Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Contract-Based Agricultural Food Supply Chain Traceability

The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people’s daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.
A Planar High Pass Filter with Quasilumped Elements for ISM, Wimax and Wlan Applications

This paper presents a quasilumped Microstrip structure High Pass Filter having a cutoff frequency 2.5 GHz. The quasilumped elements are used with the objective of obtaining the behavior of a high-pass filter with high bandwidth, reducing the size of the filter and improving the electrical performances. The proposed filter is designed on an FR-4 substrate having a thickness of 1.6mm, a dielectric permittivity constant of 4.4 and loss tangent of 0.025. The proposed filter is optimized and validated by using three electromagnetic solvers (ADS, HFSS and CST-MWS). The whole area of the proposed circuit is 19 x 16 mm2.
Exploring Category Attention for Open Set Domain Adaptation

Great success has been achieved in the area of unsupervised domain adaptation, which learns to generalize from the labeled source domain to the unlabeled target domain. However, most of the existing techniques can only handle the closed-set scenario, which requires both the source domain and the target domain to have a shared category label set. In this work, we propose a two-stage method to deal with the more challenging task of open set domain adaptation, where the target domain contains categories unseen to the source domain. Our first stage formulates the alignment of two domains as a semi-supervised clustering problem, and initially associates each target-domain sample <inline-formula> <tex-math notation="LaTeX">$x^{t}\in \mathcal {X}^{t} $ </tex-math></inline-formula> with a source-domain category label <inline-formula> <tex-math notation="LaTeX">$\ell ^{s} \in \mathcal {L}^{s}$ </tex-math></inline-formula>. To this end, we use the self-training strategy to learn a <italic>teacher network</italic> and a <italic>student network</italic>, both of which adopt the self-attention mechanism. Our second stage refines the resulting clusters by identifying the negative associations <inline-formula> <tex-math notation="LaTeX">$(x^{t}, \ell ^{s})$ </tex-math></inline-formula> and labeling the involved <inline-formula> <tex-math notation="LaTeX">$x^{t} $ </tex-math></inline-formula> as <italic>unknown</italic>. For this purpose, we investigate the compatibility of each association by replacing the self-attention maps in the last convolutional layers with the newly proposed <italic>category attention maps</italic> (CAMs), which locate the informative feature pixels for a given category. Experimental results on three public datasets show the effectiveness and robustness our method in adaptation across various domain pairs.
DatAR: An Immersive Literature Exploration Environment for Neuroscientists

Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system’s design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details.
Trust Induced Resource Provisioning (TIRP) Mechanism in IoT

Due to increased number of devices with limited resources in Internet of Things (IoT) has to serve time sensitive applications including health monitoring, emergency response, industrial applications and smart city etc. This has incurred the problem of solving the provisioning of limited computational resources of the devices to fulfill the requirement with reduced latency. With rapid increase of devices and heterogeneity characteristic the resource provisioning is crucial and leads to conflict of trusting among the devices requests. Trust is essential component in any context for communicating or sharing the resources in the network. The proposed work comprises of trusting and provisioning based on deadline. Trust quantity is measured with concept of game theory and optimal strategy decision among provider and customer and provision resources within deadline to execute the tasks is done by finding Nash equilibrium. Nash equilibrium (NE) is estimated by constructing the payoff matrix with choice of two player strategies. NE is obtained in the proposed work for the Trust- Respond (TR) strategy. The latency aware approach for avoiding resource contention due to limited resources of the edge devices, fog computing leverages the cloud services in a distributed way at the edge of the devices. The communication is established between edge devices-fog-cloud and provision of resources is performed based on scalar chain and Gang Plank theory of management to reduce latency and increase trust quantity. To test the performance of proposed work performance parameter considered are latency and computational time.
QUANTITATIVE DETERMINATION OF GLUTATHIONE IN PRESENCE OF ITS DEGRADANT IN A PHARMACEUTICAL PREPARATION USING HPLC-DAD AND IDENTIFICATION BY LC-ESI-MS

