Miracle-dz/UniFAR-FTUs huggingface dataset:
CivisAnalysis : exploring representatives'voting behaviour

In this work, we present CivisAnalysis, an open-source web-based system for the visualization of roll calls in the Brazil’s Chamber of Deputies. Using roll calls of six legislatures as well as six presidential elections, CivisAnalysis provides a unique view of the political history of our country. It combines classical roll calls visualization techniques with techniques for the visualization of temporal data. Votes of each representative are interpreted as a set of multidimensional data, which are represented in a n-dimensional space mapped to a bi-dimensional representation. The Votes of each representative determine their position in the political spectrum of deputies, and the votes of all deputies determine the political spectrum of roll calls. The work also shows how exploring subsets of deputies allows the discovery of voting patterns in the roll call spectrum, as well as exploring the roll calls spectrum allows the discovery of behavioral voting patterns in the political spectrum of deputies. For supporting a long-term political analysis, CivisAnalysis provides a time line visualization integrated with election data (election results and political alliances). The tool implements textual and visual filtering and includes auxiliary visualizations that provide an overview of the political scenario regarding deputies, parties, coalitions and their behavior along time.
100 Gbit/s End-to-End Communication: Adding Flexibility with Protocol Templates

High-speed protocol processing that provides data-rates of 100 Gbit/s and beyond to the application stresses the whole communication system up to its outer limits. Such a system can only be utilized by employing highly specialized, application specific protocols, that are tailored for certain communication parameters, such as the packet loss rate. However, the requirements for most applications are not static, and a protocol designer cannot anticipate all possible communication conditions upfront. The contradiction between specialized protocols and unknown communication parameters can be solved by adapting the protocol implementation on demand to the current communication conditions. However, such an approach needs a protocol description language that allows the automatic specialization of protocols. In this paper, we present the Protocol Engine Template Language (PETL), that allows the automatic implementation of protocols by a constructive approach for a variety of communication conditions from protocol implementation templates.
Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes.
Nonadherence to Antiepileptic Medications: Still a Major Issue to be Addressed in the Management of Epilepsy

ABSTRACT Context: Medication nonadherence is a significant barrier in achieving seizure freedom in patients with epilepsy. There is a deficiency of data about the reasons for nonadherence in Saudi population. Aims: The aim of this study is to prove the existence of nonadherence to antiepileptic drugs (AEDs) in patients with epilepsy and identify the responsible factors. Setting and Design: This is a prospective, cross-sectional study carried in the Department of Neurology at King Fahd Hospital of the University affiliated with Imam Abdulrahman Bin Faisal University. Subjects and Methods: Patients of all ages diagnosed to have epilepsy as mentioned in their medical record and taking antiepileptic medications were interviewed using a questionnaire. Statistical Analysis Used: Statistical analysis was performed using IBM Statistical Package for the Social Sciences version 21 (IBM Corp., Armonk, NY, USA). Statistical significance was defined as two-tailed with a P ≤ 0.05. Results: Among 152 participants, 52.6% were male and 47.4% were female. Mean age of the patients was 28 ± 14.3 (mean ± standard deviation) years. Of 152 patients, 48.7% were found to be nonadherent to their AED therapy. The most commonly identified factor was forgetfulness. Nonadherence was significantly associated with poor seizure control (P = 0.002). Conclusion: Nonadherence to the AED is common among patients with epilepsy and affects seizure control adversely.
A New Forecasting Framework for Bitcoin Price with LSTM

Long short-term memory (LSTM) networks are a state-of-the-art sequence learning in deep learning for time series forecasting. However, less study applied to financial time series forecasting especially in cryptocurrency prediction. Therefore, we propose a new forecasting framework with LSTM model to forecasting bitcoin daily price with two various LSTM models (conventional LSTM model and LSTM with AR(2) model). The performance of the proposed models are evaluated using daily bitcoin price data during 2018/1/1 to 2018/7/28 in total 208 records. The results confirmed the excellent forecasting accuracy of the proposed model with AR(2). The test mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for bitcoin price prediction, respectively. The our proposed LSTM with AR(2) model outperformed than conventional LSTM model. The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption. The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial time-series data.
Comparison of Image Classification and Object Detection for Passenger Seat Belt Violation Detection Using NIR & RGB Surveillance Camera Images

