Reviewerly/exHarmony huggingface dataset:
The continuous-time quantum walk on some graphs based on the view of quantum probability

In this paper, continuous-time quantum walk is discussed based on the view of quantum probability, i.e. the quantum decomposition of the adjacency matrix A of graph. Regard adjacency matrix A as Hamiltonian which is a real symmetric matrix with elements 0 or 1, so we regard [Formula: see text] as an unbiased evolution operator, which is related to the calculation of probability amplitude. Combining the quantum decomposition and spectral distribution [Formula: see text] of adjacency matrix A, we calculate the probability amplitude reaching each stratum in continuous-time quantum walk on complete bipartite graphs, finite two-dimensional lattices, binary tree, [Formula: see text]-ary tree and [Formula: see text]-fold star power [Formula: see text]. Of course, this method is also suitable for studying some other graphs, such as growing graphs, hypercube graphs and so on, in addition, the applicability of this method is also explained.
Stochastic Control for Bayesian Neural Network Training

In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models. We derive a first principle stochastic differential equation for the training dynamics of the mean and uncertainty parameter in the variational distributions. On the basis of the derived Bayesian stochastic differential equation, we apply the methodology of stochastic optimal control on the variational parameters to obtain individually controlled learning rates. We show that the resulting optimizer, StochControlSGD, is significantly more robust to large learning rates and can adaptively and individually control the learning rates of the variational parameters. The evolution of the control suggests separate and distinct dynamical behaviours in the training regimes for the mean and uncertainty parameters in Bayesian neural networks.
Canonicalisation of SPARQL 1.1 Queries

SPARQL is the standard query language for RDF as stated by the W3C. It is a highly expressive querying language that contains the standard operations based on set algebra, as well as navigational operations found in graph querying languages. Because of this, there are various ways to represent the same query, which may lead to redundancy in applications of the Semantic Web such as caching systems. We propose a canonicalisation method as a solution, such that all (monotone) congruent queries will have the same canonical form. Despite the theoretical complexity of this problem, our experiments show a good performance over real-world queries. Finally, we anticipate applications in caching, log analysis, query optimisation, etc.
EBSP

Model compression has been extensively investigated for supporting efficient neural network inference on edge-computing platforms due to the huge model size and computation amount. Recent researches embrace joint-way compression across multiple techniques for extreme compression. However, most joint-way methods adopt a naive solution that applies two approaches sequentially, which can be sub-optimal, as it lacks a systematic approach to incorporate them.
GLite

We propose a scalable graph-level optimizer named GLite to speed up search-based optimizations on large neural networks. GLite leverages a potential-based partitioning strategy to partition large computation graphs into small subgraphs without losing profitable substitution patterns. To avoid redundant subgraph matching, we propose a dynamic programming algorithm to reuse explored matching patterns. The experimental results show that GLite reduces the running time of search-based optimizations from hours to milliseconds, without compromising in inference performance.
GraphRing

Due to the irregular memory access and high bandwidth demanding, graph processing is usually inefficient on conventional computer architectures. The recent development of the processing-in-memory (PIM) technique such as hybrid memory cube (HMC) has provided a feasible design direction for graph processing accelerators. Although PIM provides high internal bandwidth, inter-node memory access is inevitable in large-scale graph processing, which greatly affects the performance. In this paper, we propose an HMC-based graph processing framework, GraphRing. GraphRing is a software-hardware codesign framework that optimizes inter-HMC communication. It contains a regularity- and locality-aware graph execution model and a ring-based multi-HMC architecture. The evaluation results based on 5 graph datasets and 4 graph algorithms show that GraphRing achieves on average 2.14× speedup and 3.07× inter-HMC communication energy saving, compared with GraphQ, a state-of-the-art graph processing architecture.
LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques

