Reviewerly/exHarmony huggingface dataset:
LegoSNARK

We study the problem of building non-interactive proof systems modularly by linking small specialized "gadget" SNARKs in a lightweight manner. Our motivation is both theoretical and practical. On the theoretical side, modular SNARK designs would be flexible and reusable. Also, previous works (e.g., Geppetto) consider They have been successfully employed in previous works.(cite prev papers ). These approaches, however, tend to be ad-hoc and to reinventing the wheel. We propose to fill this gap. In practice, specialized SNARKs have the potential to be more efficient than general-purpose schemes, on which most existing works have focused. If a computation naturally presents different "components" (e.g. one arithmetic circuit and one boolean circuit), a general-purpose scheme would homogenize them to a single representation with a subsequent cost in performance. Through a modular approach one could instead exploit the nuances of a computation and choose the best gadget for each component. Our contribution is LegoSNARK, a "toolbox" (or framework) for commit-and-prove zkSNARKs (CP-SNARKs) that includes: 1) General composition tools: build new CP-SNARKs from proof gadgets for basic relationssimply. Formalize notion of cc-SNARK. 2) A "lifting" tool: a compiler to add commit-and-prove capabilities to a broad class of existing zkSNARKsefficiently. This makes them interoperable (linkable) within the same computation. For example, one QAP-based scheme can be used prove one component; another GKR-based scheme can be used to prove another. 3) A collection of succinct proof gadgets for a variety of relations. Additionally, through our framework and gadgets, we are able to obtain new succinct proof systems. Notably: -- LegoGro16, a commit-and-prove version of Groth16 zkSNARK, that operates over data committed with a classical Pedersen vector commitment, and that achieves a 5000× speedup in proving time. -- LegoUAC, a pairing-based SNARK for arithmetic circuits that has a universal, circuit-independent, CRS, and proving time linear in the number of circuit gates (vs. the recent scheme of Groth et al. (CRYPTO'18) with quadratic CRS and quasilinear proving time). -- LegoMM, a CP-SNARK for matrix multiplication that achieves optimal proving complexity.
The influence of virtual reality on design process creativity in basic design studios

With the recent advancements in digital technologies, the design studios are transformed to virtual environments that offer both to design students and instructors a broader perspective in understanding the design process. As an integral part of design process, the supporting virtual tools enhance creativity in basic design studios. This study examines the influence of immersive and non-immersive virtual design environments on design process creativity in the first year basic design studio, through observing the factors related to creativity as the flow state and motivation. Consequently, an experiment is conducted to investigate the relationships between spatial ability, flow state and motivation in immersive and non-immersive virtual design environments. Forty-two first-year undergraduate basic design students participated in the experiment. The data analysis demonstrated that the immersive virtual design environment facilitates participants’ design process creativity more than the non-immersive one. Also, the findings indicated a positive strong correlation between motivations and flow state and a positive weak correlation between spatial ability and flow state. Study results contributed to a greater understanding of implementing immersive virtual reality as a creativity supporting tool.
Smart logistics: Study of the application of blockchain technology

Nowadays, integrating new technologies into all management processes leads to strong evolution especially in Smart Logistics which forms one of the fundamental pillar of the fourth industrial revolution ‘industry 4.0’. As an integrated technology, Blockchain, which is an emerging concept, allows decentralized and immutable storage of verified data. The purpose of this study is to define the various applications of Blockchain in Smart Logistics, as well as to present concrete examples of these applications. This work was done by classifying the applications according to four clusters: Information, Transport, Finance, and Management, in addition to presenting the applications of each cluster.
Semi-Supervised Encrypted Traffic Classification With Deep Convolutional Generative Adversarial Networks

Network traffic classification serves as a building block for important tasks such as security and quality of service management. The field has been studied for a long time, with many techniques such as classical machine learning and deep learning methods currently available. However, the emergence of stronger encryption protocols has led to the rise of new challenges. One of the challenges is capturing and labeling a large amount of encrypted traffic data especially for training deep learning classifiers, as current techniques rely on deep packet inspection tools (DPI) which perform poorly on encrypted traffic. In this paper, we propose a semi-supervised learning approach using Deep Convolutional Generative Adversarial Network (DCGAN). The basic idea is to utilize the samples generated by DCGAN generators as well as unlabeled data to improve the performance of a classifier trained on a few labeled samples. Thus, alleviating the difficulties associated with large dataset collecting and labeling. To demonstrate the efficacy of our approach, we evaluated our model using a self-collected dataset of the recently established QUIC protocol as well as publicly available ISCX VPN-NonVPN dataset. Our approach is able to achieve 89% and 78% accuracy with a very small number of labeled samples (just 10% of the dataset) on both QUIC and ISCX VPN-NonVPN datasets respectively.
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning

We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a generative model-based ZSL framework. In contrast to models based on generative adversarial networks (GAN) or variational autoencoders (VAE) that require auxiliary networks to assist the training, our model consists of a single conditional generator that maps class-level semantic features and Gaussian white noise vectors accounting for instance-level latent factors to visual features, and is trained by maximum likelihood estimation. The training process is a simple yet effective alternating back-propagation process that iterates the following two steps: (i) the inferential back-propagation to infer the latent noise vector of each observed example, and (ii) the learning back-propagation to update the model parameters. We show that, with slight modifications, our model is capable of learning from incomplete visual features for ZSL. We conduct extensive comparisons with existing generative ZSL methods on five benchmarks, demonstrating the superiority of our method in not only ZSL performance but also convergence speed and computational cost. Specifically, our model outperforms the existing state-of-the-art methods by a remarkable margin up to 3.1% and 4.0% in ZSL and generalized ZSL settings, respectively.
Training high-performance and large-scale deep neural networks with full 8-bit integers

Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as "WAGEUBN" to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on the ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability.
Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model

Whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refraction-learning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.
Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
A multimodal particle swarm optimization-based approach for image segmentation

Color image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three successive stages. In the first stage, a three-dimensional histogram of pixel colors based on the RGB model is smoothened using a Gaussian filter. This process helps to eliminate unreliable and non-dominating peaks that are too close to one another in the histogram. In the next stage, the peaks representing different clusters in the histogram are identified using a multimodal particle swarm optimization algorithm. Finally, pixels are assigned to the most appropriate cluster based on Euclidean distance. Determining the number of clusters to be used is often a manual process left for a user and represents a challenge for various segmentation algorithms. The proposed method is designed to determine an appropriate number of clusters, in addition to the actual peaks, automatically. Experiments confirm that the proposed approach yields desirable results, demonstrating that it can find an appropriate set of clusters for a set of well-known benchmark images.
Developing augmented reality capabilities for industry 4.0 small enterprises: Lessons learnt from a content authoring case study

Augmented reality (AR) has been proposed as a disruptive and enabling technology within the Industry 4.0 manufacturing paradigm. The complexity of the AR content creation process results in an inability for Small Enterprise (SE) to create bespoke, flexible AR training support “in-house” and is a potential barrier to industrial adoption of AR. Presently, AR content creation requires a range of specialist knowledge (e.g. 3D modelling, interface design, programming and spatial tracking) and may involve infrastructure changes (e.g. fiducial markers, cameras) and disruption to workflow. The research reported in this paper concerns the development and deployment of an Augmented Repair Training Application (ARTA); a template-based interface to support end user (shop floor) AR content creation. The proposed methodology and implementation are discussed and evaluated in a real-world industrial case study in collaboration with a Small Enterprise (SE) in the Used and Waste Electronic and Electrical Equipment sector (UEEE/WEEE). The need for end user friendly templates is presented in the conclusion alongside further related work.
Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language

NLP (Natural Language Processing) is a technology that enables computers to understand human languages.Deep-level grammatical and semantic analysis usually uses words as the basic unit, and word segmentation is usually the primary task of NLP.In order to solve the practical problem of huge structural differences between different data modalities in a multi-modal environment and traditional machine learning methods cannot be directly applied, this paper introduces the feature extraction method of deep learning and applies the ideas of deep learning to multi-modal feature extraction.This paper proposes a multi-modal neural network.For each mode, there is a multilayer sub-neural network with an independent structure corresponding to it.It is used to convert the features in different modes to the same-modal features.In terms of word segmentation processing, in view of the problems that existing word segmentation methods can hardly guarantee long-term dependency of text semantics and long training prediction time, a hybrid network English word segmentation processing method is proposed.This method applies BI-GRU (Bidirectional Gated Recurrent Unit) to English word segmentation, and uses the CRF (Conditional Random Field) model to annotate sentences in sequence, effectively solving the long-distance dependency of text semantics, shortening network training and predicted time.Experiments show that the processing effect of this method on word segmentation is similar to that of BI-LSTM-CRF (Bidirectional-Long Short Term Memory-Conditional Random Field) model, but the average predicted processing speed is 1.94 times that of BI-LSTM-CRF, effectively improving the efficiency of word segmentation processing.
Highly Reliable and Low-Complexity Image Compression Scheme Using Neighborhood Correlation Sequence Algorithm in WSN

Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.
Vehicular Blockchain-Based Collective Learning for Connected and Autonomous Vehicles

