Reviewerly/exHarmony huggingface dataset:
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance and energy boost of dedicated accelerators. We present DARKSIDE, a System-on-Chip with a heterogeneous cluster of 8 RISC-V cores enhanced with 2-b to 32-b mixed-precision integer arithmetic. To boost performance and efficiency on key compute-intensive Deep Neural Network (DNN) kernels, the cluster is enriched with three digital accelerators: a specialized engine for low-data-reuse depthwise convolution kernels (up to 30 MAC/cycle); a minimal overhead datamover to marshal 1-b to 32-b data on-the-fly; a 16-b floating point Tensor Product Engine (TPE) for tiled matrix-multiplication acceleration. DARKSIDE is implemented in 65nm CMOS technology. The cluster achieves a peak integer performance of 65 GOPS and a peak efficiency of 835 GOPS/W when working on 2-b integer DNN kernels. When targeting floating-point tensor operations, the TPE provides up to 18.2 GFLOPS of performance or 300 GFLOPS/W of efficiency - enough to enable on-chip floating-point training at competitive speed coupled with ultra-low power quantized inference.
Swarm Intelligence Model for Securing Healthcare Ecosystem

The healthcare sector is constantly facing challenges in ensuring security due to the sophisticated attacks by the threat actor for acquiring sensitive patient data. An attack on the system can pose any potential risk to the business continuity. The increased use of information technology in the modern healthcare system makes medical devices and systems more vulnerable to exploitation and possible cyber-security attacks. This paper proposes a flexible and decentralized cyber-security model based on the integration of multi-agent systems and swarm intelligence for tackling the propagation of attacks inside interconnected healthcare organizations and ensuring the whole healthcare ecosystem's security and resilience. The proposed model is based on the collaboration between the agents with different functions and cognitive capabilities, named primary and supervisor agents. Primary agents are lightweight BDI (Belief-Desire-Intention) agents implementing a minimum set of capabilities for monitoring a specific area of the healthcare system; supervisor agents incorporate an extended version of the BDI reasoning to provide advanced capabilities for securing the overall healthcare system by enabling collective intelligence and overall cyber-security awareness. The preliminary experimental results show that the model is robust and responsive for securing the ecosystem.
CSRS

Developers often search and reuse existing code snippets in the process of software development. Code search aims to retrieve relevant code snippets from a codebase according to natural language queries entered by the developer. Up to now, researchers have already proposed information retrieval (IR) based methods and deep learning (DL) based methods. The IR-based methods focus on keyword matching, that is to rank codes by relevance between queries and code snippets, while DL-based methods focus on capturing the semantic correlations. However, the existing methods do not consider capturing two matching signals simultaneously. Therefore, in this paper, we propose CSRS, a code search model with relevance matching and semantic matching. CSRS comprises (1) an embedding module containing convolution kernels of different sizes which can extract n-gram embeddings of queries and codes, (2) a relevance matching module that measures lexical matching signals, and (3) a co-attention based semantic matching module to capture the semantic correlation. We train and evaluate CSRS on a dataset with 18.22M and 10k code snippets. The experimental results demonstrate that CSRS achieves an MRR of 0.614, which outperforms two state-of-the-art models DeepCS and CARLCS-CNN by 33.77% and 18.53% respectively. In addition, we also conducted several experiments to prove the effectiveness of each component of CSRS.
An Efficient Approach of Vehicle Detection Based on Deep Learning Algorithms and Wireless Sensors Networks

Machine learning is applied to analyze and classify automatically images. Artificial intelligence (AI) is considered very successful in this area. Therefore, AI is exploited to evaluate the opportunities of big data and to extract value from massive and varied data sources. In order to detect any event (person, vehicle, dog, eyes, traffic, terrorist activity), ML is explored. Hence, advanced ML techniques recur to multimedia wireless sensor networks (MWSN) to detect any event in the considered area. In this work, the authors propose an enhanced architecture MWSN, which is able fly any event detection. In this context, this paper addresses the problem of vehicle detection using convolutional neural networks using a proposed architecture MWSN. Therefore, to reach this goal, the authors assess the performance of three state-of-the-art CNN algorithms, namely faster R-CNN, which is the most popular region-based algorithm; YOLO, which is known to be the fastest one; and SSD, which takes one single shot to detect multiple objects within the image.
A Semantic-Aware Transmission With Adaptive Control Scheme for Volumetric Video Service