Manual seat belt enforcement by roadside police officers on highways is known to be inefficient, costly and ineffective. Authorities have been looking for automated enforcement solution towards seat belt violation detection. In this study, we examine different approaches towards the seat belt violation of front seat passengers using a near infrared (NIR) and color (RGB) surveillance camera system pointed at the vehicle’s windshield. In these approaches an object detector; single shot multi box object detector (SSD) and image classifiers; convolutional neural network (CNN), Fisher vector (FV) are utilized to detect the usage of seat belt. Using a camera system placed on an overhead gantry installed on a highway, 13444 real world images are captured in a 20 hour time period. Our experiments conducted on this dataset show that SSD seat belt detector is superior for this problem with 91.9 % accuracy and 94.5 % precision rate.
Design, Implementation and Control of an Improved Hybrid Pneumatic-Electric Actuator for Robot Arms

Actuators used in robot arms need to be powerful, precise, and safe. We present the design, implementation, and control of a novel rotary hybrid pneumatic-electric actuator (HPEA) for use in robot arms, and collaborative robots in particular (also known as “cobots”). This HPEA is capable of producing torque 3.5 times larger than existing HPEA designs while maintaining low mechanical impedance (due to low values of friction and inertia) and inherent safety. The HPEA prototype has 450 times less inertia and 15 times less static friction in comparison to a conventional robot actuator with similar maximum continuous output torque. The HPEA combines the large slow torque generated by four pneumatic cylinders, connected to the output shaft via rack and pinion gears, with the small fast torque generated by a small DC motor directly connected to the output shaft. The direct connection of the motor avoids the higher cost and lower precision caused by a gearbox or harmonic drive. The control system consists of an outer position control loop and two inner pressure control loops. High precision position tracking control is achieved due to the combination of a model-based pressure controller, model-based position controller, adaptive friction compensator, and offline payload estimator. Experiments were performed with the actuator prototype rotating a link and payload in the vertical plane. Averaged over five tests, a root-mean-square error of 0.024° and a steady-state error (SSE) of 0.0045° were achieved for a fast multi-cycloidal trajectory. This SSE is almost ten times smaller than the best value reported for previous HPEAs. An offline payload estimation algorithm is used to improve the control system’s robustness. Finally, the superior safety of the HPEA is shown by modeling and simulating a constrained head-robot impact, and comparing the result with similar electric and pneumatic actuators.
Relationship between Sap prevalence and biofilm formation among resistant clinical isolates of Candida albicans

Background Fungal infections represent a serious health problem especially in immunocompromised individuals. Candida albicans is the most common fungi that cause superficial and systemic infections with high mortality rates. Anti-fungal resistance of C. albicans may be attributed to its virulence. Biofilm formation and proteolytic activity are major virulence determents that may influence both pathogenicity and anti-fungal resistance of Candida albicans. Objective This work studied the relation between biofilm formation, proteolytic activity and prevalence of some Sap genes with reduced susceptibility of C. albicans to different anti-fungal agents. Methods Fifty three C. albicans strains isolated from patients with systemic infections, identified by germ tube, chromogenic agar and confirmed by PCR, were subjected to evaluate their proteolytic activity, the degree of biofilm production and the prevalence of Sap9 and Sap10 genes. The susceptibility of the isolates was determined by disk diffusion method against five antifungal drugs. Results and conclusion Four of the C. albicans isolates were resistant to 3 anti-fungal drugs, strong biofilm producer, have proteolytic activity and contain either Sap9 or Sap10 or both. Conclusively, although anti-fungal resistance among the isolates was rare, a relation between the anti-fungal resistance and some major virulence factors was evidently proved in this study.
Prognostic factors of refractory NSCLC patients receiving anlotinib hydrochloride as the third- or further-line treatment

