Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding domestic energy consumption through interactive visualisation: a field study

Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy "in the wild" -- 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Matching Random Tree Models of Spatio-Temporal Patterns to Tables or Graphs

The problem of matching random tree models of multi-component patterns to tables or graphs containing components extracted from diverse data sources is considered. We focus on bi-level trees whose branches emanate from one root node and terminate on different leaf nodes. Node and branch attributes are treated as random variables. Tree nodes represent pattern components of specified types that occur in tables or graphs to be searched. For each item in the table or graph with a type match to the tree root, there is a set of components from the table or graph that are candidate leaves for optimal matches to the tree model. We adopt a view of optimal matches to random tree models as minimum cost assignments of candidate leaves to tree branches. Model-based formulas are derived for computing costs associated with assignments of specific candidate leaf components from tables or graphs to specific tree branches. We specify an ontology suitable for dynamic geo-spatial query problems in which (1) tree nodes represent physical objects or events on the ground (buildings, roads, communication transmissions.), and (2) branch attributes characterize, with uncertainty, distance or time separations between components, and angles between links connecting components. Our approach is used to search very large images for specific types of buildings in probabilistically constrained spatial arrangements, with the goal of ranking model matches for efficient inspection by human analysts.
Tudumi: information visualization system for monitoring and auditing computer logs

Computer security breaches are already a major problem in using computers. The most basic defense against it is to monitor and audit the computer logs. Computer logs, however have a huge amount of textual data. It is, therefore, almost impossible to inspect them manually using current systems. We propose a log visualization system called "Tudumi". Tudumi consists of several functions which assist system administrators to perform such tasks manually. These functions are information visualization, log summarization and reflecting known rules into the visualization method. Tudumi makes it easier to detect anomalous user activities, such as intrusion, from a huge amount of computer logs.
Hemispheric ozone variability indices derived from satellite observations and comparison to a coupled chemistry-climate model

Total column ozone is used to trace the dynam- ics of the lower and middle stratosphere which is governed by planetary waves. In order to analyse the planetary wave activity a Harmonic Analysis is applied to global multi-year total ozone observations from the Total Ozone Monitoring Spectrometer (TOMS). As diagnostic variables we introduce the hemispheric ozone variability indices one and two. They are defined as the hemispheric means of the amplitudes of the zonal waves number one and two, respectively, as traced by the total ozone field. The application of these indices as a simple diagnos- tic for the evaluation of coupled chemistry-climate models (CCMs) is demonstrated by comparing results of the CCM ECHAM4.L39(DLR)/CHEM (hereafter: E39/C) against satellite observations. It is quantified to what extent a multi- year model simulation of E39/C (representing "2000" cli- mate conditions) is able to reproduce the zonal and hemi- spheric planetary wave activity derived from TOMS data (1996-2004, Version 8). Compared to the reference observations the hemispheric ozone variability indices one and two of E39/C are too high in the Northern Hemisphere and too low in the Southern Hemisphere. In the Northern Hemisphere, where the agree- ment is generally better, E39/C produces too strong a plan- etary wave one activity in winter and spring and too high an interannual variability. For the Southern Hemisphere we reveal that the indices from observations and model differ significantly during the ozone hole season. The indices are used to give reasons for the late formation of the Antarctic ozone hole, the insufficient vortex elongation and eventually the delayed final warming in E39/C. In general, the hemispheric ozone variability indices can be regarded as a simple and robust diagnostic to quantify model-observation differences concerning planetary wave activity. It allows a first-guess on how the dynamics is rep- resented in a model simulation before applying costly and more specific diagnostics.
Reducing network load in large-scale, Peer-to-Peer Virtual Environments with 3D Voronoi Diagrams

When moving towards fully Peer-to-Peer Virtual Environments (P2P-VE), the amount of network traffic generated at each peer remains a significant concern. Multiplayer Online Games (MOG) are the largest application subset of VEs and have been shown to require high frequency of update messages and minimal network latencies. Yet, this demanding criteria must be balanced with the need to also limit the otherwise quadratic growth of network traffic amongst peers. Two-dimensional Voronoi Diagrams (2D-VD) have been proposed as a way to address the inherent traffic scalability issues by naturally clustering players (and thus their update traffic) within the game-world. However, other important issues related to game-play and overall VE performance remained and were only addressed by our recent introduction of a third dimension to the VD computations (3D-VD). As our experimentation indicates, this unique approach has significant impact on the network characteristics of a P2P-VE. Due to its 3D nature, more connections are necessary per peer but a mechanism is successfully introduced to cope with the increased bandwidth requirement. More importantly, the results obtained show considerable reduction in traffic load under varying peer topologies while still maintaining the desirable features of 3D-VD.
Prevalence of Propionibacterium acnes in Intervertebral Discs of Patients Undergoing Lumbar Microdiscectomy: A Prospective Cross-Sectional Study

