Reviewerly/exHarmony huggingface dataset:
Improving insulator fault detection with effective-YOLOv7 network

Ensuring reliable and steady operation of power equipment is paramount to safeguard the livelihoods and labor of the populace. However, traditional detection techniques face obstacles when adapting to the intricate background of transmission lines, leading to innumerable incorrect and missed detections. To resolve these issues, an improved YOLOv7 transmission line insulator defect recognition algorithm has been introduced. An anchor frame, matching the size of the transmission line fault, has been constructed using K-means++, followed by a convolutional block attention module to enhance the ability to extract features. Next, the wise-IoU loss function has been incorporated, providing a gradient gain allocation strategy, thereby enhancing the positioning performance and speed of the model. Finally, the SiLU activation function has been replaced with meta-ACON to enhance the adaptive feature capability of the YOLOv7 network. Experimental results have shown that the proposed method has an average accuracy (mAP) of 91.8%, and the detection accuracy for transmission lines can be improved to 98.8%. This model resolves the persisting issues of erroneous and missing detections by addressing the traditional technical difficulties of detecting complex backgrounds and defects with insufficient accuracy.
Electronics Engineering Perspectives on Computer Vision Applications: An Overview of Techniques, Sub-areas, Advancements and Future Challenges

This chapter provides a strategic overview of applications in the computer vision domain. We initially introduce the etymology of computer vision, main tasks, key techniques, and algorithms. Traditional feature extraction methods and deep learning techniques, including prominent algorithms like Region-Based Convolutional Neural Network (R-CNN) and You Only Look Once (YOLO), are explored. We discuss important sub-areas such as image classification, object detection, and image semantic segmentation. The versatility of computer vision is showcased, particularly in autonomous vehicles, healthcare, and surveillance. Furthermore, we delve into the challenges and potential of computer vision, highlighting the necessity for advanced algorithmic methodologies, efficient hardware, robust privacy protections, and conscientious ethical considerations. We also explore upcoming trends, including cross-modal learning, sophisticated ‘vision GPT’ models, and unified models that share architecture and parameters across different tasks. These future directions indicate a transformative impact across various sectors, encompassing autonomous driving, healthcare imaging, and e-commerce. Additionally, we outline the future challenges and trends in the field, underscoring the significance of continuous research and development to address issues such as data scarcity, model interpretability, and privacy concerns. By effectively addressing these challenges and capitalizing on emerging trends, computer vision stands poised to make profound advancements with far-reaching implications. This comprehensive overview aims to provide a solid foundation for understanding the field of computer vision and its potential impact across multiple industries and applications.
UE Power Saving with Traffic Classification and UE Assistance

A mobile device - or user equipment (UE) - using 5G can be configured to operate with large bandwidths and a large number of MIMO layers to enable high-throughput applications. This, however, comes at the cost of high power consumption. A variety of applications run at the UE with widely varying throughput requirements. For applications with low throughput requirement, over-configuration of the UE results in unnecessary power consumption. We propose to classify the traffic at the UE, and then configure the UE accordingly to meet the requirement of the current application without unnecessary power consumption. Specifically, we (i) use a machine learning (ML) based traffic classifier at the UE to detect the traffic class, (ii) find the RF parameters that are suitable for the determined traffic class in the current channel conditions, and (iii) use the UE assistance information (UAI) framework of 3GPP Release 16 to share the proposed RF parameters with the NW for UE power saving. We evaluate our proposed method using data collected in commercial networks with commercial UEs. The evaluation shows that the UE power consumption can be reduced by up to 60% in the cell-center and 50% in the cell-edge without degrading the quality-of-service (QoS).
Hybrid Tree Visualizations for Analysis of Gerrymandering

In the United States, congressional redistricting follows a decennial Census. Gerrymandering can result from selection of district lines regardless of political parties. Understanding the relationships between the multiple dimensions in electoral data is a core goal of gerrymandering analysis. In this paper, we analyze patterns of gerrymandering in election data using a hybrid tree visualization technique that supports both overview and drill-down into a hierarchy of multidimensional relationships in that data. Visualization of hierarchical data is of major interest in information visualization. The technique utilizes a left-to-right node-link diagram to show overall hierarchical structure. Nodes in the diagram depict the levels in the dimensional hierarchy. Each node is rendered as an embedded view that shows its particular dimensional combination. Edges directly connect the contents of the embedded views to provide visual bridges that aid navigation and understanding of dimensional relationships. We demonstrate the utility of this hybrid technique is demonstrated through two use cases. This work aims to both ground and inspire the design of future visualizations for exploring gerrymandering.
DoppelVer: A Benchmark for Face Verification

