OpenRaiser/Intern-Atlas huggingface dataset:
Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms
Venue: arXiv cs.CL 2023
Year: 2023

The ability to interpret spoken language is connected to natural language processing. It involves teaching the AI how words relate to one another, how they are meant to be used, and in what settings. The goal of natural language processing (NLP) is to get a machine intelligence to process words the same way a human brain does. This enables machine intelligence to interpret, arrange, and comprehend textual data by processing the natural language. The technology can comprehend what is communicated, whether it be through speech or writing because AI pro-cesses language more quickly than humans can. In the present study, five NLP algorithms, namely, Geneism, Sumy, Luhn, Latent Semantic Analysis (LSA), and Kull-back-Liebler (KL) al-gorithm, are implemented for the first time for the knowledge summarization purpose of the High Entropy Alloys (HEAs). The performance prediction of these algorithms is made by using the BLEU score and ROUGE score. The results showed that the Luhn algorithm has the highest accuracy score for the knowledge summarization tasks compared to the other used algorithms.
NLQxform: A Language Model-based Question to SPARQL Transformer
Venue: arXiv cs.CL 2023
Year: 2023

In recent years, scholarly data has grown dramatically in terms of both scale and complexity. It becomes increasingly challenging to retrieve information from scholarly knowledge graphs that include large-scale heterogeneous relationships, such as authorship, affiliation, and citation, between various types of entities, e.g., scholars, papers, and organizations. As part of the Scholarly QALD Challenge, this paper presents a question-answering (QA) system called NLQxform, which provides an easy-to-use natural language interface to facilitate accessing scholarly knowledge graphs. NLQxform allows users to express their complex query intentions in natural language questions. A transformer-based language model, i.e., BART, is employed to translate questions into standard SPARQL queries, which can be evaluated to retrieve the required information. According to the public leaderboard of the Scholarly QALD Challenge at ISWC 2023 (Task 1: DBLP-QUAD - Knowledge Graph Question Answering over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA task, demonstrating the competitiveness of the system.
Identification of Books That are Suitable for Middle School Students Using Artificial Neural Networks
Venue: arXiv cs.CL 2023
Year: 2023

Reading right books contributes to children's imagination and brain development, enhances their language and emotional comprehension abilities, and strengthens their relationships with others. Building upon the critical role of reading books in individual development, this paper aims to develop an algorithm that determines the suitability of books for middle school students by analyzing their structural and semantic features. Using methods described, an algorithm will be created that can be utilized by institutions and individuals responsible for children's education, such as the Ministry of National Education officials and schools. This algorithm will facilitate the selection of books to be taught at the middle school level. With the algorithm, the book selection process for the middle school curriculum can be expedited, and it will serve as a preliminary reference source for those who evaluate books by reading them. In this paper, the Python programming language was employed, utilizing natural language processing methods. Additionally, an artificial neural network (ANN) was trained using the data which had been preprocessed to construct an original dataset. To train this network, suitable books for middle school students were provided by the MEB, Oxford and Cambridge and with content assessed based on the "R" criterion, and inappropriate books for middle school students in terms of content were included. This trained neural network achieved a 90.06% consistency rate in determining the appropriateness of the test-provided books. Considering the obtained findings, it can be concluded that the developed software has achieved the desired objective.
CLAMP: A Contrastive Language And Molecule Pre-training Network
Venue: arXiv cs.CL 2023
Year: 2023

This paper highlights a shift in how to approach material generation. Instead of material-to-material, we propose a language-to-material generation architecture that utilizes millions of untapped data points. Using a web scraper to collect crystal text pairs from open-source research papers, a contrastive model can be trained using a convolutional graph neural network encoder and a language encoder. This would allow unsupervised zero-shot classification which can be trained by taking advantage of linguistic structure. Without any specific training data, an ~82\% accuracy was achieved and ~75\% accuracy for photocatalyst prediction with an extremely small dataset. This novel network could ideally be cross-applied to any reaction that can be described via text, opening completely new methods to think about 3D chemical framework generation. In the full experiment diffusion models would likely be incorporated to fully exploit the latent space.
Low-Cost Architecture for an Advanced Smart Shower System Using Internet of Things Platform
Venue: arXiv cs.HC 2023
Year: 2023

