly show the validity of our approach. A correct eye detection rate of 98.4% is achieved using a subset of the AR face database.
Instagram Unfiltered: Exploring Associations of Body Image Satisfaction, Instagram #Selfie Posting, and Negative Romantic Relationship Outcomes

The purpose of this study was to examine the predictors and consequences associated with Instagram selfie posting. Thus, this study explored whether body image satisfaction predicts Instagram selfie posting and whether Instagram selfie posting is then associated with Instagram-related conflict and negative romantic relationship outcomes. A total of 420 Instagram users aged 18 to 62 years (M = 29.3, SD = 8.12) completed an online survey questionnaire. Analysis of a serial multiple mediator model using bootstrapping methods indicated that body image satisfaction was sequentially associated with increased Instagram selfie posting and Instagram-related conflict, which related to increased negative romantic relationship outcomes. These findings suggest that when Instagram users promote their body image satisfaction in the form of Instagram selfie posts, risk of Instagram-related conflict and negative romantic relationship outcomes might ensue. Findings from the current study provide a baseline understanding to potential and timely trends regarding Instagram selfie posting.
An Open Source Prosodic Feature Extraction Tool

There has been an increasing interest in utilizing a wide variety of knowledge sources in order to perform automatic tagging of speech events, such as sentence boundaries and dialogue acts. In addition to the word spoken, the prosodic content of the speech has been proved quite valuable in a variety of spoken language processing tasks such as sentence segmentation and tagging, disfluency detection, dialog act segmentation and tagging, and speaker recognition. In this paper, we report on an open source prosodic feature extraction tool based on Praat, with a description of the prosodic features and the implementation details, as well as a discussion of its extension capability. We also evaluate our tool on a sentence boundary detection task and report the system performance on the NIST RT04 CTS data.
Linking Career Development Practices to Turnover Intention: the Mediator of Perceived Organizational Support

This study investigates the mediating role of perceived organizational support (POS) in linking career development practices (CDP) with turnover intention with 357 Malaysian knowledge workers. Data were collected from four different knowledge intensive industries, namely finance, information technology (IT), engineering, and education. Under individual perception of long-term CDP, structural equation modeling analysis revealed that POS completely mediates perceptions of career development opportunities, supervisory support, and internal promotion among knowledge workers. Career orientation and cultural influences of the perception of organizational support by Malaysian knowledge workers in the workplace and the impact towards human resource management policies are discussed.  Organizational support theory has received a lot of attention in its examination of the employee-employer suggested that individual attitudes and behaviors are affected by generalized perception of care and support from organizations. Employees feel obliged to repay organization with extra effort and loyalty when such favorable supportive treatments are discretionary-Defined as " employees' global beliefs concerning the extent to which organization values their contributions and cares about their well-being " , it is termed perceived organizational support (POS). To date, POS literature has been widely examined in relation to human resource (HR) policies with the aim of understanding employees' behavior and attitudes. Yet the mechanisms of how POS exerts influence on important HR policies and the mechanism to explain turnover intentions in knowledge-intensive organizations require further investigation.
Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques

Cervical cancer is the fourth most common malignant disease in women’s worldwide. In most cases, cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Most medical data sets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used data set, SMOTE is used to solve this problem. The data set consists of 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.
Towards Secure Provenance in the Cloud: A Survey

Provenance information are meta-data that summarize the history of the creation and the actions performed on an artefact e.g. data, process etc. Secure provenance is essential to improve data forensics, ensure accountability and increase the trust in the cloud. In this paper, we survey the existing cloud provenance management schemes and proposed security solutions. We investigate the current related security challenges resulting from the nature of the provenance model and the characteristics of the cloud and we finally identify potential research directions which we feel necessary t should be covered in order to build a secure cloud provenance for the next generation.
A 1.8V CMOS chopper four-quadrant analog multiplier

A 1.8V CMOS chopper four-quadrant analog multiplier, intending to serve as an autonomous IC block for low-frequency signal processing, is presented. Particular emphasis is laid upon achieving low output noise by means of chopper stabilization, while the multiplier's operation is based on the MOS Translinear Principle. The proposed design has been implemented and simulated in TSMC 0.18 µm CMOS process.
Optimal combination forecasts for hierarchical time series

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these ‘‘hierarchical time series’’. They are commonly forecast using either a ‘‘bottom-up’’ or a ‘‘top-down’’ method. In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottomup approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions. We show in a simulation study that our method performs well compared to the topdown approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region.
A multichannel CSMA MAC protocol for multihop wireless networks

