Reviewerly/exHarmony huggingface dataset:
RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.
Revealing hidden patterns in deep neural network feature space continuum via manifold learning

Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision

Abstract We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: from text to high-level semantic tokens (akin to “reading”) and from semantic tokens to low-level acoustic tokens (“speaking”). Decoupling these two tasks enables training of the “speaking” module using abundant audio-only data, and unlocks the highly efficient combination of pretraining and backtranslation to reduce the need for parallel data when training the “reading” component. To control the speaker identity, we adopt example prompting, which allows SPEAR-TTS to generalize to unseen speakers using only a short sample of 3 seconds, without any explicit speaker representation or speaker labels. Our experiments demonstrate that SPEAR-TTS achieves a character error rate that is competitive with state-of-the-art methods using only 15 minutes of parallel data, while matching ground-truth speech in naturalness and acoustic quality.
Detection of Malware using Phishing Alarm

Informal organizations have become one of the most well known stages for clients to connect with one another. Given the immensemeasure of touchy information accessible in informal community stages, client security assurance on interpersonal organizations has become one of the most dire examination issues. As a customary data taking procedure, phishing assaults actually work in their method for causing a ton of security infringement occurrences an aggressor sets up trick Web pages (professing to be a significant Website like an interpersonal organization gateway) to bait clients to enter their confidential data. As a matter of fact, the presence of Web pages is among the main variables in beguiling clients, and consequently, the comparability amongWeb pages is a basic measurement for distinguishing phishing Websites. we propose a clever visual likeness based phishing recognition plot utilizing tint data with auto refreshing data set in the paper. Likewise utilize another technique called Phishing-Alarm, That define phishing assaults utilizing highlights that are difficult to dodge by aggressors .Since a PWS (Phishing Website) is made in light of designated real site or other subspecies whose tint data is comparable each other, many PWSs can be thoroughly identified by following comparative hued subspecies. In view of this idea, the proposed conspire recognizes another PWS which has comparable shade data to currently distinguished PWSs. By the virtual experience with genuine dataset, we exhibit that the proposed plot further develops the identification execution as the quantity of recognized PWSs increments.
OJUMP: Optimization for joint unicast‐multicast panoramic VR live streaming system

Abstract Virtual reality (VR) videos have garnered increasing attention due to their ability to deliver immersive multimedia experiences to users. Nonetheless, the demanding requirements of high bandwidth and low latency for VR services present significant challenges, especially in the context of network infrastructure. In this article, we introduce a novel system called OJUMP, aimed at O ptimizing the transmission and distribution of J oint U nicast‐ M ulticast P anoramic video field of view live streaming using Bit Index Explicit Replication (BIER). The proposed system encompasses a BIER‐based network transmission approach that enhances video transmission efficiency and facilitates large‐scale service deployment. Furthermore, OJUMP includes a dynamic disk‐state‐based tile slice distribution storage algorithm and a prefetch method designed for edge nodes to enhance the distribution efficiency of live streaming services. Experimental results illustrate that the proposed system offers distinct advantages over the baseline system in terms of bandwidth, latency, disk utilization, and central processing unit utilization.
Qubit-Reuse Compilation with Mid-Circuit Measurement and Reset

A number of commercially available quantum computers, such as those based on trapped-ion or superconducting qubits, can now perform mid-circuit measurements and resets. In addition to being crucial for quantum error correction, this capability can help reduce the number of qubits needed to execute many types of quantum algorithms by measuring qubits as early as possible, resetting them, and reusing them elsewhere in the circuit. In this work, we introduce the idea of qubit-reuse compilation, which takes as input a quantum circuit and produces as output a compiled circuit that requires fewer qubits to execute due to qubit reuse. We present two algorithms for performing qubit-reuse compilation: an exact constraint programming optimization model and a greedy heuristic. We introduce the concept of dual circuits, obtained by exchanging state preparations with measurements and vice versa and reversing time, and show that optimal qubit-reuse compilation requires the same number of qubits to execute a circuit as its dual. We illustrate the performance of these algorithms on a variety of relevant near-term quantum circuits, such as one-dimensional and two-dimensional time-evolution circuits, and numerically benchmark their performance on the quantum adiabatic optimization algorithm (QAOA) applied to the MaxCut problem on random three-regular graphs. To demonstrate the practical benefit of these techniques, we experimentally realize an 80-qubit QAOA MaxCut circuit on the 20-qubit Quantinuum H1-1 trapped-ion quantum processor using qubit-reuse compilation algorithms.20 MoreReceived 7 April 2023Revised 18 August 2023Accepted 1 November 2023DOI:https://doi.org/10.1103/PhysRevX.13.041057Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum algorithms & computationQuantum circuitsQuantum computationQuantum information with trapped ionsQuantum softwareQuantum Information, Science & Technology
Shallow quantum circuits for deeper problems

