Miracle-dz/UniFAR-FTUs huggingface dataset:
Maize Disease classification using Deep Learning Techniques: A Review

Recent years have seen a significant increase in interest from both academic and commercial premises due to the benefits of autonomous learning and features extraction. Natural language processing, voice processing, picture and video processing all make extensive use of it. In addition, it has developed into a hub for research in the field of agricultural plant protection, including the identification of plant diseases and the evaluation of pest ranges. To increase agricultural productivity in a sustainable way, it’s critical to identify crop leaf diseases quickly and precisely. In this paper, we present a comprehensive assessment of recent work on crop leaf disease prediction using machine learning, image processing and deep learning techniques. Deep learning (DL) techniques, particularly those built on convolutional neural networks (CNN), are now widely used to classify plant diseases. The research articles that presented the various techniques are surveyed in this article, which assesses them in terms of the dataset, the quantity of images, the quantity of classes, the techniques applied, the convolutional neural networks (CNN) models employed, and the final results obtained. Modified DL techniques outperform conventional ML techniques in terms of performance. In order to expand the real-time autonomous system for identifying maize leaf disease, we addressed the performance measurements that were employed as well as some of the limits and future work that needs to be focused on.
Least Square Support Vector Machine based Intrusion Detection System in IoT

The IoT (Internet of Things) is an ever-expanding system of interconnected computing devices. It’s a term for when real-world items can communicate with one another via data transfers. The availability and dependability of its activities are crucial to the day-to-day functioning of modern society. As a result, it’s important to find a solution to the problem of ensuring secured data transmission across Internet of Things. An Intrusion Detection System (IDS) is a piece of software designed to keep an eye on a network or computer system and to address any suspicious activity. New threats and countermeasures must be incorporated into the network’s protected evolution. The primary function of IDS is to protect valuable resources against intrusion. Intruders can be spotted using various intrusion detection techniques, strategies, and algorithms. This major purpose of this study is to propose a comprehensive system for identifying intrusion risks by applying Least Squares Support Vector Machine approach. After receiving input, the following step is preprocessing, which was accomplished via Normalization, Discretization, and Feature selection in this study. These techniques are used for data preprocessing and feature selection. Training the model using LSSVM is the final stage. As compared to other models like SVM and RF, the proposed model attains an accuracy of roughly 97.7%.
Jointed Task of Multi-scale CNN Based Power Transformer Fault Diagnosis with Vibration and Sound Signals

The operation status of transformer is closely related to the safe and stable operation of power system. In the process of transformer mechanical fault analysis, fault diagnosis based on vibration signal and sound signal is two feasible methods. However, the acquisition cost of vibration signal is high, and the fault samples of sound signal are few. Therefore, it is a feasible method to combine vibration signal with sound signal to carry out transformer fault diagnosis. In this paper, we propose a jointed learning framework with vibration and sound signals for power transformer fault diagnosis, which integrates multi-branch input, multi-scale residual learning and joint learning technology to identify the mechanical fault types of transformers. The experimental results show that the proposed method has a good discrimination effect on fault characteristics, which can be trained through fault samples of vibration signals, and can achieve high-precision and robust identification for vibration signals or sound signals.
A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS) and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.
Coverage-based Queries: Nondiscrimination Awareness in Data Transformation

When people-related data are used in automated decision making processes, social inequities can be amplified. Thus, the development of technological solutions satisfying nondiscrimination requirements, in terms of, e.g., fairness, diversity, and coverage, is currently one of the main challenges for the data management and data analytics communities. In particular, coverage constraints guarantee that a dataset includes enough items for each (protected) category of interest, thus increasing diversity with the aim of limiting the introduction of bias during the next analytical steps. While coverage constraints have been mainly used for designing data repair solutions, in our study we investigate their effects on data processing pipelines, with a special reference to data transformation. To this aim, we first introduce coverage-based queries as a means for ensuring coverage constraint satisfaction on a selection-based query result, through rewriting. We then present two approximate algorithms for coverage-based query processing: the first, covRew , initially introduced in [3], relies on data discretization and sampling; the second, covKnn is a novel contribution and relies on a nearest neighbour approach for coverage-based query processing. The algorithms are experimentally compared with respect to efficiency and effectiveness, on a real dataset.
Plasma-Sputtered Growth of Ni-Pd Bimetallic Nanoparticles on Carbon Nanotubes for Toluene Sensing

