Miracle-dz/UniFAR-FTUs huggingface dataset:
Single reference device-based weighted least squares localization using LOS and NLOS paths

This paper proposes a novel localization scheme to estimate the location of the mobile device by using single reference device. This scheme makes use of the bidirectional information of the line of sight (LOS) and non line of sight (NLOS) paths to construct the area of possible mobile device (APMD) and line of possible mobile device (LPMD) respectively. The location of the mobile device is determined by using the weighted least squares solution based on the intersection of these APMD and LPMD. Simulation and experimental results demonstrate that the performance of our proposed localization scheme outperforms the existing localization schemes by significant margins.
Global positioning system technology (GPS) for psychological research: a test of convergent and nomological validity

The purpose of this paper is to examine the convergent and nomological validity of a GPS-based measure of daily activity, operationalized as Number of Places Visited (NPV). Relations among the GPS-based measure and two self-report measures of NPV, as well as relations among NPV and two factors made up of self-reported individual differences were examined. The first factor was composed of variables related to an Active Lifestyle (AL) (e.g., positive affect, extraversion…) and the second factor was composed of variables related to a Sedentary Lifestyle (SL) (e.g., depression, neuroticism…). NPV was measured over 4 days. This timeframe was made up of two week and two weekend days. A bi-variate analysis established one level of convergent validity and a Split-Plot GLM examined convergent validity, nomological validity, and alternative hypotheses related to constraints on activity throughout the week simultaneously. The first analysis revealed significant correlations among NPV measures- weekday, weekend, and the entire 4-day time period, supporting the convergent validity of the Diary-, Google Maps-, and GPS-NPV measures. Results from the second analysis, indicating non-significant mean differences in NPV regardless of method, also support this conclusion. We also found that AL is a statistically significant predictor of NPV no matter how NPV was measured. We did not find a statically significant relation among NPV and SL. These results permit us to infer that the GPS-based NPV measure has convergent and nomological validity.
Prediction of protein stability changes for single‐site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross‐validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy—a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb. uci.edu/servers/servers.html. Proteins 2006. © 2005 Wiley‐Liss, Inc.
The Prevalence of Problematic Video Gamers in The Netherlands

This study surveyed Dutch adolescents and adults about their video gaming behavior to assess the prevalence of problematic gaming. A representative national panel of 902 respondents aged 14 to 81 took part in the study. The results show that gaming in general is a wide-spread and popular activity among the Dutch population. Browser games (small games played via the internet) and offline casual games (e.g., offline card games) were reported as most popular type of game. Online games (e.g., massively multiplayer online role-playing games) are played by a relatively small part of the respondents, yet considerably more time is spent on these online games than on browser games, offline casual games, and offline games (e.g., offline racing games). The prevalence of problematic gaming in the total sample is 1.3 percent. Among adolescents and young adults problematic gaming occurs in 3.3 percent of cases. Particularly male adolescents seem to be more vulnerable to developing problematic gaming habits.
Facial landmark detection via self-adaption model and multi-task feature learning

Initialization and feature selection are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we proposed a new method, which includes a new initialize model and multitask feature learning, for the robust facial landmark localization. In our new method, firstly, a fast detection will be processed to locate the eyes and mouth, and the initialization model will adapt to the real location according to fast detection. Secondly, a multitask feature learning based Supervised Descent Method (SDM) is adopted on our model to achieve a better performance. For each task, multiple features will be jointly learned and their weights will be calculated during training process. Experiments on three benchmark databases show that our method achieves state-of-art performance.
Three new RelE-homologous mRNA interferases of Escherichia coli differentially induced by environmental stresses

Prokaryotic toxin – antitoxin (TA) loci encode mRNA interferases that inhibit translation, either by cleaving mRNA codons at the ribosomal A site or by cleaving any RNA site‐specifically. So far, seven mRNA interferases of Escherichia coli have been identified, four of which cleave mRNA by a translation‐dependent mechanism. Here, we experimentally confirmed the presence of three novel TA loci in E. coli. We found that the yafNO, higBA (ygjNM) and ygiUT loci encode mRNA interferases related to RelE. YafO and HigB cleaved translated mRNA only, while YgiU cleaved RNA site‐specifically at GC[A/U], independently of translation. Thus, YgiU is the first RelE‐related mRNA interferase that cleaves mRNA independently of translation, in vivo. All three loci were induced by amino acid starvation, and inhibition of translation although to different degrees. Carbon starvation induced only two of the loci. The yafNO locus was induced by DNA damage, but the transcription originated from the dinB promoter. Thus, our results showed that the different TA loci responded differentially to environmental stresses. Induction of the three loci depended on Lon protease that may sense the environmental stresses and activate TA loci by cleavage of the antitoxins. Transcription of the three TA operons was autoregulated by the antitoxins.
Perfusion-CT: A Non-Invasive Tool to Monitor Regional Brain Perfusion in Traumatic Brain Injury Patients and Therapies Impacting it

