Reviewerly/exHarmony huggingface dataset:
A Dual Security Protection Mechanism for Cloud-Based Data Storage and Sharing

Cloud-based data storage service has drawn increasing interest from both academic and industry in recent years due to their efficient and low-cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. The most widely used method is encryption to protect sensitive data from being compromised. However, simply encrypting data (e.g., via AES) cannot fully address the practical need for data management. Besides, effective access control over download requests also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service. In this project, we consider dual access control, in the context of cloud-based storage, in the sense that we design a control mechanism over both data access and download requests without loss of security and efficiency. Two dual access control systems are designed in this paper, each for a distinctly designed setting. The security and experimental analysis for the systems are also presented.
Load Balancing Using Oblivious RAM for Privacy-Preserving Access to Big Data in Cloud

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. To overcome this problem, in this work Oblivious RAM (ORAM) is applied which aims to enable privacy preserving access to big data in cloud. After applying ORAM to big data in cloud the access load balancing problem arises. Iterative Load Balancing algorithm (ILB) is applied to achieve balanced load.
TILLAGE : An Ensemble-Based Agricultural Crop Recommendation System

India is primarily an agricultural nation, and both the Indian economy and people's daily lives are heavily reliant on agriculture. In our research One flaw we discovered was that many of them thinks on one aspect (weather or soil) to judge whether crops would grow successfully.This paper presents the precision agricultural recommendation system with more parameters helps to farmer to choose right crops for their field with the help of ensemble majority voting techniques like SVM, Naive bayes, Random Forest, KNN and a Convolutional neural network and also it provide valid fertilizers and pesticide to reduce the farmer’s burden in selection right things to their field.
An unsupervised linguistic-based model for automatic glossary term extraction from a single PDF textbook

Abstract Term extraction from textbooks is the cornerstone of many different intelligent natural language processing systems, especially those that support learners and educators in the education system. This paper proposes a novel unsupervised domain-independent model that automatically extracts relevant and domain-related key terms from a single PDF textbook, without relying on a statistical technique or external knowledge base. It only relies on the basic linguistic techniques of the natural language processing: pattern recognition, sentence tokenization, part-of-speech tagging, and chunking. The model takes a PDF textbook as an input and produces a list of key terms as an output. Furthermore, the model proposes a novel classification of sentences from which the concept of defining sentences is proposed. The defining sentences are the main textual units that the model revolves around to identify the key terms. The architecture of the proposed work consists of 21 processes distributed across three phases. The first phase consists of five processes for extracting text from a PDF textbook and cleaning it for the next phases. The second phase consists of eight processes for identifying the defining sentences and extracting them from all the textbook’s sentences. The last phase consists of eight processes for identifying and extracting the key terms from every defining sentence. The proposed work was evaluated by two experiments in which two PDF textbooks from different fields are used. The experimental evaluation showed that the results were promising.
Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Summary Teaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐heuristic algorithm that was created to solve single‐objective optimization problems. The teaching‐learning mechanism of a classroom inspires it. TLBO suffers from weak exploration. As a result, its performance is not good for solving multimodal problems. To turn TLBO into a tool for solving multimodal problems and maintaining good diversity, we made significant modifications into the learning process of the fundamental TLBO. The proposed algorithm produces more diverse solutions and works better for solving multimodal problems. This newly created variant of TLBO is called “Intelligent‐Teaching‐Learning‐Based Optimization (I‐TLBO) algorithm.” I‐TLBO's performance is evaluated against the most recent standard benchmark function, CEC‐06, 2019, and it is discovered that I‐TLBO outperforms the other algorithms. After that, I‐TLBO was applied for flowtime‐aware‐cost minimization of the workflow executions in cloud datacenter. To solve these scheduling problems, I‐TLBO and other metaheuristic algorithms are simulated in CloudSim and tested over scientific workflows such as Inspiral, Montage, SIPHT, sample, Cybershake, and Epigenomics workflows. Finally, it is found that I‐TLBO reduces flowtime and cost both by 28.48%, 11.30%, 17.64%, 13.22%, 11.45%, and 14.71% in comparison to the second best performing algorithm while executing the standard workflow in cloud.
Feature Correlation Transformer for Estimating Ambiguous Optical Flow

Cost volume is widely used to establish correspondences in optical flow estimation. However, when dealing with low-texture and occluded areas, it is difficult to estimate the cost volume correctly. Therefore, we propose a replacement: feature correlation transformer (FCTR), a transformer with self- and cross-attention alternations for obtaining global receptive fields and positional embedding for establishing correspondences. With global context and positional information, FCTR can produce more accurate correspondences for ambiguous areas. Using position-embedded feature allows the removal of the context network; the positional information can be aggregated within ambiguous motion boundaries, and the number of model parameters can be reduced. To speed up network convergence and strengthen robustness, we introduce a smooth L1 loss with exponential weights in the pre-training step. At the time of submission, our method achieves competitive performance with all published optical flow methods on both the KITTI-2015 and MPI-Sintel benchmarks. Moreover, it outperforms all optical flow and scene flow methods in KITTI-2015 foreground-region prediction.
Special Issue on Failed Approaches and Insightful Losses in Cryptology — Foreword

