Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of fertigation on growth, yield, fruit quality and leaf nutrients content of strawberry (Fragaria x ananassa) cv Chandler

A field experiment was conducted during 2011-12 to study the performance of different levels of fertigation and soil fertilization on growth, yield, fruit quality and leaf nutrients content of strawberry (Fragaria × ananassa Duch.). The experiment was laid out in randomized block design with five fertigation treatments, viz. (T1) recommended dose of NPK through soil, (T2) recommended dose of NPK through drip, (T3) ¾ of recommended dose of NPK through drip, (T4) ½ of recommended dose of NPK through drip and (T5) 1/3 of recommended dose of NPK through drip and each treatment was replicated five times. The healthy runners of cv. Chandler were planted at a spacing of 25 cm × 50 cm in well prepared beds of 2 m × 2 m size in the last week of September 2011 and 2012 as the crop was taken as annual crop. The results revealed that fertigation with recommended dose of NPK gave significantly higher plant height (24.23 cm), leaf area (129.20 cm2), fruit yield (35.64 tonnes/ha) as compared to fertigation with ½ and 1/3 of recommended dose of NPK and soil fertilization, but was found at par with ¾ recommended dose of NPK fertigation treatment. The maximum fruit length (42.49 mm), fruit breadth (31.74 mm) and fruit weight (19.87 g) was also recorded in fertigation with full recommended dose of NPK. The values of TSS (9.88oB), total sugar (9.44%), anthocyanin (0.249 OD) and ascorbic acid (53.39 mg/100g) was significantly higher in fertigation with recommended dose of NPK treatment as compared to lower levels of fertigation and soil fertilization. The plants fertigated with full and ¾ recommended dose of NPK also had higher leaf nutrients content. Fertigation with ¾ of recommended dose of NPK registered 60 per cent higher fertilizer use efficiency over soil fertilization with full recommended dose of NPK and also resulted in 25 per cent saving of fertilizers without any adverse effect on growth, yield and fruit quality.
A Comparative Study of Deep Neural Network Models on Multi-Label Text Classification in Finance

Multi-Label Text Classification (MLTC) is a well-known NLP task that allows the classification of texts into multiple categories indicating their most relevant domains. However, training model tasks on texts from web user deal with redundancy or ambiguity of linguistic information. In this work, we propose a comparative study about different neural network models for a multi-label text categorisation task in finance domain. Our main contribution consists of presenting a new annotated dataset that contains ∼26k posts from users associated to finance categories. To build that dataset, we defined 10 specific-domain categories that cover financial texts. To serve as a baseline, we present a comparative study analysing both the performance and training time of different learning models for the task of multilabel text categorisation on the new dataset. The results show that transformer-based language models outperformed RNN-based neural networks in all scenarios in terms of precision. However, transformers took much more time than RNN models to train an epoch model.
Uniformization of QCM’s Mass Sensitivity Distribution by Optimizing its Metal Electrode Configurations

The quartz crystal microbalance (QCM) is an ultra-sensitive measuring device and has been commonly used in many specific applications. The mass sensitivity distribution curve of m-m type QCM (QCM with symmetrical electrode) is a Gaussian distribution. However, achieving highly accurate and repeatable measurements requires the uniform distribution of mass sensitivity. To improve the uniformity of QCM mass sensitivity while ensuring high absolute mass sensitivity, the double-ring electrode geometry is designed. This study improves the size design of the double-ring electrode, and reveals the rules for varying sensitivity and electrode parameters based on a mathematical model. Through analyzing the mass sensitivity distribution of different electrode structures of QCMs, the uniformity of the double-ring electrode QCM is proved. The effects of mass sensitivity distribution of QCM are studied using the finite element method (FEM) by the variation of the size of electrodes, and an optimal size of electrode was discovered. Our results show that the double-ring type QCM has better uniformity of the mass sensitivity distribution and it can maintain the high mass sensitivity when the size of the inner ring of the double-ring electrode is close to half of the size of the outer ring. Meanwhile, there is an optimal outer radius that enables us to obtain an ideal and desirable displacement distribution. The numerical simulation results provide design ideas for the physical design of the double-ring type QCM.
Navigation-Aided Automotive SAR for High-Resolution Imaging of Driving Environments

