Reviewerly/exHarmony huggingface dataset:
Local Features Based Deep Learning for Mammographic Image Classification: In Comparison to CNN Models

Convolutional neural networks (CNN) have achieved tremendous success in tackling various tasks of computer vision problems such as classification of images and recognition of objects. The general trend has been to make networks larger and more complex in order to achieve higher precision. In any case, these advances to improve precision are not really making the systems increasingly effective for size and speed. In this report, an attempt to overcome the aforementioned issues is described. Some well-known scale and rotation invariant local features such as SIFT, SURF, ORB, BRISK, and KAZE were first observed to check which of these having the strong potentiality to replace inclusively the convolutional layers of CNN models. To do this, while retaining the fully connected layers of the corresponding CNN model as the classifier, the convolutional parts of the model were inclusively taken out and assigned the high-level feature maps at the flatten layer with scale and rotation invariant local features. The computational experiments were carried out using the challenging mammographic images provided by MIAS (mammographic image analysis society). It contains 322 grayscale images from 2 different severity, each of which has 1024x1024 pixels. Employing only the fully connected layers of the VGG16 model as the classifier, it is found that SIFT and SURF based features offer much better training and testing accuracies compared to ORB, BRISK, and KAZE in classifying MIAS image datasets. Using SIFT and SURF local features and employing only VGG16 or MobileNetV2 fully connected layers as a classifier, it can be shown that SIFT and SURF based deep learning exhibit state-of-the-art performance on MIAS datasets. SIFT and SURF based deep learning are capable of outperforming the performance of the original VGG16 and MobileNetV2 configuration in terms of accuracy and training time needed on the same datasets.
Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set

Abstract Backdoor data poisoning attacks add mislabeled examples to the training set, with an embedded backdoor pattern, so that the classifier learns to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address posttraining detection of scene-plausible perceptible backdoors, a type of backdoor attack that can be relatively easily fashioned, particularly against DNN image classifiers. A post-training defender does not have access to the potentially poisoned training set, only to the trained classifier, as well as some unpoisoned examples that need not be training samples. Without the poisoned training set, the only information about a backdoor pattern is encoded in the DNN's trained weights. This detection scenario is of great import considering legacy and proprietary systems, cell phone apps, as well as training outsourcing, where the user of the classifier will not have access to the entire training set. We identify two important properties of scene-plausible perceptible backdoor patterns, spatial invariance and robustness, based on which we propose a novel detector using the maximum achievable misclassification fraction (MAMF) statistic. We detect whether the trained DNN has been backdoor-attacked and infer the source and target classes. Our detector outperforms existing detectors and, coupled with an imperceptible backdoor detector, helps achieve posttraining detection of most evasive backdoors of interest.
Increased-Confidence Adversarial Examples for Deep Learning Counter-Forensics

Transferability of adversarial examples is a key issue to apply this kind of attacks against multimedia forensics (MMF) techniques based on Deep Learning (DL) in a real-life setting. Adversarial example transferability, in fact, would open the way to the deployment of successful counter forensics attacks also in cases where the attacker does not have a full knowledge of the to-be-attacked system. Some preliminary works have shown that adversarial examples against CNN-based image forensics detectors are in general non-transferrable, at least when the basic versions of the attacks implemented in the most popular libraries are adopted. In this paper, we introduce a general strategy to increase the strength of the attacks and evaluate their transferability when such a strength varies. We experimentally show that, in this way, attack transferability can be largely increased, at the expense of a larger distortion. Our research confirms the security threats posed by the existence of adversarial examples even in multimedia forensics scenarios, thus calling for new defense strategies to improve the security of DL-based MMF techniques.
2D progressive fusion module for action recognition

