Miracle-dz/UniFAR-FTUs huggingface dataset:
A Document Weighted Approach for Gender and Age Prediction Based on Term Weight Measure

Author profiling is a text classification technique, which is used to predict the profiles of unknown text by analyzing their writing styles. Author profiles are the characteristics of the authors like gender, age, nativity language, country and educational background. The existing approaches for Author Profiling suffered from problems like high dimensionality of features and fail to capture the relationship between the features. In this work, a new document weighted approach is proposed in order to address the problems in existing approaches. In this approach, the term weight measure is used to assign suitable weight to the terms and these term weights are aggregated to compute the document weight. The classification model is generated with these document weights for predicting profiles of the text. The proposed approach and existing approaches are experimented on reviews domain with different classifiers. The accuracies of the proposed approach for gender and age prediction are promising than existing approaches.
Maximising influence in sensed heterogeneous social network with privacy preservation

Maximising influence to improve marketing performance has a significant impact on targeted advertisements and viral product promotion, which has become a fundamental problem in social data analysis. Most existing works neglect the fact that location data could also play an important role in the influence prorogation. This paper considers maximising influence towards both sensed location data and online social data with privacy concern. We merge location data from cyber-physical networks and relationship data from online social networks into a unified, then propose an efficient algorithm to solve the influence maximisation problem. Furthermore, our privacy-preserving mechanism could protect the sensitive location and link information during the whole process of data analysis. Real-life datasets are empirically tested with our framework and demonstrate the power of sensed and online data combination to influence maximisation. The experiment results suggest that our framework is outperforming most existing alternative resolutions and succeeds in preserving privacy.
PROSES PEMBELAJARAN BIOLOGI PADA MATERI SISTEM PEREDARAN DARAH MANUSIA DI KELAS VIII SMP NEGERI 3 RAMBATAN

Leaning process can influence students’ achievement. The lack of students’ understanding on the particular materials could decrease students’ achievement. The purpose of this research is to evaluate the leaning process of biology in human circulator system at the eight grade of Junior High School (SMP) 3 Rambatan covering the lesson plan, implementation of learning proces, evaluation, and students’ learning outcome. This researh was descriptive using qualitative approach. It was conducted at SMP 3 Rambatan. Data were collected using IPKG on lesson, interview, document, and field notes. The reseach shows as follows: (1) the format of lesson plan used by the teachers has fulfilled the IPKG and could be categorized into good; (2) the learning process had not yet referred to the IPKG but could be classified as good and enough category; (3) the evaluation of learning process had not referred to IPKG; (4) students’ learning activities were still low; and (5) students’ learning outcome were unsatisfactory. Kata Kunci : proses pembelajaran biologi, sistem peredaran darah manusia, aktivitas siswa, hasil belajar.
Enhancing Discrete Choice Models with Neural Networks

In this paper, we aim at bringing the predictive strength of Neural Networks, a powerful machine learning-based technique, to the field of Discrete Choice Models (DCM) without compromising interpretability of these choice models. We start by matching the mathematical derivation of the multinomial logit model (MNL) to its neural network equivalent. This allows us to write DCM problems in modern machine learning libraries and opens the way for our novel hybrid approach: we suggest to add a term arising from a dense neural network (DNN) in the utility function. This added value is obtained by using all discarded features from the original DCM model as input to the DNN. Not only does this greatly increases the predictive strength of the model, but it also keeps the strong parameters significance used in the original MNL. Lastly, we have reasons to believe this term fits very well in DCM theory when relating it to the random utility term , capturing all unknown or unused features of the model which may appear in the thinking process of an individual.
Robot mapping algorithm based on Kalman filtering and symbolic tags

A new method will be developed in the present work of the detection of a robot's position in a relative coordinate system based on a history of camera positions and the robot's movement, symbolic tags and on combining obtained three-dimensional depth maps that account for accuracy of their superimposition and geometric relationships between various images of the same scene. It is expected that this approach will enable one to develop a fast and accurate algorithm for localization in unknown dynamic environment.
An Approach to Similarity Search for Mathematical Expressions using MathML

