Reviewerly/exHarmony huggingface dataset:
Lightweight cryptographic algorithms for resource-constrained IoT devices and sensor networks

Over the past few years, Internet of Things (IoT) has emerged because of advancing research and practical applications. IoT is a system of enabling devices for communication and computing process through the Internet. It integrates devices and sensor nodes in the communication network. The smart chips and sensors transmit important data deployed on the physical objects in IoT, which are present in the environment everywhere. The exchange of information interconnects the devices. The transfer of information must be safe and secured in the IoT platform. Cryptography plays an important role for secure data communication. This is because it protects the information and assures the message integrity and authentication. Nowadays, security and privacy have been a major concern with the evolution of IoT devices and sensor networks. These devices have limited resources in terms of area, power, and memory. It has become a challenging task to secure resource-constrained devices in IoT, radio frequency identification (RFID) tags, wireless sensor network (WSN) nodes, etc. Therefore, lightweight cryptographic algorithms have been developed to provide adequate security in such devices. The hardware implementation of these algorithms cannot be easily modified or read by an intruder. Hence, it offers more physically secure implementation by nature. In this chapter, three architectures of KLEIN lightweight cryptographic algorithm are proposed, and FPGA implementation is performed by using Spartan and Virtex families.
PMNet: A probability map based scaled network for breast cancer diagnosis

The mortality rate of Breast Cancer in women has increased, both in west and east. Early detection is important to improve the survival rate of cancer patients. The manual detection and identification of cancer in whole slide images are critical and difficult tasks for pathologists. In this work, we introduce PMNet, a pipeline to detect regions with invasive characteristics in whole slide images. Our method employs scaled networks for detecting breast cancer in whole slide images. It classifies whole slide images on patch level into normal, benign, in situ and invasive tumors. Our approach yielded f1-score of 88.9(±1.7)% that outperforms the benchmark f1-score of 81.2(±1.3)% on patch level and achieved an average dice coefficient of 69.8% on 10 whole slide images compared to the benchmark average dice coefficient of 61.5% on BACH dataset. Similarly, on the dryad test dataset that comprises of 173 whole slide images, we achieved an average dice coefficient of 82.7% as compared to the previous state-of-art of 76% without fine-tuning on this dataset. We further proposed a method to generate patch level annotations for the image level TCGA breast cancer database that will be useful for future deep learning methods.
Keep It Simple: Data-Efficient Learning for Controlling Complex Systems With Simple Models

When manipulating a novel object with complex dynamics, a state representation is not always available, for example, deformable objects. Learning both a representation and dynamics from observations requires large amounts of data. We propose Learned Visual Similarity Predictive Control (LVSPC), a novel method for data-efficient learning to control systems with complex dynamics and high-dimensional state spaces from images. LVSPC leverages a given simple model approximation from which image observations can be generated. We use these images to train a perception model that estimates the simple model state from observations of the complex system online. We then use data from the complex system to fit the parameters of the simple model and learn where this model is inaccurate, also online. Finally, we use Model Predictive Control and bias the controller away from regions where the simple model is inaccurate and thus where the controller is less reliable. We evaluate LVSPC on two tasks; manipulating a tethered mass and a rope. We find that our method performs comparably to state-of-the-art reinforcement learning methods with an order of magnitude less data. LVSPC also completes the rope manipulation task on a real robot with 80% success rate after only 10 trials, despite using a perception system trained only on images from simulation.
Probing ultrastrong light–matter coupling in open quantum systems

Dynamically probing systems of ultrastrongly coupled light and matter by advanced coherent control has been recently proposed as a unique tool for detecting peculiar quantum features of this regime. Coherence allows in principle on-demand conversion of virtual photons dressing the entangled eigenstates of the system to real ones, with unitary efficiency and remarkable robustness. Here we study this effect in the presence of decoherence, showing that also in far from ideal regimes is it possible to probe such peculiar features.
Analyzing Forward Robustness of Feedforward Deep Neural Networks with LeakyReLU Activation Function Through Symbolic Propagation

