Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Scale Poultry Farmers’ Choice of Adaption Strategies to Climate Change in Ogun State, Nigeria

Abstract Climate risks constitute an enormous challenge to poultry production and have affected the livelihoods of the people who depend on them. Thus, farmers have adopted various strategies that can help them cope with the adverse effects of climate change. The aim of this study is to examine the factors influencing small scale poultry farmers’ choice of adaptation strategies to climate change. Data used for this study were obtained from 121 representative farmers selected through a two-stage random sampling procedure. Descriptive Statistics, Likert Scale and the Multinomial Logit Model were the tools used for analysis. Results showed that the mean age and household size of the respondents were 45 years and 5 persons respectively, while the average number of birds per farmer stood at 583 birds. Majority of the respondents had a moderate perception of the impacts of climate change on poultry farming and chose management adaptation strategies in their fight against climate change. Econometric analysis showed that the age, gender and educational status of farmers, number of birds, household size, poultry experience, access to cooperative societies, poultry housing system, access to credit, access to extension services and farm size were the factors influencing farmers’ choice of climate change adaptation strategies in the study area. Therefore, policy should focus on awareness creation on management adaptation strategies through enhancing education and extension services as well as access of poultry farmers to credit facilities to indirectly insure farmers against climate change impacts.
Simulation of digital scan conversion for ultrasound systems using a digital signal processor

B-mode ultrasound systems use digital scan conversion (DSC) to display data in a Cartesian coordinate system acquired in a polar coordinate format. Image quality depends on the quality of the interpolation algorithm applied to the unsampled pixels to avoid artefacts. In this paper, a warped distance-based adaptive bilinear interpolation technique with selective sampling is proposed and has been successfully simulated using a Texas Instruments (TI) digital signal processor series TMS320C64x+ cycle accurate simulator. The algorithm enhances both the detail in the different regions as well as preserving the smooth regions within the image, yielding better image quality with less computational complexity. Optimized implementation of the algorithm supports software pipelining. Additionally, the approach speeds up the computation by utilizing the ability of very long instruction word processor to perform multiple operations in parallel. The experimental results show that the DSC is capable of supporting 83 frames per second without sacrificing the image quality for a raw input image of size, 512 × 256 scanned with an angle of 58° and output video graphics array (640 × 480) display.
“Edu Game Monopoly” Media Development On Sosial Arithmetic Material Of Junior High School

The purpose of this research is to develop a decent monopoly edugame media product on social arithmetic material with the concept of learning while playing. To support media feasibility, process of media expert validation, material expert validation, teacher response and student trials are conducted. The feasibility assessment includes three aspects: the aspects of media engineering, social communication aspects and learning aspects. The research method used is research and development of R & D, Borg and Gall model that has been modified into five stages consisting of (1) requirement analysis stage, (2) planning stage, (3) early product development stage, (4) validation and trial stage, and (5) final product stage. The results of research and development shows that: Assessment of feasibility in terms of the above three aspects have been obtained percentvage of 90.3%. In accordance with the conversion table of achievement level on a scale of 5, the value is obtained with the qualification of "Very Good". Thus, it can be concluded that the development of monopoly edugame media in this study is "Eligible" to be used as a medium of learning on the social arithmetic material of Junior High School.
Agent Based Security Framework towards Misrouting Attack in Wireless Sensor Networks under Node Replication

Drastic development in wireless technology increased the demand for Wireless Sensor Network (WSN) applications for monitoring areas of interest. The sensible information monitored by the sensor nodes need to be securely communicated to the sink. Even after the evolution of mobility in the wireless sensor networks, a large number of application deploying static nodes. Node replication in WSN gives the attacker a chance to perform various attacks. One of the major attacks in this category was misrouting attack. The replicated node changes the route of the noncaptures nodes resulting entire network compromise. We proposed an agent based security frame work which makes the non-captures nodes will not affected by the captures nodes thereby increase the resilience of the network under misrouting attack.
Pentium 4 (partially) previewed

