Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrial Fibrillation Classification and Prediction Explanation using Transformer Neural Network

Deep learning models are ubiquitous for detecting cardiac abnormalities from Electrocardiogram (ECG) signals. Atrial Fibrillation (AFib) is one such abnormality that increases the risk of stroke, coronary mortality and is difficult to monitor in real-time due to its intermittent occurrence. This paper investigates Transformer Neural Network (TNN) for AFib detection and justifies the prediction by highlighting clinically relevant signal timestamps triggering the diagnosis. TNN captures local and global dependencies of ECG in a parameter-efficient way and achieves an average F1 score of 0.87 on the PhysioNet Computing in Cardiology Challenge 2017 database. For comparison, a baseline and attentive convolution neural network (ACNN), residual neural network are also investigated that achieve an F1 score of 0.76, 0.73, and 0.95, respectively, surpassing state-of-the-art methods. The effect of ECG segment length is also analyzed to precisely detect AFib rhythm in larger ECG segments. The ACNN and TNN explain the predictions by highlighting absent P-wave and Fibrillation waves in ECG, supporting the diagnosis. The visual explanations improve model assessment, increases transparency and the likelihood of model acceptance by the medical practitioners.
Research on Malware Detection System Using Artificial Intelligence

Malicious code is a program that accesses and destroys a system or performs malicious actions such as leaking information. Typically, viruses, Trojan horses, and worms attack the system by hiding malicious code through the same path as e-mail. As modern technology develops, the technology of malicious code is also becoming more sophisticated and diversified. Many malware creators distribute malware for the purpose of gaining financial gain. In order to achieve its purpose, it is necessary to produce the maximum effect with minimum cost and effort, so rather than elaborately produced, malicious programs with similar functions are printed quickly. In addition, there are cases where the malicious code of the creators for political purposes and the creators for the display of technology is elaborately created for the purpose of system vulnerabilities and zero-day attacks, so that it is impossible to detect them. As the core technology of the 4th industrial revolution and the technology that incorporates artificial intelligence, which lies at the center, has emerged as a core industry, major countries and many companies around the world are making intensive investments and RAD in artificial intelligence, there is. As artificial intelligence technology is being researched and invested in various fields, research and development of artificial intelligence technology is in progress in the information security industry. In line with this trend, we plan to effectively respond to malware by applying artificial intelligence to the detection of various and sophisticated malicious codes.
Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach

We propose a new task aimed at countering dis- and misinformation, called Finding Reliable Sources. Given a one-sentence claim, the challenge is to automatically find a knowledge source (e.g. a book, a research article, a web page) that could support or refute the claim. We show that this capability could be learnt by observing associations between sentences in English Wikipedia and citations provided for them. Thus, we collect a corpus of over 50 million references to 24 million identified sources with the citation context from Wikipedia, and build search indices using several meaning representation methods. For evaluation, apart from the Wikipedia corpus, we prepare another test set based on the FEVER fact-checking dataset.
LOG-a-TEC Testbed outdoor localization using BLE beacons

While the Global Positioning System (GPS) provides high accuracy it places a significant strain on the device’s battery. In search of alternative techniques for outdoor localization, several approaches have been explored and recently Bluetooth Low Energy (BLE) is becoming a viable alternative to GPS for outdoor localization. Despite its popularity, access to open-source datasets for outdoor localization is limited. In this paper, we present a new openly available BLE fingerprint-based localization dataset that has been collected on LOG-a-TEC testedbed at the Jožef Stefan Institute, Ljubljana, Slovenia. The presented dataset was also used to develop a machine learning model that is capable of correctly classifying fingerprints with an average F1-score of 96.1%. We also provide insight into the importance of each node to the performance of the model. Although fingerprint-based localization is proving to be a robust alternative to GPS, we also show how changes in the environment can negatively impact the localization performance.
From Task Analysis to Wireframe Design: An Approach to User-Centered Design of a GUI for Mobile HRI at Assembly Workplaces

While user-centered design philosophy and corresponding design recommendations are central pillars of human-robot interaction (HRI) research, the process how to move from such abstract and generalized design recommendations to concrete, context-specific design implementations remains under-researched and vague in the literature. The goal of this paper is therefore to show an approach for moving from abstract design recommendations to a concrete interface, and thus illustrates a design process that is rarely illustrated in concrete terms in HRI. This is done using a real-world use case of designing a possible user-centered interface for mobile cooperative manufacturing robots for assembly work in a medium-sized company. A study is presented to conceptualize and test a Research-through-Design approach, which combines transdisciplinary methods to determine relevant information which should be displayed on a graphical user interface (GUI) for HRI. Based on the use case, a Goal-Directed Task Analysis (GDTA) was conducted, consisting of a participatory observation and interviews with subject matter experts to analyze an assembly task from the work objective to the information units. The acquired information has been transferred to a physical model. A wireframe has been created to show how the results of the GDTA and the physical model can be applied to a GUI. The wireframe design has been evaluated through qualitative interviews with end users (n = 12) to get first estimates about its relevance. In order to validate the applied methods, design and engineering students (n = 10) repeated the process in stages followed by interviews. The results indicate that the method mix shows potential and leads to supportive user interfaces.
Ethics, Equity, & Justice in Human-Robot Interaction: A Review and Future Directions

