Reviewerly/exHarmony huggingface dataset:
Efficient Hardware and Software Design for On-device Learning

Deep neural networks (DNNs) are usually trained on servers, and then the trained model is deployed to edge devices. However, the pre-trained models are static and can be inaccurate when the inputs from the new environment are very different from the pre-training data. Therefore, the models on devices need to be continuously adapted by on-device training. To achieve efficient on-device learning, both the software and the hardware issues need to be considered. On the software level, the real-time streaming data usually follows non-independent and identically distribution (non-iid), and simply learning from the latest data can result in forgetting previous data. Besides, the storage on edge devices is usually too small to store all the input data for rehearsal. As for the hardware-level design, implementing on-device training on resource-limited edge devices is challenging because of the complex memory access with different patterns among forward propagation, backward propagation, and weight update. To solve these problems, we first propose a framework to automatically select the most representative data from the unlabeled input stream, which only requires a small data buffer for dynamic learning. Then, we propose an efficient DNN training accelerator, EF-Train, to achieve end-to-end training on resource-constrained, low-power, edge devices.
Co-designing Photonic Accelerators for Machine Learning on the Edge

Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy-efficiency and inference performance compared to CPUs and GPUs. In this chapter, we propose a novel co-designed neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimization to enable higher resolution, better energy-efficiency, and improved throughput. On average, CrossLight offers 9.5× lower energy per bit and 15.9× higher performance per watt at 16-bit resolution than state-of-the-art photonic deep learning accelerators.
Algorithm-System Co-design for Efficient and Hardware-Aware Embedded Machine Learning

We introduce efficient algorithm and system co-design for embedded machine learning to reduce the memory and computation cost. The software stack includes two sides: the inference system and the deep learning algorithms. We first introduce current inference libraries and scheduling optimization techniques. Then, we introduce efficient deep learning model designs, including model compression and neural architecture search. Finally, we discuss how we can co-design the system and algorithm to achieve the best end-to-end performance.
A Pedestrian Detection Case Study for a Traffic Light Controller

A pedestrian detection system in a traffic light controller is studied. The system is based on Deep Neural Networks (DNNs). We explore several network architectures and hardware platforms to identify the most suitable solution under the given constraints of latency, cost, and precision. Specifically, we study altogether 13 networks from the MobileNet, Yolo, ResNet, and EfficientDet families and 6 platforms based on Nvidia and Intel platforms, conducting 383 experiments. We find that several network-platform combinations meet the given requirements of maximum 100 ms inference latency and 0.9 mean average precision. The most promising are Yolo v5 networks on Nvidia Jetson TX2 and IntelNUC GPU hardware.
An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images

Tumor development is clinically characterized through the manual review of histopathological Whole Slide Images (WSI). However, the molecular attributes influencing tumor morphology are not entirely comprehended. Here, we present RNALerner, an innovative tool designed to expedite the identification of correlations between gene expression and tumor morphology as presented in H&E WSI. RNALerner achieves its efficiency by transforming the problem from linear regression to binary classification of high versus low RNA levels, and the use of Resnet18, Convolutional Neural Network (CNN) model. Furthermore, the training phase of the model is halted after only 3 iterations. Upon comparing our results with previous work, we discovered a similar number of statistically significant correlated genes but with a reduction in the number of model parameters and processing time. Analysis of the significant pathways revealed both similarities to and deviations from earlier findings, bringing forth new pathways in the process. RNALerner represents an advancement toward the practical integration of machine learning in WSI analysis, which holds the potential to substantially improve disease diagnosis and guide more effective treatments.
Cross-Layer Optimizations for Efficient Deep Learning Inference at the Edge

The capability of directly and accurately learning pattern from raw data has enabled the use of deep learning (DL) in various application domains such as image and video processing, natural language processing, autonomous driving, robotics, smart healthcare, and predictive maintenance. However, the state-of-the-art DL models are highly complex and require a large amount of compute and memory resources to generate highly accurate results. A number of DNN optimization techniques have been proposed to reduce the complexity of these models and enable their reliable and efficient deployment in resource-constrained embedded scenarios. This chapter first presents an overview of different DNN optimization and approximation techniques designed to improve area, performance, and energy efficiency of DL systems. Then, it covers different methodologies that synergistically integrate different optimization techniques in a cross-layer flow. Toward the end, the chapter discusses works related to end-to-end system-level optimizations and approximations to enable ultra-efficient DNN inference.
Compilation and Optimizations for Efficient Machine Learning on Embedded Systems

