Reviewerly/exHarmony huggingface dataset:
A Near-Duplicate Video Cleaning Method Based on AFENet Adaptive Clustering

In recent years, with the growth of video data, problems such as video quality degradation and copyright protection difficulties have arisen due to the redundancy of a large number of near-duplicate video data. Aiming at the problems of insufficient video feature extraction and low accuracy of clustering algorithms in existing near-duplicate video cleaning algorithms. This paper proposes a near-duplicate video cleaning method based on AFENet adaptive clustering. The method in this paper mainly includes two parts: Firstly, in the feature extraction stage, this paper adopts the spatiotemporal features of video and enhances the features of salient objects in each frame of image, enhancing the feature expression ability of video; Secondly, in the video cleaning stage, an adaptive feature distance threshold SFD-Means clustering algorithm is proposed, and a clustering optimization function is designed to overcome the defect of the original FD-Means algorithm that is too sensitive to threshold selection. Experimental results show that our proposed method has high recognition accuracy and cleaning performance, effectively improving the quality of video data.
Low light scene enhancement for object detection and tracking using multiscale decomposition and fusion

Object tracking in low light condition for visual camera in CCTV, Surveillance and tracking applications is really challenging task. Many methods are proposed which has limited success of augmenting the scene for human vision, but not very effective for both human and machine vision. In this paper, we have proposed a way for improving the video’s picture frames using a fusion approach, then applying a tracking methodology to demonstrate how the improved segmentation and tracking are both made possible by the enhancement. After comparison with other state-of-art techniques that improve lowlight images, our proposed methodology performs better both subjectively and objectively.
Few-Shot Segmentation via Channel Attention and Supervision Augmentation

The effect of the semantic segmentation methods has been significantly improved by introducing deep convolutional neural networks (CNNs) in recent years. But, these approaches need quantities of data with pixel-level labels and have poor generalizability. Few-shot segmentation (FSS) is subsequently proposed, which can complete the segmentation of unseen classes with only a small amount of annotated data. However, existing approaches have the following two problems. First, they ignored the connection between feature channels of support features and query features. Second, they did not fully mine the information in the support set. These two problems harm the segmentation performance. In this study, we design a new FSS approach to alleviate the above two problems. Our model consists of two parts: the channel attention module is designed to fully mine the connection of the feature channels of support and query set, and the supervision augmentation module is designed to fully utilize the information in the support set and strengthen the supervision of the model. Extensive experiments verify that our model achieves 0.9% and 1.3% mIoU (mean intersection over union) improvements over the baseline on the PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset for 1-shot and 5-shot tasks, respectively, and our approach is more lightweight than other state-of-the-art (SOTA) approaches.
Insulator Location and Segmentation Method Based on Improved S2ANet

Insulator is one of the important components of power system, which greatly affects the safe operation of transmission line. Aiming at the problem that insulator in aerial images has arbitrary rotation angle which leads to inaccurate location of insulator region and contains redundant background information, S2ANet model based on PAN is proposed to locate insulator region in aerial images. Firstly, rotation, cutting and noise were used to enhance the insulator data set and expand the number of samples. Secondly, S2ANet model based on PAN is used to locate the insulator and enhance the feature extraction ability of insulator edge. Finally, the insulator region is segmented accurately based on the image outspread clipping method. Experiments show that AP of improved S2ANEet model on the test data set is 92.6%, and outspread clipping method can completely segment the insulator region. The method proposed for insulator location and segmentation has good engineering application value.
A Roadmap to ISMS ISO 27001 Implementation Process

Due to advancement in ICT systems their usage has increased manifold now a days but at the same time these systems cyber-attacks have also increased at the same pace. In order to safeguard against such threats Information Security Management Systems are being implemented. These ISMS are certified to some standards in order to provide assurance to the customers about effectiveness of information security implementation. Present work outlines a roadmap to implementation of such a standard called ISO 27001. It may be helpful to the information owners in order to implement such standard in their organizations.
An Efficient Customized Blockchain System for Inter-Organizational Processes

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently.
Semantic Segmentation of Urban Area using Pix2Pix Generative Adversarial Networks

