Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong

Despite the exponential growth in the popularity of artificial intelligence (AI), our knowledge on the public perception of AI, especially in the context of local government services, is still limited. To bridge this gap, this study aims to provide empirical evidence and insights into public perceptions concerning the use of AI in local government services. Our methodological approach involves collecting data via an online survey from the residents of three major Australian cities—i.e., Sydney, Melbourne, Brisbane—and Hong Kong (n = 850), and performing statistical analyses. We found that: (a) Ease of using AI is significantly and positively influenced by attitude towards AI; (b) Attitude towards AI significantly and positively influences perceived usefulness of AI in local government services; (c) AI is seen useful in resource management and to improve delivery of service, reduction of cost to provide urban-service, improvement of public safety, and monitoring the effectiveness of strategies to manage environmental crisis, and; (d) AI is more positively perceived by Australians in comparison to Hong Kongers, indicating the impact of contextual and cultural differences. The research findings inform local government authorities—e.g., urban policymakers, managers, and planners—on their AI policy, planning and implementation decisions.
Integration of fractal modeling and correspondence analysis reconnaissance for geochemically high-potential promising areas, NE Iran

Most geochemical and geostatistical analysis in mining exploration requires removing regional trends in order to obtain local anomalies. In this paper, stream sediment samples, which collected from Khusf area (NE Iran), was studied based on Concentration-Area (C-A) fractal model as well as correspondence analysis methods to find high-potential areas elements. Correspondence analysis with 170 samples through 20 elements concentration values in each sample was performed. According to correspondence analysis, among one or several elements in the study area, local anomalies were separated which the highest concentration relates to the variables Pb, As, and Cd elements. After the correspondence analysis, the best variogram for the Khusf area was studied. Elemental concentration maps was then produced through estimating the values using kriging method. Therefore, using the fractal method, between three statistical sets of elements, it was concluded that the third set showed the anomaly for Pb, As, and Cd elements with local anomaly values respectively determined as 55, 7.2, and 0.88 ppm. Although Cd element is not genetically related to same source of Pb and As, utilizing integrated approach, Cd anomalies has also been detected in the area as a promising element zone. Finally, it is suggested that this region has the possibility of Basic Metals occurrence and suggests further geophysical operations on a local identification scale.
Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.
MaPLe: Multi-modal Prompt Learning

Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions. Further, we learn separate prompts across different early stages to progressively model the stage-wise feature relationships to allow rich context learning. We evaluate the effectiveness of our approach on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable performance and achieves an absolute gain of 3.45% on novel classes and 2.72% on overall harmonic-mean, averaged over 11 diverse image recognition datasets. Our code and pre-trained models are available at https://github.com/muzairkhattak/multimodal-prompt-learning.
Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank

Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network training. However, the naive mean-teacher method suffers from two main problems: (1) The consistency loss used in training might become ineffective when the teacher's prediction is wrong. (2) Using L1 distance may cause the network to overfit wrong labels, resulting in confirmation bias. To address the above problems, we first introduce a reliable bank to store the “best-ever” outputs as pseudo ground truth. To assess the quality of outputs, we conduct an empirical analysis based on the monotonicity property to select the most trustworthy NR-IQA method. Besides, in view of the confirmation bias problem, we incorporate contrastive regularization to prevent the overfitting on wrong labels. Experimental results on both full-reference and non-reference underwater benchmarks demonstrate that our algorithm has obvious improvement over SOTA methods quantitatively and qualitatively. Code has been released at https://github.com/Huang-ShiRui/Semi-UIR.
Visual Prompt Multi-Modal Tracking

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multi-modal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT.
Learning A Sparse Transformer Network for Effective Image Deraining

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer.
Multi-Concept Customization of Text-to-Image Diffusion

