Reviewerly/exHarmony huggingface dataset:
Introduction to Algorithms

Many problems in science and engineering can be formulated as optimization problems, subject to complex, nonlinear constraints. The solutions of optimization problems often require sophisticated optimization techniques. Traditional algorithms may struggle to deal with such highly nonlinear problems. Nature-inspired algorithms can be good alternatives, and they are flexible and efficient for solving problems in optimization, data mining and machine learning. This chapter introduces the fundamentals of algorithms, classification of optimization problems and algorithms as well as a brief history of metaheuristics.
Data anonymization evaluation for big data and IoT environment

The growth of big data can increase risks of re-identification in complex IoT environment. Data anonymization is widely used to prevent shared data from being re-identified private or sensitive information from anonymized data with other available data. It is very important to understand the risks of re-identification of results and process of commonly used anonymization techniques before properly mitigating. This work proposes a data anonymisation evaluation framework that can evaluate commonly used data anonymization algorithms in terms of privacy preserving level, data utility, and performance. Specifically, data utility, privacy metrics, and information loss were used to evaluate data anonymization schemes that can further optimise the data anonymization process to keep risks of re-identification low. Experiment results demonstrate that the proposed solution can effectively evaluate the performance and de-identification features that can help to prevent inappropriate usage of anonymized data.
Aspect sentiment analysis with heterogeneous graph neural networks

Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison.
Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID

Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.
Digital currencies, monetary sovereignty, and U.S.–China power competition

Abstract The rise of digital currencies challenges practices of monetary sovereignty and impacts the international monetary order. Drawing on recent IPE debates about the public‐private nature of money, the critique of the “impossible trinity” and “territorial currencies,” this article explores the competition between China and the United States over and within the international monetary system. The two largest economies display strikingly divergent regulatory approaches to cryptocurrencies and Central Bank Digital Currency (CBDC). China completely banned cryptocurrencies but became a front‐runner in developing a CBDC. It aims to expand the RMB's global role without giving up its monetary control. U.S. administrations have instead reluctantly considered regulating cryptocurrencies. Discussions on a potential digital U.S. dollar (USD) only began in 2020. Washington aims at preserving the existing cross‐border financial mechanisms and offshore infrastructure for USD‐denominated transactions and credit creation. It focuses on financial crime and maintaining the innovation dynamic of its private sector to preserve its “exorbitant privilege.” Emerging financial infrastructures and standards for digital currencies are the new technological arena for U.S.–China monetary competition.
Blockchained smart contract pyramid-driven multi-agent autonomous process control for resilient individualised manufacturing towards Industry 5.0

The production control for the mass individualisation paradigm of R&D-stage products is challenging due to the mix-flow and frequently-disturbed environment. With the convergence of the sustainable development goals and the increasing individualised demands in products, resilient manufacturing is envisioned in Industry 5.0 proposition. Concerning that conventional centralised production control methods suffer from low stability and inefficiency of decisions under frequent disruptions, this paper establishes a blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach for improving the timeliness and adaptability of control towards resilient individualised manufacturing. Firstly, a blockchain-based multi-agent system architecture is designed based on agent encapsulation of manufacturing units. Blockchained smart contracts are used as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. Secondly, a quad-play blockchained smart contract pyramid together with a series of decentralised control patterns are designed to enable the initial task dispatching of various individualised demands, as well as rapid dynamic adjustment of schedule in response to internal random disruptions. Finally, a blockchained smart contract pyramid-driven multi-agent autonomous process control system prototype is built in the ManuChain system, and experiments are conducted to analyze the proposed BSCP-MAAPC approach in different environments.HIGHLIGHTSA blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach.A quad-play blockchained smart contract pyramid together with a series of decentralized control patterns.Blockchained smart contracts as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks.
A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis

In this paper, to compute the firing strength values of type-2 fuzzy models, a soft version of minimum is presented, which endows the fuzzy model with the ability to solve large dimensional problems. In addition, a conjugate gradient method is borrowed to train the designed interval type-2 Takagi-Sugeno fuzzy model. Compared with the existing gradient-based learning strategy, this scheme can efficiently enhance the fuzzy model performance. Last but not least, convergence analysis for this modified interval type-2 Takagi-Sugeno fuzzy neural network (MIT2TSFNN) is conducted in detail, which proves that the gradient of the error function tends to zero with the iteration increasing (weak convergence) and the sequence of model parameters (weights) convergences to a fixed point (strong convergence). To validate the effectiveness of the proposed MIT2TSFNN and its theoretical results, simulation results of six regression and six classification problems are presented.
AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots

This study examines how the certainty of consumer needs affects consumers' acceptance of artificial intelligence (AI) chatbots in the online pre-purchase stage. Three experiments are conducted to demonstrate that consumers are more likely to choose AI chatbots when their needs are more certain. This effect is mediated by consumers' perceived effectiveness of AI chatbots and moderated by product type. Specifically, when the certainty of needs is higher, consumers perceive AI chatbots to be more effective, ultimately promoting consumers' acceptance of AI chatbots. For search products, higher (vs. lower) certainty of needs increases consumers' acceptance of AI chatbots, while for experience products the certainty of needs does not significantly affect consumers' acceptance of AI chatbots. These findings make important theoretical contributions to the existing literature on AI chatbots and also provide some practical implications for electronic commerce companies to implement AI chatbot strategies more effectively.
Some Very Simple Economics of Web3 and the Metaverse

The Metaverse refers to a shared vision among technology entrepreneurs of a three-dimensional virtual world, an embodied internet with humans and the physical world in it. As such, the Metaverse is thought to expand the domain of human activity by overcoming spatial, temporal, and resource-related constraints imposed by nature. The technological infrastructure of the Metaverse, i.e., Web3, consists of blockchain technology, smart contracts, and Non-Fungible Tokens (NFTs), which reduce transaction and agency costs, and enable trustless social and economic interactions thanks to decentralized consensus mechanisms. The emerging Metaverse may give rise to new products and services, new job profiles, and new business models. In this brief note, I assess the promises and challenges of the Metaverse, offer a first empirical glimpse at the emerging Metaverse economy, and discuss some simple Metaverse economics that revolve around building and operating the Metaverse.
Jigsaw

Large pre-trained language models such as GPT-3 [10], Codex [11], and Google's language model [7] are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and caution. On the optimistic side, such large language models have the potential to improve productivity by providing an automated AI pair programmer for every programmer in the world. On the cautionary side, since these large language models do not understand program semantics, they offer no guarantees about quality of the suggested code. In this paper, we present an approach to augment these large language models with post-processing steps based on program analysis and synthesis techniques, that understand the syntax and semantics of programs. Further, we show that such techniques can make use of user feedback and improve with usage. We present our experiences from building and evaluating such a tool Jigsaw, targeted at synthesizing code for using Python Pandas API using multi-modal inputs. Our experience suggests that as these large language models evolve for synthesizing code from intent, Jigsaw has an important role to play in improving the accuracy of the systems.
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments.
Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.
Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection

This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed.The hyperparameter in the feature-extraction phase was configured based on the learning rate and momentum and further improved based on the adaptive moment estimation (ADAM) optimizer and the function reducing-learning-rate-on-plateau to optimize the model's training scheme.The optimized YOLOv5s achieved a better performance, with a mean average precision of 98.6% and a high inference speed of 106 frames per second.The ADAM optimizer with a detailed learning rate (0.0001) and momentum (0.99) fine-tuning yielded a sufficient convergence rate (0.69% at 55th epoch) to assist YOLOv5s in attaining a more precise detection for underwater objects.
Neuromorphic Silicon Photonics and Hardware-Aware Deep Learning for High-Speed Inference

