Reviewerly/exHarmony huggingface dataset:
Low-earth Orbit Satellite Network Optimization and Statistical Qubit Freezing on Quantum Annealer

Recent studies have shown promising results indicating the potential of Noisy Intermediate Scale Quantum (NISQ) devices. In this work, Low-earth Orbit (LEO) satellite network design problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and Quantum Annealing (QA) is utilized to solve the resulting problem with real world setting. Compare to classical approaches, the experimental results indicate improvements in terms of network path length and number of satellite hops in network path that amount to network latency. Further, an iterative post-processing method, Statistical Qubit Freezing (SQF), which freezes initial states of qubits and reduces the size of the problem in each annealing cycle, is proposed and evaluated. Solution found with SQF indicates that SQF in fact allows the system to reach lower energy state.
An Analysis of the Trade-Off Between Sustainability and Quality of Experience for Video Streaming

The massive increase in Internet data traffic caused by video streaming applications has quickly raised energy consumption and carbon dioxide emissions ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$CO_{2}$</tex> ). Thus, sustainable system solutions have started to be studied, which aim to lower energy consumption but, at the same time, may negatively impact the Quality of Experience (QoE) perceived by the users of video streaming services. This paper aims to investigate the possibility of a trade-off between sustainability and the QoE. We have found that limiting the maximum QoE to an acceptable QoE is a possible solution to reduce energy consumption while still satisfying the consumer. Our finding is partly validated by the results of a survey we conducted to investigate the willingness of people to reduce the video streaming quality being aware that energy saving would be achieved. These results show that most consumers accept lower video quality (to a certain extent) if they have the possibility to consume less energy and save money.
Nightports at Hull Minster: Physical, Hybrid and Virtualized Live Loudspeaker Array Spatialization of Electronic Music Performance

‘Nightports at Hull Minster’ is a musical project that harnesses spatialization techniques to present music composed of the sounds of Hull Minster, UK, in both the location itself and alternative performance spaces, whilst still expressing the spatiality of the location. The project involves a live electronic music performance by Nightports (The Leaf Label), using only sounds recorded in the Minster itself, spatialized in real-time by another performer across a 25-loudspeaker array. In total, three variant approaches are detailed: a physical acousmonium in-situ; a hybrid acousmonium and virtualmonium; and headphone-targeted virtualizations. To achieve virtualization, spatial room impulse response measurements were taken across the diffusion array to capture the characteristics of the loudspeaker arrangement implemented in the Minster. A scalable and adaptable spatialization system was devised to realize the different iterations and is detailed in this paper, alongside observations on the performative ramifications, compositional preoccupations, and potential developments of the project.
TCCN: A Network Traffic Classification and Detection Model Based on Capsule Network

Privacy information theft traffic is usually detected using traffic classification methods, and deep learning-based detection methods are effective for this task. However, these methods have complex preprocessing processes as well as tend to ignore the deep features of network traffic, while the generalization ability is not outstanding. In this paper, a network traffic classification and detection model TCCN (Traffic Classification Capsule Network) based on capsule network is proposed. Meanwhile, PacketCGAN-based data balancing method is introduced to assist TCCN in traffic classification. A new feature graph vectorization method is used to improve the efficiency of TCCN. In addition, TCCN uses dynamic routing mechanism that can retain more valid traffic characteristics. In parallel, this paper proposes a new loss function to improve the generalization performance of TCCN. The results show that TCCN can show good performance in different experimental scenarios. After effective training, TCCN can also show high detection accuracy in new datasets, and the generalization ability of the model reaches a relatively excellent level.
Exploiting the Divergence Between Output of ML Models to Detect Adversarial Attacks in Streaming IoT Applications

