Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning and Computer Vision for Estimating Date Fruits Type, Maturity Level, and Weight

According to the Food and Agriculture Organization, the world production of date fruits is 8,526,218 tons and around 1,302,859 tons in the Kingdom of Saudi Arabia (KSA) in 2018. There are several types of date fruits, and the most common in KSA are Barhi, Khalas, Meneifi, Naboot Saif, and Sullaj. Moreover, there are around five main maturity levels: Immature, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar. Harvesting date fruits is performed according to its maturity level and type, which is a critical decision that significantly affects profit. In this paper, we propose a smart harvesting decision system to estimate date fruits type, maturity level, and weight using computer vision (CV) and deep learning (DL) techniques. The proposed system consists of three sub-systems: Dates maturity estimation system (DMES), type estimation system (DTES), and dates weight estimation system (DWES). We utilized four DL architectures, including ResNet, VGG-19, Inception-V3, and NASNet for both DMES and DTES and support vector machine (SVM) (regression and linear) for DWES. We evaluated the performance of the proposed system using the dataset collected by the Center of Smart Robotics Research. Using multiple performance metrics, DTES achieved maximum performance of 99.175% accuracy, an F1 score of 99.225%, 99.8% average precision, and 99.05% average recall. The maximum performance of DMES was 99.058% accuracy, F1 score of 99.34%, 99.64% average precision, and 99.08% average Recall. DWES achieved a maximum performance of 84.27% using SVM-Linear.
Intelligent and Agile Control of Edge Resources for Latency-Sensitive IoT Services

This paper presents an intelligent and agile resource control scheme for a latency-sensitive virtual network function (VNF) of Internet of things directory service (IoT-DS) deployed in a virtualized edge cloud whose computational and networking resources can be adjusted dynamically. The objective of the proposed scheme is to adjust resources dynamically such that the IoT-DS function can resolve IoT queries and provide IoT records within a bounded delay for latency-sensitive services such as automated driving, despite fluctuations in workloads. The proposed scheme leverages multiple regression models for resource demand prediction and dynamic adjustment. These models are trained offline before their deployment with a large training dataset collected from the system operating with simulated workloads. After the deployment, they are updated regularly by online retraining for more accurate performances. We aim to optimize resource allocation to satisfy both the target performance in terms of service latency and resource utilization. The results obtained from an experimental system implementation of the IoT-DS function in Docker containers show that the dynamic adjustment of CPU resources by the proposed scheme with supervised offline training reduces the CPU resource demand by 21.9% and the number of lookup latency requirement violations by 58.2% in comparison with a threshold rule-based conventional algorithm. Moreover, the proposed scheme can offer an agile control of CPU resources within a 1 s interval, which is five times faster than those reported in previous studies. The addition of unsupervised online retraining further reduces CPU resource requirements by 52% and lookup latency requirement violation cases by 62.5% compared with when no adjustments are performed.
Secure transmission for NOMA systems with imperfect SIC

This paper investigates secure transmission for non-orthogonal multiple access (NOMA) systems where the imperfect successive interference cancellation (SIC) is considered at both legitimate users and eavesdropper (Eve). A power allocation scheme is designed for the legitimate users to mitigate the effect of the SIC error caused by imperfect SIC. For characterizing the secrecy performance of the NOMA system, the closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) are derived over Nakagami-m fading channels in both NOMA and benchmark orthogonal multiple access (OMA) systems. We also provide security and reliability trade-off results (SRT) for the users in the NOMA and OMA systems. Simulation results verify our analysis and show that the strong users achieve better secrecy performance but worse reliability performance and NOMA outperforms OMA in terms of SRT.
Percutaneous treatment of tricuspid valve in refractory right heart failure

Abstract The renewed interest in tricuspid valve pathology is a consequence of the high mortality rate associated with this valve dysfunction, mostly functional, and secondary to left ventricular impairment, or pulmonary hypertension. Despite the clear relationship between tricuspid insufficiency and mortality, surgical treatment is offered to a small group of patients, due to the significant in-hospital mortality secondary also to the multiple comorbidities and the advanced stage of left ventricular dysfunction. During the last few years, new therapeutic options have been developed for the percutaneous treatment of tricuspid insufficiency which, albeit still in the experimental phase, provides an alternative to surgery in patients at very high-risk or frankly inoperable. We will describe the various percutaneous therapeutic options available today, and their potential application to clinical practice.
Deep Reinforcement Learning for Traffic Signal Control: A Review

Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC.
On the Coexistence of Aperiodic and Periodic Traffic in Cellular Vehicle-to-Everything