Volumetric video provides a more immersive holographic virtual experience than conventional video services such as 360-degree and virtual reality (VR) videos. However, due to ultra-high bandwidth requirements, existing compression and transmission technology cannot handle the delivery of real-time volumetric video. Unlike traditional compression methods and the approaches that extend 360-degree video streaming, we propose AITransfer, an AI-powered compression and semantic-aware transmission method for point cloud video data (a popular volumetric data format). AITransfer targets the semantic-level communication beyond transmitting raw point cloud video or compressed video with two outstanding contributions: (1) designing an integrated end-to-end architecture with two fundamental contents of feature extraction and reconstruction to reduce the bandwidth consumption and alleviate the computational pressure; and (2) incorporating the dynamic network condition into end-to-end architecture design and employing a deep reinforcement learning-based adaptive control scheme to provide robust transmission. We conduct extensive experiments on the typical datasets and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide extremely efficient point cloud transmission while maintaining considerable user experience with more than 30.72x compression ratio under the existing network environments.
F-PVNet: Frustum-Level 3-D Object Detection on Point–Voxel Feature Representation for Autonomous Driving

Current 3-D object detection technology for autonomous driving usually cannot efficiently utilize local sensitive points. Meanwhile, contextual feature extracted from a object is not sufficient, which easily leads to deteriorated detection accuracy of the final object estimation. For the problems, a point–voxel-based 3-D dynamic object detection algorithm is proposed. First, local points are grouped with a camera frustum. Then, the global feature extracted by the submanifold 3-D voxel CNNs is aggregated into frustum key points. Second, a module of vector pool with feature aggregation is used to aggregate multiscale features of the point cloud. Moreover, the frustum raw feature and BEV feature are used for feature extension. Subsequently, the fine multiscale feature extracted from the point cloud is used as input to a subsequent fully convolutional network for final classification and continuous estimation of oriented 3-D boxes. The proposed method was compared with other state-of-the-art algorithms on the KITTI, Waymo, and nuScenes data sets. Experimental results showed that the proposed algorithm was better in accuracy, robustness, and generalization capabilities in 3-D dynamic object detection. Experiments on a real scenario and extensive ablation studies also demonstrated that the proposed algorithm not only effectively controls computational cost but also achieved more efficient results in 3-D object detection.
Library of Fully Homomorphic Encryption on a Microcontroller

Fully homomorphic encryption technologies allow you to operate on encrypted data without disclosing it, therefore they have a lot of potential for solving personal data storage and processing issues. Because of the increased interest in these technologies, various software tools and libraries that allow completely homomorphic encryption have emerged. However, because this subject of cryptography is still in its early stages, standards and recommendations for the usage of completely homomorphic encryption algorithms are still being developed. The paper presents the main areas of application of homomorphic encryption. The analysis of existing developments in the field of homomorphic encryption is carried out. The analysis showed that existing library implementations do not support the division and subtraction operation. The analysis revealed the need to develop a library of fully homomorphic encryption, which allows performing all mathematical operations on them (addition, difference, multiplication and division), as well as the relevance of developing its own implementation of a library of homomorphic encryption on integers. Then, implement the development of a fully homomorphic encryption library in C++ and on an ESP 32 microcontroller. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the scope of application of homomorphic encryption. A method of homomorphic division and subtraction is proposed that allows performing the division and subtraction operation on homomorphically encrypted data. The level of security, the types of operations executed, the maximum length of operands, and the algorithm's running time are all described as a consequence of numerical experimentation with parameters.
A Strong Hybrid S-Box Scheme Based on Chaos, 2D Cellular Automata and Algebraic Structure