The properties of carbon nanotubes (CNTs) can be effectively tailored by decorating their surface with metal nanoparticles. For the decoration, first plasma functionalization is used to add oxygen chemical groups to the CNTs surface. Afterwards, the Ox-CNTs are decorated with Ni-Pd bimetallic nanoparticles using plasma sputtering deposition, a clean, fast, and environmentally friendly functionalization method. The grafted oxygen groups serve as nucleation sites for the growth of the bimetallic nanoparticles. Finally, the Ni-Pd nanoparticle-decorated CNTs are assessed as a sensing layer for the detection of toluene.
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery

In critical applications, causal models are the prime choice for their trustworthiness and ex-plainability. If data is inherently distributed and privacy-sensitive, federated learning allows for collaboratively training a joint model. Existing approaches for federated causal discovery share locally discovered causal model in every iteration, therewith not only revealing local structure but also leading to very high communication costs. Instead, we propose an approach for privacy-preserving federated causal discovery by distributed min-max regret optimization. We prove that max-regret is a consistent scoring criterion that can be used within the well-known Greedy Equivalence Search to discover causal networks in a federated setting and is provably privacy-preserving at the same time. Through extensive experiments, we show that our approach reliably discovers causal networks without ever looking at local data and beats the state of the art both in terms of the quality of discovered causal networks as well as communication efficiency.
The evolution of the tropical western Pacific atmosphere‐ocean system following the arrival of a dry intrusion

Recent studies using TOGA COARE data have found that extremely dry air from middle‐latitude waves frequently intrudes into the equatorial troposphere over the western Pacific. Using sounding data taken during the COARE, the magnitude of the advection of water vapour for one event is calculated, and it is estimated that the lime for the atmosphere to recover to moist conditions was ∼ 10‐20 days. From the magnitude of the drying and from the frequency of these events, it is proposed that dry intrusions must be a major contributor to the tropospheric moisture budget over the region during the COARE, making it difficult for the atmosphere to reach a radiative‐convective equilibrium, intrusions, instead, can help to recharge the tropical atmosphere by decreasing convective activity and, thus, driving the atmosphere toward unusually large values of convective available potential energy. A variety of atmospheric and oceanic measurements are also used to study the recovery process in detail. A conceptual model is proposed based on this work and previous investigations. As in past studies, the recovery of the atmosphere to moist conditions is accomplished through detrainment from convective clouds that began to form soon after the arrival of the dry air mass and slowly deepen in height as the recovery progresses. Previous investigators concluded that the entrainment of dry air into convective ceils is generally the factor that tends to suppress convective activity and limits the height of any convection that does develop under these adverse conditions. The idea that entrainment limits convective activity is consistent with the commonly held perception that the western Pacific is a region where there is little inhibition to deep convection and, when inhibition does occur, it can be removed by surface fluxes within hours. In contrast, it is found that convective inhibition can be large enough to suppress convection following dry intrusions, and that the diurnal variation in rainfall is due partly to modulations in convective inhibition. The modulations in convective inhibition are, in turn, caused by diurnal variations in the vertical profiles of radiation, in surface fluxes, and perhaps in large‐scale subsidence, leading to a minimum in convective inhibition during the late afternoon. In contrast, studies of this type of convection have generally emphasized diurnal variations in the surface fluxes, and often ignored convective inhibition and diurnal variations in atmospheric radiative heating.
Overview of EmoThreat: Emotions and Threat Detection in Urdu at FIRE 2022

Emotion and targeted abuse detection i.e threat, are problems that have been studied in many rich resource languages. However, when it comes to low-resource languages such as Urdu, we find a dearth of resources and methodologies. Our paper presents the findings of the shared task "EmoThreat: Emotions and Threat detection in Urdu", where we focused on presenting resources for multi-label emotion classification (Task A) and binary threat detection (Task B) in Urdu. Task B was further divided into group and individual threat detection, making it a multi-class problem. The paper presents a summary of the methodologies and findings of the ten different participating teams. Each team also presented a thorough error analysis for the best model. The best performing system in Task A achieved a macro-F1 score of 0.687, whereas, Task B subtask 1 and subtask 2 achieved 0.716 and 0.539 macro-F1 scores respectively.
Do Large Datasets or Hybrid Integrated Models Outperform Simple Ones in Predicting Commodity Prices and Foreign Exchange Rates?

