Reviewerly/exHarmony huggingface dataset:
Forensics in the Internet of Things: Application Specific Investigation Model, Challenges and Future Directions

The Internet of things (IoT) has already shaped a vast network to share information among things and devices. It has numerous benefits for the business, consumers, medical, and many more areas. Despite IoT's evolution and potential, it has several technical complexities and challenges to deploy. Technical challenging includes hardware, software, application framework, and communication standards and protocols. Moreover, other challenges include security, privacy, and safety. Cybercrime is a common issue in the network world and this opportunity has increased in the IoT base systems. It becomes difficult to investigate due to Internet of Things base systems that have increased complexity as the numerous IoT devices and ecosystems. As there are billions of devices connected to the internet as a result cyber criminals quickly attack the systems over the network. Identifying such crimes is difficult to trace in the IoT base application. Digital forensic investigation is not the complete solution in the IoT base systems investigation as the IoT systems work based on a different stage. In this paper, we have presented application specific investigation model to trace the IoT base systems attacker and also identified challenges of this investigation and the future direction of the IoT forensic.
Developing an Instrumentation Package to Measure Noise and Vibration in Neonatal Patient Transport

Inter-facility transport is often necessary for patients requiring specialized medical care. In the case of neonatal patients, specialized transport systems are used to ensure continuous care during transport. Concerns relating to physical stressors to which the patients are exposed during transport has motivated our ongoing study on evaluating and mitigating vibration and acoustic noise in these systems. Regardless of the various testing configurations, whether they be full-scale vehicle tests or laboratory shaker tests, reliable and consistent data collection practices are desired. An instrumentation package, designed to measure noise and vibration of the transport system, is currently in development to ultimately improve the safety of patients during transport. This system is intended to promote standard data collection practices and ultimately the aggregation of findings from independent studies into a single database. Requirements for the system have been defined, and early stages of sensor selection and prototyping are underway. The current configuration of sensors can successfully collect acceleration, angular rate, and sound level data. Additional sensors can be integrated into the system, to allow for multiple locations of interest to be studied simultaneously. Future work will improve the efficiency of the data collection script, the automation of data processing, and the physical implementation of the system.
Residential Solar Panel Object Detection Based on Multi-combination Data Augmentation and YOLOv5

The photovoltaic (PV) installation capacity is important for the planning and operation of the grid. Image recognition has been a new method to detect PV installation capacity. This paper proposes a residential solar panel (RSP) object detection model based on multi-combination data augmentation and YOLOv5. The RSP dataset is established through public satellite images. The multi-combination data augmentation method is proposed to deal with the small sample problem and the YOLOv5s is utilized as the detection algorithm. Several ablation experiments with conventional data augmentation methods are conducted. Compared to the standard yolov5-based benchmark test, our method can increase the overall average precision by 4%.
Joint Extraction of Entity Relations in Electric Power Industry Standard Based on GCN

The name, author, drafting unit, standard number and other entities of the power standard text can be extracted by the rules. Normative short sentences in the standard text need knowledge extraction to build a knowledge graph. The existing entity relationship joint extraction methods do not fully consider the complex structural characteristics of entity relationships in sentences. Therefore, a new entity relationship joint extraction method based on graph convolution neural network (GCN) is proposed. On the basis of bidirectional long short memory network to extract sequence features, an end-to-end Chinese entity relationship joint extraction model is constructed using the syntax structure information in the GCN encoding dependency analysis results and the improved entity annotation strategy. The experimental results show that the F1 value of this method can reach 72.3%, which is 2.7% higher than that of LSTM-CRF model. GCN can effectively encode the prior word relationship of text and improve the performance of entity relationship extraction.
Research on Vehicle Object Detection Based on Deep Learning

