Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunities for Web-Based Indicators in Environmental Sciences

This paper proposes a set of web-based indicators for quantifying and ranking the relevance of terms related to key-issues in Ecology and Sustainability Science. Search engines that operate in different contexts (e.g. global, social, scientific) are considered as web information carriers (WICs) and are able to analyse; (i) relevance on different levels: global web, individual/personal sphere, on-line news, and culture/science; (ii) time trends of relevance; (iii) relevance of keywords for environmental governance. For the purposes of this study, several indicators and specific indices (relational indices and dynamic indices) were applied to a test-set of 24 keywords. Outputs consistently show that traditional study topics in environmental sciences such as water and air have remained the most quantitatively relevant keywords, while interest in systemic issues (i.e. ecosystem and landscape) has grown over the last 20 years. Nowadays, the relevance of new concepts such as resilience and ecosystem services is increasing, but the actual ability of these concepts to influence environmental governance needs to be further studied and understood. The proposed approach, which is based on intuitive and easily replicable procedures, can support the decision-making processes related to environmental governance.
A hybrid method using Exponential Smoothing and Gradient Boosting for electrical short-term load forecasting

Accurate load forecasts are required in most tasks of energy planning. In this paper we present a hybrid method for short-term load forecasting. We combined Exponential Smoothing, a classical method for time series forecasting, with Gradient Boosting, a powerful machine learning algorithm. The proposed model was tested with real data and the results showed a considerable improvement in forecasting accuracy.
How the web can help Wikipedia: a study on information complementation of Wikipedia by the web

With the huge amount of data on the Web, looking for desired information can be a time consuming task. Wikipedia is a very helpful tool as it is the largest most popular general reference site on the internet. Most search engines rank Wikipedia pages among the top listed results. However, because many articles on Wikipedia are manually updated by users, there are several articles that lack information and need to be upgraded. Those lacking information can sometimes be found on the web. Uprooting this information from the web will involve a time consuming process of reading, analyzing and summarizing the information for the user. In order to support the user search process and help Wikipedia contributors in the updating process of articles, we propose a method of finding valuable complementary information on the web. Experiments showed that our method was quite effective in retrieving important complementary information from the web pages.
Probabilistic Supervised Term Weighting for Binary Text Categorization

In text categorization, the class agnostic (unsupervised) tf× idf term weighting scheme has seen widespread usage. Recently proposed supervised term weighting methods including tf×rf and tf× δidf make use of term class distribution to improve the classification accuracy. However, they only account for the presence of terms in classes, ignoring the absence of key categorical terms, which may give a very strong hint of the underlying document class. By taking into account both the presence and absence of terms, we propose a novel probabilistic family of supervised term weighting methods that are highly optimized for classification tasks. Experiments show that our robust term weighting schemes can significantly improve the performance of Support Vector Machines in various binary text categorization tasks.
The impact of stock-outs on whether, how much and what to buy

This paper investigates the impact of stockouts on category purchase incidence, purchase quantity and within-category choice, in different consumer segments. Based on scanner data for two product categories, we find that stockouts may lead to the purchase of smaller quantities, and to asymmetric (non-IIA) choice shifts. Reactions to stockouts are also found to be linked to the shopper’s basket size, shopping regularity, degree of opportunism and use rate.
Enhancing face recognition from video sequences using robust statistics

The aim of this work is to investigate a way of enhancing the performance of face recognition from video sequences by selecting only well-framed face images from those extracted from video sequences. It is known that noisy face images (e.g. not well-centered, non-frontal poses.) significantly reduce the performance of face recognition methods, and therefore, need to be filtered out during the training and the recognition. The proposed method is based on robust statistics, and more precisely, a recently proposed robust high-dimensional data analysis method, RobPCA. Experiments show that this filtering procedure improves the recognition rate by 10 to 20%.
A Case Study of the Aggregation Query Model in Read-Mostly NoSQL Document Stores

In this paper we focus on the aggregate query model implemented over NoSQL document-stores for read-mostly data bases. We discuss that the aggregate query model can be a good fit for read-mostly databases if the following design requirements are met: on-line time range queries, aggregates with predefined filters, frequent schema evolution and no ad-hoc. In our model, we present a composite object schema implementation over NoSQL document-stores, in which data associations are nested in a document under the same search key. We present the design choices to obtain a model adapted to our needs. Our schema is inspired by the star schema of Data Warehouses to reduce accessing data associations in many different documents and computing aggregates within the same composite. We present performance results of our empirical study over a 300 million records database that serves in production for the Ministry of Communications of Brazil. Results show the performance gains and penalties of our star composite schema when compared to the traditional multidimensional schema.
Students’ Perceptions of Language Learning Environment and Their Engagement in EFL Learning

