Reviewerly/exHarmony huggingface dataset:
Heterogeneous Federated Learning for Balancing Job Completion Time and Model Accuracy

Federated Learning (FL) is a secure distributed learning paradigm, which enables potentially a large number of devices to collaboratively train a global model based on their local dataset. FL exhibits two distinctive features in job requirement and client participation, where FL jobs may have different training criteria, and clients possess diverse device capabilities and data characteristics. In order to capture such heterogeneities, this paper proposes a new FL framework, Hca, which aims to strike a balance between the job completion time and model accuracy. Specifically, Hca builds upon a number of innovations in the following three phases: i) pre-estimation: we first derive the optimal set of parameters used in training in terms of the number of training rounds, the number of iterations and the number of participating clients in each round; ii) client selection: we design a novel device selection algorithm, which selects the most effective clients for participation based on both client historical contributions and data effectiveness; iii) model aggregation: we improve the classic FedAvg algorithm by integrating the model loss reduction in consecutive rounds as a weighted factor into aggregation computation. To evaluate the performance and effectiveness of Hca, we conduct theoretical analysis and testbed experiments over an FL platform FAVOR. Extensive results show that Hca can improve the job completion time by up to 34% and the model accuracy by up to 9.1%, and can reduce the number of communication rounds required in FL by up to 75% compared with two state-of-the-art FL frameworks.
Self-repair Embryonic Architecture with GA Evolved Configuration Data

The self-repair-enabled embryonic cell architecture is designed for digital circuit implementation. The embryonic cells contain complete genome data so that in case of any cell failure in the embryonic fabric, the data can be copied to non-faulty cells. The configuration data in Cartesian Genetic Programming (CGP) format is generated through a customized parallel Genetic Algorithm (GA). The Built-in Self-test (BIST) module of the embryonic fabric locates the faulty cell on which self-repair is to be applied. Self-repair to the faulty cell is achieved through scrubbing for transient errors or by spare cell replacement for permanent errors. The novel embryonic architecture integrates customized GA, BIST, and Self-repair features. The embryonic fabric design and GA implementation are implemented using Verilog and downloaded to Microsemi SmartFusion Kit for verification.
Subgraph Sampling for Inductive Sparse Cloud Services QoS Prediction

Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.
Optimizing Resource Allocation in Pipeline Parallelism for Distributed DNN Training

Deep Neural Network (DNN) models have been widely deployed in a variety of applications. Driven by privacy concerns and great improvement in the computational power of mobile devices, the idea of training machine learning models on mobile devices has become more and more important. Directly applying parallel training frameworks designed for data center networks to train DNN models on mobile devices may not achieve the ideal performance, since mobile devices usually have multiple types of computation resources such as ASIC, neural engine, and FPGA. Moreover, the communication time is not negligible when training on mobile devices. With the objective of minimizing DNN training time, we propose to extend the pipeline parallelism, which can hide the communication time behind computation for DNN training by integrating the resource allocation. Fine-tuning the ratio of resources allocated to forward and backward propagation can improve resource utilization. We focus on homogeneous workers and theoretically analyze the ideal cases where resources are linearly separable. We also discuss the model partition and resource allocation for a more realistic case. Additionally, we investigate the heterogeneous worker case. Trace-based simulation results show that our scheme can efficiently reduce the time cost of a training iteration.
SIMWIR: A Semantically Inclined Model for Annotations Based Web Image Recommendation Encompassing Integrative Intelligence

