Reviewerly/exHarmony huggingface dataset:
Control of Quantum‐Memory Induced by Generated Thermal XYZ‐Heisenberg Entanglement: <i>y</i>‐Component DM Interaction

Abstract This study explores the thermal quantum‐memory‐assisted entropic uncertainty relation (QM‐EUR) and entanglement in a general two‐qubit XYZ‐Heisenberg spin chain model in the presence of the Dzyaloshinskii–Moriya (DM) interaction. The characterization of y ‐component DM and spin–spin interactions are particularly focused. It is found that the DM and spin–spin interaction strengths highly regulate the flow behavior and the initial final levels of QM‐EUR and entanglement. In comparison, the spin–spin interaction strength in the z ‐direction remains useful in both ferromagnetic and anti‐ferromagnetic regimes for entropic uncertainty suppression and entanglement generation. Additionally, the negative and the positive directed DM values can usefully turn classical states into resourceful quantum states. The dynamics of thermal QM‐EUR and entanglement‐of‐formation have symmetric behaviors only with respect to y ‐component DM and z ‐component spin–spin interaction. Finally, different critical points of temperature, component DM as well as spin–spin interaction are encountered, which should be opted to preserve quantum correlations and degrade uncertainty.
MCNet: Multiscale Clustering Network for Two-View Geometry Learning and Feature Matching

Dear Editor, The main components of multi-view geometry and computer vision are robust pose estimation and feature matching. This letter discusses how to recover two-view geometry and match features between a pair of images, and presents MCNet (a multiscale clustering network) as an algorithm for extracting multiscale features. It can identify the true inliers from the established putative correspondences, where outliers may degenerate the geometry estimation. In particular, the proposed MCNet is based on graph clustering, in which the embedded correspondence features are mapped to a number of clusters by graph pooling. We designed a multiscale clustering layer into the two-view correspondence learning framework in order to improve correspondence representation efficiency. As a consequence of the multi-group feature fusion, we also constructed the network architectures termed MCNet-U and MCNet-M, respectively, utilizing the UNet and Pyramid techniques. Based on experimental results, the proposed model achieves state-of-the-art performance on feature matching with heavy outliers under weak supervision.
応用プロトタイプによるオンライン実習型ディープラーニング教育方式とその実践評価

In deep learning (hereinafter referred to as DL) education, universities, research institutes and large companies are promoting education by fostering educators and creating curriculums based on national policies. However, especially small and medium-sized enterprises and companies in industries that are not good at IT cannot prepare their own educational environment, and it is considered that the educational environment for improving the added value of products and services by DL is not sufficient. Furthermore, the neural network, which is a component of DL, has a feature that it is difficult to determine the feasibility for the realization target. This feature is an issue in educational methods. Based on the above situation, we proposed an educational method that combines online live lectures, reviews, and homework as DL applied education, and confirmed its practice and educational effect.
Algorithm/Hardware Co-Optimization for Sparsity-Aware SpMM Acceleration of GNNs

In recent years, graph neural networks (GNNs) have achieved impressive performance in various application fields by extracting information from graph-structured data. It contains extensive feature aggregation operations and has become a performance bottleneck, which can be abstracted as a specialized sparse-dense matrix multiplication (SpMM) operation. Previous works have leveraged the inner product or outer product to accelerate the feature aggregation process. However, inefficient execution leads to extremely unbalanced workloads and extensive intermediate data, hampering the performance of previous processors. So in this article, we demonstrate an algorithm/hardware co-optimization chance to enhance SpMM acceleration for GNNs. First, the algorithm part develops a dataflow-efficient SpMM algorithm that integrates three optimization methods to mitigate computation and memory access inefficiencies. Specifically, 1) the proposed equal-value partition method achieves fine-grained data partition and enables load balancing during data movement; 2) after observing the vertex aggregation phenomenon, a vertex-clustering optimization method is presented to enable significant data locality; and 3) the adaptive dataflow based on Gustavson’s algorithm is further implemented to enable the efficient distribution of sparse elements and improves computing resource utilization. Then, the hardware part features the proposed SpMM algorithm and customizes SDMA, a flexible and efficient accelerator to boost SpMM acceleration, which follows the adaptive dataflow to eliminate sparsity and explore the regular parallelism dimension. Finally, we prototype SDMA on the Xilinx Alveo U280 FPGA accelerator card. The results demonstrate that SDMA achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.68\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.68\times $ </tex-math></inline-formula> energy efficiency over the previous GPU implementations deployed on the Nvidia GTX 1080Ti and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.32\times $ </tex-math></inline-formula> higher throughput over the state-of-the-art FPGA prototype.
Data-Driven Participant Selection and Bandwidth Allocation for Heterogeneous Federated Edge Learning

Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-generation wireless edge systems. Smart systems across various application domains face challenges, such as data heterogeneity, limited wireless resources, and device heterogeneity, which necessitate intelligent participant selection schemes that accelerate convergence rates. Consequently, this article presents joint participant selection and bandwidth allocation schemes to address these challenges. First, we formulate an optimization problem that considers communication and computation latencies, as well as imbalanced data distribution, while meeting round deadlines and bandwidth constraints. To address the combinatorial problems of participant selection, we employ a relaxation method followed by a proposed priority selection algorithm to select near-optimal participants. The proposed algorithm initially prioritizes participants with larger datasets, effective channel states, and better CPU speeds. To address data heterogeneity, we propose a randomized deadline-controlling algorithm that diversifies updates by allowing the edge server to include different participants with fewer data samples in training rounds. The proposed algorithms offer near-optimal performance compared to the brute-force method. Experiments demonstrate that our proposed scheme accelerates the convergence rate by up to 55% under extensive non-IID settings compared to benchmarks. Furthermore, the deadline-controlling algorithm improves performance at high levels of data heterogeneity, resulting in faster FEEL systems.
First-Person Video Domain Adaptation With Multi-Scene Cross-Site Datasets and Attention-Based Methods

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person video action recognition is an under-explored problem, with a lack of benchmark datasets and limited consideration of first-person video characteristics. Existing benchmark datasets provide videos with a single activity scene, e.g. kitchen, and similar global video statistics. However, multiple activity scenes and different global video statistics are still essential for developing robust UDA networks for real-world applications. To this end, we first introduce two first-person video domain adaptation datasets: ADL-7 and GTEA_KITCHEN-6. To the best of our knowledge, they are the first to provide multi-scene and cross-site settings for UDA problem on first-person video action recognition, promoting diversity. They provide five more domains based on the original three from existing datasets, enriching data for this area. They are also compatible with existing datasets, ensuring scalability. First-person videos have unique challenges, i.e. actions tend to occur in hand-object interaction areas. Therefore, networks paying more attention to such areas can benefit common feature learning in UDA. Attention mechanisms can endow networks with the ability to allocate resources adaptively for the important parts of the inputs and fade out the rest. Hence, we introduce channel-temporal attention modules to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to this characteristic. Moreover, we propose a Channel-Temporal Attention Network (CTAN) to integrate these modules into existing architectures. CTAN outperforms baselines on the new datasets and one existing dataset, EPIC-8.
ESMO: Joint Frame Scheduling and Model Caching for Edge Video Analytics

With the advancements in Machine Learning (ML) and edge computing, increasing efforts have been devoted to <i>edge video analytics</i> . However, most of the existing works fail to consider the cooperation of edge nodes for ML model caching and video frame scheduling, thus less efficient in practical scenarios with diverse requirements. In this article, we propose a novel approach named ESMO (joint fram <b>E</b> <b>S</b> cheduling and <b>MO</b> del caching) to jointly optimize Frame Scheduling and Model Caching (FSMC), aiming at enhancing the performance of edge video analytics. In detail, we decompose the FSMC as three sub-problems, where the first two sub-problems (i.e., user's transmit power and edge computing resources allocation problems) are proven to be quasi-convex and strictly convex, respectively; while the third main sub-problem (i.e., trade-off among the video analytics (VA) accuracy, service delay and energy consumption) is NP-hard. Therefore, an efficient Two-layers Genetic Algorithm based algorithm (i.e., TGA-FSMC) is designed to find the close-to-optimal frame scheduling and the model caching decisions in an iterative manner. Finally, we deploy a target recognition prototype to comprehensively evaluate the practical performance in diverse edge nodes and CNN models. Extensive experiments demonstrate the empirical superiority of the ESMO over alternatives on real-world edge video analytics platforms, and it achieves 37.5% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 87.2% performance improvement.
Retrieving Object Motions From Coded Shutter Snapshot in Dark Environment