FeedForward Deep Neural Networks (DNNs) robustness is a relevant property to study, since it allows to establish whether the classification performed by DNNs is vulnerable to small perturbations in the provided input, and several verification approaches have been developed to assess such robustness degree. Recently, an approach has been introduced to evaluate forward robustness, based on symbolic computations and designed for the ReLU activation function. In this paper, a generalization of such a symbolic approach for the widely adopted LeakyReLU activation function is developed. A preliminary numerical campaign, briefly discussed in the paper, shows interesting results.
Distilling Visual Priors from Self-Supervised Learning

Convolutional Neural Networks (CNNs) are prone to overfit small training datasets. We present a novel two-phase pipeline that leverages self-supervised learning and knowledge distillation to improve the generalization ability of CNN models for image classification under the data-deficient setting. The first phase is to learn a teacher model which possesses rich and generalizable visual representations via self-supervised learning, and the second phase is to distill the representations into a student model in a self-distillation manner, and meanwhile fine-tune the student model for the image classification task. We also propose a novel margin loss for the self-supervised contrastive learning proxy task to better learn the representation under the data-deficient scenario. Together with other tricks, we achieve competitive performance in the VIPriors image classification challenge.
A systematic evaluation of learning rate policies in training CNNs for brain tumor segmentation

Abstract Convolutional neural networks (CNNs) have recently emerged as a powerful approach for automatic segmentation of brain tumor subregions on 3D multi-parametric MRI scans. Learning rate is a crucial hyperparameter in the training of CNNs, impacting the performance of the learned model. Different learning rate policies trace unique trajectories in the optimization landscape that converge to local minima with varying generalization properties. In this work, we empirically evaluated nine learning rate policy-optimizer pairs with two state-of-the-art architectures, namely 2D slice-based U-Net and 3D DeepMedicRes, on an augmented brain tumor dataset of 534 subjects. Segmentation performance was quantified in terms of Dice similarity coefficient and Hausdorff distance metrics. The policies were ranked based on the final ranking score (FRS) employed by the BraTS challenge, with the statistical significance of the rankings evaluated by random permutation test. For 2D slice-based U-Net architecture, an overall ranking of learning rate policies showed that the polynomial decay policy with Adam optimizer significantly outperformed other policies for the task of individual and hierarchical segmentation of tumor subregions ( p &lt; 10 −4 ). For 3D segment-based DeepMedicRes architecture, polynomial decay policy with Adam optimizer performed significantly better than all other policies, with the exception of polynomial decay with SGD optimizer for the same task ( p &lt; 10 −4 ). Based on the FRS, polynomial decay policy with Adam and SGD optimizer occupied the top two positions respectively, but the difference was not statistically significant ( p &gt; 0.3). These findings were also validated on the BraTS 2019 Validation dataset which comprised of an additional 125 subjects.
Review Paper on Data Security in Cloud Computing Environment

In today's scenario cloud computing in growing concept in the computer and information technology field for storing the data. Because on the physical storage device whatever the huge amount of data which is sensitive or confidential that cannot be stored. So the cloud computing comes into the picture for storing the data as per their requirement. But if we store the data on cloud it will be easily accessible to all and the confidentiality of that data is not maintained. So for that purpose we are going make the data on the cloud to be secure with the help of cryptography. In this Paper we talk about earlier work in this field of encryption algorithm and the key issues for encrypting the data and decoding the data in cloud environment and proposed the objectives for improving the encryption algorithm and key issues which would help to store the data in cloud environment securely and efficiently. The paper proposed the defined objectives for the secrecy of the data in the storage of cloud computing environment which is the main aspect of our paper.
Designing A Compact Convolutional Neural Network Processor on Embedded FPGAs

FPGA-based Convolutional Neural Network (CNN) processor has been widely applied for highly-parallelized computations and fast deployment. However, designing on embedded FPGA needs to consider multiple aspects, such as the feasibility of limited configurable resource on FPGA, external memory latency and the scheduling between memory and computation units. These considerations hence hinder the usage of FPGA. Addressing these issues, we elaborate a systematic design approach that allow fast deployment, which includes the parameterized computation and memory unit, which can be configured based on the target platform, and an evaluation approach for searching the optimal setting sets. To evaluate the proposed approach, we performed object detection task, YOLOv2, on PYNQ-Zl and achieved 48.23 GOPs throughputs as well as 0.611 seconds execution time. This is 42.38 and 12.8 times faster than the same inference on CPU and GPU and is 2.36 times faster than other FPGA implementations. Additionally, our created evaluation model is only 5-22% apart from the implementation result, which is 60% less than previous work.
Driver Hand Detection Using Squeeze-and-Excitation YOLOv4 Network

