Reviewerly/exHarmony huggingface dataset:
An Automated Tool for Student Journey Orchestration &amp; Optimization using Machine Learning

The customer journey is a full interaction that a customer has with a business. Every touchpoint of the business is an opportunity to provide good experiences that encourage future opportunities to become customers and consumers to be committed loyal customers through the customer journey. This research paper refers to the student’s journey at university as a customer journey & considers the student’s actions to map the next suitable actions. This paper proposed a machine learning-based novel approach to recommending the suitable next best action for the students based on their past performance at university by using customer journey orchestration and optimization. Customer journey orchestration is the process of coordinating customer experiences in real-time to encourage better engagement with the systems and organization. The journey orchestration of university students is currently a manual flow. The main goal of this research is to convert the manual flow of university journey orchestration into an automated flow. The proposed system orchestrates and optimizes the student journeys at each milestone of the university by recommending the suitable path or next best action as the outcome to help students make a successful path throughout their university journey. This research contributes to achieving the educational goals and professional career goals of university students successfully. Furthermore, from the perspective of the university, this proposed system supports everything to facilitate better directions for the students to complete their studies successfully.
Group Formation and Communication of Multitasking Multi-Robots for Smart City Cleaning Process

In this research paper, we focus on how multitasking robots team up to clean a city. In particular, we consider how they build their team, how they position themselves in their positions, how they work with teams, how they face obstacles along the way, and how to move groups out of control in an emergency. We use a leader-follower strategy here, and we are also tasked with selecting a leader for each group. The leader finds the shortest route to avoid the obstacle by considering the obstacle details such as obstacle location, obstacle width, and destination. The leader decides the best way for the team to go. If the leader wants to change the group, it gives the message to the relevant member. In the event of meeting an obstacle, it changes its shape and moves. A Robot Operating System (ROS) framework was created to perform real-time experiments with ROS-capable mobile robotic TURTLEBOTs to evaluate this control strategy. Simulations performed on a mobile robot team demonstrate the effectiveness of the proposed approach.
Real-time Multi-Objective Trajectory Optimization

In this article, a method is presented for generating a trajectory through predetermined waypoints, with jerk and duration as two conflicting objectives. The method uses a Seq2Seq neural network model to approximate Pareto efficient solutions. It trains on a set of random trajectories optimized by Sequential Quadratic Programming (SQP) with a novel initialization strategy. We consider an example pick-and-place task for a robot manipulator. Based on several metrics, we show that our model generalizes over diverse paths, outperforms a genetic algorithm, SQP with naive initialization, and scaled time-optimal methods. At the same time, our model features a negligible GPU-accelerated inference time of 5ms that demonstrates applicability of the approach for real-time control.
Remarks on Direct Controller using a Commutative Quaternion Neural Network

In this study, we investigated the capability of a high-dimensional neural network (NN) using commutative quaternion numbers in control system applications. A multilayer commutative quaternion NN was employed to develop a servo-level controller, where the network input comprised the reference output and tapped-delay inputs/outputs of the object plant, and the network output was used directly as the control input. The commutative quaternion NN in the controller was trained in an offline manner using the stochastic gradient descent method to obtain the inverse transfer function of the plant. The effectiveness of the proposed controller was evaluated in computational experiments to control a discrete-time nonlinear plant. The simulation results demonstrate the feasibility of the commutative quaternion NN for this task and the characteristics of the proposed controller.
PrimitivePose: 3D Bounding Box Prediction of Unseen Objects via Synthetic Geometric Primitives

This paper studies the challenging problem of 3D pose and size estimation for multi-object scene configurations from stereo views. Most existing methods rely on CAD models and are therefore limited to a predefined set of known object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. To address this problem we propose an oriented 3D bounding box detection method that does not require 3D models or semantic information of the objects and is learned entirely from the category-specific domain, relying on purely geometric cues. These geometric cues are objectness and compactness, as represented in the synthetic domain by generating a diverse set of stereo image pairs featuring pose annotated geometric primitives. We then use stereo matching and derive three representations for 3D image content: disparity maps, surface normal images and a novel representation of disparity-scaled surface normal images. The proposed model, PrimitivePose, is trained as a single-stage multi-task neural network using any one of those representations as input and 3D oriented bounding boxes, object centroids and object sizes as output. We evaluate PrimitivePose for 3D bounding box prediction on difficult unseen objects in a tabletop environment and compare it to the popular PoseCNN model-a video showcasing our results can be found at: https://preview.tinyurl.com/2pccumvt.
Autonomous Multirotor Landing on Landing Pads and Lava Flows

