Reviewerly/exHarmony huggingface dataset:
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks

Deep learning classifiers are crucial in the age of artificial intelligence. The device-edge-based collaborative inference has been widely adopted as an efficient framework for promoting its applications in IoT and 5G/6G networks. However, it suffers from accuracy degradation under non-i.i.d. data distribution and privacy disclosure. For accuracy degradation, direct use of transfer learning and split learning is high cost and privacy issues remain. For privacy disclosure, cryptography-based approaches lead to a huge overhead. Other lightweight methods assume that the ground truth is non-sensitive and can be exposed. But for many applications, the ground truth is the user's crucial privacy-sensitive information. In this paper, we propose a framework of Roulette, which is a task-oriented semantic privacy-preserving collaborative inference framework for deep learning classifiers. More than input data, we treat the ground truth of the data as private information. We develop a novel paradigm of split learning where the back-end DNN is frozen and the front-end DNN is retrained to be both a feature extractor and an encryptor. Moreover, we provide a differential privacy guarantee and analyze the hardness of ground truth inference attacks. To validate the proposed Roulette, we conduct extensive performance evaluations using realistic datasets, which demonstrate that Roulette can effectively defend against various attacks and meanwhile achieve good model accuracy. In a situation where the non-i.i.d. is very severe, Roulette improves the inference accuracy by 21% averaged over benchmarks, while making the accuracy of discrimination attacks almost equivalent to random guessing.
Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Breast cancer has replaced lung cancer as the number one cancer among women worldwide. In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. First, the combined sampling method SMOTE-ENN is used to solve the problem of sample imbalance, and the data are standardized to make the data have better separability. Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the XGBoost algorithm. Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. Finally, five different machine learning models are used for classification prediction, the best combination of parameters for each model is found using a grid search method, and the final results of each model are derived using a 10-fold cross-validation method. The experiments are conducted using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), and the results of the study find that after the data are processed by the hybrid strategy, the best prediction results are obtained using the RF model with 99.52% accuracy, which is better than the previous research methods.
Self-Attention-Based Transformer for Nonlinear Maneuvering Target Tracking

In the field of radar, nonlinearity has always been significant challenge in target tracking algorithms. It is evident in the complexity of the target motion model, observation model, and maneuverability of the target. Traditional model-based algorithms often rely on numerical approximations or simulations to obtain suboptimal solutions, which may lead to conversion errors and increase algorithm complexity. Model-free methods based on deep neural networks have been continuously employed in nonlinear target tracking (NTT) to improve target state estimation performance. This paper introduces two nonlinear trackers based on the Transformer that are used for smoothing, filtering, and predicting target states in the NTT task. First, a classical Transformer-based method is proposed for smoothing and prediction, improving both inference efficiency and accuracy through parallel operation. After that, to handle the recursive operation required for filtering, we introduce a novel recursive Transformer for recursive filtering and predicting of the target state. This significantly reduces computational load compared to the classical Transformer method. Simulation results indicate that the proposed algorithm outperforms traditional and recurrent neural network-based methods.
Improving Urban Traffic Mobility via a Versatile Quantum Annealing Model

The growth of cities and the resulting increase in vehicular traffic poses significant challenges to the environment and citizens' Quality of Life. To address these challenges, a new algorithm has been proposed that leverages the Quantum Annealing paradigm and D-Wave's machines to optimize the control of traffic lights in cities. The algorithm considers traffic information collected from a wide urban road network to define activation patterns that holistically reduce congestion. An in-depth analysis of the model's behaviour has been conducted by varying its main parameters. Robustness tests have been performed on different traffic scenarios, and a thorough discussion on how to configure D-Wave's quantum annealers for optimal performance is presented. Comparative tests show that the proposed model outperforms traditional control techniques in several traffic conditions, effectively containing critical congestion situations, reducing their presence, and preventing their formation. The results obtained put in evidence the state-of-the-art of these quantum machines, their actual capabilities in addressing the problem, and opportunities for future applications.
Experts Collaboration Learning for Continual Multi-Modal Reasoning