Journal Article Special Issue on Failed Approaches and Insightful Losses in Cryptology — Foreword Get access Tomer Ashur, Tomer Ashur Search for other works by this author on: Oxford Academic Google Scholar Chris J Mitchell Chris J Mitchell Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 66, Issue 6, June 2023, Page 1311, https://doi.org/10.1093/comjnl/bxac191 Published: 05 May 2023 Article history Received: 25 November 2022 Revision requested: 28 November 2022 Published: 05 May 2023
BI-DIRECTIONAL CLASSIFICATION OF ROMAN PERIOD COINS BY DEEP LEARNING METHODS

In this study, the problem of classification of coins, which have historical importance and can only be distinguished by experts, is discussed with pre-learning deep learning algorithms. In the solution of the problem, the RRC-60 dataset, which consists of the images of the coins used in the Roman Republic period, was used. In this study, pre-learning Xception, MobileNetV3-L, EfficientNetB0 and DenseNet201 models were trained using the images on both sides of the coins in the data set. As a result of the training, the best values, Precision, Recall and F1-Score metrics in the MobileNetV3-L model were 98.2%, 96.8%, 97.5%, respectively, and the test accuracy was 95.2%
Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which are used to inspect specific AP-features causing the classification decision. The outputs of the CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image sample increased the chance of a given area to be classified as a non-hotspot by up to 21.8%.
A New Optical Sensing Device for Real-Time Noncontact Vibration Measurement Considering Light Field Variation

Vibration measurement is essential for vibration monitoring and control. Noncontact vibration measurement is more applicable in practice than the contact measuring manner, but the noncontact methods usually pose a high requirement in the light field environment. To solve this issue, a new binocular vision method is proposed to enable noncontact vibration measurement with different light fields. In this new method, the multitarget objects in the same image are first recognized by a YOLOv5 model to generate the bounding boxes; meanwhile, a depth image is generated through binocular vision and kept synchronized with the target image. Then, based on each bounding box and its corresponding depth image, an optimal depth value decision algorithm is developed to determine the 3-D real-time coordinates of each object. As a result, the vibration of multitarget objects can be measured simultaneously. An experimental test system was built to evaluate the performance of the proposed method in indoor and outdoor light fields. The tests demonstrate accurate vibration measuring results, and the proposed noncontact method is able to detect very low-frequency vibrations.
COAC: Cross-Layer Optimization of Accelerator Configurability for Efficient CNN Processing

To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to efficiently deploy such networks on custom processor architectures for resource-scarce edge devices. Existing mapping exploration frameworks enable searching for the optimal execution schedules or hardware mappings of individual network layers, by optimizing each layer’s spatial (dataflow parallelization) and temporal unrolling (TU, execution order). However, these tools fail to take into account the overhead of supporting different unrolling schemes within a common hardware architecture. Using a fixed unrolling scheme across all layers is also not ideal, as this misses significant opportunities for energy and latency savings from optimizing the mapping of diverse layer types. A balanced approach assesses the right amount of mapping flexibility needed across target neural networks, while taking into account the overhead to support multiple unrollings. This article, therefore, presents cross-layer optimization of accelerator configurability (COAC), a cross-layer design space exploration and mapping framework to optimize the flexibility of neural processing architectures by balancing configurability overhead against resulting energy and latency savings for end-to-end inference. COAC does not only provide a systematical analysis of the architectural overhead in function of the supported spatial unrollings (SUs), but also builds an automated flow to find the best unrolling combination(s) for efficient end-to-end inference with limited hardware overhead. Results demonstrate that architectures with carefully optimized flexibility can achieve up to 38% energy-delay-product (EDP) savings for a set of six neural networks at the expense of a relative area increase of 9.5%.
Interval Type-2 Fuzzy Relation Matrix Model via Semitensor Product

A new modeling idea of interval type-2 fuzzy logic system (IT2 FLS) is proposed on the basis of matrix semitensor product (STP) fuzzy representation technique, to construct an interval type-2 fuzzy relationship matrix (IT2 FRM) model. The model naturally inherits the advantages of STP theory, which enables the fuzzy logic reasoning process to be translated into algebraic form for computation. In most cases, the fuzzy rules in the IT2 FRM are generally unknown. To address the above situation, the parameters of this model can be trained with observed data pairs. First, the fuzzy rules are generated by the space division method, and the initial parameters of the model are determined. Second, the parameters in the model are trained using the steepest descent algorithm. Finally, the IT2 FRM model is validated by simulation, and the results demonstrate that the IT2 FLS can be designed using the STP method.
Simultaneous Spatial and Temporal Assignment for Fast UAV Trajectory Optimization Using Bilevel Optimization

