arXiv Computer Science @arxiv_cs@qoto.org

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called UMAP is used. This method manages to reduce the dimensions while preserving part of the local and global information of the original data. K-Means is used as the clustering algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF statistics, Topic Diversity, and Topic Coherence to get the meaning of the words on the clusters. The results of the process show good values, so the topic modeling of this process is a viable option for classifying or clustering texts without labels.

Sarcasm, as defined by Merriam-Webster, is the use of words by someone who means the opposite of what he is trying to say. In the field of sentimental analysis of Natural Language Processing, the ability to correctly identify sarcasm is necessary for understanding people's true opinions. Because the use of sarcasm is often context-based, previous research has used language representation models, such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), to identify sarcasm with contextual-based information. Recent innovations in NLP have provided more possibilities for detecting sarcasm. In BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al. (2018) introduced a new language representation model and demonstrated higher precision in interpreting contextualized language. As proposed by Hazarika et al. (2018), CASCADE is a context-driven model that produces good results for detecting sarcasm. This study analyzes a Reddit corpus using these two state-of-the-art models and evaluates their performance against baseline models to find the ideal approach to sarcasm detection.

This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditional machine learning and deep neural networks for predictions. However, both approaches have their limitations. Traditional machine learning often overlooks the associations between words, while deep neural networks, despite their better classification performance, come with increased training complexity and time. This paper proposes a method utilizing the bag-of-words model approach based on the GloVe model and the CNN-BiLSTM network. The principle is to use the word vector matrix trained by the GloVe model as the input for the text embedding layer. Given that the GloVe model requires no further training, the neural network model can be trained more efficiently. The method achieves an accuracy rate of 87.26% on the test set and an F1 score of 0.8737, showcasing promising results.

Exemplar based accounts are often considered to be in direct opposition to pure linguistic abstraction in explaining language learners' ability to generalize to novel expressions. However, the recent success of neural network language models on linguistically sensitive tasks suggests that perhaps abstractions can arise via the encoding of exemplars. We provide empirical evidence for this claim by adapting an existing experiment that studies how an LM (BERT) generalizes the usage of novel tokens that belong to lexical categories such as Noun/Verb/Adjective/Adverb from exposure to only a single instance of their usage. We analyze the representational behavior of the novel tokens in these experiments, and find that BERT's capacity to generalize to unseen expressions involving the use of these novel tokens constitutes the movement of novel token representations towards regions of known category exemplars in two-dimensional space. Our results suggest that learners' encoding of exemplars can indeed give rise to abstraction like behavior.

Protecting the anonymity of authors has become a difficult task given the rise of automated authorship attributors. These attributors are capable of attributing the author of a text amongst a pool of authors with great accuracy. In order to counter the rise of these automated attributors, there has also been a rise of automated obfuscators. These obfuscators are capable of taking some text, perturbing the text in some manner, and, if successful, deceive an automated attributor in misattributing the wrong author. We devised three novel authorship obfuscation methods that utilized a Psycho-linguistic theory known as Uniform Information Density (UID) theory. This theory states that humans evenly distribute information amongst speech or text so as to maximize efficiency. Utilizing this theory in our three obfuscation methods, we attempted to see how successfully we could deceive two separate attributors. Obfuscating 50 human and 50 GPT-3 generated articles from the TuringBench dataset, we observed how well each method did on deceiving the attributors. While the quality of the obfuscation in terms of semantic preservation and sensical changes was high, we were not able to find any evidence to indicate UID was a viable guiding metric for obfuscation. However, due to restrictions in time we were unable to test a large enough sample of article or tune the parameters for our attributors to comment conclusively on UID in obfuscation.

Neural Machine Translation (NMT) models, though state-of-the-art for translation, often reflect social biases, particularly gender bias. Existing evaluation benchmarks primarily focus on English as the source language of translation. For source languages other than English, studies often employ gender-neutral sentences for bias evaluation, whereas real-world sentences frequently contain gender information in different forms. Therefore, it makes more sense to evaluate for bias using such source sentences to determine if NMT models can discern gender from the grammatical gender cues rather than relying on biased associations. To illustrate this, we create two gender-specific sentence sets in Hindi to automatically evaluate gender bias in various Hindi-English (HI-EN) NMT systems. We emphasise the significance of tailoring bias evaluation test sets to account for grammatical gender markers in the source language.

Sentiment analysis of social media data is an emerging field with vast applications in various domains. In this study, we developed a sentiment analysis model to analyze social media sentiment, especially tweets, during global conflicting scenarios. To establish our research experiment, we identified a recent global dispute incident on Twitter and collected around 31,000 filtered Tweets for several months to analyze human sentiment worldwide.

