Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Blood Pressure Monitor by Using Capacitive Pressure Sensor and Microcontroller

This paper describes the development of a Non-invasive automatic blood pressure monitor (BPM) based on a capacitive pressure sensor (CPS) with the enhanced capture, compare, pulse width modulation (PWM) module (ECCP) of the microcontroller (MCU) and Bluetooth radio communications for home health monitoring. The objective of this paper is to develop automatic blood pressure device with accuracy and low cost by using a capacitive pressure sensor and logic inverter oscillator instead of the instrumentation amplifier and bandpass filter. The biomedical real-time blood pressure (BP) signal is measured by using pressure to capacitance circuit with pulse width modulation (PWM) technology and interfaced with PIC18F4550 microcontroller. The microcontroller detects the blood pressure signal and by using enhanced module functions, with the help of proposed algorithm, the blood pressure result (systolic, diastolic, pulse) has been calculated and transmitted to the Android smartphone health application via Bluetooth communication with the presence of mini LCD display, the records are stored in the internal memory of the microcontroller. The obtained result of the proposed device was verified with standard Sphygmomanometer results.
Does Delaying Endoscopic Sinus Surgery Adversely Impact Quality‐of‐Life Outcomes?

There is little consensus regarding the prognostic value of symptom duration in predicting clinical disease severity or quality‐of‐life (QOL) outcomes in patients with chronic rhinosinusitis (CRS). Our objectives were to: 1) determine if patients with longer symptom duration have worse preoperative disease severity and/or QOL, and 2) determine if delayed surgical intervention influences outcomes of endoscopic sinus surgery (ESS).
An Enhanced Control Overhead Messages Reduction Algorithm in VANET

The mobility in Vehicular Ad-Hoc NETworks (VANETs) causes high topology changes and in turn leads to excessive control overhead messages and frequent link communication failures. Traditionally, clustering techniques have been used as the main solution to improve the stability of the clustered topology and to minimize the routing overhead messages generated due to the routing protocols. In the clustering techniques, the network is divided into multiple clusters and selecting one of the Cluster Members (CMs) as a Cluster Head (CH). Still, a problem occurs when the CMs periodically forwards of CM HELLO (CMHELLO) messages to the CH. Also, when the CH periodically broadcasts an CH ADvertiSement (CHADS) messages to announce itself to the CMs. Hence, minimizing control overhead messages in any cluster environment is an essential goal to efficiently use the network resources. In this paper, we propose an Enhanced Control Overhead messages Reduction Algorithm (ECORA) with the goal to reduce the CHADS messages that broadcasted by the CHs. Finally, we evaluate the performance of our proposed algorithm by comparing with other similar contributions.
Information Content and Efficiency in the Spoken Discourse of Individuals With Parkinson's Disease.

Purpose This study compared the information content and information efficiency of spoken language in individuals with Parkinson's disease (PD) to a healthy comparator group. Method Nineteen participants with PD and 19 healthy older adults completed the prospective, cross-sectional study. In the primary analysis, 2 language samples elicited by standardized protocols were analyzed for group differences using standard discourse informativeness measures including main events (MEs; Wright, Capilouto, Wagovich, Cranfill, & Davis, 2005) analyzed as %MEs and correct information units (CIUs; Nicholas & Brookshire, 1993) analyzed as %CIUs and CIUs/min. In exploratory analyses, the following were examined: (a) associations among conceptual (%MEs) and lexical (%CIUs and CIUs/min) measures and (b) associations among informativeness measures and age, education, disease severity/duration, global cognition, speech intelligibility, and a verb confrontation naming measure. Results In the primary analysis, the PD group differed significantly from the control group on conceptual (%MEs) and lexical measures of content (%CIUs) and efficiency (CIUs/min). In exploratory analyses, for the control group %MEs were significantly correlated with CIUs/min. Significant associations among conceptual and lexical measures of informativeness were not found in the PD group. For controls, there were no significant correlations between informativeness measures and any of the demographic or speech/cognitive/language variables. In the PD group, there was a significant and positive association between CIUs/min and Dementia Rating Scale-Second Edition scores (Mattis, 2001). A significant but negative correlation was found between CIUs/min and motor severity scores. However, %MEs and verb naming were significantly and positively correlated. Conclusions Individuals with PD without dementia demonstrated reduced discourse informativeness that reflects disruptions to both conceptual and lexical discourse processes. In exploratory analyses, reduced efficiency of information content was associated with global cognition and motor severity. Clinical and research implications are discussed within a Cognitivist framework of discourse production (Sheratt, 2007).
Managing and assessing internet service needs and use of under-served populations at public libraries

