Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Weighted AdaBoost for Animal Behavior Recognition from Sensor Data

Classifying calves' behavior from accelerometer sen-sors attached to neck collars is a promising way to assess calf welfare in commercial farms. Developing global models that achieve high performance across multiple calves is challenging due to inter-individual diversity, which limits application on commercial farms. It can be assumed that there is a greater or lesser degree of similarity between calves linked to their age, weight, breed, mobility, health status, etc. Therefore, developing global models that use calf similarity in the classification seems promising. This paper aims to develop Personalized Weighted AdaBoost (PWA) models where the weights in the training set are tuned according to the similarity with the query calves. The approach adopted consists of (i) measuring the similarity between each pair of calves by testing three different strategies (label-based, pairwise-based, and cluster-based similarity) and (ii) tuning the weights so that the training calves with the highest similarity to the query calf get higher weights. This approach was implemented with an accelerometer dataset collected from 21 pre-weaned calves. The accelerometer data was manually annotated using videos to obtain the behaviors (lying, walking, eating, running, and drinking milk) used for this work. Performance was compared against a global AdaBoost model without the assignment of weights (baseline). PWA models improve the performance compared to the baseline, regardless of the strategy used to compute the similarity metric (label-based: 0.64 +/-0.132, pairwise: 0.59 +/- 0.084, cluster-based: 0.62 +/- 0.145; baseline: 0.56 +/- 0.138 [average balanced accuracy over 10 query calves +/- standard deviation]). Although this approach still suffers from a lack of data and an unbalanced dataset, it seems extremely promising for developing robust calf behavior classification models from accelerometer data, thus offering new opportunities to improve calf welfare in commercial farms.
Disaster Event Classification on Twitter: A Comparison of LSTM and GRU Algorithms using Word Embedding FastText and GloVe

The increasing significance of handling issues pertaining to disasters in contemporary society necessitates prompt access to precise information. This study examines disaster-related matters by doing a comparative examination of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The study utilizes FastText and GloVe word embedding methodologies to categorize tweets related to disasters. The main goals are to evaluate catastrophe-related issues and compare the LSTM and GRU algorithms, with the purpose of improving disaster detection and management. The results demonstrate that the LSTM model, when combined with FastText word embeddings, surpasses previous combinations, obtaining a remarkable accuracy of 80%. The technique demonstrates a precision of 81%, recall of 83%, and an F1 score of 82%, highlighting its strong ability to effectively classify tweets connected to disasters. The study demonstrates the effectiveness of deep learning methods in efficiently sorting, categorizing, and recognizing disaster-related data.
Dectin-1/TLR2 and NOD2 Agonists Render Dendritic Cells Susceptible to Infection by X4-Using HIV-1 and Promote cis-Infection of CD4+ T Cells

HIV-1 pathogenesis is intimately linked with microbial infections and innate immunity during all stages of the disease. While the impact of microbial-derived products in transmission of R5-using virus to CD4+ T cells by dendritic cells (DCs) has been addressed before, very limited data are available on the effect of such compounds on DC-mediated dissemination of X4-tropic variant. Here, we provide evidence that treatment of DCs with dectin-1/TLR2 and NOD2 ligands increases cis-infection of autologous CD4+ T cells by X4-using virus. This phenomenon is most likely associated with an enhanced permissiveness of DCs to productive infection with X4 virus, which is linked to increased surface expression of CXCR4 and the acquisition of a maturation profile by DCs. The ensuing DC maturation enhances susceptibility of CD4+ T cells to productive infection with HIV-1. This study highlights the crucial role of DCs at different stages of HIV-1 infection and particularly in spreading of viral strains displaying a X4 phenotype.
Taxonomy of Multi-Agent Systems Attacks and their Defense Tactics in Certifying Security of Cyber Physical Systems

The utilization of multi-agent systems has been increasingly prevalent across various sectors, owing to their notable efficacy in execution. However, a multitude of hazards exist that possess the capability to undermine the integrity of the agent and pose a threat to the overall security of the system. Therefore, it is crucial to carefully evaluate and address security vulnerabilities during the process of creating Multi-Agent Systems (MAS). This survey assesses different models that aim to guarantee the security of Multi-Agent Systems (MAS). These models were developed based on principles related to the agents' functionality and communication. This study offers a comprehensive examination and categorization of the prevailing threats targeting Multi-Agent Systems (MASs). Following this, we conduct a comprehensive analysis and assessment of diverse security strategies described in scholarly sources, classifying them into prevention, detection, and resiliency approaches based on their established credibility and reliability. In conclusion, we offer a suggestion for the optimal security approach to address particular types of threats, considering the most recent breakthroughs in the field of study.
Intrinsic Dimension Estimation of Iudustrial Surface Defect Data and Its Impact on Classification

