Miracle-dz/UniFAR-FTUs huggingface dataset:
To detect and Recognize Object from Videos for Computer Vision by Parallel Approach using Deep Learning

Computer vision is the multidisciplinary domain extracts and analyses digital images in an automated manner. The application of computer vision is widespread and it ranges from agriculture to robotics. At present, computer vision adopts the concept of machine learning to build a model and solves classification problems. However, this technique becomes inefficient when it is directly applied to digital images as it ignores the structure and compositional nature of the images. Deep Convolutional Neural Network (CNN) acts as the best solution to traditional computer vision approaches as it learns to extract features from the raw images along with the classification process. In this paper, we present a deep learning based solution to computer vision problem. First, we define a CNN based approach to learn and extract features from the real time videos. Next, an extended linear support vector machine (SVM) classifier is used for object classification processes. Thus the proposed method make use of the combinational approach of the deep learning and machine learning to solve computer vision problems. Since deep CNN are massively parallel algorithms the application of CNN techniques with GPU forms the effective solution for computer vision problems. The experimental results are evaluated in terms performance, accuracy and simplicity measures.
Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

Electric vehicles emerged new applications that are strongly related to the energy constraints such as the identification of the optimal path toward the vehicle’s destination or toward the nearest recharging station, selection of the path where vehicle recovers extra energy, estimation of the need to recharge according to the actual battery state and the traffic state, etc. However, in electric vehicular networks, vehicles may provide wrong energy information due to sensors’ failure, selfish or malicious reasons. Therefore, energy-related information trustworthiness needs to be evaluated in order to preserve the quality of the presented applications. In this paper, we address the energy-related information trustworthiness to discriminate between credible and erroneous values. Therefore, we propose a new fuzzy-based trust model that deals with the information uncertainties. This model aims at detecting the wrong energy information that mismatches with the vehicle’s behavior and ensure that only trustworthy and plausible energy-information are handled. Results prove the performance of the proposed model and its capabilities to deal with several kinds of threats in different traffic densities with high precision.
Exploring Urban Mobility from Taxi Trajectories: A Case Study of Nanjing, China

Identifying urban mobility patterns is a crucial research topic in geographic information science, transportation planning, and behavior modeling. Understanding the dynamics of daily mobility patterns is essential for the management and planning of urban facilities and services. Previous studies have utilized taxi trajectories collected from the Global Positioning System (GPS) to model various types of urban patterns, such as identifying urban functional regions and hot spots. However, there is limited research on how the results of these studies can be used to inform real-world problems in urban planning. This research examines the development of sub-centers in Nanjing, China based on Taxi GPS trajectories. The results indicate a clear separation between the urban center and the sub-centers. In addition, we also clustered the time series of taxi pick-up locations to model dynamic urban movement and identify outlier patterns. The results demonstrate the importance of considering human mobility patterns in identifying urban functional regions, which provides valuable input for urban planners and policy makers.
Extended playing techniques: the next milestone in musical instrument recognition

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.
Detection and Prevention Analysis of Wormhole Attack in Wireless Sensor Network

Wireless sensor network (WSN) is a collection of various sensor nodes. These sensor nodes sense data from the surrounding and send it to its destination following a proper route. A sensor network has various applications to monitor physical phenomenon in military, agriculture and medical applications where it can be used to make human life better in many ways. There are many issues in WSN like security, energy efficiency and QoS etc. which needs attention of today's research. There are several security attacks which affect the process of data dissemination. These security attacks spawn at various layers of Internet model which hampers the security of the network. In this paper, a comparative study of some detection and prevention methods of wormhole attack is presented. The various wormhole attack models and different modes of wormhole attack are also discussed in the paper.
Rotor Shaping Method for Torque Ripple Mitigation in Variable Flux Reluctance Machines