While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to quickly acquire a new concept, given a few examples? Furthermore, can we compose multiple new concepts together? We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models. We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts while enabling fast tuning (~ 6 minutes). Additionally, we can jointly train for multiple concepts or combine multiple fine-tuned models into one via closed-form constrained optimization. Our fine-tuned model generates variations of multiple new concepts and seamlessly composes them with existing concepts in novel settings. Our method outperforms or performs on par with several baselines and concurrent works in both qualitative and quantitative evaluations, while being memory and computationally efficient.
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://github.com/ofsoundof/GRL-Image-Restoration.git.
Digital Education System During the COVID-19 Pandemic

In this chapter, the growth and utilisation of digital technologies in the Indian education system during the COVID-19 pandemic have been illustrated. During a pandemic lockdown, all humans live alone in their homes to limit COVID-19 spread. A lot of digital tools have been used in Indian schools and colleges to improve or sustain the teaching and learning processes. However, some impacts and causes have been observed while using new digital platforms during that time. The utilisation of digital tools for the school and higher education systems has been elaborately explained in this chapter. In addition, the future scope and summary of digital technology utilizations have been derived.
Temporal Resolution Multiplexing: Exploiting the limitations of spatio-temporal vision for more efficient VR rendering

Rendering in virtual reality (VR) requires substantial computational power to generate 90 frames per second at high resolution with good-quality antialiasing. The video data sent to a VR headset requires high bandwidth, achievable only on dedicated links. In this paper we explain how rendering requirements and transmission bandwidth can be reduced using a conceptually simple technique that integrates well with existing rendering pipelines. Every even-numbered frame is rendered at a lower resolution, and every odd-numbered frame is kept at high resolution but is modified in order to compensate for the previous loss of high spatial frequencies. When the frames are seen at a high frame rate, they are fused and perceived as high-resolution and high-frame-rate animation. The technique relies on the limited ability of the visual system to perceive high spatio-temporal frequencies. Despite its conceptual simplicity, correct execution of the technique requires a number of non-trivial steps: display photometric temporal response must be modeled, flicker and motion artifacts must be avoided, and the generated signal must not exceed the dynamic range of the display. Our experiments, performed on a high-frame-rate LCD monitor and OLED-based VR headsets, explore the parameter space of the proposed technique and demonstrate that its perceived quality is indistinguishable from full-resolution rendering. The technique is an attractive alternative to reprojection and resolution reduction of all frames.
Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy

Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In this paper, the proposed system aims at tracking multiple vehicles in the front view of an onboard monocular camera. The vehicle detection probes are customized to generate high precision detection, which plays a basic role in the following tracking-by-detection method. A novel Siamese network with a spatial pyramid pooling (SPP) layer is applied to calculate pairwise appearance similarity. The motion model captured from the refined bounding box provides the relative movements and aspects. The online-learned policy treats each tracking period as a Markov decision process (MDP) to maintain long-term, robust tracking. The proposed method is validated in a moving vehicle with an onboard NVIDIA Jetson TX2 and returns real-time speeds. Compared with other methods on KITTI and self-collected datasets, our method achieves significant performance in terms of the “Mostly-tracked”, “Fragmentation”, and “ID switch” variables.
A comprehensive, application-oriented study of catastrophic forgetting in DNNs.

We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that enforces typical constraints encountered in application scenarios. As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF. Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.
Region-of-Interest Compression and View Synthesis for Light Field Video Streaming

Light field videos provide a rich representation of real-world, thus the research of this technology is of urgency and interest for both the scientific community and industries. Light field applications such as virtual reality and post-production in the movie industry require a large number of viewpoints of the captured scene to achieve an immersive experience, and this creates a significant burden on light field compression and streaming. In this paper, we first present a light field video dataset captured with a plenoptic camera. Then a new region-of-interest (ROI)-based video compression method is designed for light field videos. In order to further improve the compression performance, a novel view synthesis algorithm is presented to generate arbitrary viewpoints at the receiver. The experimental evaluation of four light field video sequences demonstrates that the proposed ROI-based compression method can save 5%-7% in bitrates in comparison to conventional light field video compression methods. Furthermore, the proposed view synthesis-based compression method not only can achieve a reduction of about 50% in bitrates against conventional compression methods, but the synthesized views can exhibit identical visual quality as their ground truth.
The state of framework development for implementing reasoning mechanisms in smart cyber-physical systems: A literature review