A simple and rapid method for the quantification of the well-known multifunctional antioxidant glutathione in presence of its degradant in a pharmaceutical preparation using HPLC with a photodiode array detector was developed and validated for linearity, repeatability, limit of detection, and limit of quantitation. In addition, a high performance liquid chromatography coupled with electrospray ionization mass spectrometry method was described for its identification. Chromatographic separation and quantitation were made on a 250 mm × 4.6 mm (i.d.) C 18 column using 0.1% formic acid in water–methanol, in the ratio of (90:10, v/v) as mobile phase for the HPLC–DAD method. For structural identification chromatographic separation was performed on an YMC TM ODS-AQ 150 mm × 2 mm (i.d.) C 18 column using 0.1% formic acid in deionized water–methanol in ratio of (90:10, v/v) as mobile phase for LC-ESI-MS method. This analytical method provides a sensitive, rapid, and simple method with minimal sample preparation suitable for the stability control and routine analysis of glutathione-based commercial preparations.
A review of Machine Learning based Recommendation approaches for cricket

Cricket is one of the most watched sports in the world. Analyzing the factors, activities that affect the aftermath of the matches is of interest to many cricket lovers. Machine learning (ML) has demonstrated favorable outcomes in various fields like medical diagnosis, image processing, prediction, classification, regression etc. and is evidenced to be accurate. The ingenious frameworks built on ML have scope to learn from past experiences. The cricket pitch is the prime segment along with home game advantage, coin toss, innings, day/night match, physical fitness etc. A compendious examination of the cricket pitch, if done with imminent objectives, will be useful to precisely speculate outcomes match. This paper focuses on the existing research for analyzing the cricket pitch, predicting the performance of players and outcomes of the matches. A comparison among machine learning techniques has also been done and Naïve Bayes gave the best results due to its capability of producing accurate results with small samples while in other researches random forest turned out to be the precise classifier as it gave more accuracy than any other approach used.
Semi-Resistive Approach for Tightly Coupled Dipole Array Bandwidth Enhancement

A new approach to enhance the bandwidth of Tightly Coupled Dipole Arrays (TCDA) is presented. The new design achieves the integration of a semi-resistive Frequency Selective Surface network (FSS) composed of a non-resistive low-pass FSS and two resistive band-stop FSSs. The integration of this FSS network within a dual-polarized Tightly Coupled Dipole Array (TCDA) led to an increased impedance bandwidth of 28:1 from 0.20GHz to 5.6GHz. Notably, the use of an FSS superstrate allowed for scanning down to 60° at VSWR < 3 in the E-plane and VSWR < 4 in the D- and H-planes. Additionally, the strategic use of the inserted low-pass FSS reduces the resistive effects above 2.5GHz for improved average efficiency. A $12 \times 12$ array prototype was fabricated and tested to verify the bandwidth and gain of a finite array. The simulated radiation efficiency was demonstrated to be 83%, on average, across the band.
Artificial Intelligence based Self-Driving Car

The paper proposes a self-driving car model also called autonomous, robotic or driver-less car is one that operates and navigates using its intelligence. The basic idea behind the paper is to develop a 1/10 scale RC car to portray an automated car. The model consists of the following software and hardware components such as CNN (Convolutional neural network), Monocular vision algorithm, Haar cascade classifier, Raspberry Pi Board model B+, Pi camera, Arduino, and an Ultrasonic sensor. The Pi camera and ultrasonic sensor are attached to the raspberry pi board to collect input images along with sensor data to stream these data to the server which in our case is the laptop. The (CNN) convolutional neural network running on the server will be used to enable lane detection to provide steering predictions that are left, right, forward based on the input image. The haar cascade classifier will be used to detect signals and stop sign and monocular vision algorithms to calculate distance from them. The ultrasonic sensor will be used for front collision avoidance by stopping the car at a certain distance before the obstacle ahead. The navigation commands such as right, left, forward, stop will be sent to the car through Arduino which is connected to the RC car's remote, this will make the car drive autonomously based on neural network predictions and some hard coded rules. Thus this model will enable autonomous driving by self-navigation via lane detection, stopping at detection of stop signs, red lights and moving on green signal and front collision avoidance in a cost-effective manner. Hardcoded rules, obstacle avoidance mechanisms, machine learning models, and smart object discrimination will help the system follow traffic rules and navigate the car using artificial intelligence.
3D Reconstruction based on GAT from a Single Image

Previous methods which generate 3D shape usually use volume or a point cloud. These 3D shapes would lose details and are not smooth on the surface. This paper aims to introduce an end-to-end deep learning architecture which can generate 3D shape in triangular mesh from a single image. The architecture uses the graph attention network to continuously deform the initial ellipsoid based on the features extracted from a single image. The deformation process is constantly refined. The method can produce high precision mesh mode with richer details.
Introducing Novel Crossover and Mutation Operators into Data Replication Strategies for Distributed Systems

