Reviewerly/exHarmony huggingface dataset:
Do Image Classifiers Generalize Across Time?

Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions.
BIMFlexi-VR: A Virtual Reality Framework for Early-Stage Collaboration in Flexible Industrial Building Design

Integrated industrial building design is an interdisciplinary task, in which planning of flexible building structures requires effective communication and collaboration between all stakeholders already in early design stage. This paper presents BIMFlexi-VR, a collaborative framework which implements a real-time bidirectional link between a parametric modelling component created in Grasshopper for Rhinoceros that performs optimized structural calculations of an industrial building, and an immersive Virtual Reality environment in which the automatically calculated building is visualized. Users of BIMFlexi-VR are able to change parameters defining the outcome of the structural calculation directly inside the virtual environment and see the modified building design together with the associated fitness metrics in a matter of seconds. Providing an efficient and intuitive platform for early exploration of industrial building designs, BIMFlexi-VR enables collaborative decision making and facilitates the creation of more efficient and sustainable industrial constructions.
A Robust Reference Path Selection Method for Path Planning Algorithm

In this letter, a general robust reference path selection method (RPSM) that can be integrated into current existing motion planning algorithms is proposed to improve the mobile performance of autonomous patrol robots. The proposed RPSM maintains a dynamic array of path candidates that contains newly found paths, optimal historical paths, and hybrid paths that incorporate local trajectories. All these candidates will be evaluated in a unified criterion, and the best path with the minimal cost will be presented for trajectory tracking, ensuring the consistency and smoothness of the actual motion. Experiments with two different kinds of robots in various scenarios confirm that RPSM enriches the diversity and robustness of reference paths, and improves the execution efficiency by significantly reducing the average actual trajectory distance, orientation change, and total execution time, with an acceptable extra computational cost in comparison to the corresponding original hybrid A* algorithm.
Toward a Visual Concept Vocabulary for GAN Latent Space

A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated images. But existing techniques for identifying these transformations rely on either a fixed vocabulary of prespecified visual concepts, or on unsupervised disentanglement techniques whose alignment with human judgments about perceptual salience is unknown. This paper introduces a new method for building open-ended vocabularies of primitive visual concepts represented in a GAN’s latent space. Our approach is built from three components: (1) automatic identification of perceptually salient directions based on their layer selectivity; (2) human annotation of these directions with free-form, compositional natural language descriptions; and (3) decomposition of these annotations into a visual concept vocabulary, consisting of distilled directions labeled with single words. Experiments show that concepts learned with our approach are reliable and composable—generalizing across classes, contexts, and observers, and enabling fine-grained manipulation of image style and content.
Securing Medical Data in 5G and 6G via Multichain Blockchain Technology using Post-Quantum Signatures

Slicing a network is a key innovative feature to deliver 5G (the fifth generation) services. 5G aims to enhance mobile broadband, deliver massive machine-type and low latency communications with ultra-reliability. This generation manages the huge scale of the devices of the Mobile Internet of Things (MIoT). 5G has been invented for controlling security addressed to the threats found in 2G/3G/4G networks consequently. 5G preventive measures include enhanced authentication capabilities, subscriber identity protection, and additional security techniques. New 5G network technologies introduced new threats for the industrial organizations. GSMA (The GSM Association) announced that Mobile Internet of Things (IoT) requires more security in 5G as the number of IoT devices and connections are exponentially increasing. "The IoT needs to be securely coded, deployed and managed throughout its lifecycle". Common IoT architecture is subjected to the following common attacks: 1) attacks on IoT devices through the running application; 2) remote attacks over the internet; 3) physical attacks; 4) attacks through the cloud 5) attacks through Wi-Fi or mobile air interface. Moreover, IoT devices are being used to form DDoS attacks where each IoT device forms the specific data resulting a volume-based attacks. Blockchains are considered as a main solution to solve various issues in 5G such as Mobile IoT security problems and Electronic Healthcare Records sharing problems. We analyse Hashing based Post-Quantum Signatures for enhancing blockchain security.
SoDar: Multitarget Gesture Recognition Based on SIMO Doppler Radar

