Miracle-dz/UniFAR-FTUs huggingface dataset:
A Parallel Algorithm to Construct Concept Lattice

Concept lattice is an efficient tool for data analysis and knowledge discovery and has been used in many fields, such as data mining, machine learning, and information searching. With volumes of data growing increasingly, it's important to design the algorithms which are more efficient. Therefore, a parallel algorithm of PSTCL (parallel sub-top concept lattice) is developed in this paper. The analysis and experiments show that the time performance of PSTCL has been improved significantly over Godin algorithms. In addition, the space performance is better than the algorithm used under the single computer environment.
Comprehensive User Evaluation of Adaptive Graphical User Interfaces

Adaptive graphical user interfaces have the potential to improve the user’s experience by personalizing the interface to better suit his or her needs. However, evaluations have yielded conflicting results: some studies show that adaptive menus and toolbars are faster or preferred in comparison to static counterparts [6,8], while others show the opposite [3,10,11]. We propose an evaluation approach that should allow for more comprehensive comparison of adaptive interfaces by: (1) reporting on and isolating factors such as accuracy, stability and predictability that may contribute to the user’s experience, and (2) measuring the user’s awareness of advanced features, in addition to performance and user satisfaction.
Electric Load Forecasting Using Hybrid Machine Learning Approach Incorporating Feature Selection

Forecasting of future electricity demand is very important for the electric power industry. As influenced by various factors, it has been shown in several publications that machine learning methods are useful for electric load forecasting (ELF). On the one hand, we introduce in this paper the approach of support vector regression (SVR) for ELF. In particular, we use particle swarm optimization (PSO) algorithm to optimize SVR parameters. On the other hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. Our contribution consists of investigating the importance of applying the feature selection approach for removing the irrelevant factors of electric load. The experimental results elucidate the feasibility of applying feature selection without decreasing the performance of the SVR-PSO model for ELF.
What is the “normal” fetal heart rate?

Aim. There is no consensus about the normal fetal heart rate. Current international guidelines recommend for the normal fetal heart rate (FHR) baseline different ranges of 110 to 150 beats per minute (bpm) or 110 to 160 bpm. We started with a precise definition of “normality” and performed a retrospective computerized analysis of electronically recorded FHR tracings. Methods. We analyzed all recorded cardiotocography tracings of singleton pregnancies in three German medical centers from 2000 to 2007 and identified 78,852 tracings of sufficient quality. For each tracing, the baseline FHR was extracted by eliminating accelerations/decelerations and averaging based on the “delayed moving windows” algorithm. After analyzing 40% of the dataset as “training set” from one hospital generating a hypothetical normal baseline range, evaluation of external validity on the other 60% of the data was performed using data from later years in the same hospital and externally using data from the two other hospitals. Results. Based on the training data set, the “best” FHR range was 115 or 120 to 160 bpm. Validation in all three data sets identified 120 to 160 bpm as the correct symmetric “normal range”. FHR decreases slightly during gestation. Conclusions. Normal ranges for FHR are 120 to 160 bpm. Many international guidelines define ranges of 110 to 160 bpm which seem to be safe in daily practice. However, further studies should confirm that such asymmetric alarm limits are safe, with a particular focus on the lower bound, and should give insights about how to show and further improve the usefulness of the widely used practice of CTG monitoring.
Phototoxic Risk Assessments on Benzophenone Derivatives: Photobiochemical Assessments and Dermal Cassette-Dosing Pharmacokinetic Study

This study aimed to qualify photosafety screening on the basis of photochemical and pharmacokinetic (PK) data on dermally applied chemicals. Six benzophenone derivatives (BZPs) were selected as model compounds, and in vitro photochemical/phototoxic characterization and dermal cassette-dosing PK study were carried out. For comparison, an in vivo phototoxicity test was also conducted. All of the BZPs exhibited strong UVA/UVB absorption with molar extinction coefficients of over 2000 M−1 × cm−1, and benzophenone and ketoprofen exhibited significant reactive oxygen species (ROS) generation upon exposure to simulated sunlight (about 2.0 mW/cm2); however, ROS generation from sulisobenzone and dioxybenzone was negligible. To verify in vitro phototoxicity, a 3T3 neutral red uptake phototoxicity test was carried out, and benzophenone and ketoprofen were categorized to be phototoxic chemicals. The dermal PK parameters of ketoprofen were indicative of the highest dermal distribution of all BZPs tested. On the basis of its in vitro photochemical/phototoxic and PK data, ketoprofen was deduced to be highly phototoxic. The rank of predicted phototoxic risk of BZPs on the basis of the proposed screening strategy was almost in agreement with the results from the in vivo phototoxicity test. The combined use of photochemical and cassette-dosing PK data would provide reliable predictions of phototoxic risk for candidates with high productivity.
Strategic planning as communicative process

