Reviewerly/exHarmony huggingface dataset:
Hierarchical Reinforcement Learning Using Stepwise Subgoal Generation Based on Fuzzy ART and Experience Acquisition

Hierarchical reinforcement learning (HRL) is an approach that incorporates intrinsic motivation mechanisms into reinforcement learning. HRL divides the agent’s internal mechanism into two components: a higher-level policy (application order of subgoals) and a lower-level policy (behavior sequence to subgoals) for problem solving. It has been demonstrated that HRL can solve problems in environments with sparse rewards and environments that require learning of long action sequences, which are difficult to address with conventional reinforcement learning, provided that the definition of subgoals is appropriate. However, existing HRL assumes the availability of predefined subgoals necessary for problem solving and does not provide an algorithm for achieving autonomous reinforcement learning. In this study, we propose stepwise unified hierarchical reinforcement learning (SUHRL), a new reinforcement learning algorithm that introduces a mechanism to gradually generate necessary experiences and appropriate subgoals for problem solving. SUHRL solves problems by stepwise clustering using Fuzzy ART and experience acquisition processing to generate suitable subgoals incrementally. Evaluation experiments conducted on MiniGrid environments and Montezuma’s Revenge demonstrate that the proposed method can generate the required subgoals incrementally and achieve autonomous problem solving.
DPS: Adaptive Power Management for Overprovisioned Systems

Maximizing performance under a power budget is essential for HPC systems and has inspired the development of many power management frameworks. These can be broadly characterized into two groups: model-based and stateless. Model-based frameworks use machine learning to achieve good performance under a power budget but are highly dependent on the quality of the learned model and the data used to train it. Stateless frameworks are more robust and require no training, but are generally lower performance. In this paper, we propose a new framework that does not require a model, but does track a small amount of state in the form of recent power dynamics. We implement this idea and test it on a public cloud running both Spark and HPC jobs. We find when total power demand is low, our framework achieves equivalent performance to prior work, but when power demand is high it achieves mean 8% performance improvement (with no reliance on a learned model).
家事ロボットの服装変化がユーザの印象に与える影響

In this study, we focus on clothing as a design aspect of robots, and investigate users’ impressions when robots change their clothing to suit the task at hand. Clothing can have an effect of showing one’s role and abilities to others. By utilizing this effect in the appearance of robots, it may be possible to express multiple roles and abilities for different tasks with a single robot, which could be beneficial in robot design. In the experiment, we investigated the difference in user impressions when the robot changed its clothing to suit the task at hand compared to when it did not. This was done through a video-watching experiment conducted on a simulator. The results of the experiment showed that when the robot changed its clothing to suit the task at hand, users perceived its appearance as appropriate for the task. This suggests a potential for effectively conveying the robot’s abilities and characteristics to users through its clothing.
GraphSet: High Performance Graph Mining through Equivalent Set Transformations

Graph mining is of critical use in a number of fields such as social networks, knowledge graphs, and fraud detection. As an NP-complete problem, accelerating computation performance is the main target for current optimizations. Due to excellent performance, state-of-the-art graph mining systems mainly rely on pattern-aware algorithms. Despite previous efforts, complex control flows introduced by pattern-aware algorithms bring significant overhead and also impede further acceleration on heterogeneous hardware.
Learning Kernel Stein Discrepancy for Training Energy-Based Models

The primary challenge in unsupervised learning is training unnormalized density models and then generating similar samples. Few traditional unnormalized models know what the quality of the trained model is, as most models are evaluated by downstream tasks and often involve complex sampling processes. Kernel Stein Discrepancy (KSD), a goodness-of-fit test method, can measure the discrepancy between the generated samples and the theoretical distribution; therefore, it can be employed to measure the quality of trained models. We first demonstrate that, under certain constraints, KSD is equal to Maximum Mean Discrepancy (MMD), a two-sample test method. PT KSD GAN (Kernel Stein Discrepancy Generative Adversarial Network with a Pulling-Away Term) is produced to compel generated samples to approximate the theoretical distribution. The generator, functioning as an implicit generative model, employs KSD as loss to avoid tedious sampling processes. In contrast, the discriminator is trained to identify the data manifold, also known as an explicit energy-based model. To demonstrate the effectiveness of our approach, we undertook experiments on two-dimensional toy datasets. Our results highlight that our generator adeptly captures the accurate density distribution, while the discriminator proficiently recognizes the unnormalized approximate distribution shape. When applied to linear Independent Component Analysis datasets, the log likelihoods of PT KSD GAN improve by about 5‰ over existing methods when the data dimension is less than 30. Furthermore, our tests on image datasets reveal that the PT KSD GAN excels in navigating high-dimensional challenges, yielding authentically genuine samples.
Case Studies: AI in Action in Art and Design

