Reviewerly/exHarmony huggingface dataset:
A MATLAB-based modelling and simulation package for DPS-QKD

Quantum key distribution (QKD) is an ingenious technology utilizing quantum information science for provable secure communication. However, owing to the technological limitations and device non-idealities it is important to analyze the system performance critically and carefully define the implementation security. With an acceleration in the commercial adoption of QKD, a simulation toolkit is requisite to evaluate the functional architecture of QKD protocols. We present a simulation framework to model optical and electrical components for implementing a QKD protocol. The present toolkit aims to model and simulate the optical path of the DPS-QKD protocol with its imperfections and eventually characterize the optical path. The detailed device-level modeling and analysis capabilities of the present toolkit based on Simulink and MATLAB have the potential to provide universal toolkit for practical design and implementation of generalized QKD protocols compared to earlier works. We report a novel work on the implementation of a QKD protocol on Simulink and MATLAB platform. Further, the absence of any modeling framework for DPS QKD and its simplistic optical schematic made it an obvious choice for the authors. We are hopeful that this work will pave way for simulating other QKD protocols from the DPR family.
Sensor-Guided Optical Flow

Figure 1.Guided optical flow in action.Column (a): reference images, columns (b,c): optical flow (top) and corresponding error maps (bottom).When facing challenging conditions at test time (a), an optical flow network alone (b) may struggle, while an external guide can make it more robust (c).Both networks in (b,c) have been trained on synthetic data only.
Pathdreamer: A World Model for Indoor Navigation

People navigating in unfamiliar buildings take advantage of myriad visual, spatial and semantic cues to efficiently achieve their navigation goals. Towards equipping computational agents with similar capabilities, we introduce Pathdreamer, a visual world model for agents navigating in novel indoor environments. Given one or more previous visual observations, Pathdreamer generates plausible high-resolution 360° visual observations (RGB, semantic segmentation and depth) for viewpoints that have not been visited, in buildings not seen during training. In regions of high uncertainty (e.g. predicting around corners, imagining the contents of an unseen room), Pathdreamer can predict diverse scenes, allowing an agent to sample multiple realistic outcomes for a given trajectory. We demonstrate that Pathdreamer encodes useful and accessible visual, spatial and semantic knowledge about human environments by using it in the downstream task of Vision-and-Language Navigation (VLN). Specifically, we show that planning ahead with Pathdreamer brings about half the benefit of looking ahead at actual observations from unobserved parts of the environment. We hope that Pathdreamer will help unlock model-based approaches to challenging embodied navigation tasks such as navigating to specified objects and VLN.
RDI-Net: Relational Dynamic Inference Networks

Dynamic inference networks, aimed at promoting computational efficiency, go along an adaptive executing path for a given sample. Prevalent methods typically assign a router for each convolutional block and sequentially make block-by-block executing decisions, without considering the relations during the dynamic inference. In this paper, we model the relations for dynamic inference from two aspects: the routers and the samples. We design a novel type of router called the relational router to model the relations among routers for a given sample. In principle, the current relational router aggregates the contextual features of preceding routers by graph convolution and propagates its router features to subsequent ones, making the executing decision for the current block in a long-range manner. Furthermore, we model the relation between samples by introducing a Sample Relation Module (SRM), encouraging correlated samples to go along correlated executing paths. As a whole, we call our method the Relational Dynamic Inference Network (RDI-Net). Extensive experiments on CIFAR-10/100 and ImageNet show that RDI-Net achieves state-of-the-art performance and computational cost reduction.
A Three-Phase Noise Removal Approach to Achieve Accuracy in Line Segmentation of Odia Text

This paper focuses on segmenting lines of a scanned document in a particular Odia font or a document typed in a particular Odia font which is converted to an image. When a document is scanned in a scanner or converted to image then, the resultant image consists of some noise along with the text. The approach described in this paper deals with segmenting the lines of the Odia text in an image by removing the noise. This approach consists of three modules: Preprocessing, RemoveNoise and LineSegmenting. A scanned document or a document of Odia text converted to image is given as input to Preprocessing which is responsible for converting the input image to gray scale image and then to binary image. The scanned document that is tilted has not been considered. The output of Preprocessing module is given as input to the RemoveNoise module which is responsible for removing the white spaces that surrounds the text and the noise present in the whole text. Then the LineSegmenting module takes the output of RemoveNoise module and produces segmented lines of the text.
ValS: A Leading Visual and Inertial Dataset of Squats

