Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic deletion of the ER lectin chaperone genes reveals their roles in vegetative growth and male gametophyte development in Arabidopsis

Calnexin (CNX) and calreticulin (CRT) are homologous lectin chaperones in the endoplasmic reticulum (ER) that facilitate glycoprotein folding and retain folding intermediates to prevent their transit via the secretary pathway. The Arabidopsis genome has two CNX (CNX1 and CNX2) and three CRT (CRT1, CRT2 and CRT3) homologs. Despite growing evidence of the biological roles of CNXs and CRTs, little is understood about their function in Arabidopsis growth and development under normal conditions. Here, we report that the deletion of CNX1, but not of CNX2, in the crt1 crt2 crt3 triple mutation background had an adverse effect on pollen viability and pollen tube growth, leading to a significant reduction in fertility. The cnx1 crt1 crt2 crt3 quadruple mutation also conferred severe defects in growth and development, including a shortened primary root, increased root hair length and density, and reduced plant height. Disruption of all five members of the CNX/CRT family was revealed to be lethal. Finally, the abnormal phenotype of the cnx1 crt1 crt2 crt3 quadruple mutants was completely rescued by either the CNX1 or CNX2 cDNA under the control of the CNX1 promoter, suggesting functional redundancy between CNX1 and CNX2. Taken together, these results provide genetic evidence that CNX and CRT play essential and overlapping roles during vegetative growth and male gametophyte development in Arabidopsis.
Activity Pattern Analysis of the Mobile Phone Network During a Large Social Event

By carrying mobile phones, people leaves digital footprints in the mobile phone network that provides rich information about the transportation customs. To understand the behavior of urban environments this data, provided by a Hungarian mobile network operator, is analyzed. The behavior of the city parts has been analyzed using the number of mobile phone activities including calls, texting and data transfer. The proposed approach is to identify regions of a city with similar behavior in mobile phone activity. This paper demonstrates the introduced method using a public demonstration event as a case study. It is clear from the data that the demonstrators caused an increase in the mobile phone network load at the places of the demonstration, so it was possible to identify their path.
SYN Flood Attack Detection based on Bayes Estimator (SFADBE) For MANET

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.
A Compiler for Automatic Selection of Suitable Processing-in-Memory Instructions

Although not a new technique, due to the advent of 3D-stacked technologies, the integration of large memories and logic circuitry able to compute large amount of data has revived the Processing-in-Memory (PIM) techniques. PIM is a technique to increase performance while reducing energy consumption when dealing with large amounts of data. Despite several designs of PIM are available in the literature, their effective implementation still burdens the programmer. Also, various PIM instances are required to take advantage of the internal 3D-stacked memories, which further increases the challenges faced by the programmers. In this way, this work presents the Processing-In-Memory cOmpiler (PRIMO). Our compiler is able to efficiently exploit large vector units on a PIM architecture, directly from the original code. PRIMO is able to automatically select suitable PIM operations, allowing its automatic offloading. Moreover, PRIMO concerns about several PIM instances, selecting the most suitable instance while reduces internal communication between different PIM units. The compilation results of different benchmarks depict how PRIMO is able to exploit large vectors, while achieving a near-optimal performance when compared to the ideal execution for the case study PIM. PRIMO allows a speedup of 38× for specific kernels, while on average achieves 11.8 × for a set of benchmarks from PolyBench Suite.
Hybrid approach towards the assessment of a drill condition using deep learning and the Support Vector Machine

This paper describes an application of a novel method which relies on applying the fusion of two sets of features obtained from deep learning and hand crafted features. Moreover, in terms of deep learning, transfer learning in deep learning has been applied. A hybrid method has been compared to classical deep learning approach, and let us state that the new approach can obtain more accuracy than regular methods. Transfer learning in deep learning should be used in case of lack of data to train the use of deep learning algorithms. We have used this method for the classification of drill wear state on the basis of drilled hole images. The specialists divided the whole data set into three classes: red, yellow, and green, which correspond to 3 stages of drill wear. The red class corresponds to a drill which is worn out and should be replaced; the yellow class should send a warning message to an operator for a manual check of the state of a drill; and the last one, the green one, is connected with the state of a drill which should be further used in production (it is still sharp enough). The important advantage of this approach is that it collects features from two sources: one set is automatically extracted by means of deep learning and the other is manually extracted by a researcher. The next advantage of this approach is training classification models only on the basis of a small portion of data, which in case of popular deep learning methods is too small to achieve reasonable accuracy. Hence, in order to get around this issue connected with a small portion of training data, transfer learning in deep learning has been applied. The achieved results confirm the fact that this approach can be applied in this situation.
Real-Time Image Semantic Segmentation Networks with Residual Depth-Wise Separable Blocks

