nils-herrmann/clean_scirepeval huggingface dataset:
Analysing the Impact of a DDoS Attack Announcement on Victim Stock Prices

DDoS attacks are increasingly used by 'hackers' and 'hacktivists' for various purposes. A number of on-line tools are available to launch an attack of significant intensity. These attacks lead to a variety of losses at the victim's end. We analyse the impact of Distributed Denial-of-Service (DDoS) attack announcements over a period of 5 years on the stock prices of the victim firms. We propose a method for event studies that does not assume the cumulative abnormal returns to be normally distributed, instead we use the empirical distribution for testing purposes. In most cases we find no significant impact on the stock returns but in cases where a DDoS attack creates an interruption in the services provided to the customer, we find a significant negative impact.
Efficient attack plan recognition using automated planning

Network attacks are becoming ever more sophisticated and are able to hide more easily in the increasing amount of traffic being generated by everyday activity. Administrators are placed in the unfortunate position of distinguishing between the two. The attack graph has been in use for some time because it provides a concise knowledge representation, and has had successful security metrics developed from it. Previous methods of attack plan recognition have relied on statistical inference to capture network attacks, however they are computationally expensive and can fail to capture obvious cause and effect relationships. In this paper, we use automated planning to capture new properties of attack graphs and use it for plan recognition. Experimental results demonstrate the efficacy of our approach.
A Multi-User Surface Visuo-Haptic Display Using Electrostatic Friction Modulation and Capacitive-Type Position Sensing

This paper proposes a visuo-haptic feedback system that can provide haptic feedback to multiple users on an LCD monitor using electrostatic adhesion and built-in capacitive sensors. The prototype system consists of a 40-inch LCD monitor, an ITO electrode sheet arranged on the monitor, and multiple contact pads for electrostatic adhesion. By applying low-frequency haptic voltage and high-frequency sensing voltage to each pad, the system realizes passive haptic feedback, as well as position sensing using the same components. The passive haptic feedback force exceeds 1 N at 300 V<inline-formula><tex-math notation="LaTeX">$_\mathrm{rms}$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="nakamura-ieq1-2556660.gif"/></alternatives></inline-formula>. The performance of the sensing system is investigated in terms of interference, which shows the haptic voltage and existence of multiple pads can affect the sensing and can shift the output by a few millimeters. A pilot application, virtual hockey game, successfully demonstrates the sensing and haptic rendering capability of the proposed system. The effect of the interference on the demonstration is discussed.
Automatic power meter reading system using GSM network

The development of a GSM automatic power meter reading (GAPMR) system is presented in this paper. The GAPMR system is consists of GSM digital power meters installed in every consumer unit and an electricity ebilling system at the energy provider side. The GSM digital power meter (GPM) is a single phase IEC61036 standard compliance digital kWh power meter with embedded GSM modem which utilize the GSM network to send its power usage reading using short messaging system (SMS) back to the energy provider wirelessly. At the power provider side an ebilling system is used to manage all received SMS meter reading, compute the billing cost, update the database, and to publish billing notification to its respective consumer through SMS, email, Web portal and printed postage mailing. A working prototype of the GAPMR system was build to demonstrate the effectiveness and efficiency of automatic meter reading, billing and notification through the use of GSM network.
Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Conventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method. The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.
THE FUNCTIONAL APPROACH TO THE STUDY OF ATTITUDES

At the psychological level the reasons for holding or for changing attitudes are found in the functions they perform for the individual, specifically the functions of adjustment, ego defense, value expression, and knowledge. The conditions necessary to arouse or modify an attitude vary according to the motivational basis of the attitude. Ego-defensive attitudes, for example, can be aroused by threats, appeals to hatred and repressed impulses, and authoritarian suggestion, and can be changed by removal of threat, catharsis, and self-insight. Expressive attitudes are aroused by cues associated with the individual's values and by the need to reassert his self-image and can be changed by showing the appropriateness of the new or modified beliefs to the self-concept Brain washing is primarily directed at the value-expressive function and operates by controlling all environmental supports of old values. Changing attitudes may involve generalization of change to related areas of belief and feeling. Minimal generalization seems to be the rule among adults; for example, in politics voting for an opposition candidate does not have much effect upon party identification. The author is Professor of Psychology at the University of Michigan, former president of the Society for the Psychological Study of Social Issues, and coeditor of Research Methods in the Behavioral Sciences and Public Opinion and Propaganda.
Specification Inference from Demonstrations

Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent’s behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent’s actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent’s action, and the the agent’s choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.
Multi-Vendor Loyalty Programs: Influencing Customer Behavioral Loyalty?

Loyalty programs are a consolidated marketing instrument whose adoption in many sectors has not been associated with appropriate comprehension of either their management elements or their effects. The purpose of this research is to contribute to knowledge about the effect of loyalty programs on repeat purchase behavior. More specifically, it seeks to discover whether joining a program changes the buying behavior of its members, and, if so, to study the profile of those whose behavior changes most. The intention was also to provide new study variables pertaining to multi-vendor loyalty programs, such as where they are joined or purchases in associated outlets as a result of behavioral loyalty. Research was carried out using a sample of 1200 individuals (31,746 purchases) belonging to a multi-vendor loyalty program. The study period was 13 years, 4 months, and split into two phases: before and after the joining the program. Different methodological approaches, such as the use of transactional databases that included pre-program-enrollment data and of the same sampling units throughout the study, were incorporated into the research with the aim of advancing academic knowledge regarding multi-vendor loyalty programs. Moreover, a type of program and market hardly dealt with in the relevant literature was analyzed. The results showed while the loyalty program had managed to reduce the time between purchases, it had not affected purchase volume or average expenditure. They also demonstrated the existence of a differential profile of customers who had changed their buying behavior to a greater extent. Finally, recency was identified as being the decisive variable in behavioral change.
Power electronics for renewable energy systems: Wind turbine and photovoltaic systems

