Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards real-time free-hand biopsy navigation.

PURPOSE Performing a transrectal ultrasound (TRUS) prostate biopsy is at the heart of the current prostate cancer detection procedure. With today's 2D live ultrasound (US) imaging equipment, this task remains complex due to the poor visibility of cancerous tissue on TRUS images and the limited anatomical context available in the 2D TRUS plane. This paper presents a rigid 2D/3DUS registration method for navigated prostate biopsy. This allows continuous localization of the biopsy trajectory during the procedure. METHODS We proposed an organ-based approach to achieve real-time rigid registration without the need for any probe localization device. The registration method combines image similarity and geometric proximity of detected features. Additions to our previous work include a multi-level approach and the use of a rejection rate favouring the best matches. Their aim is to increase the accuracy and time performances. These modifications and their in-depth evaluation on real clinical cases and comparison to this previous work are described. We performed static and dynamic evaluations along biopsy trajectories on a very large amount of data acquired under uncontrolled routine conditions. The computed transforms are compared to a ground truth obtained either from corresponding manually detected fiducials or from an already evaluated registration method. RESULTS All results show that the current method outperforms its previous version, both in terms of accuracy (the average error reported here is 12 to 17% smaller depending on the experiment) and processing time (from 20 to 60 times faster compared to the previous implementation). The dynamic registration experiment demonstrates that the method can be successfully used for continuous tracking of the biopsy location w.r. t the prostate at a rate that varies between 5 and 15 Hz. CONCLUSIONS This work shows that on the fly 2D/3DUS registration can be performed very efficiently on biopsy trajectories. This allows us to plan further improvements in prostate navigation and a clinical transfer.
Nitrogen dioxide exposures from LPG stoves in a cleaner-cooking intervention trial

Background: Liquefied petroleum gas (LPG) stoves have been promoted in low- and middle-income countries (LMICs) as a clean energy alternative to biomass burning cookstoves. Objective: We sought to characterize kitchen area concentrations and personal exposures to nitrogen dioxide (NO2) within a randomized controlled trial in the Peruvian Andes. The intervention included the provision of an LPG stove and continuous fuel distribution with behavioral messaging to maximize compliance. Methods: We measured 48-hour kitchen area NO2 concentrations at high temporal resolution in homes of 50 intervention participants and 50 control participants longitudinally within a biomass-to-LPG intervention trial. We also collected 48-hour mean personal exposures to NO2 among a subsample of 16 intervention and 9 control participants. We monitored LPG and biomass stove use continuously throughout the trial. Results: In 367 post-intervention 24-hour kitchen area samples of 96 participants' homes, geometric mean (GM) highest hourly NO2 concentration was 138 ppb (geometric standard deviation [GSD] 2.1) in the LPG intervention group and 450 ppb (GSD 3.1) in the biomass control group. Post-intervention 24-hour mean NO2 concentrations were a GM of 43 ppb (GSD 1.7) in the intervention group and 77 ppb (GSD 2.0) in the control group. Kitchen area NO2 concentrations exceeded the WHO indoor hourly guideline an average of 1.3 hours per day among LPG intervention participants. GM 48-hour personal exposure to NO2 was 5 ppb (GSD 2.4) among 35 48-hour samples of 16 participants in the intervention group and 16 ppb (GSD 2.3) among 21 samples of 9 participants in the control group. Discussion: In a biomass-to-LPG intervention trial in Peru, kitchen area NO2 concentrations were substantially lower within the LPG intervention group compared to the biomass-using control group. However, within the LPG intervention group, 69% of 24-hour kitchen area samples exceeded WHO indoor annual guidelines and 47% of samples exceeded WHO indoor hourly guidelines. Forty-eight-hour NO2 personal exposure was below WHO indoor annual guidelines for most participants in the LPG intervention group, and we did not measure personal exposure at high temporal resolution to assess exposure to cooking-related indoor concentration spikes. Further research is warranted to understand the potential health risks of LPG-related NO2 emissions and inform current campaigns which promote LPG as a clean-cooking option.
Mobile Target nodes localization in WSNs by PSO Technique