In today's era of rapid development of artificial intelligence, machine vision technology based on deep learning is widely used. In this paper, the intrinsic dimension estimation of industrial defect data and its impact on classification and generalization are studied based on machine vision in the detection of industrial defects. In this work, Maximum Likelihood Estimation (MLE) algorithm is used to estimate the intrinsic dimensions of three common industrial defect data, and to verify the influence of the intrinsic dimensions on sample complexity and classification.
A Two-Stage Deep Learning Framework for Enhanced Waste Detection and Classification

With the rapid modernization progress over the past decades, waste classification has become increasingly important as cities worldwide seek to implement more sustainable waste management practices. Traditional manual sorting methods are labor-intensive, prone to inaccuracies, and hard to scale, driving the need for automated, efficient solutions. Although deep learning techniques, recognized for their ability to process complex hierarchical data, have emerged as potential aids, their successful implementation is often hampered by the challenging task of gathering diverse, large-scale, high-quality waste image datasets, leading to possible overfitting and model bias. This study proposes an innovative, two-stage waste detection framework that first identifies the bounding box of waste items, then classifies them into one of six primary categories, effectively addressing the inherent issues with previous methodologies by optimally utilizing available data and reducing overfitting and bias. Trained and evaluated on the comprehensive TACO and WaRP waste datasets, our model has demonstrated superior performance relative to existing methods, underscoring its promise as a scalable, accurate, and efficient solution for waste classification, thus offering exciting prospects for further research and practical applications in sustainable waste management.
Architectures for Speech-to-Speech Translation Using Finite-state Models

Speech-to-speech translation can be approached using finite state models and several ideas borrowed from automatic speech recognition. The models can be Hidden Markov Models for the accoustic part, language models for the source language and finite state transducers for the transfer between the source and target language. A "serial architecture" would use the Hidden Markov and the language models for recognizing input utterance and the transducer for finding the translation. An "integrated architecture", on the other hand, would integrate all the models in a single network where the search process takes place. The output of this search process is the target word sequence associated to the optimal path. In both architectures, HMMs can be trained from a source-language speech corpus, and the translation model can be learned automatically from a parallel text training corpus. The experiments presented here correspond to speech-input translations from Spanish to English and from Italian to English, in applications involving the interaction (by telephone) of a customer with the front-desk of a hotel.
Concentration inequalities for $s$-concave measures of dilations of Borel sets and applications

We prove a sharp inequality conjectured by Bobkov on the measure of dilations of Borel sets in the Euclidean space by a $s$-concave probability measure. Our result gives a common generalization of an inequality of Nazarov, Sodin and Volberg and a concentration inequality of Guedon. Applying our inequality to the level sets of functions satisfying a Remez type inequality, we deduce, as it is classical, that these functions enjoy dimension free distribution inequalities and Kahane-Khintchine type inequalities with positive and negative exponent, with respect to an arbitrary $s$-concave probability measure
Smart Substation Alarm Analysis Method Based on Semantic Rules Extraction

The alarm information generated by the security equipment of smart substation presents the characteristics of isolation, fragmentation and redundancy, which makes it a big problem to analyze the alarm information and make the corresponding disposal means. This paper proposes a smart substation alarm analysis method based on semantic rule extraction, which applies threat intelligence to smart substation threat detection. Firstly, a semantic rule model is constructed to define the attributes, relationships and semantic rules of network entities. Then, a semantic rule extraction framework is constructed to extract semantic rules from ATT&CK model attack technical texts. Finally, it matches with the alarm log data of smart substation to realize the association of alarm information and restore the attack context.
Exploring Novel CNN Architectures for Deep Learning-Based Liver Tumor Occurrence Prediction

According to the authorized findings of,” Hepatocellular carcinoma (HCC)” a common in the human species, liver located, tumor,90 % of its occurrences demand any sort of support for predicting them in advance, if possible. For this purpose, Image Processing, Text Mining, and Deep learning techniques are handled with effective pre-processing methods in the recent research works to improve the results available so far. A majority of contribution by the researchers finds it challenging mostly in selecting suitable architectures for deep learning as most of the previous literature studies show the lacking of details on feasibility and justifiability in specifying the architectures. In this article, it is aimed at the primary goal of such studies as the first attempt on varying architectures in terms of profile and position of layers and observing the performance for recognizing the liver Tumor from the Image-data sets. The CNN architecture of neural network like AlexNet, VGGnet, ResNet, LeNet are standard architectures, based on this liver tumor dataset is implemented in customized architecture and its produced best performance with accuracy of 92.4 %. And It is compared with MeanderHandPD, Breast cancer and Liver Tumor Datasets, the proposed architecture shows higher performance for Liver Tumor images.
Linear beamforming assisted receiver for binary phase shift keying modulation systems

