Reviewerly/exHarmony huggingface dataset:
Improving a neural network model by explanation-guided training for glioma classification based on MRI data

In recent years, artificial intelligence (AI) systems have come to the forefront. These systems, mostly based on Deep learning (DL), achieve excellent results in areas such as image processing, natural language processing, or speech recognition. Despite the statistically high accuracy of deep learning models, their output is often a decision of "black box". Thus, Interpretability methods have become a popular way to gain insight into the decision-making process of deep learning models. Explanation of a deep learning model is desirable in the medical domain since the experts have to justify their judgments to the patient. In this work, we proposed a method for explanation-guided training that uses a Layer-wise relevance propagation (LRP) technique to force the model to focus only on the relevant part of the image. We experimentally verified our method on a convolutional neural network (CNN) model for low-grade and high-grade glioma classification problems. Our experiments show promising results in a way to use interpretation techniques in the model training process.
CVT-MAP-Elites Introducing Local Search with Elite Individual Partial Solutions

CVT-MAP-Elites is a method of evolutionary computation that maintains diversity and searches for solutions in the feature space by maintaining the optimal solution in the segmented feature space, but there is a problem of ensuring a sufficient number of searches for local search. In this paper, we propose a local search method by transcribing partial solutions of elite individuals and report the results of applying it to multi-tasks optimization problems.
KEYWORD SPOTTING SYSTEM WITH NANO 33 BLE SENSE USING EMBEDDED MACHINE LEARNING APPROACH

Due to the obvious advancement of artificial intelligence, keyword spotting has become a fast-growing technology that was first launched a few years ago by hidden Markov models. Keyword spotting is the technique of finding terms that have been pre-programmed into a machine learning model. However, because the keyword spotting system model will be installed on a small and resource-constrained device, it must be minimal in size. It is difficult to maintain accuracy and performance when minimizing the model size. We suggested in this paper to develop a TinyML model that responds to voice commands by detecting words that are utilized in a cascade architecture to start or control a program. The keyword detection machine learning model was built, trained, and tested using the edge impulse development platform. The technique follows the model-building workflow, which includes data collection, preprocessing, training, testing, and deployment. 'On,' 'Off,' noise, and unknown databases were obtained from the Google speech command database V1 and applied for training and testing. The MFCC was used to extract features and CNN was used to generate the model, which was then optimized and deployed on the microcontroller. The model's evaluation represents an accuracy of 84.51% based on the datasets. Finally, the KWS was successfully implemented and assessed on Arduino Nano 33 BLE Sense for two studies in terms of accuracy at three different times and by six different persons.
Language and emotional domains: the environmentally sustainable behaviour of digital youth

Revolutionary technological development has been assumed to be the cause and basis of the same revolutionary changes in the modern environmental perception of youth. The purpose of the article is to show the changes in youth's sustainable perspectives on environmental issues via information and communication technologies and the changes in sustainable behaviour. The methodology of this study presents the analysis of related topics and qualitative research based on the online questionnaire. The collection of data helped to show that over 80 per cent of modern digital youth use information and communication technologies to raise their awareness of environmental concerns and their preference for the environmental issues that attract them the most by watching or reading the news online. The model of three variables ”awareness – attitude – behaviour”, was proposed. This model can be used to explain influencers and modes of action in the emotional domain of environmentally sustainable behaviour among young people. The study of changes in sustainable behavior based on the example of language and emotional domains in the era of information and communication technologies and may supplement existing research. Research may be of interest to philosophers, psychologists, sociologists, linguists and those concerned with the environmental issues.
Preface