Annotations-based web image recommendation is a needed mandate due to the reason that images are complex structures to be analyzed and their labels have a lot of information. In this paper, a strategic framework for Web image recommendation, which is compliant with Web 3.0 and semantically inclined, has been proposed. The Semantically Inclined Model for Annotations based Web Image Recommendation Encompassing Integrative Intelligence, SIMWIR integrates strategically the concepts of metadata generation, which is further classified using the RNNs. Knowledge infusing happens strategically using a Linked Open Data Cloud and the DBPedia framework and knowledge stores. All features are extracted using Heip’s evenness index and it is set to the logistic regression classifier, which in turn classifies the data set. Furthermore, the data set is automatically classified using the Inception Network B3, a deep learning classifier, and the common incidents of the classified instances based on cosine similarity are chosen. The optimal solution set is computed using the Morisitas Index as a criterion function, along with the cosine similarity with differential step events, measures, and thresholds under the Bat optimization algorithm to lead the optimal solution set, which is ranked and recommended to the user. An overall precision of 96.41% has been achieved with the lowest FDR of 0.04, which is the best model when compared to the baseline models. Experimentations are conducted on WIDER, Website object detection, and Relevant Image Datasets.
Assessing Bicep Curl Exercises by Human Pose Application: A Preliminary Study

The goal of human pose estimation is to locate human body parts and create a human body representation (e.g., a body skeleton) from input data such as images and videos. It has received a lot of attention in the last decade, and it's been used in a variety of applications like human-computer interface, motion analysis, augmented reality, and virtual reality. Even though newly developed deep learning-based algorithms have achieved great performance in human pose estimation, insufficient training data, depth ambiguities, and occlusion remain problems. Another challenge with human pose estimation was implementing evaluations during workouts and physical treatment. Evaluation aids in determining the most appropriate and correct ways to undertake physical workouts. To leverage the human pose estimation neural network to identify human joints and give users instructions on how to exercise properly, this study proposes to analyze bicep curls by measuring elbow flexion angle and identifying key points at the shoulder, elbow, and hand. Thereby, compare with the standard angle to determine if the user has reached the correct amplitude of the exercise or not. This evaluation is essential when the user is doing a bicep curls exercise. For this problem, two different solutions, which are using OpenPose open source and using MediaPipe open source, are presented. After testing on the COCO dataset and our dataset, results show that the MediaPipe method provides better results for bicep curls workout evaluation. In the future, MediaPipe will be used for developing a new application software on a mobile phone to support humans in training.
A Novel Discrete Bi-objective Optimization Method for Virtual Machine Placement

According to the cloud computing paradigm, cloud providers can offer computing infrastructure as a service in virtual machines (VMs) running on physical machines (PMs). A data center relies on a VM placement (VMP) algorithm to allocate VMs to the appropriate PMs. As VMs are running on PMs, cloud services providers need to consider operating costs and minimize energy consumption to reduce costs. Meanwhile, multiple VMs running on fewer PMs will result in excellent resource contention, affecting the user experience. How to reduce energy consumption while maintaining VM performance remains a challenge. Although some existing VMPs study VM performance degradation, they do not consider the PM’s state, which will result in errors occurring when predicting VM performance. Moreover, many current VMP algorithms converge too slowly and easily fall into the local optimum solutions. So in this paper, first, we build the energy consumption model based on real data sets to obtain more accurate energy consumption values. Second, we investigate and model VM performance in CPU and memory. Third, we formulate the VMP as a discrete optimization problem based on the energy consumption model and VM performance model. We then propose a novel bi-objective discrete VMP (BDVMP) algorithm to solve it. Finally, we evaluate the BDVMP algorithm on both the CloudSim platform and the real Openstack platform. The results show the efficiency of our BDVMP algorithm.
Towards A Scalable and Privacy-Preserving Blockchain-based European Parking System

The importance of efficient and accessible parking systems has been growing over the past decades. Steady growth in urban population and car ownership has increased problems with traffic congestion and air pollution caused by inadequate parking systems. Blockchain-based parking systems have been proposed to increase system availability and resilience and improve trust among participants. However, these systems are not transferable to the parking systems of European cities such as Copenhagen, as they are based on assumptions about the parking infrastructure, which do not hold, and are inherently incompatible with regional privacy protection regulations such as the GDPR. Furthermore, many blockchain solutions suffer from scalability issues, severely limiting their efficiency. In this work, we develop a blockchainbased parking system in Denmark, aiming to make up the gap in the existing research by directly considering GDPR compliance. Our work focuses on the municipal parking system for on-street parking in Copenhagen (Denmark), where Hyperledger Fabric is used to maintain a trusted distributed ledger for parking data shared by the network. Personal data is protected through offchain storage while maintaining on-chain verifiability. The proposed system is implemented as a proof-of-concept application, which can deliver sufficient throughput to support the needs of municipal parking.
Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we proposed a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.
G-PPG: A Gesture-related PPG-based Two-Factor Authentication for Wearable Devices