Semantic image segmentation plays a key role in obtaining pixel-level understanding of images. In recent years, researchers have tackled this problem by using deep learning methods instead of traditional computer vision methods. Because of the development of technologies like autonomous vehicles and indoor robots, segmentation techniques, that have not only high accuracy but also the capability of running in real-time on embedded platform and mobile devices, are in high demand. In this work, we have proposed a new convolutional module, named Residual depth-wise separable, and a fast and efficient convolutional neural network for segmentation. The proposed method is compared against other state of the art real-time models. The experiment results illustrate that our method is efficient in computation while achieves state of the art performance in term of accuracy.
Modelling and Analysis of Respiratory System with Congestive Heart Failure

Respiratory system (RS) is one of the most important systems inside the human body. It works with cardiovascular system and neural system. The primary function of the respiratory system is to exchange and distribute gases inside the body. The system maintains homeostasis condition of those gases inside the body by a feedback mechanism. Literature suggests major research on respiratory system is directed towards the analysis of the regulatory function of this system and it is identified that transport delay is one of the most crucial parameter for stability of the system. Literature also suggests that for stability purpose, the minimal model is sufficient. In this work, a nonlinear state-space model with two states are considered. Those are partial pressure of oxygen and carbon dioxide in an alveolar compartment and are considered as state variables. The delay occurs in the feedback control loop, as blood takes finite time to reach from alveolar compartment to the sensory location. This system is analyzed for delay-dependent stability and maximum delay tolerable by the system is calculated Physiologically. Congestive heart failure (CHF) condition is one of such condition, when delay caused by the cardiovascular system is higher than the maximum tolerable limit and it makes the system oscillatory. Such kind of oscillation is manifested by Periodic Breathing (PB) waveform. The delay-dependent stability analysis on the linearized model is done using the Pseudo-delay method (PDM).
Scattering by a Buried PEMC Cylinder Excited by a Line Source above a Planar Interface Separating Two Isorefractive Media

A solution for scattering from a perfect electro-magnetic conducting (PEMC) buried cylinder on account of an infinite electric line source is presented. The source is parallel to the cylinder axis and is positioned in a half-space. This half-space is isorefractive to that in which the cylinder is. The fields produced by the line source will be reflected and transmitted at the interface dividing the two media, and the transmitted fields will be incident on the PEMC cylinder. Field scattering by the cylinder will produce unknown fields in both of the half spaces. Applying field boundary conditions at the cylinder surface and at a point on the interface, will lead to the determination of the unknown scattered, transmitted, and reflected fields. Results are presented as reflection coefficients, computed for cylinders having different electrical sizes and admittances, for a number of impedance ratios of the isorefractive media.
Predicting Unmeasured Region of the Efficiency Map of a Speed Reducer Using a Denoising Auto-Encoder

This paper presents a Remaining Useful Life (RUL) prediction method for a speed reducer based on denoising au- to-encoder (DAE). Constructing the efficiency map of the re- ducer is an important process for predicting the life span. However, due to the situational constraints that occur, un- measured intervals hinder the completion of the efficiency map. to solve this problem, we propose a method that can pre- dict and reconstruct an unmeasured interval effectively and reliably by using DAE. In addition, we examine the applicabil- ity of the proposed algorithm through experiments that assume various situations.
A Real Time Adaptive Template Matching Algorithm in UAV Navigation Using a SoC System

Computer vision techniques employing convolution techniques are widely used to identify objects and patterns in images. This work describes an Adaptive Template Matching algorithm for tracking marks in videos. A SoC implementation is also presented for a non-adaptive case. The window used for searching a template is reduced, presuming that the template should not be far from its location in the previous frame. The proposed algorithm allows the identification through Normalized Cross-Correlation metric. The initial proposal is a computer-based implementation using computer vision libraries. As an alternative for the high time of processing, an alternative SoC implementation is presented. The results show the achievement of a real time condition for an UAV navigation application.
An Extreme Learning Machine Based Pretraining Method for Multi-Layer Neural Networks