Cellular Vehicle-to-Everything (C-V2X) communications are the key to connected and autonomous driving, and pave the way for future Intelligent Transport Systems (ITS). To support non-safety and safety critical applications in the demanding out-of-coverage scenario, the 3rd Generation Partnership Project (3GPP) has standardized the distributed C-V2X Mode 4 solution, whose behavior has been thoroughly analyzed for periodic traffic. In the current work, the problem of allocating aperiodic traffic in Mode 4 is tackled, a matter that has not been addressed before and that raises several challenging questions. A solution for serving such traffic type is put forth, and an analytical insight on the attainable performance is offered. Further, it is numerically proved that guaranteeing aperiodic flows good service levels is hard when their packets are not small sized. This holds true even for sophisticated physical layer choices and at relatively modest traffic densities, revealing that novel approaches to radio resource assignment are a necessity in Fifth Generation (5G) vehicular communications.
ATP as Phosphorus and Nitrogen Source for Nutrient Uptake by Fagus sylvatica and Populus x canescens Roots

The present study elucidated whether roots of temperate forest trees can take up organic phosphorus in the form of ATP. Detached non-mycorrhizal roots of beech (Fagus sylvatica) and gray poplar (Populus x canescens) were exposed under controlled conditions to 33P-ATP and/or 13C/15N labeled ATP in the presence and absence of the acid phosphatase inhibitor MoO42-. Accumulation of the respective label in the roots was used to calculate 33P, 13C and 15N uptake rates in ATP equivalents for comparison reason. The present data shown that a significant part of ATP was cleaved outside the roots before phosphate (Pi) was taken up. Furthermore, nucleotide uptake seems more reasonable after cleavage of at least one Pi unit as ADP, AMP and/or as the nucleoside adenosine. Similar results were obtained when still attached mycorrhizal roots of adult beech trees and their natural regeneration of two forest stands were exposed to ATP in the presence or absence of MoO42-. Cleavage of Pi from ATP by enzymes commonly present in the rhizosphere, such as extracellular acid phosphatases, ecto-apyrase and/or nucleotidases, prior ADP/AMP/adenosine uptake is highly probable but depended on the soil type and the pH of the soil solution. Although uptake of ATP/ADP/AMP cannot be excluded, uptake of the nucleoside adenosine without breakdown into its constituents ribose and adenine is highly evident. Based on the 33P, 13C, and 15N uptake rates calculated as equivalents of ATP the ‘pro and contra’ for the uptake of nucleotides and nucleosides is discussed. Short Summary Roots take up phosphorus from ATP as Pi after cleavage but might also take up ADP and/or AMP by yet unknown nucleotide transporter(s) because at least the nucleoside adenosine as N source is taken up without cleavage into its constituents ribose and adenine.
Exploring the two-dimensional nature of music notation for score recognition with end-to-end approaches

Optical Music Recognition workflows perform several steps to retrieve the content in music score images, being symbol recognition one of the key stages. State-of-the-art approaches for this stage currently address the coding of the output symbols as if they were plain text characters. However, music symbols have a two-dimensional nature that is ignored in these approaches. In this paper, we explore alternative output representations to perform music symbol recognition with state-of-the-art end-to-end neural technologies. We propose and describe new output representations which take into account the mentioned two-dimensional nature. We seek answers to the question of whether it is possible to obtain better recognition results in both printed and handwritten music scores. In this analysis, we compare the results given using three output encodings and two neural approaches. We found that one of the proposed encodings outperforms the results obtained by the standard one. This permits us to conclude that it is interesting to keep researching on this topic to improve end-to-end music score recognition.
ReTransformer: ReRAM-based Processing-in-Memory Architecture for Transformer Acceleration

Transformer has emerged as a popular deep neural network (DNN) model for Neural Language Processing (NLP) applications and demonstrated excellent performance in neural machine translation, entity recognition, etc. However, its scaled dot-product attention mechanism in auto-regressive decoder brings a performance bottleneck during inference. Transformer is also computationally and memory intensive and demands for a hardware acceleration solution. Although researchers have successfully applied ReRAM-based Processing-in-Memory (PIM) to accelerate convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the unique computation process of the scaled dot-product attention in Transformer makes it difficult to directly apply these designs. Besides, how to handle intermediate results in Matrix-matrix Multiplication (MatMul) and how to design a pipeline at a finer granularity of Transformer remain unsolved. In this work, we propose ReTransformer - a ReRAM-based PIM architecture for Transformer acceleration. ReTransformer can not only accelerate the scaled dot-product attention of Transformer using ReRAM-based PIM but also eliminate some data dependency by avoiding writing the intermediate results using the proposed matrix decomposition technique. Moreover, we propose a new sub-matrix pipeline design for multi-head self-attention. Experimental results show that compared to GPU and Pipelayer, ReTransformer improves computing efficiency by 23.21× and 3.25×, respectively. The corresponding overall power is reduced by 1086× and 2.82×, respectively.
Biodistribution of Liposome-Encapsulated Bacteriophages and Their Transcytosis During Oral Phage Therapy