Abstract Smart CPSs (S-CPSs) have been evolving beyond what was identified by the traditional definitions of CPSs. The objective of our research is to investigate the concepts and implementations of reasoning processes for S-CPSs, and more specifically, the frameworks proposed for the fuzzy front end of their reasoning mechanisms. The objectives of the paper are: (i) to analyze the framework concepts and implementations of CPS, (ii) to review the literature concerning system-level reasoning and its enablers from the points of view of the processed knowledge, building awareness, reasoning mechanisms, decision making, and adaptation. Our findings are: (i) awareness and adaptation behaviors are considered as system-level smartness of S-CPSs that are not achieved by traditional design approaches; (ii) model-based and composability approaches insufficiently support the development of reasoning mechanisms for S-CPSs; (iii) frameworks for development of reasoning in S-CPS should support compositional design. Based on the conclusions above, we argue that coping with the challenges of compositionality requires both software-level integration and holistic fusion of knowledge by means of semantic transformations. This entails the need for a multi aspect framework that is able to capture at least conceptual, functional, architectural, informational, interoperation, and behavioral aspects. It needs further investigation if a compositionality enabling framework should appear in the form of a meta-framework (abstract) or in the form of a semantically integrated (concrete) framework. Highlights Smartness in CPSs is a holistic and synergistic behavioral characteristic. Complex mental representations are compositional. Compositionality is necessary for smart CPSs. Without a rigorous unifying framework, designing synthesis reasoning remains ad hoc.
Optimizing Parameters of Multi-Layer Convolutional Neural Network by Modeling and Optimization Method

The modeling and optimization method (MAOM) proposed in this study finds the best combination of parameters for a multi-layer convolutional neural network (MCNN). This study emphasizes that in addition to the importance of the MCNN structure, the parameter design within the layers is also very important. After determining the structure of the MCNN, the parameter optimization in the layer can improve the performance of the MCNN. The MCNN parameters for convolutional layers include filter size, number of filters, padding, and filter stride. Parameters for max-pooling layers also include pooling size and pooling stride. After the MCNN architecture is designed, the major challenge is finding the combination of parameters that enhances the MCNN performance. The proposed MAOM optimizes the MCNN parameters by integrating uniform experimental design (UED), multiple regression (MR), and optimization method. After the MCNN architecture is designed, UED is used to design the MCNN parameters. The parameter layout obtained by the UED is then used in experiments to collect data that can be used for modeling. Next, MR is performed using the parameters with the average correct rate to build an MCNN parameter model. Finally, a full-factorial search algorithm is used to find the best combination of the MCNN parameters for obtaining the maximum average correct rate. Images from the modified National Institute of Standards and Technology (modified NIST or MNIST) resources, Fashion-MNIST, and PhysioNet databases are used to test the performance of the architecture and parameters of the MCNN. The experimental results demonstrate the excellent performance of the MAOM in obtaining the best combination of MCNN parameters and maximum average correct rate. The main advantage of the proposed MAOM is its systematic method of finding the best combination of the MCNN parameters for image identification and obtaining high correct rate.
An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated. The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc. To address above technical issues, an improved ant colony algorithm is proposed for path planning. In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules. Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process. The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles. The simulation results show the effectiveness of the proposed algorithm.
A Theory of Conditional Assertion

According to one tradition, uttering an indicative conditional involves performing a special sort of speech act: a conditional assertion. We introduce a formal framework that models this speech act. Using this framework, we show that any theory of conditional assertion validates several inferences in the logic of conditionals, including the False Antecedent inference (that not A implies if A, then C). Next, we determine the space of truth-conditional semantics for conditionals consistent with conditional assertion. The truth value of any such conditional is settled whenever the antecedent is false, and whenever the antecedent is true and the consequent is false. Then, we consider the space of dynamic meanings consistent with the theory of conditional assertion. We develop a new family of dynamic conditional-assertion operators that combine a traditional test operator with an update operation.