The field of automated face verification has become saturated in recent years, with state-of-the-art methods outperforming humans on all benchmarks. Many researchers would say that face verification is close to being a solved problem. We argue that evaluation datasets are not challenging enough, and that there is still significant room for improvement in automated face verification techniques. This paper introduces the DoppelVer dataset, a challenging face verification dataset consisting of doppelganger pairs. Doppelgangers are pairs of individuals that are extremely visually similar, oftentimes mistaken for one another. With this dataset, we introduce two challenging protocols: doppelganger and Visual Similarity from Embeddings (ViSE). The doppelganger protocol utilizes doppelganger pairs as negative verification samples. The ViSE protocol selects negative pairs by isolating image samples that are very close together in a particular embedding space. In order to demonstrate the challenge that the DoppelVer dataset poses, we evaluate a state-of-the-art face verification method on the dataset. Our experiments demonstrate that the DoppelVer dataset is significantly more challenging than its predecessors, indicating that there is still room for improvement in face verification technology.
Visualizing Multimodal Time Series at Scale

Today digital recording technology empowers us to understand real-world behaviors with high quality and high definition multimodal time series data. Making the presentation of these time series fit for analysis purpose, at the right scale and resolution, has become a leading data visualization challenge. In this paper, we present TimeXplore, a novel visual analysis tool to aid the exploration of time series at scale. TimeXplore allows one to query and navigate large volumes of time series and their aggregates in near real time, with a simple yet powerful interface. The visualization synchronized across modalities can provide still further capability for us to develop and verify our hypothesis in multimodal data analysis.
Local and Global Context Reasoning for Spatio-Temporal Action Localization

Localizing persons and recognizing their actions from videos is an essential task in video understanding. Recent advances have been made by reasoning the relationships between the actor and another actor, as well as between the actor and the environment. However, reasoning the relationships globally over the image is not always the efficient way, and there are cases that locally searching for the relative clues is more suitable. In this paper, we move one step further and model the relationship between an actor and the actor’s relevant surrounding context. We developed a pipeline that observes over the full image to collect context information globally and around the actor to collect context information locally. This is achieved by implementing a Near-Actor Relation Network (NARN) that focuses on reasoning the context information locally. Two key components of our NARN enable the effective accumulation of the local context information: pose encoding, which encodes the human pose information as an additional feature, and spatial attention, which discriminates the relative context information from the others. Our pipeline accumulates the global and local relation information and gathers them for the final action classification. Experimental results on the JHMDB21 and AVA datasets demonstate that our proposed pipeline outperforms a baseline approach that only reasons about the global context. Visualization of the learned attention map indicates that our pipeline is able to focus on spatial areas that contains relative context information for each action.
Self-supervised Representation Learning for Fine Grained Human Hand Action Recognition in Industrial Assembly Lines

Humans are still indispensable on industrial assembly lines, but in the event of an error, they need support from intelligent systems. In addition to the objects to be observed, it is equally important to understand the fine-grained hand movements of a human to be able to track the entire process. However, these deep learning based hand action recognition methods are very label intensive, which cannot be offered by all industrial companies due to the associated costs. This work therefore presents a self-supervised learning approach for industrial assembly processes that allows a spatio-temporal transformer architecture to be pre-trained on a variety of information from real-world video footage of daily life. Subsequently, this deep learning model is adapted to the industrial assembly task at hand using only a few labels. It is shown which known real-world datasets are best suited for representation learning of these hand actions in a regression task, and to what extent they optimize the subsequent supervised trained classification task.
Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles

Improving resolution of sugarcane crop images is crucial for extracting valuable information related to productivity, diseases, and water stress. With the rise of remote sensing technologies like Unmanned Aerial Vehicles (UAVs), the number of images available has grown exponentially. In this study, we aim to enhance image resolution using deep learning techniques, namely MuLUT, LeRF, and Real-ESRGAN, to optimize extraction of sugarcane agronomic characteristics. Although these models were initially designed for landscapes, people, cars, and anime images, our experiments with agricultural images show promising results, outperforming classic upsampling algorithms by an impressive 482.81%. Visually, the image quality improvement is significant, making our approach an attractive alternative for extracting crucial information about the crop. This research has the potential to revolutionize the analysis of sugarcane crops, opening new possibilities for precision agriculture and improved agricultural decision-making.
A Comparative Analysis of Information Systems Audit and Digital Forensics Processes