This paper examines the construction of a strategic plan as a communicative process. Drawing on Ricoeur’s concepts of decontextualization and recontextualization, we conceptualize strategic planning activities as being constituted through the iterative and recursive relationship of talk and text. Based on an in-depth case study, our findings show how multiple actors engage in a formal strategic planning process which is manifested in a written strategy document. This document is thus central in the iterative talk to text cycles. As individuals express their interpretations of the current strategic plan in talk, they are able to make amendments to the text, which then shape future textual versions of the plan. This cycle is repeated in a recursive process, in which the meanings attributed to talk and text increasingly converge within a final agreed plan. We develop our findings into a process model of the communication process that explains how texts become more authoritative over time and, in doing so, how they inscribe power relationships and social order within organizations. These findings contribute to the literature on strategic planning and on organization as a communication process.
Pre- and Postpartum Fear of Childbirth in Nulliparous and Parous Women

The link between fear of childbirth and theories of anxiety in general is discussed. A possible expression of trait (T-fear) and state (S-fear) aspects of fear of childbirth was investigated in 77 nulliparous and 85 parous women based on data from gestational week 32, at 2 hours and at 5 weeks after childbirth. Data are based on the State Trait Anxiety Inventory and the Wijma Delivery Expectancy/Experience Questionnaire. According to their scores on the Wijma Delivery Expectancy/Experience Questionnaire during late pregnancy, women were divided into 3 groups: high, moderate and low levels of fear of childbirth. In gestational week 32, women in the low level of fear of childbirth group had lower trait anxiety than those in the moderate level of fear of childbirth group, who had lower trait anxiety than the women in the high level of fear of childbirth group. Nulliparous women had a higher level of fear of childbirth but a lower level of trait anxiety than did parous women. There was a significant decreasing trend in fear of childbirth from 2 hours to 5 weeks after delivery, in a parallel way for all 3 groups. Differences in fear of childbirth between nulliparous and parous women disappeared after delivery. These findings suggest that fear of childbirth comprises a considerable part of T-fear, with the risk of a vicious cycle, i.e. that during labour women experience what they are afraid of, which also influences the women's postpartum cognitive appraisal of the delivery.
Building communities of educational enquiry

This article aims to outline key factors to be considered in the development of communities of enquiry in an educational context and to establish a conceptual and theoretical framework within which much needed empirical work can be carried out. The literature surveyed considers both different types of community and different theoretical positions on how communities develop. Seven factors emerge. 1) A community depends on its members' opportunities to engage in dialogue and other modes of participation. 2) Participation in a community is sustained through the quality of relationships. 3) Perspectives and assumptions underpin the relationships of a community and may offer insights into the dynamics and operation of the community. 4) How a community operates is governed by its structure and context, including the extent to which its structure is imposed or constrained either internally or externally. 5) As a community develops, a climate for its operation also emerges—involving aspects such as tone, environment and potential conflict. 6) The purpose of an enquiry will influence this climate and there may be a need to accommodate or harmonise a multiplicity of purposes arising from the complex interrelationships, perspectives and assumptions involved. 7) A key issue for all communities is control, in relation to who has access to the community, to resources, constraints and power within it. The authors conclude by highlighting a number of tensions or dualities arising from these themes and potential implications for empirical investigations in this field and for those who may seek to build a community of educational enquiry.
Class-wise two-dimensional PCA method for face recognition

Interests in biometric identification systems have led to many face recognition task-oriented studies. These studies often address the detection of face images taken from a camera and the recognition of faces via extracted meaningful features. To meet the requirement of defining data with fewer features, principal component analysis (PCA)-based techniques are widely used due to their efficiency and simplicity. There is a remarkable interest in the used efficiency of PCA by extending this traditional technique with various aspects. From this viewpoint, this study is specifically focused on the PCA-based face recognition techniques. By enhancing the methods in the reviewed studies, a novel class-wise two-dimensional PCA-based face recognition algorithm is presented in this study. Unlike the traditional method, this method generates more than one subspace considering within-class scattering. A system based on the presented approach can successively detect and recognise faces in not only images but also in video files. In addition, analyses were conducted to evaluate the efficiency of the proposed algorithm and its extension comparing with other addressed PCA-based methods. On the basis of the experimental results, it is clear to say that the presented approach and its extension are superior to the compared PCA-based algorithms.
Efficient, Repeated Adenovirus-Mediated Gene Transfer in Mice Lacking both Tumor Necrosis Factor Alpha and Lymphotoxin α

