Reviewerly/exHarmony huggingface dataset:
In-Network Aggregation for Privacy-Preserving Federated Learning

Cross-silo federated learning becomes popular in various fields due to its great promises in protecting training data. By carefully examining the interaction among distributed training nodes, we find that existing federated learning still suffers from security weakness and network bottleneck during model synchronization. It has no protection on training models, which also contain significant private information. In addition, many evidences have shown that model synchronization over wide-area network is slow, bottlenecking the whole learning process. To fill this research gap, we propose a novel cross-silo federated learning architecture that can protect both training data and model by using homomorphic encryption (HE). Instead of sharing the model parameters in plaintexts, we encrypt them using the HE, so that they can be aggregated in ciphertexts. In order to handle the inflated network traffic incurred by HE, we apply the in-network aggregation by exploiting the strong capability of programmable switches. A fast algorithm that jointly considers in-network aggregator placement and traffic engineering has been proposed and evaluated by extensive simulations.
Learned Enhancement Filters for Image Coding for Machines

Machine-To-Machine (M2M) communication applications and use cases, such as object detection and instance segmentation, are becoming mainstream nowadays. As a consequence, majority of multimedia content is likely to be consumed by machines in the coming years. This opens up new challenges on efficient compression of this type of data. Two main directions are being explored in the literature, one being based on existing traditional codecs, such as the Versatile Video Coding (VVC) standard, that are optimized for human-targeted use cases, and another based on end-to-end trained neural networks. However, traditional codecs have significant benefits in terms of interoperability, real-time decoding, and availability of hardware implementations over end-to-end learned codecs. Therefore, in this paper, we propose learned post-processing filters that are targeted for enhancing the performance of machine vision tasks for images reconstructed by the VVC codec. The proposed enhancement filters provide significant improvements on the target tasks compared to VVC coded images. The conducted experiments show that the proposed post-processing filters provide about 45% and 49% Bjøntegaard Delta Rate gains over VVC in instance segmentation and object detection tasks, respectively.
Signature Recognition Models: Performance Comparison

In recent times Signature Verification has become an act of absolute necessity in the area of biometric verification. Unlike other verification problems, every small detail between genuine and forged signatures needs to be observed because a skilled forgery can only differ by only some specific kind of features of the real signature. The task of verifying signatures has become even harder in writing independent scenarios. In this paper, with the help of Siamese Network, VGG16 model and DEEP CNN models we have modeled a system that will verify signatures offline. Siamese networks use two images as input with shared weights, which can be trained to learn the features of both the images to find out the similarity between them. This is done by passing sets of similar and dissimilar images to the network so that it can learn to reduce the loss and Euclidean distance between similar images and increase it in dissimilar images. As for VGG 16, it is a pre-trained 16-layer model that is based on CNN, these 16 layers consist of max layer, pooling layers, and many more. CNN is simply a convolutional neural network on which both Siamese and VGG models are based. The performance analysis shows the VGG16 to have best accuracy about 85-90%, Siamese shows 65-70% and CNN shows 65-70% and the Siamese network to have the highest speed in identification.
Deep Learning for the Identification of Decision Modelling Components from Text

Decision and process descriptions often find themselves encapsulated in long descriptions such as regulations or guidelines. Decision modelling aims at modelling the structure and logic of a decision. For decision modellers, analysing textual documents in search for relevant sentences is a time consuming activity. A promising research topic is to build decision models from text. In this paper, an automatic decision modelling component classifier using deep learning is proposed. Using a large dataset consisting of labeled sentences, the usability of deep learning techniques is investigated. In total three deep learning techniques are evaluated and compared to non-deep learning techniques using both Bag of Words and Term Frequency-Inverse Document Frequency. We conclude that classifying decision modelling components is possible and report that BERT for sequence classification is the best performing technique.
Abstractive Headline Generation from Bangla News Articles Using Seq2Seq RNNs with Global Attention

Headline generation is the process of generating headlines automatically from text articles. We model a comprehensive abstractive headline generation technique using Seq2Seq Recurrent Neural Networks with Global Attention in this work. Despite being one of the most spoken languages globally, very few significant works have been done on this particular topic in the Bangla language. Thus, our model is solely based on the Bangla language, and we find that the performance of the model is highly satisfactory at the current stage. We also propose an extensive dataset consisting of 5,14,108 filtered Bangla news articles in full and other necessary information. The dataset has been created by scrapping several online reputed Bangla newspapers. Due to the unavailability of a proper and updated dataset, the proposed datasets are freely available at https://tinyurl.com/banglaHead
Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>640</mn> <mo>×</mo> <mn>480</mn> </math> size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.
Scale Optimization of Structure from Motion for Structured Light-based All-round 3D Measurement

