Reviewerly/exHarmony huggingface dataset:
Constructions of optimal Hermitian self-dual codes from unitary matrices

We provide an algorithm to construct unitary matrices over finite fields. We present various constructions of Hermitian self-dual codes by means of unitary matrices, where some of them generalize the quadratic double circulant constructions. Many optimal Hermitian self-dual codes over large finite fields with new parameters are obtained. More precisely MDS or almost MDS Hermitian self-dual codes of lengths up to 18 are constructed over finite fields Fq, where q=32,42,52,72,82,92,112,132,172,192. Comparisons with classical constructions are made.
Automating Questions and Answers of Good and Services Tax system using clustering and embeddings of queries

Goods and Services Tax has been introduced for the first time in India in 2017 and it is a major tax reform. There have been a lot of queries posed by the users and response had to be given manually which was a very tedious task. There was a dire need to automate this Question/Answer process in an efficient manner. Embeddings e.g. BERT and ROBERTA have been used for converting the questions to make it efficient for clustering the questions. K-means and Hierarchical clustering techniques have been used for clustering the embeddings of questions, using different distance measures viz. Euclidean and Cosine. Three possible choices for answers for each query have been provided at first, and in the next step the best possible answer has been provided for each test question. Dataset of two months (October and November 2019) is used for automating the process. A high success rate in predicting the answers for the questions has been achieved.
Just-Noticeable-Difference Based Coding and Rate Control of Mobile 360° Video Streaming

In recent years, 360ovideos have gained higher and higher popularity. Nonetheless, compared to two dimensional videos, the large-scale data volume renders it a bottleneck to deliver 360ocontent with constrained bandwidth resources. In this paper, we investigate user viewing behavior when they explore in immersive environment and propose a novel 360ojust-noticeable-difference (JND) model to characterize user&#x0027;s tolerance to visual distortion. In order to maximize user&#x0027;s quality of experience (QoE), we present a scheme, named JND-Based Streaming (JBS), to jointly optimize 360o video coding and streaming over mobile devices. Specifically, tiled 360ovideos are firstly encoded with the proposed JND model to reduce video file size. Then, a quality-driven streaming approach is designed to instruct tile-level bitrate allocation, considering subjective sensation. Thanks to the video file size reduction, tiles can be delivered with higher quality, which provides users with improved QoE. Experimental results based on real-world network traces demonstrate that, on average, JBS outperforms its counterparts by 12&#x0025; and 57&#x0025; in terms of perceived quality.
The Threat of Deepfakes to Computer and Human Visions

Abstract Deepfake videos, where a person’s face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. The concern for the impact of the widespread deepfake videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes are for both human and computer visions by showing how well these videos can fool face recognition algorithms and naïve human subjects. We also show how well the state-of-the-art deepfake detection algorithms can detect deepfakes and whether they can outperform humans.
Visual Attention Analysis and User Guidance in Cinematic VR Film

Due to the character of 360° video, it is often a challenge for filmmakers to guide the attention of users to the region of interest. Visual effects as a type of user guidance is frequently applied to traditional film. Nevertheless, the influence of visual effects in 360° video has been rarely explored. For this reason, the purpose of this paper is to study how four different visual effects, respectively Desaturation, Context-based Darkening, Area Darkening, and Object to Follow, affect visual attention of users in 360° video. Therefore, we performed a subjective test as well as analyzed the saliency maps predicted with a convolutional neural network. In the subjective test, 15 participants were requested to watch four 360° videos, which were implemented with visual effects, while the position of their viewport was recorded. The results of this work are compared to earlier research on the same videos without visual effects. We show that Area Darkening has the best effect on guiding the visual attention, Context-based Darkening makes the best contribution on enhancing the saliency of the region of interest, while Desaturation has nearly no effect for user guidance and does not change the saliency of the videos. A Logo as Object to Follow create a new salient area, while the predicted saliency of areas apart from the Logo remains the same.
Investigating strategies towards adversarially robust time series classification

Deep neural networks have been shown to be vulnerable against specifically-crafted perturbations designed to affect their predictive performance. Such perturbations, formally termed ‘adversarial attacks’ have been designed for various domains in the literature, most prominently in computer vision and more recently, in time series classification. Therefore there is a need to derive robust strategies to defend deep networks from such attacks. In this work we propose to establish axioms of robustness against adversarial attacks in time series classification. We subsequently design a suitable experimental methodology and empirically validate the hypotheses put forth. Results obtained from our investigations confirm the proposed hypotheses, and provide a strong empirical baseline with a view to mitigating the effects of adversarial attacks in deep time series classification.
Elements and Overall Optimization of University Self-Organizing Physical Education Teaching System Based on Holistic Theory

