Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning based mobile application for varietal identification and ripeness grading of traditional Indian banana varieties

India is the largest producer of bananas, con-tributing to 1/5th of the world production. Traditional Indian banana varieties have their own health benefits and consumer preferences. Grading of ripening stages is essential for quality check during handling and export as well as domestic market consumer acceptance. The proposed work is aimed to develop a smart phone based mobile application capable of identification of various traditional Indian banana varieties along with the grading of its ripening stages. Image processing is the better choice for identification of banana varieties and the determination of colour dependent ripening stages. Combining multiple aspects of deep learning inclusive of Convolution neural network (CNN) and eXtreme Gradient Boosting (XGboost) algorithm (CNN-XGBoost), a varietal identification and ripeness grading model is developed. Images of the banana fruits are fed to the network, where the CNN acts as the trainable feature extractor of the images and XGboost in the last layer of the CNN acts as the identifier of variety and ripening stage. The identification accuracy of the proposed model is 95 % which is higher than other techniques such as Gaussian Naive Bayes classifier (66 %), support vector classifier (83.5 %) and k-nearest neighbourhood algorithm (90 %). The developed model is deployed into smart phone based mobile application to facilitate non-invasive varietal identification of banana fruits. The developed app is capable to identify various unique Indian traditional banana varieties and could provide detailed insights on the ripening stages. The computational complexity of the developed model is also lesser which reduces the computational burden of the mobile application. The developed mobile application could be of great help to the consumers to decide upon the right variety and the optimal stage of ripening to be consumed for their dietary requirement.
Object Detection for military restricted area monitoring

Unprecedented numbers of refugees and immigrant both legal and illegal are crossing international borders, placing pressure on the political and security capacities of numerous countries. Even though this issue has been costly and polarizing, upper-tier countries were previously able to manage it, even though it took a generation or two to fully assimilate the new demographic segments. Standard human and canine operations, as well as legitimate authorized crossings at borders, shipping ports, and airlines, and coastal marine patrols, are no longer sufficient to deal with the problem like unrestricted entry of trespasser that may cause harm to the nation.
Stress Monitoring in Humans using Biomedical Signal Analysis

Nowadays, stress is one of the major issues in every individual's life and may cause both physiological and psychological problems. Researchers have taken this into account and proposed various stress detection models, which can be measured by using biomedical signals like Electrocardiogram (ECG), Electromyogram (EMG), Electroencephalogram (EEG) and Electroneurogram (ENG). EEG signals are capable of measuring and monitoring neurological electrical activity and detecting changes in the brain. The EEG data is gathered from PhysioNet and pre-processed by filtering out noise using a fourth-order Butterworth filter and decomposing it into different frequency bands (y (>30Hz), β (13-30Hz), $a$ (S-13Hz), θ (4-8Hz), and δ (0.5-4Hz)) using Discrete Wavelet Transform (DWT) from the fifth-order Daubechies wavelet family. A variety of EEG signal characteristics were then extracted to examine EEG signal patterns under different circumstances. Support Vector Machine (SVM) classifier is used in the study to classify the results and aims to improve accuracy and efficiency of stress detection compared to existing models.
GLOBAL CLASSICAL SOLUTIONS TO QUASILINEAR HYPERBOLIC SYSTEMS WITH WEAK LINEAR DEGENERACY

Consider the following Cauchy problem for the first order quasilinear strictly hyperbolic system We let The main result of this paper is that under the assumption that the system is weakly linearly degenerated, there exists a positive constant e independent of M, such that the above Cauchy problem admits a unique global C1 solution u=u(t,x) for all t∈R, provided that
MetaMapper: Interpretable Metaphor Detection

Identifying metaphors has long been a key interest in the natural language processing (NLP) community. However, most prior research on metaphor detection has focused on token-level binary classification without specifying the metaphor’s source and target domains, which makes the results less interpretable. Our project aims to address this limitation by employing a dual-branch model based on the cutting-edge MelBERT transformer architecture. We adapted MelBERT’s transformer-based, token-level classification framework to a unique domain annotation dataset. Our findings reveal that while the token-level approach is effective in determining the target domain of a metaphor, it struggles to identify the source domain. These results provide fresh insights into the limitations of current metaphor detection techniques and suggest possible avenues for enhancement in the field of metaphor detection research.
Computer Science Technical Report Formalizing Ladder Logic Programs and Timing Charts for Fault Impact Analysis and Verification of Fault Tolerance