In this paper, we propose a novel method for 3D measurement of large structures that have sparse features. The proposed method uses a structured-light approach based on a spherical camera and an omnidirectional ring laser. The spherical camera can capture omnidirectional images which enable it to view all sparse feature points existing in the target environment. The omnidirectional ring laser can enable dense 3D measurement of cross-sections of the environment via the structured light method. Structure from Motion (SfM) is used to measure the motion of the camera to integrate the laser cross sections to obtain a dense 3D model.However, the result of SfM does not contain real-world scale information. The novelty of this research lies in a new method to obtain the real-world scale. The real-world scale is determined by comparing a mesh generated from the resultant SfM point cloud and the integrated laser sections in terms of each scale.In a simulated environment, the proposed method was found to be accurate up to 1 mm. It was also able to accurately measure the 3D shape of a real environment.
Quantum measurement classification with qudits

This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer.
Oblivious algebraic data types

Secure computation allows multiple parties to compute joint functions over private data without leaking any sensitive data, typically using powerful cryptographic techniques. Writing secure applications using these techniques directly can be challenging, resulting in the development of several programming languages and compilers that aim to make secure computation accessible. Unfortunately, many of these languages either lack or have limited support for rich recursive data structures, like trees. In this paper, we propose a novel representation of structured data types, which we call oblivious algebraic data types, and a language for writing secure computations using them. This language combines dependent types with constructs for oblivious computation, and provides a security-type system which ensures that adversaries can learn nothing more than the result of a computation. Using this language, authors can write a single function over private data, and then easily build an equivalent secure computation according to a desired public view of their data.
A Survey on Autonomous Driving Datasets

Currently the autonomous driving is one of the rapidly developing and widely concerned fields, which involves numerous artificial intelligence algorithms. Generally, these artificial intelligence algorithms require a large amount of diverse data for training and testing to ensure their performance in actual applications. In this paper, we introduce the types of data related to the autonomous driving, compare multiple datasets, and discusse the key issues in the process of dataset collection and generation.
Research On Risk Assessment Of UAV To Buildings

Nowadays, UAV has been widely used in cities to perform tasks, but at the same time, it also brings great risks to the urban environment. This paper presents a risk evaluation method to measure the risk of UAV to ground buildings. We establish a risk model to quantitative analysis of the risk caused by UAV to ground buildings. Then, we put the analysis result as the cost-function to the RRT* path planning algorithm, using RRT* algorithm to guide the UAV path planning. To further reduce the risk, we propose an improvement RRT* algorithm. Last, we do the simulation to show the effect of the risk model and the path planning algorithm and contrast the results to verify the effectiveness of our improvement measure.
The methods and automatic technology aimed at imagery georeferencing, cloud screening, atmospheric and radiometric correction of KMSS-M satellite data

In this study we present methods and automatic technology developed for routine processing of satellite imagery acquired by cameras MSU-201 and MSU-202 (KMSS-M) onboard Meteor-M №2. The developed methods were aimed at imagery georeferencing issues fixing, clouds and shadows detection as well as atmospheric and radiometric correction. Basing on these methods we built an automatic technology and complete KMSS-M data processing chain which provided analysis ready dataset for Russian grain belt and adjacent areas of neighboring countries for the year 2020. Method for imagery georeferencing was based on Pearson’s correlation localized maximization when compared to the georefenced and cloudfree coarse-resolution reference image produced in IKI RAS through MOD09 product time series processing. Method for clouds and shadows detection was based both on the spatial analysis of outputs from geocorrection step and auxiliary image, characterizing georeferenced KMSS-M image values relative accordance with the IKI reference image. The atmospheric correction was based on localized histogram matching of KMSS-M and IKI reference date-corresponding imagery, and thereby concurrently performed radiometric correction of KMSS-M data, compensating effects of varying viewing and illumination geometry which explicitly manifest across 960-km-wide swath area. The developed methods are noticeably minimalistic, requiring only one target spectral band to perform properly. Due to high flexibility and robustness, they also may be applied to raw satellite imagery acquired from various Earth observation systems, including Russian systems of high and moderate spatial resolution. The technology is currently being deployed in an operative mode for several test sites of Russia since the year 2021 onwards.
Identification of white blood cells for the diagnosis of acute myeloid leukemia

