OpenRaiser/Intern-Atlas huggingface dataset:
Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai
Venue: arXiv cs.HC 2023
Year: 2023

Farmers can now use IoT to improve farm efficiency and productivity by using sensors for farm monitoring to enhance decision-making in areas such as fertilization, irrigation, climate forecast, and harvesting information. Local farmers in Chiang Mai, Thailand, on the other hand, continue to lack knowledge and experience with smart farm technology. As a result, the 'SUNSpACe' project, funded by the European Union's Erasmus+ Program, was launched to launch a training course which improve the knowledge and performance of Thai farmers. To assess the effectiveness of the training, The Kirkpatrick model was used in this study. Eight local farmers took part in the training, which was divided into two sections: mobile learning and smart farm laboratory. During the training activities, different levels of the Kirkpatrick model were conducted and tested: reaction (satisfaction test), learning (knowledge test), and behavior (performance test). The overall result demonstrated the participants' positive reaction to the outcome. The paper also discusses the limitations and suggestions for training activities.
Developing A Personal Decision Support Tool for Hospital Capacity Assessment and Querying
Venue: arXiv cs.HC 2023
Year: 2023

This article showcases a personal decision support tool (PDST) called HOPLITE, for performing insightful and actionable quantitative assessments of hospital capacity, to support hospital planners and health care managers. The tool is user-friendly and intuitive, automates tasks, provides instant reporting, and is extensible. It has been developed as an Excel Visual Basic for Applications (VBA) due to its perceived ease of deployment, ease of use, Office's vast installed userbase, and extensive legacy in business. The methodology developed in this article bridges the gap between mathematical theory and practice, which our inference suggests, has restricted the uptake and or development of advanced hospital planning tools and software. To the best of our knowledge, no personal decision support tool (PDST) has yet been created and installed within any existing hospital IT systems, to perform the aforementioned tasks. This article demonstrates that the development of a PDST for hospitals is viable and that optimization methods can be embedded quite simply at no cost. The results of extensive development and testing indicate that HOPLITE can automate many nuanced tasks. Furthermore, there are few limitations and only minor scalability issues with the application of free to use optimization software. The functionality that HOPLITE provides may make it easier to calibrate hospitals strategically and/or tactically to demands. It may give hospitals more control over their case-mix and their resources, helping them to operate more proactively and more efficiently.
Combining feature aggregation and geometric similarity for re-identification of patterned animals
Venue: arXiv cs.CV 2023
Year: 2023

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks.
Mirror Diffusion Models
Venue: arXiv cs.LG 2023
Year: 2023

Diffusion models have successfully been applied to generative tasks in various continuous domains. However, applying diffusion to discrete categorical data remains a non-trivial task. Moreover, generation in continuous domains often requires clipping in practice, which motivates the need for a theoretical framework for adapting diffusion to constrained domains. Inspired by the mirror Langevin algorithm for the constrained sampling problem, in this theoretical report we propose Mirror Diffusion Models (MDMs). We demonstrate MDMs in the context of simplex diffusion and propose natural extensions to popular domains such as image and text generation.
A Brief Wellbeing Training Session Delivered by a Humanoid Social Robot: A Pilot Randomized Controlled Trial
Venue: arXiv cs.RO 2023
Year: 2023

Mental health and psychological distress are rising in adults, showing the importance of wellbeing promotion, support, and technique practice that is effective and accessible. Interactive social robots have been tested to deliver health programs but have not been explored to deliver wellbeing technique training in detail. A pilot randomised controlled trial was conducted to explore the feasibility of an autonomous humanoid social robot to deliver a brief mindful breathing technique to promote information around wellbeing. It contained two conditions: brief technique training (Technique) and control designed to represent a simple wait-list activity to represent a relationship-building discussion (Simple Rapport). This trial also explored willingness to discuss health-related topics with a robot. Recruitment uptake rate through convenience sampling was high (53%). A total of 230 participants took part (mean age = 29 years) with 71% being higher education students. There were moderate ratings of technique enjoyment, perceived usefulness, and likelihood to repeat the technique again. Interaction effects were found across measures with scores varying across gender and distress levels. Males with high distress and females with low distress who received the simple rapport activity reported greater comfort to discuss non-health topics than males with low distress and females with high distress. This trial marks a notable step towards the design and deployment of an autonomous wellbeing intervention to investigate the impact of a brief robot-delivered mindfulness training program for a sub-clinical population.
Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference
Venue: arXiv cs.IT 2023
Year: 2023

Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing ambient intelligent services. For enhancing the utilization of limited network resources in edge-device co-inference tasks from a systematic view, we propose a task-oriented scheme of integrated sensing, computation and communication (ISCC) in this work. In this system, all devices sense a target from the same wide view to obtain homogeneous noise-corrupted sensory data, from which the local feature vectors are extracted. All local feature vectors are aggregated at the server using over-the-air computation (AirComp) in a broadband channel with the orthogonal-frequency-division-multiplexing technique for suppressing the sensing and channel noise. The aggregated denoised global feature vector is further input to a server-side AI model for completing the downstream inference task. A novel task-oriented design criterion, called maximum minimum pair-wise discriminant gain, is adopted for classification tasks. It extends the distance of the closest class pair in the feature space, leading to a balanced and enhanced inference accuracy. Under this criterion, a problem of joint sensing power assignment, transmit precoding and receive beamforming is formulated. The challenge lies in three aspects: the coupling between sensing and AirComp, the joint optimization of all feature dimensions' AirComp aggregation over a broadband channel, and the complicated form of the maximum minimum pair-wise discriminant gain. To solve this problem, a task-oriented ISCC scheme with AirComp is proposed. Experiments based on a human motion recognition task are conducted to verify the advantages of the proposed scheme over the existing scheme and a baseline.
Evaluation of cinematic volume rendering open-source and commercial solutions for the exploration of congenital heart data
Venue: arXiv cs.GR 2023
Year: 2023

Detailed anatomical information is essential to optimize medical decisions for surgical and pre-operative planning in patients with congenital heart disease. The visualization techniques commonly used in clinical routine for the exploration of complex cardiac data are based on multi-planar reformations, maximum intensity projection, and volume rendering, which rely on basic lighting models prone to image distortion. On the other hand, cinematic rendering (CR), a three-dimensional visualization technique based on physically-based rendering methods, can create volumetric images with high fidelity. However, there are a lot of parameters involved in CR that affect the visualization results, thus being dependent on the user's experience and requiring detailed evaluation protocols to compare available solutions. In this study, we have analyzed the impact of the most relevant parameters in a CR pipeline developed in the open-source version of the MeVisLab framework for the visualization of the heart anatomy of three congenital patients and two adults from CT images. The resulting visualizations were compared to a commercial tool used in the clinics with a questionnaire filled in by clinical users, providing similar definitions of structures, depth perception, texture appearance, realism, and diagnostic ability.
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Venue: arXiv cs.LG 2023
Year: 2023

Transfer learning via fine-tuning pre-trained transformer models has gained significant success in delivering state-of-the-art results across various NLP tasks. In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. However, due to the limited communication, computation, and storage capabilities of edge devices and the huge sizes of popular transformer models, efficient fine-tuning is crucial to make federated training feasible. This work explores the opportunities and challenges associated with applying parameter efficient fine-tuning (PEFT) methods in different FL settings for language tasks. Specifically, our investigation reveals that as the data across users becomes more diverse, the gap between fully fine-tuning the model and employing PEFT methods widens. To bridge this performance gap, we propose a method called SLoRA, which overcomes the key limitations of LoRA in high heterogeneous data scenarios through a novel data-driven initialization technique. Our experimental results demonstrate that SLoRA achieves performance comparable to full fine-tuning, with significant sparse updates with approximately $\sim 1\%$ density while reducing training time by up to $90\%$.
Visualising category recoding and numeric redistributions
Venue: arXiv cs.HC 2023
Year: 2023

This paper proposes graphical representations of data and rationale provenance in workflows that convert both category labels and associated numeric data between distinct but semantically related taxonomies. We motivate the graphical representations with a new task abstraction, the cross-taxonomy transformation, and associated graph-based information structure, the crossmap. The task abstraction supports the separation of category recoding and numeric redistribution decisions from the specifics of data manipulation in ex-post data harmonisation. The crossmap structure is illustrated using an example conversion of numeric statistics from a country-specific taxonomy to an international classification standard. We discuss the opportunities and challenges of using visualisation to audit and communicate cross-taxonomy transformations and present candidate graphical representations.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Venue: arXiv cs.CL 2023
Year: 2023

Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multi-lingual raw data for data-based cross-lingual transfer. The model is trained with multi-lingual objectives on our proposed dataset OpenIE4++ by combing the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play language-specific modules and demonstrate the effectiveness of MT4CrossIE in cross-lingual OIE\footnote{\url{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}}.
An Approximation Algorithm for Ancestral Maximum-Likelihood and Phylogeography Inference Problems under Time Reversible Markov Evolutionary Models
Venue: arXiv cs.DS 2023
Year: 2023

The ancestral maximum-likelihood and phylogeography problems are two fundamental problems involving evolutionary studies. The ancestral maximum-likelihood problem involves identifying a rooted tree alongside internal node sequences that maximizes the probability of observing a given set of sequences as leaves. The phylogeography problem extends the ancestral maximum-likelihood problem to incorporate geolocation of leaf and internal nodes. While a constant factor approximation algorithm has been established for the ancestral maximum-likelihood problem concerning two-state sequences, no such algorithm has been devised for any generalized instances of the problem. In this paper, we focus on a generalization of the two-state model, the time reversible Markov evolutionary models for sequences and geolocations. Under this evolutionary model, we present a $2\log_2 k $-approximation algorithm, where $k$ is the number of input samples, addressing both the ancestral maximum-likelihood and phylogeography problems. This is the first approximation algorithm for the phylogeography problem. Furthermore, we show how to apply the algorithm on popular evolutionary models like generalized time-reversible (GTR) model and its specialization Jukes and Cantor 69 (JC69).
Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?
Venue: arXiv cs.LG 2023
Year: 2023

Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance. However, such a technique, despite being able to massively compress deep models, is hardly able to remove entire layers from a model (even when structured): is this an addressable task? In this study, we introduce EGP, an innovative Entropy Guided Pruning algorithm aimed at reducing the size of deep neural networks while preserving their performance. The key focus of EGP is to prioritize pruning connections in layers with low entropy, ultimately leading to their complete removal. Through extensive experiments conducted on popular models like ResNet-18 and Swin-T, our findings demonstrate that EGP effectively compresses deep neural networks while maintaining competitive performance levels. Our results not only shed light on the underlying mechanism behind the advantages of unstructured pruning, but also pave the way for further investigations into the intricate relationship between entropy, pruning techniques, and deep learning performance. The EGP algorithm and its insights hold great promise for advancing the field of network compression and optimization. The source code for EGP is released open-source.
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation
Venue: arXiv cs.LG 2023
Year: 2023

Graph-based diffusion models have shown promising results in terms of generating high-quality solutions to NP-complete (NPC) combinatorial optimization (CO) problems. However, those models are often inefficient in inference, due to the iterative evaluation nature of the denoising diffusion process. This paper proposes to use progressive distillation to speed up the inference by taking fewer steps (e.g., forecasting two steps ahead within a single step) during the denoising process. Our experimental results show that the progressively distilled model can perform inference 16 times faster with only 0.019% degradation in performance on the TSP-50 dataset.
Root Cross Z-Complementary Pairs with Large ZCZ Width
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we present a new family of cross $Z$-complementary pairs (CZCPs) based on generalized Boolean functions and two roots of unity. Our key idea is to consider an arbitrary partition of the set $\{1,2,\cdots, n\}$ with two subsets corresponding to two given roots of unity for which two truncated sequences of new alphabet size determined by the two roots of unity are obtained. We show that these two truncated sequences form a new $q$-ary CZCP with flexible sequence length and large zero-correlation zone width. Furthermore, we derive an enumeration formula by considering the Stirling number of the second kind for the partitions and show that the number of constructed CZCPs increases significantly compared to the existing works.
Two-Dimensional Z-Complementary Array Quads with Low Column Sequence PMEPRs
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we first propose a new design strategy of 2D $Z$-complementary array quads (2D-ZCAQs) with feasible array sizes. A 2D-ZCAQ consists of four distinct unimodular arrays satisfying zero 2D auto-correlation sums for non-trivial 2D time-shifts within certain zone. Then, we obtain the upper bounds on the column sequence peak-to-mean envelope power ratio (PMEPR) of the constructed 2D-ZCAQs by using specific auto-correlation properties of some seed sequences. The constructed 2D-ZCAQs with bounded column sequence PMEPR can be used as a potential alternative to 2D Golay complementary array sets for practical applications
Impact of Orientation on the Bias of SRAM-Based PUFs
Venue: arXiv cs.CR 2023
Year: 2023

This paper investigates the impact of memory orientation on the bias pattern of SRAM-based PUFs. We designed and fabricated a 65nm CMOS chip that contains eleven SRAM macros that exercise different memory- and chip-level parameters. At the memory level, several parameters passed to the SRAM compiler are considered, including the number of addresses, the number of words, the aspect ratio, and the chosen bitcell. Chip-level decisions are considered during the floorplan, including the location and rotation of each SRAM macro in the testchip. In this study, we conduct a comprehensive analysis of different memory orientations and their effect on the biasing direction. Physical measurements performed on 50 fabricated chips revealed that specific memory orientations, namely R270 and MY90, exhibit a distinct negative biasing direction compared to other orientations. Importantly, this biasing direction remains consistent regardless of memory type, column mux ratio, memory size, or the utilization of SRAMs with different bitcells. Overall, this study highlights the significance of careful physical implementation and memory orientation selection in designing SRAM-based PUFs. Our findings can guide designers in the selection of SRAM memories with properties that make for better PUFs that potentially require less error correction effort to compensate for instability.
Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining
Venue: arXiv cs.LG 2023
Year: 2023

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 patient's records, identified the most common signs and symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively.
BiHeartS: Bilateral Heart Rate from multiple devices and body positions for Sleep measurement Dataset
Venue: arXiv cs.HC 2023
Year: 2023

Sleep is the primary mean of recovery from accumulated fatigue and thus plays a crucial role in fostering people's mental and physical well-being. Sleep quality monitoring systems are often implemented using wearables that leverage their sensing capabilities to provide sleep behaviour insights and recommendations to users. Building models to estimate sleep quality from sensor data is a challenging task, due to the variability of both physiological data, perception of sleep quality, and the daily routine across users. This challenge gauges the need for a comprehensive dataset that includes information about the daily behaviour of users, physiological signals as well as the perceived sleep quality. In this paper, we try to narrow this gap by proposing Bilateral Heart rate from multiple devices and body positions for Sleep measurement (BiHeartS) dataset. The dataset is collected in the wild from 10 participants for 30 consecutive nights. Both research-grade and commercial wearable devices are included in the data collection campaign. Also, comprehensive self-reports are collected about the sleep quality and the daily routine.