Landing is a challenging part of autonomous drone flight and a great research opportunity. This PhD proposes to improve on fiducial autonomous landing algorithms by making them more flexible. Further, it leverages its location, Iceland, to develop a method for landing on lava flows in cooperation with analog Mars exploration missions taking place in Iceland now – and potentially for future Mars landings.
DVF-RRT: Randomized Path Planning on Predictive Vector Fields

Autonomous surface vehicle (ASV) navigation in marine environments is challenging due to the disturbances caused by water currents and their spatiotemporal variations. Existing methods take into account only spatial variations of vector fields that are measured through vehicle sensors, but neglect temporal variations of vector fields. Effective path planning for ASVs also requires critical reasoning about the prediction of spatiotemporally varying water currents in marine environments. Therefore, this paper presents a method that integrates the prediction of water vector fields with a randomized path planner. We model the water flow of an area of interest as an unknown vector field and then train a Long-Short Term Memory (LSTM) neural network to learn such an unknown vector field accurately and effectively from real ocean current data. This allows the generation of a randomized path that moves along the vector field in a continuous space. To generate a randomized path on the predicted vector field, we present a Deep Vector Field - Rapidly-exploring Random Tree (DVF-RRT) algorithm for reaching a goal configuration starting from an initial configuration that leverages the strength of the RRT algorithm. The algorithm is validated through simulated randomized paths on predictive vector fields and benchmarking with regard to an existing VF-RRT method.
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.
Efficient Neural Data Compression for Machine Type Communications via Knowledge Distillation

The anticipated huge number of devices and large traffic volumes impose new challenges on the communication system requirements and design. One of the main requirements of massive machine-type communication (mMTC) is to support network energy efficiency. Data compression is a widely adopted technique that enables higher energy efficiency, lower latency, and better bandwidth utilization. Unfortunately, the current compression techniques are mainly designed for human-type communications (HTC). Therefore, they consider the reconstruction fidelity, rather than the accuracy of inferred decisions, as the sole performance metric. In this work, we propose a novel encoder for data compression in mMTC communications, which is termed Distillation Encoder (DE). Unlike prior work, the design of the proposed DE aims to achieve high compression ratios while preserving the accuracy of the inferred decisions. DE inherits the knowledge of a large teacher model (trained on the raw data) through knowledge distillation. Evaluating the proposed framework on several public datasets shows a clear performance advantage compared with baseline models in terms of the inferred decision accuracy and generalizing to yet-unseen data. Moreover, the DE can be applied to learn efficient quantizers, as shown in the results.
Cost-Aware Hierarchical Federated Learning via Over-the-Air Computing

Federated Learning (FL) is a novel distributed learning framework to train the global model locally without collecting the raw data of clients. However, the performance of FL is greatly restricted by the limited network communication capacity between the cloud and clients. MEC-assisted Hierarchical Federated Learning (HFL) can effectively relieve the network pressure in FL, by transmitting and aggregating model parameters at the network edge based on the idea of Mobile Edge Computing (MEC). The existing researches on HFL often adopt the traditional multiple access techniques (e.g., OFDMA) for the model transmission between clients and edge servers, which may be inefficient. In this work, we consider a novel Over-the-Air Computing (AirComp) based HFL framework, where clients send model parameters to edge servers simultaneously, and edge servers can directly complete the aggregation of model in the air by exploiting the superposition property of wireless channels. In such a scenario, we study the joint client association, transmission, and computation optimization problem, aiming at minimizing the overall energy consumption and latency. The problem is challenging due to the multi-level coupling between edge servers and clients. We decouple it into a client association subproblem and a resource optimization subproblem. We first show that the second subproblem is convex and can be easily solved by a coordinate descent algorithm. We then show that the first subproblem is a combinational optimization, and propose a near-optimal solution where each client is associated with the nearest edge server. Simulation results show that the AirComp-based HFL scheme outperforms the existing OFDMA-based schemes in terms of both energy consumption and latency.
A Theoretical Framework for Information Search using Online Social Networks