Video object detection is a widely studied topic and has made significant progress in the past decades. However, the feature extraction and calculations in existing video object detectors demand decent imaging quality and avoidance of severe motion blur. Under extremely dark scenarios, due to limited sensor sensitivity, we have to trade off signal-to-noise ratio for motion blur compensation or vice versa, and thus suffer from performance deterioration. To address this issue, we propose to temporally multiplex a frame sequence into one snapshot and extract the cues characterizing object motion for trajectory retrieval. For effective encoding, we build a prototype for encoded capture by mounting a highly compatible programmable shutter. Correspondingly, in terms of decoding, we design an end-to-end deep network called detection from coded snapshot (DECENT) to retrieve sequential bounding boxes from the coded blurry measurements of dynamic scenes. For effective network learning, we generate quasi-real data by incorporating physically-driven noise into the temporally coded imaging model, which circumvents the unavailability of training data and with high generalization ability on real dark videos. The approach offers multiple advantages, including low bandwidth, low cost, compact setup, and high accuracy. The effectiveness of the proposed approach is experimentally validated under low illumination vision and provide a feasible way for night surveillance.
Maxwell-Schrödinger Hybrid Simulation for Analyzing Control and Readout of Transmon Qubits

The control and readout of a transmon qubit through applied microwave pulses is a key capability in operating many quantum technologies. Currently, the design of microwave pulses for these purposes is done through simple theoretical or numerical models that do not account for how the transmon modifies the applied microwave field. In this work, we present the formulation and discretization of a self-consistent semiclassical model that solves the Schrödinger equation describing qubit dynamics in tandem with a one-dimensional wave equation describing transmission line dynamics. Numerical results are presented using this Maxwell-Schrödinger method to characterize the control and readout of a transmon qubit. We show that our method matches simple theoretical predictions in relevant operating regimes and also look at situations where simplified models break down. In the future, our method can be used to explore these broader operating regimes to search for more effective control and readout protocols.
Embodied AI for Financial Literacy Social Robots

Financial literacy is a critical field that prepares individuals to manage their money effectively and to become economically self-suffcient by making informed financial decisions. As artificial intelligence (AI) becomes more widespread across a variety of disciplines, including K-12 education, and more financial institutions move towards digitizing their services, the demand for AI systems in financial literacy education is growing. While many financial and educational institutions alike have implemented AI systems, there remains a need for a system geared toward teaching young students these important lessons and skills. To address this demand, we have created an AI system equipped with financial literacy lessons for grade school students. The system, which is embedded into a NAO robot, a humanoid robot designed for use within education, was evaluated on its performance in the classroom across multiple metrics, including its effcacy, its robustness and feasibility in the classroom, and the students’ enjoyment in using it. The results from the experiments suggest that the system did result in the retention of material, but did not do so at a significantly different level than the more traditional teaching method of a guided worksheet. However, the students did report higher levels of enjoyment in using the robot when compared to a worksheet. These results show promise for future uses of the system, as well as shed light on areas in which the system can be improved in the future.
Deterring Adversarial Learning in Penetration Testing by Exploiting Domain Adaptation Theory

Artificial intelligence (AI) and machine learning (ML) are increasingly being used in cyber operations. Because of techniques like adversarial learning, the performance of network defenses can degrade quickly. Thus, there is an increasing need for adaptable, dynamic network defenses. Correspondingly, there has been a rise in the use of reconfiguration schemes like moving target defense in software-defined networks. However, moving target defense methods target individual adversaries and rely on an in-depth understanding of an adversary’s utility function. In contrast, domain adaptation theory suggests that learning agents are sensitive to distributional changes in their inputs, regardless of their utilities. In this paper, we identify several kinds of network changes that deter adversaries by exploiting vulnerabilities in their learned assumptions. We use an open source network attack simulator, NASim, to conduct experiments on reinforcement learning (RL)based penetration testers. We measure the time-to-relearn in order to compare the efficacy of different network changes at deterring adversaries. We find that by focusing on shifting the learning domain as a defensive strategy, we are able to degrade the performance of multiple adversaries simultaneously. With our methodology, cyber defenders have tools that allow them to raise the sophistication and cost needed by adversaries to remain a threat to network operations over time.
Energy Trading Market Simulator Blockchain-based