This chapter presents a collection of practical case studies showcasing the integration of generative AI into diverse art and design disciplines. Spanning across 3D design, drawing, and digital art, these case studies provide a comprehensive exploration of the transformative potential on the creative process. Highlighting versatile applications in graphic design, product design, architecture, and more, these case studies underscore the boundless possibilities AI offers to the creative industry. The chapter begins by delving into the realm of 3D design, unveiling how AI-driven technologies are revolutionizing the sculpting process and shaping the future of three-dimensional art. Moving to traditional drawing techniques, AI blurs the lines between tradition and innovation by enabling artists to explore novel realms of creativity. The chapter also examines the captivating world of digital art, where AI-generated content becomes a unique form of expression. From manipulating pixels to crafting intricate patterns, the role of AI tools in pushing the boundaries of digital art is vividly illustrated. Through these case studies, this chapter provides a deep insight into the profound influence of AI integration across art and design disciplines. From 3D design to drawing and digital art, these examples offer a glimpse into the transformative potential of AI, promising to reshape the creative landscape.
Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults

The advent of High-Performance Computing has led to the adoption of Convolutional Neural Networks (CNNs) in safety-critical applications such as autonomous vehicles. However, CNNs are vulnerable to DRAM errors corrupting their parameters, thereby degrading their accuracy. Existing techniques for protecting CNNs from DRAM errors are either expensive or fail to protect from large-granularity, multi-bit errors, which occur commonly in DRAMs.
TANGO: re-thinking quantization for graph neural network training on GPUs

Graph learning is becoming increasingly popular due to its superior performance in tackling many grand challenges. While quantization is widely used to accelerate Graph Neural Network (GNN) computation, quantized training faces remarkable roadblocks. Current quantized GNN training systems often experience longer training time than their full-precision counterparts for two reasons: (i) addressing the quantization accuracy challenge leads to excessive overhead, and (ii) the optimization potential exposed by quantization is not adequately leveraged. This paper introduces Tango which re-thinks quantization challenges and opportunities for graph neural network training on GPUs with three contributions: Firstly, we introduce efficient rules to maintain accuracy during quantized GNN training. Secondly, we design and implement quantization-aware primitives and inter-primitive optimizations to speed up GNN training. Finally, we integrate Tango with the popular Deep Graph Library (DGL) system and demonstrate its superior performance over the state-of-the-art approaches on various GNN models and datasets.
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training

Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more dependencies in graph events and needs to be maintained synchronously across all trainers. As a result, existing frameworks suffer from accuracy loss when scaling to multiple GPUs. Even worse, the tremendous overhead of synchronizing the node memory makes it impractical to deploy the solution in GPU clusters. In this work, we propose DistTGL --- an efficient and scalable solution to train memory-based TGNNs on distributed GPU clusters. DistTGL has three improvements over existing solutions: an enhanced TGNN model, a novel training algorithm, and an optimized system. In experiments, DistTGL achieves near-linear convergence speedup, outperforming the state-of-the-art single-machine method by 14.5% in accuracy and 10.17× in training throughput.
Mitigating Coupling Map Constrained Correlated Measurement Errors on Quantum Devices

We introduce a technique for the suppression of state-dependent and correlated measurement errors, which are commonly observed on modern superconducting quantum devices. Our method leverages previous results, establishing that correlated errors tend to be physically localised on quantum devices to perform characterisations over the coupling map of the device, and to join overlapping measurement calibrations as a series of sparse matrices. We term this 'Coupling Map Calibration'. We quantitatively demonstrate the advantages of our proposed error mitigation system design across a range of current IBM quantum devices. Our experimental results on common benchmark circuits demonstrate up to a 41% reduction in the error rate without increasing the number of executions of the quantum device required when compared to conventional error mitigation methods.
GRAPHINE: Enhanced Neutral Atom Quantum Computing using Application-Specific Rydberg Atom Arrangement