With the continuous advancement of machine learning and the increasing availability of internet-based information, there is a belief that these approaches and datasets enhance the accuracy of price prediction. However, this study aims to investigate the validity of this claim. The study examines the effectiveness of a large dataset and sophisticated methodologies in forecasting foreign exchange rates (FX) and commodity prices. Specifically, we employ sentiment analysis to construct a robust sentiment index and explore whether combining sentiment analysis with machine learning surpasses the performance of a large dataset when predicting FX and commodity prices. Additionally, we apply machine learning methodologies such as random forest (RF), eXtreme gradient boosting (XGB), and long short-term memory (LSTM), alongside the classical statistical model autoregressive integrated moving average (ARIMA), to forecast these prices and compare the models’ performance. Based on the results, we propose novel methodologies that integrate wavelet transformation with classical ARIMA and machine learning techniques (seasonal-decomposition-ARIMA-LSTM, wavelet-ARIMA-LSTM, wavelet-ARIMA-RF, wavelet-ARIMA-XGB). We apply this analysis procedure to the commodity gold futures prices and the euro foreign exchange rates against the US dollar.
Evaluating the Similarity of Location-based Corpora Identified in Reddit

Social interaction is typically studied from the context of physical movement, where geographic distance and ease of connectivity influence the strength of interaction between regions. From the point of view of social media networks however, these limitations appear to still persist, despite interactions not being reliant on physical movement, suggesting non-physical geographic characteristics influence interaction between social communities. Unlike geotags, which provide explicit geographic information about social media users as coordinates, unstructured text presents an alternative perspective for the study of social interaction between regions, instead allowing for the comparison between the language used when mentioning locations in context. Our paper analyses the corpora associated with major cities across the UK, first vectorising Reddit comments through transformer-based embeddings, which capture semantic information, then using these to establish unsupervised clusters and similarity between them. We find that distinct groups emerge which broadly conform with established regional identities of locations across the UK, but with interesting deviations.
Linear instability analysis of low- $Re$ incompressible flow over a long rectangular finite-span open cavity

TriGlobal linear instability analysis and direct numerical simulations have been performed to unravel the mechanisms ultimately responsible for transition of steady laminar flow over a long rectangular finite-span open cavity with dimensions $L$ : $D$ : $W$ $=$ 6 : 1 : 2 to unsteadiness. The steady laminar three-dimensional flow loses stability at $\mathit{Re}_{D,cr}\approx 1080$ as a consequence of linear amplification of a travelling eigenmode that is qualitatively analogous to the shear-layer mode known from analyses of flow in spanwise-periodic cavities, but has a three-dimensional structure which is strongly influenced by the cavity lateral walls. Differences in the eigenspectrum of the present and the spanwise homogeneous flow configuration are documented. Topological changes exerted on the steady laminar flow by linear amplification of the unstable shear-layer mode are reminiscent of observations in experiments at an order of magnitude higher Reynolds number.
The Application of 3D-ED to Distinguish the Superstructure of Sr 1.2 Ca 0.8 Nb 2 O 7 Ignored in SC-XRD

: Compared to X-rays, electrons have stronger interactions with matter. In electron diffraction, the low-order structure factors are sensitive to subtle changes in the arrangement of valence electrons around atoms when the scattering vector is smaller than the critical scattering vector. Therefore, electron diffraction is more advantageous for studying the distribution of atoms in the structure with atomic numbers smaller than that of sulfur. In this work, the crystal structure of Sr 1.2 Ca 0.8 Nb 2 O 7 (SCNO-0.8) was analyzed using single-crystal X-ray diffraction (SC-XRD) and three-dimensional electron diffraction (3D-ED) techniques, respectively. Interestingly, the superstructure could only be identiﬁed by the 3D-ED technique, while no signal corresponding to the superstructure was detected from the SC-XRD data. The superstructure in SCNO-0.8 was disclosed to be caused by different tilting of NbO 6 octahedra and the displacements of Sr/Ca atoms in the different atomic layers perpendicular to the a -axis. Therefore, the application of 3D-ED provides an effective method for studying superstructures caused by ordered arrangements of light atoms.
Doctoral Dissertation Proposal Using Natural Language to Aid Task Specification in Sequential Decision Making Problems

Intelligent agents that can help humans accomplish everyday tasks, such as a personal robot at home or a robot in a work environment, is a long-standing goal of artificial intelligence. One of the requirements for such general-purpose agents is the ability to teach them new tasks or skills relatively easily. Common approaches to teaching agents new skills include reinforcement learning (RL) and imitation learning (IL). However, specifying the task to the learning agent, i.e. designing effective reward functions for reinforcement learning and providing demonstrations for imitation learning, are often cumbersome and time-consuming. We aim to use natural language as an auxiliary signal to aid task specification, which reduces the burden on the end user. To make reward design easier, we propose a novel framework that is used to generate language-based rewards in addition to the extrinsic rewards from the environment for faster policy training using RL. To ameliorate the problem of providing demonstrations, we propose a new setting that enables an agent to learn a new task without demonstrations in an IL setting, given a demonstration from a related task and a natural language description of the difference between the desired task and the demonstrated task. The primary contributions of this dissertation will be new frameworks that enable incorporating natural language in RL and IL, which would enable non-expert users to specify new tasks to intelligent agents more conveniently.
A Classification Approach to Fetal Cardiotocography Dataset using R