Deep learning techniques have recently demonstrated their ability to be applied in any field, including image processing, natural language processing, speech recognition, and many other real-world problem-solving applications. Human activity recognition (HAR), on the other hand, has become a popular research topic due to its wide range of applications. The researchers began working on the new ideas by combining the two emerging areas to solve HAR problems using deep learning. Recurrent neural networks (RNNs) in deep learning (DL) provide higher opportunity in recognizing the abnormal behavior of humans to avoid any kind of security issues. The present study proposed a deep network architecture based on one of the techniques of deep learning named as residual bidirectional long-term memory (LSTM). The new network is capable of avoiding gradient vanishing in both temporal and spatial dimensions with a view to increase the rate of recognition. To understand the complexity of activities recognition and classification, two LSTM models, basic model and the proposed model, were used. Later, a comparative analysis is performed to understand the efficiencies of the models during the classification of five human activities like abuse, arrest, arson, assault, and fighting images classification. The basic LSTM model has achieved a training accuracy of just 18% and testing accuracy of 21% with higher training and classification loss values. But the proposed LSTM model has outperformed the basic model while achieving 100% classification accuracy. Finally, the observations have proved that the proposed LSTM model is best suitable in recognizing and classifying the human activities well even for real-time videos.
Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN

Real-world AI applications, such as augmented reality or autonomous driving, require processing multiple CV tasks simultaneously. However, the enormous data size and the memory footprint have been a crucial hurdle for deep neural networks to be applied in resource-constrained devices. To solve the problem, we propose an algorithm/architecture co-design. The proposed algorithmic scheme, named SqueeD, reduces per-task weight and activation size by 21.9x and 2.1x, respectively, by sharing those data between tasks. Moreover, we design architecture and dataflow to minimize DRAM access by fully utilizing benefits from SqueeD. As a result, the proposed architecture reduces the DRAM access increment and energy consumption increment per task by 2.2x and 1.3x, respectively.
VStore

Graph-based vector search that finds best matches to user queries based on their semantic similarities using a graph data structure, becomes instrumental in data science and AI application. However, deploying graph-based vector search in production systems requires high accuracy and cost-efficiency with low latency and memory footprint, which existing work fails to offer. We present VStore, a graph-based vector search solution that collaboratively optimizes accuracy, latency, memory, and data movement on large-scale vector data based on in-storage computing. The evaluation shows that VStore exhibits significant search efficiency improvement and energy reduction while attaining accuracy over CPU, GPU, and ZipNN platforms.
Using machine learning to optimize graph execution on NUMA machines

This paper proposes PredG, a Machine Learning framework to enhance the graph processing performance by finding the ideal thread and data mapping on NUMA systems. PredG is agnostic to the input graph: it uses the available graphs' features to train an ANN to perform predictions as new graphs arrive - without any application execution after being trained. When evaluating PredG over representative graphs and algorithms on three NUMA systems, its solutions are up to 41% faster than the Linux OS Default and the Best Static - on average 2% far from the Oracle -, and it presents lower energy consumption.
PatterNet

Weight clustering is an effective technique for compressing deep neural networks (DNNs) memory by using a limited number of unique weights and low-bit weight indexes to store clustering information. In this paper, we propose PatterNet, which enforces shared clustering topologies on filters. Cluster sharing leads to a greater extent of memory reduction by reusing the index information. PatterNet effectively factorizes input activations and post-processes the unique weights, which saves multiplications by several orders of magnitude. Furthermore, PatterNet reduces the add operations by harnessing the fact that filters sharing a clustering pattern have the same factorized terms. We introduce techniques for determining and assigning clustering patterns and training a network to fulfill the target patterns. We also propose and implement an efficient accelerator that builds upon the patterned filters. Experimental results show that PatterNet shrinks the memory and operation count up to 80.2% and 73.1%, respectively, with similar accuracy to the baseline models. PatterNet accelerator improves the energy efficiency by 107x over Nvidia 1080 1080 GTX and 2.2x over state of the art.
Enabling hard constraints in differentiable neural network and accelerator co-exploration

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adopting the idea of differentiable neural architecture search. However, despite the superior search efficiency of the differentiable co-exploration, it faces a critical challenge of not being able to systematically satisfy hard constraints such as frame rate. To handle the hard constraint problem of differentiable co-exploration, we propose HDX, which searches for hard-constrained solutions without compromising the global design objectives. By manipulating the gradients in the interest of the given hard constraint, high-quality solutions satisfying the constraint can be obtained.
mflowgen

Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. Unfortunately, existing approaches based on parameterized Tcl generators support very limited reuse as designers customize flows for specific designs and technologies, preventing their reuse in future flows. We present a vision and framework based on modular flow generators that encapsulates coarse-grained and fine-grained reusable code in modular nodes and assembles them into complete flows. The key feature is a flow consistency and instrumentation layer embedded in Python, which supports mechanisms for rapid and early feedback on inconsistent composition. We evaluate the design flows of successive generations of silicon prototypes built in TSMC16, TSMC28, TSMC40, SKY130, and IBM180 technologies, showing how our approach can enable significant code reuse in future flows.
Towards a formally verified hardware root-of-trust for data-oblivious computing

The importance of preventing microarchitectural timing side channels in security-critical applications has surged immensely over the last several years. Constant-time programming has emerged as a best-practice technique to prevent leaking out secret information through timing. It builds on the assumption that certain basic machine instructions execute timing-independently w.r.t. their input data. However, whether an instruction fulfills this data-independent timing criterion varies strongly from architecture to architecture.
Efficient access scheme for multi-bank based NTT architecture through conflict graph

Number Theoretical Transform (NTT) hardware accelerator becomes crucial building block in many cryptosystems like post-quantum cryptography. In this paper, we provide new insights into the construction of conflict-free memory mapping scheme (CFMMS) for multi-bank NTT architecture. Firstly, we offer parallel loop structure of arbitrary-radix NTT and propose two point-fetching modes. Afterwards, we transform the conflict-free mapping problem into conflict graph and develop novel heuristic to explore the design space of CFMMS, which turns out more efficient access scheme than classic works. To further verify the methodology, we design high-performance NTT/INTT kernels for Dilithium, whose area-time efficiency significantly outperforms state-of-the-art works on the similar FPGA platform.
Optimizing quantum circuit synthesis for permutations using recursion

We describe a family of recursive methods for the synthesis of qubit permutations on quantum computers with limited qubit connectivity. Two objectives are of importance: circuit size and depth. In each case we combine a scalable heuristic with a non-scalable, yet exact, synthesis.
PathFinder

Side-channel analysis (SCA) attacks show an enormous threat to cryptographic integrated circuits (ICs). To address this threat, designers try to adopt various countermeasures during the IC development process. However, many existing solutions are costly in terms of area, power and/or performance, and may require full-custom circuit design for proper implementations. In this paper, we propose a tool, namely PathFinder, to automatically identify leaky paths and protect the design, and is compatible with the commercial design flow. The tool first finds out partial logic cells that leak the most information through dynamic correlation analysis. PathFinder then exploits static security checking to construct complete leaky paths based on these cells. After leaky paths are identified, PathFinder will leverage proper hardware countermeasures, including Boolean masking and random precharge, to eliminate information leakage from these paths. The effectiveness of PathFinder is validated both through simulation and physical measurements on FPGA implementations. Results demonstrate more than 1000X improvements on side-channel resistance, with less than 6.53% penalty to the power, area and performance.
R2B

Big data applications have differentiated requirements for I/O resources in cloud environments. For instance, data analytic and AI/ML applications usually have periodical burst I/O traffic, and data stream processing and database applications often introduce fluctuating I/O loads based on a guaranteed I/O bandwidth. However, the existing resource isolation model (i.e., RLW) and methods (e.g., Token-bucket, mClock, and cgroup) cannot support the fluctuating I/O load and differentiated I/O demands well, and thus cannot achieve fairness, high resource utilization, and high performance for applications at the same time. In this paper, we propose a novel efficient and fair I/O resource isolation model and method called R2B, which can adapt to the differentiated I/O characteristics and requirements of different applications in a shared resource environment. R2B can simultaneously satisfy the fairness and achieve both high application efficiency and high bandwidth utilization.
A Blockchain-Based Normalized Searchable Encryption System for Medical Data

As COVID-19 continues to spread around the world, the healthcare industry has accelerated the transformation to digital healthcare services. In the era of big data, many hospitals prefer to use remote cloud servers to store and manage massive electronic medical data. However, cloud-assisted medical data systems cannot guarantee the confidentiality, integrity, and availability of data. Searchable encryption can effectively address the above challenges by enabling data search on the ciphertext, which achieves the availability of medical data while ensuring data security and privacy. However, the search server may return mismatched search results due to economic interests or single points of failure. Blockchain is a decentralized computing paradigm with public verifiability, which provides an efficient solution to this problem. However, the existing blockchain-based searchable encryption solutions do not consider the flexible search function of multiple users and the restriction of encrypted data for medical scenarios. Therefore, we propose a blockchain-based multiuser normalized searchable encryption (BNSE) scheme and design a blockchain-based normalized searchable encryption system for medical data (BNSEM) based on the scheme. To verify the practicality of the system, we evaluate the performance from both theoretical and experimental aspects.
A Variational Level Set Image Segmentation Method via Fractional Differentiation

