nils-herrmann/clean_scirepeval huggingface dataset:
Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC)

The complexity of modern HVAC systems leads to device mis-configuration in about 40% of buildings, wasting upto 40% of the energy consumed. Fault detection methods generate excessive alarms leading to operator alert fatigue, faults left unfixed and energy wastage. Sophisticated fault detection techniques developed in the literature are seldom used in practice. We investigate this gap by applying various fault detection techniques on real data from a 145,000 sqft, five floor building. We first find that none of these algorithms are designed to capture control loop configuration faults. We develop a novel algorithm, Model, Cluster and Compare (MCC) that is able to detect anomalies by automatically modeling and clustering similar entities in an HVAC system, in an unsupervised manner, and comparing them. We implemented MCC to detect faults in Variable Air Volume boxes in our building, and demonstrate that it successfully detects non-obvious configuration faults. We propose a two stage approach, where we design intelligent rules (iRules) based on anomaly exemplars from a mix of data driven algorithms. iRules are successful in capturing a large fraction of faults in our building, with only one false alarm and 78 anomalies detected out of 237 zones. Thus, comparative data mining is useful in filtering the large amount of data generated in modern buildings, but that human in the loop systems are better still.
Community structure in social and biological networks.

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
Timing and awareness of movement decisions: does consciousness really come too late?

Since Libet's seminal observation that a brain potential related to movement preparation occurs before participants report to be aware of their movement intention, it has been debated whether consciousness has causal influence on movement decisions. Here we review recent advances that provide new insights into the dynamics of human decision-making and question the validity of different markers used for determining the onset of neural and conscious events. Motor decisions involve multiple stages of goal evaluation, intention formation, and action execution. While the validity of the Bereitschaftspotential (BP) as index of neural movement preparation is controversial, improved neural markers are able to predict decision outcome even at early stages. Participants report being conscious of their decisions only at the time of final intention formation, just before the primary motor cortex starts executing the chosen action. However, accumulating evidence suggests that this is an artifact of Libet's clock method used for assessing consciousness. More refined methods suggest that intention consciousness does not appear instantaneously but builds up progressively. In this view, early neural markers of decision outcome are not unconscious but simply reflect conscious goal evaluation stages which are not final yet and therefore not reported with the clock method. Alternatives to the Libet clock are discussed that might allow for assessment of consciousness during decision making with improved sensitivity to early decision stages and with less influence from meta-conscious and perceptual inferences.
Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases

It's not surprisingly when entering this site to get the book. One of the popular books now is the genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases. You may be confused because you can't find the book in the book store around your city. Commonly, the popular book will be sold quickly. And when you have found the store to buy the book, it will be so hurt when you run out of it. This is why, searching for this popular book in this website will give you benefit. You will not run out of this book.
User churn in focused question answering sites: characterizations and prediction

Given a user on a Q&A site, how can we tell whether s/he is engaged with the site or is rather likely to leave? What are the most evidential factors that relate to users churning? Question and Answer (Q&A) sites form excellent repositories of collective knowledge. To make these sites self- sustainable and long-lasting, it is crucial to ensure that new users as well as the site veterans who provide most of the answers keep engaged with the site. As such, quantifying the engagement of users and preventing churn in Q&A sites are vital to improve the lifespan of these sites. We study a large data collection from stackoverflow.com to identify significant factors that correlate with newcomer user churn in the early stage and those that relate to veterans leaving in the later stage. We consider the problem under two settings: given (i) the first k posts, or (ii) first T days of activity of a user, we aim to identify evidential features to automatically classify users so as to spot those who are about to leave. We find that in both cases, the time gap between subsequent posts is the most significant indicator of diminishing interest of users, besides other indicative factors like answering speed, reputation of those who answer their questions, and number of answers received by the user.
Inferring Painting Style with Multi-Task Dictionary Learning

Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.
Workplace bullying in nurses.

The article reports a study of workplace bullying in community nurses in an NHS trust. The aims were to determine the prevalence of bullying, to examine the association between bullying and occupational health outcomes, and to investigate whether support at work could moderate the effects of bullying. Forty-four percent of nurses reported experiencing one or more types of bullying in the previous 12 months, compared to 35 percent of other staff. Fifty percent of nurses had witnessed the bullying of others. Nurses who had been bullied reported significantly lower levels of job satisfaction and significantly higher levels of anxiety, depression and propensity to leave. They were also more critical of aspects of the organizational climate of the trust. Support at work was able to protect nurses from some of the damaging effects of bullying.
Effects of gamification on participation and data quality in a real-world market research domain