Substitution-boxes are the main deciding components in symmetric-key cryptosystems for resisting many cryptanalytic attacks.It has been a challenging task for the designers to construct strong Sbox which satisfies multiple cryptographic properties simultaneously.A number of S-box studies have been investigated in literature; but, the generated S-box found to exhibit one single property with good score.This paper proposes a novel creation of S-boxes which possess excellent scores of multiple cryptographic properties instead of only one property.The suggested hybrid S-box method explores the science of twodimensional cellular automata theory, discrete chaotic maps, and algebraic group structure.The proposed anticipated 8×8 S-box holds excellent security performance features such as: minimum nonlinearity as high as 110, no fixed points, satisfaction of strict avalanche and bits independence criterions, differential uniformity as low as 6, linear approximation probability as low as 0.0703, and auto-correlation function (absolute indicator) of 40.The performance comparison indicates the proposed S-box has superior features, greater inherent security and robustness strength than many available state of the art S-box methods.
Transformer-Based Domain-Specific Representation for Unsupervised Domain Adaptive Vehicle Re-Identification

Fully-supervised vehicle re-identification (re-ID) methods are faced with performance degradation when applied to new image domains. Therefore, developing unsupervised domain adaptation (UDA) to transfer the knowledge from learned source domain to new unlabeled target domain becomes an indispensable task. It is challenging because different domains have various image appearances, such as different backgrounds, illuminations and resolutions, especially when cameras have different viewpoints. To tackle this domain gap issue, a novel Transformer-based Domain-Specific Representation learning network (TDSR) is proposed to dynamically focus on corresponding detailed hints for each domain. Specifically, with the source and target domain being trained simultaneously, a domain encoding module is proposed to introduce domain information into the network. The original features of source and target domains are enriched with these domain encodings first, and then sequentially processed by a Transformer encoder to model contextual information and a decoder to summarize the encoded features into the final domain-specific feature representations. Moreover, we propose a Contrastive Clustering Loss (CCL) to directly optimize the distribution of features at cluster level. Instances are overall pulled closer to the prototype of the same identity, and pushed farther from the prototypes of different identities. It helps compact the clusters in the latent space and improve the discriminative capability of the network, leading to more accurate pseudo-label assignment in TDSR. Our method outperforms the state-of-the-art UDA methods on vehicle re-ID benchmark datasets VeRi and VehicleID on both real-world to real-world and synthetic to real-world settings.
Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization

Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-level features to locate objects, making it prefers to discern global discriminative factors rather than regional object cues. Thus only the discriminative locations are activated when feeding pixel-level features into this classifier. To solve this issue, this paper elaborates a plug-and-play mechanism called BagCAMs to better project a well-trained classifier for the localization task without refining or re-training the baseline structure. Our BagCAMs adopts a proposed regional localizer generation (RLG) strategy to define a set of regional localizers and then derive them from a well-trained classifier. These regional localizers can be viewed as the base learner that only discerns region-wise object factors for localization tasks, and their results can be effectively weighted by our BagCAMs to form the final localization map. Experiments indicate that adopting our proposed BagCAMs can improve the performance of baseline WSOL methods to a great extent and obtains state-of-the-art performance on three WSOL benchmarks. Code are released at https://github.com/zh460045050/BagCAMs .
Web application performance analysis using Angular, React and Vue.js frameworks

The aim of the work is to conduct a performance analysis and, on its basis, to indicate the most user-friendly and fastest operating framework. Three Internet applications have been written to test the speed of operation of selected technologies. Some of the most popular frameworks were analyzed: Angular, React and Vue. Static comparative criteria used in the work are: favorable and generally available documentation, application development, speed of application development, development and support by creators. The practical criteria are the times measured during the execution of the tests. The tests were performed with simple operations using the CRUD (Create Read Update Delete) function. The performance analysis carried out in this way shows the differences between the frameworks. The following developerPersian tools were used for the comparative analysis: Google Analytics, Google Chrome, Mozilla Firefox, Chrome DevTools.
A Local Optima Network Analysis of the Feedforward Neural Architecture Space

This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural ar-chitecture space. The search space of feedforward neural network architectures with up to three layers, each with up to 10 neurons, is fully enumerated by evaluating trained model performance on a selection of data sets. Extracted LONs, while heterogeneous across data sets, all exhibit simple global structures, with single global funnels in all cases but one. These results yield early indication that LONs may provide a viable paradigm by which to analyse and optimise neural architectures.
Grounding Visual Representations with Texts for Domain Generalization

Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natural language supervision for the domain generalization task. We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) Visual and Textual Joint Embedder and (2) Textual Explanation Generator. The former learns the image-text joint embedding space where we can ground high-level class-discriminative information into the model. The latter leverages an explainable model and generates explanations justifying the rationale behind its decision. To the best of our knowledge, this is the first work to leverage the vision-and-language cross-modality approach for the domain generalization task. Our experiments with a newly created CUB-DG benchmark dataset demonstrate that cross-modality supervision can be successfully used to ground domain-invariant visual representations and improve the model generalization. Furthermore, in the large-scale DomainBed benchmark, our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets. The dataset and codes are available at https://github.com/mswzeus/GVRT.
3D-Aware Indoor Scene Synthesis with Depth Priors

Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the large diversity of room layouts and the objects inside. We argue that indoor scenes do not have a shared intrinsic structure, and hence only using 2D images cannot adequately guide the model with the 3D geometry. In this work, we fill in this gap by introducing depth as a 3D prior (Depth is essentially a 2.5D prior, but in this paper we use 3D for simplicity). Compared with other 3D data formats, depth better fits the convolution-based generation mechanism and is more easily accessible in practice. Specifically, we propose a dual-path generator, where one path is responsible for depth generation, whose intermediate features are injected into the other path as the condition for appearance rendering. Such a design eases the 3D-aware synthesis with explicit geometry information. Meanwhile, we introduce a switchable discriminator both to differentiate real v.s. fake domains and to predict the depth from a given input. In this way, the discriminator can take the spatial arrangement into account and advise the generator to learn an appropriate depth condition. Extensive experimental results suggest that our approach is capable of synthesizing indoor scenes with impressively good quality and 3D consistency, significantly outperforming state-of-the-art alternatives. (Project page can be found here .)
Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation

We address the problem of generating a 360-degree image from a single image with a narrow field of view by estimating its surroundings. Previous methods suffered from overfitting to the training resolution and deterministic generation. This paper proposes a completion method using a transformer for scene modeling and novel methods to improve the properties of a 360-degree image on the output image. Specifically, we use CompletionNets with a transformer to perform diverse completions and Adjust-mentNet to match color, stitching, and resolution with an input image, enabling inference at any resolution. To improve the properties of a 360-degree image on an output image, we also propose WS-perceptual loss and circular inference. Thorough experiments show that our method out-performs state-of-the-art (SOTA) methods both qualitatively and quantitatively. For example, compared to SOTA methods, our method completes images 16 times larger in resolution and achieves 1.7 times lower Fréchet inception distance (FID). Furthermore, we propose a pipeline that uses the completion results for lighting and background of 3DCG scenes. Our plausible background completion enables perceptually natural results in the application of inserting virtual objects with specular surfaces.
Physical-Layer Authentication Based on Hierarchical Variational Autoencoder for Industrial Internet of Things

Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers’ prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named “hierarchical variational autoencoder (HVAE)” for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers’ prior channel information even when trained on a few data in the complex environment. HVAE consists of an autoencoder (AE) module for CIR characteristics extraction and a variational AE (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distributions are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.
Recent Trends on Blockchain for Internet of Things based Applications: Open Issues and Future Trends

Today Blockchain, a decentralized, and distributed ledger technology have started taking over almost all the applications from banking, agriculture, finance, transportation, etc. Such enhancements are possible when this technology is enabled with some other technology like the Internet of Things, cloud computing, machine learning, etc. This paper does a thorough evaluation of the literature concerning blockchain enabling IoT-based applications in diverse sectors. The goal is to look at the current state of blockchain based IoT systems and applications and to demonstrate how special qualities of this technology can transform the working methods in the industry. We analysed high-quality papers published in high-ranking scientific journals over the last years to write this paper. This work presents a comprehensive classification of IoT and Blockchain enabled applications across many sectors such as Agriculture, Business, Supply Chain, Data Management, Finance, Healthcare, IoT, and Privacy, and in the end, we have added recent trends, emerging areas, and open challenges, for future research across various sectors and industries, based on a structured, systematic review and thematic content analysis of the discovered literature. We feel that our work will be of tremendous use to both academics and practitioners because we have discovered such critical components for this technology.