Objective: Anlotinib hydrochloride is a multitarget tyrosine kinase inhibitor that targets vascular endothelial growth factor receptor, fibroblast growth factor receptor, platelet-derived growth factor receptor, c-Kit, and c-MET; therefore, it exhibits both antitumor and anti-angiogenetic activities. A phase III trial has shown that anlotinib improved progression-free survival (PFS) and overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC), who presented with progressive disease or intolerance after standard chemotherapy. This study aimed to analyze the characteristics of patients receiving anlotinib treatment to determine the dominant populations who are fit for the treatment. Methods: Data were collected from March 2015 to January 2017 from a randomized, double-blind, placebo-controlled, multicenter, phase III trial of anlotinib (ALTER0303). A total of 437 patients were enrolled and randomly allocated (2:1) to the anlotinib and placebo groups. Kaplan–Meier analysis and log-rank test were performed to compare PFS and OS. Cox proportional hazards model was adopted for multivariate prognostic analysis. Results: Multivariate analysis indicated that high post-therapeutic peripheral blood granulocyte/lymphocyte ratio and elevated alkaline phosphatase levels were independent risk factors for PFS. Meanwhile, elevated thyroid-stimulating hormone, blood glucose, and triglyceride levels; hypertension; and hand–foot syndrome were independent protective factors of PFS. High post-therapeutic peripheral blood granulocyte/lymphocyte ratio, an Eastern Cooperative Oncology Group (ECOG) score ≥ 2, and the sum of the maximal target lesion length at baseline were independent risk factors of OS, and hypertriglyceridemia was an independent protective factor of OS. Conclusions: This study preliminarily explored the possible factors that affected PFS and OS after anlotinib treatment in patients with advanced refractory NSCLC, and the baseline characteristics of the therapeutically dominant populations were then identified.
3D Incomplete Point Cloud Surfaces Reconstruction With Segmentation and Feature-Enhancement

Raw point cloud data are often noisy, superfluous, and with topological defects, such as holes. These issues cause inaccurate geometric representation in 3D reconstruction. As a result, surface reconstruction from point cloud data is a highly challenging problem. In this paper, we address the aforementioned issues by taking advantage of the embedded information of segmentation, skeletonization, and user guidance. First, we pre-process the point cloud data with three steps, relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details; second, a segmentation method converts the input cloud into separate parts; finally, we construct curve skeletons for each part and guide the surface reconstruction with minimal user interaction, where the parts of refined smooth shapes are fused to generate the final results. The comparison studies confirmed that the proposed method is able to produce state-of-the-art results in terms of preserving sharp features, handling missing data, and requiring minimal user intervention.
Secure WiFi Fingerprinting-based Localization

Accurate indoor WiFi localization is attracting more attention nowadays with the wide-spread of location-based services. WiFi fingerprinting is an indoor localization technique, where the location is estimated by mapping the strength of measured WiFi signals from different access points against a previously collected database. WiFi fingerprinting is widely adopted as being natively supported by WiFi mobile devices without any additional costs. A major concern of this technique is that an attacker could easily change the strength of received signals in order to fake the location of the mobile device. Here, we develop novel algorithms to identify attacked access points and make accurate localization in the presence of attacks. We evaluate the performance of our developed algorithms using different WiFi fingerprinting datasets under different attack models. Experimental results show that the proposed algorithms are resistant to attacks and can achieve more robust location estimation than other strategies.
Plant growth promoting activity of nickel tolerant Bacillus cereus TS 1

Keratinase producing bacterial strain was isolated from the chicken feather dumping site. The potential strain was identified by physical, biochemical characteristics, fatty acid methyl ester (FAME) and 16S rDNA sequences method. Rice seed treated with feather hydrolysates of Bacillus cereus TSI showed 100% of vigour index in plant growth activity due to secretion of IAA, siderophore, HCN and phosphatase were synthesized. In addition to that the Bacillus cereus TSI act effectively in fertilization process of soil. Even at a concentration of 300 mg nickel/gm of soil, the metal effect was detoxified by Bacillus cereusTS1 causing enhancement in shoot length, root length, chlorophyll content, fresh weight and dry weight.
Digital 2-Dimensional Photogrammetry Simplified by Using a Marker of a Known Size

In this paper, we introduce a digital 2-dimensional (2D) photogrammetry method that utilizes a graphics viewer program to obtain a real metric linear length value from an image in which a marker of a known size is embedded. Farkas et al. [1] compared facial measurements obtained from life-size printed photographs with direct measurements and reported reliable results for 20 of the 62 measurements. Nechala et al. [2] placed a ruler beside a subject in a photograph to obtain a reproduction ratio for digital 2D photogrammetry. Those methods are difficult to use to assess the results of various types of craniofacial surgery. Moreover, although 3-dimensional (3D) anthropometric equipment is currently available, it has limited applicability in office settings [3-6]. Many physicians still use direct anthropometry or life-size printed photos [7-9]. Mulliken and Sullivan [10] discussed the importance of including some type of calibration in the images used for photogrammetry, and Price et al. [11] used the subjects’ corneal diameter for calibration. However, those authors did not include an assessment of the spherical aberration of the lens in their studies, although this is an important factor in photoJeong Woo Lee, Dong Kyu Kim, Seung Gyun Chu, Byung Chae Cho, Kyung Young Lee
DESIGN AND ASSESSMENT OF AN INTEGRATED TEACHING METHOD FOR TEACHING ECOSYSTEMS