In recent years, various intelligent activity recognition systems have been developed based on radio frequency signals such as radar, Wi-Fi, and radio frequency identification (RFID). When only one target is present, these systems can often provide high accuracy in recognizing different activities. However, such activity identification systems often fail to work due to signal interference when multiple targets coexist. To address this problem, we propose a multitarget gesture recognition system, named SoDar, based on a commercial single-input multi-output (SIMO) dual-channel Doppler radar. First, we employ endpoint detection, low-pass filtering, and discrete wavelet transform for data preprocessing. Then, we design a multitarget signal separation algorithm by maximizing the signal-to-noise ratio, and further refine the obtained signal based on principle component analysis. Afterward, we put forward a two-stage feature extraction method to extract both static and dynamic features from each separated signal. Finally, a classification model is trained to recognize the gestures of multiple targets. To verify the performance of SoDar, we selected nine different combinations of six gestures for two targets and collected more than 8000 data samples. Experimental results showed that the accuracy of two-target gesture recognition is above 90%.
Variational Quantum Circuits to Prepare Low Energy Symmetry States

We explore how to build quantum circuits that compute the lowest energy state corresponding to a given Hamiltonian within a symmetry subspace by explicitly encoding it into the circuit. We create an explicit unitary and a variationally trained unitary that maps any vector output by ansatz A(α→) from a defined subspace to a vector in the symmetry space. The parameters are trained varitionally to minimize the energy, thus keeping the output within the labelled symmetry value. The method was tested for a spin XXZ Hamiltonian using rotation and reflection symmetry and H2 Hamiltonian within Sz=0 subspace using S2 symmetry. We have found the variationally trained unitary gives good results with very low depth circuits and can thus be used to prepare symmetry states within near term quantum computers.
An Integrated Approach for Medical Image Classification Using Potential Shape Signature and Neural Network

The prime focus of computer vision research is on the development of novel methods for object recognition methods. Among various object recognition methods, shape-based methods are found to be efficient than others. So, the present chapter focuses on the design of a novel approach for object recognition with visual information. For this, the present chapter considered the input shape in complex plane and represented with the measure of vortex flow on the cylinder with angle of attack. The Fourier transform is used to describe the input object. The evaluated performance measures indicate the efficiency of the proposed approach for classifying the input medical images.
The Effect of Vlogging on Year 5 ESL Students’ Speaking Performance

Speaking skill among English for Second Language (ESL) students has always been a concern yet it has been neglected in ESL classrooms.With the increasing integration of technology in the classrooms and the overwhelming interest of students in social media, it is believed that vlogging, which is a process of creating vlog, is able to make lessons more engaging and interesting, and even improve students' speaking skills.This research reviews some past studies done on the effectiveness of vlogging in speaking skills and they evidenced that vlogging was indeed effective.However, there is very little research conducted in Malaysia and none was conducted for primary ESL students.Therefore, this study aims to explore how much vlogging helps in improving ESL Year 5 students' speaking performance.A total of 15 students from a rural primary school were selected through convenience sampling method.The study was conducted qualitatively and the participants' vlogs were analyzed thematically.Based on the data collected, it was found that the participants show improvement in terms of grammar, content, fluency and vocabulary but pronunciation was still a challenge for them.Therefore, it is hoped that this research could serve as a stepping-stone for vlogging being used as an effective way to improve students' speaking performance.In conclusion, this research proposes a new alternative in teaching speaking during ESL lessons.
Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript Dating

Automatic dating tools for historical documents can greatly assist paleographers and save them time and effort. This paper describes a novel method for estimating the date of historical Arabic documents that employs hierarchical fusions of multiple features. A set of traditional features and features extracted by a residual network (ResNet) are fused in a hierarchical approach using joint sparse representation. To address noise during the fusion process, a new approach based on subsets of multiple features is being considered. Following that, supervised and unsupervised classifiers are used for classification. We show that using hierarchical fusion based on subsets of multiple features in the KERTAS dataset can produce promising results and significantly improve the results.
Deployment of Haar Cascade Algorithm to Detect Real-Time Faces

The most difficult issue in the regions of picture preparation is human face identification [1]. Rough Haar utilized for human face area alongside an additional three weak classifiers is proposed in this paper. Classifiers distinguish human faces utilizing skin conceal histogram organizing, eyes area and mouth ID [2]. The crude-Haar classifier is used to classify images of individuals [3]. A frail classifier is used to extract the dominant art of non-human faces based on face skin shade histogram. Eyes in the human face are identified using the powerless classifier. Finally, the mouth is also identified using the same classifier. OpenCV is used to produce results on images of individuals. Experimental results show that the proposed method is successful and extracts many features from the human face, and is effective.
Differential Privacy Preservation in Robust Continual Learning