Network convergence as well as recognition accuracy are essential issues when applying Convolutional Neural Networks (CNN) to human action recognition. Most deep learning methods neglect model convergence when striving to improve the abstraction capability, thus degrading the performances sharply when computing resources are limited. To mitigate this problem, we propose a structure named 2D Progressive Fusion (2DPF) Module which is inserted after the 2D backbone CNN layers. 2DPF fuses features through a novel 2D convolution on the spatial and temporal dimensions called variation attenuating convolution and applies fusion techniques to improve the recognition accuracy and the convergency. Our experiments performed on several benchmarks (e.g., Something-Something V1&V2, Kinetics400 & 600, AViD, UCF101) demonstrate the effectiveness of the proposed method. ARTICLE INFO.
Neural Network Compression for Noisy Storage Devices

Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN model compression, there has been considerably less investigation in the actual physical storage of NN parameters. Conventionally, model compression and physical storage are decoupled, as digital storage media with error-correcting codes (ECCs) provide robust error-free storage. However, this decoupled approach is inefficient as it ignores the overparameterization present in most NNs and forces the memory device to allocate the same amount of resources to every bit of information regardless of its importance. In this work, we investigate analog memory devices as an alternative to digital media – one that naturally provides a way to add more protection for significant bits unlike its counterpart, but is noisy and may compromise the stored model’s performance if used naively. We develop a variety of robust coding strategies for NN weight storage on analog devices, and propose an approach to jointly optimize model compression and memory resource allocation. We then demonstrate the efficacy of our approach on models trained on MNIST, CIFAR-10, and ImageNet datasets for existing compression techniques. Compared to conventional error-free digital storage, our method reduces the memory footprint by up to one order of magnitude, without significantly compromising the stored model’s accuracy.
Research on pedestrian detection using optimized mask R-CNN algorithm in low-light road environment

Abstract Aiming to the performance degradation of the object detection algorithms in low-light environment, the image fusion module (MSRCR-IF) proposed in this paper is introduced into the object detection network based on the object detection algorithm of Mask R-CNN. This proposed fusion algorithm adjusts Region Proposal Network (RPN) and delete instance mask branch to achieve better pedestrian detection performance of algorithm in low-light environment. The experimental results reveal that the algorithm proposed in this paper has better detection performance than other current mainstream algorithms in COCO2017 data set, and the average detection accuracy of 85.05% was achieved under the self-built low-light road environment data set, which was 4.66% higher than before improvement. In order to verify the effectiveness of the improved algorithm, a real car data test was conducted, and the test results showed that the improved method can effectively improve the detection effect of pedestrians in low light conditions.
Work-In-Progress: Designing a Server-Side Progressive JPEG Encoder for Real-Time Applications

Images are an important part of digital communication in this era. The optimization of image file size and algorithms for image transfer are vital to ensure quality of service levels and expected time requirements for services. This paper describes and evaluates a combination of file reduction and scheduling techniques from a system design level to ensure that image transfer timing constraints are satisfied.
A Comprehensive Investigation alongwith the Statistical Evaluation for the Characterization of Ilmenite Mineral by X‐ray Fluorescence Spectrometry and Optical Emission Spectrometry

Abstract A comparative investigation on characterization of ilmenite manuscript was performed by energy dispersive X‐ray fluorescence spectrometry (EDXRF) and carrier distillation direct current arc atomic emission spectrometry (D.C. Arc AES) along with the statistical evaluation of the data obtained. 5 % AgCl was used as carrier in AES to avoid spectral interference arising out of the emission rich matrix elements. An exercise was performed to evaluate the total uncertainty associated with the methodology for characterization of base reference materials. The developed methodologies were validated using synthetic samples. ∼30 % Fe and ∼35 % Ti as major elements alongwith Ca, Mg, Cr, Si, Al, Ni, Mn, V, Zr, Eu, Mo, Na and Cd as minor and trace level were reported in real ilmenite samples. Student t test was performed at 95 % confidence limit with 10 degrees of freedom revealed that the null hypothesis persisted, i. e. the data obtained by both the methods are reliable and within the limit of statistical fluctuation. The F test on variance revealed that all the variance estimated for all the analytes were less than the critical value (Fc=2.98) and hence the variance is also within the statistical fluctuation
Design games as fieldwork: Re-visiting design games from a design anthropological perspective