The use of renewable energy sources are increased because of the depletion of natural resources and the increasing pollution level from energy production. The wind energy and the solar energy are most widely used among the renewable energy sources. Power electronics is needed in almost all kinds of renewable energy system. It controls the renewable source and interfaces with the load effectively, which can be grid-connection or working in stand-alone mode. In this paper, overview of wind and photovoltaic energy systems are introduced. Next, the power electronic circuits behind the most common wind and photovoltaic configurations are discussed. Finally, their controls and important requirements for grid connection are explained.
An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation

This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify “convergent” concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).
Beyond the 'digital natives' debate: Towards a more nuanced understanding of students' technology experiences

The idea of the ‘digital natives’, a generation of tech-savvy young people immersed in digital technologies for which current education systems cannot cater, has gained widespread popularity on the basis of claims rather than evidence. Recent research has shown flaws in the argument that there is an identifiable generation or even a single type of highly adept technology user. For educators, the diversity revealed by these studies provides valuable insights into students’ experiences of technology inside and outside formal education. While this body of work provides a preliminary understanding, it also highlights subtleties and complexities that require further investigation. It suggests, for example, that we must go beyond simple dichotomies evident in the digital natives debate to develop a more sophisticated understanding of our students’ experiences of technology. Using a review of recent research findings as a starting point, this paper identifies some key issues for educational researchers, offers new ways of conceptualizing key ideas using theoretical constructs from Castells, Bourdieu and Bernstein, and makes a case for how we need to develop the debate in order to advance our understanding.
Multi-scale Internet traffic forecasting using neural networks and time series methods

This article presents three methods to forecast accurately the amount of traffic in TCP/IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using realworld data from two large Internet service providers. In addition, different time scales (five minutes, one hour and one day) and distinct forecasting lookaheads were analyzed. The experiments with the neural ensemble achieved the best results for five minutes and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.
Why it can matter more than IQ

Goleman highlights the many reports of the disintegration of civility and safety, and the onslaught of mean-spirited impulse running amok and claims that these only reflect back to us, on a larger scale, a creeping sense of emotions out of control in our own lives, and in the lives of those people living around us. He seeks to make sense of the senselessness and bring about change which will help our children fare better in life.
Distributional Semantics Resources for Biomedical Text Processing

The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.
Affect Analysis Model: novel rule-based approach to affect sensing from text

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).
Understanding Self-Organizing Teams in Agile Software Development

Traditional software teams consist of independently focused self-managing professionals with high individual but low team autonomy. A challenge with introducing agile software development is that it requires a high level of both individual and team autonomy. This paper studies the barriers with introducing self-organizing teams in agile software development and presents data from a seven month ethnographic study of professional developers in a Scrum team. We found the most important barrier to be the highly specialized skills of the developers and the corresponding division of work. In addition we found a lack of system for team support, and reduced external autonomy to be important barriers for introducing self- organizing teams. These findings have implications for software development managers and practitioners.
Air-filled substrate integrated waveguide — A flexible and low loss technological platform

In this paper, the air-filled substrate integrated waveguide (AFSIW), offering a technological platform for the design of high performance millimeterwave self-packaged substrate integrated components and systems, is presented. AFSIW is based on low-cost printed circuit board (PCB). Several components, including antennas, filters and couplers, have been theoretically studied and experimentally demonstrated up to 60 GHz. This technology, surpassing its dielectric-filled SIW (DFSIW) counterpart, is expected by the authors to replace the rectangular waveguide technology for future low-cost extremely high data rate communication systems and highly sensitive sensors operating at millimeterwave and beyond.
Zero-forcing beamforming with block diagonalization scheme for Coordinated Multi-Point transmission

Coordinated Multi-Point (CoMP) transmission is a technology targeted for Long Term Evolution Advanced (LTE-A). The Joint Processing (JP) technique for CoMP can maximize system performance, which is achieved mainly with channel information-based beamforming algorithms. Precoding methods perform differently in various CoMP scenarios. In this paper, we propose a joint processing scheme in downlink CoMP transmission system. We apply block diagonal beamforming to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point.
A Named Entity Recognition Shootout for German

• CRF-based methods: • StanfordNER : CRF + standard features • GermaNER : CRF + distributional semantics, gazetteers, ... • Recurrent Neural Network: • BiLSTM + CRF [6] using characterand word embeddings using FastText • Wikipedia (contemporary encyclopedia) • Europeana (historic newspaper from 1703 to 1899)  Future Contemporary corpora: minor improvements GermEval: 82.93 à 82.93 CoNLL: 82.99 à 84.73 Historic corpora: major improvements LFT: 69.62 à 73.44 ONB: 70.46 à 78.56 Same setup as Exp. 2 but with transfer learning and considering only the BiLSTM-based method
Business model metrics: an open repository

Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and performance measurement literature, we develop a generic open repository of metrics related to core business model concepts. We validate and assess the practical value of the repository based on four e-Business model cases. The repository can be utilized in designing business models, specifically for networked enterprises. It is neither complete nor universally applicable, but needs adjustment with each design. Ultimately, with this paper, we hope measurement will become an integral part of the business model innovation discussion.