As social robots rapidly become mainstream technologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice-centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.
From Ethical Artificial Intelligence Principles to Practice: A Case Study of University-Industry Collaboration

The ethical artificial intelligence principal to practice gap is a significant challenge for micro and small medium businesses (SME). The policy and legal landscape is very dynamic and whilst there are limited toolkits, designed to help such businesses to embed the ethical design of responsible technology, there is generally a lack of skills, knowledge and resources on how to apply them within individual businesses. In this paper we present a small case study of practical examples that has led to the introduction of ethical AI practices into SMEs. Through a European funded university-industry collaboration, to date 102 SMEs within Greater Manchester, UK have been exposed to data and ethical AI workshops, with a subset of these choosing to deeper dive and apply tools such as consequence scanning and harms modelling within their business with support from university technical analysts and academics. The case study presents initial evaluations on embedding ethical principles, challenges faced by the SMEs and the reflections of technical analysts.
SSD Thermal Throttling Profile Prediction Using Neural Network

In the past decades, the growing capacity and performance of Solid-State Drives (SSDs) have resulted in higher power consumption and increasing thermal challenges. Thermal throttling has been widely adopted in SSD products to keep critical component temperatures within limits. While thermal throttling profiles are essential for SSD performance in firmware developments, using the traditional Computational Fluid Dynamics (CFD) approach to simulate thermal throttling profiles can be time-consuming. In this paper, a fast-prediction method using a neural network approach is proposed for predicting the thermal throttling profiles under different workloads. The Long Short-Term Memory (LSTM) neural network framework has been adopted in this method. The neural network will learn from the testing data of a single workload to model the thermal performance and hence predict thermal throttling profiles for various workloads. Results have shown that the predicted thermal throttling profiles for various workloads align with experimental test data with good accuracy. Prediction time is significantly reduced to a few minutes. This work has demonstrated that machine learning can be effectively applied to SSD thermal test data to model thermal performances with different test parameters, including power, ambient temperature, and throttling temperature limits.
Transfer Learning Framework for Forecasting Fresh Produce Yield and Price

Accurate estimates of fresh produce (FP) yields and prices are crucial for having fair bidding prices by retailers along with informed asking prices by farmers, leading to the best prices for customers. To have accurate estimates, the state-of-the-art deep learning (DL) models for forecasting FP yields and prices are improved in this work while a novel transfer learning (TL) framework is proposed for better generalizability. The proposed models are trained and tested using real world datasets for the Santa Barbara region in California, which contain environmental input parameters mapped to FP yield and price output parameters. Based on an aggregated measure (AGM), the proposed model, an ensemble of Attention Deep Feedforward Neural Network with Gated Recurrent Unit (GRU) units and Deep Feedforward Neural Network with embedded GRU units, is found to significantly outperform the state-of-the-art models. Beside finding the best DL, the TL framework is utilizing FP similarity, clustering, and TL techniques customized to fit the problem in hand and enhance the model generalization to other FPs. The literature similarity algorithms are improved by considering the time series features rather than the absolute values of their points. In addition, the FPs are clustered using a hierarchical clustering technique utilizing the complete linkage of a dendrogram to automate the process of finding the similarity thresholds and avoid setting them arbitrarily. Finally, the transfer learning is applied by freezing some layers of the proposed ensemble model and fine-tuning the rest leading to significant improvement in AGM compared to the best literature model.
Does what users say match what they do? Comparing self-reported attitudes and behaviours towards a social robot

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants’ self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant’s social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant’s behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.
CARLsim 6: An Open Source Library for Large-Scale, Biologically Detailed Spiking Neural Network Simulation

Mature simulation systems for Spiking Neural Networks (SNNs) become more relevant than ever for understanding the brain and supporting neuromorphic computing. The CARL-sim SNN platform is one of the first Open Source simulation systems that utilized CUDA GPUs to address the tremendous parallel processing demands of natural brains. It has evolved over almost a decade in numerous scientific research projects requiring efficient biologically plausible modeling at scale. With its sixth major release, CARLsim 6 respects this legacy by supporting the latest versions of operating systems, development tool chains, multi-core computers, and of course GPUs. It runs on a range of platforms; from Notebooks up to the NVIDIA DGX-A100 supercomputer, and is used in biologically plausible simulations of the hippocampus and neocortex. The latest version has added flexibility for incorporating long-term and short-term synaptic plasticity. Neuromodulation is an important property of neurobiology that can lead to rapid few shot learning, network rewiring, and neural activity modulation. Because of this, CARLsim 6 now supports four multiple neuromodulators for simulating neural excitability and synaptic plasticity.
Investigation of Transient Liquid Phase Bonding to Low Temperature Co-fired Ceramic Substrates

