Reviewerly/exHarmony huggingface dataset:
Context-aware Outlier Detection for Sensor Data Stream Processing

Sensor data streaming platforms feed pervasive applications with data through continuous processing tasks. Outliers should be constantly removed from the data. The existence of ontologies and their semantics to express the different properties of sensing devices in semantic models can be leveraged towards solving the problem of outlier detection. We present an outlier detection data stream operator based on sensor ontology-based definitions. This outlier detection operator could be defined and adjusted to fit in different use cases with different requirements. We provide a proof of concept implementation of the operator using a Data Stream Management System and evaluate the effect of increasing outlier rates in data sets on the overall performance of the outlier detection.
DCFL: Dynamic Clustered Federated Learning under Differential Privacy Settings

Federated Learning (FL) allows training machine learning models on a dataset distributed amongst multiple clients without disclosing sensitive data. Each FL client, however, might have a different data distribution, with a detrimental effect on the performance of the trained model. In this paper, we present a dynamic clustering algorithm (DCFL) that allows the server to cluster FL clients based on their model updates, letting the server adapt to changes in the data distribution and supporting the addition of new clients. Moreover, we propose a novel distance metric to estimate the distance between model updates by different clients. We evaluate our approach in a wide range of experimental settings, comparing it against the standard FedAvg algorithm and divisive clustering on the EMNIST dataset. Our approach outperforms the baselines, yielding higher accuracy and lower variance for the participating clients.
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR

Pervasive computing allows the provision of services in many important areas, including the relevant and dynamic field of health and well-being. In this domain, Human Activity Recognition (HAR) has gained a lot of attention in recent years. Current solutions rely on Machine Learning (ML) models and achieve impressive results. However, the evolution of these models remains difficult, as long as a complete re-training is not performed. To overcome this problem, the concept of Continual Learning is very promising today and, more particularly, the techniques based on regularization. These techniques are particularly interesting for their simplicity and their low cost. Initial studies have been conducted and have shown promising outcomes. However, they remain very specific and difficult to compare. In this paper, we provide a comprehensive comparison of three regularization-based methods that we adapted to the HAR domain, highlighting their strengths and limitations. Our experiments were conducted on the UCI HAR dataset and the results showed that no single technique outperformed all others in all scenarios considered.
Towards Deploying Advanced Context Provisioning for Surf Life Saving Use Case

This paper presents Context Cost and Quality computational engine (ConCQeng), a system to enhance situational awareness in context-aware pervasive computing applications (CaPCAs) through effective context provisioning. ConCQeng selects the sources that deliver a quality-adequate and cost-efficient context in the data flows from stakeholders, assets and an array of sensors, using novel quality and cost-aware selection approaches. It further measures retrieved contexts' quality metrics for compliance and assigns a reasonable cost. We demonstrate the application of conCQeng to conduct effective search and rescue operations in surf life saving using a web application. Our demonstration proves that conCQeng exhibits a compelling performance in environments with distributed and diverse context sources.
Exploring Cross Modality Feature Fusion for Activity Recognition at OpenPack Challenge 2022

This report describes team vbu211's approach to the OpenPack Challenge 2022. The goal of this task is to recognize 10 work operations from a large-scale multimodal dataset of the packing process, including data from IMUs, vision sensors, and IoT-enabled devices, etc. It aims to solve the problem of “when and what kind of work operation was performed”. In this report, we explore a fusion based method for addressing the work operation recognition task. In our method, features are first extracted from five modalities and fed to the attention-based fusion module. Finally, recognition results are obtained. Our method achieved an F1-measure of 0.959 on the submission set; it placed second in the OpenPack Challenge 2022.
Extraction of Important Temporal Order for eXplainable AI on Time-series data

We propose a new eXplainable AI method on time-series data in which multiple events are arranged in temporal order, to extract important temporal order between events for opaque model decision. Toward this, our proposed method analyzes the model behavior when inputting perturbated data generated by changing the order of the events. We evaluate our method via an exemplary classification task on a simulated dataset to confirm how accurate our method is in extracting important temporal order of events. In addition, we evaluate our method on a smart home sensor dataset to demonstrate what kind of important order is actually extracted.
Privacy-Preserving Object Detection with Veracruz

