Reviewerly/exHarmony huggingface dataset:
Trojan Signatures in DNN Weights

Deep neural networks have been shown to be vulnerable to backdoor, or trojan, attacks where an adversary has embedded a trigger in the network at training time such that the model correctly classifies all standard inputs, but generates a targeted, incorrect classification on any input which contains the trigger. In this paper, we present the first ultra light-weight and highly effective trojan detection method that does not require access to the training/test data, does not involve any expensive computations, and makes no assumptions on the nature of the trojan trigger. Our approach focuses on analysis of the weights of the final, linear layer of the network. We empirically demonstrate several characteristics of these weights that occur frequently in trojaned networks, but not in benign networks. In particular, we show that the distribution of the weights associated with the trojan target class is clearly distinguishable from the weights associated with other classes. Using this, we demonstrate the effectiveness of our proposed detection method against state-of-the-art attacks across a variety of architectures, datasets, and trigger types.
Rotation-constrained optical see-through headset calibration with bare-hand alignment

The inaccessibility of user-perceived reality remains an open issue in pursuing the accurate calibration of optical see-through (OST) head-mounted displays (HMDs). Manual user alignment is usually required to collect a set of virtual-to-real correspondences, so that a default or an offline display calibration can be updated to account for the user’s eye position(s). Current alignment-based calibration procedures usually require point-wise alignments between rendered image point(s) and associated physical landmark(s) of a target calibration tool. As each alignment can only provide one or a few correspondences, repeated alignments are required to ensure calibration quality. This work presents an accurate and tool-less online OST calibration method to update an offline-calibrated eye-display model. The user’s bare hand is markerlessly tracked by a commercial RGBD camera anchored to the OST headset to generate a user-specific cursor for correspondence collection. The required alignment is object-wise, and can provide thousands of unordered corresponding points in tracked space. The collected correspondences are registered by a proposed rotation-constrained iterative closest point (rcICP) method to optimise the viewpoint-related calibration parameters. We implemented such a method for the Microsoft HoloLens 1. The resiliency of the proposed procedure to noisy data was evaluated through simulated tests and real experiments performed with an eye-replacement camera. According to the simulation test, the rcICP registration is robust against possible user-induced rotational misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37 mm) positional error and 1. 76° rotational error by camera-based tests in the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by user tests.
Visual Question Answering with Textual Representations for Images

How far can we go with textual representations for understanding pictures? Deep visual features extracted by object recognition models are prevailing used in multiple tasks, and especially in visual question answering (VQA). However, conventional deep visual features may struggle to convey all the details in an image as we humans do. Mean-while, with recent language models’ progress, descriptive text may be an alternative to this problem. This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA.
Exploring blockchain-based Self Sovereign Identity Systems: challenges and comparative analysis

This paper highlights the challenges that an Identity Management System (IdM) must overcome and gives a comparative analysis of three Self-Sovereign Identity (SSI) ecosystems regarding these challenges. It then proposes an SSI layered architecture model detailing the actors, objects, components, and processes.
Multi-Scale Continuous Gradient Local Binary Pattern for Leaky Cable Fixture Detection in High-Speed Railway Tunnel

The feature of leaky cable fixture extracted by Local Binary Pattern (LBP) and its variants in high-speed railway tunnel has the defects of lacking description and high dimension. This paper proposes a new operator named Multi-scale Continuous Gradient Local Binary Pattern (MCG-LBP), which can realize the scale transformation of feature maps and ensure the low dimensionality of descriptors. For MCG-LBP, firstly a bi-directional triplet around the central pixel is presented to indicate the specific direction of gradient in circle neighborhood. Then, an effective dimensionality reduction strategy is introduced to perform successive down-sampling iterations. Finally, the multi-scale joint descriptors are encoded by continuous gradient sequences from different down-sampling maps, and Support Vector Machines is used to classify faulty cable fixtures. The proposed MCG-LBP can elicit a discriminative description through complementary gradient information generated by the combination of different single-scale features. While the low dimensionality of descriptor and no complex parameter to deal with both make it has higher computational efficiency. Experimental results show that the Recall and Precision of MCG-LBP reach 92.6% and 83.5% respectively on cable fixture data set, which is superior to the state-of-the-art methods.
3D Human Pose Estimation Using RGBD Camera