Background The relationship between intervertebral disc degeneration and chronic infection by Propionibacterium acnes is controversial with contradictory evidence available in the literature. Previous studies investigating these relationships were under-powered and fraught with methodical differences; moreover, they have not taken into consideration P. acnes’ ability to form biofilms or attempted to quantitate the bioburden with regard to determining bacterial counts/genome equivalents as criteria to differentiate true infection from contamination. The aim of this prospective cross-sectional study was to determine the prevalence of P. acnes in patients undergoing lumbar disc microdiscectomy. Methods and Findings The sample consisted of 290 adult patients undergoing lumbar microdiscectomy for symptomatic lumbar disc herniation. An intraoperative biopsy and pre-operative clinical data were taken in all cases. One biopsy fragment was homogenized and used for quantitative anaerobic culture and a second was frozen and used for real-time PCR-based quantification of P. acnes genomes. P. acnes was identified in 115 cases (40%), coagulase-negative staphylococci in 31 cases (11%) and alpha-hemolytic streptococci in 8 cases (3%). P. acnes counts ranged from 100 to 9000 CFU/ml with a median of 400 CFU/ml. The prevalence of intervertebral discs with abundant P. acnes (≥ 1x103 CFU/ml) was 11% (39 cases). There was significant correlation between the bacterial counts obtained by culture and the number of P. acnes genomes detected by real-time PCR (r = 0.4363, p<0.0001). Conclusions In a large series of patients, the prevalence of discs with abundant P. acnes was 11%. We believe, disc tissue homogenization releases P. acnes from the biofilm so that they can then potentially be cultured, reducing the rate of false-negative cultures. Further, quantification study revealing significant bioburden based on both culture and real-time PCR minimize the likelihood that observed findings are due to contamination and supports the hypothesis P. acnes acts as a pathogen in these cases of degenerative disc disease.
Selection bias and endogeneity issues on the relationship between information technology and firm performance

This study extends prior research on the relation between information technology (IT) and firm performance by using both univariate and multivariate econometric models to assess the hypothesized relationships. Additionally, sample selection bias and endogeneity are examined to determine their effect, if any, on the results. The univariate results indicate that, on average, IT leaders outperform non-IT leaders. After controlling for sample selection bias and endogeneity, using Wooldridge (2002) 2SLS-IV, the coefficient of the endogenous variable is higher than suggested by ordinary least squares estimation and the Hausman F-Test is significant, indicating that the relationship between IT and firm performance is endogenous. Thus, it is important to control for sample selection bias and endogeneity to properly estimate the relationship between IT and firm performance.
Top Management Teams, Strategy and Financial Performance: A Meta-Analytic Examination

A considerable amount of research has investigated the linkage between top management team (TMT) characteristics and firm financial performance. Much of this research relies on demographic data. While these data are reliable and accessible, findings across studies are not consistent. Meta-analysis of several TMT indicators and firm financial performance provides modest support for direct relationships but indicates moderating influences. Further meta-analysis and a confirmatory factor analysis enrich these findings by examining potential moderating and intervening factors.
Multimolecular complex of Par-4 and E2F1 binding to Smac promoter contributes to glutamate-induced apoptosis in human- bone mesenchymal stem cells

Neural cells undergo glutamate-induced apoptosis in ischaemic brain tissue, in which prostate apoptosis response-4 gene (Par-4) is involved. Human-bone mesenchymal stem cells can be utilized as an effective therapy for ischemic brain injury. In this study, we found that glutamate could induce apoptosis in human-bone mesenchymal stem cells, accompanied by increased expression of Par-4 gene and Smac release from mitochondria. Repressing Par-4 expression attenuated the glutamate-induced apoptosis. Both Par-4 protein and E2F1 protein could bind to E2F1-binding BS3 site on Smac promoter and participated in the formation of a proteins-DNA complex. Moreover, in the complex, E2F1, not Par-4, was found to be directly bound to the Smac promoter, suggesting that Par-4 exerted indirectly its transcriptional control on the Smac gene though interacting with E2F1. Expression of full-length Par-4 in human-bone mesenchymal cells resulted in increased activity of the Smac promoter. In addition, the indirect transcripional regulation of Par-4 on Smac depended on its COOH terminus-mediated interaction between Par-4 and E2F1. We conclude that the formation of proteins–DNA complex, containing Par-4 protein, E2F1 protein and the Smac promoter, contributes to the pro-apoptotic effect on glutamate-treated human-bone mesenchymal stem cells.
Experimental Analysis of the Prediction Model Based on String Invariants