In this paper, deep learning technology is used to study and optimize the vehicle target detection algorithm in the driving environment, and a vehicle target detection model with both real-time and accuracy is constructed. A new model is designed based on MobileNet network, and the DarkNet 53 network structure of YOLO algorithm is replaced and optimized by using deep separable convolutional neural network. The deep separable convolution is used to replace the ordinary convolution layer in the original network structure to reduce the number of parameters in the network structure and improve the learning ability of the convolutional neural network. The corresponding loss function is designed for the new model proposed in this paper. The new model target detection proposed in this paper is studied as a regression problem. Therefore, the design of the loss function comprehensively considers IoU error, coordinate error and classification error to achieve the purpose of accelerating the convergence speed of the model. The new model proposed in this paper is trained on the DeGAN extended KITTI dataset, and the experimental results are compared and analyzed. The experimental results show that the MobileNet-Yolomodel proposed in this paper has different degrees of improvement in model storage and detection speed compared with other algorithms.
WGAN-GP with Residual Network Model for Lithium Battery Thermal Image Data Expansion with Quantitative Metrics

Energy storage systems, especially lithium battery energy storage, play a significant role in microgrids. Thermal images of lithium batteries are crucial for the study of lithium batteries since lithium batteries are strongly impacted by temperature. In order to increase the sample of thermal images from lithium batteries, this paper designs the WGAN-GP with ResNet model (Wasserstein generative adversarial network with gradient penalty and residual networks). A complete WGAN-GP with ResNet model is built with the embedding of a generative adversarial network (GAN) structure, introducing gradient penalties, and combining residual networks. The experimental results demonstrate that WGAN-GP with ResNet have a greater improvement in training stability and generated image quality than GAN and Wasserstein GAN by using four evaluation indicators to examine the generated image quality.
Soft Reliability Aware Scheduling of Real-time Applications on Cloud with MTTF constraints

Nowadays the cloud system receives requests from a wide horizon of users. In order to execute a large number of modern resource-intensive, latency-sensitive applications with deadline requests from the users, the cloud systems are equipped with powerful machines, and the machines run for a significant amount of time. This leads to an increase in the probability of failures of these machines. Hence, the reliability of the cloud system is to be duly considered while designing a scheduling strategy for executing resource-intensive, latency-sensitive applications on it. This paper proposes an efficient scheduling strategy for executing real-time applications (scientific applications) maintaining the reliability constraints of both the cloud system and applications and the deadline constraints of these applications. The proposed policy assigns recoveries for an optimal number of tasks of the application while scheduling them on the cloud considering the reliability constraints of both the cloud system and the application. The experimental evaluation proves that the proposed policy outperforms the state-of-the-art policy both for the synthetic task set and scientific workflows.
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization

Federated learning enables edge devices to train a global model collaboratively without exposing their data. Despite achieving outstanding advantages in computing efficiency and privacy protection, federated learning faces a significant challenge when dealing with non-IID data, i.e., data generated by clients that are typically not independent and identically distributed. In this paper, we tackle a new type of Non-IID data, called cluster-skewed non-IID, discovered in actual data sets. The cluster-skewed non-IID is a phenomenon in which clients can be grouped into clusters with similar data distributions. By performing an in-depth analysis of the behavior of a classification model's penultimate layer, we introduce a metric that quantifies the similarity between two clients' data distributions without violating their privacy. We then propose an aggregation scheme that guarantees equality between clusters. In addition, we offer a novel local training regularization based on the knowledge-distillation technique that reduces the overfitting problem at clients and dramatically boosts the training scheme's performance. We theoretically prove the superiority of the proposed aggregation over the benchmark FedAvg. Extensive experimental results on both standard public datasets and our in-house real-world dataset demonstrate that the proposed approach improves accuracy by up to 16% compared to the FedAvg algorithm.
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference

Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due to their effectiveness in analyzing and predicting the evolution of complex interconnected graph-based systems. However, hardware deployment of DGNNs still remains a challenge. First, DGNNs do not fully utilize hardware resources because temporal data dependencies cause low hardware parallelism. Additionally, there is currently a lack of generic DGNN hardware accelerator frameworks, and existing GNN accelerator frameworks have limited ability to handle dynamic graphs with changing topologies and node features. To address the aforementioned challenges, in this paper, we propose DGNN-Booster, which is a novel Field-Programmable Gate Array (FPGA) accelerator framework for real-time DGNN inference using High-Level Synthesis (HLS). It includes two different FPGA accelerator designs with different dataflows that can support the most widely used DGNNs. We showcase the effectiveness of our designs by implementing and evaluating two representative DGNN models on ZCU102 board and measuring the end-to-end performance. The experiment results demonstrate that DGNN-Booster can achieve a speedup of up to 5.6× compared to the CPU baseline (6226R), 8.4× compared to the GPU baseline (A6000) and 2.1× compared to the FPGA baseline without applying optimizations proposed in this paper. Moreover, DGNN-Booster can achieve over 100× and over 1000× runtime energy efficiency than the CPU and GPU baseline respectively. Our implementation code and on-board measurements are publicly available at https://github.com/sharc-lab/DGNN-Booster.
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs

Node representation learning (NRL) generates numerical vectors (embeddings) for the nodes of a graph. Structural NRL specifically assigns similar node embeddings for those nodes that exhibit similar structural roles. This is in contrast with its proximity-based counterpart, wherein similarity between embeddings reflects spatial proximity among nodes. Structural NRL is useful for tasks such as node classification where nodes of the same class share structural roles, though there may exist a distant, or no path between them. Athough structural NRL has been well-studied in static graphs, it has received limited attention in the temporal setting. Here, the embeddings are required to represent the evolution of nodes' structural roles over time. The existing methods are limited in terms of efficiency and effectiveness: they scale poorly to even moderate number of timestamps, or capture structural role only tangentially. In this work, we present a novel unsupervised approach to structural representation learning for temporal graphs that overcomes these limitations. For each node, our approach clusters then aggregates the embedding of a node's neighbors for each timestamp, followed by a further temporal aggregation of all timestamps. This is repeated for (at most) d iterations, so as to acquire information from the d -hop neighborhood of a node. Our approach takes linear time in the number of overall temporal edges, and possesses important theoretical properties that formally demonstrate its effectiveness. Extensive experiments on synthetic and real datasets show superior performance in node classification and regression tasks, and superior scalability of our approach to large graphs.
Predicting the Performance-Cost Trade-off of Applications Across Multiple Systems

In modern computing environments, users may have multiple systems accessible to them such as local clusters, private clouds, or public clouds. This abundance of choices makes it difficult for users to select the system and configuration for running an application that best meet their performance and cost objectives. To assist such users, we propose a prediction tool that predicts the full performance-cost trade-off space of an application across multiple systems. Our tool runs and profiles a submitted application on a small number of configurations from some of the systems, and uses that information to predict the application's performance on all configurations in all systems. The prediction models are trained offline with data collected from running a large number of applications on a wide variety of configurations. Notable aspects of our tool include: providing different scopes of prediction with varying online profiling requirements, automating the selection of the small number of configurations and systems used for online profiling, performing online profiling using partial runs thereby make predictions for applications without running them to completion, employing a classifier to distinguish applications that scale well from those that scale poorly, and predicting the sensitivity of applications to interference from other users. We evaluate our tool using 69 data analytics and scientific computing benchmarks executing on three different single-node CPU systems with 8–9 configurations each and show that it can achieve low prediction error with modest profiling overhead.
Overcoming Challenges in DevOps Education through Teaching Method

DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.
Enabling Fast, Effective Visualization of Voluminous Gridded Spatial Datasets

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 and a distributed, in-memory cache to facilitate interactive visualizations. Our benchmarks demonstrate that deploying our lightweight models coupled with back-end caching and prefetching schemes can reduce the client's query response time by 92.3% while maintaining a high perceptual quality with a PSNR (peak signal-to-noise ratio) of 38.7 dB.
PFSL: Personalized &amp; Fair Split Learning with Data &amp; Label Privacy for thin clients

The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of slow convergence and lower accuracy. In this paper, we implement PFSL, a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel, starting with a pre-trained DL model without sharing their data or labels with a central server. We implement a lightweight step of personalization of client models to provide high performance for their respective data distributions. Furthermore, we evaluate performance fairness amongst clients under a work fairness constraint for various scenarios of non-i.i.d. data distributions and unequal sample sizes. Our accuracy far exceeds that of current SL algorithms and is very close to that of centralized learning on several real-life benchmarks. It has a very low computation cost compared to FL variants and promises to deliver the full benefits of DL to extremely thin, resource-constrained clients.
Persona-based Assessment of Software Engineering Student Research Projects: An Experience Report