As the range of public library services with new technology has diversified, defining, measuring, assessing and managing the quality of these electronic services has become a critical issue. Internet connections at public libraries have increased dramatically since 1990 due to the availability of key external funding sources. The major goal of those funding sources was to expand public access to Computers and digital information, especially for the public of low-income or under-served populations; thus, there has been continuing increase in public library Internet services at the library, for example, the services at Internet workstations. Although there have been attempts to measure the performance of public library network services by library and information science researchers using network statistics based on “technical-driven” performances, there has been little research dealing with service quality and satisfaction of Internet service users at the public libraries based on “user-driven” performance evaluation. This paper describes the process of survey research using 386 Internet service users at four public libraries and discusses how public libraries need to approach those under-served populations' Information needs and use at Internet workstations which can expand the possibility of new direction for future public library Internet services.
Transparent Ultrathin Doped Silver Film for Broadband Electromagnetic Interference Shielding

A high-performance electromagnetic interference (EMI) shielding film based on a dielectric-metal-dielectric structure incorporating ultrathin Silver is proposed. The ITO/Cu-doped Ag/ITO (ICAI) film was deposited on flexible Polyethylene terephthalate (PET) substrates at room temperature. The ICAI film transmits $\sim 96.5$% visible light (reference to PET substrate) over spectral range 400 – 700 nm. The film stack shows an excellent average EMI shielding effectiveness (SE) of $\sim 26$ dB, exhibiting a broadband shielding with the bandwidth of 32 GHz, which covers the entire X, Ku, Ka, and K bands. The dielectric-metal-dielectric design greatly relieves the trade-off between optical transmittance and EMI shielding performances. EMI SE exceeds 30 dB can be simply obtained by stacking two layers of ICAI films together. In addition, the flexible ICAI film demonstrates a stable EMI shielding performance under mechanical deformation. The outstanding optical, broadband EMI shielding and mechanical properties of ICAI film render it promising for various applications in healthcare, electronic safety, and fast-growing next-generation flexible electronics, such as roll-up displays and wearable devices.
Cost-Benefit Analysis of Public Clouds for Offloading In-House HPC Jobs

The Supercomputing Education and Research Centre (SERC) at the Indian Institute of Science (IISc), located in the South East Asian Region, has been providing state-of-the-art services and support for High Performance Computing (HPC) for the academic users of the institute since 1990. This centre, facilitates OpenMP, MPI, CUDA and OpenCL based HPC jobs for academic research. To augment or support the existing demand for compute intensive, tightly coupled HPC workloads in the institute, it was desirable to explore cloud based HPC services. This study is a comparative cost-benefit analysis of the in-house compute facility at SERC with that of the commercial cloud providers like Amazon, Google Cloud, Microsoft Azure and Sabalcore. The Total Cost of Ownership (TCO) of SahasraT (the in-house supercomputer) is computed and used to identify the cost of running a small job for 24 hours on the in-house facility. This is then compared with the estimated cost of running the same job on comparative cloud instances. Several research papers that provide a detailed evaluation of HPC on the cloud with performance benchmarks and the comparison to in-house HPC facilities, were studied. These references have been used to compare performance of compute instance in the public cloud to the performance of HPC at SahasraT. The results obtained in this study, advocate that current cloud platforms are expensive in both cost and performance as compared to the in-house facility at SERC
iFetcher: User-Level Prefetching Framework With File-System Event Monitoring for Linux

Applications face additional latency when they launch or access a disk to load data into the memory. In this paper, a user-level disk prefetching framework, called iFetcher, is introduced to hide this delay in Linux-based operating systems. This employs an inode-notification (inotify) application programming interface (API), which provides an efficient method of tracing operations in a Linux file system, and reports these events to applications in real time. During an initial training run, iFetcher traces the pattern with which data are read by an application. Subsequently, it searches the areas directly ahead of passages where it is requested that a lot of data are read, and locates events that can be used to trigger preloading. When the application is run again and a trigger event is reported (by the inotify API), the corresponding data are read into the page cache prior to actual demand. iFetcher has a low overhead, because disk reads are only need to be monitored during training. Furthermore, it does not require any modifications to the Linux kernel, because it runs at the user level. Five benchmark applications were run on a PC using a solid-state drive, and results demonstrated that iFetcher reduced the launch times by up to 41% and post-launch data loading times by up to 9%.
A Set of Multiplex Polymerase Chain Reactions for Genomic Detection of Nine Edible Insect Species in Foods

