Reviewerly/exHarmony huggingface dataset:
Adversarial Technique Validation &amp; Defense Selection Using Attack Graph &amp; ATT&amp;CK Matrix

Today cyber adversaries utilize advanced techniques to victimize target assets. To tackle the adversaries, it is of utmost importance to understand potential techniques they may use to exploit network vulnerabilities. Attack graph has always been a crucial tool for network vulnerability analysis. However, the current state-of-the-art attack graph can not predict adversarial techniques. To overcome the gap, we utilize the MITRE ATT&CK matrix in this work and map the techniques with the attack graph node descriptions. We first formulate a comprehensive dataset from ATT&CK consisting of all the adversarial strategies, subtechniques, associated tactics, and mitigation for the enterprise network. We then capture the attack graph node descriptions and apply the term frequency-inverse document frequency (TF-IDF) algorithm to map the attack techniques with the available node descriptions. Next, we generate the cosine similarity to determine an adversary’s top methods to attack a network. We then map those techniques with the associated tactics and mitigation strategies as enumerated in the ATT&CK matrix. Finally, we illustrate the analysis using a networked system’s attack graph. This proposed method would help identify and validate adversarial techniques and guide in selecting mitigation mechanisms for security enhancement.
cGAN Model-Based Radio Frequency Interference Mitigation for Radio Astronomy Data

Radio astronomy is one of the essential branches of space sciences where astronomers explore the universe by collecting data using various tools. The radio telescope is one of the principal tools for receiving celestial objects' emissions. How-ever, radio frequency interference (RFI) detection, mitigation, and avoidance are some of the main challenges in astronomical radio data. Additionally, they are essential steps for selecting the best site to initiate the radio telescope. RFI mitigation is arduous because interference can take a wide range of forms and affects different scientific goals. The substantial challenges of handling large radio data volumes make it a good application of deep learning (DL). The research aims to mitigate the interference using a DL-based approach, specifically, conditional generative adversarial network (cGAN), because of its powerful ability to differentiate the interference and the clean data.
Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning

Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without exposing their local datasets. To facilitate QKD-FL, the architectural design and routing management framework are essential. However, effective implementation is still lacking. To this end, we propose a hierarchical architecture for QKD-FL systems in which QKD resources (i.e., wavelengths) and routing are jointly optimized for FL applications. In particular, we focus on adaptive QKD resource allocation and routing for FL workers to minimize the deployment cost of QKD nodes under various uncertainties, including security requirements. The experimental results show that the proposed architecture and the resource allocation and routing model can reduce the deployment cost by 7.72% compared to the CO-QBN algorithm.
15.5 A 100Gbps Fault-Injection Attack Resistant AES-256 Engine with 99.1-to-99.99% Error Coverage in Intel 4 CMOS

Fault-injection (FI) attacks exploit corrupted ciphertexts from cryptographic engines to extract secret keys. A single fault injected into the penultimate AES round using directed laser pulses or voltage/clock glitches corrupts 4 output bytes (Fig. 15.5.1), reducing key search space to a single guess with differential fault analysis (DFA) on 8 exploitable ciphertexts. FI countermeasures using redundant concurrent/time-interleaved computations incur 2x area/performance overheads [1], [3]. Conventional linear parity checkers [2] provide insufficient fault coverage due to the non-linear characteristics of Sbox inverse operations. FI detection-based countermeasures, employing source-specific detectors such as substrate-current sensors [4] for laser attacks and frequency-locked loops [5] to detect clock glitches, respectively are ineffective against generic FI attacks. In this paper, we present a source-agnostic FI-attack resistant AES-256 accelerator with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$111\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10,000\times$</tex> improvement in minimum-time-to-disclose (MTD) against laser and undervoltage attacks, respectively compared to an unprotected AES engine. Arithmetic and parity-based checker circuits coupled with inverse and affine logic optimizations and byte-interleaved register placement enable 99.1% fault coverage against laser raster/box-scan injections (Fig. 15.5.1). Fine-grained placement of an all-digital laser detection circuit (LDC) within the AES core provides <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$13,400\times$</tex> higher margin for raster-scan laser pulse detections. Undervoltage attacks on FI-resistant AES show a measured 99.99% fault detection coverage and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$40\text{mV}$</tex> positive slack in checker datapath to capture undervoltage faults.
Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study