This work addresses a method to map the estimated 2D pose from a color image to 3D pose and obtain more accurate 3D keypoints. In this paper, the RGBD camera is used to simultaneously acquire color images and depth images. AlphaPose is used to estimate the 2D keypoints of the target, and the 2D keypoints are mapped to the corresponding depth maps to obtain the corresponding 3D keypoint. We adopt the parametric human body model, SMPL, as the 3D prior of the human body. The SMPL model and the data of point cloud are aligned by optimizing the global orientation and translation of the SMPL as well as optimizing the error of the SMPL vertices and the point cloud of the human body. The experimental results show that our method can effectively improve the accuracy of the existing 3D human pose and position estimation methods.
Mixer-Based Semantic Spread for Few-Shot Learning

Key semantics can come from everywhere on an image. Semantic alignment is a key part of few-shot learning but still remains challenging. In this paper, we design a Mixer-Based Semantic Spread (MBSS) algorithm that employs a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixer</i> module to spread the key semantic on the whole image, so that one can directly compare the processed image pairs. We first adopt a convolutional neural network to extract features from both support and query images and separate each of them into multiple Local Descriptor-based Representations (LDRs). The LDRs are then fed into the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixer</i> for semantic spread, where every LDR attracts complementary information from its peers. In this way, the objective semantic is made spread on the whole image in a data-driven manner. The overall pipeline is supervised by a voting-based loss, guaranteeing a good <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixer</i> . Visualization results validate the feasibility of our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixer</i> . Comprehensive experiments on three benchmark datasets, miniImageNet, tieredImageNet, and CUB, show that our algorithm achieves the state-of-the-art performance in both 5-way 1-shot and 5-way 5-shot settings.
Conformal Chebyshev chaotic map-based remote user password authentication protocol using smart card

Abstract With the rapid advancement and growth of computer networks, there have been greater and greater demands for remote user password authentication protocols. In current ages, smartcard-based authentication protocol has formed the standard with their incredibly insubstantial, user-friendly equipment and low-cost apps. In this study, we proposed an effective robust authentication protocol using the conformable chaotic map, where a conformable calculus is a branch of newly appearing fractional calculus. It has a magnificent property, because it formulates using a controller term. We shall also offer formal proof of smooth execution of the proposed authenticated protocol. Our new protocol is more secure as compared to several comparable protocols.
Indistinguishability and Non-deterministic Encryption of the Quantum Safe Multivariate Polynomial Public Key Cryptographic System

Multivariate Polynomial Public Key (MPPK) is a cryptographic system, over a prime Galois field. A key pair is generated using a multiplier multivariate polynomial and two multiplicand univariate solvable polynomials. They yield two product multivariate polynomials. The first variable is used for carrying the message or secret and others are used as noise sources. The public key consists of all the coefficients of the product multivariate polynomials, except the two constant coefficients, in terms with coefficients attached to the message variable, and a noise function or a polynomial of only noise variables generated from the constant term of the multiplier multivariate polynomial by multiplying a private random variable R. The private key is made of both univariate solvable multiplicand polynomials and the private R. Encryption takes a secret message and random numbers for noises, adding noise that is automatically cancelled by decryption. Decryption is achieved evaluating a solvable equation. We review security analysis that can be employed to crack MPPK secrets and private keys. Finally, we discuss indistinguishability and non-deterministic encryption, key properties of MPPK.
An Implementation of a Blockchain-based Data Marketplace using Geth

Nowadays data is one of the most important assets that can be obtained, as many applications rely on data to generate useful services. However, a very few number of companies control, in a centralized manner, a large portion of data. That, combined with inefficiencies in centralized storage and recent data leak scandals, highlights the need for new ways in which data is shared and consumed, in which privacy and access control is guaranteed by design. Based on that, in this paper we present an implementation of a blockchain-based data marketplace utilizing the Go Ethereum (Geth) library. The implementation consists of an IoT node powered by a raspberry pi zero W, which is utilized to collect data from the environment and store it in an InterPlanetary File System (IPFS) external server, a web page that displays the marketplace, and a private blockchain that records transactions. Regarding the private blockchain, three smart contracts are developed in order to: 1) record information about the data in the marketplace; 2) record transactions that occur between users; 3) allow sellers to white/blacklist buyers' access to the data. This implementation shows that a decentralized blockchain-based marketplace is feasible and scalable, and we hope it can serve as an early model for future frameworks.
Metre as a stylometric feature in Latin hexameter poetry