: This study reported students’ perceptions of language learning environment developed by one of the universities in Bandung in the form of dormitory project and their engagement in EFL learning. This study employed the qualitative descriptive method. A questionnaire adapted from McGhee (2007) and semi-structured interview using guidelines from Jones (2009) about the degree to which students are engaged in a learning experience was attempted to find out the answer to the problems proposed. The result of this study indicated that 74% students had a good perception of a language learning environment designed by the dormitory and most of them had the very high level of engagement in EFL learning. The findings of this research suggested the dormitory to improve the system so that the shortcomings felt by the students could be resolved.
Efficient perspective-accurate silhouette computation

Silhouettes of geometric models (see Fig.1) are salient for many applications, including visualization [LE97] and shape analysis [BV95, Cro77]. The video describes a technique for efficient tracking of perspective-accurate silhouettes by reducing the problem to point location queries. Perspective-accurate silhouette computation is known to be difficult [KM96, KM98, KMGL96]. Note that silhouettes formed under perspective projection are significantly different from those under parallel projection and are more difficult to compute. Given a polyhedral model with well-defined normals, the polygons (facets) whose normals point away from the viewpoint are called back-facing. Similarly, normals of front-facing polygons point toward the viewpoint. An edge shared by a back-facing polygon and a front-facing polygon is said to be on the silhouette of the model. The silhouette of a geometric model is among its most significant visual features. It may be used to describe the shape of a model with only very few details of its geometry. It can also be used for computing the visible portions of a model as it forms the boundary between the visible front-facing and the hidden back-facing polygons. Silhouettes are also useful in shadow generation [Cro77], model simplification [LE97], collision detection [BV95], image registration [LSB95], and several other applications. The video presents an incremental algorithm for computing silhouettes of polyhedral models and updating them as the viewpoint changes. Most applications of interest exhibit coherence in silhouettes between successive computations as viewpoints change slowly. Our algorithm exploits this coherence to obtain fast updates of the silhouettes. Assume that we know the silhouette of a model from . a given view point. An edge ceases to be (resp., becomes) a silhouette edge if and only if it is shared by one front-facing and one back-facing polygon (resp., two polygons of the same facing), and exactly one of its adjacent polygons changes orientation. Thus, when the viewpoint is changed we are interested in finding such orientation-change events. We reduce this problem to a point-location query in a planar-map data structure.
Allergic rhinitis, asthma, airway biology, and chronic obstructive pulmonary disease in AJRCCM in 2004.

Medical, Oncological, and Radiological Sciences, University of Modena, Modena, Italy; Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina; Department of Respiratory Medicine and Thoracic Surgery, Glenfield Hospital, Leicester, United Kingdom; National Jewish Medical and Research Center, Department of Medicine and Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Health Sciences Center, Denver, Colorado; Division of Pulmonary and Critical Care Medicine, University of California–San Francisco, San Francisco, California; MRC Centre for Inflammation Research, University of Edinburgh, Edinburgh, Scotland, United Kingdom; AP-HP, Reanimation Medicale, Hopital Universitaire Henri Mondor, Creteil, France
Full-System Critical-Path Analysis and Performance Prediction

Many important workloads today, such as web-hosted services, are limited not by processor core performance but by interactions among the cores, the memory system, I/O devices, and the complex software layers that tie these components together. Architects who optimize system designs for these workloads are challenged to identify performance bottlenecks before the systems are built. This identification is challenging because, as in any concurrent system, overheads in one component may be hidden due to overlapping with other operations. These overlaps span the user/kernel and software/hardware boundaries, making traditional tools inadequate. Common software profiling techniques cannot account for hardware bottlenecks or situations in which software overheads are hidden due to overlapping with hardware operations. Critical-path analysis is a powerful approach for identifying bottlenecks in highly concurrent systems, but typically requires detailed domain knowledge to construct the required event dependence graphs. As a result, to date it has been applied only to isolated system layers (e.g., processor micro-architectures or message-passing applications). This thesis presents a methodology for identifying true end-to-end critical paths in systems composed of multiple layers of hardware and software, particularly in the domain of high-speed networking. The state machines that implicitly or explicitly govern the behavior of all the layers are modeled and their local interactions captured to build an end-to-end dependence graph that can be used to locate bottlenecks. This is done incrementally, with modest effort and only local understanding. Furthermore, it is shown that queue-based interactions are necessary and sufficient to capture information from complex protocols, multiple connections and multiple processors. The resulting dependence graph is created and analyzed distilling the huge amount of collected data into a set bottleneck locations including where the most un-overlapped time is spent, and locations where the addition of some buffering could improve the systems performance without any other optimizations. Additionally, this technique provides accurate quantitative predictions of the benefit of eliminating bottlenecks. The end result of this analysis, minutes after the data is gathered, is: (1) the identity of the component that causes the bottleneck; (2) the extent to which a component must be improved before it is no longer the bottleneck; (3) the next bottleneck that will be exposed in the system; and (4) the performance improvement that will occur before the next bottleneck is reached The analysis can be repeated for successive bottlenecks and is far faster than the available alternatives which include: studying huge amounts of statistics to attempt to understand the problem, developing and prototyping a solution, and simulating the solution. The traditional approach could take days or weeks of implementation and simulation and does not provide any certainty that the correct bottleneck has been found until all the work to mitigate it is complete.
Multiple optodes configuration for measuring the absolute hemodynamic response using spatially resolved spectroscopy method: An fNIRS study