The paper considers adaptive beamforming assisted receiver for multiple antenna aided multiuser systems that employ binary phase shift keying (BPSK) modulation. The standard minimum mean square error (MMSE) design is based on the principle of minimising the mean square error (MSE) between the beamformer's desired output and complex-valued beamformer output. Since the desired output for BPSK systems is real-valued, minimising the MSE between the beamformer's desired output and real-part of the beamformer output can significantly improve the bit error rate (BER) performance, and we refer to this alternative MMSE design as the real-valued MMSE (RV-MMSE) to contrast to the standard complex-valued MMSE (CV-MMSE). The minimum BER (MBER) design however still outperforms the RV-MMSE solution, particularly for overloaded systems where degree of freedom of the antenna array is smaller than the number of BPSK users. Adaptive implementation of this RV-MMSE design is realised using a least mean square (LMS) type adaptive algorithm, which we refer to as the RV-LMS, in comparison to the standard CV-LMS algorithm. The RV-LMS adaptive beamformer has the same computational complexity as the adaptive least bit error (LBER) algorithm, imposing half of the computational requirements of the CV-LMS algorithm
Survey of Application of Automata Theory in Natural Language Processing

With a focus on finite-state automata (FSA), this overview explores the various uses of automata in natural language processing (NLP). Automata are used in a variety of natural language processing (NLP) applications, such as pattern matching, recognition, processing audio and handwritten text, encryption, data compression, and operating system analysis. An extensive review of theoretical automata models, including pushdown automata, Turing machines, and FSA, is provided in the abstract. With a focus on tokenization, parsing, and syntactic analysis, the paper investigates how automata might be integrated with contemporary deep learning to improve the effectiveness of natural language processing. Among the difficulties discussed in the study is the scalability of automatadriven models in big language datasets. A nuanced view of the dynamic environment of automata in NLP is offered by this synthesis.
Realization of Space Vector Pulse Width Modulation Scheme for a Quasi Z-Source Inverter in FPGA

A Quasi-Z source inverter has the ability to exceed the voltage gain normally achievable through space vector pulse width modulation (SVPWM) applied on a two-level H-bridge voltage source inverter under the linear modulation zone. These inverters use a unique power impedance network, and it is connected between the power source and the converter circuit for both buck and boost operation. A convenient approach to implement the pulse generation scheme for a simple boost control in a Quasi Z-source SVPWM voltage source inverter using Field Programming Gate Arrays(FPGA) is presented here. The computational steps to derive the switching pulses are described. Based on the computational steps, the programming in FPGA is carried out. The experimental results conform well with the analytical results.
Alberta Stroke Program Early CT Score in the Prognostication after Endovascular Treatment for Ischemic Stroke: A Meta-analysis

Purpose The Alberta Stroke Program Early CT Score (ASPECTS) was devised to quantify the extent of early ischemic changes in the middle cerebral artery territory on brain CT. We performed a systematic review and meta-analysis of studies that presented clinical outcomes and baseline ASPECTS in ischemic stroke patients managed with endovascular methods to validate the use of ASPECTS for risk prognostication. Materials and Methods We searched the MEDLINE, EMBASE, and Cochran databases for observational or interventional studies that reported clinical outcomes and baseline ASPECTS in ischemic stroke patients treated with endovascular methods. Data were pooled to perform a meta-analysis for comparisons of clinical outcomes between high and low ASPECTS patients. Results A meta-analysis of 13 studies (six observational and seven interventional) revealed favorable outcomes (mRS sore 0-2 at 90 days) for high baseline ASPECTS (odds ratio=2.22; 95% CI: 1.74-2.86). Conclusion High ASPECTS is a predictor of favorable outcome after endovascular therapy for ischemic stroke.
TURAN: Evolving non-deterministic players for the iterated prisoner's dilemma

The iterated prisoner's dilemma is a widely known model in game theory, fundamental to many theories of cooperation and trust among self-interested beings. There are many works in literature about developing efficient strategies for this problem, both inside and outside the machine learning community. This paper shift the focus from finding a “good strategy” in absolute terms, to dynamically adapting and optimizing the strategy against the current opponent. Turan evolves competitive non-deterministic models of the current opponent, and exploit them to predict its moves and maximize the payoff as the game develops. Experimental results show that the proposed approach is able to obtain good performances against different kind of opponent, whether their strategies can or cannot be implemented as finite state machines.
Bridging Subjectivity and Objectivity in Evaluation of Machine-Generated Jazz Music: A Multimetric Approach