The recent global computerization and digitization trend has helped to increase the numbers of documents with mathematical expressions on the Web. These mathematical expressions have their own unique structures, and therefore, it is not an easy task for traditional search systems targeting natural languages to deal with them. We propose a similarity search method for mathematical equations that is particularly adapted to the tree structures expressed by MathML based on this background. The similarity search system helps users acquire additional knowledge, discover concealed relationships to different fields, and compensate for some false recognition. Given an equation as a query, most of the conventional mathematical search systems return corresponding equations that exactly match the query. Contrarily, our proposed system makes it possible to return similar equations by measuring the similarity using tree-matching techniques and also by reforming the structure of Content-based MathML. In this paper, we examine our proposed techniques through preliminary experimentation using a prototype search system, and show this techniques’ effectiveness based on some conditions requested by the user.
Endocrine Regulation of Follicular Development in the Lay-incubation Cycle of Magang Geese

This study was conducted to investigate the endocrine regulatory mechanisms of follicular development in the lay-incubation cycle of Magang geese.Experiment 1: investigated the changes in palsma concentrations of PRL,LH,P4,and INB during the cycle,and the follicular development at incubation(d0),end of incubation(d10),onset of lay(d25),peak of lay(d40),end of lay(d55) and incubation(d70) in the lay-incubation cycle.Experiment 2,after being deprived of laying nest to terminate incubation behaviour on day 1(d1),Magang geese were immunized intramuscularly on days 1,23 and 45,with 1 mL of immunogen containing 1,0.8,and 0.5 mg,respectively,of recombinant chicken PRL protein.The photoperiod under which the geese were kept was increased from 11L:13D to 16L:8D on day 33.In expreiment 1,with the termination of incubation,PRL concentration in plasma decreased,whereas the concentrations of LH,P4 and INB increased.PRL concentrations fell to lowest levels prior to the onset of lay and rose to high levels during laying to the peak when incubation initiated.LH concentration in plasma exhibited a bi-phasic pattern in the whole cycle.The patterns of P4 and INB concentrations were opposite to that of PRL,reaching to high levels at peak lay and to the lowest at incubation.Laying resumed on day 24 in the lay-incubation cycle,and approximately 10 follicles were recruited into hierarchical development before onset of lay,among which 8 developed to ovulation and were laid as eggs.Ninety percent of the geese exhibited incubation behabviour after laying one clutch of approximately eight eggs in approximately 30 days.Results of experiment 2 showed that immunization against PRL increased the clutch size by one egg(8.0 vs 7.0),although initially retarded the rise of laying rate(P0.05). Development of incubation behaviour was also delayed by one week in the PRL immunized geese,which still reached to 100% as in the control geese.Results of the two experiments indicated that the alternative secretions of PRL and LH coordinated the lay incubation cycle in Magang geese.As the results of follicular development,secretions of INB and P4,the latter through PRL secretion,determine the quota of follicular development and clutch egg size.PRL triggers the incubation behaviour and atresia of eggs at bottom on developing hierarchy,however,PRL may stimulate follicular development and egg laying around the onset of lay.
Networked human motion capture system based on quaternion navigation

In this paper, from the perspective of human ergonomics, we analyze the movement of the joints in the process of human body movements, and we establish a dynamic model according to the human skeleton structure. On this basis, from the rigid body dynamics point of view, combined with the principle of inertial navigation, a body sensor network based on MEMS inertial sensors is built to capture human body motion in real time. On the basis of space trajectory of human body movement and traditional human motion solution strategy, a human motion solution strategy based on particle filter fusion solution is proposed to realize the prediction of human motion analysis. Therefore, we evaluate the performance of the designed system by comparing with the real motion. Finally, in order to verify the human motion data, the motion capture data verification platforms are established. Experimental results show that the proposed joint attitude solution algorithm can achieve a relatively smooth tracking effect and provides a certain reference value.
Molecular Characterization Of Local Human Extraintestianl Escherichia Coli Isolates