Land cover analysis using satellite images is essential for understanding how urbanization has affected a particular geographical area. In this paper we present semantic segmentation of Urban Areas using Pix2Pix Generative Adversarial Network (GAN). The GAN architecture proposed in this paper uses modified UNet as a generator and PatchGAN as a discriminator. We have trained Pix2Pix GAN which is trained using a set of two images (input and target) as input to generate the images which look like the target images. The dataset is generated using images taken from Google Earth. Comparison is made among different approaches namely Autoencoder, UNet and our Pix2Pix GAN. The results are evaluated and compared using performance metrics like Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The experimental results indicate that the proposed model outperforms the other approaches giving a PSNR of 48.26 dB and SSIM of 0.97. The performance evaluation and visual analysis both conclude that proposed approach is robust and accurate for semantic segmentation of urban area.
Graph Attention Network Text Classification Model Fused with Label Embeddings

Most of the existing text classification methods focus on improving the feature expression of the text, and only use the label in the final classification, while ignoring the semantic information of the label itself. Existing research uses the attention mechanism to calculate the correlation between text and labels through text vectors and label feature vectors, but the attention mechanism is easily disturbed by the noise generated by similar label semantic information, and cannot focus on more relevant labels characteristic words. To address the above issues, this paper proposes a graph attention network text classification model fused with label embeddings. By extracting text feature words and tags to construct a new feature word-label graph and send it to the graph attention network for joint learning to reduce the matching error between tags and feature words. Graph attention networks can better focus on feature nodes that are more relevant to classification tasks to extract richer semantic information. Experimental results on five text classification datasets show that the proposed model achieves better classification performance than other baseline models.
TKGF-NTP: Temporal Knowledge Graph Forecasting via Neural Temporal Point Process

Knowledge graphs (KGs) with real-world facts are vital for various downstream applications. However, the incomplete nature of KGs has brought lots of problems to them, and probing missing facts via reasoning or forecasting has become a promising solution. Different from the traditional KG reasoning focusing on static facts, temporal knowledge graph (TKG) forecasting incorporating time information presents more potential in event prediction, as many facts are dynamic in real-world. Despite the significance of TKG forecasting, the following inevitable problems bring great challenges for it. (1) How to alleviate the problem of temporal fact redundancy in the TKG? (2) How to merge the useful fragmented temporal facts for the given query throughout the TKG? To overcome these problems effectively, we propose a novel model entitled TKGF-NTP, which consists of two components. (1) A structural encoder is developed to aggregate the most valuable structural information and filter out the redundant temporal facts from each TKG snapshot. (2) A temporal encoder is designed to capture the evolutions of entities, while the self-attention mechanism is employed to capture the most crucial temporal information throughout the TKG. The effectiveness of TKGF-NTP is evaluated on four public datasets via link prediction, and the results demonstrate its superiority over the state-of-the-art methods.
Detection of X-ray prohibited items based on Improved YOLOv7

In order to improve the detection ability of the algorithm for overlapping objects and small targets in the X-ray prohibited items image, we propose an X-ray prohibited items detection method based on improved YOLOV7. Firstly, we introduce Bottleneck Transformers to enable networks to locate accurately in high-density scenarios. In order to further improve the effects of self -attention in Bottleneck Transformers, while reducing the calculation amount, using Hydra Attention to replace the multi-head attention of the original Bottleneck Transformers. Secondly, the convolution module is integrated in Hydra Attention to enhance the local details feature. The experiment shows that the detection accuracy of the improved YOLOv7 is increased from 90.6% to 95.2%. Compared with some of the mainstream algorithm, our algorithm has certain advantages.
Arbitrary-oriented Ship Detction Based on R<sup>2</sup>CNN

To solve the problems of arbitrary orientation, large aspect ratio and dense distribution for ship detection in SAR images, an arbitrary-oriented ship detection method based on rotational region convolutional neural network (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CNN) is proposed. Firstly, the region proposal network (RPN) is used to generate arbitrary-oriented bounding boxes. Secondly, the convolutional feature maps are input into several region of interest pooling layers (RoIPoolings) with different pool sizes and the pooled features are linked for predicting the ship/non-ship scores and bounding boxes. Finally, the detection results are got by inclined non-maximum suppression (NMS). The experimental results show that the proposed method can effectively detect arbitrary-oriented ships and is superior to other arbitrary-oriented object detection methods.
A Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift

The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users’ real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user’s historical interaction and the user’s new interest very weak. Meanwhile, the information contained in user’s sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user’s new interest from neighbors’ sessions. Then, we integrate the relevance information into user’s history sessions, which can effectively enhance the correlation between user’s history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users’ sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/LucasZen/paper_code.
TBPCS: Trustworthy Cross Department Business Process Collaboration Service Based on Blockchain

Addressing untrustworthy behavior in cross departmental business process collaboration is the focus of current research. The untrustworthiness of the business process not only leads to the misuse and leakage of business data, but also leads to cheating by the participants in the business process. This paper proposes a blockchain-based trustworthy cross-departmental business process collaboration service mechanism(TBPCS) to solve the above problems. This paper firstly maps the participants, data and data ownership of the business process to the blockchain, which prevents the business process from being tampered with. This method allows the participants in the business process to act under the constraints of a trusted environment, avoiding the illegal use of business data. Then this paper maps the business process to the blockchain in the form of smart contracts, which ensures crossdepartment business process collaboration is trustworthy and reduces the verification cost of business data. This paper innovatively proposes a rollback mechanism that supports business interruptions. The mechanism ensures that the business process is trustworthy even in abnormal situations.
Framework for e-Readiness Analysis of the Electronic Signatures Implementation in Local Government: A Case Study of Magelang City Government

The use of electronic signatures (e-sign) to replace manual signatures on government service documents plays an important role in supporting digital transformation in government. The Magelang City Government has collaborated with the Electronic Certification Agency to utilize e-sign in its e-government services. However, the implementation of e-sign has not met the expected level due to the 28 agencies, only 4 agencies are implementing e-sign into their services. The absence of a framework as a guideline for the Magelang City government to measure readiness has hindered the identification of the low adoption issues of e-signatures. Research related to the implementation of e-sign by previous researchers has mostly analyzed e-sign acceptance from user perceptions and has not measured organizational readiness in implementing e-sign. Therefore, this study aims to develop a framework that can be used to analyze the e-readiness of e-signature implementation by modifying the STOPE (Strategy Technology organization People Environment) framework and considering the applicable regulations in the local government. The framework will be tested to measure the readiness for e-signature implementation through the collection of questionnaire data. The results of the study show that the proposed framework can be used to analyze the e-readiness of e-sign implementation. Based on the ranking results, it is known that the Magelang City Government is ready to implement e-sign. However, there are still problems that hinder the implementation of e-sign related to e-leadership, future development plans, ICT support, ICT regulations, ICT awareness, ICT qualifications and jobs, and economy.
A Multicore GNN Training Accelerator

Graph neural networks (GNN) are vital for analytics on real-world problems with graph models. This work develops a multicore GNN training accelerator and develops multicore-specific optimizations for superior performance. It uses enhanced multicore-specific dynamic caching to circumvent the costs of irregular DRAM access patterns of graph-structured data. A novel feature vector segmentation approach is used to maximize on-chip data reuse with high on-chip computation per memory access, reducing data access latency, using a machine learning model for optimal performance. The work presents a major advance over prior FPGA/ASIC GNN accelerators by handling significantly larger datasets (with up to 8.6M vertices) on a variety of GNN models. On average, training speedup of 17× and energy efficiency improvement of 322× is achieved over DGL on a GPU; a speedup of 14× with 268× lower energy is shown over GPU-based GNNAdvisor; and 11× and 24× speedups are obtained over ASIC-based Rubik and FPGA-based GraphACT.
TensorCV: Accelerating Inference-Adjacent Computation Using Tensor Processors