This paper presents a novel approach for modeling, automated analysis and verification of fault tolerance in Ladder Logic (LL) programs for Programmable Logic Controllers (PLCs). The goal is to provide a framework for control engineers where they can verify LL programs in the absence and in the presence of faults in a simple-to-learn and effective formal language. To enable formal modeling of faults and fault tolerance requirements, we first devise a method for transforming LL programs and timing charts to formal specifications respectively in Promela and Linear Temporal Logic (LTL). Using such a formalization, engineers can generate Promela code from LL programs and can translate the requirements of timing charts to LTL expressions. We characterize two types of requirements for LL programs, namely intracycle and intercycle requirements. As a result, engineers can simulate LL programs in the SPIN model checker and verify them for intra-intercycle requirements. We also present a method for modeling faults and analyzing the impact of faults inside each scan cycle and across several scan cycles. We then characterize the novel notions of intracycle and intercycle fault tolerance for LL programs. We demonstrate the proposed method in the context of an industrial Carriage Line system.
A DRL-based Adaptive Cruise Control model for safe and efficient car following

Health and Safety is the main concern for car companies in the modern world as they ensure to keep the passengers comfortable and safe while driving or during a collision by maintaining standards and requirements. A study conducted in 2012 reveals that fatal road traffic occurs every 50 seconds on average and there are 1.35 million deaths caused every year due to road accidents according to the WHO. The inability of drivers to control a vehicle at its friction limits is the main cause of accidents. As a result, this study aims to maintain a safe distance between two vehicles contributing to traffic safety, traffic capacity, and efficiency. The main objective of this research is to design, implement and evaluate Machine Learning based Adaptive Cruise Control (ACC) for collision avoidance of autonomous vehicles. The research aims to develop a scalable solution for Adaptive Cruise Control using Deep Reinforcement Learning in Traffic simulation. With the recent development of Autonomous vehicles, extensive research are made in Adaptive Cruise Control (ACC) technology. Autonomous Vehicles (AV) is an interesting topic that plays a lead role in future transportation and has potential benefits to society by reducing traffic congestion and reducing the number of fatalities. With Reinforcement Learning techniques traffic optimizations are done by learning optimal policy in order to solve decision-making problems without any human interactions. Deep Reinforcement learning has been used to optimize vehicle speed control and avoid collisions. Therefore, this thesis is focused on using the Deep Reinforcement Learning method with Double Deep Q Learning (DDQN) by training two agents with different scenarios. The CARLA traffic simulator is used for these simulations where key metrics such as environment, number of agents, number of collisions, etc are initialized and detected. Throughout the simulation, three sets of actions are analyzed and tested. Double Deep Q Learning (DDQN) Method is implemented to
L OKI : Reusing Custom Concepts in Interactive Analytic Workflows

Natural language (NL) interaction enables users to be expressive with their queries when exploring data. Users often specify complex NL queries that involve a combination of grouping, aggregations, and conditionals of data attributes and values. Such queries are often reused several times by users during their analytical workflows. Existing systems offer limited support to save these bespoke queries as concepts that can be referenced in subsequent NL queries, leading to users having to respecify these queries repeatedly. To address this issue, we describe a system, L OKI that allows users to save complex and bespoke queries as reusable concepts and use these concepts in other NL queries and analytics tools. For example, users can save an NL query, “show me the opportunity amount by customer for open opportunities” in a sales dataset, as a concept ‘followup customers’ and reference this custom concept in a query such as “show me the total opportunity amount for followup customers. ” A qualitative evaluation of L OKI indicates the usefulness of supporting the reuse of custom concepts across various analytical workflows. We identify future research directions around in-situ semantic enrichment and dynamic concept maps for data exploration.
Visual linkage and interactive features of Evidente for an enhanced analysis of SNP-based phylogenies

Phylogenetic trees of a set of bacterial strains are often used to analyze their evolutionary relationships and they are commonly based on genomic features, such as single nucleotide polymorphisms (SNPs). Evidente - a recently published tool - provides visual and analytical linkage across a phylogenetic tree, SNP data and metadata, and integrates them into one interactive visual analytics platform. In contrast to other approaches, Evidente shows how SNPs agree with the tree structure. Evidente is part of the TueVis server ( https://evidente-tuevis.cs.uni-tuebingen.de/ ). Here, we give an overview of the tasks supported by Evidente . The version of Evidente described in the publication can seamlessly
Evolutionary Optimization of Neural-Network Models of Human Behavior

