Reviewerly/exHarmony huggingface dataset:
Metaverse platform ecosystems

Abstract Traditionally, the metaverse has been defined as a virtual reality space in which users can interact with a computer-generated environment and other users. Nowadays, it is much more than that—the metaverse can be described as a variety of digital platforms and ecosystems, with each ecosystem as an independent universe with its own material, complementors, and functionalities. Creating metaverse ecosystems and integrated platforms results in different roles and responsibilities for complementors, consumers, platform owners, and orchestrators. The term metaverse platforms is further structured and clarified by four perspectives: innovation, production, transaction, and social interaction. Consequently, this fundamental paper defines it as: “A Metaverse is a massively scaled and interoperable meta-ecosystem of other digital ecosystems of real-time rendered 3D virtual worlds which can be experienced synchronously and persistently by an unlimited number of complementors and consumers with an increased user experience caused by a creativity-guided co-creation of goods managed by orchestrators and supported by platform owners.” Consequently, the metaverse offers vast opportunities for digital innovations beyond traditional social media or computer games and creates new infrastructures for add-on innovations in all areas of the digital economy.
Rethinking the Person Localization for Single-Stage Multi-Person Pose Estimation

Single-stage models for multi-person pose estimation have garnered significant attention due to their streamlined approach in generating person position localization and body structure perception in a single pass. These two parts, however, are processed individually by existing methods, leading to suboptimal results, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , candidates with high confidences for person localization while poor structure estimations. To this end, we propose a simple yet effective approach, namely Structure-guided Person Localization (SPL), jointly leveraging the advantages of the two aspects to solve the multi-person pose estimation problem, with two complementary novelties. First, we propose to incorporate body structure perception to guide person position localization, consequently, we introduce the Structure-guided Center Learning (SCL) to unify the quality of the body structure perception in the displacement map with the confidence of the person existence in the center map, thus achieving more accurate keypoint position localization results even with extreme poses. Second, to facilitate the end-to-end training of SPL, we propose the efficient Agency-based Scale-adaptive Learning (ASL). Specifically, we predict an agency map of the same size as the center map, which focuses on the foreground area and can adaptively adjust the scale size for each central area with the body structure perception confidence. Comprehensive experiments on challenging benchmarks including COCO and CrowdPose clearly verify the superiority of our framework, which achieves new state-of-the-art single-stage multi-person pose estimation results. Specifically, SPL obtains 72.1 AP scores and 69.5 AP scores in COCO test-dev2017 and CrowdPose test set, respectively.
The role of sustainable finance in achieving sustainable development goals

In the article, the analysis of scientific literature leads to the definition of sustainable finance and identification of its types. This distinction allows for a more detailed examination of sustainable finance in different contexts, especially when only one type of sustainable finance is considered. The empirical study that has been carried out in the Baltic Sea states confirms that sustainable finance has an impact on the implementation of sustainable development goals. The countries that use more sustainable finance have better results in terms of the SDGs. The created research model reflects the sequence of data collection and the process of determining the relationship between sustainable development and sustainable finance. The correlation between the two is confirmed by the conducted research. The more sustainable is the country's financial model, the better is the implementation of sustainable development goals.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty of parsing the correct SQL queries especially when they involve many schema items and logic operators. This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its encoder is injected by the most relevant schema items instead of the whole unordered ones, which could alleviate the schema linking effort during SQL parsing, and its decoder first generates the skeleton and then the actual SQL query, which could implicitly constrain the SQL parsing. We evaluate our proposed framework on Spider and its three robustness variants: Spider-DK, Spider-Syn, and Spider-Realistic. The experimental results show that our framework delivers promising performance and robustness. Our code is available at https://github.com/RUCKBReasoning/RESDSQL.
Gradient-Ascent Pulse Engineering with Feedback

Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully solved using optimization techniques, including methods like gradient-ascent pulse engineering (GRAPE), relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. In this work, we introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power considering various scenarios based on cavity QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multi-qubit devices to linear-optics quantum computation strategies, quantum-enhanced sensing with adaptive measurements, and quantum error correction.
Distributed Cooperative Coevolution of Data Publishing Privacy and Transparency

Data transparency is beneficial to data participants’ awareness, users’ fairness, and research work’s reproducibility. However, when addressing transparency requirements, we cannot ignore data privacy. This article defines the multi-objective data publishing (MODP) problem, optimizing data privacy and transparency at the same time. Accordingly, we propose a distributed cooperative coevolutionary genetic algorithm (DCCGA) to optimize the MODP problem. In the population of DCCGA, each individual represents an anonymization solution to MODP. Three modules in DCCGA, i.e., grouping module, cooperative coevolutionary module, and evolving module, are proposed for distributed sub-population update and evaluation, improving DCCGA’s optimization performance and parallel efficiency. Moreover, a matrix-based crossover operator and a matrix-based mutation operator are designed to exchange and adjust anonymization information in the individuals efficiently. Experimental results demonstrate that the proposed DCCGA outperforms the competitors with respect to solution accuracy, convergence speed, and scalability. Besides, we verify the effectiveness of all the proposed components in DCCGA.
IDS in IoT using Machine ‎Learning and Blockchain

The rise of IoT devices has brought forth an urgent need for enhanced security and privacy measures, as IoT devices are vulnerable to cyber-attacks that compromise the security and privacy of users. Traditional security measures do not provide adequate protection for such devices. This study aimed to investigate the use of machine learning and blockchain to improve the security and privacy of IoT devices, creating an intrusion detection system powered by machine learning algorithms and using blockchain to encrypt interactions between IoT devices. The performance of the whole system and different machine learning algorithms was evaluated on an IoT network using simulated attack data, achieving a detection accuracy of 99.9% when using Random Forrest, demonstrating its effectiveness in detecting attacks on IoT networks. Furthermore, this study showed that blockchain technology could improve security and privacy by providing a tamper-proof decentralized communication system.
A Survey on Efficient Training of Transformers

Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transformer training faster, at lower cost, and to higher accuracy by the efficient use of computation and memory resources. This survey provides the first systematic overview of the efficient training of Transformers, covering the recent progress in acceleration arithmetic and hardware, with a focus on the former. We analyze and compare methods that save computation and memory costs for intermediate tensors during training, together with techniques on hardware/algorithm co-design. We finally discuss challenges and promising areas for future research.
GAN-Generated Faces Detection: A Survey and New Perspectives

Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.
Memory-Driven Metaheuristics: Improving Optimization Performance

Metaheuristics are stochastic optimization algorithms that mimic natural processes to find optimal solutions to complex problems. The success of metaheuristics largely depends on the ability to effectively explore and exploit the search space. Memory mechanisms have been introduced in several popular metaheuristic algorithms to enhance their performance. This chapter explores the significance of memory in metaheuristic algorithms and provides insights from well-known algorithms. The chapter begins by introducing the concept of memory, and its role in metaheuristic algorithms. The key factors influencing the effectiveness of memory mechanisms are discussed, such as the size of the memory, the information stored in memory, and the rate of information decay. A comprehensive analysis of how memory mechanisms are incorporated into popular metaheuristic algorithms is presented, and concludes by highlighting the importance of memory in metaheuristic performance and providing future research directions for improving memory mechanisms. The key takeaways are that memory mechanisms can significantly enhance the performance of metaheuristics by enabling them to explore and exploit the search space effectively and efficiently, and that the choice of memory mechanism should be tailored to the problem domain and the characteristics of the search space.
Classical and Quantum Convolutional Codes Derived From Algebraic Geometry Codes

