Reviewerly/exHarmony huggingface dataset:
Trajectory Planning of Quadrotors Flight like Binary Star

Two quadrotors flight like binary star steadily at high speed poses a challenging trajectory planning problem. In order to ensure the pitch angle and angular velocity of quadrotors are suitable and feasible, the dynamic constraints need to be taken into account in trajectory planning problem. In this paper, a quadrotor trajectory planning method considering the dynamic constraints is utilized to generate the proper trajectory. Meanwhile, a geometric controller is used to ensure the exactly tracking of the planed trajectory. The final simulation results show that the proposed trajectory planning method was feasible which can achieve stable binary star like flight within the flight capability.
Economic Growth: The Log Symmetry of Collaborative Learning Populations

Human beings are not only intelligent, but also comprise a social species capable of extensive cooperation.Moreover, they collaborate.Individual human efforts segregate into incongruous silos of multimodal distributions of expression and low performance.A social psychological, economic and mathematical review indicates that collaboration can integrate mental facilities into a congruent unimodal normal distribution of expression and high performance not otherwise possible.The ontology of symmetry is remarkable and compelling.The learning outcomes are natural for economic growth and human development.
Research on the Application of Blockchain Technology in the Weapon and Equipment Testing

Blockchain technology has a broad application prospect in the military field due to its advanced technical concept, and as an important link in the development of the weapon and equipment, the testing plays a key role in the process of development, production and combat force formation. As a new application model of computer technology such as distributed data storage, peer-to-peer transmission, consensus mechanism, cryptographic algorithms, blochchain is strongly correlated with the weapon and equipment in command and control, data management, security and confidentiality, and has great potential for application in the weapon and equipment testing. Aiming at the application of blockchain technology in the weapon and equipment testing, this paper introduced the concept and principle of blockchain technology, described the characteristics of blockchain technology, and then analyzed the application scenarios and methods of blockchain technology in the weapon and equipment testing, such as test command and control, data management, security and confidentiality according to the advantages of its technology application. Combining with the limitations of blockchain technology itself, the problems that need to be solved such as the large scale of network nodes, storage space requirements, and bearer network densities being not equal were analyzed, and the challenges that blockchian may face such as the poor compatibility of deployment methods, the low response speed and the conflict with centralized control were studied. In the end, suggestions were made for the application and development of blockchain technology in the weapon and equipment testing in the future.
Multimedia-assisted oral English teaching system based on B/S architecture

The traditional system fails to consider the practical ability of students, which results in unsatisfactory teaching results. Therefore, this paper designs a new multimedia aided oral English teaching system based on B/S architecture. Firstly, B/S architecture is used to divide the system into browser side, middle layer and server side. Among them, the browser side (foreground) can realise the daily operation of the website; server side (background) combined with AI technology to achieve system functions. This system can create a more real learning environment for students so that students can hear the Standard English pronunciation. The simulation results show that after the application of the system, students can receive the teaching content in as little as 10.30 min, and the teaching cost for six days is about 21,000 yuan. The scores from students' oral English tests are all higher than 90 points, which proves that the system has a good effect on oral English teaching.
Book Diversity and Fireworks of Events: the 35th Moscow International Book Fair

On September 2—5, 2022, the 35th Moscow International Book Fair (MIBF) was held in Moscow at the Gostiny Dvor Exhibition Centre. More than 300 publishers from 28 regions of the Russian Federation presented their books. Representatives from 21 countries, including seven CIS member states, attended the Book Fair program. Within the framework of the 35th MIBF, 344 events took place at 11 sites that were accompanied by various book exhibitions. In the context of the key events for the book industry and librarianship that took place at the 35th MIBF (the National competition “Book of the Year”, the 10th All-Russian competition of the Russian Library Association “Best Professional Book of the Year”, the conference “Russian Book Market — 2022”, the 11th competition of professional skills “Auditor”), the participation of libraries in the cultural life of the society, their achievements and initiatives aimed at promoting books and reading were noted, creative projects, including book publishing, were presented. The article summarizes the results of participation of the Russian State Library (RSL) and the RSL publishing house “Pashkov Dom” in the 35th Moscow International Book Fair.
Deep Radial Embedding for Visual Sequence Learning

