Reviewerly/exHarmony huggingface dataset:
To Think Like a Vertex (or Not) for Distributed Training of Graph Neural Networks

Graph Neural Networks (GNNs) train neural networks that combine the topological properties of a graph with the vertex and edge features to perform tasks such as node classification and link prediction. We propose a novel middleware that approaches GNN training from the perspective of a vertex-centric model (VCM) of distributed graph processing and overlays neural network training over it. Giraph Graph Neural Network (G2N2) uses a three-phase execution pattern by construction a distributed computation graph per mini-batch, and maps the forward and backward passes of the GNN training to VCM. We implement a prototype of G2N2 in Apache Giraph and report results from a preliminary evaluation using two real-world graphs on a commodity cluster.
Enhancing Interval-centric Distributed Computing to Support Incremental Graphs

Real-world graphs like social networks, fintech transactions and the World Wide Web (WWW) are dynamic in nature, and have addition and deletion of vertices and edges. The Interval-centric model (ICM) was proposed to intuitively design and execute distributed algorithms over temporal graphs having a lifespan on vertices, edges and their attributes. The temporal graphs are available a priori. The Windowed ICM (WICM) model further optimized its performance by avoiding the loading of the entire temporal graph in-memory. Here, we extend the WICM model to incrementally operate on dynamic graphs whose vertex and edge updates arrive over time. We achieve this by decoupling the receipt and application of updates to the temporal graph, and the computation on the graph using 3 strategies – JITM, DITM and AITM. We compare our results with the native WICM and strawman baseline. Our evaluation on the Reddit graph shows $\approx 40$% better performance without compromising on correctness
Towards Efficient Scheduling of Concurrent DNN Training and Inferencing on Accelerated Edges

Edge devices are typically used to perform low-latency DNN inferencing close to the data source. However, with accelerated edge devices and privacy-oriented paradigms like Federated Learning, we can increasingly use them for DNN training too. This can require both training and inference workloads to be run concurrently on an edge device, without compromising on the inference latency. Here, we explore such concurrent scheduling on edge devices, and provide initial results demonstrating the interaction of training and inferencing on latency and throughput.
Cognitive Health Assessment of Decentralized Smart home Activities using Federated Learning

The Internet of Things (IoT) and smart homes provide privacy-preserving environments for the healthcare sector to manage the care of individuals with cognitive impairment or disability. These homes, equipped with various sensors, can assist in assessing cognitive health by collecting data on daily activities. As cognitive health deteriorates over time, it can often go unnoticed until it is too late. In the literature, various machine learning and deep learning techniques have been applied to assess daily tasks and differentiate between individuals with competent and impaired cognitive abilities. However, this may compromise the privacy of those living in smart homes. This paper presents a federated learning approach based on deep neural networks to address this concern. The deep neural network model is trained on a cognitive health dataset and implemented on two clients, with a server used to receive updates from both. The results are evaluated in two rounds to reduce overfitting. The experiment demonstrates the effectiveness of the proposed approach, achieving more than 99.2% accuracy while maintaining data privacy.
Controlling Air Pollution in Data Centers using Green Data Centers

Growing air pollution has become a global threat to the environment. Controlling this global threat is very challenging and costly. Therefore, this paper proposes an air pollution control measure using green metrics (GMs). For doing this, we minimize the carbon emission from traditional data centers (DCs) by designing the ‘Green Data Centers’ (GDCs). GDCs are the control mechanism, which includes a set of different green protocols. GDCs are designed in such a way that they can minimize the carbon emission (i.e., CO, and $CO_{2})$ from the traditional DCs. The design of GDCs is also responsible for optimizing energy consumption, cost-effectiveness, efficient network infrastructures, load scheduling algorithms, and the number of used devices like switches, ports, and linecards. GDCs are constructed by taking care of the idle server because it consumes massive energy than the computing server. This paper also presents a taxonomy of the existing research work, which contains the research of GDCs related to DCs, like cloud computing and cooling techniques. Apart from this, we discuss various green metrics (GMs), green computing, and networking proposals of GDCs.
BigHOST: Automatic Grading System for Big Data Assignments

With the increasing popularity of online courses in Big Data, Data Science, and Machine Learning, the need for an efficient and reliable grading solution for assignments has become evident. Existing solutions for auto-grading assignments are limited to simple coding assignments and are unable to handle the complexity, variety, and volume of data required in Big Data applications. In order to address this need, we propose BigHOST, a custom-designed auto-grader for Big Data assignments. BigHOST employs a simple yet vertically scalable, fault-tolerant and parallel processing architecture, making it efficient and reliable for grading big data assignments. Optimizations in the architecture further result in lower execution time per submission and reduced cost of hosting on cloud platforms. Experimental results and scalability analysis demonstrate the effectiveness of the proposed architecture, with BigHOST achieving more than five times the throughput in processing big data submissions.
ScaMP: Scalable Meta-Parallelism for Deep Learning Search