Multiple technologies for realizing quantum computing are currently under development. Neutral atom quantum computing is one such promising technology; it offers advantages such as the ability to perform long-distance interactions and gates consisting of more than two qubits. A particular advantage it provides is the flexibility to arrange the qubits in different topologies by customizing atom layouts. We design Graphine, which, to the best of our knowledge, is the first technique to leverage this flexibility to design application-specific topologies for different quantum algorithms based on the structural characteristics of the algorithm circuits. This enables Graphine to improve key performance metrics like the number of gates and pulses by up to 56% and the probability of error by up to 42% on average over widely-used topology designs.
Choosing the Best Parallelization and Implementation Styles for Graph Analytics Codes: Lessons Learned from 1106 Programs

Graph analytics has become a major workload in recent years. The underlying core algorithms tend to be irregular and data dependent, making them challenging to parallelize. Yet, these algorithms can be implemented and parallelized in many ways for CPUs and even more ways for GPUs. We took 6 key graph algorithms and created hundreds of CUDA, OpenMP, and parallel C++ versions of each of them, most of which have never been described or studied. To determine which parallelization and implementation styles work well and under what circumstances, we evaluated the resulting 1106 programs on 2 GPUs and 2 CPUs using 5 input graphs. Our results show which styles and combinations perform well and which ones should be avoided. We found that choosing the wrong implementation style can yield over a 10× slowdown on average. The worst combinations of styles can cost 6 orders of magnitude in performance.
Implementing Multisignature on a Blockchain-based Land Administration System: Securing Land Rights and Enhancing Transparency

Ownership of land is a complex and multifaceted concept. Land distribution to all social categories is an even more difficult task, as in most cases we lack the instruments to capture land rights information on the ground. As part of the United Nations’s Sustainable Development Goals1 5 (Gender Equality) and 10 (Reduced Inequality), we must think of ways and means to not only capture the rightful owner of a land but also build the framework that will protect those rights regardless of the social categories of its owner. While, on the one hand, many initiatives have been taken to improve the traditional tenure arrangements and other legal aspects towards a more equitable land access distribution, in another hand the technology implication is still lacking. We argue that technology reinforcement is necessary to provide enough protection against corruption. In this paper, we propose to implement a blockchain-backed land administration system based on Hyperledger Iroha. Besides the blockchain’s inherent ability to resist corruption and enhance transparency, the proposed system would provide support for multi-signatory land transfer transactions. Such a mechanism would make sure that each shareholder agrees on any given land transaction, effectively protecting their right to the land.
A Blockchain-based Protocol of Trusted Setup Ceremony for Zero-Knowledge Proof

Zero-knowledge proof is emerging to enable privacy. Among existing techniques, zk-SNARK (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) [1] supports the shortest verification time and the smallest proof size. However, using zk-SNARK requires the execution of trusted setup ceremony in advance. The trusted setup ceremony generates common reference string (CRS) which is shared with prover and verifier. Currently, an external trusted third party is assumed for trusted setup ceremony, which causes significant security vulnerability in zk-SNARK. In this paper, we propose a blockchain-based protocol of trusted setup ceremony without trusted third party. Three different types of protocols are classified in terms of where to store CRS and how to validate CRS through pairing check. We analyze the protocol complexity of CRS pairing check computations and on-chain storage space.
Privacy Protection Scheme for Epidemiological Investigation Based on Blockchain Technology

Since Bitcoin was invented in 2008, the blockchain technology has been growing rapidly. A blockchain is a growing public ledger called blocks which are interconnected with a chain by utilizing cryptography. Utilizing blockchain, we can share information over the system safely, where everyone can see but can't do any alteration. Because of this feature of blockchain system, everyone can fetch or reason out the peer nodes’ data or even private data. To protect the privacy and data security of the blockchain system, a common practice is to use privacy computing technology to guarantee the privacy protection required. At present, the global pandemic of the COVID-19 is still going on, and it will coexist with humanity for a long time. How to screen the flow regulation quickly and make it traceable is an important research topic. This paper proposed one epidemiological data privacy protection scheme based on blockchain platform combined with secure multi-party computation based on protocols and federated learning based on modern cryptography. In this paper, we use federated learning technology to realize the construction of a federated learning model involving multiple institutions and departments in the process of epidemiological investigation to avoid the risk of user privacy leakage during epidemiological investigation. The blockchain technology is used to realize the multi-institution federated learning model On-chain operation, with the introducing of the global model competition mechanism, to realize the optimal federated learning model On-chain, and ensure the optimization of learning goals. Meanwhile, the secret sharing technology is used to realize the sharing of the validation dataset, which avoids the optimization problem of federated learning model caused by the institution knowing the global validation dataset.
Building Students' Digital Footprints with Blockchain in PBL Activities