The aim of this paper is to present and evaluate the Integrated Teaching Method (ITM) which is an innovative teaching model incorporating comprehensive teaching content, effective teaching strategies, and specifically-designed lesson plans. ITM was developed based on modern learning theories in conjunction with the latest findings in cognitive psychology and the neuroscience of the brain. ITM was implemented on Year 8 Greek secondary school students to teach the ecosystem and was assessed in comparison to Didactic Teaching Strategies (DTS), based on a traditional teaching method. The data collection tools were questionnaires on students’ knowledge, comprehension, application and attitudes/opinions towards the environment. ANOVA and t-Test showed that students who were taught with ITM as opposed to those taught with DTS: a) achieved better results in knowledge, comprehension and application of the teaching content, and b) achieved equally good results in terms of attitudes/opinions towards the environment. The findings suggest that ITM had a greater impact and improved the level of student knowledge to a larger extent than DTS. ITM can thus be considered an effective teaching methodology and is proposed to be implemented in the multi-disciplinary teaching of biology, environmental education and Ecology; its effectiveness in the other science subjects should also be assessed. Article visualizations:
MALL with WordBricks--Building Correct Sentences Brick by Brick.

Mobile-Assisted Language Learning (MALL) use is increasing and it is good to be able to provide language learners with new resources to enhance their language learning experience. One such resource is WordBricks, a non-commercial, educational app that facilitates the learning and reinforcement of grammar rules. It uses bricks and connectors of different colours and shapes for different parts of speech, and the learner can (only) form grammatically correct sentences. Learners can choose to form sentences from examples or create their own original sentences. Originally, WordBricks was designed for personalised, individual, out-of-classroom learning for English language learners. This paper provides an overview of research on the original WordBricks app for English. A version of WordBricks has been developed for Irish and adapted for use in the primary school setting. There are very few interactive resources available for Irish and the Irish WordBricks app is a new addition to the Irish Computer-Assisted Language Learning (CALL) cannon. This paper reports on how the Irish WordBricks app was used by teachers and students in a primary school and the feedback received on the app.
A highly robust estimator for computer vision

The authors present a highly robust estimator called an MF-estimator for general regression. It is argued that the kind of estimators needed by computer vision must be highly robust and that the classical robust estimators do not render a high robustness. It is explained that the high robustness becomes possible only through partially but completely modeling the unknown log likelihood function. Partial modeling explores a number of important heuristics implicit in the regression problem and takes place by taking them into consideration with the Bayes statistical decision rule, while maximizing the log likelihood function. Experiments with the simplest location estimation showed that the performance of the MF-estimator was superior to that of the classical M-estimator. <<ETX>>
A hybrid neural network system for the rainfall estimation using satellite imagery

Hybrid neural networks composed of a self-organizing map (SOM) and three-layered feedforward neural networks have been developed and applied for rainfall estimation using satellite imagery. The SOM classifies an input vector extracted from satellite imagery, then one of the feedforward neural networks is chosen according to the class to give the rainfall estimation. In order to train the hybrid neural network, adjoining seas of Japan were selected as testing area. Hourly GMS infrared imagery data and simultaneous ground truth data (the area average of rainfall observations and radar/raingage composite data) were collected from AIP/l/sup 2/ data sets. The SOM is trained to classify the textural feature vectors extracted from the imagery data, and tuned by learning vector quantization method. The feedforward neural networks are trained to give the estimation by back propagation algorithm. Fairly good correlation coefficients about 0.8 are obtained between the estimation and corresponding ground truth for the unlearned test set. Furthermore, SOM with a recurrent structure for processing the temporal information has been proposed and tested.
AC motors for high-performance drives: a design-based comparison

Three AC motors are compared for application in high-performance controlled drives: induction; brushless; and synchronous reluctance motors. Their design is optimized under common design assumptions. Then, their given stall-torques are compared. In addition, their quadrature reactances are compared, as affecting overload and flux-weakening performances. Finally, applicative considerations are given, thus defining the most suitable solution for each field.