In this paper, we propose Scalable Meta-Parallelism for Deep Learning Search (ScaMP): a distributed Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) framework that supports out-of-core models with flexible parallelism schemes. SCaMP is integrated into the modern DL ecosystem, and enables both efficient parallel training of concurrent candidate architectures and aggregate device memory saturation via a powerful load balancing engine. SCaMP estimates the memory requirements of each candidate architecture and automatically applies the appropriate model-parallel degree and maximum batch size supported for the given candidate.We evaluate the benefits of our designs on synthetic training benchmarks and in training a state-of-the-art vision transformer model. We select transformers as a candidate DL model type and demonstrate a 29% improvement in end-to-end HPO time on 32 V100 GPUs on the Lassen and ThetaGPU HPC systems. Further, we demonstrate a reduction in the proportion of NAS time spent in communication from 28% to 15%. Finally, we thoroughly verify the correctness of SCaMP by training a state-of-the-art SwinIR model.
Privacy-preserving Job Scheduler for GPU Sharing

Machine learning (ML) training jobs are resource intensive. High infrastructure costs of computing clusters encourage multi-tenancy in GPU resources. This invites a scheduling problem in assigning multiple ML training jobs on a single GPU while minimizing task interference. Our paper introduces a clustering-based privacy-preserving job scheduler that minimizes task interference without accessing sensitive user data. We perform ML workload characterization, made available publicly [1], and do exploratory data analysis to cluster ML workloads. Consequently, we build a knowledge base of inter and intra-cluster task interference to minimize task interference.
TwMiner: Mining Relevant Tweets of News Articles

In this paper, we propose a pseudo-relevance feedback-based automated approach that utilizes both the content and context attributes of a news article to determine the tweets relevant to that news article. Extensive empirical validation on a set of 1000 news articles highlights that the proposed approach can ensure high precision (0.942) in comparison to the current research works and can successfully extract relevant tweets for a majority of the news articles, around 95% of the total news articles.
Highly Scalable Large-Scale Asynchronous Graph Processing using Actors

With the accelerating growth of Big Data, real-world graph processing applications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to application scalability. Unfortunately, current approaches to implementing these applications on modern HPC systems exhibit poor scale-out performance with increasing numbers of nodes. The scalability challenges for these applications are driven by large data sizes, synchronization overheads, and fine-grained communications with irregular data accesses and poor locality. This paper presents the scalability of a novel Actor-based programming system, which provides a lightweight runtime that supports fine-grained asynchronous execution and automatic message aggregation atop a Partitioned Global Address Space (PGAS) communication layer. Evaluations of the Jaccard Index and PageRank applications on the NERSC Perlmutter system demonstrate nearly perfect scaling up to 1,000 nodes and 64K cores (one-third of the targeted 3000-nodes for Perlmutter). In addition, our Actor-based implementations of Jaccard Index and PageRank executed with parallel efficiencies of 85.7% and 63.4% for the largest run of 64K cores. This performance represents a 29.6 × speedup relative to UPC and OpenSHMEM versions of PageRank.
Power to the Applications: The Vision of Continuous Decentralized Autoscaling

Autoscaling has been one of the most active research areas since the beginning of the cloud computing era. Nearly all previously proposed approaches focus on decision-making based on averaged monitoring values of many service instances at fixed points in time. This limits responsiveness and can lead to service level objective (SLO) violations when the load suddenly increases. Our vision of continuous decentralized autoscaling avoids these issues by giving individual service instances the power to make scaling decisions in a distributed fashion. Each instance performs self-monitoring and evaluates its state. The service instance initiates upscaling if it detects an overload or downscaling if its load is below a specified threshold. By randomly determining scaling timing, we achieve quasi-continuous scaling behavior when multiple service instances are deployed. We discuss challenges regarding analytical modeling, simulation, and real-world evaluation of this approach.
Deep Learning based Approach for Fast, Effective Visualization of Voluminous Gridded Spatial Observations

Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models.
Hierarchical Clustering Architecture for Metaverse Applications