There is a growing body of evidence suggesting imaging biomarkers to select patients with traumatic brain in- jury (TBI) for therapies, and to monitor the effects of these treatments. In this setting, advanced neuroimaging techniques such as perfusion-CT (PCT) can offer more clinically relevant pathophysiologic information than purely structural imag- ing such as conventional noncontrast CT. Patients with altered brain PCT results might be considered for more aggressive and early treatment to prevent intracranial hypertension, whereas patients with preserved brain perfusion might benefit from less invasive treatment. From a clinical trial perspective, better stratification of the patient population, and the use of advanced neuroimaging as a biomarker for response to intervention, could be expected to significantly reduce the number of patients required to assess a treatment effect and, therefore, expedite the clinical trial process. This article reviews the fundamental of perfusion-CT technique and its application to TBI.
A Novel Training Algorithm for HMMs with Partial and Noisy Access to the States

This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have \emph{partial} and \emph{noisy} access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions.
Mining Sequential Patterns with Regular Expression Constraints

Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. We propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT-Sequential Pattern mining with Regular expression consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like anti monotonicity) are integrated into the mining process.
Inmates’ Adult Education in Greece—A Case Study

Correctional education in Greece has been implemented since 1980’s. Second Chance Schools (SCS) in prisons were established in 2004, aiming at combating social exclusion. This study’s aim is to look into the reasons of school dropout and to interpret why inmates take part in the educational program provided by SCS, registering the motives, expectations and barriers. According to the findings, inmates dropped out particularly due to financial problems. They engage in the educational process having strong motives, primarily the beneficial calculation of the days of their sentence as well as it is a way to get out of the pains of imprisonment. Their expectations have to do with education, improvement of their vocational status, personal development and social acceptance. There were no barriers mentioned.
Experimental evaluation of duration modelling techniques for automatic speech recognition

This paper presents an experimental evaluation of two such extensions: hidden semi-Markov models (HSMMs), and expanded state HMMs (ESHMMs). These extensions to the standard HMM (hiden Markov model) formalism permit improved duration modelling and experimental results are presented which show that they can consistently lead to improved performance. The results indicate that if sufficient training material is available, the best performance is obtained with the Fergusson model, but that with smaller training sets Poisson HSMMs or type B ESHMMs are more robust models.
A Critical Examination of the Assumptions Regarding Centralized Coordination in Large-Scale Emergency Situations

Abstract Scientists have extensively debated the effectiveness of different emergency response management models, with a particular focus on the “command and control” versus “coordination” models. This debate, which focuses on centralized coordination at the tactical and strategic levels, assumes that the activity of frontline units within and between response organizations must be aligned and that it is possible to exercise control over frontline units. In this article, we discuss these assumptions and argue that researchers overestimate the degree to which frontline units can and should be centrally coordinated during the acute phase of emergency situations. Instead, we provide a mechanism in which coordination naturally emerges from the task at hand when frontline units follow a few simple decision rules. In addition, two managerial intervention strategies are presented that only may work in specific situations when frontline units are likely to misinterpret the environment in which they operate.
On the Empirical Time Complexity of Random 3-SAT at the Phase Transition

The time complexity of problems and algorithms, i.e., the scaling of the time required for solving a problem instance as a function of instance size, is of key interest in theoretical computer science and practical applications. In this context, propositional satisfiability (SAT) is one of the most intensely studied problems, and it is generally believed that solving SAT requires exponential time in the worst case. For more than two decades, random 3-SAT at the solubility phase transition has played a pivotal role in the theoretical and empirical investigation of SAT solving, and to this day, it is arguably the most prominent model for difficult SAT instances. Here, we study the empirical scaling of the running time of several prominent, high-performance SAT solvers on random 3-SAT instances from the phase transition region. After introducing a refined model for the location of the phase transition point, we show that the median running time of three incomplete, SLS-based solvers - WalkSAT/SKC, BalancedZ and probSAT - scales polynomially with instance size. An analogous analysis of three complete, DPLL-based solvers - kcnfs, march_hi and march_br - clearly indicates exponential scaling of median running time. Moreover, exponential scaling is witnessed for these DPLL-based solvers when solving only satisfiable and only unsatisfiable instances, and the respective scaling models for each solver differ mostly by a constant factor.
Effect of Activation Time on the Pinang Frond based Activated Carbon for Remazol Brilliant Blue R Removal

