Reviewerly/exHarmony huggingface dataset:
Kafka-ML: Connecting the data stream with ML/AI frameworks

Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.
Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine learning tasks from local evolution rules. However, to date such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this innovation, our system effectively trains itself, optimizing its resistance values without use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. We demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.
Secure fusion approach for the Internet of Things in smart autonomous multi-robot systems

The application of smart autonomous multi-robot systems has received increasing attention and dramatically developed, and this situation greatly promotes the development of the Internet of Things (IoT) and brings the IoT into a new stage. However, the IoT has some emerging issues, such as security and privacy; among many concerns, trusted identity authentication and consensus protocols are the main ones. To address these issues, this study proposes a novel scheme for the identity authentication and consensus algorithm. In the proposed scheme, an identity-based authentication model is designed, and all communication nodes utilize this model to establish connection and conduct data exchange. Meanwhile, a hash pool-based joint consensus algorithm is constructed. All transmission data are strictly protected by permuting hash functions from hash pool and utilizing the generated random number. This feature greatly enhances the security for the IoT. Analysis and experimental results demonstrate that the proposed scheme provides enhanced security and reliability for the IoT. Furthermore, the time efficiency is encouraging, and the communication and storage cost is promising.
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments

Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider side, flexible resource management is required to meet fluctuating demand. It can be enabled through automated provisioning and deprovisioning of resources. A common approach among both commercial and open source serverless computing platforms is workload-based auto-scaling, where a designated algorithm scales instances according to the number of incoming requests. In the recently evolving serverless framework Knative a request-based policy is proposed, where the algorithm scales resources by a configured maximum number of requests that can be processed in parallel per instance, the so-called concurrency. As we show in a baseline experiment, this predefined concurrency level can strongly influence the performance of a serverless application. However, identifying the concurrency configuration that yields the highest possible quality of service is a challenging task due to various factors, e.g. varying workload and complex infrastructure characteristics, influencing throughput and latency. While there has been considerable research into intelligent techniques for optimizing auto-scaling for virtual machine provisioning, this topic has not yet been discussed in the area of serverless computing. For this reason, we investigate the applicability of a reinforcement learning approach to request-based auto-scaling in a serverless framework. Our results show that within a limited number of iterations our proposed model learns an effective scaling policy per workload, improving the performance compared to the default auto-scaling configuration.
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed networks in various web-based applications such as social recommendation and web search. Nevertheless, in high-stake decision-making scenarios such as online fraud detection, there is an increasing societal concern that GNNs could make discriminatory decisions towards certain demographic groups. Despite recent explorations on fair GNNs, these works are tailored for a specific GNN model. However, myriads of GNN variants have been proposed for different applications, and it is costly to fine-tune existing debiasing algorithms for each specific GNN architecture. Different from existing works that debias GNN models, we aim to debias the input attributed network to achieve fairer GNNs through feeding GNNs with less biased data. Specifically, we propose novel definitions and metrics to measure the bias in an attributed network, which leads to the optimization objective to mitigate bias. We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks. EDITS works in a model-agnostic manner, i.e., it is independent of any specific GNN. Experiments demonstrate the validity of the proposed bias metrics and the superiority of EDITS on both bias mitigation and utility maintenance. Open-source implementation: https://github.com/yushundong/EDITS.
Federated transfer learning: Concept and applications

Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.
A hybrid particle swarm optimization with crisscross learning strategy

As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains two crucial problems. To overcome these drawbacks of PSO, a hybrid particle swarm optimization with crisscross learning strategy (PSO-CL) algorithm is proposed in this paper. In PSO-CL, in order to well balance the global exploration and local exploitation capabilities of PSO, a search direction adjustment mechanism based on subpopulation division operation is proposed. Meantime, to avoid the premature convergence and enhance the global search ability, a crossover-based comprehensive learning strategy (CCL) is adopted. Additionally, a stochastic example learning strategy (SEL) is introduced, which can assist collective information to be spread among separate sub-swarms, improve the local exploitation ability of the algorithm. 15 classic benchmark functions, CEC2017 test suite and two real-world optimization problems are utilized to verify the promising performance of PSO-CL, experimental results and statistical analysis indicate that PSO-CL has competitive performance compared with state-of-the-art PSO variants.
Is there a role for statistics in artificial intelligence?

