Reviewerly/exHarmony huggingface dataset:
Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.
A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras

Abstract The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone.
A prompt model with combined semantic refinement for aspect sentiment analysis

Recently, pre-trained language models (PLMs), especially pre-trained bidirectional encoder representations from transformers (BERT), have improved the performance of aspect-based sentiment analysis (ABSA) tasks to some extent. However, due to the imbalance of training data in different polarities, the following shortcomings remain in PLM-based ABSA methods: (1) for small corpus scenarios with polarized emotions, an unbalanced performance problem exists; and (2) for delicate and obscure scenes dominated by neutral emotions, PLM-based performance gains are limited. To address these shortcomings, we use BERT as an instance of PLMs to propose a general-purpose prompt model with combined semantic refinement for ABSA. First, we utilize a BERT without fine-tuning to automatically induce prompts for various ABSA datasets to enhance the adaptability of the model to different application scenarios. We then leverage multi-prompt learning to propose a data augmentation method to address the imbalance of training data in different polarities. Moreover, to further deepen the model's understanding and analysis of reviews with prompts, we also propose an improved BERT semantic refinement method that combines global semantic refinement and local semantic extraction. Experiments on five public datasets show that compared with existing methods, our macro-average F1 improvement is over 10% on polarized small datasets and over 7% on an emotionally delicate and obscure dataset.
SketchFaceNeRF: Sketch-based Facial Generation and Editing in Neural Radiance Fields

Realistic 3D facial generation based on Neural Radiance Fields (NeRFs) from 2D sketches benefits various applications. Despite the high realism of free-view rendering results of NeRFs, it is tedious and difficult for artists to achieve detailed 3D control and manipulation. Meanwhile, due to its conciseness and expressiveness, sketching has been widely used for 2D facial image generation and editing. Applying sketching to NeRFs is challenging due to the inherent uncertainty for 3D generation with 2D constraints, a significant gap in content richness when generating faces from sparse sketches, and potential inconsistencies for sequential multi-view editing given only 2D sketch inputs. To address these challenges, we present SketchFaceNeRF, a novel sketch-based 3D facial NeRF generation and editing method, to produce free-view photo-realistic images. To solve the challenge of sketch sparsity, we introduce a Sketch Tri-plane Prediction net to first inject the appearance into sketches, thus generating features given reference images to allow color and texture control. Such features are then lifted into compact 3D tri-planes to supplement the absent 3D information, which is important for improving robustness and faithfulness. However, during editing, consistency for unseen or unedited 3D regions is difficult to maintain due to limited spatial hints in sketches. We thus adopt a Mask Fusion module to transform free-view 2D masks (inferred from sketch editing operations) into the tri-plane space as 3D masks, which guide the fusion of the original and sketch-based generated faces to synthesize edited faces. We further design an optimization approach with a novel space loss to improve identity retention and editing faithfulness. Our pipeline enables users to flexibly manipulate faces from different viewpoints in 3D space, easily designing desirable facial models. Extensive experiments validate that our approach is superior to the state-of-the-art 2D sketch-based image generation and editing approaches in realism and faithfulness.
A systematic decision-making framework for tackling quantum software engineering challenges

Abstract Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.
Continual Causal Inference with Incremental Observational Data

The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new industrial problem of causal effect estimation from incrementally available observational data and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect estimation and the effectiveness of our method.
TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems

The scalability problem has been one of the most significant barriers limiting the adoption of blockchains. Blockchain sharding is a promising approach to this problem. However, the sharding mechanism introduces a significant number of cross-shard transactions, which are expensive to process.This paper focuses on the transaction allocation problem to reduce the number of cross-shard transactions for better scalability. In particular, we systematically formulate the transaction allocation problem and convert it to the community detection problem on a graph. A deterministic and fast allocation scheme TxAllo is proposed to dynamically infer the allocation of accounts and their associated transactions. It directly optimizes the system throughput, considering both the number of cross-shard transactions and the workload balance among shards.We evaluate the performance of TxAllo on an Ethereum dataset containing over 91 million transactions. Our evaluation results show that for a blockchain with 60 shards, TxAllo reduces the cross-shard transaction ratio from 98% (by using traditional hash-based allocation) to about 12%. In the meantime, the workload balance is well maintained. Compared with other methods, the execution time of TxAllo is almost negligible. For example, when updating the allocation every hour, the execution of TxAllo only takes 0.5 seconds on average, whereas other concurrent works, such as BrokerChain (INFOCOM’22) leveraging the classic METIS method, require 422 seconds.
Data Ambiguity Profiling for the Generation of Training Examples