This paper provides a comparison of two essential cybersecurity disciplines: Information Systems Audit (ISA) and Digital Forensics (DF) by drawing on a thorough examination of the literature and empirical studies. The security and integrity of digital systems are critical in today's connected and data-driven world. Both ISA and DF play a critical role in protecting digital assets, detecting incidents, and facilitating an effective incident response. This study examines the methodology, goals, tools, and strategies used in these two fields, emphasizing their parallels, contrasts. Organizations can improve their cybersecurity strategy and strengthen their defenses against evolving cyber threats by understanding the unique yet interconnected nature of ISA and DF. The rapid spread of cyber threats and the potential impact of cyber incidents on organizations underscore the critical importance of comprehensive and effective cyber security measures. Therefore, this paper seeks to make a significant contribution to the topic of cybersecurity by providing an in-depth comparative analysis of two critical areas. The importance of this paper lies in exploring how these disparate but interconnected areas could work together to strengthen an organization's cybersecurity posture. Additionally, it highlights the relative contributions of ISA and DF to protecting digital assets, identifying and responding to incidents, and maintaining regulatory compliance by looking at their approaches, goals, tools, and techniques. Moreover, it will highlight the potential synergies between the two areas and provide insights into how organizations can leverage the strengths of ISA and DF to improve their overall cybersecurity resilience. Organizations can optimize their strategies, streamline procedures, and develop a more coherent and proactive approach to cybersecurity by acknowledging the symbiotic relationship between ISA and DF.
Probabilistic Local Equivalence Certification for Robustness Evaluation

To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. For a given function $$f$$ , probabilistic local equivalence evaluates whether, when sampling a normally-distributed point $$x'$$ in a neighborhood of a point $$x$$ , there is a probability $$> 0.5$$ that $$f(x')$$ is equivalent to $$f(x)$$ , according to a user-defined notion of equivalence. We use probabilistic local equivalence to evaluate the effect of data augmentation methods for improving robustness, including adversarial training, on a model’s performance. We also use probabilistic local equivalence to evaluate the effect on robustness of model architecture, number of parameters, pre-training, quantization, and other model properties.
Spiro: Order-Preserving Visualization in High Performance Computing Monitoring

As High-Performance Computing (HPC) is moving closer to entering the exascale era, monitoring HPC systems is becoming increasingly important and continuously more complex. The innovations in interactive visualization provide tremendous assistance in presenting complex HPC data to be more comprehensible and promote the roles of humans in the analysis process. With the larger number of system components in the monitoring tasks, representing them in order requires a scalable mechanism for handling multiple visual elements and simultaneously preserving the context of ordering. In this paper, we propose a novel method for utilizing the spiral layout for order-preserving visualization in HPC monitoring, called Spiro. To demonstrate the effectiveness and usefulness of Spiro, we present the case studies of the application to a real-world temporal, multivariate HPC dataset.
Extras and Premiums: Local PCN Routing with Redundancy and Fees

Payment channel networks (PCNs) are a promising solution to the blockchain scalability problem. In PCNs, a sender can route a multi-hop payment to a receiver via intermediaries. Yet, Lightning, the only prominent payment channel network, has two major issues when it comes to multi-hop payments. First, the sender decides on the path without being able to take local capacity restrictions into account. Second, due to the atomicity of payments, any failure in the path causes a failure of the complete payment. In this work, we propose Forward-Update-Finalize (FUFi): The sender adds redundancy to a locally routed payment by initially committing to sending a higher amount than the actual payment value. Intermediaries decide on how to forward a received payment, potentially splitting it between multiple paths. If they cannot forward the total payment value, they may reduce the amount they forward. If paths for sufficient funds are found, the receiver and sender jointly select the paths and amounts that will actually be paid. Payment commitments are updated accordingly and fulfilled. In order to guarantee atomicity and correctness of the payment value, we use a modified Hashed Time Lock Contract (HTLC) for paying that requires both the sender and the receiver to provide a secret preimage. FUFi furthermore is the first local routing protocol to include fees and specify a fee policy to intermediaries on how to determine their fair share of fees. We prove that the proposed protocol achieves all key security properties of multi-hop payments. Furthermore, our evaluation on both synthetic and real-world Lightning topologies shows FUFi outperforms existing algorithms in terms of fraction of successful payments by about 10%.
AI Based Online Education by using Smart Infrastructure