State-of-the-art quantum computers can only reliably execute circuits with limited qubit numbers and computational depth. This severely reduces the scope of algorithms that can be run. While numerous techniques have been invented to exploit few-qubit devices, corresponding schemes for depth-limited computations are less explored. This work investigates to what extent we can mimic the performance of a deeper quantum computation by repeatedly using a shallower device. We propose a method for this purpose, inspired by Feynman simulation, where a given circuit is chopped into two pieces. The first piece is executed and measured early on, and the second piece is run based on the previous outcome. This method is inefficient if applied in a straightforward manner due to the high number of possible outcomes. To mitigate this issue, we propose a shallow variational circuit, whose purpose is to maintain the complexity of the method within predefined tolerable limits, and provide an optimization method to find such a circuit. The composition of these components of the methods is called ``reduce'' As we discuss, this approach works for certain cases of interest. We believe this work may stimulate new research towards exploiting the potential of shallow quantum computers.
Welcome to the Gemini era: Google DeepMind and the information industry

Purpose The purpose of this paper is to examine the motivation behind Google’s development of Gemini and its potential impact on the information industry. Design/methodology/approach This viewpoint paper relies on a comprehensive analysis of the advancements in artificial intelligence (AI) technology, specifically in the field of chatbots. Findings The findings reveal that Gemini is designed to enhance user experiences by providing personalized and contextually relevant information. It aims to streamline information retrieval, improve customer service and offer tailored content recommendations. The competition among companies in building AI-powered chatbots is driving rapid advancements and innovation in the field. Originality/value The originality of this paper lies in its analysis of Google DeepMind’s Gemini and its potential impact on the information industry. It highlights the significance of AI-powered chatbots in transforming how users access and interact with information. This paper contributes to the existing literature by examining the competition in building AI tools and its implications for the future of the information industry. It offers insights into the motivation behind Google’s development of Gemini and its potential value in enhancing user experiences and driving monetization opportunities.
Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning

Streaming data processing has attracted much more attention and become a key research area in the fields of machine learning and data mining. Since the distribution of real data may evolve (called concept drift) with time due to many unforeseen factors and real data is usually with imbalanced cluster/class distributions during streaming data processing, drifts occurred in distributions with fewer data objects are easily masked by the larger distributions. This paper, therefore, proposes an unsupervised drift detection approach called Multi-Imbalanced Cluster Discriminator (MICD) to address the more challenging imbalance problem of unlabeled data. It first partitions data into compact clusters, and then learns a discriminator for each cluster to detect drift. It turns out that MICD can detect drift occurrence, locate where the drift occurs, and quantify the extent of the drift. MICD is efficient, interpretable, and has easy-to-set parameters. Extensive experiments on synthetic and real datasets illustrate the superiority of MICD.
Effective Whole-body Pose Estimation with Two-stages Distillation

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an image. This task is challenging due to multi-scale body parts, fine-grained localization for low-resolution regions, and data scarcity. Meanwhile, applying a highly efficient and accurate pose estimator to widely human-centric understanding and generation tasks is urgent. In this work, we present a two-stage pose Distillation for Whole-body Pose estimators, named DWPose, to improve their effectiveness and efficiency. The first-stage distillation designs a weight-decay strategy while utilizing a teacher’s intermediate feature and final logits with both visible and invisible keypoints to supervise the student from scratch. The second stage distills the student model itself to further improve performance. Different from the previous self-knowledge distillation, this stage finetunes the student’s head with only 20% training time as a plug-and-play training strategy. For data limitations, we explore the UBody dataset that contains diverse facial expressions and hand gestures for real-life applications. Comprehensive experiments show the superiority of our proposed simple yet effective methods. We achieve new state-of-the-art performance on COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from 64.8% to 66.5%, even surpassing RTMPose-x teacher with 65.3% AP. We release a series of models with different sizes, from tiny to large, for satisfying various downstream tasks. Our code and models are available at https://github.com/IDEA-Research/DWPose.
LLaViLo: Boosting Video Moment Retrieval via Adapter-Based Multimodal Modeling

