Reviewerly/exHarmony huggingface dataset:
Human Personality Identification Based on Handwriting Analysis

The study of handwritten patterns to recognise, assess, and comprehend a person's personality qualities is known as graphology. Handwriting analysis is pricy and error-prone, and its accuracy is dependent on the analyst's skill. The suggested method is therefore concentrated on creating a Web App with flask that can predict personality traits using machine learning without the involvement of a human. 657 authors' handwritten samples were used in the study as datasets. The user-uploaded image undergoes initial pre-processing, which removes undesirable properties, enhances quality, and applies transformations to make image data acceptable for feature extraction. We take into consideration seven manuscript patterns for filtering undesirable qualities, increasing the quality, and executing transformations for picture feature extraction. Eight Support Vector Machines are required to develop the unique personality and behaviour of the user after extracting all these features from the image. Predicting a writer's eight personality traits is fairly accurate. This includes emotional stability, mental acuity or willpower, humility, personal harmony and flexibility, lack of discipline, poor concentration, lack of communication, and social isolation classifiers with great accuracy.
Smart Contracts For NGOs and Startups using Blockchain

This paper proposes and emphasizes the requirement of an Blockchain based smart contract for NGO's and startup crowdfunding in the present circumstances. It also highlights the need of an online financial system for indigenous NGO's and seed fund utilization of startups. Conventionally, most charity organizations make use of hard cash for settling its transactions making the process less transparent. However, due to the COVID-19 pandemic, financial system has been largely affected. In this case an online financial transaction cum procurement portal would be crucial for the candidates applying relief in remote locations. The system analyses their eligibility based on their Curriculum Vitae (CV). Proposed system uses Ethereum based smart contract and Truffle Box to build a complete Dapp (decentralized application). Authors have used MetaMask Extension as a cryptocurrency wallet and Ganache blockchain to develop, deploy and test the decentralized application.
A Sliding Window Blockchain Architecture for the Internet of Things

The Internet of Things is a concept that allows people to connect to the Internet and access services through new, unconventional computing and communication practices. But there are two issues in the Internet of Things. These are security and data protection. Due to the limited processor, storage, and energy resources of IoT devices existing security frameworks are unable to meet the essential security requirements. Centralized security is not good for IoT as they tend towards a single point of failure and are more expensive for protecting centralized resources against attacks. Therefore, decentralized IoT security must be adapted. Blockchain is a a decentralised security system that can be used to secure many different things. Blockchain is not currently appropriate technology for IoT because it is too complicated and poorly scalable. This paper shows research on and implementation of Sliding Window Blockchain (SWBC), a conventional blockchain architecture used in Internet of Things applications. SWBC's performance is computed using live data which is supplied by a smart home testbed.
Analysis of Statistical and Spatial Spectral Characteristics of Distortions in Lossy Image Compression

Image lossy compression is currently widely employed in different fields. Compared to lossless compression, it allows providing a considerably larger compression ratio but distortions are introduced inevitably. Properties of these distortions depend on a used coder, an image subject to compression, and compression parameters. Distortions affect visual perception of compressed images and efficiency of their further processing. Thus, statistical and spatial spectral properties of introduced distortions have to be understood well to explain effects observed in compressed image processing and, possibly, enabling their simulation. In this paper, we describe the tools that can be used in analysis of distortion properties and give an example of such analysis for grayscale images compressed by AGU coder. It is shown that distortions, in general, have spatially variant statistics where they are more intensive in locally active areas. Besides, distortions are spatially uncorrelated for all considered quantization steps and, respectively, compression ratios. Experiments are carried out using four typical remote sensing images.
UniQ: A Unified Programming Model for Efficient Quantum Circuit Simulation

Quantum circuit simulation is critical for verifying quantum computers. Given exponential complexity in the simulation, existing simulators use different architectures to accelerate the simulation. However, due to the variety of both simulation methods and modern architectures, it is challenging to design a high-performance yet portable simulator. In this work, we propose UniQ, a unified programming model for multiple simulation methods on various hardware architectures. We provide a unified application abstraction to describe different applications, and a unified hierarchical hardware abstraction upon different hardware. Based on these abstractions, UniQ can perform various circuit transformations without being aware of either concrete application or architecture detail, and generate high-performance execution schedules on different platforms without much human effort. Evaluations on CPU, GPU, and Sunway platforms show that UniQ can accelerate quantum circuit simulation by up to 28.59× (4.47× on average) over state-of-the-art frameworks, and successfully scale to 399,360 cores on 1,024 nodes.
Positive-Phase Temperature Scaling for Quantum-Assisted Boltzmann Machine Training