design goals and microarchitecture of the Pentium 4's new core. The announcement answered some questions but begged others. For example, the P4 will be announced at " at least " 1.4GHz, according to Intel, but the company has said nothing about the P4's performance relative to the Pentium 3, currently shipping at 1.13GHz. The higher operating frequency of the new part is made possible by a hyperpipelined core with 20 stages. Intel calls this new architecture NetBurst rather than P7, or some other sequential code-name of the type used in the past. As Figure 1 shows, the NetBurst pipeline is twice as deep as that of the P6, which in turn had twice the depth of the P5's. Increasing pipeline depth increases logic complexity and branch penalties, but it also allows clock speeds to increase. We expect the new core to reach 2GHz—a speed demonstrated at IDF—before it moves to a 0.13-micron process in 2001. The two Drive stages shown in Figure 1 represent time required to move signals across the chip. No other work is done during these stages. As far as we know, NetBurst is the first pipeline with dedicated stages for wire delays. Although the new pipeline has one execution stage, the P4's two ALUs execute many operations in one-half of a clock period. Shifts and some other operations still spend one full clock period in the ALU, and these operations must start at the beginning of the period. Since the rest of the pipeline can process only two ALU operations per clock, the faster ALUs don't increase peak throughput—but they do boost sustained throughput. When two ALU operations are ready for execution, one of which depends on the results of the other, the Pentium 4 can complete the first operation in the Intel has released a few more details of its next IA-32 processor, formerly code-named Willa-mette and now slated to be sold as the Pentium 4. At the Intel Developer Forum last week, Intel's CEO Craig Barrett and vice president Albert Yu provided interesting insights into the Figure 1. The new hyperpipelined NetBurst microarchitecture of the Pentium 4 allows its clock rate to be increased significantly over that of the Pentium 3. This figure, based on information provided by Intel, shows the portion of each pipeline involved in ALU operations under branch mispredictions.
Multidisciplinary Approach to Improve Venous Thromboembolism Risk Assessment and Prophylaxis Rates in Hospitalized Patients

Background: Venous thromboembolism (VTE) is a preventable disease in hospitalized patients; however, VTE prophylaxis is underutilized. Effective strategies for the assessment of individual patients' VTE risk and the provision of VTE prophylaxis are needed. Objective: To evaluate the efficacy of a multidisciplinary intervention designed to improve VTE risk assessment and prophylaxis in at-risk hospitalized patients. Methods: The multidisciplinary intervention to improve VTE risk assessment and prophylaxis consisted of 3 strategies: a broad educational effort, nursing assessment, and pharmacist follow-up. Educational programs were delivered to nursing, pharmacy, and physician staff. Upon admission, all patients were assessed for VTE risk factors by nursing staff. Pharmacists reviewed reports of patients screened to have at least 1 VTE risk factor; for patients not prescribed VTE prophylaxis, pharmacists placed a progress note and VTE prophylaxis order form in the chart. If no prophylaxis was prescribed by the following day, the pharmacist contacted the physician with a verbal recommendation. The impact of this intervention was evaluated by comparing the proportion of patients assessed for VTE risk factors on admission and the proportion of VTE prophylaxis candidates who received prophylaxis, both before and after implementation. Results: A total of 310 patients were included during the 2-month study period. An increase in patients assessed for the presence of VTE risk factors was observed after the intervention (41% vs 87%, p < 0.001). Similarly, an increase in patients prescribed prophylaxis was observed after the intervention (36% vs 63%, p < 0.001). Conclusions: This multidisciplinary approach including education, nursing assessment, and pharmacist follow-up resulted in a significant increase in the rates of VTE risk assessment and prophylaxis.
Novel procedure model for an improved comparison of simulation and component crash test results

Fur die effektive Vorauslegung und fur verbesserte Validierungscrashversuche von Komponenten wird am Institut fur Fahrzeugkonzepte ein Vorgehensmodell zur Konzeption und Auslegung von Komponentenprufstanden fur Schlittenversuche entwickelt. Ziel des Vorgehensmodells ist ein effizienter Aufbau von Schlittenversuchen fur Fahrzeugkomponenten, mit der Anforderung die Bauteile reproduzierbar so zu belasten und einzubinden, dass ihr Crashverhalten nahe dem realen Einsatz im Fahrzeug ist. Fokus ist die Reproduzierbarkeit und Vergleichbarkeit zwischen Versuchen untereinander und zwischen Versuch und Simulation. Das Vorgehensmodell wurde prasentiert und ausgewahlte Inhalte am Beispiel eines Turprufstandes mit Belastungen in Anlehnung an den Seitenaufprall ECE-R95 detaillierter erlautert.
Psychological well-being and relationship changes in women after obesity surgery