This article explores how design games might serve as a form of design anthropological fieldwork drawing on examples from the project 'Changing Perspectives'- a social design project aimed at designing games to support family consultants in working with incarcerated fathers' empathic understanding of how their children and family experience the collateral effects of imprisonment. In this design anthropological approach to design games, focus shifts from how games facilitate collaboration to how they can help design researchers understand the ambiguous roles of the design researcher vis-à-vis participants, the gap between research intentions and situated practices, and stakeholder agonism to help us discover and challenge implicit assumptions and roles embedded in the design project.
CRUFT: Context Recognition under Uncertainty using Fusion and Temporal Learning

Human context recognition (HCR), which involves determining a user's current situation (or context), has long been an important task in context-aware systems. With the widespread ownership of smartphones, HCR methods that utilize signals from its built-in sensors have recently received increased attention. We propose Context Recognition under label Uncertainty using Fusion and Temporal Learning (CRUFT), a novel method to recognize a diverse set of smartphone user contexts, including long-term human activities, short-term human activities, and phone placement (pocket or bag in which the smartphone is carried). Context recognition is formulated as a multi-label classification task. CRUFT uses both handcrafted features and auto-learned deep learning features extracted from raw time-series data in two separate arms. The handcrafted arm includes a Multi-Layer Perceptron (MLP), while the raw data arm utilizes a Convolutional Neural Network (CNN) along with a Bi-Directional Long Short Term Memory (Bi-LSTM) model that exploits temporal correlations in the input stream. As smartphone sensor readings, assigned timestamps, and labels can be wrong sometimes, CRUFT integrates an uncertainty module. CRUFT outperforms the state-of-the-art baselines achieving 94.25% in overall Balanced Accuracy (BA), which improves the best performing baseline by 2.7%. Our detailed analyses demonstrate the non-trivial contributions of each component in CRUFT.
Deep R-Learning for Continual Area Sweeping

Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics. We also present a video demonstration on a real service robot.
Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper. Firstly, the radial polynomials of integer-order PHFMs are modified to obtain fractional-order radial polynomials, and FrPHFMs are constructed based on the fractional-order radial polynomials; subsequently, the strong reconstruction ability, orthogonality, and geometric invariance of the proposed FrPHFMs are proven; and, finally, the performance of the proposed FrPHFMs is compared with that of integer-order PHFMs, fractional-order radial harmonic Fourier moments (FrRHFMs), fractional-order polar harmonic transforms (FrPHTs), and fractional-order Zernike moments (FrZMs). The experimental results show that the FrPHFMs constructed in this paper are superior to integer-order PHFMs and other fractional-order continuous orthogonal moments in terms of performance in image reconstruction and object recognition, as well as that the proposed FrPHFMs have strong image description ability and good stability.
A new logistic distribution based crossover operator for real-coded genetic algorithm

This paper proposed a new crossover operator called the Logistic crossover (LogX) which is used in conjunction with a well-known mutation operators Makinen, Periaux and Toivanen mutation (MPTM), non-uniform mutation (NUM) and power mutation (PM). The defined algorithm used the real encoded crossover and mutation operator. A set of 15 test problems have been taken from global optimization literature to test the performance of the proposed algorithm. Results are compared with some popular genetic algorithms (GAs) existing in the literature. The evaluation of performance of the proposed algorithm has been done by analysing the mean of the objective function values and by the Performance Index (PI). This comparative study shows that Logistic crossover operator (LogX) with three mutation operators outperform the other crossover operators.
An Overview of Image-to-Image Translation Using Generative Adversarial Networks