Human movement recognition has sparked a lot of attention because of its wide range of applications in sports, animation, simulation, and entertainment. These applications necessitate the analysis of datasets in order to distinguish motions in either the visual or inertial motion data. The use of camera and motion sensors, particularly, the gyroscope and accelerometer, has expanded recently as a result of the availability of mobile smartphones, smartwatches, etc., and is now being employed in real-world applications. In this paper, we present a new Visual and Inertial dataset ValS Dataset focusing on performing the squats exercises. The same actors and activities were captured using various hardware systems in two capture rounds, including video using mobile cameras and inertial measurement units (IMUs). The data from the IMUs and the videos are synced. Squats are being performed by 24 males and 3 females. To provide the highest data diversity, we recorded some sessions outdoors in the daylight and at night while others to be indoors. In this paper, we offer further details about the nature of the data and how would the dataset can be of many benefits, as well as how the dataset was collected and post-processed, including the synchronization process. Seeking evaluating the quality of the data, we ran certain tests to produce a proper data analysis.
Fine-Grained Chinese Named Entity Recognition Based on MacBERT-Attn-BiLSTM-CRF Model

Named Entity Recognition (NER) is to identify pre-defined types of entities (such as people, organizations, locations) from texts. NER is a key component of information retrieval, relationship extraction and other tasks in practice. However, traditional entity recognition models have a series of problems: high cost of artificial feature design, weak model robustness, and coarse granularity of entities. To solve these problems, we propose a model called MacBERT-Attn-BiLSTM-CRF based on pre-trained language models. The experiments on a fine-grained Chinese NER dataset show that our model outperforms existing models significantly.
El Gamal Cryptosystem on a Montgomery Curves Over Non Local Ring

Let Fq be the finite field of q elements, where q is a prime power. In this paper, we study the Montgomery curves over the ring Fq[X]/(X^2−X), denoted by MA,B(Fq[X]/(X^2−X) ); (A,B) ∈ (Fq[X]/(X^2−X))^2. Using the Montgomery equation, we define the Montgomery curves MA,B(Fq[X]/X^2−X) and we give a bijection between this curve and product of two Montgomery curves defined on Fq. Furthermore, we study the addition law of Montgomery curves over the ring Fq[X]/X^2−X. We close this paper by introducing a public key cryptosystem which is a variant of the ElGamal cryptosystem on a Montgomery curves over the same ring.
Dynamic Gesture Recognition Based On Multimodal Fusion Model

Aiming at the problem that the RGB modal is susceptible to illumination, skin color, complex background, and lack of motion information, resulting in low recognition accuracy. A dynamic gesture recognition algorithm based on the joint ResNeXt-3D-CBAM model is proposed. The algorithm uses a 3D convolution kernel to replace the traditional 2D convolution kernel in the ResNeXt network, and effectively extracts the spatiotemporal features of dynamic gestures in RGB, Depth and optical flow data. Then use the method of multi-model fusion to predict dynamic gestures. Experimental results show that the method proposed in this paper can recognize dynamic gestures more accurately. Compared with the traditional single-modal information, the accuracy of dynamic gesture recognition is improved, which verifies the feasibility and superiority of the proposed method.
Facial recognition using grey wolf optimization

Nowadays, multimedia data on social media platforms of the Internet, especially images, are proliferating, due to which image retrieval systems based on content are one of the major research areas. Despite the introduction of various content-based images retrieval approaches, face recognition remains a problematic task. Consequently, this paper developed a face recognition framework using modifying the bag-of-features (BOF) from a grey wolf optimisation (GWO) algorithm in which a variable weight is used. Variable weight proposes a sine cos function to find the best score. The convergence behaviour of the variable weight GWO algorithm is performed over some benchmark functions. Furthermore, it has been used to get the histograms fed into training and testing function to get the model accuracy using the Yale face database.
BroadSurv: A Novel Broad Learning System-based Approach for Survival Analysis