A significant part of human activity today consists of searching for pieces of information online, an endeavor that may be time-consuming if the individual searching for the information is unfamiliar with the subject matter. However, online social networks exist where experts can aid individuals by answering questions posed by these users. This paper describes a theoretical framework to model the dynamic process by which requests for information arrive in a social network of experts, who then answer the requests after a random time interval. Results on the requests-handling capacity of the network are provided.
Double Perturbation-Based Privacy-Preserving Federated Learning against Inference Attack

Federated Learning (FL) is a well discussed distributed training framework, which allows scattered clients to collaboratively train a central model without directly sharing raw data. However, recent researches have stated that the model updates or gradients uploaded by FL can be used to infer sensitive data of clients, and this attack poses severe threats to FL. Several solutions are developed to address this threat. Although these solutions can achieve privacy preservation to a certain extent, their accuracy is severely degraded, and they are unable to provide strong privacy protection. Under this background, we propose a double perturbation-based privacy-preserving federated learning method, in which a feature extractor and an additional blurry function are utilized to improve the objective function of Conditional Generative Adversarial Networks (CGANs) and the generated data by CGANs are mixed with real data to construct fake-training data. Meanwhile, we design an algorithm to perturb the information contained in the gradient of fully connected layers that is most favorable for the attacker to reconstruct data. Finally, simulation results show that our developed method can effectively resist inference attack with a negllgible decline in accuracy.
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition

Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem that is essential yet rarely exploited. In this paper, we propose to relieve this challenging problem by introducing the segmentation technology into HAR, yielding joint activity segmentation and recognition. Especially, we introduce the Multi-Stage Temporal Convolutional Network (MS-TCN) architecture for sample-level activity prediction to joint segment and recognize the activity sequence. Furthermore, to enhance the robustness of HAR against the inter-class similarity and intra-class heterogeneity, a multi-level contrastive loss, containing the sample-level and segment-level contrast, has been proposed to learn a well-structured embedding space for better activity segmentation and recognition performance. Finally, with comprehensive experiments, we verify the effectiveness of the proposed method on two public HAR datasets, achieving significant improvements in the various evaluation metrics.
Device Identification in the Presence of MAC Randomization

Wi-Fi (IEEE 802.11) is the most-used protocol for wireless internet access on customer premises. The MAC address of each connected device, which used to be static, is being recently randomized (by the device's operating system) as frequently as daily to prevent tracking and fingerprinting of devices and users. While this feature might be useful in public areas, it disturbs some day-to-day functionalities, such as firewalls, parental control, and similar applications that require a static identifier per device. In this work, we present methods to ensure the functionalities of these applications, even when the MAC address is changed every time the device connects to the network. Our methods work even if the latest MAC randomization techniques are applied and provide these device identifications only to the gateway router. (Potentially malicious) devices that are connected to the same LAN, still see the randomized MAC address, and therefore, cannot identify or track the device.
Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo

This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers. The standard one-shot, or embarrassingly parallel, Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized by proposing two straggler-resilient solutions based on grouping and coding. Two main challenges in designing straggler-resilient algorithms for CMC are the need to estimate the statistics of the workers' outputs across multiple shots, and the joint non-linear post-processing of the outputs of the workers carried out at the server. This is in stark contrast to other distributed settings like gradient coding, which only require the per-shot sum of the workers' outputs. The proposed methods, referred to as Group-based CMC (G-CMC) and Coded CMC (C-CMC), leverage redundant computing at the workers in order to enable the estimation of global posterior samples at the server based on partial outputs from the workers. Simulation results show that C-CMC may outperform G-CMC for a small number of workers, while G-CMC is generally preferable for a larger number of workers.
An Architectural Approach for Heterogeneous Data Access in Serverless Platforms