One approach in training a deep neural network to perform effectively is to do unsupervised pretraining on each layer, followed by fine-tuning the whole network. A common way is to train an unsupervised model of neural network such as restricted Boltzmann machines or autoencoders and stack them on top of another. Although these unsupervised pretraining approaches yield good performance, relying on back-propagation, due to iterative learning process, they still suffer from a long pretraining time. Extreme learning machine (ELM) is an analytical training approach which is extremely fast and gives a solution with a good generalization performance. In this paper, we apply a new ELM based unsupervised learning, named backward ELM based autoencoder (BELM-AE), to pretrain each layer of a neural network before using a back-propagation based learning algorithm to fine-tune the whole network. Experimental results show that the new pretraining method requires significantly shorter training time and also yields better testing performance on various datasets.
In Vitro Dielectric Properties of Rat Skin Tissue for Microwave Skin Cancer Detection

Dermal tissue characterization based on dielectric properties can be utilized as a non-invasive method for diagnosis of skin cancers. To enable such technology, there is a need to develop techniques that can rapidly and accurately collect the dielectric properties of the skin tissues. Therefore, the current measurement techniques and tools has to be optimized for skin cancer detection. To this end, this study presents dielectric property measurements with open-ended coaxial probes having small apertures customized for detection of skin cancer. Relative permittivity and conductivity of rat skin tissue is characterized with open-ended coaxial probes with outer diameters of 0.9mm and 0.5mm between 0.5GHz-6GHz and the measurement results are compared with the traditional probes having diameter of 2.2mm. The results agree well with the reported literature data.
Stock Price Prediction Based on Information Entropy and Artificial Neural Network

Stock market is one of the most important components of the financial system. It directs money from investors to support the activity and development of the associated company. Therefore, understanding and modeling the stock price dynamics become critically important, in terms of financial system stability, investment strategy, and market risk control. To better model the temporal dynamics of stock price, we propose a combined machine learning framework with information theory and Artificial Neural Network (ANN). This method creatively uses information entropy to inform non-linear causality as well as stock relevance and uses it to facilitate the ANN time series modeling. Our analysis with Google, Amazon, Facebook, and Apple stock prices demonstrates the feasibility of this machine learning framework.
Decentralised Image Sharing and Copyright Protection using Blockchain and Perceptual Hashes

In this paper, we aim to address the major problem of correct image attribution for photos being uploaded on the stock photo websites and peer-to-peer image sharing marketplaces, ensuring that original photographers are rightfully recognized and credited for their works. There are now millions of royalty-free images being shared every week on stock photo websites. There is a major discontent among photographers and graphics designers who share images on such websites regarding a widespread malpractice where others try to take credit for and benefit from original author’s work. This happens when someone other than the original author uploads the same image as one already shared on the marketplace or uses image processing methods such as rotation, cropping, gray scale conversion etc and then uploads this tampered image to avoid copyright violation detection. To counter such problems, we propose a decentralised peer-to-peer photo sharing marketplace built on top of Ethereum test chain and demonstrate how it is fair, trustworthy and practical. Our decentralised application leverages perceptual hashes and robust smart contracts of Ethereum to automatically detect and reject tampered images that are perceptually similar to images already present on the marketplace. Due to inherent properties of Blockchain, our marketplace has no central authority controlling it, no third party interference, no single point of failure, zero-censorship and preserves online user privacy. To the best of our knowledge, this is the first work that provides a practical solution for automatically detecting and rejecting perceptually similar images on a decentralised image sharing platform, thus protecting the copyrights of genuine image authors.
RRP1, a Saccharomyces cerevisiae gene affecting rRNA processing and production of mature ribosomal subunits

The Saccharomyces cerevisiae mutant ts351 had been shown to affect processing of 27S pre-rRNA to mature 25S and 5.8S rRNAs (C. Andrew, A. K. Hopper, and B. D. Hall, Mol. Gen. Genet. 144:29-37, 1976). We showed that this strain contains two mutations leading to temperature-sensitive lethality. The rRNA-processing defect, however, is a result of only one of the two mutations. We designated the lesion responsible for the rRNA-processing defect rrp1 and showed that it is located on the right arm of chromosome IV either allelic to or tightly linked to mak21. This rrp1 lesion also results in hypersensitivity to aminoglycoside antibiotics and a reduced 25S/18S rRNA ratio at semipermissive temperatures. We cloned the RRP1 gene and provide evidence that it encodes a moderately abundant mRNA which is in lower abundance and larger than most mRNAs encoding ribosomal proteins.
Embedded Device Free Passive (EDfP) System: Sensitivity of ESP32