The evolution of Advanced Driver Assistance Systems (ADAS) towards the ultimate goal of autonomous driving relies on a conspicuous number of sensors, to perform a wide range of operations, from parking assistance to emergency braking and environment mapping for target recognition/classification. Low-cost Mass-Market Radars (MMRs) are today widely used for object detection at various ranges (up to 250 meters) but they might not be suited for high-precision environment mapping. In this context, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising technique to augment radar imaging capability by exploiting the vehicle motion to provide two-dimensional (2D), or even three-dimensional (3D), images of the surroundings. SAR has a higher resolution compared to standard automotive radars, provided that motion is precisely known. In this regard, one of the most attractive solutions to increase the positioning accuracy is to fuse the information from multiple on-board sensors, such as Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. This paper proposes a multi-sensor fusion technique to support automotive SAR systems, experimentally validating the approach and demonstrating its advantages compared to standard navigation solutions. The results show that multi-sensor-aided SAR images the surrounding with centimeter-level accuracy over typical urban trajectories, confirming its potential for practical applications and leaving room for further improvements.
How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies

Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETwEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step. Second, we use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets. The resulting classifier is evaluated on the new ReTweeT dataset, showing promising results, especially considering that it has been trained without any manually labeled data. Both the dataset and the baseline implementation are publicly available.
Improved Boundary Uncertainty Estimation For Classifier Selection

Classifier evaluation is a key component of optimal classifier design that traditionally requires validation data separate from the training data. In contrast, a recent classifier evaluation method enabled classifier evaluation directly on the training data, by introducing a new evaluation measure called boundary uncertainty. However, the boundary uncertainty estimation proposed by this method suffered costs that hindered its applicability and accuracy. Therefore, we propose an improved boundary uncertainty estimation method and demonstrate its high utility. Compared to the original method, our improved method is more accurate, transparently applicable, hyperparameter-free, and roughly 104 times faster on the presented benchmark dataset.
Detection Bottleneck links without multiple nodes

A "Link Flooding Attack" is that does not directly attack the target node, but instead creates a state in which packets cannot reach the target node by congestion of the intermediate link. In general, it is best way to distribute multiple measurement nodes to measure the network and monitor the links that are bottlenecks. In this paper, we propose a method to detect a bottleneck link, that is attacked, by a single node, without requiring multiple measurement nodes. Next, we show the effectiveness of our proposal method using a network topology by a topology generator. Finally, we detect links that may be bottleneck links by applying our proposal method to the actual Traceroute data.
Improvements of Centroid Localization Algorithm for Wireless Sensor Networks

The accurate position of nodes in Wireless Sensor Networks (WSNs) is considered a critical problem in the majority of the Internet of Things (IoT) applications. Recently a large number of contribution in localization have been recommended to determine the location of nodes. However, a number determinated of these techniques have been presented to precisely determine the nodes locations in the IoT. In this work, we discuss the new three localization techniques, named Centroid + 4A, ICentroid, and ICentroid + 4A respectively, based on the Centroid localization technique and a new weighted formula to estimate the target nodes’ positions. The OMNeT++ network simulator was used to figure out and the performance of the discussed solutions in comparison with the Centroid localization technique. The examined results reveal that a significant improvement in the localization precision of the discussed contributions in Wireless Sensor Networks.
Making Sense of Blockchain for AI Deepfakes Technology

Deepfakes generally refers to a new breed of adversarial deep learning technology to create non-consensual contents (mostly videos) for nefarious purposes. Most researches focus on the ‘detection’ of deepfakes using AI-assisted approaches to take on this problem. This has been the common method operandi used by researchers thus far. However, there is one missing aspect of the deepfake problem, which is ‘authentication’. Instead of attempting to detect what content is fake, in this paper, we focus on techniques to provide tamper-proof evidence of what content is real. Blockchain has been advocated to be helpful with the authentication aspect of many real-world scenarios. Despite the scattered efforts around such solutions, there are no studies that can shed light on where it makes sense to adopt blockchain technology to better take on the deepfake problem. This paper aims to provide a one-stop guide to using blockchain to navigate deepfake artificial intelligence. We discuss potential use cases and solutions to tackle deepfakes technology via blockchain functionalities and features.
Analysis of Information Pollution on Social Media Platform

Social networking sites have made the communication very easy and each one can express its views and opinions. But this also opens up an opportunity of spreading anecdotal information intentionally. With the increase in content on social network, it is not easy to differentiate between fake information or genuine information. In this paper, authors have analyzed the anecdotal information based on different social platforms and proposed a methodology for the detection of fake news using Term Frequency & Inverse Document frequency (TFIDF) feature vector and different classification models. Authors have analyzed that random forest classifier outperformed best with accuracy upto 95.75%.
Synthesis, Pharmacological Evaluation and QSAR Study of 2-aryl- 1H -(1, 3,4) oxadiazino (5, 6-b) quinoxalines as Antidepressants