The advancements in AI/ML accelerators have made the core AI/ML computation relatively insignificant in application pipelines. For example, inferencing only accounts for 3% of the latency in an image-based ML pipeline with the help of Tensor Cores. The mismatch in performance growth between ML model computation and ML-adjacent computation, the producer and consumer of ML models, will become the bottleneck leading to system inefficiency. This paper presents a set of innovative algorithms to allow the entire ML-based computer vision pipelines to leverage AI/ML accelerators. Our proposed algorithms feature matrix-based operations that AI/ML accelerators specialize in. Simply compiler optimizations cannot take full advantage of hardware acceleration without revisiting algorithms. This paper implements the proposed algorithms as an open-source library, TensorCV, in a system platform with Tensor Cores. TensorCV shows a 6.12 × speedup in optimized ML-adjacent functions and saves 81 % energy consumption on modern heterogeneous computers. The code is available at https://github.com/escalab/TensorCV.
Enabling DVFS Side-Channel Attacks for Neural Network Fingerprinting in Edge Inference Services

The Inference-as-a-Service (IaaS) delivery model provides users access to pre-trained deep neural networks while safeguarding network code and weights. However, IaaS is not immune to security threats, like side-channel attacks (SCAs), that exploit unintended information leakage from the physical characteristics of the target device. Exposure to such threats grows when IaaS is deployed on distributed computing nodes at the edge. This work identifies a potential vulnerability of low-power CPUs that facilitates stealing the deep neural network architecture without physical access to the hardware or interference with the execution flow. Our approach relies on a Dynamic Voltage and Frequency Scaling (DVFS) side-channel attack, which monitors the CPU frequency state during the inference stages. Specifically, we introduce a dedicated load-testing methodology that imprints distinguishable signatures of the network on the frequency traces. A machine learning classifier is then used to infer the victim architecture. Experimental results on two commercial ARM Cortex-A CPUs, the A72 and A57, demonstrate the attack can identify the target architecture from a pool of 12 convolutional neural networks with an average accuracy of 98.7% and 92.4%
Processing-in-Memory Using Optically-Addressed Phase Change Memory

Today's Deep Neural Network (DNN) inference systems contain hundreds of billions of parameters, resulting in significant latency and energy overheads during inference due to frequent data transfers between compute and memory units. Processing-in-Memory (PiM) has emerged as a viable solution to tackle this problem by avoiding the expensive data movement. PiM approaches based on electrical devices suffer from throughput and energy efficiency issues. In contrast, Optically-addressed Phase Change Memory (OPCM) operates with light and achieves much higher throughput and energy efficiency compared to its electrical counterparts. This paper introduces a system-level design that takes the OPCM programming overhead into consideration, and identifies that the programming cost dominates the DNN inference on OPCM-based PiM architectures. We explore the design space of this system and identify the most energy-efficient OPCM array size and batch size. We propose a novel thresholding and reordering technique on the weight blocks to further reduce the programming overhead. Combining these optimizations, our approach achieves up to 65.2 × higher throughput than existing photonic accelerators for practical DNN workloads.
Energy-Efficient Missing Data Recovery in Wearable Devices: A Novel Search-Based Approach

Wearable and internet of things (IoT) devices are transforming a number of high-impact applications. Machine learning (ML) algorithms on wearable devices assume that data from all sensors is available at runtime. However, one or more sensors may be unavailable at runtime due to malfunction, energy constraints or communication challenges. Loss of sensor data can potentially lead to severe degradation in application accuracy and quality of service. Commonly employed generative ML methods to recover missing data are not suitable for resource-constrained wearables because they incur significant memory, execution time, and energy overhead at runtime. In contrast to prior methods, this paper presents a novel search-based accuracy-preserving imputation (AIM) algorithm that obtains most likely imputation patterns of sensor data for each missing data scenario via offline analytics. Specifically, for each missing data condition, we store the most likely recovery patterns which preserve ML classifier-based application accuracy in a look up table and use it appropriately at runtime. The key insight behind AIM is that we do not need exact recovery of the missing data as long as the ML classifier-based application accuracy (e.g., health assessment) is preserved. To further improve the overall effectiveness of AIM, we train the ML classifiers to be robust to small errors in data recovery. Experiments on four diverse wearable sensor based time-series benchmarks demonstrate that AIM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing, and improves the overall accuracy by 15% compared to a state-of-the-art baseline. AIM achieves this improvement with negligible energy consumption overhead.