The accuracy of the ML model is essential for the further development of AI-enabled CAVs. With the increasing complexity of on-board sensor systems, the large amount of raw data available for learning can however cause big communication burdens and data security issues. To alleviate the communication cost yet improve the accuracy of machine learning with preserved data privacy is an important issue to address in CAVs. In this article, we survey the existing literature toward efficient and secured learning in a dynamic wireless environment. In particular, a BCL framework for AI-enabled CAVs is presented. The framework enables distributed CAVs to train ML models locally and upload to blockchain network to overall utilize the "collective intelligence" of CAVs while avoiding large amounts of data transmission. Blockchain is then applied to protect the distributed learned models. We evaluate the performance of the presented framework by simulations and discuss a range of open research issues that need to be addressed in the future.
Fractional Charlier moments for image reconstruction and image watermarking

In this paper, we propose a new set of discrete orthogonal polynomials called fractional Charlier polynomials (FrCPs). This new set will be used as a basic function to define the fractional discrete orthogonal Charlier moments (FrCMs). The proposed FrCPs are derived algebraically using the spectral decomposition of Charlier polynomials (CPs), then the Lagrange interpolation formula is used to derive the spectral projectors. Then, each spectral projector matrix is decomposed by the singular value decomposition (SVD) technique in order to build a basic set of orthonormal eigenvectors which help to develop FrCPs. FrCMs are deduced in matrix form from the proposed FrCPs and are applied for image reconstruction and watermarking. The experimental results show the capacity of the FrCMs proposed for image reconstruction and image watermarking against different attacks such as noise and geometric distortions.
Ego-Pose Estimation and Forecasting As Real-Time PD Control

We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos. The method learns directly from unsegmented egocentric videos and motion capture data consisting of various complex human motions (e.g., crouching, hopping, bending, and motion transitions). We propose a video-conditioned recurrent control technique to forecast physically-valid and stable future motions of arbitrary length. We also introduce a value function based fail-safe mechanism which enables our method to run as a single pass algorithm over the video data. Experiments with both controlled and in-the-wild data show that our approach outperforms previous art in both quantitative metrics and visual quality of the motions, and is also robust enough to transfer directly to real-world scenarios. Additionally, our time analysis shows that the combined use of our pose estimation and forecasting can run at 30 FPS, making it suitable for real-time applications.
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it. However, we argue that the future interactions after a target interaction, which are also available during training, provide valuable signal on user preference and can be used to enhance the recommendation quality.
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices

Low-precision integer arithmetic is a necessary ingredient for enabling Deep Learning inference on tiny and resource-constrained IoT edge devices. This brief presents CMix-NN, a flexible open-sourceCMix-NN is available at https://github.com/EEESlab/CMix-NN. mixed low-precision (independent tensors quantization of weight and activations at 8, 4, 2 bits) inference library for low bitwidth Quantized Networks. CMix-NN efficiently supports both Per-Layer and Per-Channel quantization strategies of weights and activations. Thanks to CMix-NN, we deploy on an STM32H7 microcontroller a set of Mobilenet family networks with the largest input resolutions (224×224) and higher accuracies (up to 68% Top1) when compressed with a mixed low precision technique, achieving up to +8% accuracy improvement concerning any other published solution for MCU devices.
Digital Annealer for High-Speed Solving of Combinatorial optimization Problems and Its Applications

A Digital Annealer (DA) is a dedicated architecture for high-speed solving of combinatorial optimization problems mapped to an Ising model. With fully coupled bit connectivity and high coupling resolution as a major feature, it can be used to express a wide variety of combinatorial optimization problems. The DA uses Markov Chain Monte Carlo as a basic search mechanism, accelerated by the hardware implementation of multiple speed-enhancement techniques such as parallel search, escape from a local solution, and replica exchange. It is currently being offered as a cloud service using a second-generation chip operating on a scale of 8,192 bits. This paper presents an overview of the DA, its performance against benchmarks, and application examples.
Breast cancer diagnosis from histopathological images using textural features and CBIR

Currently, breast cancer diagnosis is an extensively researched topic. An effective method to diagnose breast cancer is to use histopathological images. However, extracting features from these images is a challenging task. Thus, we propose a method that uses phylogenetic diversity indexes to characterize images for creating a model to classify histopathological breast images into four classes – invasive carcinoma, in situ carcinoma, normal tissue, and benign lesion. The classifiers used were the most robust ones according to the existing literature: XGBoost, random forest, multilayer perceptron, and support vector machine. Moreover, we performed content-based image retrieval to confirm the classification results and suggest a ranking for sets of images that were not labeled. The results obtained were considerably robust and proved to be effective for the composition of a CADx system to help specialists at large medical centers.