The Eleventh International Conference on Educational Innovation through Technology (EITT) was organized by Society of International Chinese in Educational Technology (SICET) and hosted by St. John's University virtually from December 16 to December 17, 2022. EITT 2022 engages scholars and researchers to share research advancement and to promote academic exchanges. Founded in 2003, SICET is a nonpolitical, non-profit academic organization, and is affiliated with the Association for Educational Technology and Communications (AECT) in 2005. SICET aims to establish and strengthen academic international connections, exchanges, research, and studies in educational technology for teaching and learning; as well as to promote the applications of educational technology in education. The previous ten EITT conferences have been successfully held in Beijing (2012), Williamsburg (2013), Brisbane (2014), Wuhan (2015), Tainan (2016), Osaka (2017), Auckland (2018), Biloxi (2019), virtually (2020) and Chongqing in hybrid format (2021).
Study on path planning and tracking algorithm for autonomous parking system of semi-trailers

Semi-trailers have the advantage that the maximum load capacity is large. However, there are some problems that high driving skill is required because it is necessary to perform unique steering during turning or backward driving. This paper proposes a path planning and tracking algorithm for autonomous parking of semi-trailers. The path planner determines the parking path to the target parking position in accordance with the given parking environment including obstacles. Furthermore, the steering control system is desinged to handle the path tracking and stabilize the hitch angle. The proposed path planning method and tracking control are both unique in that it focuses on the vehicle’s terminal state at the target parking position, and finds the solution in the time-inverse order of the actual driving sequence. The parking simulations is performed and the effectiveness of the proposed method is verified.
Introduction to the theory of open quantum systems

This manuscript is an edited and refined version of the lecture script for a one-semester graduate course given originally at the PhD school in the Institute of Physics of Polish Academy of Sciences in the Spring/Summer semester of 2022. The course expects from the student only a basic knowledge on graduate-level quantum mechanics. The script itself is largely self-contained and could be used as a textbook on the topic of open quantum systems. The program of this course is based on a novel approach to the description of the open system dynamics: It is showed how the environmental degrees of freedom coupled to the system can be represented by a multi-component quasi-stochastic process. Using this representation one constructs the super-quasi-cumulant (or super-qumulant) expansion for the system's dynamical map -- a parametrization that naturally lends itself for the development of a robust and practical perturbation theory. Thus, even an experienced researcher might find this manuscript of some interest.
Improving the EFL Students’ Oral Communication through the Implementation of Business Meetings Project Using a Digital Pocket Presentation Guide

This study aimed to implement a Business Meetings Project using a Digital Pocket Presentation Guide to improve EFL students' oral communication. Therefore, the action research design was implemented to match the purpose of the study. The data collection techniques were started by asking students to do a pretest, implement the model, do observations during the activities, distribute a questionnaire and do interviews to see the students' improvement by asking students to do the post-test. The result of the research showed that the student's progress sustainably improved from the first evaluation, which was 48,75, to the second evaluation, which was 53,760 and to the third evaluation, which was 56,25 for the mean. The score continuously improved from the first evaluation to the third evaluation. The results of the research show that the students gave good attitudes and responses during the process of implementing the model. Questionnaire and interview reports show that students agreed on the implemented model as it motivated and improved their self-confidence and capability in oral communication skills. In conclusion, implementing the business meeting projects through a digital pocket presentation guide enhanced students' verbal communication skills.
Discrete representation learning for handwritten text recognition

Abstract Handwritten text recognition, i.e., the conversion of scanned handwritten documents into machine-readable text, is a complex exercise due to the variability and complexity of handwriting. A common approach in handwritten text recognition consists of a feature extraction step followed by a recognizer. In this paper, we propose a novel DNN architecture for handwritten text recognition that extracts discrete representation from the input text-line image. The proposed model is constructed of an encoder–decoder network with an added quantization layer which applies a dictionary of representative vectors to discretize the latent variables. The dictionary and the network parameters are trained jointly through the k -means algorithm and back propagation, respectively. The performance of the suggested model is evaluated through conducting extensive experiments on five datasets, analyzing the effect of discrete representation on handwriting recognition. The results demonstrate that the use of feature discretization improves the performance of deep handwriting text recognition models when compared to the conventional DNN models with continuous representation. Specifically, the character error rate is decreased by $$22\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>22</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$21.1\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>21.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on IAM and ICFHR18 datasets, respectively.
Machine Perception-Driven Image Compression: A Layered Generative Approach