Psychological Well-Being and Relationship Changes in Women after Obesity Surgery by Lindsey Bocchieri Ricciardi Dr. Marta Meana, Examination Committee Chair Associate Professor o f Psychology University o f Nevada, Las Vegas The present study explored changes in patients’ psychological well-being and relationship adjustment at threeand six-months after either Lap-Band or Gastric Bypass Surgery. Sixty-eight women completed the battery, which consisted of standardized questionnaires o f self-esteem, body-image, psychological symptomatology, and assertiveness, as well as marital and sexual functioning, prior to surgery and then at the two follow-ups. Also included was the assessment of patient expectations of relationship change and the pilot Weight-Related Relationship Adjustment Questionnaires (WRRAQ’s). Pilot WRRAQ’s were designed for the purpose of the study, based on retrospective patient narratives, and assessed parenting, work, family, and friend relationship domains. Psychometric properties of the WRRAQ’s were examined and determined to be acceptable to good. Results indicate that obesity surgery had a positive effect on all aspects of psychological well-being, and on a number of relationships, with the exception o f marital and sexual functioning. Patients’ expectations of relationship improvement were positively correlated with ftiendship improvement, but negatively
Development of a Measurement Scale to Evaluate Self-Care Stability for Patients Diagnosed with Type 2 Diabetes at an Older Age

Objectives: For the medical care of older adults diagnosed with type 2 diabetes later in life, it is necessary to provide medical staff support considering their life stage and to ensure that they live a stable life without fatigue. However, there is no measure for evaluating whether older diabetic patients have a stable life at the time of diagnosis. This study aimed to develop a scale to evaluate self-care stability for diabetic patients and to verify its reliability and validity. Methods: A cross-sectional study was conducted at four hospitals in Japan. A draft scale comprising 39 items was developed to identify patients’ stability of diabetes self-care in life. A total of 69 patients diagnosed with diabetes for the first time at age 60 or older responded effectively to questionnaires. Results: As a result of item analysis and factor analysis, seven factors comprising 20 items were extracted. The cumulative contribution ratio before rotation was 70.577%. Cronbach’s α was 0.700 overall. The total score on this scale and the sum of the revised Philadelphia Geriatric Center Morale Scale scores were significantly positively correlated (r = 0.379). On comparing the total scale score based on glycated hemoglobin level, the scale score of the stable group was found to be higher. Therefore, this scale was found to be reliable and valid. Conclusions: This scale may have utility in the measurement of self-care stability in patients diagnosed with type 2 diabetes later in life.
ASEDS: Towards Automatic Social Emotion Detection System Using Facebook Reactions

The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for "Joy" emotion.
An Overview of Machine Learning and HPC in Open Sources for Bioinformatics*

With development of recent open source software in HPC, such as Hadoop project, bioinformatics applications with large scale biological data can be solved more efficiently. The emerging machine learning algorithms are widely adapted for analysis, recognition and prediction. This paper will survey recent literatures about open source software of machine learning and HPC for bioinformatics applications. The commonly used machine learning algorithms for bioinformatics applications will be summarized here, and the situations of HPC adaption are also provided. Finally, the issues of future usages of machine learning for bioinformatics applications will be included.
Forest Fire Detection with Color Features and Wavelet Analysis Based on Aerial Imagery

Unmanned aerial vehicles (UAVs), equipped with vision-based systems, can be used for forest fire monitoring and detection due to their low cost, fast response capability, and high safety. This paper proposes a novel approach to forest fire detection, which uses the color characteristics of the images taken by the UAVs and uses wavelet analysis to further process. Firstly, according to the color characteristics of forest flame and smoke, a low computational cost algorithm is adopted to extract pixels from its related regions. In order to correct the inaccuracy of color feature extraction, a two-dimensional discrete wavelet transform (nWT) is implemented to distinguish flame and the smoke area from other high-frequency noise signals. Multiple sets of experiments have proved that the algorithm proposed can effectively detect the forest flame and smoke part of the image. The good performance is anticipated to significantly improve the accuracy of forest fire detection on the basis of less computational cost and can perform real-time detection on the UAVs platform.
Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application

The combination of Condition Based monitoring techniques with the predictive capabilities of neural networks represents a topic of central importance when it comes to maximizing production profits and consequently reducing costs and downtime. The ability to plan the best strategy based on the prediction of potential damaging events can represent a significant contribution, especially for the maintenance function. In fact, optimization of the management of the equipment is a fundamental step to guarantee the competitiveness of companies in the current market. In this paper, a tool based on the implementation of Radial Basis Function Neural Networks was developed to support the maintenance function in the decision-making process. In addition to providing an indication of the status of the equipment, the current approach provides an additional level of information in terms of predicting the confidence interval around the prediction of the neural network. The confidence interval combined with the prediction of the future state of the equipment can be of fundamental importance in order to avoid strategic decisions based on a low level knowledge of the system status or prediction performance of the applied algorithm. The developed tool is tested on the prediction of a naval propulsion system gas turbine performance decay, where the statuses of both the turbine and the compressor of the system are predicted as well as predicting their confidence intervals.
Modeling and Analysis of Safety Requirements in Robot Navigation with an Extension of UML MARTE