The fact that self-organized physical education teaching systems in universities should have diversity and imbalance has been weakened, and we need to focus on the self-thinking, self-learning, and uncertain factors in the teaching process. The components of the entire teaching system are known as teaching elements. We cannot understand the integrity of teaching elements without understanding system theory. The teaching in physical education classes is the same way. This paper analyzes the elements and overall optimization of university self-organizing physical education teaching system using the methods of literature review and logical argumentation, starting from the perspective of holism and combining with the characteristics of physical education. The effectiveness and superiority of this model are tested by comparing sports performance data with ACOA (Ant Colony Optimization Algorithm) to optimize the sports video of SVM (Support Vector Machine). The findings show that the model can improve sports performance prediction accuracy, and the prediction results are more reliable, providing useful information for sports training.
Control of Robot Arm Based on EOG Signals

This paper presents an Electrooculography (EOG) acquisition system for Human-Computer Interaction (HCI) based on Arduino microcontroller. The proposed system is used to acquire vertical and horizontal eye movements for control of wireless robot arm. The NRF24L01 - 2.4G wireless transceiver module is used to transfer data from master to slave circuits. The L298N driver circuit is mounted on a special robotic arm. The EOG is a signal produced in both positive and negative directions by eye movement. The EOG acquisition system is interfaced with Arduino Uno and Arduino Nano microcontrollers. Robotic arm movement is controlled by five basic eye movements in main directions. Five Ag-Agcl disposable electrodes are used to measure the potential difference for eye movement between cornea and retina. Different volunteers of different ages at different times have been treated with the proposed system to obtain data. For controlling the wireless robot arm, the Arduino microcontroller is used to serialize the EOG signals for calibration. The performance and accuracy of the presented system are really promising due to simplicity and reliability. This trend can help the disabled patients in wheelchair regulation, rehabilitation aids and other applications by simply moving their eyes via the proposed system.
Research on Knowledge Distillation Algorithm of Object Detection

The Algorithms of object detection are usually difficult to deploy on low-end devices due to the large amount of computation, but knowledge distillation can solve this problem by training small models to learn the already trained complex network models, realizing model compression, and effectively reducing the amount of computation. How to transfer rich knowledge from teachers to students is a key step in the knowledge distillation. To solve this problem, this paper uses the knowledge of the teacher to guide the student network training in feature extraction, target classification and frame prediction, and proposes a distillation algorithm based on multi-scale attention mechanism, which uses attention mechanism to integrate different scale features. The correlation of features between different channels is learned by assigning weights to the features of each channel. The distillation algorithm proposed in this paper is based on YOLOv4, so it can strengthen the student network to learn the key knowledge of the teacher network, and make the knowledge of the teacher network How to the student network better. Experimental analysis shows that it can effectively improve the detection accuracy of the student network. The size of the model is only 6.4% of the teacher network, but the speed is increased by 3 times, and mAP is 5.7% higher than the original student network and 2.1% lower than the teacher network.
Intelligibility assessment of impaired speech using Regularized self-representation based compact supervectors

Automatic intelligibility assessment of impaired speech is essential and helps in assisting people with speech impairment in therapy and clinical diagnosis. It is important to learn compact and discriminative representations to assess the quality of impaired speech. We propose a regularized self-representation (RSR) based compact supervectors for intelligibility assessment of the impaired speech. Supervectors of huge dimension are used as base representations to obtain a regularized self-representation based compact supervectors. 10-digits and 100-common words datasets of the UA-SPEECH database are used to evaluate the performance of the proposed RSR-based compact supervector approach. The above two datasets have four intelligibility levels: high, medium, low, and very low. The proposed RSR-based compact supervector approach performs significantly better than the hybrid GMM/SVM, supervector, x-vector, i-vector, and Bag of Models-based approaches for both datasets. Compactness and discriminative ability of the proposed representation help to improve the performance of intelligibility assessment of the impaired speech.
On the infiniteness of a family of APN functions

APN functions play a fundamental role in cryptography against attacks on block ciphers. Several families of quadratic APN functions have been proposed in the recent years, whose construction relies on the existence of specific families of polynomials. A key question connected with such constructions is to determine whether such APN functions exist for infinitely many dimensions or do not. In this paper we consider a family of functions recently introduced by Li et al. in 2021 showing that for any dimension m≥3 there exists an APN function belonging to such a family. Our main result is proved by a combination of different techniques arising from both algebraic varieties over finite fields connected with linearized permutation rational functions and partial vector space partitions, together with investigations on the kernels of linearized polynomials.
A Chaotic Encryption System Based on DNA Coding Using a Deep Neural Network