Abstract On 1 January 2018, a new regulation on ‘Novel Food’ has come into application in the EU. Insects and insect-based products are therefore included among the categories of food which constitute novel foods. Insects are nutrient-rich, produce fewer greenhouse gases and ammonia than conventional livestock, and have high feed conversion efficiency. Insects may be an alternative food source in the near future, but consideration of insects as a food requires scrutiny due to the risk of allergens. The aim of the present study was to develop a set of multiplex polymerase chain reaction (PCR) to detect nine edible insect species directly in foods. Four sets of mPCRs were designed to detect Locusta migratoria migratorioides (Reiche & Fairmaire, 1849) (Orthoptera: Acrididae), Tenebrio molitor (Linnaeus, 1758) (Coleoptera: Tenebrionidae) (mPCR-I), Acheta domesticus (Linnaeus, 1758) (Orthoptera: Gryllidae), Bombyx mori (Linnaeus, 1758) (Lepidoptera: Bombycidae (mPCR-II), Alphitobius diaperinus (Panzer, 1797) (Coleoptera: Tenebrionidae), Schistocerca gregaria (Forskål, 1775) (Orthoptera: Acrididae), Zophobas atratus (Fabricius, 1775) (Coleoptera: Tenebrionidae) (mPCR-III), Galleria mellonella (Linnaeus, 1758) (Lepidoptera: Pyralidae), and Gryllodes sigillatus (Walker, 1869) (Orthoptera: Gryllidae) (mPCR-IV). Results demonstrate that the panel of mPCRs allowed a rapid genetic identification of the insect species and has proved to be a sensible and highly discriminatory method. The assay is a potential tool in issues related to the labeling of products and food safety, in case of allergic consumers.
When Deep Learning Meets Inter-Datacenter Optical Network Management: Advantages and Vulnerabilities

To realize cost-effective and adaptive network control and management (NC&M) on inter-datacenter optical networks (IDCONs), people have considered network virtualization to let the operator of an IDCON work as an infrastructure provider (InP), which can create virtual optical networks (VONs) over the IDCON for tenants. In this paper, we use this network scenario as the background, and try to integrate deep learning (DL) based traffic prediction in the NC&M of the IDCON and the VONs created over it. We first design the service provisioning framework in which each tenant uses a DL module to predict the traffic in its VON and will submit a VON reconfiguration request to the InP, when it sees a significant mismatch between future traffic and the allocated resources in its VON. Then, the InP will invoke the VON reconfiguration to make the VON be better prepared for future traffic. An adaptive and scalable DL-based traffic predictor is proposed together with a cognitive service provisioning algorithm to exploit the temporal and spatial characteristics of interDC traffic and achieve effective service provisioning based on precise and timely traffic prediction. Next, we consider the situation where a tenant leverages “machine-learning-as-a-service” and outsources the training of its DL module to a third-party entity for overcoming its resource limitations, and analyze the induced vulnerabilities due to data poisoning. Our simulation results indicate that with our proposal, the InP can invoke VON reconfigurations timely and improve the service provisioning performance of each VON significantly. Meanwhile, the results also demonstrate that our data poisoning scheme can easily bypass the normal validation of the DL module and generate significant adversarial effects.
Traffic State Prediction Using Convolutional Neural Network

Traffic state prediction methods have been considered by many researchers since accurate traffic prediction is an important part of the successful implementation of the Intelligent Transportation System (ITS). This study develops the traffic prediction model based on real traffic data in congested hours of expressways in Bangkok, Thailand. Unlike most studies, this model utilizes data from 40 nodes along the expressway instead of a single sensor. A Convolutional Neural Network (CNN) model was applied and compared to other widely used models. The result shows that the accuracy of CNN model is higher than other models.
Continuous and Integrated Software Development using DevOps

Devops is all about culture, automation, measurement and sharing (CAMS). It is gaining popularity because of its continuous approach - continuous integration(CI), continuous deployment (CD) and software delivery. CI requires the developer to commit the code several times in a day followed by automatic build and test and immediate feedback to the developer whenever any bug is encountered. If no bug is encountered, the committed code is pushed to the production. According to the 2017 State Of Devops Report, high performance companies like Amazon and Netflix deploy thousands of times per day. Configuration management allows the rollback of previous code for the developer. However several factors prevent the organizations from adopting these approaches like lack of fully automated acceptance test, poor rollback methodology, manually driven quality check etc. This research paper provides various methodologies and tools that can constitute an effective CD/CI pipeline.
Feature Selection via Incorporating Stiefel Manifold in Relaxed K-Means

The task of feature selection is to find the optimal feature subset such that an appropriate criterion is optimized. It can be seen as a special subspace learning task, where the projection matrix is constrained to be selection matrix. In this paper, a novel unsupervised graph embedded feature selection (GEFS) method is derived from the perspective of incorporating the projected k-means with Stiefel manifold regularization. To achieve more statistical and structural properties, we directly embed unsupervised feature selection algorithm into a clustering algorithm via sparse learning to suppress the projected matrix to be row sparse. Comparative experiments demonstrate the effectiveness of our proposed algorithm in comparison with the traditional methods for feature selection.
Contact lens wear and dry eyes: challenges and solutions