Recent graph-based models for multi-intent SLU have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existing methods (1) only model the unidirectional guidance from intent to slot, while there are bidirectional inter-correlations between intent and slot; (2) adopt homogeneous graphs to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance. In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks. In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances. Specifically, we propose two heterogeneous graph attention networks working on the proposed two heterogeneous semantics label graphs, which effectively represent the relations among the semantics nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which exploits the single-task and dual-task semantics contrastive relations. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning, which considers the two tasks' mutual guidances in the contrastive learning procedure. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin, obtaining a relative improvement of 21.3% over the previous best model on MixATIS dataset in overall accuracy. We also evaluate our model on the zero-shot cross-lingual scenario and the results show that our model can relatively improve the state-of-the-art model by 33.5% on average in terms of overall accuracy for the total 9 languages.

The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI and law have found applications in various domains, including image recognition, automatic text generation, and interactive chat. With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry. However, the application of legal large language models (LLMs) is still in its nascent stage. Several challenges need to be addressed. In this paper, we aim to provide a comprehensive survey of legal LLMs. We not only conduct an extensive survey of LLMs, but also expose their applications in the judicial system. We first provide an overview of AI technologies in the legal field and showcase the recent research in LLMs. Then, we discuss the practical implementation presented by legal LLMs, such as providing legal advice to users and assisting judges during trials. In addition, we explore the limitations of legal LLMs, including data, algorithms, and judicial practice. Finally, we summarize practical recommendations and propose future development directions to address these challenges.

This research paper presents a comprehensive evaluation of the performance of three artificial 10 intelligence chatbots: Bing, ChatGPT, and GPT-4, in addressing standardized test questions. Graduate record examination, known as GRE, serves as a case study in this paper, encompassing both quantitative reasoning and verbal skills. A total of 137 quantitative reasoning questions, featuring diverse styles and 157 verbal questions categorized into varying levels of difficulty (easy, medium, and hard) were administered to assess the chatbots' capabilities. This paper provides a detailed examination of the results and their implications for the utilization of artificial intelligence in standardized test preparation by presenting the performance of each chatbot across various skills and styles tested in the exam. Additionally, this paper explores the proficiency of artificial intelligence in addressing image-based questions and illustrates the uncertainty level of each chatbot. The results reveal varying degrees of success across the chatbots, demonstrating the influence of model sophistication and training data. GPT-4 emerged as the most proficient, especially in complex language understanding tasks, highlighting the evolution of artificial intelligence in language comprehension and its ability to pass the exam with a high score.

The latest advances in artificial intelligence (AI) present many unprecedented opportunities to achieve much improved bandwidth saving in communications. Unlike conventional communication systems focusing on packet transport, rich datasets and AI makes it possible to efficiently transfer only the information most critical to the goals of message recipients. One of the most exciting advances in generative AI known as diffusion model presents a unique opportunity for designing ultra-fast communication systems well beyond language-based messages. This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework. To better control the regenerated message at the receiver output, our diffusion system design includes a local regeneration module with finite dimensional noise latent. The critical significance of noise latent control and sharing residing on our Diff-GO is the ability to introduce the concept of "local generative feedback" (Local-GF), which enables the transmitter to monitor the quality and gauge the quality or accuracy of the message recovery at the semantic system receiver. To this end, we propose a new low-dimensional noise space for the training of diffusion models, which significantly reduces the communication overhead and achieves satisfactory message recovery performance. Our experimental results demonstrate that the proposed noise space and the diffusion-based generative model achieve ultra-high spectrum efficiency and accurate recovery of transmitted image signals. By trading off computation for bandwidth efficiency (C4BE), this new framework provides an important avenue to achieve exceptional computation-bandwidth tradeoff.

We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object's 6D pose and camera's focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods.

The subgraph isomorphism finding problem is a well-studied problem in the field of computer science and graph theory, and it aims to enumerate all instances of a query graph in the respective data graph. In this paper, we propose an efficient method, SubISO, to find subgraph isomorphisms using an objective function, which exploits some isomorphic invariants and eccentricity of the query graph's vertices. The proposed objective function is used to determine pivot vertex, which minimizes both number and size of the candidate regions in the data graph. SubISO also limits the maximum recursive calls of the generic SubgraphSearch function to deal with straggler queries for which most of the existing algorithms show exponential behaviour. The proposed approach is evaluated over three benchmark datasets. It is also compared with three well known subgraph isomorphism finding algorithms in terms of execution time, number of identified embeddings, and ability to deal with the straggler queries, and it performs significantly better.

