Reviewerly/exHarmony huggingface dataset:
Double Medical Image Cryptosystem Based on Quantum Walk

With the growing capability of realizing quantum computers, most medical image cryptosystems may crash.This work proposes a novel double medical-image encryption algorithm using quantum walks and logistic mapping.In the proposed encryption technique, the double plain images are split into two different images, one of which has the high 4 bits of each image and the other has the low 4 bits.The high-bit image is encrypted using quantum walks because it possesses a large portion of information, and the other image, which possesses a small portion of information, is encrypted using the logistic map.The moderate values of information entropy, NPCR, UACI, and PSNR for the tested images are 7.9993, 99.6143%, 33.4659%, and 7.4819, respectively.The experimental results prove the efficiency of the proposed double medical image encryption technique.
Determination of the statistical distribution of drag and lift coefficients of refuse derived fuel by computer vision

This research investigates the flight behavior of refuse-derived fuel (RDF) in a drop shaft using Computer Vision to obtain statistical data on the aerodynamic properties of the particles. Methods to determine 3D geometry models of complex-shaped particles by photogrammetry and to obtain time resolved particle positions and velocities are described. Furthermore, an approach to obtain the frequency distribution of drag and lift coefficients from photogrammetric analysis and drop shaft experiments is presented. The image evaluation is based on algorithms of the open-source libraries OpenCV, COLMAP as well as MeshLab and Open3D. The precision of the system is validated employing model particles with known geometry. The 3D particle models overestimate the particle surface area by 4.58 %, the position detection works with a mean deviation of 2.73 %. The average sink rate is calculated with an accuracy of 4.87 % and the drag coefficient with an accuracy of 2.08 %. Finally, the frequency distribution of four RDF fractions, namely, textiles, cardboard, 3D plastic particles and 2D plastic foils are presented.
Style-Content-Aware Adaptive Normalization Based Pose Guided for Person Image Synthesis

Most of the tasks based on pose-guided person image synthesis have obtained accurate target pose, but still have not obtained reasonable style texture mapping. In this paper, we propose a new two-stage network to decouple style and content, which aims to enhance the accuracy of pose transfer and the realism of a person appearance. Firstly, we propose an Aligned Multi-scale Content Transfer Network(AMSNet) to predict the target edge map for pose content transfer in advance, which can not only preserve clearer texture content but also alleviate spatial misalignment through advancing to transfer pose information. Secondly, we propose a new Style Texture Transfer Network(STNet) to transfer gradually the source style features to the target pose to achieve a reasonable distribution of styles. In order to make the appearance texture highly similar to the source style, a style-content-aware adaptive normalization method was proposed. The source style features were mapped into the same latent space with aligned content images(the target pose and edge), and the consistency of the style texture and content was enhanced through adaptive adjustment of the source style and target pose. To enhance the realism of the target generated texture through preserving the source style features. The experimental results show, both quantitatively and qualitatively, that the model can synthesize target images that are consistent with the source style and obtain superior results.
Multi-Encoder Context Aggregation Network for Structured and Unstructured Urban Street Scene Analysis

Developing computationally efficient semantic segmentation models that are suitable for resource-constrained mobile devices is an open challenge in computer vision research. To address this challenge, we propose a novel real-time semantic scene segmentation model called Multi-encoder Context Aggregation Network (MCANet), which offers the best combination of low model complexity and state-of-the-art (SOTA) performance on benchmark datasets. While we follow the multi-encoder approach, our novelty lies in the varying number of scales to capture both global context and local details effectively. We introduce suitable lateral connections between sub-encoders for improved feature refinement. We also optimize the backbone by exploiting the residual block of MobileNet for resource-constrained applications. On the decoder side, the proposed model includes a new Local and Global Context Aggregation (LGCA) module that significantly enhances semantic details in the segmentation output. Finally, we use several known efficient convolution techniques for the classification module to make the model more computationally efficient. We provide a comprehensive evaluation of MCANet on multiple datasets containing structured and unstructured urban street scenes. Among the existing real-time models with less than 3 million parameters, the proposed model is more competitive as it achieves the SOTA performance without ImageNet pre-trained weights on both structured and unstructured environments while being more compact for resource-constrained applications.
A Topicality Relevance-Aware Intent Model for Web Search