In a distributed paradigm, data replication plays a vital role in achieving high availability and fault tolerance of a system. In a connected network, faults are inevitable, replication masks those faults at run-time while users are unaware of it and the system continues to work as expected. There are different strategies to enforce such fault-tolerant behavior on a system. However, there are numerous scenarios reflecting different trade-offs between several quality metrics and to identify a relevant strategy for a specific scenario is quite cumbersome since there could exist potentially infinite scenarios and solutions are limited. This requires designing new solutions satisfying the constraints of such scenarios, which may be left unaddressed otherwise. In this regard, this paper develops a mechanism to automatically design new replication strategies (up-to-now unknown), optimized for given scenarios. The paper uses genetic programming to explore unknown replication strategies. It evolves the population of replication strategies (representing each a computer program) gradually, but consistently to make them optimized to eventually meet the desired criteria. Furthermore, it introduces strong multi-crossover and multi-mutation operators into replication, which strengthens our machine learning framework, at the same time guaranteeing consistency of the solutions, to generate innovative hybrid replication strategies.
Indian Traffic Sign Board Recognition and Driver Alert System Using CNN

Affirmation of traffic signs (TSR) is a popular bit of some ADA (ADAS) and vehicle drivers ' (ADS) schemes. As either the leading main technology of TSR, unveiling of traffic signs (TSD) is a worrying issue due to various styles, small size, complicated riding scenarios and obstacles. From late on various TSD figurations also relied on the view of the computer and even the pattern. A full description of the TSD structure is given in this article. We recommend splitting the field procedures under review into two rule groups: possibility-based, form-based systems. The proposed system is exhaustively apportioned into, data planning, data gathering, and getting ready and testing. System uses variety of picture planning strategies to improve the image quality and to oust non-illuminating pixel, and recognizing edges. Feature extractors are used to find the features of picture. Moved AI figuring Convolutional Neural Networks (CNN) is used to gather the differing traffic sign pictures reliant on their features by using the progressing camera.
Managing Container QoS with Network and Storage Workloads over a Hyperconverged Platform

Container resource management is non-trivial over hyperconverged platforms where the storage is shared among host servers. Therefore, the same backbone network is used by storage and regular network traffic. In this paper, we first characterize this problem by analyzing the nature of the traffic from storage workloads and its impact on the network workloads in a container-based virtualization environment. Accordingly, we develop CONtrol, a resource management approach for assuring network workloads’ performance in the presence of storage workloads. CONtrol uses a proportional-integral-derivative controller to dynamically decide the bandwidth redistribution among various workloads. Additionally, it uses a container migration strategy for balancing the workloads across different servers of a hyperconverged platform. We have implemented CONtrol over a hyperconverged platform with 5 physical servers. Thorough testing indicates that it can significantly improve the performance of various benchmark applications over a containerized hyper-converged platform.
“The Robot-Arm Talks Back to Me” - Human Perception of Augmented Human-Robot Collaboration in Virtual Reality

The usage of AI enhanced robots in shared task environments is likely to become more and more common with the increase of digitalization in different industrial sectors. To take up this new challenge, research on the design of Human-Robot-Collaboration (HRC) involving AI-based systems has yet to establish common targets and guidelines. This paper presents results from an explorative qualitative study. Participants (N= 80) were either exposed to a virtual representation of an industrial robot-arm equipped with several augmentation channels for communication with the human operator (lights, textual statements about intentions, etc.) or one with no communicative functions at all. Across all conditions, participants recognized the benefit of collaborating with robots in industrial scenarios regarding work efficiency and alleviation of working conditions. However, a communication channel from the robot to the human is crucial for achieving these benefits. Participants interacting with the non-communicative robot expressed dissatisfaction about the workflow. In both conditions we found remarks about the insufficient speed of the robot-arm for an efficient collaborative process. Our results indicate a wider spectrum of questions to be further explored in the design of collaborative experiences with intelligent technological counterparts considering efficiency, safety, economic success and well-being.
Efficient Transfer Laerning Used in the Classification of Gastroscopic Images with Small Dataset

