Reviewerly/exHarmony huggingface dataset:
New Modified Controlled Bat Algorithm for Numerical Optimization Problem

Bat algorithm (BA) is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems. BA leverages the echolocation feature of bats produced by imitating the bats’ searching behavior. BA faces premature convergence due to its local search capability. Instead of using the standard uniform walk, the Torus walk is viewed as a promising alternative to improve the local search capability. In this work, we proposed an improved variation of BA by applying torus walk to improve diversity and convergence. The proposed. Modern Computerized Bat Algorithm (MCBA) approach has been examined for fifteen well-known benchmark test problems. The finding of our technique shows promising performance as compared to the standard PSO and standard BA. The proposed MCBA, BPA, Standard PSO, and Standard BA have been examined for well-known benchmark test problems and training of the a.pngicial neural network (ANN). We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning (ML) repository of UCI. Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness, with more superiority compared to the traditional methodologies. The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.
Quantum Coding with Low-Depth Random Circuits

Random quantum circuits have played a central role in establishing the computational advantages of near-term quantum computers over their conventional counterparts. Here, we use ensembles of low-depth random circuits with local connectivity in $D\ge 1$ spatial dimensions to generate quantum error-correcting codes. For random stabilizer codes and the erasure channel, we find strong evidence that a depth $O(\log N)$ random circuit is necessary and sufficient to converge (with high probability) to zero failure probability for any finite amount below the optimal erasure threshold, set by the channel capacity, for any $D$. Previous results on random circuits have only shown that $O(N^{1/D})$ depth suffices or that $O(\log^3 N)$ depth suffices for all-to-all connectivity ($D \to \infty$). We then study the critical behavior of the erasure threshold in the so-called moderate deviation limit, where both the failure probability and the distance to the optimal threshold converge to zero with $N$. We find that the requisite depth scales like $O(\log N)$ only for dimensions $D \ge 2$, and that random circuits require $O(\sqrt{N})$ depth for $D=1$. Finally, we introduce an "expurgation" algorithm that uses quantum measurements to remove logical operators that cause the code to fail by turning them into additional stabilizers or gauge operators. With such targeted measurements, we can achieve sub-logarithmic depth in $D\ge 2$ below capacity without increasing the maximum weight of the check operators. We find that for any rate beneath the capacity, high-performing codes with thousands of logical qubits are achievable with depth 4-8 expurgated random circuits in $D=2$ dimensions. These results indicate that finite-rate quantum codes are practically relevant for near-term devices and may significantly reduce the resource requirements to achieve fault tolerance for near-term applications.
Classification and reconstruction of optical quantum states with deep neural networks

We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feedforward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed [arXiv:2008.03240] QST technique with conditional generative adversarial networks (QST-CGAN). We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than a standard iterative maximum likelihood (iMLE) method. Further, the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than iMLE.
Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
AutoPedestrian: An Automatic Data Augmentation and Loss Function Search Scheme for Pedestrian Detection

Pedestrian detection is a challenging and hot research topic in the field of computer vision, especially for the crowded scenes where occlusion happens frequently. In this paper, we propose a novel AutoPedestrian scheme that automatically augments the pedestrian data and searches for suitable loss functions, aiming for better performance of pedestrian detection especially in crowded scenes. To our best knowledge, it is the first work to automatically search the optimal policy of data augmentation and loss function jointly for the pedestrian detection. To achieve the goal of searching the optimal augmentation scheme and loss function jointly, we first formulate the data augmentation policy and loss function as probability distributions based on different hyper-parameters. Then, we apply a double-loop scheme with importance-sampling to solve the optimization problem of data augmentation and loss function types efficiently. Comprehensive experiments on two popular benchmarks of CrowdHuman and CityPersons show the effectiveness of our proposed method. In particular, we achieve 40.58% in MR on CrowdHuman datasets and 11.3% in MR on CityPersons reasonable subset, yielding new state-of-the-art results on these two datasets.
Video Index Point Detection and Extraction Framework Using Custom YoloV4 Darknet Object Detection Model