Metaverse systems are virtual spaces that integrate 3D models, conversations, and data visualization for user interactions and collaboration. While these systems have primarily been used in the gaming industry, their application in other industries, such as industrial and business, has faced multiple challenges. One of the main challenges is the lack of responsiveness due to the size and interaction complexities of 3D models, particularly when supporting a massive number of simultaneous users. This research proposes a scalable hierarchical cluster-based architecture that simplifies the metaverse business application platform and maintenance, provides flexibility for multiple functionalities, and addresses the challenges faced by current metaverse platforms in supporting massive simultaneous users.
Privacy Regulations-Aware Storage Service Selection in Multi-Cloud

Cloud computing involves the dispersal of data across several data centers which could be geographically distributed. The way in which cloud transcends data through national boundaries poses privacy and security threats. In this work, we propose a decision support framework to assist enterprises in cloud storage service selection, taking into consideration varied data regulations such as strict and sectorspecific data localization, and GDPR. We demonstrate the implications of data regulations and data distributions across multiple cloud service providers.
Optimizing Memory Allocation in a Serverless Architecture through Function Scheduling

In a serverless architecture, a function does not fully utilize the allocated memory. Such memory over-allocation increases node utilization and wastes resources, causing cold-start and latency issues. This paper presents a fine-grained scheduling approach for a serverless architecture that aims to address the issue of over-memory allocation and improve data locality. The proposed approach estimates how much memory each function uses so that similar functions can be scheduled on the same node. As a result, it makes less use of each node and keeps the state within a single node. We evaluated our approach through the existing FaaS applications and real-world data.
Blockchains’ federation: Developing Personal Health Trajectory-centered health systems

The current world is a globalized and connected one. Even without moving around, a person interacts with personnel from different institutions treating him as a patient in their daily life. Each of these institutions keeps their patients’ data stored in their own information system, in an isolated way. Due to this, each patient has their data scattered among all these institutions and services with which she interacts along her life. This can complicate the take of the proper decision when the patient is under treatment. To solve this situation, new patient-centered health systems have been proposed as a replacement to the actual institution-centered ones, storing all health information of a patient into a unique global vision. However, some questions have arisen around the actual proposals, as who should store and maintain this vision of a given patient, and how should this information be made available for other systems. The proposal presented in this paper advocate for the achievement of a Personal Health Trajectory that can be useful both for patients and health professionals, using the concept of blockchains’ federation. The proposal has been validated using 5689 records from 50 different institutions, belonging to 1156 actors.
Competitor Attack Model for Privacy-Preserving Deep Learning

Since deep learning models usually handle a large amount of data, the ensuing problems of privacy leakage have attracted more and more attention. Although various privacy-preserving deep learning (PPDL) methods have been proposed, these methods may still have the risk of privacy leakage in some cases. To better investigate the security of existing PPDL methods, we establish a new attack model, which may be exploited by the competitors of the owners of private data. Specifically, we assume that the competitor has some data that belongs to the same domain as the private data of the other party, and he applies the same PPDL methods to his data as the other party to obtain the perturbed data, and then he trains a model to inverse the perturbed data. Data perturbation-based PPDL methods are selected in four scenarios and their security against the proposed competitor attack model (CAM) is investigated. The experimental results on three public datasets, i.e. MNIST, CIFAR10 and LFW, demonstrate that the selected methods tend to be vulnerable to CAM. On average, the recognition accuracy for the images reconstructed by CAM is about 10% lower than that for the original images, and PSNR is more than 15. The outline of the image and other information can be seen by the naked eye.
A Blockchain-based Security Management Framework for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are becoming increasingly common in various domains, including industrial control systems, smart cities, and healthcare. However, the security of CPS is a major concern due to its interconnected and heterogeneous nature. In this paper, we propose a Blockchain-based security management framework for CPS to address the security challenges of CPS. The framework leverages the unique features of Blockchain, such as decentralization, immutability, and transparency, to ensure the integrity and confidentiality of data exchanged among devices. We describe the architecture of the proposed framework and explain its key components, including Blockchain nodes, smart contracts, and security policies. Our work contributes to the field of CPS security by providing a novel approach to secure CPS using Blockchain technology. The proposed framework can be applied to various domains, including smart cities, healthcare, and industrial control systems. The framework can also be extended to handle more advanced security scenarios, such as dynamic security policies and real-time threat detection. In this framework, we build a decentralized controller-to-controller (C2C) communication system to make CPS more scalable, secure, private, and reliable. We make a miner-as-a-controller selection algorithm to choose a miner controller for a given session.
MUAR: Maximizing Utilization of Available Resources for Query Processing