Extraintestinal pathogenic E.coli are major pathogens responsible for causing urinary tract infections.These are associated with multiple drug resistance (MDR) and high virulence.A triplex PCR was employed to segregate each isolate into different phylogenetic groups.Among fifty nine uropathogenic E.coli isolates majority belonged to phylogenetic group B2 (50%) where as 19% each belonged to groups A and B1, and 12% to group D. Phylogenetic group D isolates were found to be less frequent but harbored multiple drug resistance and virulence related genes to a greater extent. Drug resistance was evaluated by disc diffusion method and relevant drug resistance gene detection. It was found that most effective drugs were gentamicin and amikacin among aminoglycosides; aztreonam, ceftriaxone, cefoperazone and cefixime among β lactams, and ciprofloxacin among quinolones. Streptomycin, trimethoprim-sulfamethoxazole, tetracycline and nalidixic acid were relatively ineffective.Chloramphenicol was found to be very effective for urinary tract infections. Highly prevalent drug resistance gene was mutated gyrA (83%) followed by tem (46%), bltTEM (41%), tetB (34%), catP (31%), and tem β lactamase (15%); blaSHV and tetA were not detected. Among virulence related genes most prevalent was hlyA (37%) followed by sfaDE (27%), papC (24%), cnf1 (20%), eae A (19%) and afaBC3 (14%). Some of isolates were highly hemolytic to human erythrocytes and some were highly cytotoxic to Vero cells. Highly hemolytic isolates mainly belonged to group D, where as highly verotoxic isolates belonged to group B2 followed by group D. It was also noted that in cnf1 negative isolates cytotoxicity was related to papC, sfaDE, afaBC3, eae A and hlyA genes.
Experimenting with reproducibility in bioinformatics

Reproducibility has been shown to be limited in many scientific fields. This question is a fundamental tenet of the scientific activity, but the related issues of reusability of scientific data are poorly documented. Here, we present a case study of our attempt to reproduce a promising bioinformatics method [1] and illustrate the challenges to use a published method for which code and data were available. First, we tried to re-run the analysis with the code and data provided by the authors. Second, we reimplemented the method in Python to avoid dependency on a MATLAB licence and ease the execution of the code on HPCC (High-Performance Computing Cluster). Third, we assessed reusability of our reimplementation and the quality of our documentation. Then, we experimented with our own software and tested how easy it would be to start from our implementation to reproduce the results, hence attempting to estimate the robustness of the reproducibility. Finally, in a second part, we propose solutions from this case study and other observations to improve reproducibility and research efficiency at the individual and collective level. Availability last version of StratiPy (Python) with two examples of reproducibility are available at GitHub [2]. Contact yang-min.kim@pasteur.fr
Actuated Leg Prosthesis for Above-knee Amputees

Of the present invention, the knee member (12), the knee member (12), the socket connector 22, a lower leg member (14) extends below it equipped with a man-made to the lower end portion (14C) that (20) is connected which is provided on the It relates to a working artificial leg consisting including, and linear operating body 16. It is the lower leg member (14) coupled to drive the knee member (12) is implemented by a first pivot portion 30. Connecting to drivingly the upper end (16A) of the operating body 16 to the knee member (12) is implemented by the second pivot portion 80. It is the bottom end (16B) of the operating body 16 drivingly connected to the lower end portion (14C) of the lower leg member 14 is implemented by a third pivot member (100). Prosthetic foot 10 is to operate either of the front or rear working configuration chesik chesik configuration may also be provided.
PREDICTION OF ISTANBUL STOCK EXCHANGE (ISE) DIRECTION BASED ON NEWS ARTICLES