The continuous digitalization and application of modern ICT technologies in the Smart City and Tourism domains are paving the way to new, integrated and connected experiences with strong economic and social impact. However, the integration of services and data coming from different providers is hindered by a rapidly evolving ecosystem of tools and techniques, introducing substantial delays and costs in solution design, deployment, testing, and refinement. Serverless computing is a novel cloud computing model where the customers' business logic, structured as lightweight functions, is automatically put into execution in response to an incoming event. This emergent paradigm promises to be an appealing solution, lowering the development and management barrier for the adaptation, integration, and roll-out of services. However, the ephemeral nature of serverless functions clashes with the necessity of creating and maintaining persistent connections to the various data providers. In this work, we present the Serverless Persistence Support (SPS) service, a novel, distributed, and scalable service implementing an adaptation layer able to improve data access performance from serverless functions. To validate our proposal, we conduct a thorough analysis on a realistic testbed, contrasting various data layer supports and assessing SPS performance when compared to the classic approach where connections and operations are executed inside the business logic. The experimental analysis shows that our solution exhibits better performance both in terms of latency and throughput, while also improving resource utilization.
COVID-LPS: Location-Protected Services for COVID-19 Prevention

The COVID-19 pandemic has caused not only worldwide health problems but also economic damage. Numerous researchers and intuitions have attempted to visualize confirmed COVID-19 cases with maps to provide timely information to users (e.g., warnings upon entry of crowded areas) and prevent the spread of COVID-19. However, such systems are limited by their poor protection of private information because they must collect sensitive information, such as the locations of individuals. We propose a practical method of obtaining a distribution of users while anonymizing their location data that can be used in location-based services for the prevention of the spread of COVID-19. Generalization and local differential privacy are used to guarantee user and data anonymity while maintaining high data utility and accuracy. To our knowledge, COVID-LPS is not only the first COVID-19 tracing system in Taiwan but also the first system to visualize user distributions for location-based services while protecting user privacy through generalization and local differential privacy.
Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology Datasets

Deep learning-based methods have powered recent advancements in medical image segmentation, accelerating the field past previous statistical and Machine Learning-based methods [1]. This, however, has simultaneously created a need for large quantities of labeled data, which is difficult in domains such as medical imaging where labeling is expensive and requires expert knowledge. Semi-supervised learning (SSL) addresses these limitations by augmenting labeled data with large quantities of more widely available unlabeled data. Existing semi-supervised frameworks based on pseudo-labeling [2] or contrastive methods [3], however, struggle to scale to the high resolution of medical image datasets. In this work, we propose the Histopathology DatasetGAN (HDGAN) framework, an extension of the DatasetGAN framework for image generation and segmentation that scales well to large-resolution histopathology images. We make several adaptations on the original framework, including updating the generative backbone, selectively extracting latent features from the generator, and switching to memory-mapped arrays. These changes reduce the memory consumption of the framework, improving its applicability to medical imaging domains.
DYNAMIC ENCODING OF THE TRANSFORMER VIDEO IMAGES WITH REFINEMENT OF THE BASE SYSTEM

This article creates a method of dynamic encodiofng the sequence of video segments based on the adaptive update of the system fundamentals the structural positional basis. In this case, the processing of part of the components is carried out in one pass. It is shown that it is necessary to take into account new patterns that are inherent in the sequence of transformed segments. It is argued that the elimination of structural redundancy creates the conditions for additional reduction in the bit volume. It is substantiated that the increase in the volume of digital representation compressed video image by an amount equal to the number of digits spent on the marker matrix is compensated by a decrease in the number of digits for the presentation of the code and a reduction in the number bases used to form it. The practical significance lies in the fact that the technology created in relation to the method, which is implemented in the formats of the JPEG and JPEG2000 families, allows you to further increase the compression ratio of realistic video images without making an error and reduce the time for their processing.