In the present study the synthesis, QSAR study and evaluation of 2-aryl-1H[1,3,4]oxadiazino[5,6-b]quinoxalines as antidepressant agents is described. Number of compounds exhibited antidepressant activity measu red in terms of % decrease in immobility duration (%DID). The activity was evaluated in albi no mice using tail suspension test. The QSAR analysis between antidepressant activity and t he two descriptors: aqueous solubility of the molecule (CIQPlogS) and predicted polarizability in cubic angstroms (QPpolrz) generated highly predictive model while hydrophobic component attributable to saturated carbon and attached hydrogen of the solvent accessible surface area (FOSA) indicated less significance for the activity.
Post-Quantum Era Privacy Protection for Intelligent Infrastructures

As we move into a new decade, the global world of Intelligent Infrastructure (II) services integrated into the Internet of Things (IoT) are at the forefront of technological advancements. With billions of connected devices spanning continents through interconnected networks, security and privacy protection techniques for the emerging II services become a paramount concern. In this paper, an up-to-date privacy method mapping and relevant use cases are surveyed for II services. Particularly, we emphasize on post-quantum cryptography techniques that may (or must when quantum computers become a reality) be used in the future through concrete products, pilots, and projects. The topics presented in this paper are of utmost importance as (1) several recent regulations such as Europe’s General Data Protection Regulation (GDPR) have given privacy a significant place in digital society, and (2) the increase of IoT/II applications and digital services with growing data collection capabilities are introducing new threats and risks on citizens’ privacy. This in-depth survey begins with an overview of security and privacy threats in IoT/IIs. Next, we summarize some selected Privacy-Enhancing Technologies (PETs) suitable for privacy-concerned II services, and then map recent PET schemes based on post-quantum cryptographic primitives which are capable of withstanding quantum computing attacks. This paper also overviews how PETs can be deployed in practical use cases in the scope of IoT/IIs, and maps some current projects, pilots, and products that deal with PETs. A practical case study on the Internet of Vehicles (IoV) is presented to demonstrate how PETs can be applied in reality. Finally, we discuss the main challenges with respect to current PETs and highlight some future directions for developing their post-quantum counterparts.
Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services

Offloading tasks to the edge or the Cloud has the potential to improve accuracy of classification and detection tasks as more powerful hardware and machine learning models can be used. The downside is the added delay introduced for sending the data to the Edge/Cloud. In delay-sensitive applications, it is usually necessary to strike a balance between accuracy and latency. However, the state of the art typically considers offloading all-or-nothing decisions, e.g., process locally or send all available data to the Edge (Cloud). Our goal is to expand the options in the accuracy-latency trade-off by allowing the source to send a fraction of the total data for processing. We evaluate the performance of image classifiers when faced with images that have been purposely reduced in quality in order to reduce traffic costs. Using three common models (SqueezeNet, GoogleNet, ResNet) and two data sets (Caltech101, ImageNet) we show that the Gompertz function provides a good approximation to determine the accuracy of a model given the fraction of the data of the image that is actually conveyed to the model. We formulate the offloading decision process using this new flexibility and show that a better overall accuracy-latency tradeoff is attained: 58% traffic reduction, 25% latency reduction, as well as 12% accuracy improvement.
Real-Time Parameter Estimation of a Fuel Cell for Remaining Useful Life Assessment

This article presents a real-time parameter estimation of a proton exchange membrane fuel cell (PEMFC). The proposed strategy estimates online the PEMFC's resistance, since it is directly correlated to its remaining useful life assessment. The estimation of the PEMFC's parameters is a difficult task to undertake due to various uncertainties, like temperature and aging, that lead to a drift in parameters and limit the performance of the overall energy system. Therefore, online system identification is essential to track online the PEMFC's time-varying parameters. Unlike other identification techniques, the proposed strategy is based on a simple yet accurate PEMFC's model and adjusts its parameters in real-time using a Lyapunov-based adaptation law, which yields guaranteed stability. Experiments are conducted on a 500-W Horizon PEMFC and results along with a comparison against the well-known Kalman filter highlight the effectiveness of the proposed approach, which is instrumental for its numerous applications, such as the energy management of hybrid fuel cell vehicles.
Causal Quantification of Cannibalization During Promotional Sales in Grocery Retail