Several applications, such as text-to-SQL and computational fact checking, exploit the relationship between relational data and natural language text. However, state of the art solutions simply fail in managing "data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between text and data. Given the ambiguity in language, text can be mapped to different subsets of data, but existing training corpora only have examples in which every sentence/question is annotated precisely w.r.t. the relation. This unrealistic assumption leaves the target applications unable to handle ambiguous cases. To tackle this problem, we present an end-to-end solution that, given a table D, generates examples that consist of text, annotated with its data evidence, with factual ambiguities w.r.t. D. We formulate the problem of profiling relational tables to identify row and attribute data ambiguity. For the latter, we propose a deep learning method that identifies every pair of data ambiguous attributes and a label that describes both columns. Such metadata is then used to generate examples with data ambiguities for any input table. To enable scalability, we finally introduce a SQL approach that can generate millions of examples in seconds. We show the high accuracy of our solution in profiling relational tables and report on how our automatically generated examples lead to drastic quality improvements in two fact-checking applications, including a website with thousands of users, and in a text-to-SQL system.
Quantitative Metrics for Benchmarking Human-Aware Robot Navigation

Social robots have recently gained popularity, and many human-aware navigation approaches have emerged. This work presents a comprehensive benchmark for quantitatively assessing robot navigation methods. As an automated quantitative approach, our Social Robot Planner Benchmark (SRPB) produces invariable indicators of the algorithm’s performance that can assist the system designer in selecting the best method for the specific application. Our benchmark extends state-of-the-art task performance scores and proposes novel social metrics regarding robot motion naturalness and the perceived safety of humans surrounding the robot. Our social metrics take human tracking reliability into account. Using the SRPB integrated with the TIAGo robot, we assessed the robot’s behaviour operating with traditional and human-aware trajectory planners in simulated and real-world environments. Our experiments tested whether state-of-the-art human-aware trajectory planners significantly improve human-awareness indicators over traditional approaches yet still maintain reasonable navigation performance. An open-source implementation of our benchmark, compatible with the Robot Operating System, is provided.
SK-Gradient: Efficient Communication for Distributed Machine Learning with Data Sketch

With the explosive growth of data volume, distributed machine learning has become the mainstream approach for training deep neural networks. However, distributed machine learning incurs non-trivial communication overhead. To this end, various compression schemes are proposed to alleviate the communication volume among nodes. Nevertheless, existing compression schemes, such as gradient quantization or gradient sparsification, suffer from low compression ratios and/or high computational overheads. Recent studies advocate leveraging sketch techniques to assist these schemes. However, the limitations of gradient quantization and gradient sparsification remain. In this paper, we propose SK-Gradient, a novel gradient compression scheme that solely builds on sketch. The core component of SK-Gradient is a novel sketch namely FGC Sketch that is tailored for gradient compression. FGC Sketch precomputes the costly hash functions to alleviate computational overheads. Its simplified design makes it convenient for GPU acceleration. In addition, SK-Gradient leverages various techniques including selective gradient compression and periodic synchronization strategy to improve computational efficiency and compression accuracy. Compared with the state-of-the-art schemes, SK-Gradient achieves up to 92.9% reduction in computational overhead and up to 95.2% improvement in training speedups at the same compression ratio.
Federated IoT Interaction Vulnerability Analysis

IoT devices provide users with great convenience in smart homes. However, the interdependent behaviors across devices may yield unexpected interactions. To analyze the potential IoT interaction vulnerabilities, in this paper, we propose a federated and explicable IoT interaction data management system FexIoT. To address the lack of information in the closed-source platforms, FexIoT captures causality information by fusing multi-domain data, including the descriptions of apps and real-time event logs, into interaction graphs. The interaction graph representation is encoded by graph neural networks (GNNs). To collaboratively train the GNN model without sharing the raw data, we design a layer-wise clustering-based federated GNN framework for learning intrinsic clustering relationships among GNN model weights, which copes with the statistical heterogeneity and the concept drift problem of graph data. In addition, we propose the Monte Carlo beam search with the SHAP method to search and measure the risk of subgraphs, in order to explain the potential vulnerability causes. We evaluate our prototype on datasets collected from five IoT automation platforms. The results show that FexIoT achieves more than 90% average accuracy for interaction vulnerability detection, outperforming the existing methods. Moreover, FexIoT offers an explainable result for the detected vulnerabilities.
Static Entanglement Analysis of Quantum Programs