Quantum-assisted sampling is a promising technique to enable training probabilistic ML models, which otherwise depend on slow-mixing classical sampling methods; such as, the use of Quantum Annealing Processors (QAP) to train Boltzmann Machines (BMs). Previous work has shown that QAPs can sample from a Boltzmann distribution, although, at an unknown instance-dependent temperature. Due to this distribution divergence, existing training algorithms have resorted to negative-phase temperature scaling. This method, although effective under arduous tuning, introduces unwanted noise to the sampleset due to the quantization errors caused by the underutilization of the QAP bias ranges; and is prone to bias overflow. We introduce a change in the training algorithm to allow positive-phase temperature scaling; an approach that reduces the impact of quantization noise, while still incorporating temperature scaling. As a result, we see an overall improvement in the convergence rate and testing accuracy, when compared to the state-of-the-art approach.
WholeGraph: A Fast Graph Neural Network Training Framework with Multi-GPU Distributed Shared Memory Architecture

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets, encoding graph data into low dimensional vectors. In this paper, we present a fast training graph neural network framework, i.e., WholeGraph, based on a multi-GPU distributed shared memory architecture. Whole-Graph consists of partitioning the graph and corresponding node or edge features to multi-GPUs, eliminating the bottleneck of communication between CPU and GPUs during the training process. And the communication between different GPUs is implemented by GPUDirect Peer-to-Peer (P2P) memory access technology. Furthermore, WholeGraph provides several optimized computing operators. Our evaluations show that on large-scale graphs WholeGraph outperforms state-of-the-art GNN frameworks, such as Deep Graph Library (DGL) and Pytorch Geometric (PyG). The speedups of WholeGraph are up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$57.32\mathrm{x}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$242.98\mathrm{x}$</tex> compared with DGL and PyG on a single machine multi-GPU node, respectively. The ratio of GPU utilization can sustain above 95% during GNN training process.
Multivariate Time-Series Anomaly Detection in IoT Using Attention-Based Gated Recurrent Unit

The explosively-growing Internet of Things (IoT) is generating massive amount of multivariate time series data. Anomaly detection for multivariate time series is of great importance in detecting and locating system failures, device malfunctions and malicious attacks in IoT. In this paper, we propose a new anomaly detection model, called Graph Attention-based Gated Recurrent Unit (GAGRU), to exploit the complex spatiotemporal features in multivariate time series. The proposed GAGRU is basically an attention-based Gated Recurrent Unit (GRU), which embeds the graph attention mechanism into the GRU to integrate the spatial features while exploiting the temporal dependency of data using GRU. Moreover, the GAGRU model is also designed to automatically learn the graph structure and score anomalies under their own TopK criterion. Experiments on two real-world datasets demonstrate the performance improvement of the proposed GAGRU model.
Mapping Out the HPC Dependency Chaos

High Performance Computing (HPC) software stacks have become complex, with the dependencies of some applications numbering in the hundreds. Packaging, distributing, and administering software stacks of that scale is a complex undertaking anywhere. HPC systems deal with esoteric compilers, hardware, and a panoply of uncommon combinations. In this paper, we explore the mechanisms available for packaging software to find its own dependencies in the context of a taxonomy of software distribution, and discuss their benefits and pitfalls. We discuss workarounds for some common problems caused by using these composed stacks and introduce Shrinkwrap: A solution to producing binaries that directly load their dependencies from precise locations and in a precise order. Beyond simplifying the use of the binaries, this approach also speeds up loading as much as 7× for a large dynamically-linked MPI application in our evaluation.
Insurance Fraud Detection using Machine Learning

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders’ premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share.
HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization

Graph neural networks (GNNs) have shown to significantly improve graph analytics. Existing systems for GNN training are primarily designed for homogeneous graphs. In industry, however, most graphs are actually heterogeneous in nature (i.e., having multiple types of nodes and edges). Existing systems train a heterogeneous GNN (HetGNN) as a composition of homogeneous GNN (HomoGNN) and thus suffer from critical limitations such as lack of memory optimization and limited operator parallelism. To address these limitations, we propose HGL - a heterogeneity-aware system for GNN training. At the core of HGL is an intermediate representation, called HIR, which provides a holistic representation for GNNs and enables cross-relation optimization in HetGNN training. We devise tailored optimizations on HIR, including graph stitching, operator fusion and operator bundling. Compared with DGL and PyG, HGL achieves a speedup from 7 to 22 times for training HetGNNs.
Semantics-Guided Contrastive Joint Source-Channel Coding for Image Transmission

Deepjoint source-channel coding (D-JSCC) provides several advantages over conventional coding schemes, in which source and channel coding are designed separately. For example, D-JSCC schemes suffer from smaller delays and are more robust to rapid channel variation. However, D-JSCCs are often designed without explicit structure or insight, making them less adaptive, hard to control, and theoretically unfounded. In this paper, we propose a contrastive joint-source-channel coding (C-JSCC) design, which uses supervised contrastive learning (SCL) to make the latent space of a conventional D-JSCC more structured and meaningful. By testing on the CIFAR-10 dataset, we show that C-JSCC consistently outperforms its D-JSCC counterpart in both tasks of image reconstruction and classification. Moreover, C-JSCC is shown to output images with perceptual quality better than the classic BPG image codec in the low bits-per-pixel (bpp) region. The roles of various hyper-parameters in C-JSCC are investigated by analytical approximations, experiments, and visualization techniques.
STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations

Graph pattern matching is a fundamental task in many graph analytics and graph mining applications. As an NP-hard problem, it is often a performance bottleneck in these applications. Previous work has proposed to use GPU to accelerate the computation. However, we find that the existing GPU solutions fail to show a performance advantage over the state-of-the-art CPU implementation due to their subgraph-centric design. This work proposes a novel stack-based graph pattern matching system on GPU that avoids the synchronization and memory consumption issues of the previous subgraph-centric systems. We also propose a two-level work-stealing and a loop-unrolling technique to improve the inter-warp and intra-warp GPU resource utilization of our system. The experiments show that our system significantly advances the state-of-the-art for graph pattern matching on GPU.
Quantum Computation Using Coherent Ising Machines Based on Spiking Neural Networks

Coherent Ising machines (CIMs) are a promising approach for quantum computation. It exhibits excellent per-formance in solving the combinatorial optimization problems, however, the existence of amplitude heterogeneity limits the applications of CIMs. Here in this work, we present a system of the spiking neural network composed of antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. It works based on the principles of CIM during solving the combinatorial optimization problems. Moreover, a nonlinear transfer function is chosen to mitigate the amplitude inhomogeneities and destabilize the resulting local minima ac-cording to the dynamical behavior of spiking neurons.
Large Kernel Attention Hashing for Efficient Image Retrieval

Since the Vision Ttransformer can capture long-range dependencies in images, it has been successfully applied in hashing image retrieval. Unfortunately, the existing Transformer-based hashing methods suffer from the problem of dividing 2D images into ID patch sequences, which ignores the 2D structure of patches and destroys local feature details. Indeed, the Conv Stem in Convolutional Neural Network can avoid the above-mentioned problem. However, the convolution operation is expert in extracting local contextual information of the image, which is difficult to capture the global representations in contrast to Transformer. Therefore, this paper proposes a hybrid hash network structure, termed Large Kernel Attention Hashing method (LKAH). It cleverly introduces a large convolution kernel and an attention mechanism into the design of the hash network, so that the hash network can simultaneously extract local features and long-range dependencies of images under a certain number of parameters. While fully combining the advantages of convolution and Transformer, LKAH also conquers the defects of both, and achieves state-of-the-art retrieval performance on two commonly used datasets, CIFAR-10 and NUS-WIDE.
Parametric activation functions modelling fuzzy connectives for better explainability of neural models

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository.
Efficient Shared Feature Learning for Cross-modality Person Re-identification