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, virtual reality, etc. However, DNN-based ML applications also bring much increased computational and storage requirements, which are particularly challenging for embedded systems with limited compute/storage resources, tight power budgets, and small form factors. Challenges also come from the diverse application-specific requirements, including real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we introduce a series of effective design methodologies, including efficient ML model designs, customized hardware accelerator designs, and hardware/software co-design strategies to enable efficient ML applications on embedded systems.
Efficient Neural Networks and Their Acceleration Techniques for Embedded Machine Learning

This chapter focuses on hardware, software, and machine learning co-design techniques for efficient neural networks. These techniques affect performance, cost, and inference accuracy. This chapter introduces basic compression techniques of machine learning models and some specific cases of lightweight neural network architecture for improving the cost and performance efficiency. This chapter also introduces an SoC (System-on-a-Chip) platform and a simple implementation of DNN (Deep Neural Network) accelerator written in C/C++ on the platform. Selected design techniques, such as quantization, loop unrolling, and pipelining, on the DNN accelerator are quantitatively evaluated in terms of accuracy, latency, and hardware resources to see the design trade-offs.
Design of Sparsity Optimized Photonic Deep Learning Accelerators

Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accuracy. Because of the sparsity in parameter matrices, sparse neural networks can, in principle, be exploited in accelerator architectures for improved energy efficiency and latency. However, to realize these improvements in practice, there is a need to explore sparsity-aware hardware-software co-design. In this chapter, we discuss a novel silicon photonics-based sparse deep neural network inference accelerator called SONIC. SONIC takes advantage of the high energy efficiency and low latency of photonic devices along with software co-optimization to accelerate sparse neural networks. Experimental analysis shows that SONIC can achieve up to 5.8× better performance per watt and 8.4× lower energy per bit than state-of-the-art sparse electronic neural network accelerators and up to 13.8× better performance per watt and 27.6× lower energy per bit than the best known photonic neural network accelerators, at the time.
Rate-Distortion Optimization for Cross Modal Compression

Recently, cross modal compression (CMC) is proposed to compress highly redundant visual data into a compact, common, human-comprehensible domain (such as text) to preserve semantic fidelity for semantic-related applications. However, CMC only achieves a certain level of semantic fidelity at a constant rate, and the model aims to optimize the probability of the ground truth text but not directly semantic fidelity. To tackle the problems, we propose a novel scheme named rate-distortion optimized CMC (RDO-CMC). Specifically, we model the text generation process as a Markov decision process and propose rate-distortion reward which is used in reinforcement learning to optimize text generation. In rate-distortion reward, the distortion measures both the semantic fidelity and naturalness of the encoded text. The rate for the text is estimated by the sum of the amount of information of all the tokens in the text since the amount of information of each token is a lower bound of coding bits. Experimentally, RDO-CMC effectively controls the rate in the CMC framework and achieves competitive performance on MSCOCO dataset.
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders

End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are on par with the latest compression standard like VVC/H.266 on objective metrics, practical implementation and complexity remain strong issues for real-world applications. We propose a practical implementation suitable for realistic applications. We demonstrate that some gains can be achieved on top low-complexity AE, even when using simpler implementation. The proposed implementation also allows a direct integration of such approaches on a variety of platforms and code is made available as a pure C++ standalone codec [1]:
Assessing Challenges Facing Implementation of Information Security Critical Success Factors: A Case of National Examination Council, Tanzania