Ucam is a user-centric, blockchain-based and end-to-end secure home IP camera system designed by IoTeX. In a Ucam system, all the video clips captured by the camera are encrypted using a user-controlled symmetric key before storing them in the cloud. In this demo paper, we describe the system architecture and implementation of a privacy-preserving object detection proof-of-concept (PoC) for the Ucam system using Veracruz, a confidential computing framework developed by Arm Research. The resulting PoC demonstrates the viability of bringing the object detection capability to the Ucam system without leaking users sensitive information.
Content-Aware Dynamic Resource Allocation Framework for Video Analytic Applications at Edge

Recent Machine Learning (ML) techniques enable new features on video analytic applications. Still, video pro-cessing requires intensive computing power and communication bandwidth. Processing tasks are shifted to Edge nodes close to video sources to reduce bandwidth usage. Thus, critical events can be detected in real-time and notified to users. However, the amount of resources at Edge is restricted. Insufficient resources can lower the prediction accuracy of ML models. In this paper, we designed a dynamic allocation framework and implemented it to improve resource utilization at the Edge node while the prediction accuracy is maintained.
Applying Differential Privacy to Medical Questionnaires

Currently, the medical sector sees a lot of digitization efforts. Besides the already available and growing amount of data there is still a need to receive data from patients. A special form of data donation are medical questionnaires. They come with a fixed set of questions regarding medical conditions and can be performed regularly or once. Such data donations show promising potential for research but come with open questions regarding privacy. There are many examples that show that re-identification is even possible with anonymized data. To mitigate such risks privacy enhancing technologies like Differential Privacy (DP) can be used. This paper presents a procedure for privacy preserving questionnaires using an implementation of DP called RAPPOR. Through its Randomized Response mechanism RAPPOR is well suited to be used with questionnaires. To proof feasibility of this procedure various experiments are conducted that show that this approach can improve privacy while still retaining a good level of utility.
On Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic/static activities (e.g, walking, lying, cycling, etc.) has become a well-studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non-periodic, and occur less frequently when compared with the often large amount of irrelevant “background” activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling non-periodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, i.e., a delay model and an inverse model. The delay model can effectively exploit pre-defined delay intervals as additional contextual information, while the inverse model can learn patterns from the time-series in an inverse manner, which can be complementary to the forward model (i.e., LSTM). These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two public datasets, and the promising results suggested the effectiveness of our approaches in recognizing sporadic activities.
Precise Human Activity Recognition for The OpenPack Challenge 2022

This technical report provides an overview of our method for the OpenPack Challenge 2022, which is part of the Behavior Analysis and Recognition for Knowledge Discovery Workshop at PerCom 2023. The challenge involves proposing an activity recognition approach by predicting activity classes for each 1-second-long time slot using sensor data from packaging works in the logistics industry. To accomplish this, we employed various data augmentation techniques and trained a deep neural network that incorporates spatio-temporal features from multiple sensor time-series data and a self-attention mechanism for selecting and learning essential time points. Our model achieved an average macro F1-score of 91.12% and generalised well on the submission set. This experiment is a part of the challenge conducted by our team “Shubham Wagh” and secured 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> place in the competition.
CASPER: Context-Aware Anomaly Detection System for Industrial Robotic Arms

With the arrival of industry 4.0, industrial control systems are converted into “smart” industrial cyber-physical systems that depend on high interconnectivity enabled by ubiquitous applications. As these applications can significantly reduce maintenance and supervision costs, the integration of these applications is done with the “cost” being the focus overlooking the security aspect that suffers from the vulnerabilities that occurred due to increased attack surface. The adversaries aim to create physical alterations by exploiting these cyber vulnerabilities via so-called “cyber-physical” attacks. In this work, we introduce CASPER, a context-aware ubiquitous machine learning-based anomaly detection infrastructure that utilizes ubiquitous computing to detect anomalies of an industrial robotic arm. CASPER monitors the robotic arm's movements to ensure the arm follows a predetermined trajectory. The CASPER can reach an accuracy and F1 score of 97% which is promising for an industrial domain. We modify the joint velocity of an industrial robotic arm to create anomalies which we detect via CASPER.(Demo Video)
ChoEn: A Smart Contract Based Choreography Enforcer