Abstract This article demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, classification experiments are performed between the works of six authors using four different machine-learning models. The results showed a pairwise classification accuracy of at least 90% with samples as small as ten lines and no greater than seventy-five lines (up to around 500 words). In a multiclass setting, classification accuracy exceeded 95% for all four algorithms when using eighty-one-line chunks. These sample sizes are an order of magnitude smaller than those typically recommended for BOW (‘bag of words’) or n-gram approaches, and the reported accuracy is outstanding. Additionally, this article explores the potential for outlier (forgery) detection, or ‘one-class classification’. As an example, analysis of the disputed Aldine Additamentum (Sil. Ital. Pun. 8:144–223) concludes (P &amp;lt; 0.0001) that the metrical style differs significantly from that of the rest of the poem.
FUSIONET: A Hybrid Model Towards Image Classification

Image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. Contextual here means this approach is focusing on the relationship of the nearby pixels also called neighborhood. An open topic of research in computer vision is to devise an effective means of transferring human’s informal knowledge into computers, such that computers can also perceive their environment. However, the occurrence of object with respect to image representation is usually associated with various features of variation causing noise in the image representation. Hence, it tends to be very difficult to actually disentangle these abstract factors of influence from the principal object. In this paper, we have proposed a hybrid model: FUSIONET, which has been modeled for studying and extracting meaning facts from images. Our proposition combines two distinct stack of convolution operation (3 × 3 and 1 × 1, respectively). Successively, these relatively low-feature maps from the above operation are fed as input to a downstream classifier for classification of the image in question.
A Survey on Object Detection Performance with Different Data Distributions

Detecting objects in a dynamic scene is a critical step for robotic navigation. A mobile robot may need to slow down in presence of children, elderly or dense crowds. A robot’s movement needs to be precise and socially adjustable especially in a hospital setting. Identifying key objects in a scene can provide important contextual awareness to a robot. Traditional approaches used handcrafted features along with object proposals to detect objects in images. Recently, object detection has made tremendous progress over the past few years thanks to deep learning and convolutional neural networks. Networks such as SSD, YOLO, and Faster R-CNN have made significant improvements over traditional techniques while maintaining real-time inference speed. However, current existing datasets used for benchmarking these models tend to contain mainly outdoor images using a high-quality camera setup that is usually different from a robotic vision setting where a robot moves around in a dynamic environment resulting in sensor noise, motion blur, and change in data distribution. In this work, we introduce our custom dataset collected in a realistic hospital environment consisting of distinct objects such as hospital beds, tables, and wheelchairs. We also use state-of-art object detectors to showcase the current performance and gaps in a robotic vision setting using our custom CHART dataset and other public datasets.
Embedded Intelligence in a System for Automatic Test Generation for Smoothly Digital Transformation in Higher Education

Automated test generation is an important component in the digital transformation of education. Assessing knowledge using tests is an objective, fast and effective. Electronic tests are effective in verifying knowledge but they also can be used for self-learning. Generating tests by teachers is a laborious and time-consuming process. The development of an automatic test generation system is a key component for adapting education to the ever-changing digital environment. This paper presents a logical approach to assessing student success in the testing process. The approach satisfies the two goals of testing: knowledge testing and self-learning. A process for intelligent transition from one level of complexity of the testing system to another has been developed. This is a new and innovative approach to developing an intelligent automatic test generation system. None of our known training management systems has a self-test module with self-generated questions. The system we developed generates questions independently using templates. The system also navigates students through the different levels of complexity of the content, using the puzzle method and the curiosity function. Adding intelligence to an automated e-test generation system can be a milestone of digital transformation in education and can increase the efficiency of the teaching and learning process during a crisis. Today we are facing the crisis with COVID-19, which affects all areas of work, study and life. Our intelligent approach helps educational institutions to overcome atypical situations like the one we face now. It can also support the universities for a smooth digital transformation and better handling with future atypical situations.
Alzheimer Detection Using Deep Convolutional GAN