Enhancing the privacy of machine learning (ML) algorithms has become crucial with the presence of different types of attacks on AI applications. Continual learning (CL) is a branch of ML with the aim of learning a set of knowledge sequentially and continuously from a data stream. On the other hand, differential privacy (DP) has been extensively used to enhance the privacy of deep learning (DL) models. However, the task of adding DP to CL would be challenging, because on one hand the DP intrinsically adds some noise that reduce the utility, on the other hand the endless learning procedure of CL is a serious obstacle, resulting in the catastrophic forgetting (CF) of previous samples of ongoing stream. To be able to add DP to CL, we have proposed a methodology by which we cannot only strike a tradeoff between privacy and utility, but also mitigate the CF. The proposed solution presents a set of key features: (1) it guarantees theoretical privacy bounds via enforcing the DP principle; (2) we further incorporate a robust procedure into the proposed DP-CL scheme to hinder the CF; and (3) most importantly, it achieves practical continuous training for a CL process without running out of the available privacy budget. Through extensive empirical evaluation on benchmark datasets and analyses, we validate the efficacy of the proposed solution.
Detector-Free Weakly Supervised Grounding by Separation

Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, most recent SotA methods for WSG assume an existence of a pre-trained object detector, relying on it to produce the ROIs for localization. In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector. The key idea behind our proposed Grounding by Separation (GbS) method is synthesizing ‘text to image-regions’ associations by random alpha-blending of arbitrary image pairs and using the corresponding texts of the pair as conditions to recover the alpha map from the blended image via a segmentation network. At test time, this allows using the query phrase as a condition for a non-blended query image, thus interpreting the test image as a composition of a region corresponding to the phrase and the complement region. Our GbS shows an 8.5% accuracy improvement over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual Genome, and ReferIt, as well as a complementary improvement (above 7%) over the detector-based approaches for WSG.
Locating terrane boundaries in South China with big geochemical data mining

The basis for understanding continental tectonics to a large degree relies on terrane recognition. However, discrepant identifications and interpretations of the same terrane as well as its boundary are still commonly seen in South China. For years, the central debate has been revolving around the Yangtze-Cathaysia boundary and West-East Cathaysia boundary. Here we characterized the geochemical patterns (or differences) of the proposed terranes and located their boundaries by using a big geochemical dataset of South China, which consists of compositions of 75 elements in 5231 sediment samples. To extract valuable information from this big geochemical dataset, we applied multiple data mining techniques, including principal component analysis, sequential binary partition, Sobel operator, and support vector machine. Our results show that the geochemical patterns vary distinctly at regional scale across both the subblocks (i.e., Yangtze and Cathaysia) and terranes (i.e., Jiuling, Huaiyu, and Youjiang). Accordingly, we conclude that 1) the Northeast Jiangxi fault marks not only the Jiuling-Huaiyu boundary but also the north-eastern Yangtze-Cathaysia boundary; 2) the Yangtze-Cathaysia boundary extends westward along the Jiangshan-Pingxiang-Chaling fault; and 3) a fault zone comprising the Northwest Fujian fault and Zhenghe-Dapu fault possibly demarcates the West and East Cathaysia. The boundaries defined by the contrasting geochemical patterns in this study are also supported by geophysical, Nd isotopic, and geological evidence. Our observations will allow better explanation of where the proposed terranes originated and how South China was assembled. Our work demonstrates that data mining techniques are effective in deriving and integrating information for solving geoscience problems.
Crystals-Dilithium on ARMv8

Crystals-Dilithium is one of the digital-signature algorithms in NIST’s ongoing post-quantum cryptography (PQC) standardization final round. Security and computational efficiency concerning software and hardware implementations are the primary criteria for PQC standardization. Many studies were conducted to efficiently apply Dilithium in various environments; however, they are focused on traditionally used PC and 32-bit Advanced RISC Machine (ARM) processors (Cortex-M4). ARMv8-based processors are more advanced embedded microcontrollers (MCUs) and have been widely used for various IoT devices, edge computing devices, and On-Board Units in autonomous driving cars. In this study, we present an efficient Crystals-Dilithium implementation on ARMv8-based MCU. To enhance Dilithium’s performance, we optimize number theoretic transform (NTT)-based polynomial multiplication, the core operation of Dilithium, by leveraging ARMv8’s architectural properties such as large register sets and NEON engine. We apply task parallelism to NTT-based polynomial multiplication using the NEON engine. In addition, we reduced the number of memory accesses during NTT-based polynomial multiplication with the proposed merging and register-holding techniques. Finally, we present an interleaved NTT-based multiplication simultaneously executed with ARM processor and NEON engine. This implementation can further optimize performance by eliminating the ARM processor latency with NEON overheads. Through the proposed optimization methods, for Dilithium 3, we achieved a performance improvement of about 43.83% in key pair generation, 113.25% in signing, and 41.92% in verification compared to the reference implementation submitted to the final round of the NIST PQC competition.
Sentiment Analysis Of Amazon Fine Food Reviews