In this paper, we examined the effects of financial news on Istanbul Stock Exchange and we tried to predict the direction of ISE National 100 Index open price after the news articles were published. In order to do this study, we got news articles from three big financial websites and we represented them as feature vectors. We obtained class labels from the ISE National 100 Index open price and assigned them to these feature vectors. After creating the datasets, we selected the informative features using Mutual Information (MI) and Term Frequency-Inverse Document Frequency (TF-IDF) Weighting methods. According to the selected features, we created feature subsets with different number of features and we trained a Naive Bayes Classifier with them. We used precision, recall, accuracy and F-Measure metrics to evaluate the performances of our methods. We found out that feature selection can help us use a smaller number of features for classification and as a measure of how useful a feature is, TF-IDF is better than MI.
Improved Chase Detection Algorithm Based on SNR Maximum Sorted

Multiple-input multiple-output (MIMO) wireless communication systems could increase the spectral efficiency and system performance significantly. Maximum likelihood detection (MLD) algorithm provided the best bit error rate (BER) performance for MIMO communication system. Calculating complexity of MLD increased exponentially with the constellation size and the transmit antenna number. Original Chase detector, with lower calculating complexity, was combined of a list detector followed with some V-BLAST sub-detectors. There were error propagations in this algorithm. The order of signal detection was critical because of serial detection in Chase detector. In order to get better trade-off between detection performance and calculating complexity in MIMO wireless communication system, an improved Chase detection algorithm based on SNR maximum was presented here which could reduce error propagation efficiently. Parallel linear detectors were used in sub detectors to improve the bit error performance with lower complexity further more. The proposed algorithm could obtain proper trade-off between calculating complexity and detection performance, and simulation experiment results show its validity.
INTRAFORCE: Intra-Cluster Reinforced Social Transformer for Trajectory Prediction

Predicting mobile users' trajectories accurately is essential for improving the performance of wireless networks and autonomous systems. In this paper, we tackle the problem of tra-jectory prediction in a multi-agent scenario where the social inter-action among users is taken into consideration. We propose Intra-Cluster Reinforced Social Transformer (INTRAFORCE), a novel system to design and train Social-Transformer neural networks that learn the spatio-temporal interactions among neighboring mobile users and predict their joint future trajectories. Unlike state-of-the-art social-aware trajectory predictors that either miss the large-distance interactions or are computationally expensive due to the pooling of all users' interactions, INTRAFORCE clusters users with similar trajectories and learns their interactions. INTRAFORCE performs Neural Architecture Search to optimize each transformer's architecture to fit each cluster's user mobility features using Reinforcement Learning. Through experimental validation, we show that INTRAFORCE outperforms several state-of-the-art trajectory predictors on five widely used small-scale pedestrian mobility datasets and one large-scale privacy-oriented cellular mobility dataset by achieving lower prediction error. training time, and computational complexity.
The Karyotype of Grouper Epinephelus fuscoguttatus

The chromosome specimens of grouper Epinephelus fuscoguttatus were obtained from metaphase of kidney cells by in vivo injection of PHA and colchicin,hypatoic-air drying technique,and Giemsa staining.The results showed that the grouper had a diploid chromosome number of 48 and its karyotype formula was 2sm+46t,NF=50.Its karyotype was accordance with the typical characters of higher location karyotype group of fish.
Effects of tamoxifen and melatonin on mammary gland cancer induced by N-methyl-N-nitrosourea and by 7,12-dimethylbenz(a)anthracene, respectively, in female Sprague-Dawley rats.