In this age of information, images are a critical medium for storing and transmitting information. With the rapid growth of image data amount, visual compression and visual data perception are two important research topics attracting a lot attention. However, those two topics are rarely discussed together and follow separate research path. Due to the compact compressed domain representation offered by learning-based image compression methods, there exists possibility to have one stream targeting both efficient data storage and compression, and machine perception tasks. In this paper, we propose a layered generative image compression model achieving high human vision-oriented image reconstructed quality, even at extreme compression ratios. To obtain analysis efficiency and flexibility, a task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks while reserves outstanding reconstructed perceptual quality, compared with traditional and learning-based codecs. In addition, joint optimization schedule is adopted to acquire best balance point among compression ratio, reconstructed image quality, and downstream perception performance. Experimental results verify that our proposed compressed domain-based multi-task analysis method can achieve comparable analysis results against the RGB image-based methods with up to 99.6% bit rate saving (i.e., compared with taking original RGB image as the analysis model input). The practical ability of our model is further justified from model size and information fidelity aspects.
Incentivising Building Data Availability and Accessibility Using Tokenized Data Assets

Smart cities are data driven and collect data from a variety of sources. Certain types of data such as building data is under-represented and remains harder to find despite its value. Our goal is to incentivise the stakeholders to make building data easier to avail by turning it into an asset. We use tokenized building data assets on a blockchain to improve data accessibility. This is achieved by connecting building data owners with the consumers of building information via tokens (fungible and non-fungible), which serves the purpose of coordinating the activities of the built ecosystem. Further, we present our system architecture designed to sustain the economic incentives for interested parties and individuals.
M2T: Masking Transformers Twice for Faster Decoding

We show how bidirectional transformers trained for masked token prediction can be applied to neural image compression to achieve state-of-the-art results. Such models were previously used for image generation by progressivly sampling groups of masked tokens according to uncertainty-adaptive schedules. Unlike these works, we demonstrate that predefined, deterministic schedules perform as well or better for image compression. This insight allows us to use masked attention during training in addition to masked inputs, and activation caching during inference, to significantly speed up our models (~4 higher inference speed) at a small increase in bitrate.
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation

Self-supervised pre-training strategies have recently shown impressive results for training general-purpose feature extraction backbones in computer vision. In combination with the Vision Transformer architecture, the DINO self-distillation technique has interesting emerging properties, such as unsupervised clustering in the latent space and semantic correspondences of the produced features without using explicit human-annotated labels. The STEGO method for unsupervised semantic segmentation contrastively distills feature correspondences of a DINO-pre-trained Vision Transformer and recently set a new state of the art. However, the detailed workings of STEGO have yet to be disentangled, preventing its usage in safety-critical applications. This paper provides a deeper understanding of the STEGO architecture and training strategy by conducting studies that uncover the working mechanisms behind STEGO, reproduce and extend its experimental validation, and investigate the ability of STEGO to transfer to different datasets. Results demonstrate that the STEGO architecture can be interpreted as a semantics-preserving dimensionality reduction technique.
HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper number of negative samples. However, there is no work that optimizes SimpleX on multi-core CPUs, leading to limited performance. To this end, we perform an in-depth profiling and analysis of existing SimpleX implementations and identify their performance bottlenecks including (1) irregular memory accesses, (2) unnecessary memory copies, and (3) redundant computations. To address these issues, we propose an efficient CF training system (called HEAT) that fully enables the multi-level caching and multi-threading capabilities of modern CPUs. Specifically, the optimization of HEAT is threefold: (1) It tiles the embedding matrix to increase data locality and reduce cache misses (thus reduces read latency); (2) It optimizes stochastic gradient descent (SGD) with sampling by parallelizing vector products instead of matrix-matrix multiplications, in particular the similarity computation therein, to avoid memory copies for matrix data preparation; and (3) It aggressively reuses intermediate results from the forward phase in the backward phase to alleviate redundant computation. Evaluation on five widely used datasets with both x86- and ARM-architecture processors shows that HEAT achieves up to 45.2X speedup over existing CPU solution and 4.5X speedup and 7.9X cost reduction in Cloud over existing GPU solution with NVIDIA V100 GPU.
Remote Procedure Call as a Managed System Service