Solar energy devices in homes and businesses eliminate the energy supplier-consumer divide. Energy prosumers have new roles. Blockchain technology allows prosumers, consumers, energy, and amenities to exchange energy in a low-cost, safe, and novel way using cipher encoding and wide-ranging arrangement proof. This job builds and operates an unknown and exact Agent-Based Modeling (ABM) model and Geographic Information System (GIS) to handle energy dealing in a domestic neighborhood market for Qatar's Education City Community Housing. (ECCH). The method for reproducing minor market spatial and timing aspects collects and analyzes energy usage data. These models help you build an automated energy trading system and understand trading markets. GPS data and an agent-based design can be used to study community-based property market deals by quickly changing parameters. High-performance calculations will improve large-scale simulation prototypes with many legislators, providing more accurate results and an accessible environment to study the energy blockchain community's impact on Qatar's economy, society, and technology.
Identifying Ineffective Cooling Implementation to Increase Energy Efficiency in Existing Data Centres

With the rapid development of data centres worldwide and keeping climate change in mind, more people are looking at “green data centres”. There is a significant need to cut power consumption as much as possible to become such. Because the cooling system’s power consumption almost matches the power consumption of IT equipment, it vastly increases the PUE coefficient, meaning low energy efficiency of data centres. High levels of bypass airflow and recirculation of air in the server room are the main factors causing a considerable drop in the energy efficiency of data centres. However, the main areas of poor cooling should be identified before performance improvement actions can be taken in existing data centres. This paper addresses the most-known issues impacting the cooling system’s energy efficiency and proposes a possible solution to improve it.
The Latest in Natural Language Generation: Trends, Tools and Applications in Industry

Natural Language Generation (NLG) has experienced rapid progress in recent years with advancements in artificial intelligence contributing to its evolution. In this paper, we present a comprehensive review of the latest trends, models, tools, and applications of NLG across various industries. We discuss the increasing use of deep learning algorithms and neural networks, the development of multilingual NLG models, and the integration of NLG with other artificial intelligence (AI) technologies such as natural language understanding (NLU) and machine translation (MT). Furthermore, we examine the different pre-trained language models available, including autoregressive models, masked language models, encoder-decoder models, and hybrid models, along with their evaluation and improvement. We also explore the applications of NLG in business intelligence, customer service, healthcare, education, and multimodal language models, highlighting the potential of NLG in communication and decision-making, as well as its significant implications for cybersecurity. This paper aims to provide a thorough understanding of the current state of NLG and its potential to revolutionize various industries in the digital era.
Analysis of Energy Efficient Workflow Allocation in Cloud Computing

Growing demand for high performance computing such as cloud since exponential increase data and computing complexity. Cloud provides elastic and on-demand provisioning of high performance computing capabilities using a pay-per-use paradigm, which over the past few years has seen a rapid increase in its usage by scientists and engineers. Many applications consist of many cooperating tasks requiring more processing power than a single computer can provide. Scientific workflows, big data processing workflows and multitier web service workflows are known to be the examples of these applications. Workflow scheduling is an actively researched area in the IaaS cloud environment. Such applications are a big problem that require attention since they required high performance computing and consume excessive amount of energy in cloud data centers. Massive energy use in cloud data centers has an adverse effect on the environment and raises operational costs. As a result, it cannot be ignored. While satisfying the Quality-of-Service constraints set by the user, effective scheduling techniques can considerably reduce energy consumption. Due to the NP-hard nature of the problem, researchers have spent a lot of time studying cloud workflow scheduling. In this study, we offer an overview of existing research for workflow scheduling algorithm in the cloud environments. It provides a perceptive of heuristic and meta heuristic approaches used to solve workflow scheduling. This excerpt is from a full-text study that conducts a thorough analysis of the literature on workflow application in relation to cloud computing. By responding to the three research questions, initial findings are given. 96 research papers were reviewed, and 24 articles were chosen. This study explores that there is a need to find an energy efficient workflow allocation strategy while complying Quality-of-Service parameters set by the user.
Hyperledger Fabric Enabled Vaccine Intelligent Network to Implement Immunization Program

Blockchain is a disruptive technology and incredibly popular nowadays based on decentralized ledger technology. It is a tampered-proof technology that saves the transaction data permanently in a distributed ledger with an immutable time-stamped series record of data, and it is built on the idea of a peer-to-peer network. Blockchain efficiently creates trust between people and machines. It provides new approaches and solutions that cater to the business demands oriented to the fourth industrial revolution. Healthcare is one of the key research use case of blockchain wherein health records are required to be electronic for e-governance. In this paper, we propose the blockchain based Vaccine Intelligent Network for pregnant women, included vaccines covered under the Universal Immunization Program. Our solution is based on the Hyperledger Fabric framework to develop the Electronic Immunization Record of vaccinated pregnant women. The proposed system transforms the immunization record from paper to a reliable digital platforms that will benefit all nation citizens and further reduce anomalies.