Aim of this study was to assess challenges facing implementation of information security critical success factors. The study employed quantitative research approach and survey research design where case study design was used. A sample of 79 respondents derived from the population sample of 372 were used by using Slovin’s formula sampling technique, 86% of respondents questionnaire filled effectively were used. Descriptive data analysis was used to analyze variables based on research questions while, statistical tables and figures were used in data presentation. Results of this study indicate that, there are challenges in implementation of information security critical success factors such as security training program, security policy, risk assessment, regular system update, system auditing and committed of top management. The study found reasons for challenges of implementation from respondent views as availability of limited resources, weak financial support from top management, lack of understanding of needed technology from information technology professionals; poor security awareness program for top management who may think that information security is the issue of information technology department only and not the whole organization. It is therefore concluded that organization should identify their specific information security critical success factors to enhance useful of organization limited resource, without investing in generalization and give solutions based on risk priority, in order to make organization secure also utilization of information security critical success factors holds significant importance in ensuring security of an organization's data. It is crucial to address and eliminate any challenges that are within the scope of affordability or manageability.
A decentralized public-permissioned blockchain framework for enhanced security of health records in fog computing

Medical health records comprise sensitive patient data for precise diagnosis and successive treatment. However, it must be stored and shared securely to protect data privacy. Generally, health records are kept on centralized servers, which raise the risk of security breaches and involve trust in a single authority that cannot efficiently defend data from internal attacks. Blockchain (BC) is extensively used in medical health records management because of its decentralized and tamper-proof properties. This work introduces a public-permissioned BC technology with a decentralized ledger (DL) to manage medical health records in the fog computing layer. The considered BC is decentralized and allows the transmission of records within the decentralized network of records. The data blocks are hashed using the SHA-256 hash algorithm. Especially, an Adaptive RSA Digital Signature Algorithm (ARSA-DS) is developed to prevent data tampering with medical health records in the fog computing layer. Moreover, an Ebola Search Optimization based Key Selection (ESO-KS) technique is employed to find the ideal key from the randomly generated keys to reduce processing time and increase overall efficiency. The proposed decentralized BC framework will help to preserve patient privacy and prevent the tampering of health records by attacks; moreover, it is efficient in terms of confidentiality, integrity, and availability.
RenderBench: The CPU Rendering Benchmark Suite Based on Microarchitecture-Independent Characteristics

This research addresses the issue of evaluating CPU rendering performance by introducing the innovative benchmark test suite construction method RenderBench. This method combines CPU microarchitecture features with rendering task characteristics to comprehensively assess CPU performance across various rendering tasks. Adhering to principles of representativeness and comprehensiveness, the constructed benchmark test suite encompasses diverse rendering tasks and scenarios, ensuring accurate capture of CPU performance features. Through data sampling and in-depth analysis, this study focuses on the role of microarchitecture-independent features in rendering programs, including instruction-level parallelism, instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance. The research findings reveal significant variations in rendering programs across these features. For instance, in terms of instruction-level parallelism, rendering programs demonstrate a high level of ILP (instruction-level parallelism), with an average value of 5.70 for ILP256, surpassing benchmarks such as Mibench and NAS Parallel Benchmark. Furthermore, in aspects such as instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance, rendering programs exhibit distinct characteristics. Through the application of the RenderBench method, a scalable and highly representative benchmark test suite was constructed, facilitating an in-depth exploration of CPU performance bottlenecks in rendering tasks. By delving into microarchitecture-independent features, this study provides profound insights into rendering program performance, offering valuable guidance for optimizing CPU rendering performance. The application of ensemble learning models, such as random forest, XGBoost, and ExtraTrees, reveals the significant influence of features like floating-point computation, memory access patterns, and register usage on CPU rendering program performance. These insights not only offer robust guidance for performance optimization but also underscore the importance of feature selection and algorithm choice. In summary, the results of feature importance ranking in this study provide beneficial directions and deep insights for the optimization and enhancement of CPU rendering program performance. These findings are poised to exert a positive impact on future research and development endeavors.
High-Level Hessian-Based Image Processing with the Frangi Neuron

The Frangi neuron proposed in this work is a complex element that allows high-level Hessian-based image processing. Its adaptive parameters (weights) can be trained using a minimum number of training data. In our experiment, we showed that just one image is enough to optimize the values of the weights. An intuitive application of the Frangi neuron is to use it in image segmentation process. In order to test the performance of the Frangi neuron, we used diverse medical datasets on which second-order structures are visualized. The Frangi network presented in this paper trained on a single image proved to be significantly more effective than the U-net trained on the same dataset. For the datasets tested, the network performed better as measured by area under the curve receiver operating characteristic (ROC AUC) than U-net and the Frangi algorithm. However, the Frangi network performed several times faster than the non-GPU implementation of Frangi. There is nothing to prevent the Frangi neuron from being used as part of any other network as a component to process two-dimensional images, for example, to detect certain second-order features in them.
Federated learning for rare disease detection: a survey

