Reviewerly/exHarmony huggingface dataset:
Blockchain-based Data Sharing Framework for Malaysia Government Aid Management System

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem.
Orchestrating Measurement-Based Quantum Computation over Photonic Quantum Processors

Quantum computing has rapidly evolved in recent years and has established its supremacy in many application domains. While matter-based qubit platforms such as superconducting qubits have received the most attention so far, there is a rising interest in photonic qubits lately, which show advantages in parallelism, speed, and scalability. Photonic qubits are best served by the paradigm of measurement-based quantum computation (MBQC). To deliver the promise of measurement-based photonic quantum computing (MBPQC), the photon cluster state depth and photon utilization are two of the most important metrics. However, little attention has been paid to optimizing the depth and utilization when mapping quantum circuits to the photon clusters. In this paper, we propose a compiler framework that achieves automatic and dynamic depth and utilization optimizations. Our approach consists of an MBPQC mapping mechanism that maps optimized measurement patterns on a cluster state and a cluster state pruning strategy that removes all possible redundancies without impacting the circuit functions. Experimental results on five quantum benchmark with three different qubit numbers indicate our approach achieves an average of 63.4% cluster depth reduction and 22.8% photon utilization improvements.
Development of Mobile Apps for Miri Geology: Enhancing Student Learning

Smartphone embedded with various apps has become necessary device in our daily life for communication, obtaining information, and online transactions. Mobile apps can serve as a viable learning tool to support, enhance, and enrich students learning. This study aims to develop a mobile app to support students in learning the basic principles of geology and experiencing geology in the real world. Developed for Android devices, this mobile app offers an extensive collection of geological content in Miri covering the rock type, rock deformation, sedimentary structures, and geohazard. The app provides users an interactive map and a visual presentation of the geological content. Users can navigate and explore the map, accessing the location where the geological content is identified. We wish that students will be inspired by the beauty of the geological features in Miri and be motivated to learn.
Mckeycutter: A High-throughput Key Generator of Classic McEliece on Hardware

Classic McEliece is a code-based quantum-resistant public-key scheme characterized with relative high encapsulation/decapsulation speed and small ciphertexts, with an in-depth analysis on its security. However, slow key generation with large public key size make it hard for wider applications. Based on this observation, Mckeycutter, a high-throughput key generator in hardware, is proposed to accelerate the key generation in Classic McEliece based on algorithm-hardware co-design. Meanwhile the storage overhead caused by large-size keys is also minimized. First, compact large-size GF(2) Gauss elimination method is presented by adopting naive processing array and memory-friendly scheduling strategy. Second, an optimized constant-time hardware sorter is proposed to support regular memory accesses with less comparators and storage. Third, algorithmlevel pipeline is enabled for high-throughput processing, allowing for concurrent key generations. Our FPGA implementation results achieve around 4× improvements in throughput with 9~14× less memory-time product compared with the existing FPGA solutions.
HDSuper: Algorithm-Hardware Co-design for Light-weight High-quality Super-Resolution Accelerator

Super-resolution (SR) networks have been gradually applied to embedded devices with good-quality image reconstruction. However, the hardware performance and power efficiency are limited by a large number of algorithm parameters, computation complexity, and hardware resources, obstructing the development of a high-quality SR accelerator. This paper proposes an end-to-end platform with a lightweight super-resolution network (LSR) and an efficient, high-quality super-resolution architecture HDSuper, to perform algorithm-hardware co-design for the SR accelerator. For algorithm design, we employ depth-wise separable convolution and pixelshuffle to reduce network size and computation complexity by considering the hardware constraints. For hardware design, we provide a unified computing core (UCC) combined with an efficient flattening-and-allocation (F-A) mapping strategy to support various operators with high computational utilization. We adopt the patch training method to reduce the external memory access of the hardware architecture. Based on the evaluation, the proposed algorithm achieves high-quality image reconstruction with 37.44dB PSNR. Finally, we implement the image reconstruction in FPGA demonstration, achieving high-quality image reconstruction with 2.08W power consumption under the lowest hardware resources compared to the state-of-the-art works.
Lightning Talk: Trinity - Assured Neuro-symbolic Model Inspired by Hierarchical Predictive Coding

