Reviewerly/exHarmony huggingface dataset:
Object Detection Algorithm Based on Multi-Scaled Convolutional Neural Networks

Object detection algorithms must first identify all the objects inside an image before machine vision can properly categorize and localize them. Many methods have been proposed to handle this problem, with most of the motivation coming from computer vision and deep learning methods. However, prevailing technologies have never effectively recognized tiny, dense things and often failed to detect objects that have undergone random geometric alterations. We analyze the current state of the art in object identification and propose a deformable convolutional network with adjustable depths to address these concerns. The results of our research suggest that they are better than the current best practices, blend deep convolutional networks with flexible convolutional structures to account for geometric variations, and get multi-scaled features. Next, we perform the remaining phases of object identification and region regress by up-sampling the fusion of multi-scaled elements. Experimental validation of our proposed framework demonstrates a considerable improvement in accuracy relative to time spent recognizing small target objects with geometric distortion.
Responsibly strategizing with the metaverse: Business implications and DEI opportunities and challenges

This viewpoint article aims to lay out the foundations for IS research on the strategic implications as well as diversity, equity, and inclusion (DEI) opportunities and challenges of the metaverse – a sociotechnical system providing users with an immersive, synchronous 3D experience of (virtual) reality through interoperable platforms and sensor devices. We first examine the metaverse’s technologies and its underlying business models, which hold immense strategic implications for organizations and industries, and might require rethinking the future of work. As with most emerging technologies able to generate, collect, and process user data, the metaverse comes at a price; leveraging the metaverse strategically will require ethical and DEI considerations. After examining all these aspects of the metaverse, we reflect on ways IS scholarship engaged in strategizing can advance theoretical and practical knowledge by generating novel research questions and outlining a research agenda. Our contribution is twofold: first, we analyze the metaverse using a sociotechnical lens, thereby setting the ground for future studies of the phenomenon. Second, we identify specific strategic implications as well as DEI opportunities and challenges associated with the metaverse, and discuss how scholarship versed in IS strategizing can contribute to addressing them, both theoretically and empirically.
Symbols and grounding in large language models

Large language models (LLMs) are one of the most impressive achievements of artificial intelligence in recent years. However, their relevance to the study of language more broadly remains unclear. This article considers the potential of LLMs to serve as models of language understanding in humans. While debate on this question typically centres around models' performance on challenging language understanding tasks, this article argues that the answer depends on models' underlying competence, and thus that the focus of the debate should be on empirical work which seeks to characterize the representations and processing algorithms that underlie model behaviour. From this perspective, the article offers counterarguments to two commonly cited reasons why LLMs cannot serve as plausible models of language in humans: their lack of symbolic structure and their lack of grounding. For each, a case is made that recent empirical trends undermine the common assumptions about LLMs, and thus that it is premature to draw conclusions about LLMs' ability (or lack thereof) to offer insights on human language representation and understanding. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Fast random opposition-based learning Golden Jackal Optimization algorithm

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems.
Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

This article's innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother's care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA's high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of 12 often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Precisely, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems. The findings of the statistical analysis from the Wilcoxon signed-rank test show that MOA has a significant statistical superiority compared to the twelve well-known metaheuristic algorithms in managing the optimization problems studied in this paper.
Web3: A comprehensive review on background, technologies, applications, zero-trust architectures, challenges and future directions

Web3, the next generation web, promises a decentralized and democratized internet that puts users in control of their data and online identities. However, Web3 faces significant challenges, including scalability, interoperability, regulatory compliance, and energy consumption. To address these challenges, this review paper provides a comprehensive analysis of Web3, including its key advancements and implications, as well as an overview of its major applications in Decentralized Applications (DApps), Decentralized Finance (DeFi), Non-fungible Tokens (NFTs), Decentralized Autonomous Organizations (DAOs), and Supply Chain Management and Provenance Tracking. The paper also discusses the potential social and economic impact of Web3, as well as its integration with emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), and smart cities. This article then discusses importance of zero-trust architecture for Web3. Ultimately, this review highlights the importance of Web3 in shaping the future of the internet and provides insights into the challenges and opportunities that lie ahead.
Visual Language Maps for Robot Navigation

Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image captions). While this is useful for matching images to natural language descriptions of object goals, it remains disjoint from the process of mapping the environment, so that it lacks the spatial precision of classic geometric maps. To address this problem, we propose VLMaps, a spatial map representation that directly fuses pretrained visual-language features with a 3D reconstruction of the physical world. VLMaps can be autonomously built from video feed on robots using standard exploration approaches and enables natural language indexing of the map without additional labeled data. Specifically, when combined with large language models (LLMs), VLMaps can be used to (i) translate natural language commands into a sequence of open-vocabulary navigation goals (which, beyond prior work, can be spatial by construction, e.g., “in between the sofa and the TV” or “three meters to the right of the chair”) directly localized in the map, and (ii) can be shared among multiple robots with different embodiments to generate new obstacle maps on-the-fly (by using a list of obstacle categories). Extensive experiments carried out in simulated and real-world environments show that VLMaps enable navigation according to more complex language instructions than existing methods. Videos are available at https://vlmaps.github.io.
Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

This study presents a new nature-inspired optimization algorithm, namely the Mantis Search Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying mantises. In brief, MSA consists of three optimization stages, including the search for prey (exploration), attack prey (exploitation), and sexual cannibalism. Those operators are simulated using various mathematical models to effectively tackle optimization challenges across diverse search spaces. The performance of MSA is rigorously tested on fifty-two optimization problems and three real-world applications (five engineering design problems, and the parameter estimation problem of photovoltaic modules and fuel cells) to show its versatility and adaptability to different scenarios. To disclose the MSA’s superiority, it is compared to two categories from the rival optimizers: the first category involves well-established and highly-cited optimizers, like Differential evolution; and the second category contains recently-published algorithms, like African Vultures Optimization Algorithm. This comparison is conducted using several performance metrics, the Wilcoxon rank-sum test and the Friedman mean rank to disclose the MSA’s effectiveness and efficiency. The results of this comparison highlight the effectiveness of this new approach and its potential for future optimization applications. The source codes of the MSA algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/131833-mantis-search-algorithm-msa.
Automated Repair of Programs from Large Language Models