Multi-modal reasoning, which aims to capture logical and causal structures in visual content and associate them with cues from other modality inputs (e.g., texts) to perform various types of reasoning, is an important research topic in artificial intelligence (AI). Existing works for multi-modal reasoning mainly exploit offline learning, where the training samples of all types of reasoning tasks are assumed to be available at once. Here we focus on continual learning for multi-modal reasoning (i.e., continual multi-modal reasoning), where the model is required to continuously learn to solve novel types of multi-modal reasoning tasks in a lifelong fashion. Continual multi-modal reasoning is challenging since the model needs to be able to effectively learn various types of new reasoning tasks, meanwhile avoiding forgetting. Here we propose a novel brain-inspired exp erts [Formula: see text]llaboration network (Expo), which incorporates multiple learning blocks (experts). When encountering a new task, our network dynamically assembles and updates a set of task-specific experts that are most relevant to learning the current task, by either utilizing learned experts or exploring new experts. This thus enables effective learning of new tasks, and meanwhile consolidates previously learned reasoning skills. Moreover, to automatically find optimal task-specific experts, an effective experts selection strategy is designed. Extensive experiments demonstrate the efficacy of our model for continual multi-modal reasoning.
Attribute Based Conjunctive Keywords Search With Verifiability and Fair Payment Using Blockchain

Cloud computing has brought great convenience to data storage and resource sharing, however, there are still concerns about data security and service quality. Attribute-based encryption (ABE) and searchable encryption (SE) are always adopted to achieve data access authorization and data retrieval on encrypted data in data sharing, respectively. But more efficient and accurate methods have been always pursued. Moreover, the spoofing attacks is another important aspect that raises concerns, especially when it comes to reliability of search results and online payments. Blockchain, an emerging technology that can be used to solve the problem of trust, has shown great application potential in finance and data sharing. Based on blockchain, we propose an attribute-based conjunctive keyword search (ABCKS) scheme with verifiability and fairness. In this paper, data privacy-preserving, fine-grained access control, and multi-keywords search can be supported simultaneously. Blockchain and smart contract are employed to facilitate the search result verification process and ensure fair payment in the trustless case. Furthermore, we reduce the user's decryption load to a constant level by performing partial decryption on the cloud side. Finally, the results of the performance evaluation indicate that our scheme has higher efficiency and security.
A 3D WebGIS-Enhanced Representation Method Fusing Surveillance Video Information

In Internet applications, there are more and more demands for geographic information expression technology that integrates video surveillance information, but there are generally problems such as camera position, direction, field of view and other parameters that are difficult to calculate accurately, texture occlusion, etc., which seriously affect the fusion expression of 3D scenes and video information Effect. To address the above problems, in this study, movable objects and trees are removed from the model surface, and an enhanced representation framework is designed for the fusion of video and 3D WebGIS under weak constraints. At the same time, the proposed method is verified and analyzed from three aspects: feature point matching, line coincidence degree and occlusion effect. The experimental results show the following. (1) In terms of the feature point matching ability, the feature point matching ability of the proposed method is 22.22% higher than that under strong constraints. (2) In terms of the occlusion effect, the proposed method achieves a 74.12% reduction in the area of the occlusion area, which is 1.63% higher than the occlusion area reduction rate after video projection under strong constraints. (3) In terms of the straight line coincidence, the experimental results of the proposed method are significantly better than those obtained using the strong constraints method. Both the length difference of the straight line with the same name and the relative offset angle are smaller than the result of the method using the strong constraint conditions. Also, the FPS is stabilized at about 30f/s during system operation with high rendering efficiency, which further proves the feasibility and stability of this system. Experiments show that using the proposed method can reflect real geospatial information, better enhance geographic information expression and be easy to implement, providing a valuable reference for the construction of applications such as real-world 3D models and smart cities.
Deep Contextual Grid Triplet Network for Context-Aware Recommendation