This work deals with the automatic generation of smart contracts to enforce compliance to a given BPMN choreography of requests sent by a set of independent actors. The choice of smart contracts is justified by the desire not to centralize the enforcement process when there are multiple independent actors characterized by low mutual trust. The choice of BPMN choreographies as an alternative to BPMN processes and collaborations is instead justified by the desire to leave the actors free to dynamically organize their internal processes in the way they prefer, provided that the interactions with the other actors respect some agreed protocol. This work proposes a formal framework to interpret choreographies and translate them onto smart contract enforcers. The framework has been implemented on a free open-source tool named ChoEn and tested by means of a running example.
Differential Privacy with Weighted ∊ for Privacy-Preservation in Human Activity Recognition

Many services based on human activity recognition (HAR) have been developed; however, user activity data include a large amount of private information. Although privacy protection is important in activity recognition, it has not been sufficiently explored. Therefore, we propose a privacy-preserving mechanism for HAR services that uses differential privacy. The proposed method reduces the user recognition accuracy to a level that satisfies the privacy requirements by adding weighted noise to the features in the learning model construction and then improves the activity recognition accuracy (service usefulness). The results indicate that when the privacy requirement is defined as less than the probability of a user being identified by chance, the proposed method improves the activity recognition accuracy by approximately 10 % compared to the conventional method.
Wearables for In-Situ Monitoring of Cognitive States: Challenges and Opportunities

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve as proxies for cognitive/affective states and show that earable-mounted pressure, EMG, and ultrasonic sensing hold promise for canturing such markers.
A Secure Bandwidth-Efficient Treatment for Dropout-Resistant Time-Series Data Aggregation

Aggregate statistics derived from time-series data collected by individual users are extremely beneficial in diverse fields, such as e-health applications, IoT-based smart metering networks, and federated learning systems. Since user data are privacy-sensitive in many cases, the untrusted aggregator may only infer the aggregation without breaching individual privacy. To this aim, secure aggregation techniques have been extensively researched over the past years. However, most existing schemes suffer either from high communication overhead when users join and leave, or cannot tolerate node dropouts. In this paper, we propose a dropout-resistant bandwidth-efficient time-series data aggregation. The proposed scheme does not incur any interaction among users, involving a solo round of user→aggregator communication exclusively. Additionally, it does not trigger a re-generation of private keys when users join and leave. Moreover, the aggregator is able to output the aggregate value by employing the re-encrypt capability acquired during a one-time setup phase, notwithstanding the number of nodes in the ecosystem that partake in the data collection of a certain epoch. Dropout-resistancy, trust-less key management, low-bandwidth and non-interactive nature of our construction make it ideal for many rapid-changing distributed real-world networks. Other than bandwidth efficiency, our scheme has also demonstrated efficiency in terms of computation overhead.
On-Device AI: Quantization-Aware Training of Transformers in Time-Series

Artificial Intelligence (AI) models for time-series in pervasive computing keep getting larger and more complicated. The Transformer model is by far the most compelling of these AI models. However, it is difficult to obtain the desired performance when deploying such a massive model on a sensor device with limited resources. My research focuses on optimizing the Transformer model for time-series forecasting tasks. The optimized model will be deployed as hardware accelerators on embedded Field Programmable Gate Arrays (FPGAs). I will investigate the impact of applying Quantization-aware Training to the Transformer model to reduce its size and runtime memory footprint while maximizing the advantages of FPGAs.
APS: An Auditable Positioning System Based on Angle-of-Arrival Proof of Location and Graph of Trust

With the fast growth of the Internet ofThings, location-based services are becoming more important for quality-relevantapplications. Quality-relevant applications often require to be auditable inorder to ensure quality of products and services. However, positioning systemstypically only provide position claims that are not secured and can be forgedor manipulated. This work proposes an auditable positioning system to tacklethis issue. The proposed system performs a proof of location to assert that thetracked asset tag was at the claimed position and immutably stores thisinformation on a distributed ledger. Additionally, an adaptive storage rate wasproposed to save disk storage on the distributed ledgers. A testbed based onBluetooth's Angle of Arrival positioning was implemented for the evaluation ofthe auditable positioning system. The evaluation shows that auditablepositioning is secure, feasible for low-power devices, and that adaptivestorage rates can significantly reduce disk usage and improve scalability.
OS<sup>3</sup>: The Art and the Practice of Searching for Open-Source Serverless Functions