Remote Procedure Call (RPC) is a widely used abstraction for cloud computing. The programmer specifies type information for each remote procedure, and a compiler generates stub code linked into each application to marshal and unmarshal arguments into message buffers. Increasingly, however, application and service operations teams need a high degree of visibility and control over the flow of RPCs between services, leading many installations to use sidecars or service mesh proxies for manageability and policy flexibility. These sidecars typically involve inspection and modification of RPC data that the stub compiler had just carefully assembled, adding needless overhead. Further, upgrading diverse application RPC stubs to use advanced hardware capabilities such as RDMA or DPDK is a long and involved process, and often incompatible with sidecar policy control. In this paper, we propose, implement, and evaluate a novel approach, where RPC marshalling and policy enforcement are done as a system service rather than as a library linked into each application. Applications specify type information to the RPC system as before, while the RPC service executes policy engines and arbitrates resource use, and then marshals data customized to the underlying network hardware capabilities. Our system, mRPC, also supports live upgrades so that both policy and marshalling code can be updated transparently to application code. Compared with using a sidecar, mRPC speeds up a standard microservice benchmark, DeathStarBench, by up to 2.5$\times$ while having a higher level of policy flexibility and availability.
Limiting Sets for Digital Cones and Suspensions

Cone and suspension constructions have been introduced in digital topology, modeled on those of classical topology. For digital cones and suspensions, and for some related digital images, we find (m, n)-limiting sets; especially (0, 0)-limiting (freezing) sets.
Learning To Optimize Quantum Neural Network Without Gradients

Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. The similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r.t objective function have to be computed on the quantum device. This computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a \emph{meta-optimizer} network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets.
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy

Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mechanisms are leveraged to provide formal privacy guarantee. However, when deploying FL at the wireless edge with over-the-air computation, ensuring client-level DP faces significant challenges. In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy. The key idea of PFELS is for each device to first compress its model update and then adaptively design the transmit power of the compressed model update according to the wireless channel status without any artificial noise addition. We provide a privacy analysis for PFELS and prove the convergence of PFELS under general non-convex and non-IID settings. Experimental results show that compared with prior work, PFELS can improve the accuracy with the same DP guarantee and save communication and energy costs simultaneously.
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems

Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples? In this work, we propose to explore this problem based on a design approach. Starting from a small initial set of samples, we adaptively discover critical samples to achieve increasingly accurate learning of the system evolution. One central challenge here is that we do not know the network modeling error since the ground-truth system state is unknown, which is however needed for critical sampling. To address this challenge, we introduce a multi-step reciprocal prediction network where forward and backward evolution networks are designed to learn the temporal evolution behavior in the forward and backward time directions, respectively. Very interestingly, we find that the desired network modeling error is highly correlated with the multi-step reciprocal prediction error, which can be directly computed from the current system state. This allows us to perform a dynamic selection of critical samples from regions with high network modeling errors for dynamical systems. Additionally, a joint spatial-temporal evolution network is introduced which incorporates spatial dynamics modeling into the temporal evolution prediction for robust learning of the system evolution operator with few samples. Our extensive experimental results demonstrate that our proposed method is able to dramatically reduce the number of samples needed for effective learning and accurate prediction of evolution behaviors of unknown dynamical systems by up to hundreds of times.