Reviewerly/exHarmony huggingface dataset:
ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark.
Automatic Bangla Article Content Categorization using a Hybrid Deep Learning Model

The process of categorizing or marking documents based on their content is known as document classification or categorization. Bangla is the world's fifth most widely spoken native language and the sixth most widely spoken language overall. Proper methods for automatic document classification are very limited. In this paper, we have introduced a hybrid deep learning model which is the combination of CNN-BiLSTM for automatic article categorization for Bangla language. We have also compared our model with different classifiers to measure its efficiency. Our proposed model acheived 83.48% accuracy.
A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors

Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities.
Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data, especially where modern data is considerably different than legacy data, it is an expensive exercise. The risk associated with modernizing such legacy data lies within its uncertainty in return (e.g., the possibility of new discoveries, in primarily greenfield settings). Without any advanced knowledge of yet unanalyzed elements, the importance of re-analyses remains ambiguous. To address this uncertainty, we apply machine learning to multivariate geochemical data from different regions in Canada (i.e., the Churchill Province and the Trans-Hudson Orogen) in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses. Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data (e.g., prospectivity mapping). Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.
Towards a systematic multi-modal representation learning for network data

Learning the right representations from complex input data is the key ability of successful machine learning (ML) models. The latter are often tailored to a specific data modality. For example, recurrent neural networks (RNNs) were designed having sequential data in mind, while convolutional neural networks (CNNs) were designed to exploit spatial correlation in images. Unlike computer vision (CV) and natural language processing (NLP), each of which targets a single well-defined modality, network ML problems often have a mixture of data modalities as input. Yet, instead of exploiting such abundance, practitioners tend to rely on sub-features thereof, reducing the problem to single modality for the sake of simplicity. In this paper, we advocate for exploiting all the modalities naturally present in network data. As a first step, we observe that network data systematically exhibits a mixture of quantities (e.g., measurements), and entities (e.g., IP addresses, names, etc.). Whereas the former are generally well exploited, the latter are often underused or poorly represented (e.g., with one-hot encoding). We propose to systematically leverage language models to learn entity representations, whenever significant sequences of such entities are historically observed. Through two diverse use-cases, we show that such entity encoding can benefit and naturally augment classic quantity-based features.
High-resolution facial expression image restoration via adaptive total variation regularization for classroom learning environment

Infrared videos play an important role in recording the learning process. Facial expression images are an important part of infrared videos. High-resolution facial images can reflect the emotion of students or teachers in the classroom. However, recorded infrared videos inevitably have random noise and image blur, which influence facial expression recognition and head pose estimation. In this study, we introduce a blind image restoration method with wavelet transform and total variation regularization. The difference between the low-resolution facial expression image and high-resolution one is revealed by the wavelet transform and total variation regularization. The distribution of the wavelet transform coefficient of high-resolution images is sparser than the coefficient distribution of original low-resolution images. The major novelty of this work is that the sparsity of coefficient distribution is described by the wavelet transform and total variation regularization. Furthermore, the proposed method is conducted on real facial images to verify the effectiveness of priori knowledge. Numerical experiments demonstrate that the proposed method can recover high-resolution facial image and facilitate the application on facial expression recognition.
Investigating Orthographic Versus Auditory Cross‐Situational Word Learning With Online and Laboratory‐Based Testing

Abstract Research has shown that novel words can be learned through the mechanism of statistical or cross‐situational word learning (CSWL). So far, CSWL studies using adult populations have focused on the presentation of spoken words. However, words can also be learned through their written form. This study compared auditory and orthographic presentations of novel words with different degrees of phonological overlap using CSWL in a laboratory‐based and an online‐based approach. In our analyses, we first compared accuracy across modalities, with our findings showing more accurate recognition performance for CSWL when novel words were presented through their written forms (orthographic condition) rather than through their spoken forms (auditory condition). Bayesian modeling suggested that accuracy for the orthographic condition was higher in the laboratory compared to online, whereas performance in the auditory condition was similar across both experiments. We discuss the implications of our findings for presentation modality and the benefits of our online testing protocol for future research.
Multiple Inputs Neural Networks for Fraud Detection

This study aims to use artificial neural network based classifiers to predict fraud, particularly that related to health insurance. Medicare fraud results in considerable losses for governments and insurance companies and results in higher premiums from clients. Medicare fraud costs around 13 billion euros in Europe and between 21 billion and 71 billion US dollars per year in the United States. To detect medicare frauds, we propose a multiple inputs deep neural network based classifier with an autoencoder component. This architecture makes it possible to take into account many sources of data without mixing them and makes the classification task easier for the final model. We use the data sets from the Centers for Medicaid and Medicare Services (CMS) of the US federal government and four benchmarks fraud detection data sets. Our results show that although baseline artificial neural network give good performances, they are outperformed by our multiple inputs neural networks. We have shown that using an autoencoder to embed the provider behavior gives better results and makes the classifiers more robust to class imbalance.
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search

Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network design knowledge across multiple tasks. In this line of research, effectively modeling task correlations is vital yet highly neglected. Therefore, we propose Arch-Graph, a transferable NAS method that predicts task-specific optimal architectures with respect to given task embeddings. It leverages correlations across multiple tasks by using their embeddings as a part of the predictor's input for fast adaptation. We also formulate NAS as an architecture relation graph prediction problem, with the relational graph constructed by treating candidate architectures as nodes and their pairwise relations as edges. To enforce some basic properties such as acyclicity in the relational graph, we add additional constraints to the optimization process, converting NAS into the problem of finding a Maximal Weighted Acyclic Subgraph (MWAS). Our algorithm then strives to eliminate cycles and only establish edges in the graph if the rank results can be trusted. Through MWAS, Arch-Graph can effectively rank candidate models for each task with only a small budget to finetune the predictor. With extensive experiments on TransNAS-Bench-101, we show Arch-Graph's transferability and high sample efficiency across numerous tasks, beating many NAS methods designed for both single-task and multi-task search. It is able to find top 0.16% and 0.29% architectures on average on two search spaces under the budget of only 50 models. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/Centaurus982034/Arch-Graph
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection

Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems. The wide-ranging anomalies in images, such as various sizes, shapes, and colors, make automatic visual inspection challenging. Previous work on image anomaly detection has achieved significant advancements. However, there are still limitations in terms of detection performance and efficiency. In this paper, a novel Position Encoding enhanced Feature Mapping (PEFM) method is proposed to address the problem of image anomaly detection, detecting the anomalies by mapping a pair of pre-trained features embedded with position encodes. Experiment results show that the proposed PEFM achieves better performance and efficiency than the state-of-the-art methods on the MVTec AD dataset, an AUCROC of 98.30% and an AUCPRO of 95.52%, and achieves the AUCPRO of 94.0% on the MVTec 3D AD dataset.
Efficient Public Key Encryption With Outsourced Equality Test for Cloud-Based IoT Environments

Cloud-based Internet of Things (IoT) system is becoming a promising architecture in our modern society. However, cloud-based IoT system brings a number of challenges in the security aspect while improving the efficiency of data analytics. Especially, searching on encrypted data is challenging given today’s technology. Thus searchable encryption has emerged as one of the important research fields. Public key encryption with equality test (PKEET) provides a simple but useful mechanism to cryptographically protect data while keeping it available for equality test on ciphertexts. However, PKEET schemes in the literature are not suitable for cloud-based IoT system with privacy protection enhancement since the untrustworthy cloud server may be interested in query results by itself and hence reveal the private information of data owner out of the expectation of data user. Even worse, it could launch offline message recovery attack (OMRA) based on the returned query results. In this paper, we introduce a new notion of public key encryption with outsourced equality test (PKE-OET) for adapting to cloud-based IoT environments as well as providing a flexible solution to resist against OMRA without taking any information about entrusted parties as input in encryption. We formally define the security model of PKE-OET against three types of adversary including IND-CCA-I, IND-CCA-II and IND-CCA-III. We present a generic PKE-OET construction using a new variant of smooth projective hash function (SPHF) with a novel Lin-Hom property, which is of independent interest. Then we provide an efficient PKE-OET instantiation from Symmetric eXternal Diffie-Helllman (SXDH) assumption and show its practicality for cloud-based IoT environments through a series of experiments on Cloud Server and Raspberry Pi.
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification

In person re-identification (ReID), very recent researches have validated pre-training the models on unlabelled person images is much better than on ImageNet. However, these researches directly apply the existing self-supervised learning (SSL) methods designed for image classification to ReID without any adaption in the framework. These SSL methods match the outputs of local views (e.g., red T-shirt, blue shorts) to those of the global views at the same time, losing lots of details. In this paper, we propose a ReID-specific pre-training method, Part-Aware Self-Supervised pre-training (PASS), which can generate part-level features to offer fine-grained information and is more suitable for ReID. PASS divides the images into several local areas, and the local views randomly cropped from each area are assigned a specific learnable [PART] token. On the other hand, the [PART]s of all local areas are also appended to the global views. PASS learns to match the outputs of the local views and global views on the same [PART]. That is, the learned [PART] of the local views from a local area is only matched with the corresponding [PART] learned from the global views. As a result, each [PART] can focus on a specific local area of the image and extracts fine-grained information of this area. Experiments show PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves 92.2%/90.2%/88.5% mAP accuracy on Market1501 for supervised/UDA/USL ReID. Our codes are available at https://github.com/CASIA-IVA-Lab/PASS-reID .
BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms

Barren plateaus are a notorious problem in the optimization of variational quantum algorithms and pose a critical obstacle in the quest for more efficient quantum machine learning algorithms. Many potential reasons for barren plateaus have been identified but few solutions have been proposed to avoid them in practice. Existing solutions are mainly focused on the initialization of unitary gate parameters without taking into account the changes induced by input data. In this paper, we propose an alternative strategy which initializes the parameters of a unitary gate by drawing from a beta distribution. The hyperparameters of the beta distribution are estimated from the data. To further prevent barren plateau during training we add a novel perturbation at every gradient descent step. Taking these ideas together, we empirically show that our proposed framework significantly reduces the possibility of a complex quantum neural network getting stuck in a barren plateau.
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis

Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work, we propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable of discovering semantic attributes during training and controlling them in an unsupervised manner. After that, we inject the prior of SURF-GAN into StyleGAN to obtain a high-fidelity 3D-controllable generator. Unlike existing latent-based methods allowing implicit pose control, the proposed 3D-controllable StyleGAN enables explicit pose control over portrait generation. This distillation allows direct compatibility between 3D control and many StyleGAN-based techniques (e.g., inversion and stylization), and also brings an advantage in terms of computational resources. Our codes are available at https://github.com/jgkwak95/SURF-GAN .
Duple Color Image Encryption System Based on 3-D Nonequilateral Arnold Transform for IIoT

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, which contains sensitive information. Therefore, how to protect data security is an important challenge in the development of IIoT. To this end, we propose a duple color image encryption method for IIoT. Different from the traditional algorithm that encrypts one plain image into one ciphertext image, our algorithm encrypts two color images into one color ciphertext image which can cause great confusion to the attacker who illegally breaks the ciphertext image. First, the proposed algorithm converts two color images with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N\times M$</tex-math></inline-formula> into a 3-D bit-level matrix with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N\times M\times 48$</tex-math></inline-formula> . Next, 3-D nonequilateral Arnold transform (3D-NEAT) is applied to permutate the positions of the elements of the resulted 3-D bit-level matrix. Then, the permutated 3-D bit-level matrix is transformed into three 2-D pixel-level images and then diffused by the random diffusion sequences that 3-D Lorenz system (3D-LS) generates. Finally, the scrambling matrices generated by 3D-LS are used to scramble three diffused 2-D pixel-level images, and the output is considered as three color components of the encrypted image. The numerical experiments and security analyses show that the proposed image encryption scheme has strong resistance to several known attacks, and yields near-zero correlation and near-eight entropy for the RGB cipher image, and its performance is better than some of the recently proposed image encryption algorithms.
Auction Algorithm: Peer-To-Peer System Based on Hybrid Technologies for Smallholder Farmers to Control Demand and Supply

The demand for agricultural goods could be affected by variables such as population growth and consumer tastes. Environmental and zoonotic risks jeopardise crop yields as well as the dependability of distribution and storage networks. Governments can aid in addressing issues of supply and demand by enacting policies like price controls and subsidies. Climate change threatens agricultural output and livestock health, while other climate variables have been polluted by outside sources. Pesticides not only endanger wildlife that isn't intended to be harmed, but they also poison the environment, killing beneficial insects and other organisms. The rising costs of agricultural goods may be mitigated, at least in part, through regional and global trade agreements. When third parties intervene in farming, farmers stand to lose money. One example is the utilisation of low-quality inputs, while another is predatory financing. So we are proposing an Auction Algorithm: peer-to-peer system based on hybrid technologies for smallholder farmers to control demand and supply. To implement an auction algorithm, you need to set up the auction platform and back-end systems. You may also need to integrate the platform with hybrid technologies such as block chain and machine learning. Together, machine learning and blockchain technology can help agriculture companies improve their forecasts of demand and supply, make it easier to keep track of inventory, and reduce risks and inefficiencies in the supply chain. Designing and constructing the blockchain, establishing the P2P network, testing and deploying the system these are the stages of bringing a peer-to-peer system to life using blockchain technology. When we implement the above technologies in the agriculture sector, they have the potential to improve efficiency, promote transparency, and eventually help farmers increase their earnings. This technology can help increase agricultural quality by enhancing industrial efficiency, transparency, and sustainability.