To solve the issues with conventional level set segmentation algorithms, which are sensitive to the initial contours and less noise-resistant, a segmentation model based on the coupling of texture information and structural information is developed. In this model, a rotation invariant mask produced by fractional-order differentiation is used to first describe the image’s global information. Then, the power function of the energy generalization function is solved by applying factorization theory, and for each pixel of the image, not only its information but also its surrounding pixel information is taken into account and integrated into the energy generalization function via weight scaling. At the same time, the L2 norm of the fractional-order image and the difference from the fitted image are used to generate the energy generalization function of the model. The final results of this study demonstrate that the proposed model achieved a better segmentation performance than the current active contour models in terms of robustness to Gaussian noise and pretzel noise, as well as the segmentation accuracy and algorithm running time. These results were obtained in synthetic images, real images, and natural images.
Analysis of recent advancements in support vector machine

Summary The current researches are primarily focused on optimizing the available classification methods in support vector machines (SVMs). The basic idea of this article to highlight the fundamentals of the proposed SVM variants and their optimization techniques. In this article, we have collectively identified the major issues in different variations of SVMs. This article has discussed a detailed explanation of the optimization and advancement of SVM and its kernel variants. The study elaborates on certain SVM optimization issues like model selection, novelty detection and so forth. In contrast, it discussed the identification of problems raises after every optimization and advancement of SVM. This is the first novel attempt to explain the optimization raises for SVMs. This article is about the majority of the necessary advancements in the optimization and cost‐effectiveness of computational algorithms of SVM with their detailed computational analysis. The approaches of such study helps to understand the optimization challenges in SVM.
An Overview on Technologies for the Distribution and Participation in Live Events

COVID-19 has impacted several sectors, designing news way of collaboration and interaction with customers and partners. The performing arts sector is one of the most affected; activities have been stopped for months and months but, at the same time, the sector has embarked on a systemic transition where old, unsustainable practices have been replaced with more sustainable and technology-based alternatives. Through a narrative literature review, the paper discusses needs, current studies based on immersive technologies, strengths and weakness to be managed, opportunities to be leveraged and threats to be overcome, in order to improve competitiveness and plan future actions. Academics and practitioners can benefit from the results to address their current research and activities.
XRShip: Augmented Reality for Ship Familiarizations

PT Pertamina Trans Kontinental (PTK) is a state-owned maritime company in Indonesia. PTK's vision and mission are to combine innovation with a dedication to digitization. As a result, we created the ShipXR Augmented Reality (AR) application to carry out that commitment and support business activities. The applications have been tested on a variety of devices. Metafrate Tools are evaluative measurement tools that we have created ourselves. The evaluation found that the program functions normally on both devices or that any serious difficulties were discovered. Furthermore, the result reveals that the application is very stable as a result of Metafrate Tools’ output.
Coupling Mobile AR with a Virtual Agent for End-User Engagement

Virtual agents are introduced in our daily lives to guide end-users in a variety of tasks, contributing to a convenient user experience and boosting motivation and productivity. Along with the high data availability and modern algorithms, they contribute to the changing way end-users interact with their environment to obtain information, train, and socialize. In this study, we leverage the ability of mobile Augmented Reality (AR) technology to engage end-users to an immersive environment. Given the deep penetration of mobile phones in daily lives, we developed a mobile AR application that offers unique user-environment interaction. The AR application allows media visualizations, content manipulation, educational and gamification functionalities, and a virtual agent ensuring seamless interaction with the technology. The app aims to foster end-user engagement in sustainable practices (i.e., circular economy, water management, etc.) with a great potential to be applied in touristic and cultural appreciation, art and heritage promotion, etc. The purpose and functioning of the AR app, along with the system design and architecture, implementation challenges, and the end-users’ feedback on the interactions with a virtual agent are thoroughly presented.