Critical to computer vision applications, deep learning demands a massive volume of training data for great performance. However, encrypting the sensitive information in a photograph is fraught with difficulty, despite rapid technological advancements. The Advanced Encryption System (AES) is the bedrock of classical encryption technologies. The Data Encryption Standard (DES) has low sensitivity, with weak anti-hacking capabilities. In a chaotic encryption system, a chaotic logistic map is employed to generate a key double logistic sequence, and deoxyribonucleic acid (DNA) matrices are created by DNA coding. The XOR operation is carried out between the DNA sequence matrix and the key matrix. Finally, the DNA matrix is decoded to obtain an encrypted image. Given that encrypted images are susceptible to attacks, a rapid and efficient Convolutional Neural Network (CNN) denoiser is used that enhances the robustness of the algorithm by maximizing the resolution of rebuilt images. The use of a key mixing percentage factor gives the proposed system vast key space and great key sensitivity. Its implementation is examined using statistical techniques such as histogram analysis, information entropy, key space analysis and key sensitivity. Experiments have shown that the suggested system is secure and robust to statistical and noise attacks.
Implementing Quantum-Kernel-Based Classifiers in the NISQ Era

There is an intrinsic link between operations that can be performed on a quantum computer and kernel methods. This has inspired the development of quantum-kernel-based classifiers that exploit the ability of quantum computers to efficiently perform operations in large Hilbert spaces. This work performs a proof of principle demonstration of a quantum-kernel-based classifier applied to the binary classification of various non-linearly separable datasets. For each classification task, a quantum device provided by the IBM Quantum (IBMQ) platform is used to estimate a kernel matrix. A number of novel strategies comprised of combinations of existing post-processing methods are then applied to the matrix to mitigate the effects of noise from the quantum device, readout error and account for the effects of finite sampling. The application of certain strategies is shown to improve the quality of the kernel matrices estimated by the quantum device. The raw and post-processed kernel matrices are fed into a classical support vector machines (SVM) that learns a model to perform the classification. For each classification task, the classifiers exhibits high accuracies that are comparable to the classifiers that use ideal, simulated kernel matrices. The classifiers that use certain post-processed kernel matrices exhibit higher accuracies than the classifiers that use the raw kernel matrices. This demonstrates the effectiveness of quantum-kernel-based classifiers in the Noisy Intermediate Scale Quantum (NISQ) computing era as well as the power of certain of post-processing strategies.
BirdView Retina-Net: Small-Scale Object Detector for Unmanned Aerial Vehicles

Recent advancements in computer vision and deep learning techniques have led to many applications in robotics. These techniques play a significant part in the development of artificially intelligent autonomous and semi-autonomous robotic systems, e.g., Unmanned Aerial Vehicles (UAVs) and autonomous cars. Object detection and tracking is an important pillar for such robotic systems. For object detection, a wide variety of deep learning frameworks have been proposed over the past few years. However, objects in the video from a UAV are small and thus very hard to localize and classify. This research proposes a deep learning framework based object detection approach named as BirdView Retina-Net (BV-RNet). BV-RNet is an object detection framework capable of efficiently detecting small-scaled objects from an aerial view-point. BV-RNet extracts dense features and optimizes pre-defined anchors for inference to learn features effectively. VisDrone dataset is used to evaluate the performance of the framework. We have compared the results of BV-RNet with past VisDrone challenge competitors and other commonly employed past object detection frameworks. We have reported that our algorithm achieves mean average precision (mAP) of 31.8 on test-dev dataset, which is among the highest mAP achieved so far on VisDrone. Furthermore, it also outperforms several of the top class-wise mAP of various classes when compared with VisDrone past challenge competitors.
Multiple‐Qubit C<sub><i>k</i></sub>U<sup><i>m</i></sup> Logic Gates of Rydberg Atoms via Optimized Geometric Quantum Operations

Abstract Multiple‐qubit logic gates, as an essential part of quantum logic gates, have wide practical application in quantum computation. Here, a scheme to directly construct a multiple‐qubit logic gate , in which k control atoms apply an universal operation U on each of m target qubits in the Rydberg atom system is proposed. The scheme, based on Rydberg blockade and geometric quantum operations as well as optimized control theory, can greatly reduce the qubit number and the operation steps, improving the speed and robustness of the gating compared to the conventional multiple‐qubit gates consisting of single‐ and two‐qubit gates. Taking the C 2 U 2 gate as an example, the performance of the proposal is numerically evaluated in terms of the fidelity of the quantum gate as well as the robustness against the static systematic errors and spontaneous emission. This work may provide a significative reference for constructing universal multiple‐qubit logic gates in future Rydberg atom experiments.
Optimal navigation over spatiotemporally varying hazardous fields