Abstract The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.
Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images

Semantic segmentation for unmanned aerial vehicle (UAV) remote sensing images has become one of the research focuses in the field of remote sensing at present, which could accurately analyze the ground objects and their relationships. However, conventional semantic segmentation methods based on deep learning require large-scale models that are not suitable for resource-constrained UAV remote sensing tasks. Therefore, it is important to construct a light-weight semantic segmentation method for UAV remote sensing images. With this motivation, we propose a light-weight neural network model with fewer parameters to solve the problem of semantic segmentation of UAV remote sensing images. The network adopts an encoder-decoder architecture. In the encoder, we build a light-weight convolutional neural network model with fewer channels of each layer to reduce the number of model parameters. Then, feature maps of different scales from the encoder are concatenated together after resizing to carry out the multiscale fusion. Moreover, we employ two attention modules to capture the global semantic information from the context and the correlation among channels in UAV remote sensing images. In the decoder part, the model obtains predictions of each pixel through the softmax function. We conducted experiments on the ISPRS Vaihingen dataset, UAVid dataset, and UDD6 dataset to verify the effectiveness of the light-weight network. Our method obtains quality semantic segmentation results evaluated on UAV remote sensing datasets with only 9 M parameters the model owns, which is competitive among popular methods with the same level of parameters.
Blind and Robust Watermarking Scheme in Hybrid Domain for Copyright Protection of Medical Images

This work presents a robust watermarking technique in hybrid domain for the copyright claim of medical images. The scheme is a fusion of three popular transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is first separated into region of interest (ROI) and region of non-interest (RONI). The DWT is applied on RONI to get low and high frequency bands. The low frequency band is then segmented into 4×4 blocks. The Human Visual System (HVS) is applied to select the potential blocks for implanting watermark content. Each 4×4 selected block is further subdivided into four 2×2 carrier matrices. The SVD is applied to each carrier matrix. Finally, the hidden information is implanted by altering the largest diagonal singular values of four 2×2 matrices. The technique is blind, so host image is not needed for the extraction of hidden information. The proposed scheme achieves higher values of imperceptibility as well as robustness. Experimental results reveal that the proposed technique outperforms the techniques currently reported in the literature by achieving higher values of imperceptibility in the form of PSNR with value of 44.0567 decibels (dB) and SSIM value of 0.9800. At the same time it achieves excellent values of robustness with maximum NCC value of 1.000 and minimum BER with value of 0.000.
A Flexible Permission Ascription (FPA)-Based Blockchain Framework for Peer-to-Peer Energy Trading With Performance Evaluation

With the proliferation of smart grid and deregulation of the energy market, a wide variety of peer-to-peer (P2P) energy trading systems have emerged. Common challenges for designing such systems include prosumers’ privacy and security threats. To this end, Blockchain-based solutions have gained a lot of attention, though most existing solutions have either employed permissionless blockchain, which is far from pragmatic for a P2P energy trading system with peers permitted to join or leave the network at their whim; or relatively secure yet inefficient permissioned blockchains. Hence, this article presents a flexible permissioned ascription (FPA) scheme that uses on-chain and off-chain permissioning scheme via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Orion</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metamask</i> wallet. It also employs contract permissioning through a JavaScript based chain code deployed over Hyperledger Besu (an Ethereum based permissioned Blockchain network) with istanbul byzantine fault tolerant (IBFT) 2.0 consensus algorithm. Additionally, the proposed framework is emulated for development of a working prototype for a P2P energy trading system. Its performance evaluation has been conducted and monitored with Grafana, Prometheus, Hyperledger Caliper, and Kibana for parameters such as latency, throughput, success rate, CPU time, block time, block behind time, memory usage, garbage collection (GC) time, and performance of the validator nodes. The latency of IBFT 2.0 was found five times lesser than that of Ethereum and two times lesser than HF RAFT and KAFKA under varying conditions. Also, the measured throughput was 1.5 times higher than RAFT and Kafka and three times higher than that of Ethereum. The average block confirmation time measured is 5–6 s. The GC usage measured very less, i.e., 0.5–0.8%, with the proposed framework. It has been observed that the proposed energy-trading framework provides an efficient performance for deploying, transferring, and querying the energy transaction to a P2P energy-trading Blockchain network when compared with other consensus mechanisms.
PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds

3D single object tracking is a key issue for robotics. In this paper, we propose a transformer module called Point-Track-Transformer (PTT) for point cloud-based 3D single object tracking. PTT module contains three blocks for feature embedding, position encoding, and self-attention feature computation. Feature embedding aims to place features closer in the embedding space if they have similar semantic information. Position encoding is used to encode coordinates of point clouds into high dimension distinguishable features. Self-attention generates refined attention features by computing attention weights. Besides, we embed the PTT module into the open-source state-of-the-art method P2B to construct PTT-Net. Experiments on the KITTI dataset reveal that our PTT-Net surpasses the state-of-the-art by a noticeable margin $\left( {\sim 10\% } \right)$. Additionally, PTT-Net could achieve real-time performance (~40FPS) on NVIDIA 1080Ti GPU. Our code is open-sourced for the robotics community at https://github.com/shanjiayao/PTT.
Long-Term Co-Design Guidelines: Empowering Older Adults as Co-Designers of Social Robots

Users can provide valuable insights for designing new technologies like social robots, with the right tools and methodologies. Challenges in inviting users as co-designers of social robots is due to lack of guidelines or methodologies to (1) organize co-design processes and/or (2) engage with people long-term to develop technologies together. The main contribution of this work is to provide guidelines for long- term co-design for how other researchers can adopt long- term co-design, informed by a 12-month co-design with older adults designing a social social robot. We leveraged human- centered, tactile and experiential design activities, including participatory design, based upon the following design principles: scenario specific exploration, long-term lived experiences, supporting multiple design activities, cultivating relationships, and employing divergent and convergent processes. We present seven different sessions across three stages as examples of this methodology that build on each other to engage users as co- designers, successfully deployed in a co-design project of home social robots with 28 older adults. Lastly, we detail 10 long- term divergent-convergent co-design guidelines for designing social robots. We demonstrate the value of leveraging people's lived technology experiences and co-design activities to generate actionable social robot design guidelines, advocating for more applications of the methodology in broader contexts as well.
Energy-efficient blockchain implementation for Cognitive Wireless Communication Networks (CWCNs)

Considering the computation resources available with sensor devices and the value and validity of Cognitive Wireless Communication Network (CWCN), traditional blockchain is not feasible for CWCN. Further, considering the security and privacy for CWCN that can directly impact human life (as in the case of ambient assisted living applications), blockchain provides a good solution for such applications, however, with some simplicity in the computation of Proof of Work (PoW). Therefore, the fourth objective solution comes up with a simplified energy-efficient blockchain implementation for CWCN that consumes less energy in computation time. The energy-hungry blockchain has been implemented on resource-constrained CWCN for ambient assisted living applications specialized for elderly care. The process includes a collection of physical environmental parameters on a single board computer-based CWCN. The implementation includes possible simplification in the most energy-consuming process, i.e., the mining process, which makes it energy efficient in computation time as energy consumption is a computation time factor.
How cryptocurrency affects economy? A network analysis using bibliometric methods

This paper analyses existing literature about the economic effects of cryptocurrency using bibliometric methods. 1850 articles are collected from "Web of Science core database" from 2013 to 2020. We explore research hotspots and development trends through descriptive statistical analysis, keyword co-occurrence and literature co-citation network analysis. We find that literature of computer sciences and interdisciplinary fields mainly research the technologies' effects on the economic phenomenon, the technique progress in overcoming the negative economic effects of cryptocurrency, and appeal to establish a new payment system based on the underlying technologies. In comparison, the literature of economic field is concerned about the economic outcomes of cryptocurrency. Especially with the occurrence of various altcoins and the steady progress of legal digital currency in some countries, scholars have paid more attention to the investment properties of cryptocurrency. Based on these findings, this paper holds that the macroeconomic effects of cryptocurrency, the influence mechanism of cryptocurrency and legal digital currency will become research hotspots in the future.
An empirical study on challenges of application development in serverless computing

Serverless computing is an emerging paradigm for cloud computing, gaining traction in a wide range of applications such as video processing and machine learning. This new paradigm allows developers to focus on the development of the logic of serverless computing based applications (abbreviated as serverless-based applications) in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, it also introduces new challenges on the design, implementation, and deployment of serverless-based applications, and current serverless computing platforms are far away from satisfactory. However, to the best of our knowledge, these challenges have not been well studied. To fill this knowledge gap, this paper presents the first comprehensive study on understanding the challenges in developing serverless-based applications from the developers’ perspective. We mine and analyze 22,731 relevant questions from Stack Overflow (a popular Q&A website for developers), and show the increasing popularity trend and the high difficulty level of serverless computing for developers. Through manual inspection of 619 sampled questions, we construct a taxonomy of challenges that developers encounter, and report a series of findings and actionable implications. Stakeholders including application developers, researchers, and cloud providers can leverage these findings and implications to better understand and further explore the serverless computing paradigm.