To accurately understand user information needs and provide better search experiences, various methods have been proposed to model user search intent from their search logs. Most traditional methods based on query understanding only consider the similarity between the query and documents, while ignoring the user’s session interaction sequence. Some researchers also adopt neural network-based methods to model user search intent. However, most of neural network-based models mainly focus on the user’s session interaction sequence, without considering the role of topic relevance in modeling user search intent. In this paper, we propose a novel topicality relevance-aware intent model (TRIM) for web search. TRIM consists of a topic relevance predictor and a user short-term intent predictor. The topic relevance predictor utilizes the BERT model to predict the topic relevance between the query and documents. The user short-term search intent predictor utilizes session context information to predict user short-term search intent. We further investigate several fusion strategies to integrate the topic relevance and user short-term search intent for user search intent prediction. We conduct our experiments on two public web search datasets named TianGong-QRef and TianGong-ST. The experiments show that TRIM outperforms all baselines in the document ranking task. On TianGong-QRef dataset, TRIM achieves a 15.19% increase over the best performing baselines M-Match in terms of Mean Average Precision (MAP). On TianGong-ST dataset, TRIM achieves a 5.77% increase over the best performing baselines CARS in terms of MAP. The experimental results indicate that the effectiveness of topic relevance in user search intent modeling.
Multi-Strategy Fusion Improved Adaptive Hunger Games Search

Aiming at the drawbacks of Hunger Games Search (HGS) algorithm, such as slow convergence speed and the tendency to fall into local optimum, a Multi-strategy fusion Improved Adaptive Hunger Games Search (MIA-HGS) algorithm is proposed. Firstly, a good point set is employed to generate a more diverse initial population. Secondly, the control strategy selection parameter is fixed in the original HGS algorithm; an adaptive adjustment parameter is proposed to replace the fixed parameters, whose dynamically tuned update strategy strengthens the global searching ability. Finally, to further jump out of the local optimum, a mutation operation based on Logarithmic spiral opposition-based learning is performed on a population for a certain condition. Simulation experiments are carried out for 23 benchmark functions and the UAV aerial planning problem. The results show that MIA-HGS solves more accurately and converges more rapidly than the original HGS algorithm on 23 benchmark functions, with MIA-HGS leading on 69.5% of the tested functions and tying with HGS on 21.7% of the tested functions. It also showed better performance than the other algorithms on the UAV flight planning problem.
A High-Stability All-Optical Nonlinear Activator for Optical Computing

We propose an all-optical nonlinear activator based on the fiber-optic ring resonator (FORR) to implement physically nonlinear activation at low optical signal intensity. The FORR is fabricated from a simple passive optical element, suggesting that optical nonlinearity can be generated by the inherent properties of the device without the need for additional energy consumption. Our device exhibits advantages of excellent nonlinear traits, high stability, versatility, and compatibility in combination with existing optical chips. These features have potential advantages for the scalability of optical neural (ONNs). As a proof of concept, we show the capability of FORR as a nonlinear unit for nonlinear mapping in ONNs. For classification and super-resolution reconstruction tasks, the performance of our model, obtained from experimental measurements, is comparable to that of activation functions commonly used in computers. Our work may provide a feasible avenue for novel layout and wider deployment of all-optical neural networks.
Guard Cache: Creating Noisy Side-Channels

Microarchitectural innovations such as deep cache hierarchies, out-of-order execution, branch prediction and speculative execution have made possible the design of processors that meet ever-increasing demands for performance. However, these innovations have inadvertently introduced vulnerabilities, which are exploited by side-channel attacks and attacks relying on speculative executions. Mitigating the attacks while preserving the performance has been a challenge. In this letter we present an approach to obfuscate cache timing, making it more difficult for side-channel attacks to succeed. We create <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">false cache hits</i> using a small <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guard Cache</i> with randomization, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">false cache misses</i> by randomly evicting cache lines. We show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">false hits</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">false misses</i> cause very minimal performance penalties and our obfuscation can make it difficult for common side-channel attacks such as Prime &Probe, Flush &Reload or Evict &Time to succeed.
A New Zero Knowledge Argument for General Circuits and Its Application