Serverless computing enables service developers to focus on creating useful services, without being concerned about how these services would be deployed and provisioned. Many developers reuse existing open-source serverless functions to create their own functions. However, existing technologies for searching open-source software repositories have not taken into consideration the unique features of serverless functions. This paper presents a novel approach to searching for serverless functions, called Open-Source Serverless Search (OS3) that maximizes the utility of the returned serverless functions by (1) basing the search process on both descriptive keywords and library usages, thus increasing the search results' precision and completeness; (2) filtering and ranking the search results based on the software license, to accommodate the unique requirements of deploying serverless functions on dissimilar platforms, including cloud and edge computing. Implemented in 3K lines of Python, with a search space of 5,981 serverless repositories from four major serverless platforms, OS3 outperforms existing search approaches in terms of the suitability of the search results, based on our evaluation with realistic use cases.
Analysis of Accelerometer Data for Personalised Mood Detection in Activities of Daily Living

This paper proposes a novel approach to identify moods in Activities of Daily Living (ADLs) using accelerometer sensor data from 15 participants over 7 sessions each. Monitoring ADLs and detecting moods are of particular importance due to the potential life-changing consequences. The ADLs considered relate to preparing and drinking a hot beverage, and they were segmented into four sub-activities: (i) entering kitchen, (ii) preparing beverage, (iii) drinking beverage, and (iv) exiting kitchen. The accelerometer was attached to the participants' wrists, and prior to collecting the data, they were asked about their current mood. Two approaches were considered in the analysis according to the moods reported by the participants (happy, calm, tired, stressed, excited, sad, and bored), firstly using all trials, and secondly using a balanced sample of data. A set of statistical, temporal, and spectral features were extracted from acceleration data, and personalised classification models were built and evaluated using the Random Forest algorithm. The experimental results showed that the average F -measure for all personalized classifiers was 0.75 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sigma\ 0.20)$</tex> considering all data, and 0.76 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sigma\ 0.22)$</tex> using balanced data. The best classification results were obtained with the “preparing” and “drinking” activities, and with the “happy”,”calm”, and “stressed” moods. This suggests that the use of accelerometers, such as those incorporated into smartwatches or activity trackers, may be useful in detecting moods in ADLs.
A QoS-Aware 3D Point Cloud Streaming from Real Space for Interaction in Metaverse

In this paper, we present a point cloud streaming method of real-space objects such as humans and animals for real-time 3D reconstruction in VR space. The system uses a depth camera to scan a human or animal, divides the point cloud into parts of the body, and then controls the quality of the point cloud (i.e., resolution and frame rate) for each part in real-time according to the object's motion and context. This enables point cloud streaming with limited resources (computational and network resources) and maximizes the user's quality of experience (QoE). We have implemented and evaluated a series of systems incorporating the proposed method to enhance the user experience in realistic environments and scenarios while maintaining interactivity in VR-based online communication. The results show that the proposed system is feasible under resource-constrained environments without significantly affecting the user's QoE.
Is Querying Users Acceptable for Human Activity Recognition Based on Active Learning?

Labeled data scarcity is one of the major open problems of sensor-based Human Activity Recognition (HAR). To mitigate this issue, several research groups proposed solutions based on Active Learning (AL), where the user is explicitly asked to provide feedback about the performed activity when the classifier's confidence is low. Despite existing methods trigger a limited number of queries that decreases over time, they do not take into account user's context. Indeed, a subject may not wish to be interrupted when receiving the query's notification. Delaying the query may be critical, since it is known that the feedback's quality decreases with the length of the delay. In this work, we aim at answering the following question: is real-time AL-based HAR practical in real-world scenarios? By using a novel evaluation methodology based on the Wizard of Oz approach, we performed a user-based study with 30 subjects performing physical activities and using earphones and a smartwatch as vocal and touch interfaces. Our results evaluate the impact of the AL strategy, the user's context, and the interaction modality on the effectiveness of the AL approach.