Students enrolled in software engineering degrees are generally required to undertake a research project in their final year through which they demonstrate the ability to conduct research, communicate outcomes, and build in-depth expertise in an area. Assessment in these projects typically involves evaluating the product of their research via a thesis or a similar artifact. However, this misses a range of other factors that go into producing successful software engineers and researchers. Incorporating aspects such as process, attitudes, project complexity, and supervision support into the assessment can provide a more holistic evaluation of the performance likely to better align with the intended learning outcomes. In this paper, we present on our experience of adopting an innovative assessment approach to enhance learning outcomes and research performance in our software engineering research projects. Our approach adopted a task-oriented approach to portfolio assessment that incorporates student personas, frequent formative feedback, delayed summative grading, and standards-aligned outcomes-based assessment. We report upon our continuous improvement journey in adapting tasks and criteria to address the challenges of assessing student research projects. Our lessons learnt demonstrate the value of personas to guide the development of holistic rubrics, giving meaning to grades and focusing staff and student attention on attitudes and skills rather than a product only.
Model-Platform Optimized Deep Neural Network Accelerator Generation through Mixed-Integer Geometric Programming

Although there are distinct power-performance advantages in customizing an accelerator for a specific combination of FPGA platform and neural network model, developing such highly customized accelerators is a challenging task due to the massive design space spans from the range of network models to be accelerated, the target platform's compute capability, and its memory capacity and performance characteristics. To address this architectural customization problem, an automatic design space exploration (DSE) framework using a mixed-integer geometric programming (MIGP) approach is presented. Given the set of DNN models to be accelerated and a generic description of the target platform's compute and memory capabilities as input, the proposed framework automatically customizes an architectural template for the platform-model combination and produces the associated I/O schedule to maximize its end-to-end performance. By formulating DNN inference as a multi-level loop tiling problem, the proposed framework first customizes an accelerator template that consists of a parameterizable array architecture with SIMD execution cores and a customizable memory hierarchy using a MIGP to maximize the expected resource utilization. Subsequently, a second MIGP is used to schedule memory and compute operations as tiles to improve on-chip data reuse and memory bandwidth utilization. Experimental results from a wide range of neural network models and FPGA platform combinations show that the proposed scheme is able to produce accelerators with performance comparable to the state-of-the-art. The proposed DSE framework and the resulting hardware/software generator are available as an open-source package called AGNA with the hope that it may facilitate vendor-agnostic DNN accelerator development from the research community in the future.
Accelerating Graph Analytics with oneAPI and Intel FPGAs

Graphs are a popular and effective way to represent relationships between data points in a network due to their simple data abstraction and low storage cost per data point. However, as data continues to grow, the processing complexity of graph operations also significantly increases. Thus, there is a need to accelerate graph processing through specialized techniques and hardware in order to improve analytical throughput. This research uses the oneAPI toolkit with SYCL for FPGAs and leverages the increased productivity when creating designs, as compared to traditional hardware description language methodologies. The oneAPI toolkit is used in the creation of minimum-spanning-tree (MST) and breadth-first search (BFS) accelerators, evaluating the impact of different high-level design choices on the overall performance observed.
Recurrent DETR: Transformer-Based Object Detection for Crowded Scenes

Recent Transformer-based object detectors have achieved remarkable performance on benchmark datasets, but few have addressed the real-world challenge of object detection in crowded scenes using transformers. This limitation stems from the fixed query set size of the transformer decoder, which restricts the model’s inference capacity. To overcome this challenge, we propose Recurrent Detection Transformer (Recurrent DETR), an object detector that iterates the decoder block to render more predictions with a finite number of query tokens. Recurrent DETR can adaptively control the number of decoder block iterations based on the image’s crowdedness or complexity, resulting in a variable-size prediction set. This is enabled by our novel Pondering Hungarian Loss, which helps the model to learn when additional computation is required to identify all the objects in a crowded scene. We demonstrate the effectiveness of Recurrent DETR on two datasets: COCO 2017, which represents a standard setting, and CrowdHuman, which features a crowded setting. Our experiments on both datasets show that Recurrent DETR achieves significant performance gains of 0.8 AP and 0.4 AP, respectively, over its base architectures. Moreover, we conduct comprehensive analyses under different query set size constraints to provide a thorough evaluation of our proposed method.