Gamification has become an increasingly popular way to improve user engagement and motivation, but there is currently a lack of empirical research to demonstrate that increased gamification provides these benefits. To help address this problem we designed three versions of a gamified market research survey and tested them alongside the established industry standard in a study of over 600 participants. We also highlight examples where game elements compromise respondent data, and provide design solutions that correct the problem without losing the motivational benefits of gamification.
Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log  k) and the cost of the tree is O(log  k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.
Multicategory Support Vector Machines

The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be optimal for the original multicategory problem. We propose multicategory SVMs, which extend the binary SVM to the multicategory case, and encompass the binary SVM as a special case. The multicategory SVM implements the optimal classification rule as the sample size gets large, overcoming the suboptimality of the conventional one-versus-rest approach. The proposed method deals with the equal misclassification cost and the unequal cost case in unified way.
A Schema Theory of Discrete Motor Skill Learning

A number of closed-loop postulations to explain motor skills learning and performance phenomena have appeared recently, but each of these views suffers from either (a) logical problems in explaining the phenomena or (b) predictions that are not supported by the empirical evidence. After these difficulties are discussed, a new theory for discrete motor learning is proposed that seems capable of explaining the existing findings. The theory is based on the notion of the schema and uses a recall memory to produce movement and a recognition memory to evaluate response correctness. Some of the predictions are mentioned, research techniques and paradigms that can be used to test the predictions are listed, and data in support of the theory are presented.
The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being

Most people use Facebook on a daily basis; few are aware of the consequences. Based on a 1-week experiment with 1,095 participants in late 2015 in Denmark, this study provides causal evidence that Facebook use affects our well-being negatively. By comparing the treatment group (participants who took a break from Facebook) with the control group (participants who kept using Facebook), it was demonstrated that taking a break from Facebook has positive effects on the two dimensions of well-being: our life satisfaction increases and our emotions become more positive. Furthermore, it was demonstrated that these effects were significantly greater for heavy Facebook users, passive Facebook users, and users who tend to envy others on Facebook.
Probabilistic Recurrent State-Space Models

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. We propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. This combination allows efficient incorporation of latent state temporal correlations, which we found to be key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem.
Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway.

Dengue virus (DENV) belongs to the family Flaviviridae and can cause major health problems worldwide, including dengue fever and dengue shock syndrome. DENV replicon in human cells inhibits interferon α and β with the help of its non-structural proteins. Non-structural protein 5 (NS5) of DENV is responsible for the proteasome-mediated degradation of signal transducer and activator of transcription (STAT) 2 protein, which has been implicated in the development of resistance against interferon-mediated antiviral effect. This degradation of STAT2 primarily occurs with the help of E3 ubiquitin ligases. Seven in absentia homologue (SIAH) 2 is a host protein that can mediate the ubiquitination of proteins and is known for its interaction with NS5. In this study, comprehensive computational analysis was performed to characterize the protein-protein interactions between NS5, SIAH2, and STAT2 to gain insight into the residues and sites of interaction between these proteins. The objective of the study was to structurally characterize the NS5-STAT2, SIAH2-STAT2, and NS5-SIAH2 interactions along with the determination of the possible reaction pattern for the degradation of STAT2. Docking and physicochemical studies indicated that DENV NS5 may first interact with the host SIAH2, which can then proceed towards binding with STAT2 from the side of SIAH2. These implications are reported for the first time and require validation by wet-lab studies.
A Robust Density-Based Clustering Approach Using DBCURE – MapReduce Techniques

Clustering is the process of grouping similar data into clusters and dissimilar data into different clusters. Density-based clustering is a useful clustering approach such as DBSCAN and OPTICS. The increasing volume of data and varying size of data sets lead the clustering process challenging. So that we propose a parallel framework of clustering with advanced approach called MapReduce. We develop a new algorithm DBCURE which is modified from DBSCAN. Next we propose a newly developed parallel algorithm called DBCURE-MR that is DBCURE algorithm is parallelized with MapReduce framework. This proposed method finds clusters in parallel manner efficiently.  MapReduce, DBCURE, density based clustering, parallelization algorithm.
A systematic test case selection methodology for product lines: results and insights from an industrial case study