The quantity of sensor hubs acclimatizes the data and convey remotely make Wireless Sensor Network (WSN). In greater part of utilizations of WSNs viz. health monitoring, military, ecological detecting, salvage besides organic following, area of sensor node is fundamental. The way toward deciding the directions of obscure hubs in a system is named as confinement process. The accessible confinement plots in the writing by which limitation procedure should be possible are portrayed by set of sets like fixed and movable restriction, unified as well as circulated limitation, outside and indoor limitation, two dimensional &three dimensional restrictions and so on. The principle target of these confinement plans is to locate the general directions of the concerned nodes from where the occasion is happening in various situations. This paper proposes the utilization of PSO calculation for ideal restriction of arbitrarily moving sensors in the specific region. All reproductions utilizing various situations have been performed on MATLAB programming.
3D segmentation in medical images

—Medical imaging modalities like CT, MRI provide good image of bones, hard tissue, soft tissues and tumors etc. Researcher will process and analyze these images to extract meaningful information like shape, growth of tumor, motion of organs and any kind of abnormalities in the patient body. This paper presents thresholding and clustering techniques like k-means and FCM with three dimension visualization by using volume rendering method. Experiments on Sheep-Logan phantom data sets are conducted to demonstrate the effectiveness of segmentation methods with performance measure
Ridgeness Based Vessel Feature Detection for Endoscopic Images

: Distinctive features are crucial to many tasks in computer assisted minimally invasive surgeries (MIS). Most existing methods are difficult to extract distinctive features in MIS images. For better analysis of MIS images, resort to blood vessels that are abundant and distinctive on the tissue surfaces. In our paper, based on vascular branching points, propose a new type of vascular feature, branching segment. For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. Abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. Here two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, Ridgeness-Based Circle Test and Ridgeness-Based Branching Segment Detection are presented to extract branching points and branching segments, respectively.
ACTIVITY RECOGNITION USING SMARTPHONE EMBEDDED SENSORS WITH K-NEAREST NEIGHBOR ALGORITHM

: Human Activity Recognition(HAR) is behind many human-centric applications such as Smart Homes, and public healthcare. This paper presents a way of detecting six basic physical human activities (Sitting, Laying, Standing, Walking, Walking Downstairs, Walking Upstairs), with accelerometer embedded in an android smartphone. K-Nearest Neighbor (KNN) algorithm, and Relief F feature selector in WEKA Machine Learning Toolkit were adopted to recognize these activities on a publicly available dataset. The average recognition accuracy of 95.2% demonstrated the feasibility of the proposed solution, and can be considered adequate for HAR. This work is significant because it can provide contextual information
Detecting Trending Skills in Information Technology Industry Using Job Ads

Due to the rapid development in Information Technology (IT), knowing what skills are trending in the job market can not only help job seekers equip themselves with proper competencies but also help training and education association improve their training curricula. In this paper, we collected IT job advertisements (job ads) from an online job board and applied methods from natu-ral language processing and trend detection on job titles and job descriptions to identify trending skills. We found that “Cloud Computing" is a trending skill for IT job from the results of all analysis. Also, there are more and more job ads that listed the requirement of business skills. In addition to investigating trending skills in the context of job ads, we also inserted these skills into Google Trends to see whether these skills are also trendy in the context of the public’s interests. The comparison between Google Trends and job ads shows that a trend in the job market is not necessarily considered as a trend in the public’s interests. We believe that our pipeline of how to analyze job ads can help the subsequent studies to better process the job ads and detect trending jobs and skills. The mismatch between Google Trends and job ads also shows the information gap between what people think the companies require and what the companies actually want.
A Two Time-Scale Update Rule Ensuring Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER

We prove under commonly used assumptions the convergence of episodic, that is, sequence-based, actor-critic-like reinforcement algorithms for which the policy becomes more greedy during learning. The most prominent example of such algorithms is the recently introduced RUDDER method, which speeds up the learning of delayed reward problems by reward redistribution. RUDDER is based on simultaneously learning a policy and a reward redistribution network similar to actor-critic methods. We show the convergence of RUDDER which can be generalized to similar actor-critic-like algorithms. In contrast to previous convergence proofs for actor-critic-like methods, we consider whole episodes as learning examples, undiscounted reward, and a policy that becomes more greedy during learning. We employ recent techniques from two time-scale stochastic approximation theory which are equipped with a controlled Markov process to account for the policy getting more greedy. We expect our framework to be useful to prove convergence of other algorithms based on reward shaping or on attention mechanisms.
Spectral-domain optical coherence tomography finding in cytomegalovirus retinitis in AIDS patients.

AIM To observe the findings of spectral domain optical coherence tomography (SD-OCT) scan in cytomegalovirus retinitis (CMVR). METHODS Forty-six eyes of 33 patients with acquired immunodeficiency syndrome and CMVR were enrolled in the study. Complete ophthalmologic examinations, color fundus photography, SD-OCT and fundus autofluorescence (FAF) were performed for all patients at the first visit and each follow-up visit. Retinal necrosis in CMVR was analyzed on SD-OCT and classified into two types, the typical type and the atypical type. RESULTS Forty-one eyes of active CMVR and 4 eyes of recurrent CMVR were classified into typical type, and 4 eyes with graying retinal lesion without hemorrhage or only punctate hemorrhage were classified into atypical type. In active stage of CMVR, the retina in typical type was significant thickened with hyperreflective lesion and full-thickness disruption of retinal architecture with enlarged vessel; while in atypical type, the retina was also destroyed in all layers but without thickening or slightly thinned. The choroid, vitreous and retinal vessels were not significantly involved. In healed stage, the retina was thin with destroyed layers in both types. In typical type, FAF showed mottled hypofluorescence mixed with punctuate hyperfluorescence. In atypical type, the retina showed some "cavity" in outer nuclear layer, and FAF showed mild hyperfluorescence. CONCLUSION SD-OCT show different changes in the retina in typical type and atypical type of CMVR, which should be useful in assisting diagnosis and follow-up management of the disease.
GSJ: Volume 8, Issue 4, April 2020, Online: ISSN 2320-9186

Object detection is a subfield of computer vision that is currently heavily based on machine learning. For the past decade, the field of machine learning has been dominated by so-called deep neural networks, which take advantage of improvements in computing power and data availability. A subtype of a neural network called a convolutional neural network (CNN) is well-suited for image-related tasks. The network is trained to look for different features, such as edges, corners and color differences, across the image and to combine these into more complex shapes. For project, we reviewed the current literature on convolutional object detection and tested the implement ability of one of the methods. We found that convolutional object detection is still evolving as a technology, despite outranking other object detection methods. By virtue of free availability of datasets and pre-trained networks, it is possible to create a functional implementation of a deep neural network without access to specialist hardware. Pre-trained networks can also be used as a starting point for training new networks, decreasing costly training time.
Développement de membranes céramiques à architecture optimisée pour l'oxycombustion