The trend of learning from videos instead of documents has increased.There could be hundreds and thousands of videos on a single topic, with varying degrees of context, content, and depth of the topic.The literature claims that learners are nowadays less interested in viewing a complete video but prefers the topic of their interests.This develops the need for indexing of video lectures.Manual annotation or topic-wise indexing is not new in the case of videos.However, manual indexing is time-consuming due to the length of a typical video lecture and intricate storylines.Automatic indexing and annotation is, therefore, a better and efficient solution.This research aims to identify the need for automatic video indexing for better information retrieval and ease users navigating topics inside a video.The automatically identified topics are referred to as "Index Points."137-layer YoloV4 Darknet Neural Network creates a custom object detector model.The model is trained on approximately 6000 video frames and then tested on a suite of 50 videos of around 20 hours of run time.Shot Boundary detection is performed using Structural Similarity fused with a Binary Search Pattern algorithm which outperformed the state-of-the-art SSIM technique by reducing the processing time to approximately 21% and providing around 96% accuracy.Generation of accurate index points in terms of true positives and false negatives is detected through precision, recall, and F1 score, which varies between 60-80% for every video.The results show that the proposed algorithm successfully generates a digital index with reasonable accuracy in topic detection.
Multi-attention augmented network for single image super-resolution

How to improve the representational power of visual features extracted by deep convolutional neural networks is of crucial importance for high-quality image super-resolution. To address this issue, we propose a multi-attention augmented network, which mainly consists of content-, orientation- and position-aware modules. Specifically, we develop an attention augmented U-net structure to form the content-aware module in order to learn and combine multi-scale informative features within a large receptive field. To better reconstruct image details in different directions, we design a set of pre-defined sparse kernels to construct the orientation-aware module, which can extract more representative multi-orientation features and enhance the discriminative capacity in stacked convolutional stages. Then these extracted features are adaptively fused through channel attention mechanism. In upscale stage, the position-aware module adopts a novel self-attention to reweight the element-wise value of final low-resolution feature maps, for further suppressing the possible artifacts. Experimental results demonstrate that our method obtains better reconstruction accuracy and perceptual quality against state-of-the-art methods.
RFNet: Region-aware Fusion Network for Incomplete Multi-modal Brain Tumor Segmentation

Most existing brain tumor segmentation methods usually exploit multi-modal magnetic resonance imaging (MRI) images to achieve high segmentation performance. However, the problem of missing certain modality images often happens in clinical practice, thus leading to severe segmentation performance degradation. In this work, we propose a Region-aware Fusion Network (RFNet) that is able to exploit different combinations of multi-modal data adaptively and effectively for tumor segmentation. Considering different modalities are sensitive to different brain tumor regions, we design a Region-aware Fusion Module (RFM) in RFNet to conduct modal feature fusion from available image modalities according to disparate regions. Benefiting from RFM, RFNet can adaptively segment tumor regions from an incomplete set of multi-modal images by effectively aggregating modal features. Furthermore, we also develop a segmentation-based regularizer to prevent RFNet from the insufficient and unbalanced training caused by the incomplete multi-modal data. Specifically, apart from obtaining segmentation results from fused modal features, we also segment each image modality individually from the corresponding encoded features. In this manner, each modal encoder is forced to learn discriminative features, thus improving the representation ability of the fused features. Remarkably, extensive experiments on BRATS2020, BRATS2018 and BRATS2015 datasets demonstrate that our RFNet outperforms the state-of-the-art significantly.
Foreground Activation Maps for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, with only image-level labels, learning object classification models tends to activate object parts and ignore the whole object, while expanding object parts into the whole object may deteriorate classification performance. To alleviate this problem, we propose foreground activation maps (FAM), whose aim is to optimize object localization and classification jointly via an object-aware attention module and a part-aware attention module in a unified model, where the two tasks can complement and enhance each other. To the best of our knowledge, this is the first work that can achieve remarkable performance for both tasks by optimizing them jointly via FAM for WSOL. Besides, the designed two modules can effectively highlight foreground objects for localization and discover discriminative parts for classification. Extensive experiments with four backbones on two standard benchmarks demonstrate that our FAM performs favorably against state-of-the-art WSOL methods.
Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes’ formula are conceivable as fuzzy sets and Bayes’ inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.
A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization

Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for "transfer learning" in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.
LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability

Deep learning-based fingerprinting techniques have recently emerged as potential enablers of various wireless applications. However, their resiliency to time, location, and/or configuration changes in the operating environment undoubtedly remains one major challenge that lies ahead in their deployment pathway. In this paper, we present an experimental framework that aims to disclose, understand and overcome the sensitivity of LoRa device fingerprinting to variations in deployment settings. We first began by presenting our RF fingerprinting datasets, collected from 25 different LoRa devices. The datasets cover a comprehensive set of experimental scenarios, considering both indoor and outdoor environments with varying network deployment settings, such as varying the distance between the transmitters and the receiver, the configuration of the LoRa protocol, the physical location of the conducted experiment, and the receiver hardware used for capturing the fingerprints. We then proposed a new technique that leverages out-of-band spectrum distortions, that are caused by device-specific hardware impairments, to provide unique device signatures that we exploit to improve fingerprinting accuracy. Finally, we conducted an experimental study that discloses the sensitivity of deep learning-based RF fingerprinting to changes in various deployment settings while considering three data representations of the learning model input: time-domain IQ, frequency-domain FFT, and Amplitude/Phase polar-coordinate. We found that the learning models perform relatively well when trained and tested under the same deployment settings, with FFT representation yielding the best performance followed by IQ representation. However, when trained and tested under different settings, the models (i) fail to maintain their high accuracy when the channel conditions change, and (ii) completely lose their ability to classify devices when the LoRa configuration and/or the USRP receiver hardware change. In addition, we interestingly observed that FFT representation performs exceptionally poorly when training and testing are done under different deployment settings, regardless of the type of the setting change.
PPVF: Privacy-Preserving Protocol for Vehicle Feedback in Cloud-Assisted VANET

The vehicular ad hoc network (VANET) is a platform for exchanging information between vehicles and everything to enhance driver’s driving experience and improve traffic conditions. The reputation system plays an essential role in judging whether to communicate with the target vehicle based on other vehicles’ feedback. However, existing reputation systems ignore the privacy protection of feedback providers. Additionally, traditional VANET based on wireless sensor networks (WSNs) has limited power, storage, and processing capabilities, which cannot meet the real-world demands in a practical VANET deployment. Thus, we attempt to integrate cloud computing with VANET and proposes a privacy-preserving protocol of vehicle feedback (PPVF) for cloud-assisted VANET. In cloud-assisted VANET, we integrate homomorphic encryption and data aggregation technology to design the scheme PPVF, in which with the assistance of the roadside units (RSU), cloud service provider (CSP) obtains the total number of vehicles with the corresponding parameters in the feedback for reputation calculation without violating individual feedback privacy. Simulation results and security analysis confirm that PPVF achieves effective privacy protection for vehicle feedback with acceptable computational and communication burden. Besides, the RSU is capable of handling 1999 messages for every <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300ms$ </tex-math></inline-formula> , so as the number of vehicles in the communication domain increases, the PPVF has a lower message loss rate.
Pre-training Graph Transformer with Multimodal Side Information for Recommendation

Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate items by common user activities, e.g., co-purchase, and construct a homogeneous item graph. This graph provides a unified view of item relations and their associated side information in multimodality. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction. Experimental results on real datasets demonstrate that the proposed PMGT model effectively exploits the multimodality side information to achieve better accuracies in downstream tasks including item recommendation and click-through ratio prediction. In addition, we also report a case study of testing PMGT in an online setting with 600 thousand users.