The paper presents a method for measuring the absolute hemoglobin concentration using a multiple optodes configuration. The functional near-infrared spectroscopy (fNIRS) is used for obtaining the data of the right individual two-finger movement (i.e., thumb and index fingers) in the left motor cortex. The measured light intensities of four healthy male subjects are converted to the absolute hemoglobin concentration levels by using the spatially resolved spectroscopy approach. The t-value of each channel is computed for generating the activation brain map. It is noteworthy that our work is firstly about detection of the active positions in the same brain region using the fNIRS technique with multiple optodes configuration. The experimental results revealed that the proposed method could measure the absolute hemoglobin concentration. The active channels of individual two-finger movement were found in the different territories. However, it also had the active overlapped areas between the index and thumb finger movement.
Increasing end‐to‐end fairness over IEEE 802.11e‐based wireless mesh networks

In this paper, we discuss the issue of fairness in the IEEE802.11e over wireless mesh networks. Fairness is an important factor that we have to achieve before talking about QoS. Inspired by social networks approximations, and to achieve fairness and provide QoS by regulating heterogeneous traffic, we extended the original IEEE802.11e protocol by introducing a new algorithm based on the ‘token bucket’ concept. We also treat the problem of exposed/hidden nodes. Simulation results show that the proposed approach offers better performance than the IEEE802.11e one. Copyright © 2011 John Wiley & Sons, Ltd.
Automatic Recommendation of Web Pages for Online Users Using Web Usage Mining

Now a days, user rely on the web for information, but the currently available search engines often gives a long list of results, much of which are not always relevant to the user's requirement. Since a user has a specific goal when searching for information, personalized search may provide the results that accurately satisfy user's specific goal and intent for the search. Personalization of web search is to retrieve information according to user's interests which may be inferred from user's action, browsed documents or past query history etc. A real problem before web master of a website is to match the user needs and keep their attention focused to his website. In this paper, we propose a web recommendation approach which recommends user a list of pages based upon user's historic pattern and a list of web pages which have not been visited yet. This approach results in the improvement in the accuracy of the pages presented to the users.
Self-Resemblance Modulates Processing of Socio-Emotional Pictures in a Context-Sensitive Manner: Evidence From Startle Modification and Heart Rate Deceleration

Relevance of emotional information varies with self-involvement. The current study was undertaken to test whether subtle facial self-resemblance is sufficient to affect attentional and affective processing of complex socio-emotional pictures. Faces digitally manipulated to resemble the participants’ (final sample: N = 21) own versus unfamiliar control faces (form and color morphs) were presented in pictures of emotionally evocative social interactions, that is, threat versus sex scenes. At stimulus onset asynchronies (SOAs) of either 300 ms or 3,000 ms after picture onset, startle responses were elicited by acoustic white noise bursts (50 ms, 105 dB), and recorded at the orbicularis oculi via electromyography (EMG). The majority of participants remained unaware of the morphing manipulation, and awareness did not affect the principal results. As indexed by inhibition of startle at short lead intervals and by picture-evoked heart rate deceleration, facial self-resemblance modulates effects of motivational salience in a context-sensitive way, increasing prepulse inhibition with threat-related, but not erotic cues. Overall, the present study suggests that visual cues of general motivational significance and of self-relevance are integrated in a fast, presumably automatic manner.
Learning pedestrian activities for semantic mapping

This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.
Ortaokul öğrencilerinin üstbilişsel farkındalık düzeyi ile matematik öz yeterlik algısının matematik başarısına etkisinin incelenmesi