In this paper, we have proposed a driver hand detection method using YOLOv4 combined with Squeeze-and-Excitation (SE) module, which we called SE-YOLOv4. In our proposed SE-YOLOv4, we add the SE module to the CSPDarknet in the backbone of YOLOv4. The SE module optimizes the learning ability of YOLOv4, which improves the performance of driver hand detection. Furthermore, we have established a driver hand detection dataset. We evaluate the proposed hand detection method and compare it with some other network model, and experimental results show that the hand detection accuracy of our method was improved by 2%-4%.
Research of mobile robot path planning based on improved A* algorithm

Aiming at the problem of low efficiency, large path turning angle and insufficient path smoothness of mobile robot path planning using traditional A* algorithm, an novel path planning method based on improved A* algorithm is proposed in this paper. The procedure of the proposed method is described as follows. Firstly, the grid method is used to model the working environment map of the mobile robot. Then, in order to improve the evaluation function of the A* algorithm, not only the node steering angle and the node direction angle are integrated into the actual cost function and heuristic function of the evaluation function, but also the exponential distance adjustment factor and parent node heuristic function information are added to the heuristic function. Finally, a rotation prediction method, to which the judgment of the minimum safety distance is added, is designed to optimize the two-way smoothing of the planned path. The simulation experiment results show that the path length planned by the improved A* algorithm is shorter, and the turning angle and number of turns are smaller. The algorithm designed in this paper can improve the search efficiency and adaptability of the A* algorithm, and increase the smoothness and safety of the path.
Effective Feature Enhancement and Model Ensemble Strategies in Tiny Object Detection

We introduce a novel tiny-object detection network that achieves better accuracy than existing detectors on TinyPerson dataset. It is an end-to-end detection framework developed on PaddlePaddle. A suit of strategies are developed to improve the detectors performance including: 1) data augmentation based on scale-match that aligns the object scales between the existing large-scale dataset and TinyPerson; 2) comprehensive training methods to further improve detection performance by a large margin; 3) model refinement based on the enhanced PAFPN module to fully utilize semantic information; 4) a hierarchical coarse-to-fine ensemble strategy to improve detection performance based on a well-designed model pond.
Deidentifying MRI Data Domain by Iterative Backpropagation

Medical images acquired at various hospitals can differ significantly in their data distribution. We can find multiple sources of divergence evaluating images from different clinical centers, patient diseases, vendors, or even configurations on the same scanner. Typically at deployment, when we are facing real-world domain, data is collected from it and the trained model is adapted. This is not practical in all scenarios like medical imaging due to the lack of data and the strict protection to which it is subjected. We investigate this challenging problem by evaluating a novel domain adaptation procedure. First, a classifier model is trained to distinguish between which data distribution comes from. Once trained, the images from each vendor are modified iteratively using the gradients of the error obtained when the target is set arbitrarily. Finally, when we have a new sample we only have to carry out the same process of domain adaptation by error backpropagation. The experiments were performed on Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms), comparing the segmentation metrics obtained for studies from vendors present in the training set and an additional studies from an unseen vendor.
Bio-inspired Algorithms in Software Fault Prediction: A Systematic Literature Review

Bio-inspired (and meta-heuristic) algorithms are successfully employed in different domains and the research is going on to accommodate them in all the contexts where optimization is required. In software engineering, and especially Software Fault Prediction (SFP), they are investigated in various forms, e.g., to extract the most relevant features in a dataset or to select the most appropriate set of parameter values in the application of estimation techniques. In SFP, feature selection and optimization/tuning of estimation technique's parameters are an active research area, where recently various bio-inspired algorithms have been employed for both strategies. In this work, we present a Systematic Literature Review (SLR) about the use of bio-inspired algorithms for feature selection and parameter optimization aiming at increasing fault prediction accuracy of the models built with various estimation techniques. To the best of our knowledge, there is no SLR in SFP which covers the use of bioinspired algorithms, both for feature selection and parameter optimization. Since, the use of bio-inspired algorithms in the area of SFP started to be investigated in the late 2000, we have considered studies published between 2007 and 2019. As result, we have selected about 19 studies related to parameter optimization and 15 dealing with feature selection (in total 34 studies), extracted from five well-known digital libraries (ACM digital library, IEEE explore, Springer, ScienceDirect, and Scopus). Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are the widely used bio-inspired algorithms, both for parameter optimization and feature selection. Among them, GA is the better performed algorithm when evaluating its performance against the baseline (i.e., estimation techniques without any algorithm for feature selection or parameter optimization and trained with their default values). The SLR results also suggests that bio-inspired algorithms seem to provide more accurate predictions for feature selection than for parameter optimization.
Handwritten Digit Recognition Based on Convolutional Neural Network