In this paper, four rotor shaping methods, i.e., eccentric circular, inverse cosine, inverse cosine with third harmonic, and multi-step shaping methods, are developed and compared for torque ripple mitigation in variable flux reluctance machines (VFRMs). By using a 6-stator-pole/7-rotor-pole (6/7) VFRM as an example, the design criterions and capabilities of these four methods are illustrated. It is found that all the rotor shaping methods are capable of torque ripple mitigation and applicable to all the VFRMs except those with 6<italic>k</italic>/(6<italic>i</italic> ± 2) <italic>k</italic> (<italic>k</italic>, <italic>i</italic> = 1, 2, 3…) stator/rotor pole combinations. Moreover, the inverse cosine with third harmonic and multi-step shaping methods are found to have the best performance. They are able to reduce the torque ripple by 90% at a cost of only 3% torque density reduction. A 6/7 VFRM with both conventional and shaped rotors is prototyped and tested for verification.
Suitability Analysis of Fractal Compression Technique for Medical Image

Images occupy the increasing part of the medical information systems. The Large volume of image contents needs to be compressed for effective storage and transmission. Fractal Image Compression (FIC) is a technique based on Quad tree decomposition. This compression represents an image by a contractive Affine transforms. In the existing literatures this technique has been analyzed mostly on natural images and found to be efficient. In this paper an attempt is made with medical images namely MRI, CT, Angiogram and Ultrasounds. The performance of FIC is compared with traditional SPIHT compression technique in terms of PSNR, compression Ratio, Encoding and Decoding time. From the simulation results, it is inferred that FIC technique outperforms SPIHT in terms of compression ratio with acceptable quality of reconstructed image which makes it suitable for medical image transmission applications.
Assessment of industry data on pulmonary and immunosuppressive effects of IQOS

Introduction Heated tobacco products are being touted as novel reduced-harm tobacco products by tobacco companies. In the USA, Philip Morris International submitted a modified risk tobacco product (MRTP) application to the US Food and Drug Administration in 2016 in which it purports that its heated tobacco product, I-Quit-Ordinary-Smoking (IQOS), is associated with reduced harm compared with conventional cigarettes. Methods We reviewed Philip Morris International’s MRTP application to assess the pulmonary and immune toxicities associated with IQOS use in both animal and human studies. Results Among rats exposed to IQOS, there was evidence of pulmonary inflammation and immunomodulation. In human users, there was no evidence of improvement in pulmonary inflammation or pulmonary function in cigarette smokers who were switched to IQOS. Conclusion IQOS is associated with significant pulmonary and immunomodulatory toxicities with no detectable differences between conventional cigarette smokers and those who were switched to IQOS in Philip Morris International’s studies. Philip Morris International also failed to consider how dual use and secondhand aerosol exposure may further impact, and likely increase, the harms associated with these products.
EEG Signal Classification for Real- Time Neuro Marketing Applications

This paper aims to find out the functionality and mechanism and procedure of various brainwaves produced in a human and how non-invasive EEGs are used to read electrical activity that takes place inside our brain. This information can be further used for EEG signal classification for real time neuro marketing applications. In this paper we are going to compare the efficiency of the results of using various algorithms required for signal classification. All this is done by making use of the tool EEGLAB which is implemented in MATLAB. EEGLAB version 14.1. 1 was used for the same. The dataset used was the default dataset provided by EEGLAB. Various documentations provided by Swartz Center for Computational Neuroscience were read and the directions to use the tool were followed as per the documentation provided by them.
Estimate the Lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method

This paper presents a novel algorithm for recovering missing data of phasor measurement units (PMUs). Due to the low-rank property of PMU data, missing measurement estimation can be formulated as a low-rank matrix-completion problem. Based on maximum-margin matrix factorization, we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) for solving the matrix completion problem. Comparing to existing approaches, the proposed ADMM based algorithm does not need to estimate the rank of the target data matrix and provides better performance in computation complexity. In addition, we consider the case of measurements missing from all PMU channels and provide a strategy of reshaping the matrix which contains the received PMU data for estimation. Numerical results using PMU measurements from IEEE 68-bus power system model illustrate the effectiveness and efficiency of the proposed approaches.
Knapsack Optimisation Versus Cell Range Expansion for Mobility Load Balancing in Dense Small Cells

We present a new approach for mobility load balancing (MLB) and user association in dense small cell scenarios. This Self Organizing Network (SON) approach relies on Knapsack Optimisation (KO) to evenly distribute users across a cluster of small cells subject to constraints. It is shown that the new technique referred to as (MLB-KO) achieves substantial improvements (better than four times reduction) in blocking ratios compared to the case when no MLB strategy is deployed. Comparisons with other approaches relying on Cell Range Expansion (CRE) and Almost Blank Subframes (ABS) are also presented highlighting the effectiveness of the new approach as a centralised self optimisation technique for future dense small cell network deployments.
Component-based Engineering of Web User Interface Designs for Evolutionary Optimization