Recent studies have explored the potential of large language models (LLMs) for understanding the semantic information in images. However, the use of LLMs to understand videos, which contain continuous contextual information, remains limited. In this paper, we propose LLaV-iLo (LLaMa-Video-Localizer), a video moment retrieval pipeline powered by a large language model. LLaViLo has two key features: 1) In contrast to fine-tuning the entire LLM, we introduce and optimize only 1.7% of additional parameters in adapter modules, freezing the pre-trained LLM to enable efficient alignment of video and text. 2) A multi-objective optimization framework concurrently op-timizes two objectives: a set prediction objective and a captioning objective. The joint training of these two objectives allows the proposed framework to produce high-quality time coordinates. Compared with other state-of-the-art methods, the proposed LLaViLo achieves significant performance improvement on QVHighlights and Charades-STA datasets.
A Novel Dynamic S-Box Generation Scheme Based on Quantum Random Walks Controlled by a Hyper-Chaotic Map

For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems.
Metaverse customer journeys in tourism: building viable virtual worlds

Purpose This study aims to examine the use of the metaverse in tourism and hospitality to comprehend better how the technology might shape customer journey management, especially relative to information provision, experiences and customer benefits. Design/methodology/approach This explanatory research used a two-stage approach of media analysis and practitioner interviews to analyse the interactions among tourism information provision, customer experiences and customer benefits in the metaverse. It conceptualized and mapped the consumer journey of the emerging metaverse experience, focusing on the ideas and practices of metaverse design pioneers in tourism and hospitality. Findings Based on the media analysis and interviews with 27 designers, the metaverse – information – experiences – benefits (MIEB) model was proposed, containing three parts (information characteristics, customer experiences and customer benefits) and 31 supporting items grouped into nine components. Originality/value One of the unique contributions of this research is the MIEB model for applying the metaverse in customer journey management (pre-, during- and post-trip). The findings contribute to the current literature with this model based on the practical perspectives of metaverse designers and provide insights on how to incorporate the MIEB model in applying the metaverse in tourism and hospitality management. The findings also address existing literature gaps of insufficient research on metaverse management and design through all stages of the customer travel journey and by paying attention to stakeholders’ viewpoints, including the media and designers of metaverse applications. Engaging in semi-structured interviews with pioneers of the metaverse to gain insights into the design of tourism experiences was also different from other metaverse tourism research, although this is not claimed as a significant point of innovation.
The moral embeddedness of cryptomarkets: text mining feedback on economic exchanges on the dark web

Abstract Reputation systems promote cooperation in large-scale online markets for illegal goods. These so-called cryptomarkets operate on the Dark Web, where legal, social, and moral trust-building mechanisms are difficult to establish. However, for the reputation mechanism to be effective in promoting cooperation, traders have to leave feedback after completed transactions in the form of ratings and short texts. Here we investigate the motivational landscape of the reputation systems of three large cryptomarkets. We employ manual and automatic text mining methods to code 2 million feedback texts for a range of motives for leaving feedback. We find that next to self-regarding motives and reciprocity, moral norms (i.e. unconditional considerations for others’ outcomes) drive traders’ voluntary supply of information to reputation systems. Our results show how psychological mechanisms interact with organizational features of markets to provide a collective good that promotes mutually beneficial economic exchange.
Narain CFTs from nonbinary stabilizer codes

A bstract We generalize the construction of Narain conformal field theories (CFTs) from qudit stabilizer codes to the construction from quantum stabilizer codes over the finite field of prime power order ( $$ {\mathbbm{F}}_{p^m} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:msub> </mml:math> with p prime and m ≥ 1) or over the ring ℤ k with k &gt; 1. Our construction results in rational CFTs, which cover a larger set of points in the moduli space of Narain CFTs than the previous one. We also propose a correspondence between a quantum stabilizer code with non-zero logical qubits and a finite set of Narain CFTs. We illustrate the correspondence with well-known stabilizer codes.
Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation

The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Network (MAINet). The MAINet uses a modified ResNet50 to extract features and introduces a Detail Feature Extraction module to extract multi-level information and reduce the loss of details. In the last down-sampling, the Deep Multi-head Information Enhancement module combines a CNN and a Transformer structure to make deep semantic features more distinct and reduce redundant information. Then, the Feature Interactive and Fusion Up-sampling module enhances the information extraction of deep and shallow information and, then, guides and aggregates each to make the learned semantic features more comprehensive, which can better recover remote sensing images and increase the prediction accuracy. The MAINet model we propose performed satisfactorily in handling cloud and snow detection and segmentation tasks in multiple scenarios. Experiments on related data sets also showed that the MAINet algorithm exhibited the best performance.
A Survey of Object Detection for UAVs Based on Deep Learning

With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.