Deep learning (DL) has gained success in image classification. However, DL always needs a large number of labeled training data which are not usually available for gastroscopic image analysis. Therefore, the pre-trained convolutional neural networks (CNNs) models are important. The transfer learning (TL) method could leverage the pre-trained model in natural images to gastroscopic images. In this paper, we have studied different TL methods on small dataset of gastroscopic images. We have fine-tuned the current deep CNNs: EfficientNet, ResNet and DenseNet on three kinds of gastroscopic images from kvasirv2 dataset. The results demonstrated that the pre-trained models of current deep CNNs can get considerable classification results on gastroscopic images with small dataset.
Deep Convolutional Neural Network based Feature Extraction with optimized Machine Learning Classifier in Infant Cry Classification

Crying is the only mode of communication for babies to share information with the surrounding environment. Expert knowledge is required to analyze the audio signals in pre-processing and extract the features from it. Deep learning doesn't require much pre-processing and it automatically extracts the crucial features directly from the data. This paper presents the deep learning based feature extraction and machine learning based classification approach in infant cry classification and compares the various machine learning algorithms in infant cry classification. The audio signal of length 4 seconds data was converted into spectrogram image which was taken as input, deep convolutional neural network extract the features based on that the data were classified using various machine learning algorithms such as SVM, Naïve Bayes and KNN then compare their performance with Bayesian Hyper-parameter optimization technique. The experimental result shows that SVM provides better performance than K Nearest Neighbor and Naïve Bayes in the classification of infants hunger, pain and sleepy cries.
Data Imputation-Based Learning Models for Prediction of Diabetes

The accuracy of automated diabetes prediction models using the past health record of the patient is highly dependent on the correctness of the used data. If patient data is inconsistent and contains lots of missing values, then the prediction is more challenging. In this paper, the impact of missing value imputation (MVI) techniques is evaluated in diabetes prediction with existing missing values. The experiments are performed on the Pima Indians diabetes dataset, which contains many missing values. In this paper, first, MVI techniques are used for handling the missing values. Second, K-Means clustering is used to analyze the best imputation technique based on the percentage of incorrectly classified instances in each imputed dataset. Third, principal component analysis (PCA) is used for feature extraction, and Info Gain is used for selecting the optimal set of features. Six different classification models, such as multi-layer perceptron (MLP), support vector machine (SVM), Naïve Bayes (NB), decision tree (J48), AdaBoost, and Bagging are used for experiments. Eight different techniques such as CMC, Case Deletion, KMI, SVMI, WKNNI, KNNI, FKMI, and MC are used for missing value imputation. The experimental result shows that case deletion and KMI imputed datasets have the lowest number of incorrectly classified instances. On these two datasets, when to six classifiers are applied, we obtained that MLP classifier attained the highest accuracy of 98.9967 % with the case deletion imputed dataset and accuracy of 99.2767% with the KMI imputed dataset when six principal components are used. The other classifiers used in comparison obtained accuracies ranging between 93% - 98%.
Classification of Landslide Stability Based on Fine Topographic Features

In this paper, we propose an extraction method for landslide topographic features and based on these fine features, we trained a landslide stability classification model. Topographic factors have a huge impact on the stability of landslides, and our new feature extraction methods can provide more accurate portraits of landslides. Considering the factors affected landslide stability, this paper studied the factors used in traditional quantitative stability calculations and analyzed the factors affecting stability based on landslide data. So, the proposed method is a combination of theoretical method and numerical method. Based on support vector classification (SVC), random forest (RF), and extreme gradient boosting (XGBoost) models, this paper compares and analyzes the rationality and reliability of the landslide stability classification results output by three models. After adopting the new fine feature extraction method, the accuracy of the stable classification results has been improved by about 5% on average, and the interpretability of the classification results has also been greatly enhanced.
A REFERENCE ARCHITECTURE FOR THE INTERNET OF THINGS

The result is that no single architecture will suit all these areas and the requirements each area brings. However, a modular scalable architecture that supports adding or subtracting capabilities, as well as supporting many requirements across a wide variety of these use cases is inherently useful and valuable. It provides a starting point for architects looking to create IoT solutions as well as a strong basis for further development. This paper proposes such a reference architecture. The reference architecture must cover multiple aspects including the cloud or serverside architecture that allows us to monitor, manage, interact with and process the data from the IoT devices; the networking model to communicate with the devices; and the agents and code on the devices themselves, as well as the requirements on what sort of device can support this reference architecture.