Connectionist Temporal Classification (CTC) is a popular objective function in sequence recognition, which provides supervision for unsegmented sequence data through aligning sequence and its corresponding labeling iteratively. The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC. In this study, we propose an objective function named RadialCTC that constrains sequence features on a hypersphere while retaining the iterative alignment mechanism of CTC. The learned features of each non-blank class are distributed on a radial arc from the center of the blank class, which provides a clear geometric interpretation and makes the alignment process more efficient. Besides, RadialCTC can control the peaky behavior by simply modifying the logit of the blank class. Experimental results of recognition and localization demonstrate the effectiveness of RadialCTC on two sequence recognition applications.
Research on the application of artificial intelligence in diagnosing power system faults

This paper mainly analyzes the application of artificial intelligence technology in power system fault diagnosis, and focuses on the power system fault diagnosis system developed by artificial intelligence technology to conduct a full study, in order to ensure that power system faults can comprehensively improve the safety and stability of power system operation after diagnosis by artificial intelligence technology, and then ensure the efficiency of power system.
CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM

Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.
Context-Switching Neural Node for Constrained-Space Hardware

Artificial neural networks are a mathematical abstraction that models the nerve cells and their connections in the biological brain. Their use to solve a wide range of nonlinear problems in the field of approximation, machine learning, and artificial intelligence may require significant computing power. This requires the search for approaches to more efficient use of hardware resources. The article describes an early stage in the development of an optimized neural network designed for use on hardware with constrained resources, but with the ability to run parallel algorithms. Circuits that meet these requirements are the field programmable gate arrays. In the research an approach is proposed to significantly reduce the amount of logic used by contextually switching the weight matrices and the matrices of the activation functions which will result in reducing the number of network layers. This suggests the ability to create more affordable and smarter devices without compromising performance significantly.
OpenSSL 3.0.0

Context: The OpenSSL project released version 3.0.0 in September 2021. This release was a departure from previous versions of OpenSSL in several ways, including a new versioning system and the first use of public software design documents.
FAR: Fourier Aerial Video Recognition

We present an algorithm, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background. Our disentanglement technique operates in the frequency domain to characterize the extent of temporal change of spatial pixels, and exploits convolution-multiplication properties of Fourier transform to map this representation to the corresponding object-background entangled features obtained from the network. To encapsulate contextual information and long-range space-time dependencies, we present a novel Fourier Attention algorithm, which emulates the benefits of self-attention by modeling the weighted outer product in the frequency domain. Our Fourier attention formulation uses much fewer computations than self-attention. We have evaluated our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night, Drone Action, and NEC Drone. We demonstrate a relative improvement of 8.02%–38.69% in top-1 accuracy and up to 3 times faster over prior works.
Semantic Map Injected GAN Training for Image-to-Image Translation

Image-to-image translation is the recent trend to transform images from one domain to another domain using a generative adversarial network (GAN). The existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Let us refer to the original training as the training for the translation of the input image into the target domain. The injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. The semantic map is only utilized at the training time and is not required at the test time. The experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID, and KID scores after injecting semantic training as compared to the original training.
English Abstract Writing Based on Natural Language Generation

Natural Language Generation has a very wide range of application, which can effectively reduce manual work. This paper introduces several text generation models, such as recurrent neural networks, long short-term memory Network, sequence-to-sequence (Seq2Seq) model etc. English Abstract writing is a new text generation task, which helps people write abstracts more effectively, but it still faces some challenges.
An Inertial Representation of Search Results

Search engines are ubiquitous in academic research and modern internet culture, but most only parse information into large blocks of text. This paper explores a visual search representation that encodes publication metadata in an inertial, interactive, force-directed graph. The ‘inertial’ in the title emphasizes that all previously and usefully encoded visible nodes remain stationary when one node is examined in detail, a property that vastly reduces the conventional need to scroll to retrieve and explore unseen search results.
Animated Transitions for Small-Scale Visualizations