The main objective of the online examination system is to provide equal opportunity to every student who is not going to the exam due to the lack of geographical areas. If you are regularly watching TV in India, you show one advertisement about online education to remove geographical barriers. In the Ad, one girl is leaving in the mountain region and can’t go to the learning institute due to the lack of infrastructure (buses or transport). So, with the help of mobile phones and the internet, the girl educates herself and prepares for her future. However, Online examinations use the same for the assessment of students’ yearly learning. As a result, candidates from remote locations can participate in examinations without physically being present, enhancing inclusion and facilitating access to education. In this project, we are going to implement unique question-answer types like, Fill in the blanks, String Matching, Ordering/Sequence, etc. in online examinations with Restrict Users to Single Sessions to prevent malpractice..
Trapped-ion quantum computing with integrated photonics

Quantinuum has developed a trapped-ion quantum computer based on the QCCD architecture which exhibits high-delity operations, mid-circuit measurements and full connectivity. This talk will introduce the QCCD architecture and discuss how we can address scaling challenges with integrated photonics for visible light to facilitate large-scale quantum computing.
Optics-informed deep learning over silicon photonic hardware

Photonic Neural Networks (PNNs) implemented on silicon photonic (SiPho) platforms stand out as a promising candidate to endow neural network hardware, offering the potential for energy efficient and ultra-fast computations through exploiting the unique primitives of light i.e., THz bandwidth, low-power and low-latency. In this paper, we review the state-of-the-art photonic linear processors discuss their challenges and propose solutions for future photonic-assisted machine learning engines. Additionally, we will present experimental results on the recently introduced SiPho 4x4 coherent crossbar (Xbar) architecture, that migrates from existing Singular Value Decomposition (SVD)-based schemes while offering single time-step programming complexity. The Xbar architecture utilizes silicon germanium (SiGe) Electro-Absorption Modulators (EAMs) as its computing cells and Thermo-Optic (TO) Phase Shifters (PS) for providing the sign information at every weight matrix node. Towards experimentally evaluating our Xbar architecture, we performed 10,024 arbitrary linear transformations over the SiPho processor, with the respective fidelity values converging to 100%. Followingly, we focus on the execution of the non-linear part of the NN by demonstrating a programmable analog optoelectronic circuit that can be configured to provide a plethora of non-linear activation functions, including tanh, sigmoid, ReLU and inverted ReLU at 2 GHz update rate. Finally, we provide a holistic overview on optics-informed neural networks towards improving the classification accuracy and performance of optics-specific Deep Learning (DL) computational tasks by leveraging the synergy of optical physics and DL.
Efficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation

In the era of big data, it is essential to securely and efficiently combine the large amounts of private data owned by different companies or organizations to make correct decisions. Secure Multiparty Computation (SMPC) works as a general cryptographic primitive, which enables distributed parties to collaboratively compute an arbitrary functionality without revealing their own private inputs. While SMPC may potentially address this task, several issues, such as computation efficiency and correctness validation, have to be overcome for practical realizations. To tackle these issues, we designed a secure and efficient decision-making scheme to enable clients to outsource data and computations to cloud servers while ensuring the integrity and confidentiality of the input and output, in addition to the correctness of the results. Moreover, we implemented our scheme based on an SMPC computation framework named MP-SPDZ. The experimental evaluation results showed that our proposed scheme is feasible and efficient for practical realizations.
FMDL: Federated Mutual Distillation Learning for Defending Backdoor Attacks

Federated learning is a distributed machine learning algorithm that enables collaborative training among multiple clients without sharing sensitive information. Unlike centralized learning, it emphasizes the distinctive benefits of safeguarding data privacy. However, two challenging issues, namely heterogeneity and backdoor attacks, pose severe challenges to standardizing federated learning algorithms. Data heterogeneity affects model accuracy, target heterogeneity fragments model applicability, and model heterogeneity compromises model individuality. Backdoor attacks inject trigger patterns into data to deceive the model during training, thereby undermining the performance of federated learning. In this work, we propose an advanced federated learning paradigm called Federated Mutual Distillation Learning (FMDL). FMDL allows clients to collaboratively train a global model while independently training their private models, subject to server requirements. Continuous bidirectional knowledge transfer is performed between local models and private models to achieve model personalization. FMDL utilizes the technique of attention distillation, conducting mutual distillation during the local update phase and fine-tuning on clean data subsets to effectively erase the backdoor triggers. Our experiments demonstrate that FMDL benefits clients from different data, tasks, and models, effectively defends against six types of backdoor attacks, and validates the effectiveness and efficiency of our proposed approach.