This research explores different musical metrics to bridge the gap between subjectivity and objectivity in jazz music generation by employing diverse metrics including pitch class histogram entropy, groove pattern similarity, and creating two new metrics based on jazz theoretical concepts: the chord tone emphasis and the swing deviation metric. The study utilizes both a Markov model and a Recurrent Neural Network for music generation, with a dataset consisting of choruses of F-blues. A human survey is conducted on the preferred model for jazz generation to align these objective metrics with the subjective preference of humans. The results suggest that combining these objective metrics with subjective survey responses provides a more comprehensive understanding of generative jazz music’s quality. While challenges remain, these metrics contribute to a more nuanced and balanced evaluation of the artistic output. Future work may involve exploring the integration of additional metrics and theoretical concepts to further enrich the evaluation process.
Self-Supervised Learning Based Target-Aware Session Recommendation Algorithm

Session-based recommender systems aim to predict user behavior in the next moment based on anonymous sessions. Existing approaches suffer from the limitations of sparse data while ignoring the rich diversity of information embedded within items, resulting in the inability to capture the complete user interest in the target session. In order to better model user interests, we propose to better model user interests, we proposes a self-supervised learning-based target-aware session recommendation algorithm (SSL-TA). On the one hand, the idea of self-supervised learning is introduced to alleviate data sparsity; on the other hand, aggregation operations are performed from multiple levels of user preference implicit relationships in the target session to achieve finer-grained user interest inference. The effectiveness of the proposed model is verified by outperforming other benchmark models in experiments on two public datasets.
Analyzing Embedding Models for Embedding Vectors in Vector Databases

Vector databases have emerged as the computation engine that enables us to successfully interact with vector embeddings in our applications as a result of the exponential rise of vector embeddings in disciplines like NLP (Natural Language Processing), computer vision, and other AI applications. In order to address the issues that arise when handling vector embeddings in production applications, vector databases were specifically created. Vector databases that offer quick and precise nearest-neighbor search, clustering, and similarity matching, and that are simple to deploy on cloud infrastructure or distributed computing systems, are more likely to be well-liked by users. They therefore provide a number of advantages over conventional scalar-based databases and independent vector indexes. This research reveals that embedding vectors are frequently utilized for analyzing and exploring unstructured data with the creation of learning-based embedding models. Completely managed and horizontally scalable vector databases are required as vector collections reach billion-scale numbers. The proposal relaxes the data model and consistency restrictions in exchange for the aforementioned benefits because the majority of vector data applications do not call for intricate data models and robust data consistency. VectorDB of Python has been used for implementation and test case which does faster similarity search.
Development of Extreme Learning Machine Method for Detecting Cadmium Concentration in Beche-De-Mere Using Hyperspectral Imaging Technology

Detecting heavy metal contamination, such as cadmium, in seafood, particularly beche-de-mer (BDM), is very important for food safety and human health. However, conventional methods for detecting cadmium are often invasive, time-consuming, labor-intensive, and very expensive. So in this research, these challenges were addressed by utilizing hyperspectral imaging (HSI) combined with an Extreme Learning Machine (ELM) algorithm to predict cadmium concentrations in beche-de-mer. This approach provides a non-invasive, rapid, cheaper, and precise alternative for assessing heavy metal contamination. We applied the ELM model to hyperspectral data acquired from six beche-de-mer specimens, which were purchased from local markets in Indonesia, to derive spectral signatures correlating with cadmium concentrations. Our findings reveal a significant correlation between the hyperspectral data and the measured cadmium concentrations, achieving a prediction accuracy of $R_{P}^{2}=0.95186$ and $RMSEP=3.7802$. The results demonstrate the effectiveness of integrating HSI with advanced machine learning algorithms such as ELM in addressing detection of heavy metal contamination in seafood, especially in BDM.
Induction Motor Inter-turn Short Circuit Fault Classification by Extracting Auto Features Using LSTM Neural Network

Inter Turn Short Circuit (ITSC) fault in stator winding of three phase induction motor (IM) is one of the most recurrent type of failures, which counts more than 30% of total electrical faults in machine. Thus, the investigation of ITSC fault detection in early stage is essential, since IMs are widely used in industries and household applications. The proposed work exploits the facility of deep learning-based approaches for ITSC fault detection in IMs using auto feature extraction enabled Long Short-Term Memory (LSTM) Neural network. Initially, three phase stator current envelop is extracted by Hilbert Transform and the envelop samples are used further for auto feature extraction. Along with, modified weighted batch normalization (BN) is used to improve the training capability of proposed method. MATLAB/ SIMULINK model with three different IM parameters along with three different loading conditions are used to train the proposed diagnostic system for eight different stator fault conditions. The method is also experimentally tested with 1 hp IM to check the accuracy of the diagnostic system. The performance of proposed method is compared with statistical feature extraction with ANN and from the result, it is observed that auto feature extraction using LSTM neural network is more accurate (up to 98%) than previous one.