In this paper, an unsupervised generative modeling method that generates synthetic images with the help of Deep Convolutional Generative Adversarial Networks (DCGANs). A method has been put forward that uses labelled Magnetic Resonance Imaging (MRI) dataset and then applies DCGAN on the limited amount of data, hence enlarging the dataset size as well as its diversity through the use of GAN. Further the synthetic images and training dataset are merged together and trained into a Convolutional Neural Network (CNN) and its different architectures for the classification of four different stages of Alzheimer's disease. The classification performance using CNN yielded an accuracy of 69%, ResNet50 yielded respectively. On increasing the number of epochs as well as the number of dataset images more accuracy can be obtained.
A Secure Cross-Shard View-Change Protocol for Sharding Blockchains

A complete sharding blockchain consists of many vital components, the two most important of which are the intra-shard consensus algorithm and the cross-shard transaction processing method. The latter usually requires a two-phase commit protocol, which usually relies on the shard leaders to transfer critical messages among different shards. In the process, a leader might behave maliciously. In response to possible problems in cross-shard transaction processing, this paper makes the following contributions. First, this paper proposes a cross-shard transaction censorship attack that may be launched by a shard leader. The leader might behave honestly inside the shard while does not transfer key messages between the shards. Second, a cross-shard view-change protocol is proposed to defend against the attack. When a shard leader behaves maliciously between shards, a related shard’s members can run an intra-shard consensus algorithm to generate a proof of the leader’s malicious behavior and forward the proof to the corresponding shard’s members. The shard members could launch an intra-shard view-change operation to replace the malicious leader with a new one. Third, it is proved that the proposed protocol satisfies consistency and liveness. The secure cross-shard view-change protocol can be applied to most sharding blockchains to ensure the safe and efficient execution of cross-shard transactions.
Machine Learning approach to classify and predict different Osteosarcoma types

Family physicians rarely see a malignant bone cancer because it is hard to find, and most of the time, bone cancer is benign. It is very time-consuming and complicated for the pathologist to classify Osteosarcoma histopathological images. Typically Osteosarcoma classifies into viable, Non-viable, and Non-tumor classes, but intra-class variation and inter-class similarity are complex tasks. This paper used the Random Forest(RF) machine learning algorithm, which efficiently and accurately classifies Osteosarcoma into Viable, Non-viable, and Non-tumor classes. The Random Forest method gives a classification accuracy of 92.40%, a sensitivity of 85.44%, and specificity 93.38% with AUC=0.95.
Prototypical Classifier for Robust Class-Imbalanced Learning

Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise and class imbalance. While both learning with noisy labels and class-imbalanced learning have received tremendous attention, existing works mainly focus on one of these two training set biases. To fill the gap, we propose Prototypical Classifier, which does not require fitting additional parameters given the embedding network. Unlike conventional classifiers that are biased towards head classes, Prototypical Classifier produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. By leveraging this appealing property, we can easily detect noisy labels by thresholding the confidence scores predicted by Prototypical Classifier, where the threshold is dynamically adjusted through the iteration. A sample reweighting strategy is then applied to mitigate the influence of noisy labels. We test our method on both benchmark and real-world datasets, observing that Prototypical Classifier obtains substaintial improvements compared with state of the arts.
Metric Indexing for Graph Similarity Search

Finding the graphs that are most similar to a query graph in a large database is a common task with various applications. A widely-used similarity measure is the graph edit distance, which provides an intuitive notion of similarity and naturally supports graphs with vertex and edge attributes. Since its computation is \(\mathsf {NP}\)-hard, techniques for accelerating similarity search have been studied extensively. However, index-based approaches for this are almost exclusively designed for graphs with categorical vertex and edge labels and uniform edit costs. We propose a filter-verification framework for similarity search, which supports non-uniform edit costs for graphs with arbitrary attributes. We employ an expensive lower bound obtained by solving an optimal assignment problem. This filter distance satisfies the triangle inequality, making it suitable for acceleration by metric indexing. In subsequent stages, assignment-based upper bounds are used to avoid further exact distance computations. Our extensive experimental evaluation shows that a significant runtime advantage over both a linear scan and state-of-the-art methods is achieved.