The number of contact lens wearers worldwide has remained relatively stable over the past decade, despite the investment that has gone into contact lens technology. This is largely because 10%–50% of wearers dropout of contact lens wear within 3 years of commencement; the most common reason cited being contact lens discomfort (CLD). Of the symptoms reported, sensation of dry eye is the most common. Given the outcome of reduced wearing time, increased chair time, and ultimate contact lens discontinuation, the challenge is to identify the warning signs of CLD early on. Clinically detectable changes such as conjunctival staining, conjunctival indentation, conjunctival epithelial flap formation, lid wiper epitheliopathy, Demodex blepharitis, and meibomian gland dysfunction have been linked to CLD, highlighting the need to perform regular aftercare visits to identify these changes. At a cellular level, conjunctival metaplasia and reduced goblet cell density have been linked to CLD, leading to a downstream effect on the tear film breakup time of contact lens wearers. These factors suggest a strong link between CLD and friction, raising the need to target this as a means of minimizing CLD. The purpose of this review is to identify the clinical signs that relate to CLD as a means of earlier detection and management in order to combat contact lens dropout.
Robust Energy Harvesting FD Transmission: Interference Suppression Versus Exploitation

We explore robust designs to jointly minimize the total uplink and downlink transmit power and maximize the total harvested energy in a full duplex system with imperfect channel state information. We first formulate an optimization, where multiuser interference (MUI) is suppressed. We then propose an optimization, where the MUI is rather exploited, both as useful energy and information power, for guaranteeing quality of service and energy harvesting constraints. To tackle the non-convexity of the formulations, we employ convex relaxations. Simulation results show the effectiveness of interference exploitation compared with interference suppression in terms of both power consumption and energy transfer.
Latency Measurement of Fine-Grained Operations in Benchmarking Distributed Stream Processing Frameworks

This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmarking of fine-grained individual stages of a processing pipeline. By determining the latency of distinct common operations in the processing flow instead of the end-to-end latency, we can form guidelines for efficient processing pipeline design. Additionally, we address the issue of defining time in distributed systems by capturing time on one machine and defining the baseline latency. We validate our benchmark for Apache Flink using a processing pipeline comprising common stream processing operations. Our results show that joins are the most time consuming operation in our processing pipeline. The latency incurred by adding a join operation is 4.5 times higher than for a parsing operation, and the latency gradually becomes more dispersed after adding additional stages.
Validation of Urine-based Gene Classifiers for Detecting Bladder Cancer in a Chinese Study

Background: Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. We have previously reported in an international European study four non-invasive tests for BC diagnosis based on the gene expression patterns of urine. Objective: to validate the tests in an independent Asian cohort. Design, setting and participants: Prospective blinded study in which consecutive voided urine samples from BC patients and controls (n=520) were collected in the Fudan University Shanghai Cancer Center from 2014-2016. Gene expression values were quantified using TaqMan Arrays. The same cut-off as previously reported for discrimination between tumours and controls was used in this validation study. Results and limitations: Finally, a total of 257 tumour and 132 control urine samples were analysed. We found a high accuracy for the four gene classifiers in this independent Asian set, the classifiers composed of 5 and 10 genes achieved the best sensitivity (80.54% and 81.32%, respectively) maintaining a high specificity (91.67% and 85.61%, respectively). Sensitivity of 5-gene (GS_D5) and 10-gene (GS_D10) expression classifiers in recurrent BC cases (78 and 79%, respectively) is comparable to that of primary BC cases (82%). Cytology and NMP22 identified 67% and 40%, respectively, of tumours that have been diagnosed with our tests. In addition, influence of each studied gene was analyzed and showed similar gene rank between Chinese and Caucasian population. Conclusions: Our study proves that our non-invasive diagnostic BC tests can be reproduced in independent cohorts and in an external laboratory. All the four gene classifiers have shown equal or superior performance to the current gold standard in the present and previously reported validation studies. Consequently, they may be taken for consideration as molecular tests applicable to clinical practice in the management of BC. Patient summary: Our gene classifiers achieve sensitivities up to 90% in HR NMIBC and MIBC patients, while this achievement is comparatively lower in LR NMIBC ones.
DeepProteomics: Protein family classification using Shallow and Deep Networks

The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequences. This arises the need to use computational techniques to classify proteins based on their functions. In our work, we have collected the data from Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass it to recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) model and compare it by applying trigram with deep neural network and shallow neural network on the same dataset. Through this approach, we could achieve maximum of around 78% accuracy for the classification of protein families.