Modeling contextual information is a vital part of developing effective recommender systems. Still, existing work on recommendation algorithms has generally put limited focus on the effective treatment of contextual information. Moreover, adding context to recommendation models is challenging since it increases the dimensionality and complexity of the model. Therefore, an efficient learning method is required to extract an association and inter-relationship between user/item features and contextual features for preference-driven modeling. The engineering of features through the exploration of adjacent correlations between the user/item and their context, and their further learning through a distance-based metric, is critical for effective personalization. Motivated by this, we introduce a context-aware recommendation strategy using a ’contextual grid triplet network.’ This strategy uses a contextual grid topology to capture robust semantic representations of users, items, and contextual data. We present a learning methodology that merges a triplet network with a convolutional neural network. This fusion enables the exploration of associations both ’within’ the contextual grid, such as between users or items, and ’between’ different contextual grids, like between a user and items of input. Moreover, we present a variant of a hinge loss function using a triplet network for improved performance and fast convergence. In this work, we study how these aspects boost the quality of top-N recommendations. Furthermore, We show through extensive ablation-based experiments that the proposed method outperforms existing state-of-the-art techniques, demonstrating its robustness and feasibility.
Evaluating Student Risk Group Mitigation Strategies

We analyzed reports from three courses based on an existing risk framework for academic projects. Our contribution is a set of insights that we will apply in the creation of a risk mitigation resource for students.
Deploying Patch-Based Segmentation Pipeline for Fibroblast Cell Images at Varying Magnifications

Cell culture monitoring necessitates thorough attention for the continuous characterization of cultivated cells. Machine learning has recently emerged to engage in a process, such as a microscopy segmentation task; however, the trained data may not be comprehensive for other datasets. Most algorithms do not encompass a wide range of data attributes and require distinct system workflows. Thus, the main objective of the research is to propose a segmentation pipeline specifically for fibroblast cell images on phase contrast microscopy at different magnifications and to achieve reliable predictions during deployment. The research employs patch-based segmentation for predictions, with U-Net as the baseline architecture. The proposed segmentation pipeline demonstrated significant performance for the UNet-based network, achieving an IoU score above 0.7 for multiple magnifications, and provided predictions for cell confluency value with less than 3% error. The study also found that the proposed model could segment the fibroblast cells in under 10 seconds with the help of OpenVINO and Intel Compute Stick 2 on Raspberry Pi, with its optimal precision limited to approximately 80% cell confluency which is sufficient for real-world deployment as the cell culture is typically ready for passaging at the threshold.
High-Frequency Cybersickness Prediction Using Deep Learning Techniques With Eye-Related Indices

Cybersickness is a growing concern in the field of virtual reality (VR). It is characterized by symptoms, such as headache, sweating, disorientation, and nausea. These symptoms can considerably hinder the users’ immersive experience in VR environments, leading to a pressing need for effective solutions to combat cybersickness. In this study, we aim to tackle cybersickness by presenting a novel high-frequency approach for detecting the timing at which users experience cybersickness. Our approach uses 1-, 5-, or 10-s time-series eye-related indices processed by deep learning algorithms to predict cybersickness severity. In five-fold cross-validation, we achieved 71.09% accuracy in classifying four classes of cybersickness severity when individuals were not distinguished. Furthermore, with individualized cross-validation, we achieved an accuracy of up to approximately 80%. Our approach outperforms other cybersickness prediction studies as it provides the highest frequency in predicting cybersickness. It is anticipated that our approach will be valuable not only for immediate evaluation by researchers investigating cybersickness mitigation but also for early detection and notification of users experiencing cybersickness symptoms. By predicting cybersickness, our approach has the potential to promote the future advancement of VR technology.
A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems’ decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images.
Dual Level Adaptive Weighting for Cloth-Changing Person Re-Identification

For the long-term person re-identification (ReID) task, pedestrians are likely to change clothes, which poses a key challenge in overcoming drastic appearance variations caused by these cloth changes. However, analyzing how cloth changes influence identity-invariant representation learning is difficult. In this context, varying cloth-changed samples are not adaptively utilized, and their effects on the resulting features are overshadowed. To address these limitations, this paper aims to estimate the effect of cloth-changing patterns at both the image and feature levels, presenting a Dual-Level Adaptive Weighting (DLAW) solution. Specifically, at the image level, we propose an adaptive mining strategy to locate the cloth-changed regions for each identity. This strategy highlights the informative areas that have undergone changes, enhancing robustness against cloth variations. At the feature level, we estimate the degree of cloth-changing by modeling the correlation of part-level features and re-weighting identity-invariant feature components. This further eliminates the effects of cloth variations at the semantic body part level. Extensive experiments demonstrate that our method achieves promising performance on several cloth-changing datasets. Code and models are available at https: //github.com/fountaindream/DLAW.
Shared Avatar for Hand Movement Imitation: Subjective and Behavioral Analyses