Data completeness is one of the most common challenges that hinder the performance of data analytics platforms. Different studies have assessed the effect of missing values on different classification models based on a single evaluation metric, namely, accuracy. However, accuracy on its own is a misleading measure of classifier performance because it does not consider unbalanced datasets. This paper presents an experimental study that assesses the effect of incomplete datasets on the performance of five classification models. The analysis was conducted with different ratios of missing values in six datasets that vary in size, type, and balance. Moreover, for unbiased analysis, the performance of the classifiers was measured using three different metrics, namely, the Matthews correlation coefficient (MCC), the F1-score, and accuracy. The results show that the sensitivity of the supervised classifiers to missing data differs according to a set of factors. The most significant factor is the missing data pattern and ratio, followed by the imputation method, and then the type, size, and balance of the dataset. The sensitivity of the classifiers when data are missing due to the Missing Completely At Random (MCAR) pattern is less than their sensitivity when data are missing due to the Missing Not At Random (MNAR) pattern. Furthermore, using the MCC as an evaluation measure better reflects the variation in the sensitivity of the classifiers to the missing data.
Research on Intelligent Disinfection-Vehicle System Design and Its Global Path Planning

We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.
Underwater target detection algorithm based on improved YOLOv4 with SemiDSConv and FIoU loss function

Underwater target detection is an indispensable part of marine environmental engineering and a fast and accurate method of detecting underwater targets is essential. Although many target detection algorithms have achieved great accuracy in daily scenes, there are issues of low-quality images due to the complex underwater environment, which makes applying these deep learning algorithms directly to process underwater target detection tasks difficult. In this paper, we presented an algorithm for underwater target detection based on improved You Only Look Once (YOLO) v4 in response to the underwater environment. First, we developed a new convolution module and network structure. Second, a new intersection over union loss was defined to substitute the original loss function. Finally, we integrated some other useful strategies to achieve more improvement, such as adding one more prediction head to detect targets of varying sizes, integrating the channel attention into the network, utilizing K-means++ to cluster anchor box, and utilizing different activation functions. The experimental results indicate that, in comparison with YOLOv4, our proposed algorithm improved the average accuracy of the underwater dataset detection by 10.9%, achieving 91.1%, with a detection speed of 58.1 frames per second. Therefore, compared to other mainstream target detection algorithms, it is superior and feasible for applications in intricate underwater environments.
Sat2rain: Multiple Satellite Images to Rainfall Amounts Conversion By Improved GAN

This paper presents a conversion method of cloud to precipitation images based on an improved Generative Adversarial Network (GAN) using multiple satellite and radar images. Since heavy rainfall events have been yearly increasing everywhere on the earth, precipitation radar images on lands become more important to use and predict, where much denser data is observed than on-the-ground sensor data. However, the coverage of such radar sites is very limited in small regions like land and/or near the sea. On the other hand, satellite images, i.e., Himawari-8, are available globally, but no direct precipitation images, i.e., rain clouds, can be obtained. GAN is a good selection for image translation, but it is known that high edges and textures can be lost. This paper proposes ‘sat2rain’, a two-step algorithm with a new constraint of the loss function. First, multiple satellite band and topography images are input to GAN, where block-wised images from overall images are used to cover over 2500 km x 2500 km. Second, enhanced GAN-based training between satellite images and radar images is conducted. Experimental results show the effectiveness of the proposed sat2rain mesh-wise method over the previous point-wise Random Forest method in terms of high edge and texture.
On-Board Pedestrian Trajectory Prediction Using Behavioral Features

This paper presents a novel approach to pedestrian trajectory prediction for on-board camera systems, which utilizes behavioral features of pedestrians that can be inferred from visual observations. Our proposed method, called Behavior-Aware Pedestrian Trajectory Prediction (BA-PTP), processes multiple input modalities, i.e. bounding boxes, body and head orientation of pedestrians as well as their pose, with independent encoding streams. The encodings of each stream are fused using a modality attention mechanism, resulting in a final embedding that is used to predict future bounding boxes in the image.In experiments on two datasets for pedestrian behavior prediction, we demonstrate the benefit of using behavioral features for pedestrian trajectory prediction and evaluate the effectiveness of the proposed encoding strategy. Additionally, we investigate the relevance of different behavioral features on the prediction performance based on an ablation study.
Using Synthetic Data to Reduce Model Convergence Time in Federated Learning