In this letter, we propose a framework for fast trajectory planning for unmanned aerial vehicles (UAVs). Our framework is reformulated from an existing bilevel optimization, in which the lower-level problem solves for the optimal trajectory with a fixed time allocation, whereas the upper-level problem updates the time allocation using analytical gradients. The lower-level problem incorporates the safety-set constraints (in the form of inequality constraints) and is cast as a convex quadratic program (QP). Our formulation modifies the lower-level QP by excluding the inequality constraints for the safety sets, which significantly reduces the computation time. The safety-set constraints are moved to the upper-level problem, where the feasible waypoints are updated together with the time allocation using analytical gradients enabled by the OptNet. We validate our approach in simulations, where our method's computation time scales linearly with respect to the number of safety sets, in contrast to the state-of-the-art that scales exponentially.
VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments

Having a profound understanding of the surrounding environment is considered one of the crucial tasks for the reliable operation of future self-driving cars. Light Detection and Ranging (LiDAR) sensor plays a critical role in achieving such understanding due to its capability to perceive the world in 3D. Similar to 2D perception tasks, current state-of-the-art methods in 3D perception tasks rely on deep neural networks (DNNs). However, the performance of 3D perception tasks, specially point-wise semantic segmentation, is not on par with their 2D counterparts. One of the main reasons is the lack of publicly available labelled 3D point cloud datasets (PCDs) from 3D LiDAR sensors. In this work, we are introducing the VoxelScape dataset, a large-scale simulated 3D PCD with 100K annotated point cloud scans. The annotations in the VoxelScape dataset include both point-wise semantic labels and 3D bounding box labels. Additionally, we used a number of baseline approaches to validate the transferability of VoxelScape to real 3D PCD for two challenging 3D perception tasks. The promising results have shown that training DNNs on VoxelScape boosted the performance of the 3D perception tasks on the real PCD. Furthermore, we are also releasing the proposed data generation pipeline for the research community to facilitate realistic simulation of 3D LiDAR point cloud data for different scenarios beyond those covered in our VoxelScape dataset. The VoxelScape dataset and the corresponding LiDAR simulation codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://voxel-scape.github.io/dataset</uri>
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving

As learning-based methods make their way from perception systems to planning/control stacks, robot control systems have started to enjoy the benefits that data-driven methods provide. Because control systems directly affect the motion of the robot, data-driven methods, especially black box approaches, need to be used with caution considering aspects such as stability and interpretability. In this letter, we describe a differentiable and hierarchical control architecture. The proposed representation, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-abstractive neural controller</i> , uses the input image to control the transitions within a novel discrete behavior planner (referred to as the visual automaton generative network, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vAGN</i> ). The output of a vAGN controls the parameters of a set of dynamic movement primitives which provides the system controls. We train this neural controller with real-world driving data via behavior cloning and show improved explainability, sample efficiency, and similarity to human driving.
Improving Seq2Seq TTS Frontends With Transcribed Speech Audio

Due to the data inefficiency and low speech quality of grapheme-based end-to-end text-to-speech (TTS), having a separate high-performance TTS linguistic frontend is still commonly regarded as necessary. However, a TTS frontend is itself difficult to build and maintain, since it requires abundant linguistic knowledge for its construction. In this paper, we start by bootstrapping an integrated sequence-to-sequence (Seq2Seq) TTS frontend using a pre-existing pipeline-based frontend and large amounts of unlabelled normalized text, achieving promising memorization and generalisation abilities. To overcome the performance limitation imposed by the pipeline-based frontend, this work proposes a Forced Alignment (FA) method to decode the pronunciations from transcribed speech audio and then use them to update the Seq2Seq frontend. Our experiments demonstrate the effectiveness of our proposed FA method, which can significantly improve the word token accuracy from 52.6% to 91.2% for out-of-dictionary words. In addition, it can also correct the pronunciation of homographs from transcribed speech audio and potentially improve the homograph disambiguation performance of the Seq2Seq frontend.
PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.
Wheelchair-Centered Omnidirectional Gaze-Point Estimation in the Wild

In this article, we propose a dataset named Wheelchair-OmniGaze and a method of estimating the gaze point of a wheelchair user that can cope with large head motion as the first step for understanding the intention of a wheelchair user. In the proposed method, the wheelchair user's face is observed by two remote cameras, called face cameras, mounted on the two front sides of the wheelchair, while a spherical camera with a 360° field of view, called a spherical scene camera, is mounted to the top rear to observe the surrounding scenes. A user's gaze point at the environment is determined by combining the facial information acquired by the face cameras with the spherical image acquired by the spherical scene camera. Concretely, a convolutional neural network–long short-term memory network is used to learn gaze points of the spherical image from the two face image sequences. Using two face cameras enables a user's face to be observed even for a large head motion. Using a spherical scene camera mounted on a wheelchair, the representation of a user's gaze points at a wheelchair-centered spherical camera coordinate can be obtained, which is independent of a user's head motion. The experimental results show that our method achieved competitive performance in comparison with the state-of-the-art method. Since our problem definition is different from the existing research, the experimental results of this article can be seen as a baseline for our built dataset.