The majority of streaming Internet of Things (IoT) applications use machine learning models to identify and classify streaming inputs before forwarding them for further processing. These streaming IoT systems, however, are vulnerable to poisoning and adversarial attacks. An adversary deliberately modifies the input by adding a small perturbation during the communication to fool the class label into producing an arbitrary or specific output. The increasing number of well-developed, imperceptible attacks necessitates more sophisticated countermeasures. To this end, this paper underlines this problem and proposes a new scheme based on committee-based machine learning models: some have experience with only benign inputs, and others with benign and adversarial inputs. Then, the probabilities of the outputs of these pairs' models are utilized. The KL-divergence after that is applied to identify, detect, and mitigate such streaming attacks. Specifically, we use the uncertainty measures between the output of mitigation and non-mitigation ML models as a proxy to identify adversely attacked inputs. We use traffic sign classification in autonomous vehicle technology as a streaming IoT application. Our experiments demonstrate that the proposed approach can detect and mitigate adversarial attacks with high confidence for the white-box attack.
Optimizing Privacy-Preserving Content-Centric Networks

In this paper, we consider a multi-server content-centric networking (CCN) architecture, where the servers have distinct trust credentials toward the user while the requested files convey the user's personal information. The servers can infer personal information when serving the user such that privacy concerns are triggered. Unlike the pre-processing and protocol-based solutions in literature, e.g., encryption, requiring additional processing or lacking quantitative information leakage measure, in this paper, an optimization-based collaborative content delivery strategy is developed to minimize the information leakage to the servers in the content delivery phase. Gaussian mixture model (GMM) is used to construct the user profile and the file contents. We leverage Kullback Leibler (KL) divergence between the exact and estimated user profiles to measure the privacy leakage. The formulated non-convex mixed-integer program is solved with a polyhedral outer approximation (POA) algorithm. We show that our scheme ultimately reduces information leakage over random content delivery while approaching the probabilistic method.
An Efficient GDPR-Compliant Data Management for IoHT Applications

The rapid rise of Internet of Health Things (IoHT) makes preserving data privacy become a critical task. In 2016, European Union has published General Data Protection Regulation (GDPR) which urges service providers to protect user privacy. Existing fine-grained encryption methods, such as key-policy attribute-based encryption (KP-ABE), belong to pairing-based cryptography which is impractical in resource-constraint IoHT devices. Also, habitual data-sharing systems fail to satisfy the right to access, the right to data portability, and the right to erasure claimed by GDPR at the same time. Thus, this paper proposes the GDPR-compliant Data Management (GCDM) composed of pairing-free KP-ABE (PF-KP-ABE) and GDPR-compliant Revocable Blockchain (GRBC) to provide IoHT applications efficient and secure data-sharing service. PF-KP-ABE exploits the pairing-free mechanism to improve the performance of fine-grained access control on IoHT devices. GRBC applies the special-designed Create/ Read/ Update/ Delete (CRUD) operations to comply with GDPR. Security analysis demonstrates the GDPR compliance of GRBC as well as the correctness of PF-KP-ABE. Then, the Perfect Forward Secrecy of PF-KP-ABE is also proved. Experiment results show that the proposed PF-KP-ABE outperforms existing KP-ABE on both Control Centre and IoHT devices. Hence, GCDM is the most practical data management for various IoHT applications.
Multi-modal Representation Learning for Social Post Location Inference

Inferring geographic locations via social posts is essential for many practical location-based applications such as product marketing, point-of-interest recommendation, and infector tracking for COVID-19. Unlike image-based location retrieval or social-post text embedding-based location inference, the combined effect of multi-modal information (i.e., post images, text, and hashtags) for social post positioning receives less attention. In this work, we collect real datasets of social posts with images, texts, and hashtags from Instagram and propose a novel Multi-modal Representation Learning Framework (MRLF) capable of fusing different modalities of social posts for location inference. MRLF integrates a multi-head attention mechanism to enhance location-salient information extraction while significantly improving location inference compared with single domain-based methods. To overcome the noisy user-generated textual content, we introduce a novel attention-based character-aware module that considers the relative dependencies between characters of social post texts and hashtags for flexible multi-model information fusion. The experimental results show that MRLF can make accurate location predictions and open a new door to understanding the multi-modal data of social posts for online inference tasks.
Modeling of Diverse Computing Requirements in Azure Task Traces