In order to meet the needs of paperless offices and greatly improve work efficiency, it is necessary to research and implement a handwritten digit recognition system. Handwritten digit recognition plays an important role in large-scale data statistics and the financial business, such as industry annual inspection, population census, tax statements and checks, etc. This paper proposes a new type of handwritten digit recognition system based on convolutional neural network (CNN). In order to improve the recognition performance, the network was trained with a large number of standardized pictures to automatically learn the spatial characteristics of handwritten digits. For model training, according to the loss function, the convolutional neural network continuously updates the network parameters with the data set in MNIST, which contains 60,000 examples. For model test, the system uses the camera to capture the pictures composed of the images generated by the test data set of MNIST and the samples written by different people, then continuously processes the captured graphics and refreshes the output every 0.5 seconds. With the trained deep learning model, we got a recognition accuracy of 97.3% in test process. Good performance in this experiment shows that our system can automatically recognize the handwritten digital content appearing in the target area and output the content label in real time.
Query-Efficient Hard-Label Black-Box Attacks Using Biased Sampling

In recent years, deep learning has developed rapidly and achieved great success in many fields. However, it has been demonstrated that deep neural networks are very vulnerable to artificially designed adversarial examples which are difficult to visually observe by human. In this paper, the practical hard-label black box attack in which attackers can only query the output labels to generate adversarial examples, is studied on image classification tasks. Existing attacks proposed for this setting require a lot of queries. To improve the attack efficiency, the unbiased sampling in existing attacks is replaced with two biased sampling methods: low image frequency and regional mask. The two biased methods integrate domain knowledge into the process of sampling and searching for adversarial directions, which can significantly limit the search space and thus reduce query times. Experimental results on ImageNet show that the biased sampling methods can improve the efficiency of existing hard-label black box attacks.
Basic Electrical Installation Trainer Boards : Virtual Reality based Laboratory for Electrical Basic Education

Expensive practicum equipment, frequent accidents in learning, and lack of practicum equipment because damage after students use them, they are problems found in vocational schools. Due to these conditions, teachers need the effective learning media to minimize accidents and damage. Therefore, the author aims to design a learning media based on Virtual Reality (VR). VR is a virtual technology that allow user activities like in the real world in a different dimension. The method in this study using survey to prepare spec and design. The author designed a virtual laboratory, inside there are experiment boards to enable teachers and students to do practicum virtually. The goal, by practicing using VR, teachers and students can still practicum without worry about the limitations of the equipment. Expensive equipment also can be simulated virtually without fear of damage.
Modeling and verifying a resource allocation algorithm for secure service migration for commercial cloud systems

Abstract Cloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments.
Study on DID Application Methods for Blockchain-Based Traffic Forensic Data

Traffic accident investigation has been used to reconstruct the cause and condition of the accident by using images and driving record data that introduce IT technology, and to analyze both sides of the accident, skid marks, and vehicle damage to identify the perpetrators and victims. However, level 3 self-driving vehicles are the most important factor in determining the cause and imputation of the accident by the driver or manufacturer with control information at the time of the accident. It is also developing into a network and connected vehicle with various restrictions such as the burden of the price of sensors equipped with self-driving cars and climate and rapidly changing road traffic information. In addition, network and connected vehicle driving data are stored on the outside, or various devices and sensors are installed to store information on the outside for convenience in operation, and efforts to enact laws are continuing. This paper attempts to propose a traffic investigation digital framework using digital data generated by these devices and sensors.