In the context of product lines, test case selection aims at obtaining a set of relevant test cases for a product from the entire set of test cases available for a product line. While working on a research-based innovation project on automated testing of product lines of Video Conferencing Systems (VCSs) developed by Cisco, we felt the need to devise a cost-effective way of selecting relevant test cases for a product. To fulfill such need, we propose a systematic and automated test selection methodology using: 1) Feature Model for Testing (FM_T) to capture commonalities and variabilities of a product line; 2) Component Family Model for Testing (CFM_T) to model the structure of test case repository; 3) A tool to automatically build restrictions from CFM_T to FM_T and traces from CFM_T to the actual test cases. Using our methodology, a test engineer is only required to select relevant features through FM_T at a higher level of abstraction for a product and the corresponding test cases will be obtained automatically. We evaluate our methodology by applying it to a VCS product line called Saturn with seven commercial products and the results show that our methodology can significantly reduce cost measured as test selection time and at the same time achieves higher effectiveness (feature coverage, feature pairwise coverage and fault detection) as compared with the current manual process. Moreover, we conduct a questionnaire-based study to solicit the views of test engineers who are involved in developing FM_T and CFM_T. The results show that test engineers are positive about adapting our methodology in their current practice. Finally, we present a set of lessons learnt while applying product line engineering at Cisco for test case selection.
Driving Hotzenplotz: A Hybrid Interface for Vehicle Control Aiming to Maximize Pleasure in Highway Driving

A prerequisite to foster proliferation of automated driving is common system acceptance. However, different users groups (novice, enthusiasts) decline automation, which could be, in turn, problematic for a successful market launch. We see a feasible solution in the combination of the advantages of manual (autonomy) and automated (increased safety) driving. Hence, we've developed the Hotzenplotz interface, combining possibility-driven design with psychological user needs. A simulator study (N=30) was carried-out to assess user experience with subjective criteria (Need Scale, PANAS/-X, HEMA, AttrakDiff) and quantitative measures (driving behavior, HR/HRV) in different conditions. Our results confirm that pure AD is significantly less able to satisfy user needs compared to manual driving and make people feeling bored/out of control. In contrast, the Hotzenplotz interface has proven to reduce the negative effects of AD. Our implication is that drivers should be provided with different control options to secure acceptance and avoid deskilling.
The double-edged sword of leader charisma: Understanding the curvilinear relationship between charismatic personality and leader effectiveness.

This study advanced knowledge on charisma by (a) introducing a new personality-based model to conceptualize and assess charisma and by (b) investigating curvilinear relationships between charismatic personality and leader effectiveness. Moreover, we delved deeper into this curvilinear association by (c) examining moderation by the leader's level of adjustment and by (d) testing a process model through which the effects of charismatic personality on effectiveness are explained with a consideration of specific leader behaviors. Study 1 validated HDS charisma (Hogan Development Survey) as a useful trait-based measure of charisma. In Study 2 a sample of leaders (N = 306) were assessed in the context of a 360-degree development center. In line with the too-much-of-a-good-thing effect, an inverted U-shaped relationship between charismatic personality and observer-rated leader effectiveness was found, indicating that moderate levels are better than low or high levels of charisma. Study 3 (N = 287) replicated this curvilinear relationship and further illustrated the moderating role of leader adjustment, in such a way that the inflection point after which the effects of charisma turn negative occurs at higher levels of charisma when adjustment is high. Nonlinear mediation modeling further confirmed that strategic and operational leader behaviors fully mediate the curvilinear relationship. Leaders low on charisma are less effective because they lack strategic behavior; highly charismatic leaders are less effective because they lack operational behavior. In sum, this work provides insight into the dispositional nature of charisma and uncovers the processes through which and conditions under which leader charisma translates into (in)effectiveness.
Clearing the RF smog: making 802.11n robust to cross-technology interference

Recent studies show that high-power cross-technology interference is becoming a major problem in today's 802.11 networks. Devices like baby monitors and cordless phones can cause a wireless LAN to lose connectivity. The existing approach for dealing with such high-power interferers makes the 802.11 network switch to a different channel; yet the ISM band is becoming increasingly crowded with diverse technologies, and hence many 802.11 access points may not find an interference-free channel.
 This paper presents TIMO, a MIMO design that enables 802.11n to communicate in the presence of high-power cross-technology interference. Unlike existing MIMO designs, however, which require all concurrent transmissions to belong to the same technology, TIMO can exploit MIMO capabilities to decode in the presence of a signal from a different technology, hence enabling diverse technologies to share the same frequency band. We implement a prototype of TIMO in GNURadio-USRP2 and show that it enables 802.11n to communicate in the presence of interference from baby monitors, cordless phones, and microwave ovens, transforming scenarios with a complete loss of connectivity to operational networks.
Pure Exploration in Multi-armed Bandits Problems

We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of strategies that explore sequentially the arms. The strategies are assessed in terms of their simple regrets, a regret notion that captures the fact that exploration is only constrained by the number of available rounds (not necessarily known in advance), in contrast to the case when the cumulative regret is considered and when exploitation needs to be performed at the same time. We believe that this performance criterion is suited to situations when the cost of pulling an arm is expressed in terms of resources rather than rewards. We discuss the links between simple and cumulative regrets. The main result is that the required exploration–exploitation trade-offs are qualitatively different, in view of a genera l lower bound on the simple regret in terms of the cumulative regret. We then refine this statement.