This paper presents an extension of the UML MARTE profile for modeling and quantitative analysis of robotic-specific non functional requirements. The UML models of a robot control system annotated with the proposed profile are automatically transformed in the model of a Constraint Satisfaction Problem (CSP). By solving the derived CSP problem, it is possible to adjust the configuration parameters of the control system when the operational scenario changes. In particular, this paper focuses on safety in autonomous navigation. The proposed approach is exemplified with a set of hospital logistics scenarios.
Context Aware Recommender System for Large Scaled Flash Sale Sites

Flash Sale Sites popularize because they save great money for users. Good recommender systems can further save users’ time to improve their online shopping experiences. Although there exsit a lot of studies on recommender system, very few focus on flash sale sites. Big Data, Context Sensitivity, and Feature Engineering are three key challenges for one to build a good recommender system. This paper proposes two deep learning oriented models: Tensor-AutoRec and Hybrid-AutoRec to cope with the problems within an industrial context. First, these two models can handle storage and speed problem caused by big data. Second, both models incorporate context information, so they can generate more relevant recommendations by adapting to specific contextual situations. Third, our deep learning-based models can be trained end-to-end without tedious feature engineerings. Extensive experiments with a half year real transcation data demonstrate that our models can outperform classifcal ones in terms of different evaluation metrices. Finally, online A/B testing results showed that our model can improve our old recommendation system over various online performance indicators.
On accurate, automated and insightful deviation analysis of clinical protocols

Clinical guidelines, pathways and protocols are introduced to standardize and provide best-practice care. Analyzing deviations of actual care against the documented best practices is useful to find opportunities for complying better in the future. Prior work demonstrates that deviation analyses should be accurate, automated and insightful but only few studies manage to satisfy all three intentions. In this paper, we manage to reconcile accuracy with automation and insightfulness by combining the previously disconnected steps of checking and mining in compliance analysis software. Results are achieved using an algorithm that consists of three steps. We demonstrate the effectiveness of the algorithm via a real-life case from the intensive care unit of a Dutch hospital.
A Bezier Curve-Based Font Generation Algorithm for Character Fonts

This paper presents an algorithm that automatically generates fonts based on Bezier curve. We locate the feature points by computing the intersection of the adjoining segments-elments, and draw the feature segment which named Linear Bézier curves or quadratic Bézier curves between any adjacent feature points, and automatically generates the stroke of the Chinese characters. This algorithm greatly improves the shape of the corner of the Chinese characters fonts. It can be used to dynamically generate the Chinese character of the personalization true type font, and overcome the problem on Chinese handwriting font design and font generation. This algorithm is efficacious in Chinese information cloud storage and cloud service, and also provides an effective strategy and methods for the design of a deeper cloud character information service.
HE-SLAM: A Stereo SLAM System Based on Histogram Equalization and ORB Features

In the real-life environments, due to the sudden appearance of windows, lights, and objects blocking the light source, the visual SLAM system can easily capture the low-contrast images caused by over-exposure or over-darkness. At this time, the direct method of estimating camera motion based on pixel luminance information is infeasible, and it is often difficult to find enough valid feature points without image processing. This paper proposed HE-SLAM, a new method combining histogram equalization and ORB feature extraction, which can be robust in more scenes, especially in stages with low-contrast images. Because HE-SLAM uses histogram equalization to improve the contrast of images, it can extract enough valid feature points in low-contrast images for subsequent feature matching, keyframe selection, bundle adjustment, and loop closure detection. The proposed HE-SLAM has been tested on the popular datasets (such as KITTI and EuRoc), and the real-time performance and robustness of the system are demonstrated by comparing system runtime and the mean square root error (RMSE)of absolute trajectory error (ATE)with state-of-the-art methods like ORB-SLAM2.
Online 3D CoM Trajectory Generation for Multi-Contact Locomotion Synchronizing Contact

This paper proposes a fast computation method for 3D multi-contact locomotion. The contributions of this paper are (a) the derivation of the prospect centroidal dynamics by introducing a force distribution ratio, where the centroidal dynamics in multi-contact can be represented with a formulation similar to the inverted pendulum's one, and (b) the development of a fast computation method for generating a 3D center of mass (CoM) trajectory. The proposed method allows to generate a trajectory sequentially and to change the locomotion parameters at any time even under variable CoM height. Then, the contact timing of each end-effector can be adjusted to synchronize with the actual contact with the environment by shortening or extending the desired duration of the support phase. This can be used to improve the robustness of the locomotion. In this paper, we deal with a multi-contact locomotion which can be fully received a vertical reaction force from the environment and the validity of the proposed method is confirmed by several numerical results: the CoM motion while changing the contact timing and a multi-contact locomotion considering a transition between biped and quadruped walking in a dynamics simulator.