Federated Learning (FL) is a hot new topic in collaborative training of machine learning problems. It is a privacy-preserving distributed machine learning approach, allowing multiple clients to jointly train a global model under the coordination of a central server, while keeping their sensitive data private. The problem with FL systems is that they require intense communication between the server and clients to achieve the final machine learning model. Such complexity increases with the number of clients participating and the complexity of the model sought. In this paper, we introduce synthetic data generation into FL systems with the intention of reducing the number of iterations required for model convergence. In this novel method, clients generate synthetic datasets modeling their private data. The synthetic datasets are then sent to the central server and are used to generate a cognizant initial model. Our experiments show that such conscious method for generating the initial model lowers the number of iterations by a factor of more than 4 without affecting the model accuracy. As such it enhances the overall efficiency of FL systems.
Social Network Analysis of Popular YouTube Videos via Vertical Quantitative Mining

Frequent itemset (or frequent pattern) mining is a technique used in big data mining to discover frequently occurring sets of items (such as popular co-purchased merchandise) and has numerous applications in the field of databases. Traditional frequent pattern mining algorithms only look at Boolean mining; that is, considering only the presence or absence of an item in an itemset. In this paper, we present an algorithm for mining interesting quantitative frequent patterns. Our qEclat (or Q-Eclat) algorithm extends the common Eclat algorithm to be able to vertically mine quantitative patterns. When compared with the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), our evaluation results show that qEclat mines quantitative frequent patterns faster.
Agamotto: A Performance Optimization Framework for CNN Accelerator With Row Stationary Dataflow

We propose a software/hardware co-design framework called Agamotto for the complete design automation and performance optimization of the row stationary-based CNN accelerator. We design a scalable accelerator template whose critical design parameters can be configured. Based on the hardware template, Agamotto estimates the performance of the numerous possible hardware implementations for the target FPGA device and CNN model using the latency modeling tool. It chooses the best hardware design and generates the instructions and optimal runtime variables for each target CNN layer. As a result, Agamotto can generate the best hardware design within 61.67 seconds, achieving up to 2.8x higher hardware utilization than the original accelerator. In addition, experimental results show that the performance estimation is accurate, showing only 4.8% difference against the FPGA runtime for the end-to-end CNN model execution. The accelerator implemented on the Xilinx VCU118 evaluation board achieves 402 giga operations per second (GOPS) at 200 MHz, resulting in 13 frames per second (FPS) for the end-to-end execution of VGG-16. It is flexible enough to run more complex CNN models such as ResNet-50 and DarkNet-53, achieving 29.3 FPS and 16.9 FPS, respectively.
Learning Graph Enhanced Spatial-Temporal Coherence for Video Anomaly Detection

Video Anomaly Detection (VAD) is a critical yet challenging task in the signal processing community. Since part abnormal events cannot be detected by analyzing spatial or temporal information alone, learning spatial-temporal coherence has been proven the key to effective VAD. To this end, we propose a Graph Enhanced Spatial-Temporal Attention (GESTA) to address unsupervised VAD by learning the spatial-temporal coherence of normal events. Firstly, we propose a Dynamic Graph Recurrent Neural Network (DGRNN) to extract the motion patterns. Then, we propose a Spatial-Temporal Attention Module (STAM) to better model spatial-temporal coherence by integrating the prototypical spatial and temporal information. Finally, the fused spatial-temporal features are fed into the decoder to predict future frames. In testing phase, the anomaly with irregular information will result in poor prediction results. Experiments on three benchmarks demonstrate that our GESTA performs comparably to the state-of-the-art methods, and extensive analysis proves the effectiveness of DGRNN and STAM.
Promoting the Sustainability of Blockchain in Web 3.0 and the Metaverse Through Diversified Incentive Mechanism Design