Wastage of water is a critical issue amongst the various global crises. This paper proposes an architecture model for a low-cost, energy efficient SMART Shower system that is ideal for efficient water management and be able to predict reliably any accidental fall in the shower space. The sensors in this prototype can document the surrounding temperature and humidity in real time and thereby circulate the ideal temperature of water for its patron, rather than its reliance on predictive values . Three different scenarios are discussed that can allow reliably predicting any accidental fall in the shower vicinity. Motion sensors, sound sensors and gesture sensors can be used to compliment prediction of possible injuries in the shower. The integration with the Internet of Things (IoT) platform will allow caretakers to monitor the activities in the shower space especially in the case of elderly individuals as there have been reported cases of casualties in the slippery shower space. The proposed proof-of-concept prototype is cost effective and can be incorporated into an existing system for the added precedence of safety and convenience. The intelligent system is conserving water by optimizing its flow temperature and the IoT platform allows real time monitoring for safety.
The Disagreement Problem in Faithfulness Metrics
Venue: arXiv cs.LG 2023
Year: 2023

The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-hoc feature attributions to any model architecture. While the pace of innovation around novel methods has slowed down, the question remains of how to choose a method, and how to make it fit for purpose. Recently, efforts around benchmarking XAI methods have suggested metrics for that purpose -- but there are many choices. That bounty of choice still leaves an end user unclear on how to proceed. This paper focuses on comparing metrics with the aim of measuring faithfulness of local explanations on tabular classification problems -- and shows that the current metrics don't agree; leaving users unsure how to choose the most faithful explanations.
Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain
Venue: arXiv cs.CV 2023
Year: 2023

Integrating information from multiple modalities is arguably one of the essential prerequisites for grounding artificial intelligence systems with an understanding of the real world. Recent advances in video transformers that jointly learn from vision, text, and sound over time have made some progress toward this goal, but the degree to which these models integrate information from modalities still remains unclear. In this work, we present a promising approach for probing a pre-trained multimodal video transformer model by leveraging neuroscientific evidence of multimodal information processing in the brain. Using brain recordings of participants watching a popular TV show, we analyze the effects of multi-modal connections and interactions in a pre-trained multi-modal video transformer on the alignment with uni- and multi-modal brain regions. We find evidence that vision enhances masked prediction performance during language processing, providing support that cross-modal representations in models can benefit individual modalities. However, we don't find evidence of brain-relevant information captured by the joint multi-modal transformer representations beyond that captured by all of the individual modalities. We finally show that the brain alignment of the pre-trained joint representation can be improved by fine-tuning using a task that requires vision-language inferences. Overall, our results paint an optimistic picture of the ability of multi-modal transformers to integrate vision and language in partially brain-relevant ways but also show that improving the brain alignment of these models may require new approaches.
Covering Codes, List-Decodable Codes and Strong Singleton-Like Bounds in the Sum-Rank Metric
Venue: arXiv cs.IT 2023
Year: 2023

Codes in the sum-rank metric have received many attentions in recent years, since they have wide applications in the multishot network coding, the space-time coding and the distributed storage. Fundamental bounds, some explicit or probabilistic constructions of sum-rank codes and their decoding algorithms have been developed in previous papers. In this paper, we construct covering codes in the sum-rank metric from covering codes in the Hamming metric. Then some upper bounds on sizes of covering codes in the sum-rank metric are presented. Block length functions of covering codes in the sum-rank metric are also introduced and studied. As applications of our upper bounds on covering codes in the sum-rank metric and block length functions, several strong Singleton-like bounds on sum-rank codes are proposed and proved. These strong Singleton-like bounds are much stronger than the Singleton-like bound for sum-rank codes, when block lengths are larger and minimum sum-rank distances are small. An upper bound on sizes of list-decodable codes in the sum-rank metric is also given, which leads to an asymptotic bound on list-decodability of sum-rank codes. We also give upper bounds on block lengths of general MSRD codes.
Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering
Venue: arXiv cs.LG 2023
Year: 2023

Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform incremental learning challenge its widespread application. To overcome these limitations, this paper introduces a novel approach called parametric spectral clustering (PSC). By extending the capabilities of spectral clustering, PSC addresses the challenges associated with big data and real-time scenarios and enables efficient incremental clustering with new data points. Experimental evaluations conducted on various open datasets demonstrate the superiority of PSC in terms of computational efficiency while achieving clustering quality mostly comparable to standard spectral clustering. The proposed approach has significant potential for incremental and real-time data analysis applications, facilitating timely and accurate clustering in dynamic and evolving datasets. The findings of this research contribute to the advancement of clustering techniques and open new avenues for efficient and effective data analysis. We publish the experimental code at https://github.com/109502518/PSC_BigData.
Replay Clocks
Venue: arXiv cs.DC 2023
Year: 2023

In this work, we focus on the problem of replay clocks (RepCL). The need for replay clocks arises from the observation that analyzing distributed computation for all desired properties of interest may not be feasible in an online environment. These properties can be analyzed by replaying the computation. However, to be beneficial, such replay must account for all the uncertainty that is possible in a distributed computation. Specifically, if event 'e' must occur before 'f' then the replay clock must ensure that 'e' is replayed before 'f'. On the other hand, if 'e' and 'f' could occur in any order then replay should not force an order between them. After identifying the limitations of existing clocks to provide the replay primitive, we present RepCL and identify an efficient representation for the same. We demonstrate that RepCL can be implemented with less than four integers for 64 processes for various system parameters if clocks are synchronized within 1 ms. Furthermore, the overhead of RepCL (for computing/comparing timestamps and message size) is proportional to the size of the clock. Using simulations, we identify the expected overhead of RepCL based on the given system settings. We also identify how a user can the identify feasibility region for RepCL. Specifically, given the desired overhead of RepCL, it identifies the region where unabridged replay is possible.
On the IRS Deployment in Smart Factories Considering Blockage Effects: Collocated or Distributed?
Venue: arXiv cs.IT 2023
Year: 2023

In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LOS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment.
Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography
Venue: arXiv cs.LG 2023
Year: 2023

Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine, essential for identifying various sleep disorders. By capturing physiological signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a patient's sleep architecture. However, its dependency on complex equipment and expertise confines its use to specialized clinical settings. Addressing these limitations, our study aims to perform PSG by developing a system that requires only a single EEG measurement. We propose a novel system capable of reconstructing multi-signal PSG from a single-channel EEG based on a masked autoencoder. The masked autoencoder was trained and evaluated using the Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the similarity between original and reconstructed signals. The model demonstrated proficiency in reconstructing multi-signal data. Our results present promise for the development of more accessible and long-term sleep monitoring systems. This suggests the expansion of PSG's applicability, enabling its use beyond the confines of clinics.
Dual-channel Prototype Network for few-shot Classification of Pathological Images
Venue: arXiv cs.CV 2023
Year: 2023

In pathology, the rarity of certain diseases and the complexity in annotating pathological images significantly hinder the creation of extensive, high-quality datasets. This limitation impedes the progress of deep learning-assisted diagnostic systems in pathology. Consequently, it becomes imperative to devise a technology that can discern new disease categories from a minimal number of annotated examples. Such a technology would substantially advance deep learning models for rare diseases. Addressing this need, we introduce the Dual-channel Prototype Network (DCPN), rooted in the few-shot learning paradigm, to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer (PVT) framework for few-shot classification via self-supervised learning and integrates it with convolutional neural networks. This combination forms a dual-channel architecture that extracts multi-scale, highly precise pathological features. The approach enhances the versatility of prototype representations and elevates the efficacy of prototype networks in few-shot pathological image classification tasks. We evaluated DCPN using three publicly available pathological datasets, configuring small-sample classification tasks that mirror varying degrees of clinical scenario domain shifts. Our experimental findings robustly affirm DCPN's superiority in few-shot pathological image classification, particularly in tasks within the same domain, where it achieves the benchmarks of supervised learning.
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
Venue: arXiv cs.CV 2023
Year: 2023