This paper considers the optimal navigation problem wherein the agent has to traverse a spatiotemporally varying hazardous field that has severely lethal effects on the agent. The accumulated effects of the value of the spatiotemporally varying field along the path, represented by the line integral of the spatial field along the chosen trajectory, constitutes the destructive effects of the hazardous field. When this accumulated amount exceeds a prescribed threshold, the agent is incapacitated and can no longer move. We formulate the navigation problem as an optimal control problem in which the agent has to traverse a 2D enclosed domain and reach its goal destination at a free final time while minimizing the cumulative exposure to the hazardous field. Due to practical considerations, the solution to the optimal control problem is admissible only if the accumulated exposure of the agent when it reaches the final destination is well below the deadly threshold. The evolution of the spatiotemporally varying field is described by a 2D advection-diffusion PDE with a moving source that realistically captures rapidly evolving hazardous fields. Extensive numerical studies are included to highlight the various aspects of the proposed constrained navigation problem.
A Random Number Generator Based on Metastability of Oscillators

An irregular sampling of regular waveform based true random number generator(TRNG) utilizing a tetrahedral oscillator as entropy source is proposed. The tetrahedral oscillator is composed of multiple nested rings which increases entropy due to metastability. By integrating tetrahedral oscillator with irregular sampling method, the proposed RNG is faster and more robust to external interference attacks than regular sampling of tetrahedral oscillator, it uses much less hardware resources than regular ring oscillator based RNGs. In total, 5 tetrahedral oscillators are used as the entropy source and they are combined with an XOR gate. This XOR output is used to sample a faster regular clock, which should have a duty cycle as close to 50% as possible, to generate the random bit. The randomness of the bit stream was proved with National Institute of Standard and Technology (NIST) 800–22 Test Suites. The proposed design uses 93 LUTs and 14 FFs while consuming 100mW of power and having a throughput of 1.7Mb/s. The speed is 17 times faster than regular sampling of tetrahedral and the resource usage is 16 times lower than regular ring oscillator based RNG designs.
Faster Isogenies for Post-quantum Cryptography: SIKE

In the third round of the NIST PQC standardization process, the only isogeny-based candidate, SIKE, suffers from slow performance when compared to other contenders. The large-degree isogeny computation performs a series of isogenous mappings between curves, to account for about 80% of SIKE's latency. Here, we propose, implement, and evaluate a new method for computing large-degree isogenies of an odd power. Our new strategy for this computation avoids expensive recomputation of temporary isogeny results. We modified open-source libraries targeting x86, ARM64, and ARM32 platforms. Across each of these implementations, our new method achieves 10% and 5% speedups in SIKE's key encapsulation and decapsulation operations, respectively. Additionally, these implementations use 3% less stack space at only a 48 byte increase in code size. Given the benefit and simplicity of our approach, we recommend this method for current and emerging SIKE implementations.
Robust, accurate, and improved measurement of structural deformation based on off-axis digital image correlation

We propose a noncontact method for measuring structural deformation using off-axis digital image correlation. An efficient and high-precision algorithm that is insensitive to the accuracy of the initial guess is proposed and validated through numerical simulation. Image displacements in pixels are converted to physical displacements in millimeters using a calibration model based on a new method of measuring the objective distance. A new image-based structural deformation measurement system is proposed and validated using laboratory test results. The proposed method is easy to implement and accurate for structural deformation measurements.
Role of Temporal Information for Multi-step Ahead Forecasting of Solar Irradiance

Multi-step ahead solar irradiance time series forecasting is a challenging problem. In this paper, we propose a machine-learning based model for multi-step ahead forecasting. We represent the diurnal variation of solar irradiance in our machine learning models using trignometric transformation of time step information. We observe that the use of temporal information in the forecasting model significantly increases the accuracy of the predictive model.
Project Activities for the Training of Specialists in the Context of Digitalization

The purpose of the work is to study issues related to the use of the project method, which involves a flexible, innovative, creative and open approach focused on the training of a highly qualified specialist who is able to build an individual training route taking into account digitalization projects. The contribution presents the data of a survey of 240 students of 1–2 university courses studying under the bachelor’s degree program in Economics and Management. Taking into account the survey results, the authors see the need to involve students in industry-specific project activities in a foreign (English) language and develop mini-project tasks aimed at acquiring and/or expanding existing project experience, as well as related to existing and anticipated problems in the implementation of digital transformation.