Processing large datasets requires significant hardware resources and energy. Researchers have observed that most database management systems could not utilize available resources efficiently, increasing data to result time and application running costs. This research explores techniques that can maximize the utilization of available resources to efficiently process large datasets on limited resource systems. The work implemented single and multiple resource maximization techniques and observed improvements in total workload execution time (WET). Results showed that combining CPU and RAM resource maximization techniques can reduce WET by 61-81% compared to the original WET observed with default resource allocation configuration. This work proposes a lightweight real-time resource allocation and task scheduling algorithm MUAR (Maximizing Utilization of Available Resources). It maximizes the utilization of available resources considering the real-time availability of resources and workload task complexity. The algorithm identifies complex multi-join queries and allocates maximum available resources for faster execution. MUAR is capable of estimating work memory value with 15-20% error required to achieve the best query performance with only single query run data. A comparison of MUAR with machine learning-based techniques like PCC and AutoToken is also presented.
Scavenger: A Cloud Service for Optimizing Cost and Performance of DL Training

Deep learning (DL) models learn non-linear functions and relationships by iteratively training on given data. To accelerate training further, data-parallel training [1] launches multiple instances of training process on separate partitions of data and periodically aggregates model updates. With the availability of VMs in the cloud, choosing the “right“ cluster configuration for data-parallel training presents non-trivial challenges. We tackle this problem by considering both the parallel and statistical efficiency of distributed training w.r.t. the cluster size configuration and batch-size in training. We build performance models to evaluate the pareto-relationship between cost and time of DL training across different cluster and batch-size configurations and develop Scavenger as a cloud service for searching optimum cloud configurations in an online, blackbox manner.
Vulnerability Disclosure Considered Stressful

Vulnerability disclosure is a widely recognized practice in the software industry, but there is a lack of literature detailing the firsthand experiences of researchers who have gone through the process. This work aims to bridge that gap by sharing our personal experience of accidentally discovering a DNS vulnerability and navigating the vulnerability disclosure process for the first time. We document our mistakes and highlight the important lessons we learned, such as the fact that public disclosure can be effective but can also be more time-consuming and emotionally taxing than anticipated. Additionally, we discuss the ethical considerations and potential consequences that may arise during each step of the disclosure process. Lastly, drawing from our own experiences, we identify and discuss issues with the current disclosure process and propose recommendations for its improvement. Our ultimate aim is to provide valuable insights to fellow researchers who may encounter similar challenges in the future and contribute to the enhancement of the overall disclosure process for the benefit of the wider community.
Research on Evolutionary Game of Blockchain Enabling Finance-Transportation and Warehouse Financing

Based on the financing business of the finance-transportation and warehouse (FTW) enabled by blockchain, this paper takes the two default modes of small and medium-sized enterprises (SMEs), unilateral default and collusion default as the entry point, and constructs an evolutionary game (EG) model between the key players, financial institutions (FIs) and SMEs of the FTW before and after the introduction of blockchain technology (BT), and compares and discusses the evolution of the game system before and after the introduction of BT. Finally, numerical simulation is carried out. The findings are as follows: (1) The blockchain enabled FTW financing mode can effectively prevent the default behavior of SMEs. (2) BT can supervise the default behaviors of SMEs through strict regulatory environment formed by high default costs. (3) The increase of inventory pledge loan rate and FTW financing loan rate can accelerate the evolution of FIs to the loan stability strategy; The greater the return rate of production and the fraud cost of colluding to defraud the loan, the more the FIs and SMEs will be encouraged to choose the active financing strategy; The increase in the probability of conspiracy to cheat loans will shorten the time for SMEs to evolve the strategy of keeping contracts, and increase the time for FIs to evolve the strategy of lending.
Exploring the Potential of Information Flow Diagram (IFD) in Graduate Digital Library System Design and Development

The increasing importance of digital library systems in education has made the design and development of these systems a crucial task. To address the challenges faced in this process, the authors propose the utilization of the Information Flow Diagram (IFD) approach in the design and development of a graduate digital library system. This research provides a comprehensive overview of the IFD approach and its potential benefits in digital library system design and development. Our detailed case study demonstrates the feasibility and effectiveness of the proposed approach. The authors also conduct a comparative analysis between the IFD approach and traditional methods to evaluate the contributions and limitations of each approach. User studies were conducted to assess the usability and effectiveness of the digital library system designed using the IFD approach. The results indicate that the IFD approach is a promising method for designing and developing graduate digital library systems, offering a highly usable and effective system. Overall, this research highlights the potential of the IFD approach in digital library system design and development.