When visualizations are used to augment text, geographical information on a map, or on mobile devices, there often is not enough space to show complex data with approaches like juxtaposed visualizations or coordinated views. To alleviate this issue, we propose the use of animated transitions between several small-scale visualizations. We discuss design considerations for animated transitions in small-scale visualizations. We further present the results of a study on the effectiveness of those animated transitions in conveying information and attribute relations, and the mental load of following the animated transitions.
Splitting Fields for the Rational Polynomials X<sup>2</sup>−2, X<sup>2</sup>+X+1, X<sup>3</sup>−1, and X<sup>3</sup>−2

Summary In [11] the existence (and uniqueness) of splitting fields has been formalized. In this article we apply this result by providing splitting fields for the polynomials X 2 − 2, X 3 − 1, X 2 + X + 1 and X 3 − 2 over Q using the Mizar [2], [1] formalism. We also compute the degrees and bases for these splitting fields, which requires some additional registrations to adopt types properly. The main result, however, is that the polynomial X 3 − 2 does not split over <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>𝒬</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mroot> <m:mn>2</m:mn> <m:mn>3</m:mn> </m:mroot> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:mrow> </m:math> \mathcal{Q}\left( {\root 3 \of 2 } \right) . Because X 3 − 2 obviously has a root over <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>𝒬</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mroot> <m:mn>2</m:mn> <m:mn>3</m:mn> </m:mroot> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:mrow> </m:math> \mathcal{Q}\left( {\root 3 \of 2 } \right) this shows that the field extension <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>𝒬</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mroot> <m:mn>2</m:mn> <m:mn>3</m:mn> </m:mroot> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:mrow> </m:math> \mathcal{Q}\left( {\root 3 \of 2 } \right) is not normal over Q [3], [4], [5] and [7].
DLP in semigroups: Algorithms and lower bounds

Abstract The discrete logarithm problem (DLP) in semigroups has attracted some interests and serves as the foundation of many cryptographic schemes. In this work, we study algorithms and lower bounds for DLP in semigroups. First, we propose a variant of the deterministic algorithm for solving the cycle length of torsion elements and show the lower bound of computing the DLP in a semigroup. Then, we propose an algorithm for solving the multiple discrete logarithm (MDL) problem in the semigroup and give the lower bound for solving the MDL problem by considering the MDL problem in the generic semigroup model. Besides, we solve the multidimensional DLP and product DLP in the semigroup.
Semi-supervised Labeling Model Based on Gaussian Mixture in the Context of E-commerce Price Fraud

E-commerce transaction fraud has become an increasingly serious problem in recent years. Mining and analyzing e-commerce transaction data can identify potential transaction frauds, promote fair competition in the market and facilitate regulation. However, there are inevitable problems of missing values and unlabeled data obtained from e-commerce platforms. In this paper, we propose a semi-supervised data labeling method, which interpolates missing values before labeling, solves the problem of removing a large amount of valuable feature data to remove missing values, and then selects the semi-supervised labeling model with the highest accuracy through experiments. The data processed by the above method is helpful to improve the accuracy of the model, which is a practical guidance for e-commerce transaction fraud detection.
On Black-Box Constructions of Time and Space Efficient Sublinear Arguments from Symmetric-Key Primitives

Zero-knowledge proofs allow a prover to convince a verifier of a statement without revealing anything besides its validity. A major bottleneck in scaling sub-linear zero-knowledge proofs is the high space requirement of the prover, even for NP relations that can be verified in a small space. In this work, we ask whether there exist complexity-preserving (i.e. overhead w.r.t time and space are minimal) succinct zero-knowledge arguments of knowledge with minimal assumptions while making only black-box access to the underlying primitives. We design the first such zero-knowledge system with sublinear communication complexity (when the underlying $$\textsf {NP}$$ relation uses non-trivial space) and provide evidence why existing techniques are unlikely to improve the communication complexity in this setting. Namely, for every NP relation that can be verified in time T and space S by a RAM program, we construct a public-coin zero-knowledge argument system that is black-box based on collision-resistant hash-functions (CRH) where the prover runs in time $$\widetilde{O}(T)$$ and space $$\widetilde{O}(S)$$ , the verifier runs in time $$\widetilde{O}(T/S+S)$$ and space $$\widetilde{O}(1)$$ and the communication is $$\widetilde{O}(T/S)$$ , where $$\widetilde{O}()$$ ignores polynomial factors in $$\log T$$ and $$\kappa $$ is the security parameter. As our construction is public-coin, we can apply the Fiat-Shamir heuristic to make it non-interactive with sample communication/computation complexities. Furthermore, we give evidence that reducing the proof length below $$\widetilde{O}(T/S)$$ will be hard using existing symmetric-key based techniques by arguing the space-complexity of constant-distance error correcting codes.
A Lightweight Foreign Object Debris Detection Algorithm for Airport Runway