A new approach of the string theory called the Prediction Model Based on String Invariants (PMBSI) was applied here to time-series forecast. We used 2-end-point open string that satisfies the Dirichlet and Neumann boundary conditions. The initial motivation was to transfer modern physical ideas into the neighboring field called econophysics. The physical statistical viewpoint has proved to be fruitful, namely in the description of systems where many-body effects dominate. However, PMBSI is not limited to financial forecast. The main advantage of PMBSI include absence of the learning phase when large number of parameters must be set. Comparative experimental analysis of PMBSI vs. SVM was performed and the results on artificial and real-world data are presented. PMBSI performance was in a close match with SVM.
Estimating the Inf-Sup Constant in Reduced Basis Methods for Time-Harmonic Maxwell’s Equations

The reduced basis method (RBM) generates low-order models of parametrized partial differential equations. These allow for the efficient evaluation of parametrized models in many-query and real-time contexts. We use the RBM to generate low-order models of microscale models under variation of frequency, geometry, and material parameters. In particular, we focus on the efficient estimation of the discrete stability constant used in the reduced basis error estimation. A good estimation of the discrete stability constant is a challenging problem for Maxwell's equations, but is needed to yield rigorous bounds on the model approximation error. We therefore test and compare multiple techniques and discuss their properties in this context.
Compressive demodulation of mutually interfering signals

The challenge of Multiuser Detection (MUD) is that of demodulating mutually interfering signals given that at any time instant the number of active users is typically small. The promise of compressed sensing is the demodulation of sparse superpositions of signature waveforms from very few measurements. This paper considers signature waveforms that are are drawn from a Gabor frame. It describes a MUD architecture that uses subsampling to convert analog input to a digital signal, and then uses iterative matching pursuit to recover the active users. Compressive demodulation requires K log N samples to recover K active users whereas standard MUD requires N samples. The paper provides theoretical performance guarantees and consistent numerical simulations.
Investigating Privacy Attitudes and Behavior in Relation to Personalization

This article presents an experimental study of privacy-related attitudes and behaviors regarding a music recommender service based on two types of user modeling: personality traits and musical preferences. Contrary to prior expectations and attitudes reported by participants, personality traits are frequently disclosed to the system and other users, indicating that embedded modeling of user personality does not represent an acceptance barrier. Discrepancies between privacy attitudes and behaviors have been reported before in the context of e-commerce applications, but the corresponding studies could not exclude several conflicting hypotheses, such as participants expressing attitudes outside the context of specific privacy dilemmas and contact with researchers, which may have mitigated perceived privacy risks. Arguably, these are fundamental problems in empirical investigations into privacy that apply to most published works relating to privacy and user modeling. Measures to control these factors in this study are discussed, and methodological suggestions for future research are presented.
Time-weighted Social Network: Predict when an item will meet a collector

“For what else is this collection but a disorder to which habit has accommodated itself to such an extent that it can appear as order? ”. Unpacking his library, Walter Benjamin describes how a collection is singular [2]. Collections are not unified wholes, but rather chains of undefined objects. Classify, search, arrange or browse collections are personal processes influenced by internal reflexions. Working on figural and non-figural collections, Piaget and Inhelder explain how space and time influence the way a collector looks to his collection [13]. As a result, representing collections is an issue for computer scientists. Here, we propose a time-based method, which consideres chronological events and draws a time-weighted graph defining patterns of items. We therefore show how this graph outputs different results depending on when it is requested. This work is based on an architecture, designed by Openrendezvous.com, a collaborative web-based application helping to make appointments. Our goal is to adapt a social graph used to define the perfect moment for two people to meet, to the collection case. We discuss how we can build a structure that helps to compute the ideal moment for an item to meet a collector.
Implementation of data mining approach for building automated decision support systems

Decision support systems (DSS) has remarkably increased in today's competitive business environment. Organizations are aggressively emphasizing on computerized support for building comprehensive automated DSS. The main purpose behind this approach is to build intelligent business and reduces the business pressure from the competitors. In this paper, we proposed a model for building efficient DSS with connection of data mining view. The presented model guide the business leaders to get extra support from data mining abstract to create effective DSS and compete the business world with more appropriate manner. Model represent the combination of DSS components and data mining tasks for the generation of better decisions, results, rules, and patterns from operational databases.
Properties and Classification of the Wheels of the OLS Polytope.