In food and grocery retail, sales cannibalisation during promotions occurs when a promoted product has a knock-on effect on the sales of a non-promoted one. The quantification of its effect is important for retailers, as cannibalisation can lead to wasted food and lost profits. The performance of promotions is ultimately dependent on their features but also on the characteristics of the stores, i.e. type of store or its location. Generally speaking, there is no homogeneous response to a promotion, and by extension to sales cannibalisation. Accordingly, in this paper we describe a framework to analyse the effects of cannibalisation due to individual promotions based on the relationship amongst their sales. The novelty of our work resides in understanding cannibalisation as a causal effect where the increase in sales of the promoted product is partly due to the decrease in sales of the non-promoted one. As such, we propose to use causal inference to measure the impact of cannibalisation due to promotions. Our method reviews each product that has been on promotion, searching for potential cannibals, given by products whose promotion have resulted in large sales uplifts, and for the fall-outs, given by those products experiencing a reduction in sales due to the cannibals. Then each cannibal-victim pair is analysed with Causal Impact, a time-series method which allows one to infer the causal effect of an intervention. We demonstrate the practical application of detecting cannibalisation on vast datasets of promotions within several stores and their many departments. To provide with an overview of the cannibalisation for entire departments and not simply for individual products, we build a directed graph. This is both unique and of utmost value to store managers and marketing teams. Additionally, we discuss in the Appendix the application of explainable forecasting to cannibalisation on a surrogate model.
Adaptive Robust Operation for Battery Energy Storage Systems with SoC Management in a Three-phase Unbalanced Microgrid

Energy management plays a significant role in microgrid economical and reliable operation. To deal with the stochastic nature of renewable energy sources and improve renewable energy utilization efficiency, battery energy storage systems (BESSs) have been widely deployed in microgrids. However, BESS dispatch decisions made based on current short-term uncertainty prediction may not be efficient in a whole-day aspect, leading to an insufficient SoC for the following scheduling. To tackle this issue, this paper proposes an adaptive robust BESS operation method with SoC management in a three-phase unbalanced microgrid, aiming to minimize the microgrid operating cost. In a day-ahead stage, a SoC interval is determined. Then the BESS is optimally dispatched within the SoC interval to track uncertainty realization during the day. To address uncertainties, the problem is formulated into an adaptive robust optimization model with integer recourse variables and further solved by a nested column-and-constraint generation (CCG) algorithm. Simulation results verify high efficiency and solution robustness of the proposed adaptive robust operation method.
Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder

The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation algorithms is the assumption of a single health degradation trend for different machine health stages. To improve the RUL prediction accuracy with various degradation trends, this article proposes an algorithm dubbed degradation-aware long short-term memory (LSTM) autoencoder (AE) (DELTA). First, the Hilbert transform is adopted to evaluate the degradation stage and factor with the real-time sensory signal. Second, we adopt LSTM AE to predict RUL based on multisensor time-series data and the degradation factor. Distinct from the existing studies, the proposed framework is able to dynamically model the degradation factor and explore latent variables to improve RUL prediction accuracy. The performance of DELTA is evaluated with the open-source FEMTO bearing data set. Compared with the existing algorithms, DELTA achieves appreciable improvements in the RUL prediction accuracy.
Thermally-Compensated Magnetic Core Loss Model for Time-Domain Simulations of Electrical Circuits

This article introduces a thermally-compensated magnetic hysteresis model, capable of accurately determining core losses of ferrite materials while accounting for core temperature variations. The employed model is based on permeance-capacitance analogy and it captures the frequency-independent hysteresis effect with the help of the Preisach model. For validation purposes, a 100 kW, 10 kHz realized medium frequency transformer prototype is selected and modeled as a part of a full-bridge LLC resonant converter in a time-domain simulation environment. Conducted simulations show the ability of the thermally-compensated magnetic hysteresis model to impact the generated core losses for core temperatures of up to 120 $^{\circ }$C. Finally, a temperature-feedback loop within the transformer is successfully closed inside of a system-level simulation, allowing for a more precise determination of the core and winding steady-state temperatures.
Permutation Design for Ultra-Low Latency Communication and Spatial Permutation Modulation (SPM)

Recently, spatial permutation modulation (SPM) and path-permutation code (PPC) are proposed for the baseband multiple-input multiple-output (MIMO) system and the virtual MIMO system on the network/session layer for ultra-low latency communication, e.g. vehicular network. These techniques modulate data bits by selecting a certain vector from a predefined permutation set. In this work, we first prove that generating such a permutation set is NP-complete. With the required throughput and reliability, a greedy algorithm is then proposed to efficiently design the permutation set. Theoretical analysis shows that this algorithm can achieve optimal performance in most practical cases. In addition to the permutation generation algorithm, we also generalize SPM by constructing the virtual transmit antennas, i.e., the combination vectors used to concurrently activate multiple antennas. Numerical results demonstrate the optimality of the proposed algorithm for the generation of permutation set and the superior of such generalized SPM using the permutation set acquired by the proposed algorithm.