Vehicle anomaly detection plays a vital role in highway safety applications such as accident prevention, rapid response, traffic flow optimization, and work zone safety. With the surge of the Internet of Things (IoT) in recent years, there has arisen a pressing demand for Artificial Intelligence (AI) based anomaly detection methods designed to meet the requirements of IoT devices. Catering to this futuristic vision, we introduce a lightweight approach to vehicle anomaly detection by utilizing the power of trajectory prediction. Our proposed design identifies vehicles deviating from expected paths, indicating highway risks from different camera-viewing angles from real-world highway datasets. On top of that, we present VegaEdge - a sophisticated AI confluence designed for real-time security and surveillance applications in modern highway settings through edge-centric IoT-embedded platforms equipped with our anomaly detection approach. Extensive testing across multiple platforms and traffic scenarios showcases the versatility and effectiveness of VegaEdge. This work also presents the Carolinas Anomaly Dataset (CAD), to bridge the existing gap in datasets tailored for highway anomalies. In real-world scenarios, our anomaly detection approach achieves an AUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform, processes 738 trajectories per second in a typical highway setting. The dataset is available at https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set .
RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations
Venue: arXiv cs.RO 2023
Year: 2023

Slip and crumple detection is essential for performing robust manipulation tasks with a robotic hand (RH) like remote surgery. It has been one of the challenging problems in the robotics manipulation community. In this work, we propose a technique based on machine learning (ML) based techniques to detect the slip, and crumple as well as the shape of an object that is currently held in the robotic hand. We proposed ML model will detect the slip, crumple, and shape using the force/torque exerted and the angular positions of the actuators present in the RH. The proposed model would be integrated into the loop of a robotic hand(RH) and haptic glove(HG). This would help us to reduce the latency in case of teleoperation
Instruction-Following Evaluation for Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eval
Iterative missing value imputation based on feature importance
Venue: arXiv cs.LG 2023
Year: 2023

Many datasets suffer from missing values due to various reasons,which not only increases the processing difficulty of related tasks but also reduces the accuracy of classification. To address this problem, the mainstream approach is to use missing value imputation to complete the dataset. Existing imputation methods estimate the missing parts based on the observed values in the original feature space, and they treat all features as equally important during data completion, while in fact different features have different importance. Therefore, we have designed an imputation method that considers feature importance. This algorithm iteratively performs matrix completion and feature importance learning, and specifically, matrix completion is based on a filling loss that incorporates feature importance. Our experimental analysis involves three types of datasets: synthetic datasets with different noisy features and missing values, real-world datasets with artificially generated missing values, and real-world datasets originally containing missing values. The results on these datasets consistently show that the proposed method outperforms the existing five imputation algorithms.To the best of our knowledge, this is the first work that considers feature importance in the imputation model.
Contrastive Learning for Multi-Object Tracking with Transformers
Venue: arXiv cs.CV 2023
Year: 2023

The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Previous works typically add expensive modules to DETR to perform Multi-Object Tracking (MOT), resulting in more complicated architectures. We instead show how DETR can be turned into a MOT model by employing an instance-level contrastive loss, a revised sampling strategy and a lightweight assignment method. Our training scheme learns object appearances while preserving detection capabilities and with little overhead. Its performance surpasses the previous state-of-the-art by +2.6 mMOTA on the challenging BDD100K dataset and is comparable to existing transformer-based methods on the MOT17 dataset.