Visible-infrared person re-identification is a challenging task, which aims to achieve cross-modal pedestrian retrieval. How to reduce the intra- and cross-modal discrepancies of the same pedestrian is the difficult part of this task. In response to the aforementioned issue, we propose a new framework named Shared Local Feature Learning Network (SLFL-Net) for mining more efficient shared features. Focusing on network designing, SLFL-Net uses ResNet50 as the backbone and employs the mixed attention mechanism to highlight important features. In addition, to obtain the fine-grained information of heterogeneous pedestrian images, we explore the local feature learning module for cross-modality person re-identification. For metric learning, we present two center-based losses to shorten intra- and cross-modal distances. The learned representation is more effective when the proposed loss function and local feature learning module are combined. Numerous experiments are performed on the public dataset, and the experimental results illustrate that our method can significantly improve the performance of the cross-modality task.
Attention-based Models in Self-supervised Monocular Depth Estimation

Depth estimation problem has various real world applications where high quality of depth prediction is crucial. Depth maps can be estimated from labeled data. However, it is hard to obtain a good quality and dense ground truth. Nevertheless, it is possible to use unlabeled sequences of images in the self-supervised learning setting as they contain the information about changes of camera's position. Thus, this study is focused on the monocular self-supervised depth estimation task. In the study three different visual attention mechanisms are compared. Also, the novel attention mechanism is proposed. It aggregates inter-channel information and spatial information to capture long-range dependencies. The proposed modifications to the Monodepth2 architecture lead to the improved performance on the public KITTI dataset. The provided experimental results show that including attention to the depth decoder significantly improves model's quality. In comparison, adding attention to the encoder does not lead to better performance. Additionally, the proposed attention mechanism shows high quality in terms of the all used metrics on the chosen dataset.
Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform

Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck in realizing parallel MCTS on CPUs. In this work, we develop a scalable CPU-FPGA system for Tree-Parallel MCTS. We propose a novel decomposition and mapping of MCTS data structure and computation onto CPU and FPGA to reduce communication and coordination. High scalability of our system is achieved by encapsulating in-tree operations in an SRAM-based FPGA accelerator. To lower the high data access latency and inter-worker synchronization overheads, we develop several hardware optimizations. We show that by using our accelerator, we obtain up to 35× speedup for in-tree operations, and 3× higher overall system throughput. Our CPU-FPGA system also achieves superior scalability wrt number of parallel workers than state-of-the-art parallel MCTS implementations on CPU.
Numba-accelerated parameter estimation for artificial pancreas applications

General-purpose computing on graphics processing units (GPU) has gained more attention over the last years in scientific computing. Tasks like population-based optimizations are widely used and can be efficiently parallelized. While Python is considered generally “slow”, it is one of the most popular programming languages. In this study, we evaluate the acceleration possibilities of a parameter estimation problem based on optimization utilizing the Numba library on the central processing unit (CPU) and on the GPU. Runtimes indicate that Numba achieves high runtime improvements with little modifications in the syntax. On the other hand, utilization of the GPU is beneficial when high parallelization can be guaranteed and using cloud computing platforms.
A Survey on Distributed Reinforcement Learning

In many settings, reinforcement learning (RL) has proven to be an effective tool for tackling difficult decision-making challenges. Traditional RL algorithms, on the other hand, frequently hit walls when confronted with issues of a sufficiently great scale or complexity. Distributed reinforcement learning (DRL) is a new area of study that hopes to circumvent these restrictions by dividing the learning workload among several computers. In this work, we offer a thorough overview of DRL, discussing its history, difficulties, applications, evaluation, scalability, and outstanding issues. We classify DRL approaches and frameworks and examine their similarities and differences. We also highlight the difficulties and restrictions of using DRL in real-world circumstances and examine its practical applicability in a variety of fields. We also describe current trends and future directions for evaluating DRL algorithms, and we analyse the performance of DRL algorithms on benchmark tasks. We also go through various options for distributed computing in DRL, as well as other methods for increasing the scalability and efficiency of DRL algorithms. We conclude by outlining key concerns and obstacles in DRL study, and by making suggestions for moving the subject forward. Overall, the goal of this survey is to give readers a picture of where things stand in terms of DRL study and application at the moment.