Neural network models are essential tools in understanding how behavior arises from information processing in the brain. Recent advances in computing power and neural network algorithms have made more complex models possible, increasing their explanatory power. However, it is difﬁcult to make such models work: they have many conﬁguration parameters that have to be set right for the model to work properly. Consequently, automated methods are needed to optimize them. This paper proposed an evolutionary approach to this problem. An Age-Layered Evolutionary Algorithm is introduced and evaluated by ﬁtting training parameters for BiLex, a self-organizing map model of lexical access in bilinguals. The resulting con-ﬁgurations are highly optimized and able to generalize to previously unseen human data, showing that evolutionary optimization of complex models has the potential to play an integral role in cognitive modeling in the future.
Hear We Are: Spatial Audio Benefits Perceptions of Turn-Taking and Social Presence in Video Meetings

Relative to in-person meetings, conversations in video meetings have long been reported as stilted. Spatial audio in video meetings can simulate the way we hear the world by separating audio streams based on speakers’ virtual locations. We report on a within-subject experiment in which 75 employees of a global technology company completed two group survival tasks with spatial audio enabled or disabled. Spatial audio increased perceptions of interactivity, shared space, and ease of understanding. Women experienced effects for social presence while men experienced effects for turn-taking. We discuss implications for inclusion, task performance, fatigue, and future research.
Divergent Features of the Tropopause Aerosol Layer: Effects of Monsoon Dynamics and Pollution Emissions in Asia, South America, and Africa

The tropopause aerosol layer (TAL) represents the increase of aerosols in tropopause. It was first discovered over Asia but was found in this study to also occur over South America and Africa owing to the combined effects of monsoon dynamics and pollutant emissions. Over Asia, the TAL has the highest altitude and widest spread due to strong deep convection and the upper troposphere anticyclonic system there. TAL intensity is highest in South America due to heavy pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TAL centers, whereas widespread wildfires result in single centers over South America and Africa. TAL radiative forcing induced by carbonaceous aerosols at the top of the atmosphere has warming effects over Asia (+0.21 W m-2), whereas cooling effects occur over South America (-0.47 W m-2) and Africa (-0.12 W m-2) owing to the divergent strengths of black-carbon absorption and organic-carbon scattering.
Comparing Costs for Cloud-based Data Archives

Research data management is an expensive enterprise. Computing infrastructure for storing, retrieving, and preserving data is one area of expenses, and computing infrastructure costs grow as the size and number of datasets and demands for their retrieval grow. This paper compares the costs and performance of two database infrastructures, PostgreSQL and Elasticsearch, for digital data archives. We used benchmarking experiments and data from social media to estimate the costs of loading, indexing
A Review: Artificial Intelligence in Medical Field

: Artificial intelligence has two phases. “will it be beneficial or dangerous for a human being? In this way we can review the benefits of medical practice. It examines how artificial intelligence helps the medical field. how artificial intelligence can influence medical science. AI has also begun the introduction of drugs to improve patient care by speeding up processes and achieving greater accuracy, paving the way for the provision of better overall health care. Radiological imaging, pathology slides, and electronic medical records (EMR) medical records of patients, assisted by machine-assisted diagnostic and therapeutic procedures and enhanced physician skills. Here we describe the current state of AI in medicine, how it is used in different fields and the methods to come.
BERT Goes to College: Exploring additional pretraining and multitask fine-tuning strategies with minBERT

Though the depth of the BERT model is key to its computational power, BERT’s breadth of knowledge is not to be underestimated. Specifically, its ability to adapt to multiple downstream tasks calls for a thorough and expanded investigation into the possibilities of multitask fine-tuning, in which multiple tasks are optimized simultaneously after pretraining. Our objectives in this project were to implement the minBERT model and experiment with multitask fine-tuning paradigms to improve performance across three downstream tasks: sentiment analysis, paraphrase detection, and semantic textual similarity. In our research we compare a number of different strategies, including sequential training, basic multitask training, and two algorithms of our own design (weighted and stochastic). Additionally, we highlight techniques and training methods that seem crucial to accomplishing the provided tasks, including contrastive learning and additional pretraining. Ultimately, we find that multitask fine-tuning (including stochastic and weighted methods) provides a small improvement on other downstream fine-tuning methodologies and more balanced performance across tasks, though other facets of our implementation—like training on additional datasets and freezing weights—are promising areas for the perfection of multitask fine-tuning.
Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots

Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and view the output of these models as no more than randomised plagiarism, remix, or collage of the source data. On these grounds many have argued for stronger regulations on the deployment, use, and attribution of the output of these models. However, these issues are not new or unique to artificial intelligence. In this position paper, using examples from literary criticism, the history of art, and copyright law, I show how creativity and originality resist definition as a notatable or information-theoretic property of an object, and instead can be seen as the property of a process, an author, or a viewer. Further alternative views hold that all creative work is essentially reuse (mostly without attribution), or that randomness itself can be creative. I suggest that creativity is ultimately defined by communities of creators and receivers, and the deemed sources of creativity in a workflow often depend on which parts of the workflow can be automated. Using examples from recent studies of AI in creative knowledge work, I suggest that AI shifts knowledge work from material production to critical integration. This position paper aims to begin a conversation around a more nuanced approach to the problems of creativity and credit assignment for generative models, one which more fully recognises the importance of the creative and curatorial voice of the users of these models, and moves away from simpler notational or information-theoretic views.
Legal-SBERT: Creating a Sentence Tranformer for the Legal Domain and Generating Data

Legal texts are characterized by a variety of differences from everyday language to the extent that legal language can be classified as a sublanguage. One of the differences between legal and everyday texts is that, in legal contexts, semantically significant words are used with extremely specific meanings (which can be entirely different from the words’ everyday meanings). There have been some efforts to create BERT-based encoders that capture the nuanced meanings of legal words, but there exists no sentence transformer model specialized for the legal domain. We leverage an existing legal BERT model and the Sentence-BERT architecture in order to create a legal sentence transformer and name it Legal-SBERT. Furthermore, we demonstrate methods to finetune and test sentence transformers on legal-domain specific data, addressing the lack of legal data labeled suitably for sentence transormers, by generating data from a Large Language Model, adapting binary classification data, and adapting multi-class classification data. Our work indicates that the Legal-SBERT architecture presented leads to performance gains on legal NLP tasks of reasonable difficulty and furthers the discussion around when pretraining on legal corpora is useful for improving performance on legal NLP tasks.
Examining Misinformation via Search Directives

Web searches are a common mechanism by which people find information on the internet; however, the relationship between web searches and misinformation spread has generally been understudied. One way people may encounter misleading web search results is through social media posts called search directives, which encourage users to search for potentially dubious queries. For this project, we seek to develop 1) a classifier, which can identify whether or not any given post is a search directive, and 2) a query extractor, which, given a search directive, can extract the query being suggested. We collected a labelled dataset of 2,811 examples from across four social media platforms. We fine-tuned a pretrained BERT classifier on our dataset, and were able to identify search directives with 88% accuracy. We then fine-tuned HuggingFace’s T5 model to extract the queries from a set of search directives, reaching 74% accuracy. These models could be used in order to generate a large dataset of search directives, which then could be studied to better understand what sorts of web searches may spread misinformation.
Targeted Syntactic Evaluations with Pseudo-languages

In this paper, we propose a novel method called 001 Targeted Syntactic Evaluations with pseudo-002 languages , which can (i) test whether language 003 models capture linguistic phenomena without 004 relying on other superficial cues (e.g., lexical 005 information), (ii) control the factors out of in-006 terest (e.g., vocabulary size), and (iii) easily 007 test linguistic phenomena that only exist in few 008 languages without collecting corpora of those 009 languages. Specifically, we create four types 010 of pseudo-languages with the abstracted vocab-011 ulary of different sizes to control the effect of 012 lexical information and vocabulary size, and 013 with different levels of syntactic complexity: 014 (Adj) n NP , NP n VP n , Nested Dependency, 015 and Cross Serial Dependency. We evaluate 016 four different language models (LSTM, BiL-017 STM, Transformer Encoder, and Transformer 018 Decoder) on these pseudo-languages, using a 019 binary classification of strings based on their 020 grammaticality. Our result demonstrated that 021 the language models have successfully cap-022 tured the (Adj) n NP type phenomenon irre-023 spective of vocabulary size, while they failed 024 to capture the other phenomena as the vocabu-025 lary size increases. These results are not con-026 sistent with the previous findings that LSTM 027 or Transformer-based language models can 028 capture syntactic dependencies in natural lan-029 guages to some extent (Hu et al., 2020; Wilcox 030 et al., 2019; Warstadt et al., 2020), suggesting 031 that these language models may not necessarily 032 capture the rules behind these phenomena but 033 rather use some other superficial cues such as 034 co-occurrence or frequency. 035