This paper describes the core concepts and challenges in developing Trinity - a high-assurance neuro-symbolic approach to trustworthy and resilient machine learning for applications in open-world, contested, and rapidly-evolving environments. The two central concepts in Trinity are a neuro-symbolic factored world model that identifies entities, activities, and complex events, and the notion of surprise against this world model that is used for self-adaptation and learning, as well as runtime assurance. The world model is not derived purely as a bottom-up inference from sensors treating each observation as independent uncorrelated input; instead, we iteratively interleave bottom-up inference (conditioned on context) with top-down predictions and context identification using a three-layered hierarchical predictive processing (HPP) stack. Thus, the neuro-symbolic inference in Trinity is bidirectional – learning-based bottom-up pull that is uncertainty-driven and reasoning-based symbolic top-down push that is decision-driven. The progressively symbolic higher layers capture a larger context than the bottom layers finally culminating in the highest layer implemented using large language models. Any surprise arising from the mismatch between the top-down prediction and the bottom-up inference is used for the continual adaptation of Trinity. The inference in Trinity produces a factored temporal world model as the result of perception. The predictions are accompanied by a quantitative measure of surprise from the 3-layered HPP stack. This surprise corresponds to the confidence of the model in its current inference. The continuous monitoring and adaptation accompanied by risk analysis make Trinity robust to semantic adversarial perturbations and more efficiently generalizable to novelties. The hierarchical nature of Trinity also enables adaptation of the architecture to the available compute resources.
Convolutional Neural Networks with Feature Fusion Method for Automatic Modulation Classification

The analogy and application of Automatic modulation classification (AMC) detects the modulation type of received signals. Henceforth, the received signals can be correctly demodulated and, consequently, the transmitted message can be recovered. In Deep Learning (DL) based modulation classification, one major category, challenge is to pre-processing a received signal and representing it in a proper format-manner, before passing the desired-signal into the neural network. However, most existing modulation classification algorithms are neglecting the fact of mixing features between different representations, and the importance of features fusion method. This paper, however, attempted a Feature fusion scheme, for AMC, using convolutional neural networks (CNN). The approach was taken, in order to attempt fuse features extracted from In-phase & Quadrature (IQ) sequences, as well as, the Amplitude & Phase (AP) Sequences and Constellation Diagram images. Finally, simulation results show that fusing features from different representations can incorporate and leads to the best accuracy figures, achieved from each representation separately. Furthermore, our model achieved a classification accuracy of 84.68% at 0dB and 75.29% at -2dB and over 90% accuracy for high SNRs with a maximum accuracy of 94.65%, were available.
A Carbon Emissions Verification Mechanism Based on Blockchain and Reverse Auction

With the establishment of the Carbon Peaking and Carbon Neutrality targets, Carbon Emissions Trading(CET), as an important emissions reduction policy tool, needs to be supported by more accurate carbon emissions data. However, the problems of low credibility of data and mismatch between government budget and work requirements are existing in the current carbon emissions data verification business. In this paper, we proposed a blockchain-based carbon verification mechanism to address the problem of low credibility of carbon emissions data, and realized the full traceability of verification data through a data control model of "private chain + alliance chain". Then we designed a reverse auction algorithm based on ring signature to address the problem of mismatch between government budget and work requirements. We also use the alliance chain platform FISCO BCOS as the underlying architecture, designed the corresponding smart contract, and verify the feasibility of the system. The results are conducive to promoting the market-oriented and benign development of carbon verification business.
Efficient Transformer Inference with Statically Structured Sparse Attention

Self-attention matrices of Transformers are often highly sparse because the relevant context of each token is typically limited to just a few other tokens in the sequence. To reduce the computational burden of self-attention on Transformer inference, we propose static, structured, sparse attention masks that split attention matrices into dense regions, skipping computations outside these regions while reducing computations inside these regions. To support the proposed mask structure, we design an entropy-aware finetuning algorithm to naturally encourage attention sparsity while maximizing task accuracy. Furthermore, we extend a typical dense deep learning accelerator to efficiently exploit our structured sparsity pattern. Compared to a dense baseline, we achieve 56.6% reduction in energy consumption, 58.9% performance improvement with <1% accuracy loss and 2.6% area overhead.
Estimation of Solar PV Power Plant Output Using Machine Learning Algorithms