Internet of Things (IoT) concept fueled the commercial market to produce cheap, small and energy efficient IoT enablers working in a multitude of communication protocols. Among those, 802.11 WiFi based devices have a winning edge as WiFi infrastructure is ubiquitously available in Malaysia. In this study, we evaluate the sensitivity of an ESP32 to the presence of human entity in a residential living hall of 13.44 square meters. The key objective of this study is to prove the potential of an IoT enabler to perform Device Free Passive (DfP) tasks such as detection, localization and tracking of human entity using a single link with an Access Point (AP) without any intervention from an application server. The sensitivity is measured using embeddable deterministic methods on Received Signal Strength Indicator (RSSI) data. Sensitivity measurement using average and variance resulted with average accuracy of 100% and 73.5% respectively for human presence detection. In overall, it is possible to perform DfP tasks using IoT enablers especially ESP32.
Dynamically Packet Routing for QoS Assurances on Internet Networks

An adaptive packet routing on the Internet networks based on reinforcement learning has been reported on our empirical study. We show that the approach of Q-routing previously demonstrated on small size of networks which under 100 nodes, can be expanded to large realistic sizes of networks. The performance of such a routing approach on three different network connectivity has been studied: random network, preferential attachment (PA) and a specific architecture known as heuristically optimized topology (HOT). In addition, designed HOT network is related to the Internet’s router level topology. Our experimental results show that the HOT network is able to represent significant advantage over a PA network which is characterized by hubs at which communication bottlenecks form.
Quantitative ultrasound of the tongue and submental muscles in children and young adults

Introduction: The purpose of this study was to assess the feasibility of quantitative muscle ultrasound (QMUS) to visualize oral muscles and to establish normative data for muscle thickness and echo intensity of submental and tongue muscles in healthy children and young adults. The data were compared with those of 5 patients with Duchenne muscular dystrophy (DMD). Methods: Ultrasound images from the suprahyoid region and from the surface of the tongue were made in 53 healthy subjects aged 5 to 30 years. Results: All measurements were feasible in all subjects and patients with good reproducibility except for the mylohyoid muscle. Muscle thickness depended on height, and echo intensity depended on weight. Our findings suggest gradual involvement of oral muscles in DMD. Conclusions: QMUS in oral muscles is feasible in healthy children, adults and patients with DMD. These data show that it is possible to differentiate between healthy persons and patients with DMD. Muscle Nerve 46:31–37, 2012
Accurate Cost Estimation of Memory Systems Inspired by Machine Learning for Computer Vision

Hardware/software co-designs are usually defined at high levels of abstractions at the beginning of the design process in order to allow plenty of options how to eventually realize a system. This allows for design exploration which in turn heavily relies on knowing the costs of different design configurations (with respect to hardware usage as well as firmware metrics). To this end, methods for cost estimation are frequently applied in industrial practice. However, currently used methods for cost estimation oversimplify the problem and ignore important features – leading to estimates which are far off from the real values. In this work, we address this problem for memory systems. To this end, we borrow and re-adapt solutions based on Machine Learning (ML) which have been found suitable for problems from the domain of Computer Vision (CV) – in particular age determination of persons depicted in images. We show that, for an ML approach, age determination from the CV domain is actually very similar to cost estimation of a memory system.
Self-configuration of Wireless Access Points Based on Mechanisms of Biological Development and Evolution

We propose a method for self-configuration of wireless access points (APs) that is inspired by mechanisms of biological development and evolution. The purpose of the proposed configuration is to simultaneously achieve power savings in the APs and achieve reliable connection services through the APs. The configuration target is the cycle time at which each AP enters the power-off state, which is referred to as the sleep cycle time. Biological development mechanisms form bodies of a variety of shapes using different genomes as design information, and these genomes are modified evolutionarily. Similarly, in the present study, a method inspired by biological development forms a variety of the sleep cycle times of APs, and an evolutionary method modifies the parameter values of the method inspired by the biological development in order to obtain better configurations of the APs. In addition, the regulation method is able to configure APs in a distributed manner without knowing global information, the total number of APs, their locations, or their identifiers, which is actually suitable for the situation assumed in the present study, in which APs are established and removed freely by their owners and AP users freely appear. Simulation results suggest that the proposed method is able to achieve power savings and reliable connection services under several assumptions.