L’etude de materiaux conducteur mixtes (ionique et electronique) connait un interet croissant depuis plusieurs annees dans le domaine de l’energie, principalement lie au developpement des electrodes pour les piles a combustible de type SOFC (Solid Oxide Fuel Cell) ou des reacteurs catalytiques membranaires (CMR) pour le reformage du methane de synthese ou pour le procede d’oxycombustion. Dans ce dernier cas, la realisation de membranes conductrices mixtes de structure perovskite du type La1-xAxFe1-yByO3- permet la separation de l’oxygene de l’air a haute temperature (900°C) avec une selectivite quasiment infinie sans circuit electrique exterieur. Les mecanismes limitant le transport de l’oxygene a travers la membrane ont ete etudies a l’aide d’un dispositif de caracterisation original compose d’electrodes, permettant la mesure du potentiel electrochimique de l’oxygene a la surface de la membrane. L’influence de la substitution du cation en site A puis en site B sur les proprietes de semi-permeabilite a l’oxygene a ete etudiee au sein des materiaux perovskites La0,5A0,5Fe0,7B0,3O3-(A = Ca, Sr, Ba et B = Al, Co, Cu, Ga, Mg, Mn, Ni, Sn, Ti, Zn). Les resultats obtenus avec cette technique originale nous ont permis de mieux cerner les mecanismes limitant le transport d’oxygene a travers la membrane. L’influence de la microstructure de la membrane sur les proprietes de semi-permeabilite a l’oxygene a egalement ete etudiee et un modele d’evolution des proprietes de semi-permeabilite en fonction de la microstructure a ete propose. Cette comprehension des mecanismes de transport nous a permis d’orienter les recherches vers l’elaboration de nouvelles architectures de membranes.
An Investigation on the User Behavior in Social Commerce Platforms: A Text Analytics Approach

Nowadays, the tourism industry accounts for approximately 10% of the global GDP, while it only contributes 3% of the economy in Iran. Since the pressure of US sanctions increases day after day on the Iranian economy, the necessity of paying attention to this industry as a source of foreign currency is felt more than ever. The purpose of this research is to analyze the reviews of users of social commerce websites by using a combination of text mining and data mining techniques. For this purpose, the database of TripAdvisor website (TripAdvisor.com) was evaluated, and all profile information of users who commented on hotels in Iran was collected. These comments on all the content of the website, such as hotels, restaurants, and attractions, were then extracted and analyzed. The optimal number of clusters was considered four clusters by calculating the Davies-Bouldin index, namingly water therapy tourists, boutique hotels style and Iran urban tourists, travelholics and food tourists, business and health tourists. Every single cluster possesses unique attributes and features. Afterward, the association rules were further identified for each cluster according to the characteristics of each cluster and the information in the users' profiles. Finally, a solution is proposed to increase the participation of the users on the website, and targeted promotional plans are expressed in accordance with the well-known features of each cluster.
Improving Multilevel Writes on Vertical 3-D Cross-Point Resistive Memory

Resistive memory is promising to be constructed as a high-density storage-class memory. Multilevel cell, access-transistor-free cross-point array structure, and 3-D array integration are three approaches to scale up the density of resistive memory. However, composing the three approaches together strengthens the interactions between array-level and cell-level nonidealities (interconnect resistance-induced IR drop, sneak current, and device variability) of resistive memory arrays during write operations and significantly degrades write performance and reliability. In this article, we analyze the dynamic voltage-dividing effect along a selected write current path in 3-D cross-point memory arrays. We propose a nonideality-tolerant high-density resistive memory (HD-RRAM) architecture, that can weaken the interactions between nonidealities and mitigate their degradation effects on the performance and reliability of array multilevel write operations. HD-RRAM is equipped with a double-transistor array architecture with two-transistor- $n$ -resistor (2TnR) cell organization along pillars to reduce the current driving requirement and the large undesired voltage drop across each vertical pillar access transistor. Moreover, multiside asymmetric bias improves the resistive switching velocity by leveraging current-dividing effects. Variability-aware multilevel state partition reduces the worst-case write error rate by leveraging target state dependency of variability. Proportional-control multilevel state tuning reduces the average number of required write-and-verify iterations by leveraging pulse amplitude dependency of variability. Multilevel cell parallel writing improves the cell-level parallelism by leveraging the pass-through feature of intermediate resistance states. The evaluations show that HD-RRAM reduces both memory access latency and energy consumption over an aggressive baseline.
Scaling Crowdsourcing with Mobile Workforce