Photovoltaic (PV) systems have become ubiquitous worldwide, harnessing solar energy to generate power on a global scale. Solar output power is varying and largely dependent on environmental factors. These factors consist of humidity, wind speed, irradiance, and the PV module surface temperature, etc. Since photovoltaic generation is not constant in nature, it is critical to prepare for contingencies related to its generation, as the prediction of solar power plant output is important for the electric grid to be designed to accommodate any fluctuations. This paper delves into an examination of how diverse environmental factors impact the output of a solar PV system. Additionally, it explores the application of various Machine Learning (ML) algorithms, including Linear regression, Polynomial regression, Ridge regression, and Lasso regression techniques. Recurrent Neural Network are also implemented on the dataset discussed in [10] to forecast the solar power output. The effectiveness of these techniques is evaluated using nRMSE values. Out of these techniques, polynomial regression outperformed the other regression models with high accuracy.
Lightning Talk: The New Era of Computational Cognitive Intelligence

The triple whammy of variability in platforms (e.g., process variability), applications (e.g., dynamic use cases in autonomous systems), and the environment (e.g., context) renders ineffective the classical computational/algorithmic/numerical computing paradigm in dealing with the inherent runtime dynamism and uncertainty faced by emerging systems. We posit that this requires a fundamental change from classical "static" computing to a new era that deploys a computational cognitive intelligence (CCI) paradigm that is able to learn and evolve at runtime. The CCI paradigm empowers systems to be adaptable and evolvable by exploiting biologically-inspired cognitive intelligence principles.
Differentiable Representation of Warping Based on Lie Group Theory

Warping is a type of signal distortion where the sequential order is not changed. This operation is important for representing the alignment between series in signal processing. From the motivation of end-to-end optimization of series transformation processes using deep learning, some differentiable warping representations have been proposed. However, the conventional representations are not necessary and sufficient. That is, it cannot represent complex warping or contains non-warping. In this paper, we analyze the Lie group of warping, which is called General Warping group (GW), and investigate the representation of warping for continuous signals. We derive a complete matrix representation of warping that acts in the frequency domain. We show that this method can represent all warping without omission or error while remaining differentiable. Numerical experiments show that the warping curve can be optimized by gradient descent.
ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks

Training contemporary AI models requires investment in procuring learning data and computing resources, making the models intellectual property of the owners. Popular model watermarking solutions rely on key input triggers for detection; the keys have to be kept private to prevent discovery, forging, and removal of the hidden signatures. We present ZKROWNN, the first automated end-to-end framework utilizing Zero-Knowledge Proofs (ZKP) that enable an entity to validate their ownership of a model, while preserving the privacy of the watermarks. ZKROWNN permits a third party client to verify model ownership in less than a second, requiring as little as a few KBs of communication.
Return-to-Non-Secure Vulnerabilities on ARM Cortex-M TrustZone: Attack and Defense

ARM Cortex-M is one of the most popular microcontroller architectures designed for embedded and Internet of Things (IoT) applications. To facilitate efficient execution, it has some unique hardware optimization. In particular, Cortex-M TrustZone has a fast state switch mechanism that allows direct control-flow transfer from the secure state program to the non-secure state userspace program. In this paper, we demonstrate how this fast state switch mechanism can be exploited for arbitrary code execution with escalated privilege in the non-secure state by introducing a new exploitation technique, namely return-to-non-secure (ret2ns). We experimentally confirmed the feasibility of four variants of ret2ns attacks on two Cortex-M hardware systems. To defend against ret2ns attacks, we design two address sanitizing mechanisms that have negligible performance overhead.
FLNA: An Energy-Efficient Point Cloud Feature Learning Accelerator with Dataflow Decoupling

Grid-based feature learning network plays a key role in recent point-cloud based 3D perception. However, high point sparsity and special operators lead to large memory footprint and long processing latency, posing great challenges to hardware acceleration. We propose FLNA, a novel feature learning accelerator with algorithm-architecture co-design. At algorithm level, the dataflow-decoupled graph is adopted to reduce 86% computation by exploiting inherent sparsity and concat redundancy. At hardware design level, we customize a pipelined architecture with block-wise processing, and introduce transposed SRAM strategy to save 82.1% access power. Implemented on a 40nm technology, FLNA achieves 13.4 − 43.3× speedup over RTX 2080Ti GPU. It rivals the state-of-the-art accelerator by 1.21× energy-efficiency improvement with 50.8% latency reduction.
HBP: Hierarchically Balanced Pruning and Accelerator Co-Design for Efficient DNN Inference