There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous computing devices. Thus, an important challenge is addressing the holistic environmental impacts of these next-generation computing systems. To accomplish this, a life-cycle view of sustainability for computing advancements is necessary to reduce environmental impacts such as greenhouse warming gas emissions from these computing choices. Unfortunately, decadal efforts to address operational energy efficiency in computing devices have ignored and in some cases exacerbated embodied impacts from manufacturing these edge and cloud systems, particularly their integrated circuits. During this time FPGA architectures have not changed dramatically except to increase in size. Given this context, we propose REFRESH FPGAs to build new FPGA devices and architectures from recently retired FPGA dies using 2.5D integration. To build REFRESH FPGAs requires creative architectures that leverage existing chiplet pins with an inexpensive to-manufacture interposer coupled with creative design automation. In this paper, we discuss how REFRESH FPGAs can leverage industry trends for renewable energy integration into data centers while providing an overall improvement for sustainability and amortizing their significant embodied cost investment over a much longer ``first'' lifetime.

This research focuses on the critical process of upgrading a Design System from Web Content Accessibility Guidelines (WCAG) 2.0 to WCAG 2.1, which is an essential step in enhancing web accessibility. It emphasizes the importance of staying up to date on increasing accessibility requirements, as well as the critical function of Design Systems in supporting inclusion in digital environments. The article lays out a complete strategy for meeting WCAG 2.1 compliance. Assessment, strategic planning, implementation, and testing are all part of this strategy. The need for collaboration and user involvement is emphasized as critical strategies and best practices for a successful migration journey. In addition, the article digs into migration barriers and discusses significant lessons acquired, offering a realistic view of the intricacies of this transforming road. Finally, it is a practical guide and a necessary resource for organizations committed to accessible and user-centered design. The document provides them with the knowledge and resources they need to navigate the changing world of web accessibility properly.

Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also becoming increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90\% of vulnerabilities emerged from misuse cases and human errors. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate misuse cases while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring schemas to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native micro-services of authentication, encryption, logging, and authorizations. Second, creating a bond trust scoring to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model to generate the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent to create a scoring system. The advantage of this method is that it is applicable to any language and adapts to all attributes as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting misuse cases.

Advances in gene sequencing have enabled in silico analyses of microbial genomes and have led to the revision of concepts of microbial taxonomy and evolution. We explore deficiencies in existing multiple sequence global alignment algorithms and introduce a new indexing scheme to partition the dynamic programming algorithm hypercube scoring tensor over processors based on the dependency between partitions to be scored in parallel. The performance of algorithms is compared in the study of rpoB gene sequences of Mycoplasma species.

In the world reference context, though mixed reality have been an emerging methodology for several years, only today technological and scientific advances have made them suitable to revolutionize in the industry and among people's daily life through the provision of enhanced functionalities and improved services. This systematic review provides the state-of-the-art applications and utilities of the Microsoft HoloLens 2 by reviewing the related papers during 2022-2023. Focusing on the potential that this technology has in providing digitally supported simulations and other utilities, highlighting the potential and limitations of the HoloLens 2-based innovative solutions and also bringing focus to emerging research topics, such as telemedicine, remote control and optimization of direct volume rendering.

Recently, Gavazzo has developed a relational theory of symbolic manipulation, that allows to study syntax-based rewriting systems without relying on specific notions of syntax. This theory was obtained by extending the algebra of relations with syntax-inspired operators. Within the algebras thus obtained, it is possible to encode notions of parallel and full reduction for first-order rewriting systems, as well as to prove nontrivial properties about them in an algebraic and syntax-independent fashion. Sequential reduction, however, was not explored, but it was conjectured that it could be studied through a differential relational theory of rewriting. This manuscript proves the above conjecture by defining differential algebras of term relations, viz. algebras of term relations extended with novel operators inspired by the theory of functor derivatives. We give a set of axioms and rules for such operators and show that the resulting theory is expressive enough to define notions of parallel, full, and sequential reduction. We prove fundamental results relating all these notions in a purely algebraic and syntax-independent way, and showcase the effectiveness of our theory by proving the soundness of a proof technique for weak confluence akin to the so-called Critical Pair Lemma.

The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. The geometry of ice shelves, and hence their buttressing strength, is determined by ice flow as well as by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions. Contemporary methods resolve one of these rates, but typically not both. Moreover, there is little information of how they changed in time. We present a new method to simultaneously infer the surface accumulation and basal melt rates averaged over decadal and centennial timescales. We infer the spatial dependence of these rates along flow line transects using internal stratigraphy observed by radars, using a kinematic forward model of internal stratigraphy. We solve the inverse problem using simulation-based inference (SBI). SBI performs Bayesian inference by training neural networks on simulations of the forward model to approximate the posterior distribution, allowing us to also quantify uncertainties over the inferred parameters. We demonstrate the validity of our method on a synthetic example, and apply it to Ekström Ice Shelf, Antarctica, for which newly acquired radar measurements are available. We obtain posterior distributions of surface accumulation and basal melt averaging over 42, 84, 146, and 188 years before 2022. Our results suggest stable atmospheric and oceanographic conditions over this period in this catchment of Antarctica. Use of observed internal stratigraphy can separate the effects of surface accumulation and basal melt, allowing them to be interpreted in a historical context of the last centuries and beyond.