Survival analysis (time-to-event analysis) is a set of statistic methods to analyze time-to-event data and is widely used in many fields such as economics, finance and medicine. One of the fundamental problems in survival analysis is to explore the relationship between the covariates and the survival time. Recently, with the development of deep learning-based techniques, various approaches have been proposed for survival analysis. To better handle the censoring, special cost functions or sophisticated network structures are usually designed for these methods. In this paper, a novel two-stage method is proposed to model the survival data. In the first stage, pseudo conditional probabilities are computed, which can act as the quantitative response variables in regression problems. In the second stage, with these pseudo values, a complicated survival analysis problem is transformed into a regression problem that can be effectively solved by broad learning system. The experimental results show that, with a flexible structure and a simple cost function, our proposed method has a better performance in handling the censored problems.
SmartShots: An Optimization Approach for Generating Videos with Data Visualizations Embedded

Videos are well-received methods for storytellers to communicate various narratives. To further engage viewers, we introduce a novel visual medium where data visualizations are embedded into videos to present data insights. However, creating such data-driven videos requires professional video editing skills, data visualization knowledge, and even design talents. To ease the difficulty, we propose an optimization method and develop SmartShots, which facilitates the automatic integration of in-video visualizations. For its development, we first collaborated with experts from different backgrounds, including information visualization, design, and video production. Our discussions led to a design space that summarizes crucial design considerations along three dimensions: visualization, embedded layout, and rhythm. Based on that, we formulated an optimization problem that aims to address two challenges: (1) embedding visualizations while considering both contextual relevance and aesthetic principles and (2) generating videos by assembling multi-media materials. We show how SmartShots solves this optimization problem and demonstrate its usage in three cases. Finally, we report the results of semi-structured interviews with experts and amateur users on the usability of SmartShots.
An efficient method to remove mixed Gaussian and random-valued impulse noise

Mixed Gaussian and Random-valued impulse noise (RVIN) removal is still a big challenge in the field of image denoising. Existing denoising algorithms have defects in denoising performance and computational complexity. Based on the improved "detecting then filtering" strategy and the idea of inpainting, this paper proposes an efficient method to remove mixed Gaussian and RVIN. The proposed algorithm contains two phases: noise classification and noise removal. The noise classifier is based on Adaptive center-weighted median filter (ACWMF), three-sigma rule and extreme value processing. Different from the traditional "detecting then filtering" strategy, a preliminary RVIN removal step is added to the noise removal phase, which leads to three steps in this phase: preliminary RVIN removal, Gaussian noise removal and final RVIN removal. Firstly, RVIN is processed to obtain a noisy image approximately corrupted by Gaussian noise only. Subsequently, Gaussian noise is re-estimated and then denoised by Block Matching and 3D filtering method (BM3D). At last, the idea of inpainting is introduced to further remove RVIN. Extensive experimental results demonstrate that the proposed method outperforms quantitatively and visually to the state-of-the-art mixed Gaussian and RVIN removal methods. In addition, it greatly shortens the computation time.
Mapping YOLOv4-Tiny on FPGA-Based DNN Accelerator by Using Dynamic Fixed-Point Method

In the past few decades, with the large-scale application of deep learning technology, the neural network inference speed problem is becoming more and more severe, especially in some mobile and embedded real-time processing systems. To improve the inference speed, some software optimization techniques have been proposed by previous researchers. However, these methods only focused on the model self and still cannot meet the rapid development of large and complex deep learning models. Fortunately, deep neural network (DNN) accelerators based on FPGA SoC has opened a promising opportunity for the real-time inference. In this paper, we proposed a novel 16-bit dynamic fixed-point number quantization method to map the object detection network YOLOv4-tiny into FPGA-based heterogeneous deep learning accelerators. We evaluate this model in Xilinx Zynq-7020 SoC on the ZedBoard platform. Experiment on coco dataset shows that this approach can improve the model inference speed to 4× than pure CPU platform, and the accuracy loss is negligible with only 3-5%. This mapping method can be competitive with or even superior to other state-of-the-art accelerating method in deep learning fields.
Frequency-band down-sampled stereo-DIC: Beyond the limitation of single frequency excitation