The modernized world with recent inventions in technology seen nowadays has become more digitized. By making the products available online, e-commerce is taking advantage of this digitized world by making the customers get whatever they want without stepping out. The importance of the online review has become higher these days because the number of people depending on e-commerce websites for purchasing things have increased. As people believe in other opinions, going through lots of reviews before buying a product has become a common thing. For a better understanding of the product, in this busy world people don't have time to go through lots of, so, there is a need for developing a model which can polarize those reviews and generate an appropriate result. With the advancement in the area of machine learning and different technology, this task has become much more comfortable. For this project, machine learning algorithms are used on amazon fine food reviews dataset to analyze if the given review is a positive review or a negative review.
Commonsense based text mining on urban policy

Local laws on urban policy, i.e., ordinances directly affect our daily life in various ways (health, business etc.), yet in practice, for many citizens they remain impervious and complex. This article focuses on an approach to make urban policy more accessible and comprehensible to the general public and to government officials, while also addressing pertinent social media postings. Due to the intricacies of the natural language, ranging from complex legalese in ordinances to informal lingo in tweets, it is practical to harness human judgment here. To this end, we mine ordinances and tweets via reasoning based on commonsense knowledge so as to better account for pragmatics and semantics in the text. Ours is pioneering work in ordinance mining, and thus there is no prior labeled training data available for learning. This gap is filled by commonsense knowledge, a prudent choice in situations involving a lack of adequate training data. The ordinance mining can be beneficial to the public in fathoming policies and to officials in assessing policy effectiveness based on public reactions. This work contributes to smart governance, leveraging transparency in governing processes via public involvement. We focus significantly on ordinances contributing to smart cities, hence an important goal is to assess how well an urban region heads towards a smart city as per its policies mapping with smart city characteristics, and the corresponding public satisfaction.
Learned Compression Framework With Pyramidal Features and Quality Enhancement for SAR Images

Current image compression algorithms based on transforms can achieve ideal performance for natural images, but do not do well with synthetic aperture radar (SAR) images. We propose a learned compression framework with pyramidal features and quality enhancement to fully exploit the redundancy among image pixels and to improve the compression bitrate and reconstruction quality. Based on the variational autoencoder (VAE) architecture, pyramidal decomposition is performed at the first autoencoder to extract both global and coarse feature maps. The latent distribution is modeled by the second hyperprior autoencoder with a single-Gaussian model for more accurate and flexible entropy estimation. Universal quantization is applied to consolidate the entropy estimation accuracy of the hyperprior network. To further improve reconstruction quality, a residual dense network (RDN) is adopted to fully capture local and global features. Experimental results demonstrate that the proposed framework provides a better rate-distortion tradeoff than standard codecs such as JPEG, JPEG2000, and learning-based methods on both the Sandia and ICEYE datasets.
Adaptive Convolutions with Per-pixel Dynamic Filter Atom

Applying feature dependent network weights have been proved to be effective in many fields. However, in practice, restricted by the enormous size of model parameters and memory footprints, scalable and versatile dynamic convolutions with per-pixel adapted filters are yet to be fully explored. In this paper, we address this challenge by de-composing filters, adapted to each spatial position, over dynamic filter atoms generated by a light-weight network from local features. Adaptive receptive fields can be supported by further representing each filter atom over sets of pre-fixed multi-scale bases. As plug-and-play replacements to convolutional layers, the introduced adaptive convolutions with per-pixel dynamic atoms enable explicit modeling of intra-image variance, while avoiding heavy computation, parameters, and memory cost. Our method preserves the appealing properties of conventional convolutions as being translation-equivariant and parametrically efficient. We present experiments to show that, the proposed method delivers comparable or even better performance across tasks, and are particularly effective on handling tasks with significant intra-image variance.