Verifying the correctness of computation without revealing the input is a critical issue intensively studied in real-world applications. The recent surge of zero knowledge arguments has been focusing on its efficiency and practicality. Among them, GKR-based arguments have received wide attention and become the foundation of many zero-knowledge proof protocols. However, GKR-based protocols are restricted to layered arithmetic circuits. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Terrace</i> , a new, efficient zero-knowledge argument system for general circuits, based on GKR. By dynamically patching cross-layer claims to the original circuit for verification instead of verifying those claims separately, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Terrace</i> is able to reduce the total circuit size and thus enjoys a logarithmic factor less verification time and proof size. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Terrace</i> is further extended to include the verification of non-arithmetic operations by rewriting those claims in the multilinear extension form. Experimental results demonstrate that Terrace enjoys a competitive performance on efficiency, and shows great promise in enabling low-cost verification of neural networks.
Multisensors Fusion for Trajectory Tracking Based on Variational Normalizing Flow

The problem of multi-sensors fusion target trajectory tracking under the Bayesian variational inference is to find the jointly accurate posterior distribution. In this paper, a joint optimization method, called VINFNet, combining inference modeling and data-driven is proposed. The proposed VINFNet incorporates the respective advantages of state-space latent inference models and deep generative network that can explicitly model the physical process of target motion and construct complex posterior distributions of target trajectory through a series of invertible mappings. Specifically, the joint probabilistic representation of the multi-sensors latent variable is generated by variational inference, with the optimization on the evidence lower bound (ELBO) to guarantee convergence. However, finding the approximate posterior distribution of targets in variational inference is a crucially intractable problem. Therefore, a normalized flow generation model under a multi-layers perceptron strategy is proposed to recover the approximate posterior distribution of targets, overcoming the challenge that the choosing posterior distribution during variational inference. The proposed VINFNet neither requires the computation of complex Jacobi matrices as in model-based algorithms such as KF, nor lacks interpretability as in data-driven neural networks. Simulations and ablation experiments evaluate that the VINFNet algorithm outperforms prevalent methods regarding convergence, accuracy, robustness, and effectiveness via fusing different data sources.
An Energy-Efficient GeMM-Based Convolution Accelerator With On-the-Fly im2col

Systolic array architectures have recently emerged as successful accelerators for deep convolutional neural network (CNN) inference. Such architectures can be used to efficiently execute general matrix–matrix multiplications (GeMMs), but computing convolutions with this primitive involves transforming the 3-D input tensor into an equivalent matrix, which can lead to an inflation of the input data, increasing the OFF-chip memory traffic which is critical for energy efficiency. In this work, we propose a GeMM-based systolic array accelerator that uses a novel data feeder architecture to perform ON-chip, on-the-fly convolution lowering (also known as im2col), supporting arbitrary tensor and kernel sizes as well as strided and dilated (or atrous) convolutions. By using our data feeder, we reduce memory transactions and required bandwidth on state-of-the-art CNNs by a factor of two, while only adding an area and power overhead of 4% and 7%, respectively. Application specific integrated circuit (ASIC) implementation of our accelerator in 22-nm technology fits in less than 1.1 mm 2 and reaches an energy efficiency of 1.10 TFLOP/sW with 16-bit floating-point arithmetic.
APQ: Automated DNN Pruning and Quantization for ReRAM-Based Accelerators

Emerging ReRAM-based accelerators support in-memory computation to accelerate deep neural network (DNN) inference. Weight matrix pruning is a widely used technique to reduce the size of DNN models, thereby reducing the resource and energy consumption of ReRAM-based accelerators. However, existing pruning works for ReRAM-based accelerators have three major issues. First, they use heuristics or rules from domain experts to prune the weights, leading to sub-optimal pruning policies. Second, they use row or column-level coarse-granularity methods to prune weights, resulting in poor compression rates with model accuracy constraints. Third, they only apply the weight pruning technique individually, losing the compression opportunity of both pruning and quantization. In this article, we propose an Automated DNN Pruning and Quantization framework, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APQ</small> , for ReRAM-based accelerators. First, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APQ</small> adopts reinforcement learning (RL) to automatically determine the pruning policy for DNN layers for a global optimum. Second, it prunes and maps weight matrices to a ReRAM-based accelerator in a finer granularity of column-vector, which improves the compression rates with the accuracy constraints. To address the dislocation problem, it uses a new data path in ReRAM-based accelerators to correctly index and feed input to matrix-vector computation. Third, to further reduce resource consumption, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APQ</small> also leverages reinforcement learning to automatically determine the quantization bitwidth of each layer of the pruned DNN model. Experimental results show that, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APQ</small> achieves up to 4.52X compression rate, 4.11X area efficiency, and 4.51X energy efficiency with similar or even higher model accuracy, compared to the state-of-the-art work.
Efficient Feature Relation Learning Network for Cross-Spectral Image Patch Matching