A low-speed stereo-camera DIC setup was used in this paper to measure the down-sampled bandpass vibration signal of a plate in a given frequency range, which is much higher than the available frame rate. Down-sampled vibration measurements are well known in the literature. However, they are always subject to the strong hypothesis of having a single frequency component in the excitation source (e.g., pure sinusoidal excitation). In this scenario, several approaches can be found in the literature to reconstruct the actual response from the down-sampled data. In this paper, a data postprocessing algorithm is newly introduced to properly reconstruct the target response in the case of a frequency band excitation, thus relaxing the single frequency excitation constraint and allowing to explore a given frequency range with a single measurement. Additionally, a custom excitation signal is presented in this paper to achieve a constant intensity in the studied frequency range. The proposed approach is presented and experimentally validated by measuring a cantilever plate vibration in the kHz range using a 100 fps acquisition. A single acquisition of 98 frames allowed to describe the deformed shapes at 49 different frequency values in the range 1110–1160 Hz, highlighting two resonance peaks. The comparison with the results of multiple conventional single-frequency tests and LDV measurements confirmed the effectiveness of the proposed approach.
Usage of Selected Swarm Intelligence Algorithms for Piecewise Linearization

The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original function and the estimation from the piecewise linear function. Here, the piecewise linearization of 2D functions is studied. Each of the employed swarm intelligence algorithms is enhanced by a newly proposed automatic detection of the number of piecewise linear parts that determine the discretization points to calculate the distance between the original and piecewise linear function. The original algorithms and their enhanced variants are compared on several examples of piecewise linearization problems. The results show that the enhanced approach performs sufficiently better when it creates a very promising approximation of functions. Moreover, the degree of precision is slightly decreased by the focus on the speed of the optimization process.
Group movement of UAVs in environment with dynamic obstacles: a survey

Purpose The successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission. Design/methodology/approach This paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described. Findings The analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group. Originality/value The results of scientific research progress in the tasks of UAV group control have been summed up.
Medical Surgical Video Recognition and Retrieval Based on Novel Unified Approximation

Video retrieval recognition is a linear characterized action constituted by many frame similarity-based videos. This medical video recognition and classification can be a great extent in medical research, such as Endoscopic, radiological, pathological, and applied health informatics. General Video Retrieval Recognition (GVRR) cannot address a problem with recognition alone. GVRR can be solving the Multi-Input-Multi-Output (MIMO) interface mixed video retrieval system. To generalize the conventional video retrieval interface like Multi-user MIMO, WiMAX MIMO, single-user MIMO, several types of research made excused. In fine-tuning existing video retrieval, this research gives the authentic procedure for a frame-based cognitive operation called Secure Approximation and sTability Based Secure Video Retrieval recognition (SAT-SR) recognition proposed. In this research article, the process of recognition has three processes generalized by the video retrieval system. Initially, the virtual dissection and connection weights of input video were established using the mathematical and numerical analysis of interpolation estimation. Secondly, the interpolation approximation and activation function were figured out using the Open Mcrypt Stimulus (oMs) for video security fragments. Similarly, systematic investigations are accomplished for approximation error computation. The result for this widely circulated utilization of three processes on the video retrieval recognition prevents the occurrence of the cybercrime abuse of stored video registers. The proposed technique was used to identify the virtual dissection, interpolation, and activation function for decoding the videos. Using this information, the abusers identified cybercrime rate might be reduced considerably.
Method for developing and using high quality reference images to evaluate tone mapping operators

With the advancement of imaging technology, high dynamic range (HDR) images can now be captured and displayed to produce realistic effects. Tone mapping operators (TMOs) are used to map HDR radiance to the displayable range. A reliable TMO would play a significant role in the accurate reproduction of HDR scenes. The present study aimed to establish an image quality metric based on external references to evaluate various TMOs. Two psychophysical experiments were conducted to develop reference images and to investigate the performance of TMOs. In experiment 1, a set of high quality reference images was developed by rendering image features in terms of contrast, sharpness, and colorfulness, to achieve good rendering for each image. The images were used as reference in experiment 2 to evaluate the performance of 14 TMOs using a six-point categorical judgment method. The TMOs were evaluated using four scales, i.e., contrast, sharpness, colorfulness, and overall performance. The hierarchical relationship among TMOs was established. The results were further compared with previous studies, and high correlation was found between the current experiments and previous studies.
Design and implementation of efficient hash functions

With the rapid popularity of the network, the development of information encryption technology has a significant role and significance in securing network security. The security of information has become an issue of concern to the whole society, and the study of cryptography has been increasingly concerned, and the hash function is the core of modern cryptography, the most common hash algorithms are MD5 series of algorithms, SHA series of algorithms. MD5 is a popular and excellent typical Hash encryption technology today, which is used for password management, electronic signature, spam screening. In this paper, we focus on the improved MD5 algorithm with more efficiency, focusing on the internal structure of MD5, and finally making it more efficient in retrieval.