Component-based approach has proved its effectiveness in modern web engineering, where programmers increasingly rely on web development frameworks. In front-end web development parts of available solutions cannot be reused directly, due to technical and legal obstacles, so we propose a specific process to generate web user interfaces (WUI) designs from configurable components. The similarity between the generated designs and the exemplary high-quality solutions retrieved from case base is then optimized within the evolutionary algorithm. In our current paper we designate the structure of the components and justify the employment of Drupal as the organizational framework. We also describe the implementation details, including the two supplemental software tools that we developed: 1) web intelligence miner that collects website-related data from linked open data sources and 2) web UI screenshot analyzer that converts it into semantic-spatial representation in JSON format. Finally, we specify the new solutions generation algorithm, per the three WUI dimensions: functionality, layout and visual appearance.
Electrical conductivity of nutrient solution influenced photosynthesis, quality, and antioxidant enzyme activity of pakchoi (Brassica campestris L. ssp. Chinensis) in a hydroponic system

To find an electrical conductivity (EC) in the nutrient solution used for pakchoi (Brassica campestris L. ssp. Chinensis) cultivation that optimizes the plant’s physiology, growth, and quality, we conducted an experiment with eight EC treatments (from EC0 to EC9.6) in a hydroponic production system (i.e. soilless culture) under greenhouse condition in Shanghai, China. Plants biomass production, leaf photosynthesis, vegetable quality variables, tissue nitrate and nitrite contents, and antioxidant enzyme activities were measured. The results showed that very high (EC9.6) or low EC (EC0-0.6) treatments clearly decreased plants fresh weight (FW) and dry weight (DW), leaf size, leaf water content, leaf net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and taste score. Nitrite content, and antioxidant enzyme activities were low in medium EC treatments (EC1.8 and EC2.4), but high in very high or low EC treatments. Leaf relative chlorophyll, ascorbic acid, and nitrate contents increased gradually from low EC to high EC treatments, while crude fiber and soluble sugar contents decreased. Based on growth and quality criteria, the optimal EC treatment would be EC1.8 or EC2.4 for pakchoi in the hydroponic production system. Too high or too low EC would induce nutrient stress, enhance plant antioxidant enzyme activities, and suppress pakchoi growth and quality.
CDK4/6 inhibitors in the treatment of patients with breast cancer: summary of a multidisciplinary round-table discussion

This article is the result of a round-table discussion organised by ESMO Open in Vienna in December 2017. Its purpose is to discuss the background and advances in the evidence regarding cyclin-dependent kinase 4/6 inhibitors (palbociclib, ribociclib and abemaciclib) in the treatment of metastatic and early-stage breast cancer and to explore what the key open research questions are and next steps should be.
Updates on Old and Weary Haematopoiesis

Blood formation, or haematopoiesis, originates from haematopoietic stem cells (HSCs), whose functions and maintenance are regulated in both cell- and cell non-autonomous ways. The surroundings of HSCs in the bone marrow create a specific niche or microenvironment where HSCs nest that allows them to retain their unique characteristics and respond rapidly to external stimuli. Ageing is accompanied by reduced regenerative capacity of the organism affecting all systems, due to the progressive decline of stem cell functions. This includes blood and HSCs, which contributes to age-related haematological disorders, anaemia, and immunosenescence, among others. Furthermore, chronological ageing is characterised by myeloid and platelet HSC skewing, inflammageing, and expanded clonal haematopoiesis, which may be the result of the accumulation of preleukaemic lesions in HSCs. Intriguingly, haematological malignancies such as acute myeloid leukaemia have a high incidence among elderly patients, yet not all individuals with clonal haematopoiesis develop leukaemias. Here, we discuss recent work on these aspects, their potential underlying molecular mechanisms, and the first cues linking age-related changes in the HSC niche to poor HSC maintenance. Future work is needed for a better understanding of haematopoiesis during ageing. This field may open new avenues for HSC rejuvenation and therapeutic strategies in the elderly.
Chronic clozapine treatment improves the alterations of prepulse inhibition and BDNF mRNA expression in the medial prefrontal cortex that are induced by adolescent social isolation