In an effort to capitalize on the high temperature potential of wide band gap semiconductors, this paper implements transient liquid phase sintering bonds on gold low temperature co-fired ceramic conductors. The strength of these bonds is characterized by die shear tests at 260 degrees Celsius. Two sample groups are used to determine the benefit, if any to the inclusion of an electroplated copper layer in the process of copper, silver, tin TLPS bonding on gold LTCC ink. The shear strength and shear modulus of each are reported.
Predicting Human Decision Making with LSTM

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.11The data and codes to reproduce the empirical results can be accessed and reproduced at https://github.com/doerlbh/HumanLST M. A extended version of this work is to appear in [1].
Quantum Machine Learning for Audio Classification with Applications to Healthcare

Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented.
Time Assisted SAR ADC with Bit-guess and Digital Error Correction

This work presents an original SAR ADC architecture for low-power ADC applications. The proposed architecture uses a Time-to-Digital converter (TDC) to apply a window switching scheme in the SAR algorithm that predicts the switching value of the three MSB CDAC capacitors in just one SAR cycle. The switching scheme also implements a correlated-reversed switching (CRS), improving the converter linearity. The proposed archi-tecture is demonstrated on a 10-bit SAR ADC implementation, which takes ten SAR cycles to provide a l2-bit word to a digital error correction (DEC) block that translates it into a final 10-bit digital output. Considering a Gaussian random distribution to model the variability of unit capacitances, MATLAB simulations demonstrate an ADC linearity that achieves 52% of DNL and 69% of INL values of a conventional VCM-based switching method. The switching scheme reduces by 50% the average switching energy compared with the conventional VCM-based switching method, considering a design with the redundancy searching range of the implemented CDAC. The proposed SAR ADC architecture is designed and simulated in a 28nm CMOS technology. The proposed architecture, working with a 600mV power supply with 10MHz sample frequency, demonstrates an improvement of 28% in the ADC power dissipation compared with a 10-bit SAR ADC with traditional implementation designed to have the same linearity.
Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense

In this paper, we investigate hand gesture classifiers that rely upon the abstracted ‘skeletal’ data recorded using the RGB-Depth sensor. We focus on ‘skeletal’ data represented by the body joint coordinates, from the Praxis dataset. The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease, performing a Praxis test under the direction of a clinician. In this paper, we propose hand gesture classifiers that are more effective with the PRAXIS dataset than previously proposed models. Body joint data offers a compressed form of data that can be analyzed specifically for hand gesture recognition. Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8% using only body joint data. In addition, we investigated a long-short-term-memory (LSTM) to extract and analyze the movement of the joints through time to recognize the hand gestures being performed and achieved a gesture recognition rate of 74.3% and 67.3% for static and dynamic gestures, respectively. The proposed approach contributed to the task of developing an automated, accurate, and inexpensive approach to diagnosing cortical pathologies for multiple healthcare applications.
A Hierarchical Reinforcement Learning Framework for Stock Selection and Portfolio

Investment is a common economics task in which investors maximize future profits by continuously reallocating their current assets. A large number of studies are based on specifying stocks and constantly adjusting the ratio between these stocks to gain more benefits. However, the question of which stocks should be included in the portfolio is not addressed, while some investment strategies only select stocks and buy them without portfolio optimization, which may also cause unexpected loss owing to market oscillation. We try to integrate stock selection and portfolio optimization as a complete process to address this problem using hierarchical reinforcement learning. The high-level policy selects stocks with a high profitable probability, and then the low-level policy makes portfolio optimization on the selected stocks to gain more profit. The performance in China market demonstrates that our hierarchical agents can over performance a single stock selection agent.
Recognition of Lower Limb Motions via Surface Electromyography

Wearable exoskeleton can help people with mobility impairments, such as stroke and amputation patients, to improve their rehabilitation. Traditional exoskeleton control signals include plantar pressure and joint angle. These signals can only reflect the current state and human motion, but cannot predict the motion. As electromyography (EMG) signal occurs before the motion, it can be used as the input signal to predict the subject's motion intention. In this paper, EMG signals are used to identify human lower limb motions, and different algorithms are compared to find the most suitable one for motion recognition. Seven types of lower limb motions are involved, i.e., walking, going upstairs, going downstairs, walking uphill, walking downhill, squatting, and standing. The surface EMG signals from 6 muscles are measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, gastrocnemius, and tibial anterior. Butterworth filter and wavelet threshold are used to denoise the raw EMG signals, and support vector machine (SVM), C4.5 decision tree, and backpropagation (BP) neural network are used to recognize the different motions. To improve the accuracy and speed of recognition, principal component analysis (PCA) is adopted to reduce the feature dimensionality. Before dimensionality reduction, the BP neural network shows the highest accuracy, i.e., up to 97.32%, but it takes 17.205 seconds on average. After dimensionality reduction, the computational cost of each algorithm is significantly reduced; and instead, the SVM shows the highest accuracy, i.e., up to 97.44%, and it takes only 0.004 seconds. The results show that the combination of SVM and PCA has the best performance in lower limb motion recognition.