The detection of rare diseases utilizing advanced artificial intelligence (AI) techniques has garnered considerable attention in recent years. Numerous approaches have been proposed to detect diverse rare diseases by leveraging a range of medical data, including medical images, electronic health records, and sensory data. In order to safeguard the privacy of health data, considerable investigation has been undertaken on a novel learning paradigm known as federated learning, which has been applied to the domain of rare disease detection. Nonetheless, this nascent research direction remains in its infancy, necessitating greater scrutiny and attention. Within this survey, our primary focus lies in providing fresh perspectives, deliberating the challenges, and enumerating potential research directions concerning the application of federated learning techniques in rare disease detection. Furthermore, we provide a succinct summary of existing advancements using AI techniques for rare disease detection, as well as the utilization of federated learning within healthcare informatics. Moreover, we furnish a compilation of publicly available datasets that can be employed to validate novel federated learning algorithms for the purpose of detecting rare diseases.
Analysis of Invitation Letters to Academic Conference Using Language Network Analysis - Focusing on French Studies Conferences in Korea -

Purpose This study aims to examine the impact of recent periodic revolutions such as the 4th industrial revolution and the corona pandemic on the academic field, and in particular, to examine the impact of these changes on French studies.Methods This study analyzed the invitation letters to academic conference through language network analysis and examined the influence of the current trend of change on French Studies research.Results French studies between 2021 and 2023, when the corona was in vogue and the 4th industrial revolution accelerated, showed a change in the theme of the conference compared to before the pandemic. In other words, topics such as virtual reality, metaverse, and artificial intelligence, along with the search for the direction of French Studies research in the corona era and the post-corona era were selected, which reflected the trend of the new era.Conclusion Trends of the times, such as the acceleration of the 4th industrial revolution and the corona pandemic, have been shown to affect research trends in academic fields such as French Studies in Korea.
Computational theories should be made with natural language instead of meaningless code

The target article claims that we should speak in code to understand property, because natural language is too ambiguous. Yet the best computer programmers tell us the opposite: Arbitrary code is too ambiguous, so we should use natural language for variables, functions, and classes. I discuss how meaningless code makes Boyer's theory too enigmatic to properly debate.
Improving Head Pose Estimation with Neural Network Optimization on the AFLW2000 Dataset

The problem of large regression loss values in the performance of the AlexNet model based on deep learning has been solved. The best combination of hyperparameters, including batch size, number of iterations, and learning rate, was obtained through comparative experiments while improving the structure of the neural network layers of the model. On the AFLW2000 dataset, the average absolute error loss value decreased from 59.89 to 15.86, and the prediction performance of Euler angles increased by 73.5%. The experiment made the AlexNet CNN model perform better on the AFLW2000 dataset, proving the superiority of feature regression in predicting head pose angles in all directions and improving the robustness of the AlexNet model.
CSFINet: Cross-scale Feature Interaction For Medical Image Segmentation

Recently, vision transformers have emerged as excellent segmentation structures for their global modeling capability. In current transformer-based models for medical image segmentation, transformers are often used instead of convolutional layers or are added to the deepest layer of the encoder. However, for the extracted multi-scale feature information, most existing methods tend to ignore the cross-scale dependencies, which are important to render powerful representations. In this paper, we propose a cross-scale feature interaction network (CSFINet) that can alleviate this problem to some extent. Specifically, our approach consists of two feature interaction modules: the patch-wise interaction unit (PIU) and the feature-wise interaction unit (FIU). PIU uses a transformer to aggregate patches from different scale features to enhance the feature representations, and FIU uses the flow field to realize the interaction of adjacent scale features for image upsampling. Experimental results on four medical datasets demonstrate that CSFINet can achieve promising results. The code is publicly available on https://github.com/CSFINet/CSFINet.