In the cloud data centers of large technology companies, there are various resources such as computing, communication and storage, which provide stable and quality-guaranteed services for subscribers. To get a comprehensive understanding of real load trends and to observe the characteristics of user tasks consuming resources, we analyzed the workload of Microsoft Azure Virtual Machines (VMs). Specifically, we analyzed the workload of all VMs in the Azure cluster within one month in 2017 and 2019. The existing public data provides complete information for each schema. Through data analysis and fitting, we revealed the consistency and difference in delay and memory usage of workload tasks during the time span from 2017 to 2019. In addition, we found the distribution characteristics of the task computing requirement, i.e., it conforms to a large scale exponential distribution superimposed with a small scale Sample function. We propose two modeling approaches to fit the 2017 and 2019 traces and find that the complex one is able to reduce the Sum of Squares due to Error (SSE) by 72.9% and 66.9% compared to the simple one. The task characteristics we discovered can help researchers understand the workload and provide a modeling basis for the simulation framework.
Online Computation of Reduced Egonet Features for Anomaly Detection in Bank Transactions Graphs

Anomaly detection in bank transactions graphs can be an effective tool for revealing potentially fraudulent activities. Online algorithms are the most interesting in this context, since they can lead to early analysis of dubious transactions. We propose an online algorithm for computing vertex features related to egonet and reduced egonet. Such features have been proved effective for anomaly detection in bank transactions graphs, but in a batch approach. The proposed online algorithm needs to explore only sets of common neighbors of two vertices, without isolating the egonet as a subgraph. Hence, it is very efficient and has execution times suited for practical use. Also, the performance in detecting anomalies is quite similar to that of an a posteriori algorithm that computes the features and the anomaly scores only at the end of each day.
Post-Quantum Based Oblivious Transfer for Authentication in Heterogeneous Internet of Everything

Quantum security in multi-party authentication is a critical aspect to be addressed in Internet of Everything (IoE), especially when considering the possible future network attacks by the usage of quantum computers. To ensure an invulnerable multi-party computation, the Post-quantum based Oblivious Transfer for authentication in heterogeneous internet of everything Systems (POTS) protocol proposed in this paper integrates 1-out-n oblivious transfer along with the post-quantum encryption to preserve the network from suspicious parties during communication. The protocol further intends to evaluate resilience by considering various metrics namely, hit ratio, execution time and memory consumption, in order to attain considerable accuracy in an IoE environment. The POTS protocol achieves an execution time of 66.69 seconds, device memory consumption of 131.95 MB and hit ratio of 20.46% for the session size of 300.
Improved Vocal Effort Transfer Vector Estimation For Vocal Effort-Robust Speaker Verification

Despite the maturity of modern speaker verification technology, its performance still significantly degrades when facing non-neutrally-phonated (e.g., shouted and whispered) speech. To address this issue, in this paper, we propose a new speaker embedding compensation method based on a minimum mean square error (MMSE) estimator. This method models the joint distribution of the vocal effort transfer vector and nonneutrally-phonated embedding spaces and operates in a principal component analysis domain to cope with non-neutrallyphonated speech data scarcity. Experiments are carried out using a cutting-edge speaker verification system integrating a powerful self-supervised pre-trained model for speech representation. In comparison with a state-of-the-art embedding compensation method, the proposed MMSE estimator yields superior and competitive equal error rate results when tackling shouted and whispered speech, respectively.
Optimizing hyperparameters of linear SVM classifiers using the support method

The support vector machine (SVM) is a popular classifier known for its powerful generalization capabilities and its wide application in a variety of fields. In order to achieve optimal performance of this model, the user is required to provide values for the tuning parameters: the regularization cost parameter and kernel parameters. In this work, we focus on the optimization of the regularization parameter, by developing a new algorithm based on the direct support method that allows for computing all breakpoints that minimize the number of misclassified examples and maximize the margin. This approach is iterative and formed by two phases. Phase 1 is used to calculate the first optimal solution corresponding to the initial value of the regularization parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> = 1/C, while phase 2 is used to iteratively calculate the rest of the breakpoints.
Semantic Communication Protocol: Demystifying Deep Neural Networks via Probabilistic Logic

In this paper, we suggest a method to transform a communication protocol based on deep neural network (NN) into a semantic communication protocol. We need such transformation to alleviate the issues posed by NN's lack of interpretability and redundant parameters due to overparametrization. However, transformation process is challenging because it is difficult to disambiguate the semantics while reducing the protocol's complexity. We solve the challenge by employing NN's activation patterns and probabilistic logic. Lastly, we validate our method by transforming an NN trained for a medium access control (MAC) protocol and verifying its contention performance compared to ALOHA based protocols.
Graph-Attention-Based Decentralized Edge Learning for Non-IID Data