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic engines aspire to synergize the low-power and high-bandwidth credentials of light-based deployments with novel architectures, towards surpassing the computing performance of their electronic counterparts. In this paper, we review recent progress in integrated photonic neuromorphic architectures and analyze the architectural and photonic hardware-based factors that limit their performance. Subsequently, we present our approach towards transforming silicon coherent neuromorphic layouts into high-speed and high-accuracy Deep Learning (DL) engines by combining robust architectures with hardware-aware DL training. Circuit robustness is ensured through a crossbar layout that circumvents insertion loss and fidelity constraints of state-of-the-art linear optical designs. Concurrently, we employ DL training models adapted to the underlying photonic hardware, incorporating noise- and bandwidth-limitations together with the supported activation function directly into Neural Network (NN) training. We validate experimentally the high-speed and high-accuracy advantages of hardware-aware DL models when combined with robust architectures through a SiPho prototype implementing a single column of a 4:4 photonic crossbar. This was utilized as the pen-ultimate hidden layer of a NN, revealing up to 5.93% accuracy improvement at 5GMAC/sec/axon when noise-aware training is enforced and allowing accuracies of 99.15% and 79.8% for the MNIST and CIFAR-10 classification tasks. Channel-aware training was then demonstrated by integrating the frequency response of the photonic hardware in NN training, with its experimental validation with the MNIST dataset revealing an accuracy increase of 12.93% at a record-high rate of 25GMAC/sec/axon.
GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

We propose GA-Nav, a novel group-wise attention mechanism to identify safe and navigable regions in unstructured environments from RGB images. Our group-wise attention method extracts multi-scale features from each type of terrain independently and classifies terrains based on their navigability levels using coarse-grained semantic segmentation. Our novel loss can be embedded within any backbone network to explicitly focus on the different groups’ features, at a low spatial resolution. Our design leads to efficient inference while maintaining a high level of accuracy compared to existing SOTA methods. Our extensive evaluations on the RUGD and RELLIS-3D datasets shows that GA-Nav achieves the state-of-the-art performance on RUGD and RELLIS-3D datasets. We interface GA-Nav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains. We integrate our GA-Nav-based navigation algorithm with ClearPath Jackal and Husky robots, and observe an improvement in terms of navigation success rate and better trajectory selections.
Bayesian Inference for State-Space Models With Student-<i>t</i> Mixture Distributions

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.
Choose your programming copilot

GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for software developers. This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance in automatic program synthesis, has not yet been carried out. In this paper, we evaluate GitHub Copilot on standard program synthesis benchmark problems and compare the achieved results with those from the genetic programming literature. In addition, we discuss the performance of both approaches. We find that the performance of the two approaches on the benchmark problems is quite similar, however, in comparison to GitHub Copilot, the program synthesis approaches based on genetic programming are not yet mature enough to support programmers in practical software development. Genetic programming usually needs a huge amount of expensive hand-labeled training cases and takes too much time to generate solutions. Furthermore, source code generated by genetic programming approaches is often bloated and difficult to understand. For future work on program synthesis with genetic programming, we suggest researchers to focus on improving the execution time, readability, and usability.
SWIPENET: Object detection in noisy underwater scenes

Deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst accompanying severe noise that confuses the detectors and (2) objects in real applications are usually small. In this paper, we propose a Sample-WeIghted hyPEr Network (SWIPENET), and a novel training paradigm named Curriculum Multi-Class Adaboost (CMA), to address these two problems at the same time. Firstly, the backbone of SWIPENET produces multiple high resolution and semantic-rich Hyper Feature Maps, which significantly improve small object detection. Secondly, inspired by the human education process that drives the learning from easy to hard concepts, we propose the noise-robust CMA training paradigm that learns the clean data first and then move on to learns the diverse noisy data. Experiments on four underwater object detection datasets show that the proposed SWIPENET+CMA framework achieves better or competitive accuracy in object detection against several state-of-the-art approaches.