In this paper, we construct new families of classical convolutional codes (CCC's) and new families of quantum convolutional codes (QCC's). The CCC's are derived from (block) algebraic geometry (AG) codes. Furthermore, new families of CCC's are constructed by applying the techniques of puncturing, extending, expanding, and by the direct product code construction applied to AG codes. In addition, utilizing the new CCC's constructed here, we obtain new families of QCC's. The parameters of these new codes are good. More precisely, in the classical case, a family of almost near maximum distance separable (MDS) codes is presented; in the quantum case, we construct a family of MDS (optimal) quantum convolutional codes.
Swapping intraphoton entanglement to interphoton entanglement using linear optical devices

We propose a curious protocol for swapping the intraphoton entanglement between path and polarization degrees of freedom of a single photon to interphoton entanglement between two distance photons that have never interacted. This is accomplished by using an experimental setup consisting of three suitable Mach-Zehnder interferometers along with number of beam splitters, polarization rotators, and detectors. Using the same setup, we have also demonstrated an interesting quantum state transfer protocol, symmetric between Alice and Bob. Importantly, the Bell-basis discrimination is not required in both the swapping and state transfer protocols. Our proposal can be implemented using linear optical devices.
Analysis of e-Recruitment System Design

The process of applying for a job is a first step for unemployment to get a job who matches the criteria.An e-recruitment system such as LinkedIn, Reed, and Indeed is a tool for prospective workers looking for jobs.On this site, workers must register the email address, name, type of bachelor and salary desired.After that, system will send company's link that is offers vacancies to the email of candidates, so the jobsekeer can directly register to the system.However, the system is ineffective because the prospective worker must register itself on the website where the e-recruitment site only provides such services.In contrast to prospective workers and e-recruitment systems, recruiters (firms) also have bigger problems.To open the selection of new recruiting candidates, the company has to spend a lot of energy, time and money.From that point, this paper is designed to create heading posts for users (prospective workers) and companies to be on the same system.It will help users in finding work and help companies in finding workers who are competent in their field.
Sampling-Based Tour Generation of Arbitrarily Oriented Dubins Sensor Platforms

This paper describes a formulation and develops a novel procedure for a fleet of unmanned aerial vehicles (UAVs) from the perspective of remotely executable tasks. In a complex mission environment, the characteristics of vehicles can be different in terms of sensing capability, range, direction, or the motion constraints. The purpose of this paper is to find a set of paths that minimizes the sum of costs while every task region is visited exactly once under certain reasonable assumptions. The heterogeneous multi-UAV path planning problem is formulated as a generalized heterogeneous multiple depot asymmetric traveling salesman problem (GHMDATSP), which is a variant of the traveling salesman problem. The proposed transformation procedure changes an instance of the GHMDATSP into a format of an asymmetric traveling salesman problem (ATSP) to obtain tours for which the total cost of a fleet of vehicles is minimized. The instance of the ATSP is solved using the Lin–Kernighan–Helsgaun heuristic, and the result is inversely transformed to the GHMDATSP-formatted instance to obtain a set of tours. An additional local optimization-based path refinement process helps obtain a high-quality solution. Numerical experiments investigate and confirm the validity and applicability of the proposed procedure.
The Automation of C Program Verification by the Symbolic Method of Loop Invariant Elimination

In the deductive verification of programs written in the imperative programming languages, the generation and proving of the verification conditions corresponding to loops are of particular complexity, as each of them must be provided with an invariant, whose construction is often a challenge. As a rule, the methods for the synthesis of loop invariants have a heuristic character, which complicates their application. An alternative is the symbolic loop invariant elimination method proposed by V.A. Nepomniaschy in 2005. Its idea is to represent a loop body in the form of a special replacement operation under certain constraints. Such an operation in the symbolic form expresses the loop effect, which allows introducing an inference rule for the loops without invariants into axiomatic semantics. This work is the further development of this method. It extends the proposed method of mixed axiomatic semantics for the verification of C-light programs. This extension incorporates the method for the verification of iterations over changeable arrays with the possible exit from the loop body in C-light programs. The method contains the inference rule for iterations without loop invariants. This rule has been implemented in the verification condition generator, which is a part of the automated system for the verification of C-light programs. To perform automated verification in the used ACL2 system, two algorithms, one of which generates the replacement operation in the ACL2 language, and the second generates the auxiliary lemmas resulting in the successful automated proof of the obtained verification conditions in the ACL2 system have been developed and implemented. The application of the above mentioned methods and algorithms is illustrated with an example.
Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems

In machine learning research, many emerging applications can be (re)formulated as the composition optimization problem with nonsmooth regularization penalty. To solve this problem, traditional stochastic gradient descent (SGD) algorithm and its variants either have low convergence rate or are computationally expensive. Recently, several stochastic composition gradient algorithms have been proposed, however, these methods are still inefficient and not scalable to large-scale composition optimization problem instances. To address these challenges, we propose an asynchronous parallel algorithm, named Async-ProxSCVR, which effectively combines asynchronous parallel implementation and variance reduction method. We prove that the algorithm admits the fastest convergence rate for both strongly convex and general nonconvex cases. Furthermore, we analyze the query complexity of the proposed algorithm and prove that linear speedup is accessible when we increase the number of processors. Finally, we evaluate our algorithm Async-ProxSCVR on two representative composition optimization problems including value function evaluation in reinforcement learning and sparse mean-variance optimization problem. Experimental results show that the algorithm achieves significant speedups and is much faster than existing compared methods.
Waking up to data challenges

Yuanfang Guan explains how taking part in data challenges has helped her learn new analytical techniques and creatively apply them on a variety of datasets.
Cloud Application Portability: Issues and Developments

Cloud computing is a standard that is fast gaining momentum in the IT world. The availability of storage capacity that can be accessed and increased as the need arises makes computing easier. Applications can also be deployed using services provided by a cloud service provider. Portability allows utilization of applications and services across various domains. Portability could be in the area of programming language, application programming interface, data storage or data migration. Clearly, the easier it is to move services across various providers, the more attractive cloud computing becomes. The study was executed by means of review of some literature available on cloud application portability. This chapter examines current trends in cloud application portability area and gives focus for future research. In the present work, the objective is to answer the following question: what is the current trend and development in cloud application portability? Papers published in journals, conferences, white papers, and reputable magazines were analyzed. Some core topic facets were used in this review for the identification of trends in cloud application portability. The finding is that discussions on virtualization and API-specific issues are not adequate. This will be of benefit to prospective cloud users and even cloud providers.
The influence of word frequency on word reading speed when individuals with macular diseases read text

People with central field loss (CFL) use peripheral vision to identify words. Eccentric vision provides ambiguous visual inputs to the processes leading to lexical access. Our purpose was to explore the hypothesis that this ambiguity leads to strong influences of inferential processes, our prediction being that increasing word frequency would decrease word reading time. Individuals with bilateral CFL induced by macular diseases read French sentences displayed with a self-paced reading method. Reading time of the last word of each sentence (target word) was recorded. Each target word (in sentence n) was matched with a synonym word (in sentence n+1) of the same length. When using absolute frequency value (Analysis 1), we found that reading time of target words decreased when word frequency increases, even when controlling for word length. The amplitude of this effect is larger than reported in previous investigations of reading with normal subjects. When comparing the effect of relative frequency (low vs. high) within each pair of synonyms (Analysis 2), results show the same pattern as the one observed in Analysis 1. Our results demonstrate clear-cut frequency effects on word reading time and suggest that inferential processes are stronger in CFL readers than in normally sighted observers. These results might also help design text simplification tools tailored for low-vision patients.
Big Data Anonymization in Cloud using k-Anonymity Algorithm using Map Reduce Framework

Anonymization techniques are enforced to provide privacy protection for the data published on cloud. These techniques include various algorithms to generalize or suppress the data. Top Down Specification in k anonymity is the best generalization algorithm for data anonymization. As the data increases on cloud, data analysis becomes very tedious. Map reduce framework can be adapted to process on these huge amount of Big Data. We implement generalized method using Map phase and Reduce Phase for data anonymization on cloud in two different phases of Top Down Specification.