We describe a new carrier–sense multiple access (CSMA) protocol for multihop wireless networks, sometimes also called ad hoc networks. The CSMA protocol divides the available bandwidth into several channels and selects an id le channel randomly for packet transmission. It also employs a notion of “soft” channel reservation as it gives preference to the channel that was used for the last successful transmission. We show via simulations that this multichannel CSMA protocol provides a higher throughput compared to its single channel counterpart by reducing the packet loss due to collisions. We also show that the use of channel reservation provides better performance than multichannel CSMA with purely random idle channel selection.
MIDeA: a multi-parallel intrusion detection architecture

Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.
AIDA - Abstraction for Advanced In-Database Analytics

With the tremendous growth in data science and machine learning, it has become increasingly clear that traditional relational database management systems (RDBMS) are lacking appropriate support for the programming paradigms required by such applications, whose developers prefer tools that perform the computation outside the database system. While the database community has attempted to integrate some of these tools in the RDBMS, this has not swayed the trend as existing solutions are often not convenient for the incremental, iterative development approach used in these fields. In this paper, we propose AIDA an abstraction for advanced in-database analytics. AIDA emulates the syntax and semantics of popular data science packages but transparently executes the required transformations and computations inside the RDBMS. In particular, AIDA works with a regular Python interpreter as a client to connect to the database. Furthermore, it supports the seamless use of both relational and linear algebra operations using a unified abstraction. AIDA relies on the RDBMS engine to efficiently execute relational operations and on an embedded Python interpreter and NumPy to perform linear algebra operations. Data reformatting is done transparently and avoids data copy whenever possible. AIDA does not require changes to statistical packages or the RDBMS facilitating portability. PVLDB Reference Format: Joseph Vinish D’silva, Florestan De Moor, Bettina Kemme. AIDA Abstraction for Advanced In-Database Analytics. PVLDB, 11(11): 1400-1413, 2018.
Application of DIP to Detect Power Transformers Axial Displacement and Disk Space Variation Using FRA Polar Plot Signature

While frequency response analysis (FRA) technique has been successfully used to assess the mechanical integrity of active parts within power transformers, it still exhibits some drawbacks including its inability to detect incipient and minor winding deformations and the requirement for an expert to analyze the results. Although several papers have investigated the impact of various faults on the transformer FRA signature, no attempt was made to automate and improve the fault detection accuracy of the current technique. The main contribution of this paper is the presentation of a new approach for the FRA technique through incorporating the magnitude and phase angle plots that can be measured using any commercial frequency response analyzer into one polar plot. Contrary to the current industry practice that only relies on the magnitude of the measured FRA signature for fault identification and quantification, the proposed polar plot that comprises more features than the magnitude plot will facilitate the use of digital image processing (DIP) techniques to improve the detection accuracy, standardize, and automate the FRA interpretation process. In this regard, three-dimensional models for two three-phase power transformers of different ratings, sizes, and windings structures are modeled using a finite element analysis technique to simulate various levels of axial displacement (AD) and disk space variation (DSV) at different locations of the transformer windings. Impact of minor fault levels on the proposed polar plot signature is investigated through the application of various DIP techniques. Simulation results are validated through practical measurements on a scaled-down transformer. Results show that the proposed polar plot along with the DIP technique is able to detect minor fault levels of AD and DSV with high accuracy.
Attention-based Belief or Disbelief Feature Extraction for Dependency Parsing

Existing neural dependency parsers usually encode each word in a sentence with bi-directional LSTMs, and estimate the score of an arc from the LSTM representations of the head and the modifier, possibly missing relevant context information for the arc being considered. In this study, we propose a neural feature extraction method that learns to extract arcspecific features. We apply a neural network-based attention method to collect evidences for and against each possible head-modifier pair, with which our model computes certainty scores of belief and disbelief, and determines the final arc score by subtracting the score of disbelief from the one of belief. By explicitly introducing two kinds of evidences, the arc candidates can compete against each other based on more relevant information, especially for the cases where they share the same head or modifier. It makes possible to better discriminate two or more competing arcs by presenting their rivals (disbelief evidence). Experiments on various datasets show that our arc-specific feature extraction mechanism significantly improves the performance of bi-directional LSTMbased models by explicitly modeling long-distance dependencies. For both English and Chinese, the proposed model achieve a higher accuracy on dependency parsing task than most existing neural attention-based models.
A dynamic understanding of customer behavior processes based on clustering and sequence mining

In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the ‘‘behavior process’’ as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.