The main trends and changes of project-based learning are considered. The method of organization of project-based learning is demonstrated on the practical example. The analysis of the learning process at universities is carried out on the example of several methods. The issues of integration of new project-based and practice-oriented methods of education into conventional education are considered. The scheme of the educational process with the use of effective methods of project-based learning of students in high school and university is shown. Examples of design solutions for undergraduate programs are given. The analysis of the effectiveness of the proposed project-based learning methodology is given in terms of students acquiring new competencies and real experience working in a team. The model of the student's competence profile from the point of view of project-based learning is described. The concept of designing a student's digital footprint based on blockchain is proposed. The technique allows you to organize a system for issuing digital certificates that form a single array of personal data.
Proof of Review: Trust Me, It's Been Reviewed

We present a novel consensus model called Proof of Review (PoRev) which uses reputation, evaluation of reviews/assessments, blacklisting, and minimum reputation to come to an agreement on a block of transactions, while securing the data on the blockchain. PoRev is a tool that can be used to aid applications by ensuring honest and unbiased reviews and assessments. The motivation to develop this type of consensus lies in applications to decentralized marketplaces, micro-accreditation, autonomous vehicles infrastructures and other community-based blockchain systems. Our proposed architecture is based on the Algorand platform whose engine we modified to fit our purposes. The reviews and evaluations are stored on the blockchain, driving the reputation model, where the latter is used to regulate users’ participation in the system—similarly to the Proof of Reputation systems. This regulation is also aided by the blacklisting and minimum-reputation components which we introduce to deny the malicious and uncooperative parties control over the system. Our simulation results showed that the proposed blockchain system maintains liveliness and completeness, even in the presence of the blacklisting and minimum-reputation requirements, when they are properly tuned.
Enhancing Fairness in Federated Learning: A Contribution-Based Differentiated Model Approach

Federated learning (FL) has emerged as a promising framework for collaborative machine learning, allowing the training of machine learning models on distributed devices without centralizing sensitive data. However, FL falls short in terms of fairness, as each client receives the same model regardless of their individual contributions. This unfairness discourages active client participation in FL. To address this challenge, we propose a contribution-based differentiated global model mechanism. Specifically, we introduce the contribution score as a metric to assess client contributions in FL and utilize deep Q-networks (DQN) to dynamically update the contribution scores. Subsequently, we allocate clients to different clusters based on their contributions by using a clustering algorithm, where each cluster is associated with a distinct global model. This mechanism encourages clients to make greater contributions for improved global models. Experimental results confirm the effectiveness of our approach in enhancing fairness in FL.
Preset Conditional Generative Adversarial Network for Massive MIMO Detection

In recent years, extensive research has been conducted to obtain better detection performance by combining massive multiple-input multiple-output (MIMO) signal detection with deep neural network (DNN). However, spatial correlation and channel estimation errors significantly affect the performance of DNN-based detection methods. In this study, we consider applying conditional generation adversarial network (CGAN) model to massive MIMO signal detection. First, we propose a preset conditional generative adversarial network (PC-GAN). We construct the dataset with the channel state information (CSI) as a condition preset in the received signal, and train the detector without direct involvement of CSI, which effectively resists the impact of imperfect CSI on the detection performance. Then, we propose a noise removal and preset conditional generative adversarial network (NR-PC-GAN) suitable for low-signal-to-noise ratio (SNR) communication scenarios. The noise in the received signal is removed to improve the detection performance of the detector. The numerical results show that PC-GAN performs well in spatially correlated and imperfect channels. The detection performance of NR-PC-GAN is far superior to the other algorithms in low-SNR scenarios.
Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Handwritten signatures are one of the most extensively utilized biometrics used for authentication, and forgeries of this behavioral biometric are quite widespread. Biometric databases are also difficult to access for training purposes due to privacy issues. The efficiency of automated authentication systems has been severely harmed as a result of this. Verification of static handwritten signatures with high efficiency remains an open research problem to date. This paper proposes an innovative introselect median filter for preprocessing and a novel Gaussian gated recurrent unit neural network (2GRUNN) as a classifier for designing an automatic verifier for handwritten signatures. The proposed classifier has achieved an FPR of 1.82 and an FNR of 3.03. The efficacy of the proposed method has been compared with the various existing neural network-based verifiers.