Bu arastirma, ortaokul ogrencilerinin ustbilissel farkindalik duzeyi ile matematik oz yeterlik algisinin matematik basarisina etkisini belirlemek amaciyla yapilmistir. Bu arastirmada nicel arastirma yontemlerinden iliskisel tarama modeli kullanilmistir. Arastirmanin orneklemini Konya ili Kadinhani ilcesinde bulunan Milli Egitim Bakanligi'na bagli dort devlet okulunda, 2015-2016 egitim- ogretim yili II. Donemi’nde ogrenim goren 680 ortaokul ogrencisi olusturmaktadir. Arastirmada veri toplama araci olarak Ustbilissel Farkindalik Envanteri (UFE) ve Matematik Oz Yeterlik Algisi Olcegi kullanilmistir. Elde edilen verilerin analizinde; Bagimsiz Gruplar t testi, tek yonlu varyans analizi (ANOVA), Pearson Momentler Carpimi Korelasyon analizi ve coklu regresyon analizinden yararlanilmistir. Arastirmanin bulgularina gore ortaokul ogrencilerinin ustbilissel farkindalik duzeyinin cinsiyete, sinif duzeyine ve matematik karne notu degiskenine gore anlamli farklilastigi; matematik oz yeterlik algisinin sinif duzeyi ve matematik karne notuna gore farklilasirken cinsiyet degiskenine gore farklilasmadigi gorulmustur. Ustbilissel farkindalik duzeyi ve matematik oz yeterlik algisinin matematik basarisini %47 oraninda acikladigi ve matematik basarisi uzerinde anlamli etkiye sahip olduklari gorulmustur.
WiFi-assisted multi-floor indoor localization with inertial sensors

With the rapid development of Location Based Service (LBS) applications and the continual improvement of intelligent terminal technology, study on smartphone-based indoor positioning approach has turned to be the hot topic quickly in recent years. In this paper, a WiFi-assisted Pedestrian Dead Reckoning (PDR) algorithm based on activity recognition is proposed on the smartphone platform to realize the pedestrian's trajectory tracking and positioning in the multi-floor indoor environment. Our algorithm combines the Inertial Measurement Unit (IMU) data with the Received Signal Strength Indication (RSSI) data from the smartphone to distinguish pedestrian's activities. Then, based on the recognition result, we implement adjustment and optimization on the traditional PDR algorithm. The test results show that our algorithm performs with better accuracy and reliability compared with the traditional algorithm on realistic scenario.
PERFORMANCE EVALUATION OF UWB SYSTEMS USING DIFFERENT ORDER OF GAUSSIAN PULSES

The ultra-wideband impulse radio (UWB-IR) transmits information by using extremely short pulses that are very sensitive to the timing error. In this paper, the contribution of pulse shapes to the timing error tolerance is considered. Three well known Gaussian derivative pulses are investigated. The simulation results show that different pulse shapes cause substantial difference in achievable bit error rate. The pulse with lower derivative order tends to offer better performance
A Semantic Web Approach for E-learning Platforms

When lecturers publish contents in an e-learning platform like a course degree or the bibliography for a given course, normally they do that either by uploading a document (MSWord, pdf) or by creating a resource that will be part of a webpage. In both cases, this kind of information is static and is only useful for human readers, the information is not open to other systems, and in general to the world. In this work, we propose to enrich an e-learning platform with semantic web content so that information is available to external systems. Concretely, we will develop our approach over the Moodle platform, the widest e-learning platform used. Moreover, we will focus on representing the course’s degree and its bibliography.
Measurement and Estimation of Renal Size by Computed Tomography in Korean Children

Adequate organ growth is an important aspect of growth evaluation in children. Renal size is an important indicator of adequate renal growth; computed tomography (CT) can closely estimate actual kidney size. However, insufficient data are available on normal renal size as measured by CT. This study aimed to evaluate the relationships of anthropometric indices with renal length and volume measured by CT in Korean pediatric patients. Renal length and volume were measured using CT images in 272 pediatric patients (age < 18 years) without renal disease. Data for anthropometric indices—including height, weight, and body surface area (BSA)—were obtained using medical records. Using the equation for an ellipsoid, renal volume was calculated in cubic centimeters. Height showed greatest correlation with renal length on stepwise multiple linear regression analysis; BSA showed the strongest significant correlation with renal volume. The mean renal size for each age group and height group was determined; it showed a tendency to increase with age and height. This is the first Korean study to report the relationship between body indices and renal size measured by CT. These results can serve as normative standards for assessing adequate renal growth.