Activated carbons are regularly used for dyes wastewater treatment. It can be produced from various organics materials which having high level of carbon content. In this study, pinang frond based activated carbon (PFAC) was produce by using physical activation process to study the effect of difference activation time in the range of 1-7 hours. The optimum PFAC sample is found on activation time of 3 hours which gives the highest BET surface area and pore volume of 958.23 m2/g and 0.5469 mL/g, respectively. This sample shows well-developed pore structure with high fixed carbon content of 79.74%. The removal of remazol brilliant blue R (RBBR) by 87.6 % for initial RBBR concentration of 50 mg/L and 40.8 % for 500 mg/L are achieved via this sample.
Improving Disease Gene Prioritization by Comparing the Semantic Similarity of Phenotypes in Mice with Those of Human Diseases

Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are “translated” into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are “translated” into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene–disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene–disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google. com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net.
An Ontological User Modeling Approach Using Wikipedia's Content for Cross-System Personalization

In this work, we are building a personalization engine for web pages across systems by using Wikipedia’s content as an ontological model. The model captures domain topics of any web page for user modeling purposes. Our system formulates a semantic user profile by analyzing the user’s browsing behavior, assuming that frequent browsing in a topical area implies a user’s interest in that area. We also address semantic interoperability via using Wikipedia as a shared platform to construct a protocol for profiling and data exchange. Preliminary tests of our system in the computer science domain may indicate that the proposed ontological model fairly captures a user’s topical interests with minor errors.
Collective entrepreneurship: Employees’ perceptions of the influence of leadership styles

Abstract Collective entrepreneurship (CE) is the ability of several individuals to jointly innovate and create within organisations. In this exploratory study, we investigate whether the relationship between some specific leadership styles and more effective CE is affected by moderator variables. The unit of analysis were staff members of a Portuguese group of small- and medium-sized enterprises (SMEs). A total of 204 questionnaires were returned, representing almost half the employee population we approached. For measurement, we employed 19 items to gather the six latent variables related to our model. For data analysis, we used partial least squares. We found that participative leadership style had the highest joint and indirect effect on more effective CE. Job satisfaction appears to be an important moderator for the occurrence of CE. The impact of employees’ organisational commitment and collaboration on CE is rather weak. Our study contributes to advancing knowledge in the fields of organisational psychology and entrepreneurship. We combine and extend previous research, which allows us to reconcile the sometimes contradictory findings so far concerning leadership and CE in the realm of small- and medium-sized enterprises.
Volumetric Data Modeling and Reduction in a Compressive Sensing Framework

We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly proportional to the number of features, as opposed to the original resolution of the dataset. These experiment results motivate further research in the direction of customdesigned sparse representations for large-scale volumetric data.
INTERNATIONAL STUDENTS' SELF-REGULATED LEARNING AND ITS RELATION TO MATHEMATICS ACHIEVEMENT IN AN OFF- SHORE AUSTRALIAN PROGRAM

The study examines any differences in the use of self-regulated learning (SRL) strategies between domestic and international students early and late in the semester, and also investigates the relation between SRL strategies and mathematics achievement. A sample consists of 32 domestic students and 38 international students who are in their first semester in an off-shore Australian pre-university program in Malaysia. The Learning and Study Strategies Inventory (LASSI) is employed to measure self-reported SRL strategies for the same sample twice during the semester. The results indicate that international students score higher in almost all LASSI subscales than domestic students at the beginning and towards the end of their semester. The results also reveal Anxiety subscale is significantly related to mathematics achievement attained early and late in the semester for domestic students. No significant relation between LASSI subscales and mathematics achievement is obtained early in the semester, while Anxiety, Concentration, Selecting Main Ideas, Time Management and Test Strategies subscales are significantly related to mathematics achievement attained late in the semester for international students.
Exploring the Evolutionary History of the Differentially Expressed Genes between Human Populations: Action of Recent Positive Selection

Though debates exist on the early human evolutionary models such as the “Out of Africa” theory, which hypothesizes that modern humans migrated from Africa to Europe about 50,000 to 100,000 years ago, Africans and Europeans were geographically separated with minimal gene flow for tens of thousands of years. The variations between the current European and African populations, therefore, should have evolved during this timeframe. To gain more insights into the evolutionary history of human phenotypes including gene expression, it is critical to tell how recent positive selection has played a role in the variations observed in the current populations. Using the list of differentially expressed genes we previously identified between the HapMap samples derived from individuals of African (from Ibadan, Nigeria) and European (from Utah, USA) ancestry, we searched for evidence of selection among these differential genes. We found that 27 differentially expressed genes (out of 356 tested) between these two European and African populations have been under recent positive selection. Our findings suggest that the variation between these two populations appears to be affected primarily by neutral genetic drift and/or stabilizing selection and to a lesser degree by positive selection. Further annotation enrichment analyses showed that these 27 genes under selection were overrepresented in certain Gene Ontology biological processes, molecular functions and cellular components such as transcription, lipid binding and lysosome. Our results can provide unique insights into the evolutionary history of the variation in the gene expression phenotype between these two human populations.