Weight pruning is studied to accelerate DNN inference by reducing the parameters and computations. Irregular pruning achieves high sparsity while incurring low computation parallelism and imbalanced workloads. The coarse-grained structured pruning sacrifices sparsity for higher parallelism. To strike a better balance, we propose Hierarchically Balanced Pruning by applying fine-grained but structured adjustments based on irregular pruning. Besides, it partitions the weight matrix into hierarchical blocks and constrains the sparsity of the blocks for balanced workloads. Furthermore, an accelerator is proposed to unleash the power of the pruning method. Experimental results show our method achieves 1.1×-6 higher sparsity than prior studies, and the accelerator achieves 1.2×-13× speedup and 3.3× energy efficiency improvement than its counterparts.
Invited: Building Robust Quantum System Software for Technology-Specific Characteristics

This paper discusses the various technologies used for quantum computing and highlights the need for compiler and software stack solutions that are portable across different technologies (beyond superconducting qubit quantum computers) while providing a higher-level interface that allows scientists to run their programs in a technology-agnostic manner. To achieve this, quantum compilers and architecture designs must not be bound by classical-style standards and specifications. As a first step toward tackling this challenge, this paper then focuses on developing a compiler solution for neutral atom quantum computing technology, which has several potential benefits over superconducting qubit quantum computing technology.These benefits include a greater connectivity of qubits within the Rydberg interaction radius, which allows for fewer SWAP operations, and the ability to execute multi-qubit gates directly. However, neutral atom quantum computers have a different set of constraints and requirements, including interaction blockades, which can result in potential serialization of operations, reducing some of the gains due to the better connectivity of neutral atom quantum computers. The paper then concludes by stating that addressing these challenges requires further research and development in the field.
AVX Timing Side-Channel Attacks against Address Space Layout Randomization

Modern x86 processors support an AVX instruction set to boost performance. However, this extension may cause security issues. We discovered that there are vulnerable properties in implementing masked load/store instructions. Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the significance of our attack by showing User and Kernel ASLR breaks on the recent Intel and AMD processors in various environments, including cloud computing systems, an SGX enclave (a fine-grained ASLR break), and major operating systems. We further demonstrate that our attack can be used to infer user behavior, such as Bluetooth events and mouse movements. We highlight that stronger isolation or more fine-grained randomization should be adopted to successfully mitigate our presented attacks.
NNTesting: Neural Network Fault Attacks Detection Using Gradient-Based Test Vector Generation

Recent studies have shown Neural Networks (NNs) are highly vulnerable to fault attacks. This work proposes a novel defensive framework, NNTesting, for detecting the fault attack and recovering the model. We first leverage gradient-based optimization to generate a set of high-quality Test Vectors (TVs) that effectively differentiate faulty profile models and further optimize the TV set by reducing the TVs through compression. The selected final TV set is then used to recover the model. The effectiveness of the proposed method is comprehensively evaluated on a wide range of models across various benchmark datasets. For instance, we successfully generate more than thousands of TV candidates using a gradient-based generation method. After compression, we achieve up to 94.76% detection success rate with only 140 TVs on the CIFAR-10 dataset.
Processing-In-Hierarchical-Memory Architecture for Billion-Scale Approximate Nearest Neighbor Search

Graph-based approximate nearest neighbor search (ANNS) algorithms achieve the best accuracy for fast high-recall searches on billion-scale datasets. Because of the irregular and large-volume data access, existing CPU-based systems suffer from heavy data movements when dealing with graph-based ANNS algorithms. Near-memory-computing (NMC) architectures have demonstrated great potential in boosting the performance of big-data processing. However, existing NMC architectures face two serious problems when processing graph-based ANNS algorithms: (1) the memory capacity of main memory level NMC (e.g., 64GB) cannot meet the storage requirement of ANNS on billion-scale datasets (e.g., 800GB), resulting in heavy data transfers between main memory and storage; (2) the contradiction between the irregular and fine-grained graph access and the page-level read granularity hinder the throughput of storage level NMC.This paper proposes Pyramid, the processing-in-hierarchical-memory architecture for graph-based ANNS on billion-scale datasets. Pyramid combines the internal bandwidth benefits of main memory level NMC with the capacity benefits of storage level NMC. A hierarchical graph-cluster-based ANNS is also proposed for Pyramid. It transforms the irregular data access on large-scale graphs into the irregular access on small-scale graphs at the main memory level and regular sequential in-cluster access at the storage level. Experimental results show that with the same recall of 0.9, Pyramid improves the throughput by 21.1~72.8× and 26.0~50.7× compared with existing CPU/GPU-based ANNS systems on million-scale and billion-scale datasets, respectively.