ABSTRACT The efficiency of adenovirus-mediated gene transfer is now well established. However, the cellular and the humoral immune responses triggered by vector injection lead to the rapid elimination of the transduced cells and preclude any efficient readministration. The present investigation focuses on the role of tumor necrosis factor alpha (TNF-α), a proinflammatory cytokine, and the related cytokine lymphotoxin α (LTα), in mounting an immune reaction against recombinant adenovirus vectors. After gene transfer in the liver, mice genetically deficient for both cytokines (TNF-α/LTα−/−), in comparison with normal mice, presented a weak acute-phase inflammatory reaction, a reduction in cellular infiltrates in the liver, and a severely impaired T-cell proliferative response to both Adenoviral and transgene product antigens. Moreover, we observed a strong reduction in the humoral response to the vector and the transgene product, with a drastic reduction of anti-adenovirus immunoglobulin A and G antibody isotypes. In addition, the reduction in antibody response observed in TNF-α/LTα−/− and TNF-α/LTα+/− mice versus TNF-α/LTα+/+ mice links antibody levels to TNF-α/LTα gene dosage. Due to the absence of neutralizing antibodies, the TNF-α/LTα knockout mice successfully express a second gene transduced by a second vector injection. The discovery of the pivotal role played by TNF-α in controlling the antibody response against adenovirus will allow more efficient adenovirus-based strategies for gene therapy to be proposed.
DrivingStyles: A smartphone application to assess driver behavior

The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classification of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months.
Lifetime-aware scheduling and power control for cellular-based M2M communications

In this paper the uplink scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in cellular networks. A lifetime metric, based on the accurate energy consumption model for cellular-based machine devices, is provided and used to formulate the uplink scheduling and power control problems as network-lifetime maximization problems. Then, lifetime-aware uplink scheduling and power control protocols which maximize the overall network-lifetime are investigated based on the different lifetime definitions. Besides the exact solution, a low-complexity suboptimal solution is presented in this work which can achieve near optimal lifetime-performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended under proposed protocols.
Rejection of dysphoric actors and implications of depressive symptom display

Abstract This study is an examination of the role of gender, symptom display and gender-role stereotypes on interpersonal rejection in depression. Recent research suggests that differential display of depressive symptomatology during interactions may depend on the gender of the interaction partner. Such differences may account for the reported disparate levels of interpersonal rejection. Male and female undergraduates were videotaped while viewing videotapes of male or female actors, depicting dysphoric or nondysphoric roles, whom they expected to meet in an upcoming interaction. Self-report measures indicated that actors portraying dysphoric roles were more rejected than actors portraying nondysphoric roles by both males and females. Observer ratings of videotapes revealed that participants showed less body movement when watching dysphoric actors, and less eye diversion when watching the female dysphoric actor. Males showed fewer adaptors when watching dysphoric actors. Lower participant empathy was rela.
A medical volume visualization system supporting head-tracked stereoscopic viewing and direct 3D interaction

Introduction We have developed an experimental medical volume visualization system supporting head-tracked stereoscopic viewing registered with direct 3Dinteraction. We aim to assess the suitability of these techniques for surgical planning tasks in real medical settings. In particular, vascular surgeons examining the distal site of the aneurismatic sack are assisted by visualizing the artery aneurism in depth. A better understanding of such complex spatial structures is achieved by incorporating motion parallax and stereoscopic cues to depth perception not available from static images. Our display when positioned as a surgical table provides the impression of looking down at the patient in a naturalistic way. With simple head motions, good positions for observing the pathology are quickly established.
Toward Cooperation by Carrier Aggregation in Heterogeneous Networks: A Hierarchical Game Approach