Traditional urban-scale crowdsourcing approaches suffer from three caveats - lack of complete spatiotemporal coverage, lack of accurate information and lack of sustained engagement of crowd workers. In this paper, we argue that these caveats can be addressed by embedding crowdsourcing tasks into the daily routine of mobile workforces that roam around an urban area. As a use case, we take the bpost who deliver the letters and parcels to the citizens across entire Belgium. We present a study that explores the behavioural attributes of these mobile postal workers both quantitatively (6.3K) and qualitatively (6) to assess the opportunity of leveraging them for crowdsourcing tasks. We report their mobility pattern, workflow, and behavioural traits which collectively inform the design of a purpose-built crowdsourcing solution. In particular, our solution operates on two key techniques - route augmentation, and on-wearable interruptibility management. Together, these mechanisms enhance the spatial coverage, response accuracy and increase workers' engagement with crowdsourcing tasks. We describe these principal components in a wearable smartwatch application supported by a data management infrastructure. Finally, we report a first-of-its-kind real-world trial with ten postal workers for two weeks to assess the quality of road signs at the city centre of Antwerp. Our findings suggest that our solution was effective in achieving 89% spatial coverage and increasing response rate (83.6%) and accuracy (100%) of the crowdsourcing tasks. Although limited in scale, these and the rest of our findings highlight the way of building an efficient and purposeful crowdsourcing solution of the future.
An Improved Items Recommendation for Memory-Based Collaborative Filtering Technique

- This research aimed to improve Improved Items Recommendation for Memory-based Collaborative Filtering Technique. In this research: 1) to concern rating item about users and 2) to know new prediction rating based on Memory-based Collaborative Filtering Technique. The experimental results from tested propose equation appear γ is [0, 1) and from the equation, γ is equal to 0.2. Get the minimization then recommend item was improved. Mean Absolute Error (MAE) compared to exiting approach was decrease 99.9 %.
University of Birmingham Asymmetric distribution in twin screw granulation

PEPT (Positron Emission Particle Tracking) was successfully employed to validate measured transverse asymmetry in material distribution in the conveying zones of a Twin Screw Granulator (TSG). Flow asymmetry was established to be a property of the granulator geometry and dependent on fill level. The liquid distribution of granules as a function of fill level was determined. High flow asymmetry at low fill level negatively affects granule nucleation leading to high variance in final uniformity. Wetting of material during nucleation was identified as a critical parameter in determining final granule uniformity and fill level is highlighted as a crucial control factor in achieving this. Flow asymmetry of dry material in conveying zones upstream of binder fluid injection leads to poor non-uniform wetting at nucleation and results in heterogeneous final product. The granule formation mechanism of 60 ° F kneading blocks is suggested to be primarily breakage of agglomerates formed during nucleation. Optimisation of screw configuration would be required to provide secondary growth. This work shows how fill dependent flow regimes affect granulation mechanisms.
Low Complexity Classification of Power Asset Faults for Real Time IoT-based Diagnostics

This paper investigates a new application of Capsule Neural Network (CapsNet), in combination with Constant-Q Transform (CQT), for insulation fault signal detection in High Voltage (HV) power plants. First, a mapping from insulation fault time-series signals to time-frequency images is obtained using the CQT, providing both time and frequency information. Different ways of exploiting the resulting complex-valued CQT are proposed; the CQT magnitude as a 1-channel image and the real-imaginary values of the CQT as a 2-channel image. This paper presents novel work in HV condition monitoring by utilising the CQT and CapsNet methods. Feature extraction and classification, from the produced CQT spectrum, is performed by CapsNet and the Residual Neural Network (ResNet). A performance comparison between both models, shows that CapsNet outperforms the ResNet in terms of classification accuracy with lower computation. The reduced computation and improved classification accuracy proves ideal, for system implementation on an edge embedded device incorporated in an Internet of Things (IoT) arrangement.