Verifying the user identity of wearable devices is crucial for system security, especially before sensitive operations like making financial payments. A PPG-based two-factor authentication can be a promising solution with widely deployed PPG (Photoplethysmography) sensors within wearable devices. Our observations find PPG readings reveal a significant relevance to the user’s hand motions, i.e., gestures, while the user’s heartbeat characteristics and wearing habits are also implicitly related, which can be utilized for user authentication. In this paper, we design G-PPG, a gesture-related PPG-based two-factor authentication mechanism that can non-intrusively validate the user’s identity. In G-PPG, gesture detection and segmentation and a specific feature set are proposed for accurate gesture-related PPG characteristic extraction. Moreover, an adaptive update scheme is proposed for the high accuracy of long-term authentication. Our experiments among 15 participants demonstrate that G-PPG can achieve a 90% accuracy in the long-term study.
WMDRS: Workload-Aware Performance Model Based Multi-Task Dynamic-Quota Real-Time Scheduling for Neural Processing Units

To further improve the capacity of airborne embedded system for dealing with deep learning (DL) applications and reduce overall power consumption, it is necessary to equip Neural Processing Units (NPUs). Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent NPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability for the airborne embedded system. At present, there are few researches on multi-task real-time scheduling for NPUs. Therefore, we propose WMDRS, a workload-aware performance model multi-task dynamic-quota real-time scheduling for Neural Processing Units. The NPU performance model based on workload-awareness can accurately predict the remaining execution time of a task, which is running concurrently with other tasks on NPU. The multi-task dynamic-quota real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting NPU computing resources for active applications. In addition, we implement a prototype NPU scheduler without any hardware extension. Furthermore, the proposed NPU performance model and real-time scheduling algorithm are evaluated in realistic application sets. Experimental results demonstrate that WMDRS can achieve low prediction error and high scheduling success ratio.
Dispersal Foraging Strategy With Cuckoo Search Optimization Based Path Planning in Unmanned Aerial Vehicle Networks

Unmanned aerial vehicles (UAVs) are assumed to be a promising model of automatic emergency tasks in dynamic marine ecosystems. But, the real-time communication efficacy betwixt UAVs and base platforms is developing a serious challenge. The compact-sized powerful flying robots can be wirelessly controlled and accomplish end tasks with and without human involvement. UAVs still face severe challenges that limit the dream of completely autonomous unmanned flying machines. The main difficulties contain path planning and hindrance avoidance of such unmanned flying robots, which are mandatory but carry out the application-specific functionality in either indoor or outdoor environments. This study introduces a new Dispersal Foraging Strategy with Cuckoo Search Optimization based Path Planning (DFSCSO-PP) technique for UAV networks. In the presented DFSCSO-PP technique, the identification of optimal paths for data transmission is performed in the UAV network. In addition, the presented DFSCSO-PP technique involves the optimal allocation of resources while finding the optimal paths in the network. Moreover, the DFSCSO technique can be designed by integrating the DFS concept into the CSO method to avoid local optima problems. A widespread simulation analysis is performed to exhibit the enhanced outcome of the DFSCSO-PP approach. A detailed set of comparative studies assured the improved performance of the DFSCSO-PP technique over other approaches.
Activity Recognition in Missing Data Scenario Using MICE Algorithm