This study sheds light on the biodistribution of orally administered, liposome-encapsulated bacteriophages, and their transcytosis through intestinal cell layers. Fluorochrome-labeled bacteriophages were used together with a non-invasive imaging methodology in the in vivo visualization of bacteriophages in the stomach and intestinal tract of mice. In those studies, phage encapsulation resulted in a significant increase of the labeled phages in the mouse stomach, even 6 h after their oral administration, and without a decrease in their concentration. By contrast, the visualization of encapsulated and non-encapsulated phages in the intestine were similar. Our in vivo observations were corroborated by culture methods and ex vivo experiments, which also showed that the percentage of encapsulated phages in the stomach remained constant (50%) compared to the amount of initially administered product. However, the use of conventional microbiological methods, which employ bile salts to break down liposomes, prevented the detection of encapsulated phages in the intestine. The ex vivo data showed a higher concentration of non-encapsulated than encapsulated phages in liver, kidney, and even muscle up to 6 h post-administration. Encapsulated bacteriophages were able to reach the liver, spleen, and muscle, with values of 38% ± 6.3%, 68% ± 8.6%, and 47% ± 7.4%, respectively, which persisted over the course of the experiment. Confocal laser scanning microscopy of an in vitro co-culture of human Caco-2/HT29/Raji-B cells revealed that Vybrant-Dil-stained liposomes containing labeled bacteriophages were preferably embedded in cell membranes. No transcytosis of encapsulated phages was detected in this in vitro model, whereas SYBR-gold-labeled non-encapsulated bacteriophages were able to cross the membrane. Our work demonstrates the prolonged persistence of liposome-encapsulated phages in the stomach and their adherence to the intestinal membrane. These observations could explain the greater long-term efficacy of phage therapy using liposome-encapsulated phages.
Investigation of Accumulative Switching in Ferroelectric FETs: Enabling Universal Modeling of the Switching Behavior

Accumulative switching (AS) in ferroelectric field-effect transistors (FeFETs), which takes place under a train of subcritical voltage pulses, represents not only a concern in terms of disturbs for classical nonvolatile memory but also a source of novel opportunities for neuromorphic applications. Here, we report a detailed experimental investigation of AS for large-area and scaled devices and compare it with the conventional one-shot switching. We show that both of them obey the same exponential dependence between the switching time and voltage, irrespective of the pulse polarity, the investigated interval duration between the pulses, and the device size. This unified physical picture sheds new light on the FeFET operation and holds promise to considerably simplify the modeling of both memory and neuromorphic functionalities in these devices.
Click-Through Rate Prediction Combining Mutual Information Feature Weighting and Feature Interaction

Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems. It is used to estimate the likelihood that a user will click on ads. The method used in the traditional CTR prediction task is to improve the prediction effect through a large number of feature engineering. However, these methods are time-consuming and laborious, and the construction process is not universal. Due to the sparse and high-dimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions. In this paper, a novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed. First, the mutual information of each sparse feature and the click result is calculated as the weight of each sparse feature. Subsequently, an interactive method combining the outer product and inner product is constructed to carry out the feature interaction. Then, the resulting feature interactions and the dense features set of the original input are taken as DNN inputs. We verify the proposed model on four datasets. In addition, several widely known models are introduced for comparison. The experimental results indicate the superiority of the model.
Critical Information Infrastructure Protection Framework Development: Preliminary Findings from the Malaysian Public Sector

This paper reports preliminary findings on the Malaysian Public Sector Critical Information Infrastructure Protection (MPS CIIP) Framework development. Malaysia Public Sector (MPS) requires sectoral references in the form of directive and guidelines to facilitate its organization’s management and operations. MPS organizations also require a framework to maintain CII operational security and resilience as stated in the National Cyber Security Policy (NCSP). As sectoral references pertaining to CIIP is currently lacking, this research aims to fill in the void and provide feasible CIIP Framework based on the Secure and Resilience attribute mentioned in the NCSP. This paper outlined ongoing work to develop a secure and resilient CIIP using the Grounded Theory method. As part of an observational descriptive study, we conducted a pilot interview among CII personnel in the MPS to acquire preliminary input and to test our interview questions. The outcome of this phase yielded a better understanding of the MPS CIIP landscape and provided future direction in terms of revised interview questions and better respondent selection criteria. The pilot interview process garnered up to date opinion on current public sector CIIP issues and challenges. Respondents provided valuable up to date insights such as top-to-bottom directive operating model, governance, finance and resource constraints, information sharing, dependency on focal point agency, CIIP monitoring and preparedness level. MPS CIIP Framework will be developed in line with NCSP vision to create secure, resilient, and self-reliant CIIs. The framework would assist related MPS agencies to deliver its initiatives such as preparedness and capacity building programs to increase its security and resilience.
Routability-Aware Pin Access Optimization for Monolithic 3D Designs*