Large language models such as Codex, have shown the capability to produce code for many programming tasks. However, the success rate of existing models is low, especially for complex programming tasks. One of the reasons is that language models lack awareness of program semantics, resulting in incorrect programs, or even programs which do not compile. In this paper, we systematically study whether automated program repair (APR) techniques can fix the incorrect solutions produced by language models in LeetCode contests. The goal is to study whether APR techniques can enhance reliability in the code produced by large language models. Our study revealed that: (1) automatically generated code shares common programming mistakes with human-crafted solutions, indicating APR techniques may have potential to fix auto-generated code; (2) given bug location information provided by a statistical fault localization approach, the newly released Codex edit mode, which supports editing code, is similar to or better than existing Java repair tools TBar and Recoder in fixing incorrect solutions. By analyzing the experimental results generated by these tools, we provide several suggestions: (1) enhancing APR tools to surpass limitations in patch space (e.g., introducing more flexible fault localization) is desirable; (2) as large language models can derive more fix patterns by training on more data, future APR tools could shift focus from adding more fix patterns to synthesis/semantics based approaches, (3) combination of language models with APR to curate patch ingredients, is worth studying.
Blockchain technology applications in waste management: Overview, challenges and opportunities

Rapid population growth and urbanisation have accelerated waste generation, and effective waste management has become a major challenge worldwide. With advances in technology and management methods, waste management strategies have begun embracing digitalisation, leveraging the Internet of Things (IoT), big data analytics, cloud/edge computing, machine learning, 5G communication, and blockchain technologies. Amongst them, the blockchain technology has the structural features of achieving information security and integrity without central guarantees. Blockchain also meets the data record/storage needs of waste management and the design of new mechanisms for effective waste management. These benefits make blockchain an attractive technology in the field of waste management, with researchers and practitioners alike investigating its broad applications to support sustainable waste management. However, this emerging technology has not yet been widely accepted by potential users. To further champion the application of blockchain technology, this review paper provides a systematic overview of the various pathways in which the technology has been applied in the waste management industry and further discusses its related challenges and opportunities via considering the promising prospect of combining blockchain technology with IoT, artificial intelligence (AI) and life cycle assessment (LCA). This review also provides insights for interpreting some emerging applications of blockchain in the field of waste management and clarifying the research paths in the context of blockchain, digitalised waste management, and circular economy.
Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and finetuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$512 \times 1024$</tex> , achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pretrained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1280 \times 2048$</tex> . We show that the temporal layers trained in this way generalize to different finetuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://nv-tlabs.github.io/VideoLDM/
GLIGEN: Open-Set Grounded Text-to-Image Generation

Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN's zero-shot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.
DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation

Abstract This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth estimation. Moreover, the Transformer and convolution are good at long-range and close-range depth estimation, respectively. Therefore, we propose to adopt a parallel encoder architecture consisting of a Transformer branch and a convolution branch. The former can model global context with the effective attention mechanism and the latter aims to preserve the local information as the Transformer lacks the spatial inductive bias in modeling such contents. However, independent branches lead to a shortage of connections between features. To bridge this gap, we design a hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features and model the affinity between the heterogeneous features in a set-to-set translation manner. Due to the unbearable memory cost introduced by the global attention on high-resolution feature maps, we adopt the deformable scheme to reduce the complexity. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets demonstrate that our proposed model, termed DepthFormer, surpasses state-of-the-art monocular depth estimation methods with prominent margins. The effectiveness of each proposed module is elaborately evaluated through meticulous and intensive ablation studies.
Using large language models in psychology

Large language models (LLMs), such as OpenAI's GPT-4, Google's Bard or Meta's LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to advance psychological measurement, experimentation and practice, they are not yet ready for many of the most transformative psychological applications — but further research and development may enable such use. Next, we examine four major concerns about the application of LLMs to psychology, and how each might be overcome. Finally, we conclude with recommendations for investments that could help to address these concerns: field-initiated 'keystone' datasets; increased standardization of performance benchmarks; and shared computing and analysis infrastructure to ensure that the future of LLM-powered research is equitable. Large language models (LLMs), which can generate and score text in human-like ways, have the potential to advance psychological measurement, experimentation and practice. In this Perspective, Demszky and colleagues describe how LLMs work, concerns about using them for psychological purposes, and how these concerns might be addressed.
Vision-Motion Codesign for Low-Level Trajectory Generation in Visual Servoing Systems

The separate design of vision algorithms and motion control algorithms in existing vision servo systems limits their application in high-speed and high-precision industrial scenarios. To address this problem, a vision-motion codesign method is proposed for integrating visual servo systems with motion controllers via real-time Ethernet connections. During the engineering design phase of our proposal, we developed a control kernel architecture to clarify the system design logic. Then, we proposed a runtime module for real-time trajectory generation to facilitate the unified deployment of different planning methods in the kernel. On this basis, we implemented a self-adaptive trajectory generation synthesis method capable of dynamically adjusting the motion resources for real-time interpolation. The experimental results demonstrate that in visual servo static tracking experiments with the SCARA robot, our proposed method has a faster response time and smaller impact than other mainstream trajectory generation methods in 1 ms real-time interpolation cycles.
Choosing Path Replanning Strategies for Unmanned Aircraft Systems

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no-fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may invalidate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required. Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learning techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.