Virtual co-embodiment enables sharing avatars with others in virtual environments and can be applied for training motor skills by allowing teachers to share movements with learners in first-person perspective. We conducted a task where participants were asked to imitate pre-recorded hand movements of a teacher as accurately as possible. The participant’s virtual hand movements were averaged in real-time with those of the teacher (shared avatar hand). We compared their usability ratings and behavior against a controlled condition with full control of the hand (solo avatar hand). The teacher’s hand was displayed facing the same or the opposite direction as the participant’s hand. We hypothesized that using the shared avatar hand would improve imitation over using the solo hand, and the teacher’s hand presented in the same direction is better than that in the opposite direction. Subjective ratings showed that the shared hand was easier to use than the solo hand, and the teacher’s hand when presented in the same direction was easier to imitate than when presented in the opposite direction. Spatial error was less with the opposite-direction presentation than the same-direction presentation of the teacher’s hand, irrespective of movement sharing. Time delay was less when the participants used the shared hand compared to when they used the solo hand, irrespective of the teacher’s hand direction. These results suggest that sharing movements enhances usability and matching speed during movement imitation, and the same-direction presentation of the teacher’s hand improves usability while the opposite-direction presentation improves spatial accuracy of motor imitation.
Live Is Life: Teaching Software Engineering on Live Systems

In this article, we present the course “DevOps, Software Evolution, and Software Maintenance” that we developed and teach at IT University of Copenhagen. It differs from similar courses in that it focuses student project work on maintenance and evolution of a live software system.
Constant-time Quantum Algorithm for Homology Detection of Closed Curves

Given an oracle that could query the inclusion of edges on a closed curve, we give a constant-time (single query usage) quantum algorithm that determines whether or not that curve is homologous to zero on a two-dimensional manifold.
Quantum key distribution with model-based polarization correction using entangled photons

We present a Polarization correction module and a model-based polarization correction technique that are assessed and implemented for establishing Quantum key distribution on terrestrial free-space quantum communication link using an Entangled photon source.
Integrated Quantum Photonic Processors in a Femtosecond Laser Writing Platform

Universal photonic processors are enabling novel experiments in quantum in-formation science. A femtosecond-laser-written 6-mode processor shows an unprecedented performance. The perspective of a remarkably higher complexity is discussed in light of the most recent results.
Development of an Experimentally-Inspired Quantum Internet Stack

We present a bottom-up design of a Quantum Internet stack. Our concept is based on a quantum/classical approach combining three optical networks: quantum, quantum-enabling, and classical dictating a tripartite stack with individual layers matching each network’s functionality but acting as a whole to enable the overall network operations.
Numerical analysis of quantum circuits for state preparation and unitary operator synthesis

We use numerical optimal-control-theory techniques to calculate the number of CNOT gates needed to perform quantum state preparation and unitary operator synthesis. Our approach pro-vides new results and previously inaccessible insight.
Generation of four-photon GHZ states on a laser written integrated platform

We present a laser-written glass photonic circuit that, in synergy with a quantum dot based single photon source, generates a four-photon Greenberger-Horne-Zeilinger entangled state at the rate of 0.5 Hz with 86.0 ± 0.4% fidelity.
Digital Tavis-Cummings Simulation on Superconducting Quantum Hardware with Error Mitigation

We explore digital quantum simulation of the dynamics of N emitters coupled to an optical cavity (Tavis-Cummings model) on superconducting quantum hardware and successfully mitigate errors using randomized compiling and noiseless output extrapolation methods.
Towards Transverse-Mode Entanglement Generation In Few-Mode Optical Fiber

We present progress towards generating transverse-mode-entangled photon-pair states in few-mode birefringent optical fiber using spontaneous four-wave mixing. We also describe how transverse-mode quantum state tomography is employed to verify such entanglement.