In conventional decentralized learning, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribution among agents, the unified learning model becomes inefficient for each agent to process its locally accessible data. To address this problem, we propose a graph-attention-based personalized training algorithm (GATTA) for decentralized deep learning. The GATTA enables each agent to train its local personalized model while exploiting its correlation with neighboring nodes and utilizing their useful information for aggregation. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph and the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited. Instead of aggregation simply based on averaging, GATTA learns the specific weights for different neighboring nodes without requiring any prior knowledge about the graph structure or the neighboring nodes' data distributions. Numerical results validate the effectiveness of the proposed algorithm under different datasets as well as various network topologies.
Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data

In this paper, we propose a method to design the training data that can support robust generalization of trained neural networks to unseen channels. The proposed design that improves the generalization is described and analysed. It avoids the requirement of online training for previously unseen channels, as this is a memory and processing intensive solution, especially for battery powered mobile terminals. To prove the validity of the proposed method, we use the channels modelled by different standards and fading modelling for simulation. We also use an attention-based structure and a convolutional neural network to evaluate the generalization results achieved. Simulation results show that the trained neural networks maintain almost identical performance on the unseen channels.
Htgtm: Hybrid Temporal-Graph Tabular Model For Complex Multimodal Tabular Data Processing

Understanding the behaviour of volunteers is an important research area in social science and management. This paper tackles the Volunteer Retention Prediction task of the IEEE MLSP 2023 Data Challenge, utilizing a dataset from the COVID-19 pandemic volunteer coordination in Shenzhen, China in 2020 ‒ 2021, with the objective of forecasting volunteer retention in the next ten months. Our paper proposes Hybrid Temporal-Graph Tabular Model (HTGTM), a deep learning-based hybrid model designed to extract and analyze temporal and graph information within complex tabular data. In this data challenge, we compared our model, ensembled with XGBoost, against traditional machine learning methods and deep learning models that are specifically tailored for tabular data. Our method exhibited robust performance, validated by its 1st-ranking Root-Mean-Square-Error (RMSE) score of 76.36 in MLSP 2023 Data Challenge Kaggle private Leaderboard. This research sheds light on pertinent volunteer retention prediction tasks and highlights the incorporation of deep learning techniques in complex, multimodal tabular data processing tasks.
Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.
Cooperative Task-Oriented Communication for Multi-Modal Data with Transmission Control

Real-time intelligence applications in Internet of Things (IoT) environment depend on timely data communication. However, it's challenging to transmit and analyse massive data of various modalities. Recently proposed task-oriented communication methods based on deep learning have showed its superiority in communication efficiency. In this paper, we propose a cooperative task-oriented communication method for the transmission of multi-modal data from multiple end devices to a central server. In particular, we use the transmission result of data of one modality, which is with lower rate, to control the transmission of other modalities with higher rate in order to reduce the amount of transmitted data. We take the human activity recognition (HAR) task in a smart home environment and design the semantic-oriented transceivers for the transmission of monitoring videos of different rooms and acceleration data of the monitored human. The numerical results demonstrate that by using the transmission control based on the obtained results of the received acceleration data, the transmission is reduced to 2% of that without transmission control while preserving the performance on the HAR task.
Bayesian Deep Learning Detection of Anomalies and Failure: Application To Medical Images

Deep learning models trained solely on in-distribution data may not generalize well to anomalous data and may exhibit high confidence in incorrect predictions, leading to significant consequences in safety-critical applications such as medical diagnosis or autonomous driving. It is important for these models to be able to handle anomalous or Out-of-Distribution (OOD) data which are prevalent in many real-world scenarios. In this work, we leverage the predictive variance, i.e., second-order moment of the predictive distribution, of Bayesian models to identify anomalous data points. We test our anomaly and misclassification mechanism on medical image datasets and compare the detection performance of several Bayesian frameworks under various distributional shifts, i.e., noisy conditions, adversarial attacks and OOD data.