With the increasing design complexity (especially, the increasing number of design rules), pin access for detailed routing has attracted much attention. Monolithic 3D design (M3D for short) has emerged as a promising technology to alleviate this pin access challenge by providing redundant pins and free routing tracks. This paper addresses the problem of pin access optimization for M3D designs to improve routability during detailed routing. Unlike existing work that uses integer linear programming (ILP) to maximize the total number of pre-routed nets on free routing tracks without considering the influence of a net on pin access congestion, we present new pin access congestion estimation based on the probability of pin access conflicts and a novel weighted ILP formulation. A dynamic programming algorithm is proposed to refine detailed placement to obtain a better initial solution. Experimental results show that our algorithm achieves significant routability improvements.
Energy-efficient XNOR-free In-Memory BNN Accelerator with Input Distribution Regularization

SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks to their compactness in terms of hardware and neural network parameter size. However, previous works had to modify SRAM cells to support XNOR operations on memory array resulting in limited area and energy efficiencies. In this work, we present a conversion method which replaces the signed inputs (+1/-1) of BNN with the unsigned inputs (1/0) without computation error, and vice versa. The method enables BNN computing on conventional 6T SRAM arrays and improves area and energy efficiencies. We also demonstrate that further energy saving is possible by skewing the distribution of binary input data based on regularization during network training. Evaluation results show that the proposed techniques improve the inference energy efficiency by up to 9.4x for various benchmarks over previous works.
Multi-step forecasting strategies for wind speed time series

A time series is a sequence of observations, measured at certain moments in time, ordered chronologically and evenly spaced, so that the data are usually dependent on each other. Currently, time series are used to estimate wind gusts, which are highly non-linear, unknown, and at times unpredictable. A good estimation of wind gusts implies correct planning on the generation of clean wind energy. In this work, we use Artificial Intelligence (AI) techniques such as the use of convolutional neural networks for wind gust estimation. One of the best models for dealing with this type of information is the Large Short Term Memory (LSTM) network because it is a type of recurrent network that specializes in sequence information. In this work, an LSTM prediction model is implemented for five different wind speed data sets using different multi-step forecasting strategies. The strategies used are Recursive, Direct, MIMO (multiple-input to multiple-output), DIRMO (Combination of direct strategy and MIMO), and DirREC (Combination of direct and recursive strategy).
Toxicogenomics of Gold Nanoparticles in a Marine Fish: Linkage to Classical Biomarkers

In the present study, the underlying short-term effects of gold nanoparticles (AuNP, spheres, citrate coated, ~40 nm) on the hepatic function of gilthead sea bream (Sparus aurata) was assessed, using a species-specific enriched oligonucleotide microarray platform (SAQ). Two distinct concentrations of AuNP (0.5 and 50 μg/L) were tested via 24 h waterborne exposure. The transcriptional profile was complemented with outcomes at higher levels of biological organization, including hepatic health indicators and DNA damage indicators. The DNA damaging potential of AuNP was assessed in whole peripheral blood, assessing DNA strand breaks (using the comet assay) and chromosome damage (scoring the erythrocytic nuclear abnormalities, ENA). The overall genetic response showed a differential hepatic transcriptional profile, both in terms of number and intensity, of differentially expressed genes (DEG). Concerning functional pathways affected, the main changes were found for genes encoding proteins involved in the response to xenobiotics, oxidoreductase activity, immunomodulation, DNA repair and programmed cell death types I and II. The hepatic health indicators measured revealed that AuNP can induce liver injury, as demonstrated by the plasma alanine aminotransferase (ALT) and aspartate aminotransferase (AST) significantly increased activities after exposure to the highest AuNP concentration (50 μg/L). Exposure to AuNP also caused DNA strand breaks, however, without causing clastogenesis or aneugenesis, since no ENA were detected. Overall, data show that a short-term exposure to AuNP can modulate gene expression in liver and affects several biochemical/genetic functions in fish.