Quantum entanglement plays a crucial role in quantum computing. Entangling information has important implications for understanding the behavior of quantum programs and avoiding entanglement-induced errors. Entanglement analysis is a static code analysis technique that determines which qubit may entangle with another qubit and establishes an entanglement graph to represent the whole picture of interactions between entangled qubits. This paper presents the first static entanglement analysis method for quantum programs developed in the practical quantum programming language Q#. Our method first constructs an interprocedural control flow graph (ICFG) for a Q# program and then calculates the entanglement information not only within each module but also between modules of the program. The analysis results can help improve the reliability and security of quantum programs.
Schmidt decomposition of parity adapted coherent states for symmetric multi-quDits

Abstract In this paper we study the entanglement in symmetric N -quDit systems. In particular we use generalizations to U ( D ) of spin U (2) coherent states (CSs) and their projections on definite parity <?CDATA ${\mathbb{c}}\in\mathbb{Z}_2^{D-1}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="double-struck">c</mml:mi> </mml:mrow> </mml:mrow> <mml:mo>∈</mml:mo> <mml:msubsup> <mml:mrow> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> (multicomponent Schrödinger cat) states and we analyse their reduced density matrices when tracing out M &lt; N quDits. The eigenvalues (or Schmidt coefficients) of these reduced density matrices are completely characterized, allowing to prove a theorem for the decomposition of a N -quDit Schrödinger cat state with a given parity <?CDATA ${\mathbb{c}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="double-struck">c</mml:mi> </mml:mrow> </mml:mrow> </mml:math> into a sum over all possible parities of tensor products of Schrödinger cat states of N − M and M particles. Diverse asymptotic properties of the Schmidt eigenvalues are studied and, in particular, for the (rescaled) double thermodynamic limit ( <?CDATA $N,M\rightarrow\infty,\,M/N$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>,</mml:mo> <mml:mi>M</mml:mi> <mml:mo stretchy="false">→</mml:mo> <mml:mi mathvariant="normal">∞</mml:mi> <mml:mo>,</mml:mo> <mml:mi>M</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>N</mml:mi> </mml:math> fixed), we reproduce and generalize to quDits known results for photon loss of parity adapted CSs of the harmonic oscillator, thus providing an unified Schmidt decomposition for both multi-quDits and (multi-mode) photons. These results allow to determine the entanglement properties of these states and also their decoherence properties under quDit loss, where we demonstrate the robustness of these states.
FedMCC: Federated multi-center clustering algorithm to improve privacy healthcare

In recent years, healthcare data from various sources such as clinical institutions, patients, and pharmaceutical industries have become increasingly abundant. However, due to the complex healthcare system and data privacy concerns, aggregating and utilizing these data in a centralized manner can be challenging. Federated learning (FL) has emerged as a promising solution for distributed training in edge computing scenarios, utilizing on-device user data while reducing server costs. In traditional FL, a central server trains a global model sampled client data randomly, and the server combines the collected model from different clients into one global model. However, for not independent and identically distributed (non-i.i.d.) datasets, randomly selecting users to train server is not an optimal choice and can lead to poor model training performance. To address this limitation, we propose the Federated Multi-Center Clustering algorithm (FedMCC) to enhance the robustness and accuracy for all clients. FedMCC leverages the Model-Agnostic Meta-Learning (MAML) algorithm, focusing on training a robust base model during the initial training phase and better capturing features from different users. Subsequently, clustering methods are used to ensure that features among users within each cluster are similar, approximating an i.i.d. training process in each round, resulting in more effective training of the global model. We validate the effectiveness and generalizability of FedMCC through extensive experiments on public healthcare datasets. The results demonstrate that FedMCC achieves improved performance and accuracy for all clients while maintaining data privacy and security, showcasing its potential for various healthcare applications.