Electronic fetal heart monitoring to check fetal status during pregnancy is common. Cardiotocography is a technique for assisting obstetricians in obtaining clear details during the time of childbirth as a method of monitoring the health condition, especially in pregnant women who are at risk of difficulties. This paper deals with the classification of the fetal cardiotocography dataset using R. The supervised machine learning-based approach is applied for the categorization of fetal datasets. It is classified as normal, suspect, and pathologic based on the random forest classifier. It produces an accuracy of 99.94% in training and 93.57% in testing which is found to be a better performance. It also provides the best results in terms of sensitivity, and specificity in the classification of normal, suspect, and pathology in both training and testing datasets. It is found that this method provides a greater accuracy compared to all other methods.
Elaborative Trademark Similarity Evaluation Using Goods and Services Automated Comparison

Trademark description comparison is an important problem during registering a new trademark. Manual search for the most similar trademark often faces the plenty of similar trademarks already registered and filed, so subjective evaluation of the trademark similarity imposes some deviations in the comparison. This paper discusses the methods that aim the automatic evaluation of the trademark’s similarity/identity based on the text description of goods and services. The analysis included trademarks covering legal services. Various models (count vectorizer, TF-IDF, word2vec, doc2vec) pretrained and trained by ourselves, and sets of vectors comparison approaches (cosine similarity, Tanimoto similarity) have been evaluated. Own simple greedy matching algorithm that can optionally include difference between size of sets which is useful for trademarks comparison has been proposed. The article shows that some combinations of model/similarity measure are successful in searching for correct hierarchy of trademarks, while some other are better in terms of human relevance. The quality and performance analysis of the discussed methods allowed us to choose the most effective of them according to the available data and technical requirements and implement them in practical applications to perform search for the similar or identical trademarks by example. We contribute to the study of methods for automating legal processes and offer a mean to reduce the risk of intellectual property disputes.
Adaptive Filtering Strategies for Social Media Streams

Information produced on social media platforms can serve a wide range of applications, yet the ability to obtain and refine this information is hindered. Social media data naturally exhibit a multi-modal and relational nature, with challenges associated with its volume, pace, variability, and a variety of sources of noise. Especially in time-sensitive scenarios, these characteristics call for efficient, automated filtering approaches capable of extracting the available relevant data with minimal human supervision. How these automated approaches can be optimized while respecting context-dependent quality constraints is an understudied issue. Moreover, adaptation to non-stationarity in both data and relevance enriches the perspective of this research topic. The study of a method for overcoming these challenges by leveraging an adaptive architecture is proposed. Adaptivity pertains to selecting data representations, their aggregation, and the filtering decision policy. These choices are subject to operating constraints over the quality dimensions of accuracy, completeness and timeliness. The research question is contextualized in the state of the art, and its novel aspects are discussed. Preliminary results are described, together with a research plan outline.
Sentiment and Homophobia Detection on YouTube using Ensemble Machine Learning Techniques

Internet users frequently express themselves through posts, comments, and articles. The examination of such posts/comments has recently attracted the research community’s attention. Sentiment analysis and the identification of homophobic comments are two key research areas in this field. Sentiment analysis reveals that people’s emotions reflect positive, negative, or mixed feelings about a certain topic or article. Further, Homophobia refers to a wide range of attitudes and feelings toward people who identify as homosexual, transgender, lesbian, gay, or queer. To encourage research in this direction, the organisers of the Dravidian LangTech shared task as part of FIRE 2022 have set two shared tasks. Task A consists of a message-level polarity detection problem, in which the given YouTube comments system has to recognise positive, negative, and mixed emotions. Task B involves detecting transphobic and homophobic YouTube comments. Our team participated in both subtasks; we worked on the Kannada dataset for sentiment analysis, and our best-performing model secured 11th place among the participating teams. For Task B, we participated in all four languages (Tamil, English, Malayalam, and Tanglish) and received 6, 6, 2, and 4th positions, respectively. In our proposed approach, we employed several Machine learning models, the Ensemble method and Deep learning models to achieve the desired result.