Human Activity Recognition (HAR) using sensors offers a wide range of applications in healthcare, smart homes for the elderly, sports, and other fields. There have been numerous attempts in existing methods to distinguish diverse human actions using sensor data but all those studies are based on clean data patterns. As a result, investigation of detecting activity based on sensor values even if some data is lost in a random pattern. In this paper, the authors present a strategy for improving activity detection. A random pattern (Missing at random) for missing data since it is a realistic problem for sensor-based activities. First, recognize the activity using the clean dataset (HAR) and then generate different percentages of missing data in test and train datasets. At the time of classification of various activities, if a classifier is learned with missing data, it reinforces the model to regulate the missing data. This method proves that machine learning models plausibility by demonstrating that this kind of approach can predict the activity. In machine learning models, random forest and logistic regression are considered for activity classification. Using the mice algorithm in the training dataset authors observed that it can effectively boost recognition accuracy from 87% to 98%.
Fuzzy Bi-GRU Based Hybrid Extractive and Abstractive Text Summarization for Long Multi-documents

As a massive amount of information is produced on the internet nowadays, the need for extracting the most useful and relevant information from that massive list is one of the most attractive research and it can be possible through a mechanism called automatic text summarization (ATS). This summarization mechanism is classified into single and multi-documents based on the number of source documents. When multiple source documents communicate similar information called multi-documents and it is the biggest challenge in the field of ATS. This motivates us to work on the long multi-documents by calculating the sentence scores using a fuzzy inference system. From the extracted sentences, the similarity or redundancy has to be removed using Bi- GRU, and then an abstractive summary need to be generated for those identified sentences has to produce. The proposed system is validated and tested using Standard datasets namely, DUC, BBC news, and CNN/daily mail. The proposed Fuzzy Bi-GRU is compared with other cutting-edge models, and empirical results indicate that it outperforms all other models in terms of ROUGE- N and L scores.
A Variable Sliding Window Algorithm Based on Concept Drift for Frequent Pattern Mining Over Data Streams<sup>*</sup>

Sliding window is a widely used model in the process of mining frequent patterns in data streams. In order to determine the exact size of the sliding window, it is necessary to know the time and scale of the change over data streams in advance. However, the prior knowledge is difficult to determine, and the fixed-size sliding window in the traditional algorithms obviously cannot adapt to this change, which have poor performance on the latest concepts of data streams. Based on the above shortcomings, this paper proposes a new frequent pattern mining algorithm based on concept drift using variable sliding window: VSWCDD (Variable Sliding Window-Concept Drift Detection), which is suitable for mining frequent patterns in changing data streams. The window size is dynamically determined by whether the concept drift occurs in the data stream. During the mining process, the mining result variables and the cause variables about the concept drift are monitored simultaneously. When the data stream is stable and there is no concept change, the window size keeps expansion. When the concept drift occurs, the window size changes according to the difference between detection nodes. Extensive experiments on both real and synthetic data show that the VSW-CDD algorithm proposed in this paper can timely detect the concept drift in the data stream and adapt to the new concepts by adjusting the window size. Finally, the latest frequent patterns in the data streams are mined and has a better effect for click data streams on e-commerce sites and medical data. Compared with other algorithms, the algorithm in this paper also has better performance in terms of recall and adaptation.
Comprehensive Potentiality Maximization to Improve and Interpret Multi-Layered Neural Networks

The present paper aims to propose a new method to maximize the potentiality of components to deal with new inputs in multi-layered neural networks. This means that the implicit ability of components is supposed to be maximized, namely, comprehensive potentiality maximization. The paper in particular states that learning in neural networks should focus not on specific components for specific inputs but on all available components to cope with as many different inputs as possible. In terms of learning for specific inputs, the main objective of learning lies in learning as little as possible. However, for the first approximation, we deal here with two types of potentiality among many to be maximized, defined for connection weights and hidden layers, namely, inner and outer potentiality. Under the comprehensive potentiality maximization, the inner and outer potentiality should be as large as possible. When the potentiality cannot be easily increased, it is forced to be increased by making the strength of weights larger excessively and repeatedly. We applied the method to the bankruptcy data set. The results show that by forcing networks to have larger inner and outer potentiality, generalization performance was improved. In addition, by the comprehensive interpretation, taking into account as many representations as possible and treating them as equally as possible, we could extract new important inputs.