Image-to-image translation is an important and challenging problem in computer vision. It aims to learn the mapping between two different domains, with applications ranging from data augmentation, style transfer, to super-resolution, etc. With the success of deep learning methods in visual generative tasks, researchers have applied deep generative models, especially generative adversarial networks (GANs), to image-to-image translation since the year of 2016 and gained fruitful progress. In this survey, we have conducted a comprehensive review of the literature in this field, covering supervised and unsupervised methods, among which unsupervised approaches include one-to-one, one-to-many, many-to-many categories and some latest theories. We highlight the innovation aspect of these methods and analyze different models employed and their components. Besides, we summarized some commonly used normalization techniques and evaluation metrics, and finally, present several challenges and future research directions in this area.
3D-Skeleton Estimation based on Commodity Millimeter Wave Radar

The extraction of human pose from the 3D point-cloud data is an important research challenge in the field of human behavior recognition, which will play a vital role in smart home, elderly-care, gaming, etc. Among the variety of technologies proposed, mmWave sensor based technology has been attracting more attention in the behavior recognition due to its advantages in privacy protection. However, previous studies have focused on using sophisticated mmWave radars (which are typically costly), whereas commodity mmWave sensors have not received much attention due to their low spatial resolutions. In this paper, we propose a skeleton estimation method based on commodity millimeter-wave radar. Through data calibration, feature extraction, and model training, we demonstrate the feasibility and promising prospects of this method. We have built a database that includes 14 people and 52,805 frames. And our experimental evaluations demonstrate that our algorithm is effective.
Path Negotiation for Self-interested Multirobot Vehicles in Shared Space

This paper addresses the problem of path negotiation among self-interested multirobot operators in shared space. In conventional multirobot path planning problems, most of the research thus far has focused on the coordination and planning of collision-free paths for multiple robots with some common objectives. On the contrary, the recent progress of technologies in autonomous vehicles, including automated guidance vehicles, unmanned aerial vehicles, and manned autonomous cars, has increased demand for solving coordination and conflict avoidance in these autonomous and self-interested agents that pursue their own objectives. In this research, we tackle this problem from the operator perspective. We assume a problem setting where collisions between robots are avoided based on path reservation and negotiation. Under that circumstance, we propose a task-oriented utility function and a path negotiation algorithm for robot operators to maximize their task utility during path negotiation. The simulation and experiment results demonstrate the effectiveness of our task-based negotiation method over a simple path-based negotiation approach.
Privacy-Preserving Decision Trees Training and Prediction

In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize Fully Homomorphic Encryption (\(\mathsf {FHE }\)) to enable machine learning over encrypted data, thus providing resiliency against information leakage. However, computing over encrypted data incurs a high computational overhead, thus requiring the redesign of algorithms, in an "\(\mathsf {FHE }\)-friendly" manner, to maintain their practicality.
A MADM Location Privacy Protection Method Based on Blockchain

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.
A Secure Shard Reconfiguration Protocol for Sharding Blockchains Without a Randomness

In permissionless blockchains, due to the corruption attack of an adversary, nodes participating the protocol need to be updated regularly. In the process of node selection and committee reconfiguration, there may exist some problems. First, a complicated secure randomness generation protocol is in need. Besides, an adversary might obtain a mining puzzle in advance and start mining in ahead of honest nodes. Moreover, an adversary usually has an advantage of network delay. In order to solve the above problems, we conduct the following research. Firstly, we propose a PoW solution withhold attack against PoW-based member selection methods. An adversary might withhold his mining results in an epoch to obtain the mining puzzle of the next epoch in advance of honest nodes. Secondly, a secure shard reconfiguration protocol is designed, which does not rely on any complicated randomness generation protocol. Our shard reconfiguration protocol is proved rigorously to be secure, which means that in each selected committee, the honest node fraction exceeds a predefined target value. Thirdly, we implement our shard reconfiguration protocol. By carefully setting related system parameters, our protocol could be applied easily to most sharding blockchains. To our best knowledge, the shard reconfiguration protocol proposed in this paper is the first protocol that could safely implement node selection and committee reconfiguration of a sharding blockchain without using a secure randomness, which greatly reduces the communication and time overhead caused by the generation of a randomness.