Aiming at the problem that small target detection for foreign object debris(FOD) on airport runways, the deep learning target detection algorithm is difficult to deploy on mobile devices due to the large size of the model and too many parameters. A lightweight FOD detection algorithm YOLO-ACIR is proposed based on YOLOX detection model. The spatial pyramid structure is improved, and the spatial pyramid structure with atrous convolution is utilized to reduce the loss of local information and edge information while expanding the receptive field. Secondly, the inverted residual structure and depth-separable convolution are introduced to improve the feature extraction module to provide richer features and reduce the loss of image information. Faster model inference by fusing convolutional and BN layers. Finally, FOD-A data set is used to verify the algorithm. Experimental results show that, in comparison with YOLOX-Nano with A few parameters added, the improved YOLO-ACIR, [email protected]:0.95, increases by 3.5 percentage points, and the final inference speed increases by 35.34 percentage points after the convolution layer and BN layer are fused. The comprehensive performance is better than original model, which is a more suitable target detection algorithm for mobile devices.
A Novel Logistics Scheme Based on Zero-Trust Model

Big data and cloud computing have turned the old logistics sector into an intelligent and digital one, enhancing supply chain efficiency and enabling real-time information sharing. While digitization improves quality, reduces prices, and boosts efficiency in logistics, it also introduces new security risks. A combination of traditional security protection systems, network security borders collapsing and increased risks from both sides. An investigation into how to preserve logistic privacy and information security is critical given present system flaws such as data security, arbitrary data access rights allocation, disappearance of security limits, and absence of privacy protection. We developed a zero-trust logistics scheme based on commercial encryption, employing the SDP architecture to establish a zero-trust security system, and precisely managing network boundary access behavior. The system’s underlying blockchain concept uses the domestic alliance chain FISCO BCOS to ensure transaction data traceability and party identification. Results show that this technique effectively protects user privacy, secures data storage, and allows autonomous operation of the logistics system. The solution places identity at the center, creates a new security border, protects user privacy, and aids in the digital transformation of logistics organizations.
Constrained Mean Shift Using Distant yet Related Neighbors for Representation Learning

We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its nearest neighbors (NNs). Since most NNs are close to the query by design, the averaging may not affect the embedding of the query much. On the other hand, far away NNs may not be semantically related to the query. We generalize the mean-shift idea by constraining the search space of NNs using another source of knowledge so that NNs are far from the query while still being semantically related. We show that our method (1) outperforms MSF in SSL setting when the constraint utilizes a different augmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query. Our code is available here: https://github.com/UCDvision/CMSF .
An Efficient VCGen-Based Modular Verification of Relational Properties

Deductive verification typically relies on function contracts that specify the behavior of each function for a single function call. Relational properties link several function calls together within a single specification. They can express more advanced properties of a given function, such as non-interference, continuity, or monotonicity, or relate calls to different functions, possibly run in parallel, for instance, to show the equivalence of two implementations. However, relational properties cannot be expressed and verified directly in the traditional setting of modular deductive verification. Recent work proposed a new technique for relational property verification that relies on a verification condition generator to produce logical formulas that must be verified to ensure a given relational property. This paper presents an overview of this approach and proposes important enhancements. We integrate an optimized verification condition generator and extend the underlying theory to show how relational properties can be proved in a modular way, where one relational property can be used to prove another one, like in modular verification of function contracts. Our results have been fully formalized and proved sound in the Coq proof assistant.