This paper explores the role of blockchains in the development of Web 3.0 and the Metaverse. The success of these technologies is dependent on the utilization of decentralized systems like blockchains, which can store and validate data on identities and reputations and facilitate the exchange of virtual assets. Full nodes, which store the entire blockchain state and validate all transactions, are essential for the decentralization and reliability of the network. However, operating a full node is resource-intensive and can be expensive. To tackle this challenge, we propose an incentive mechanism that utilizes contract-theoretic methods to economically motivate users to support the sustainability and growth of the blockchain network. Our contract design addresses the problem of information asymmetry (e.g., users' revenue-generating capabilities and efforts) between users and the blockchain network. Additionally, we recommend providing diverse incentives based on the user's revenue-generating capabilities and efforts to assist the blockchain network in funding incentives. Our experimental results demonstrate that our proposed mechanism increases the blockchain network's utility by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$48.48\%-54.52\%$</tex-math></inline-formula> and reduces the users' cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$38.46\%-62.5\%$</tex-math></inline-formula> compared with the state-of-the-art implementations such as Celo, Vipnode, and Pocket Network.
An Efficient Accelerator Based on Lightweight Deformable 3D-CNN for Video Super-Resolution

Deformable convolutional networks (DCNs) have shown outstanding potential in video super-resolution with their powerful inter-frame feature alignment. However, deploying DCNs on resource-limited devices is challenging, due to their high computational complexity and irregular memory accesses. In this work, an algorithm-hardware co-optimization framework is proposed to accelerate the DCNs on field-programmable gate array (FPGA). Firstly, at the algorithm level, an anchor-based lightweight deformable network (ALDNet) is proposed to extract spatio-temporal information from the aligned features, boosting the visual effects with low model complexity. Secondly, to reduce intensive multiplications, an innovative shift-based deformable 3D convolution is developed using low-cost bit shifts and additions, maintaining comparable reconstruction quality. Thirdly, at the hardware level, a dedicated critical processing core, together with a block-level interleaving storage scheme, is presented to avoid dynamic and irregular memory accesses caused by the deformable convolutions. Finally, an overall architecture is designed to accelerate the ALDNet and implemented on an Intel Stratix 10GX platform. Experimental results demonstrate that the proposed design can provide significantly better visual perception than other FPGA-based super-resolution implementations. Meanwhile, compared with the prior hardware accelerators, our design can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.75\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.63\times $ </tex-math></inline-formula> improvements in terms of throughput and energy efficiency, respectively.
Evaluating Action-Based Temporal Planners Performance in the RoboCup Logistics League

Due to increased demands related to flexible product configurations, frequent order changes, and tight delivery windows, there is a need for flexible production using AI methods. A way of addressing this is the use of temporal planning as it provides the ability to generate plans for complex goals while considering temporal aspects such as deadlines, concurrency, and durations. A drawback in applying such methods in dynamic environments is their high and unpredictable planning time. In this paper, we present an evaluation of the current state-of-the-art temporal planners within the RoboCup Logistics League. Among the many factors that impact automated planners applicability, the level of abstraction of the planning model is paramount. We center our study on the effect that modeling choices have on the performance of the assessed planners. Our experimental results suggest that seeking for the right level of abstraction of planning domain models allows for compromising solutions between plan quality and plan solving time.
Learning bilingual word embedding for automatic text summarization in low resource language

Studies in low-resource languages have become more challenging with the increasing volume of texts in today's digital era. Also, the lack of labeled data and text processing libraries in a language further widens the research gap between high and low-resource languages, such as English and Indonesian. This has led to the use of a transfer learning approach, which applies pre-trained models to solve similar problems, even in different languages by using bilingual or cross-lingual word embedding. Therefore, this study aims to investigate two bilingual word embedding methods, namely VecMap and BiVec, for Indonesian – English language and evaluates them for bilingual lexicon induction and text summarization tasks. The generated bilingual embedding was compared with MUSE (Multilingual Unsupervised and Supervised Embeddings) as the existing multilingual word created with the generative adversarial network method. Furthermore, the VecMap was improved by creating shared vocabulary spaces and mapping the unshared ones between languages. The result showed the embedding produced by the joint methods of BiVec, performed better in intrinsic evaluation, especially with CSLS (Cross-Domain Similarity Local Scaling) retrieval. Meanwhile, the improved VecMap outperformed the regular type by 16.6% without surpassing the BiVec evaluation score. These methods enabled model transfer between languages when applied to cross-lingual-based text summarization. Moreover, the ROUGE score outperformed classical text summarization by adding only 10% of the training dataset of the target language.