This paper studies the resource-allocation problem for a heterogeneous network (HetNet) in which the spectrum owned by a macrocell operator (MCO) can be shared by both unlicensed users (UUs) and licensed users (LUs). We formulate a novel hierarchical game theoretic framework to jointly optimize the transmit powers and subband allocations of the UUs and the pricing strategies of the MCO. In our framework, an overlapping coalition formation (OCF) game has been introduced to model the cooperative behaviors of the UUs. We then integrate this OCF game into a Stackelberg game-based hierarchical framework. We prove that the core of our proposed OCF game is nonempty and introduce an optimal subband-allocation scheme for UUs. A simple distributed algorithm is proposed for UUs to autonomously form an optimal coalition formation (CF) structure. The Stackelberg equilibrium (SE) of the proposed hierarchical game is derived, and its uniqueness and optimality are proved. A distributed joint optimization algorithm is also proposed to approach the SE of the game with limited information exchanges between the MCO and the UUs.
Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients.

BACKGROUND Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented. METHODS We compared an autoregressive (AR) model using only glucose information, an AR model with external insulin input (ARX), and an artificial neural network (ANN) using both glucose and insulin information. Online adaptive models were used to account for the intra- and inter-subject variability of the population with diabetes. The evaluation of the predictive ability included prediction horizons (PHs) of 30 min and 45 min. RESULTS The AR model presented root mean square error (RMSE) values of 14.0-21.6 mg/dL and correlation coefficients (CCs) of 0.92-0.95 for PH=30 min and 23.2-35.9 mg/dL and 0.79-0.87, respectively, for PH=45 min. The respective values for the ARX models were slightly better (PH=30 min, 13.3-18.8 mg/dL and 0.94-0.96; PH=45 min, 22.8-29.4 mg/dL and 0.83-0.88). For the ANN, the RMSE values ranged from 2.8 to 6.3 mg/dL, and the CC was 0.99 for all cases and PHs. The sensitivity of hypoglycemia prediction was 78% for AR, 81% for ARX, and 96% for ANN for PH=30 min and 65%, 67%, and 95%, respectively, for PH=45 min. The corresponding specificities were 96%, 96%, and 99% for PH=30 min and 93%, 93%, and 99% for PH=45 min. CONCLUSIONS The ANN appears to be more appropriate for the prediction of glucose profile based on glucose and insulin data.
2016 Ieee International Conference on Big Data (big Data) Word Embeddings for Arabic Sentiment Analysis

— Manual feature extraction is a challenging and time consuming task, especially in a Morphologically Rich Language (MRL) such as Arabic. In this paper, we rely on word embeddings as the main source of features for opinion mining in Arabic text such as tweets, consumer reviews, and news articles. First, we compile a large Arabic corpus from various sources to learn word representations. Second, we train and generate word vectors (embeddings) from the corpus. Third, we use the embeddings in our feature representation for training several binary classifiers to detect subjectivity and sentiment in both Standard Arabic and Dialectal Arabic. We compare our results with other methods in literature; our approach—with no hand-crafted features—achieves a slightly better accuracy than the top hand-crafted methods. To reproduce our results and for further work, we publish the data and code used in our experiments 1 .
Understanding and Working With Attention Deficit Disorder Students

From a holistic perspective the term attention refers to a student’s capacity to focus, direct and sustain their attention on a particular stimulus within their environment for a significant period of time. The development of students’ attention spans develops progressively from the time they enter school. From the beginning some students have more innate ability to pay attention than their peers. One may think of attention capacity as running from high to low with all learners falling somewhere along the continuum. Different issues hold varying levels of interest for students, and their attention spans will vary accordingly. Unfortunately, for some students the development of their attention does not progress normally. These students frequently need education accommodations.
Evaluating the streaming of FGS-encoded video with rate-distortion traces

The MPEG–4 video coding standard has recently been enriched with Fine Granularity Scalability (FGS) a new coding technique expressly designed for video streaming. With FGS coding the video stream can be flexibly truncated at very fine granularity to adapt to the available network resources. In this report we provide a framework for the evaluation of the streaming of FGS-encoded video. The framework consists of evaluation metrics and a library of rate-distortion traces. This framework enables networking researchers without access to video codecs and video sequences to develop and evaluate rate-distortion optimized streaming mechanisms for FGS-encoded video. We apply our evaluation framework to investigate the rate-distortion optimized streaming at different video frame aggregation levels. We find that compared to the optimization for each individual video frame, optimization at the level of video scenes reduces the computational effort dramatically, while reducing the video quality only very slightly.