Recently, cross-spectral image patch matching methods based on feature difference aggregation have achieved excellent performance, but they introduce a large number of parameters, limit matching speed and have poor scalability. At the same time, only using feature difference learning to extract differential features will lead to the loss of consistent features between cross-spectral image patches. Therefore, we construct a novel four-branch efficient feature relation learning network (EFR-Net) without feature difference aggregation. Specifically, a new four-branch feature relation learning strategy is proposed, which reasonably combines multiple feature relation learning to comprehensively and effectively extract the differential features and consistent features between image patches. At the same time, we construct an efficient local attention (ELA) module with negligible parameters, which can learn some global context information, enhance the interaction of local information and promote the extraction of discriminative features. In addition, a combined metric network is introduced to facilitate network optimization and improve network generalization. Furthermore, a public optical and SAR image patch matching dataset with a patch size of 64 × 64 pixels is constructed based on the OS dataset, which is called the OS patch dataset. We also establish an experimental benchmark on this new dataset. Extensive experimental results show that the proposed EFR-Net achieves excellent performance on cross-spectral image patch matching (OS patch dataset, VIS-NIR patch dataset) and single spectral image patch matching (Brown dataset).
Introducing Massive Open Metaverse Course and Its Enabling Technology

As metaverse becomes one of the most popular buzzwords in technology, there is still a lack of support to integrate true metaverse learning experiences in Massive Open Online Courses (MOOCs). This paper introduces a new framework of Massive Open Metaverse Courses (MOMCs) and its major enabling technologies, which add immersive and three-dimensional learning experiences lacking in MOOCs. It then describes a detailed case study, the President's First Lecture at the Hong Kong University of Science and Technology (Guangzhou), which we consider the world's first true MOMC environment, enabled by the latest volumetric video and related virtual and augmented reality technologies. We describe in detail how this course is created and discuss the major advantages of MOMC over MOOC as well as its current limitations.
On the Mean-Field Belavkin Filtering Equation

Following Kolokoltsov’s work [1], we present an extension of mean-field control theory in quantum framework. In particular such an extension is done naturally by considering the Belavkin quantum filtering and control theory in a mean-field setting. In this setting, the dynamics is described by a controlled Belavkin equation of McKean-Vlasov type. We prove the well-posedness of such an equation under imperfect measurement records. Furthermore, we show under purification assumption the propagation of chaos for perfect measurements. Finally, we apply particle methods to simulate the mean-field Belavkin equation and we provide numerical simulations showing the stabilization of the mean-field Belavkin equation by a feedback control strategy towards a chosen target state.
Fast Offline Policy Optimization for Large Scale Recommendation

Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem resulting in policy learning or REINFORCE style learning algorithms. Unfortunately, this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show that our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.
Reflection as a Component of Formative Assessment of Younger School Students in Modern Primary School

The article characterizes the essence of the concepts «control and evaluation activity», «reflection», defines the place and role of reflection as an important component of formative assessment.&#x0D; In the conditions of educational reforms in Ukraine today, the issue of improving and increasing the efficiency of the educational process of junior high school students is particularly acute, which is why it is determined that the issues of assessment and reflection require study and analysis.&#x0D; The purpose of the article is to carry out a theoretical analysis of the concept of reflection in the educational activity of a junior high school student, to clarify the role of reflection in formative assessment.&#x0D; The study of the scientific works of Ukrainian and foreign scientists and practicing teachers made it possible to determine the peculiarities of the use of reflection in the formative assessment of educational achievements of younger schoolchildren.&#x0D; The article demonstrates that reflection can be a useful tool for elementary school students, after which it can help them better understand their actions and achievements, and develop the ability to analyze their own learning and growth. Here are some ways that can help elementary school students incorporate reflection into their learning: portfolios, islands, palms, analyze difficulties.&#x0D; Reflection can help students develop their ability to work independently and grow, understand their goals and focus on achieving them, and improve the quality of their learning. It is important to provide students with appropriate support and mentoring so that they can successfully incorporate reflection into their learning.