Abstract The diagnosis of hematologic malignancies such as leukemia includes identifying malignant white blood cells. Manual microscopic analysis of white blood cells is time‐consuming and involves the assistance of medical specialists, and its accuracy may be affected by their abilities. Our objective is to develop an automated system that can identify 15 categories of white blood cells to assist medical practitioners in diagnosing acute myeloid leukemia. The proposed approach uses an ensemble model built with transfer learning‐based pre‐trained networks to categorize 15 kinds of white blood cells. An over‐sampling strategy is used to alleviate the problem of class imbalance, followed by data augmentation. For experimentation, a microscopic blood image dataset containing 18 365 cells images from 200 individuals representing 15 different forms of leukocytes is used. For cells with more than 400 images available, the suggested technique achieves an F1 score of more than 91%. Myeloblasts, which are frequent in myeloid leukemias and are detected in the peripheral blood, are recognized with an average precision of 95.74%. This work describes an image processing strategy that employs ensemble learning to assist in diagnosing acute myeloid leukemia by classifying 15 different types of leukocytes. Experiments show that our technique is both practical and effective. Extensive research supports the use of the leukocyte classifier in real‐world medical applications, such as assisting clinicians in diagnosing diseases and decreasing human resource requirements.
Quantum Machine Learning: A comprehensive review on optimization of machine learning algorithms

Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.
Forgetting in Answer Set Programming – A Survey

Abstract Forgetting – or variable elimination – is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this article, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.
Explore on Application of BDS-3 RSMC in the Field of Image Transmission

As a core of the national spatial-info assets, the BeiDou satellite navigation system (BDS) has a unique function is short messages, it is different from GPS, GLONASS and other global navigation satellite systems (GNSS). In the white paper, the regional short messages communication (RSMC) has reached 1,000 Chinese characters per slot (or 14k bits per slot), and the system capacity has been increased to 10 million times per hour. For BDS-3 RSMC service, this paper proposes an engineering scheme of image transmission and processing based on “cloud-edge-end”, and via simulation verification, it is found that for high-definition (HD) images with a resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1024\times 768\mathrm{p}$</tex> , H.265 and proposed compression method compares the average data, compression ratio, standard deviation, subjective evaluation and time and other indicators, and finally verifies that the RSMC can realize high-definition image transmission under the VP8 compression coding condition. The engineering scheme of “cloud-edge-end” is feasible. To this point, the RSMC will have the ability to escape the scope of text and audio in real-time. At the same time, the improvement of its coverage and system capacity will also increase the worth of the BDS-3 RSMC image transmission, and further improve the communication on transportation, emergency and other fields.
CorbFuzz: Checking Browser Security Policies with Fuzzing

Browsers use security policies to block malicious behaviors. Cross-Origin Read Blocking (CORB) is a browser security policy for preventing side-channel attacks such as Spectre. We propose a web browser security policy fuzzer called CorbFuzz for checking CORB and similar policies. In implementing a security policy, the browser only has access to HTTP requests and responses, and takes policy actions based solely on those interactions. In checking the browser security policies, CorbFuzz uses a policy oracle that tracks the web application behavior and infers the desired policy action based on the web application state. By comparing the policy oracle with the browser behavior, CorbFuzz detects weaknesses in browser security policies. CorbFuzz checks the web browser policy by fuzzing a set of web applications where the state-related queries are symbolically evaluated for increased coverage and automation. CorbFuzz collects type information from database queries and branch conditions in order to prevent the generation of inconsistent data values during fuzzing. We evaluated CorbFuzz on CORB implementations of Chromium and Webkit, and Opaque Response Blocking (ORB) policy implementation of Firefox using web applications collected from GitHub. We found three classes of weaknesses in Chromium’s implementation of CORB.
An Agile Software Development Life Cycle Model for Machine Learning Application Development

Software development teams are often hampered when aligning machine learning production with standard software development processes. Iterative experimentation is needed to address the inherent complexities of data collection and preparation, model entanglement, and the technical debt of machine learning. The complexity of this process is compounded due to dependencies on the production environment and real- time data. We propose a unified framework which facilitates the planning, development, and deployment of a machine learning application through parallel processes for software and machine learning engineering. This allows for the risk of both the project and machine learning development to be significantly reduced through continuous integration, evaluation, and production. The framework, named MLASDLC, unifies concepts from standard software development life cycle methodologies (SDLC), development operations (DevOps) and machine learning operations (MLOps) to present a framework for the development of machine learning applications.
Hardware Resource and Computational Density Efficient CNN Accelerator Design Based on FPGA

Convolutional neural networks (CNNs) have become more and more popular in machine vision tasks, including image classification and object detection. As one of the most promising platforms for accelerating CNNs, Field Programmable Gate Array (FPGA) can achieve a better trade-off between speed, flexibility, cost, and power consumption, thus is more suitable in Edge-AI scenarios, which is generally constrained by cost and power. In this paper, by flexibly using line buffer and tiling, we propose an efficient CNN accelerator design based on FPGA. The implementation results indicate the peak performance of the proposed accelerator can reach 172.8 GOP/s, only occupying 24K LUTs (Look-Up-Table), 40.5 BRAMs (Block Ram), and 296 DSP48s, which shows the highest efficiency of hardware resource and computational density compared with previous works.
A Deep Learning-based Audio-in-Image Watermarking Scheme

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.