A wheel in a graph G(V;E) is an induced subgraph consisting of an odd hole and an additional node connected to all nodes of the hole. In this paper, we study the wheels of the column intersection graph of the OLS polytope (PI). These structures induce valid inequalities for this polytope, which are facet defining for its set packing relaxation. Our work builds on simple structural properties of wheels which are used to categorise them into a number of collectively exhaustive classes. Each such class gives rise to a set of valid inequalities for PI. Moreover, this classification allows us to estimate the cardinality of the whole wheel class as well as to derive a recognition algorithm for the circulant matrices corresponding to wheels of a particular type. In a forthcoming paper, we show for some of the wheel classes presented here that they give rise to facet-defining inequalities for PI.
Bioactivity and Chemical Synthesis of Caffeic Acid Phenethyl Ester and Its Derivatives

Caffeic acid phenethyl ester (CAPE), as one of the main active ingredients of the natural product propolis, shows the unique biological activities such as anti-tumor, anti-oxidation, anti-inflammatory, immune regulation, and so on. These have attracted the attention of many researchers to explore the compound with potent biological activities. This review aims to summarize its bioactivities, synthetic methods and derivatives, which will be helpful for further study and development of CAPE and its derivatives.
From Parkinsonian thalamic activity to restoring thalamic relay using deep brain stimulation: new insights from computational modeling

We present a computational model of a thalamocortical relay neuron for exploring basal ganglia thalamocortical loop behavior in relation to Parkinson's disease and deep brain stimulation (DBS). Previous microelectrode, single-unit recording studies demonstrated that oscillatory interaction within and between basal ganglia nuclei is very often accompanied by synchronization at Parkinsonian rest tremor frequencies (3–10 Hz). These oscillations have a profound influence on thalamic projections and impair the thalamic relaying of cortical input by generating rebound action potentials. Our model describes convergent inhibitory input received from basal ganglia by the thalamocortical cells based on characteristics of normal activity, and/or low-frequency oscillations (activity associated with Parkinson's disease). In addition to simulated input, we also used microelectrode recordings as inputs for the model. In the resting state, and without additional sensorimotor input, pathological rebound activity is generated for even mild Parkinsonian input. We have found a specific stimulation window of amplitudes and frequencies for periodic input, which corresponds to high-frequency DBS, and which also suppresses rebound activity for mild and even more prominent Parkinsonian input. When low-frequency pathological rebound activity disables the thalamocortical cell's ability to relay excitatory cortical input, a stimulation signal with parameter settings corresponding to our stimulation window can restore the thalamocortical cell's relay functionality.
An adaptable time-delay neural-network algorithm for image sequence analysis

In this letter we present an algorithm based on a time-delay neural network with spatio-temporal receptive fields and adaptable time delays for image sequence analysis. Our main result is that tedious manual adaptation of the temporal size of the receptive fields can be avoided by employing a novel method to adapt the corresponding time delay and related network structure parameters during the training process.
Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: a factorial screening experiment.

AIMS To screen promising intervention components designed to reduce smoking and promote abstinence in smokers initially unwilling to quit. DESIGN A balanced, four-factor, randomized factorial experiment. SETTING Eleven primary care clinics in southern Wisconsin, USA. PARTICIPANTS A total of 517 adult smokers (63.4% women, 91.1% white) recruited during primary care visits who were willing to reduce their smoking but not quit. INTERVENTIONS Four factors contrasted intervention components designed to reduce smoking and promote abstinence: (1) nicotine patch versus none; (2) nicotine gum versus none; (3) motivational interviewing (MI) versus none; and (4) behavioral reduction counseling (BR) versus none. Participants could request cessation treatment at any point during the study. MEASUREMENTS The primary outcome was percentage change in cigarettes smoked per day at 26 weeks post-study enrollment; the secondary outcomes were percentage change at 12 weeks and point-prevalence abstinence at 12 and 26 weeks post-study enrollment. FINDINGS There were few main effects, but a significant four-way interaction at 26 weeks post-study enrollment (P = 0.01, β = 0.12) revealed relatively large smoking reductions by two component combinations: nicotine gum combined with BR and BR combined with MI. Further, BR improved 12-week abstinence rates (P = 0.04), and nicotine gum, when used without MI, increased 26-week abstinence after a subsequent aided quit attempt (P = 0.01). CONCLUSIONS Motivation-phase nicotine gum and behavioral reduction counseling are promising intervention components for smokers who are initially unwilling to quit.