Chemopreventive effects were analysed of antioestrogen TAM and of MEL on NMU- or DMBA-induced mammary gland cancer, respectively, in female Sprague-Dawley rats. NMU was administered intraperitoneally in two doses each of 50 mg/kg b.w. between 46th-57th postnatal days. DMBA was given by gavage in one dose (20 mg per animal) between 50th-54th postnatal days. The treatment with MEL began 12 days and the treatment with TAM 10 days before carcinogen administration; both chemopreventive substances were administered until the end of the experiment (24 weeks after carcinogen application). TAM was administered subcutaneously twice a week in a dose 2.5 mg/kg b.w. MEL was given in tap water (20 mg/ml) daily between 3 p.m. to 8 a. m. The tumour incidence, tumour frequency per group and animal, latency period, tumour volume, body weight gain in the rats and weight of uterus (in the experiment with NMU) were evaluated. TAM suppressed carcinogenesis to 0% incidence like TAM+MEL in both the NMU and DMBA models. In NMU-induced mammary carcinogenesis MEL lowered the tumour volume (although statistically non-significantly) by 30% in comparison with the control group; in DMBA-induced mammary carcinogenesis it lowered the tumour volume (2.70 +/- 0.81 cm3 vs. 0.90 +/- 0.33 cm3) and lengthened (non-significantly) the latency period (by 12 days). The weight gain of animals in both NMU and DMBA models and relative uterus weight in the NMU model were significantly lower in the groups treated with TAM and TAM+MEL as compared to the control group and the group treated with MEL. Evaluation of the combined effect of TAM+MEL was not possible due to total suppression of carcinogenesis by TAM. TAM and TAM+MEL are highly effective agents in rat mammary carcinogenesis prevention, but the side effects of TAM in humans limits its use in clinical oncology.
Détection automatique d’opinions dans des corpus de messages oraux

Telephone surveys are often used by Customer Services to evaluate their clients' satisfaction and to improve their services. Large amounts of data are collected to observe the evolution of customers' opinions. Within this context, the automatization of the process of these databases becomes a crucial issue. This paper addresses the automatic analysis of audio messages where customers are asked to give their opinion over several dimensions about a Customer Service. Interpretation methods that integrate automatically and manually acquired knowledge are proposed. Experimental results, done on a database collected from a deployed Customer Service in real conditions with real customers are given.
Folliculogenesis in the sheep as influenced by breed, season and oestrous cycle.

: In the sheep the total duration of folliculogenesis, i.e. from the start of development of a primordial follicle to ovulation, is thought to be about 6 months. The initiation of follicular growth as follicles enter the growth phase is influenced by gonadotrophins and, in the sheep, by factors such as breed, season and nutrition. Preantral follicles are characterized by a slow growth rate and no atresia. The number of preantral follicles is influenced by gonadotrophins, age, nutrition, season and unilateral ovariectomy. Antral follicles grow rapidly and produce steroids in response to gonadotrophins. The number of antral follicles varies according to factors such as breed of sheep, season, cycle, unilateral ovariectomy and gonadotrophins. Ovulation rate can be influenced in the short term by factors such as PMSG stimulation, short-term nutrition and unilateral ovariectomy that probably act by changing the number of follicles undergoing atresia. Factors such as breed, age and season probably act by changing the number of follicles entering the growth phase.
수학 및 언어 영재아동의 영재성 판별변인 분석

Background NQF has endorsed more than 100 performance measures that are directly related to patient safety. These endorsed measures are relevant in several different environments of care and are applicable to a variety of healthcare professionals. As with the preceding measures, the measures presented in this report address broad issues within patient safety, including capacity, productivity, and improving patient outcomes.
Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Tone mapping has been used to transfer HDR (high dynamic range) images to low dynamic range. This paper describes an algorithm to display high dynamic range images. Although local tone-mapping operator is better than global operator in reproducing images with better details and contrast, however, local tone mapping algorithm usually requires a huge amount of computation and it takes a long time to display an HDR image. We have designed a highly parallel method using Graphics Processing Unit (GPU) to accelerate the computation in order to achieve a real-time display. The algorithm can be highly parallelized. In order to run on different heterogeneous systems, we choose OpenCL, instead of CUDA, for our implementation. We have demonstrated the speed-up can be as high as 63 times for a 1280x960 image. Keywords— OpenCL, HDR, tone mapping, GPU, parallel programming