Isolation rearing produces significant behavioral and neurochemical dysfunctions in rodents, which resemble the symptoms of schizophrenia. Clozapine, one of the atypical antipsychotics, is widely used in the treatment of schizophrenia patients and in experimental studies. In this study, male Sprague Dawley rats were randomly assigned to either group-reared or isolation-reared conditions during postnatal days (PNDs) 21–34. During PNDs 46–55, the rats were subjected to chronic clozapine (1.0 mg/kg for 10 days) or saline treatment. On PND 56, all rats underwent behavioral testing and then were sacrificed for biochemical testing. The results indicated that adolescent social isolation induced impairments in prepulse inhibition and reversal learning, and clozapine injection improved the prepulse inhibition disruption but not reversal learning ability. Furthermore, clozapine administration reversed the increased brain-derived neurotrophic factor (BDNF) mRNA level in the medial prefrontal cortex (mPFC) that was induced by adolescent isolation. However, clozapine decreased the BDNF mRNA level in the mPFC in group-reared rats. Together, our findings provide additional evidence that a low dose of chronic clozapine treatment could improve information filtering/sensorimotor gating and alterations in the BDNF mRNA level in the mPFC induced by adolescent social isolation.
DEMANDS: Distributed Energy Management Using Noncooperative Scheduling in Smart Grid

In this paper, the problem of energy scheduling and energy exchange between microgrids and customers is studied as a multi-leader multi-follower noncooperative Stackelberg game. The customers act as leaders, and decide the amount of required energy to be taken in each time slot. On the other hand, the microgrids act as the followers, which need to decide the price per unit energy based on the total requested energy by the connected customers. Using variational inequality, it is shown that the proposed distributed energy management using scheduling (DEMANDS) scheme has a Nash equilibrium solution, which is also socially optimal. In the proposed scheme, DEMANDS, each customer gets energy from any of the available microgrids within a coalition neither by paying higher price per unit energy, nor by waiting for the next time slot for service. The proposed scheme, DEMANDS, which enables the microgrids and the customers to reach the equilibrium state, is evaluated theoretically as well as through simulations.
Measuring Team Effectiveness in the Health Care Setting: An Inventory of Survey Tools

Background: Guidance for measuring team effectiveness in dynamic clinical settings is necessary; however, there are no consensus strategies to help health care organizations achieve optimal teamwork. This systematic review aims to identify validated survey instruments of team effectiveness by clinical settings. Methods: PubMed, MEDLINE, and ISI Web of Knowledge were searched for team effectiveness surveys deployed from 1990 to 2016. Validity and reliability were evaluated using 4 psychometric properties: interrater agreement, internal consistency, content validity, and structural integrity. Two conceptual frameworks, the Donabedian model and the Command Team Effectiveness model, assess conceptual dimensions most measured in each health care setting. Results: The 22 articles focused on surgical, primary care, and other health care settings. Few instruments report the required psychometric properties or feature non-self-reported outcomes. The major conceptual dimensions measured in the survey instruments differed across settings. Team cohesion and overall perceived team effectiveness can be found in all the team effectiveness measurement tools regardless of the health care setting. We found that surgical settings have distinctive conditions for measuring team effectiveness relative to primary or ambulatory care. Discussion: Further development of setting-specific team effectiveness measurement tools can help further enhance continuous quality improvements and clinical outcomes in the future.
Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.
3D Pose Estimation of Bin Picking Object using Deep Learning and 3D Matching

In this paper, we propose a method to estimate 3D pose information of an object in a randomly piled-up environment by using image data obtained from an RGB-D camera. The proposed method consists of two modules: object detection by deep learning, and pose estimation by Iterative Closest Point (ICP) algorithm. In the first module, we propose an image encoding method to generate three channel images by integrating depth and infrared images captured by the camera. We use these encoded images as both the input data and training data set in a deep learning-based object detection step. Also, we propose a depth-based filtering method to improve the precision of object detection and to reduce the number of false positives by preprocessing input data. ICP-based 3D pose estimation is done in the second module, where we applied a plane-fitting method to increase the accuracy of the estimated pose.