Reviewerly/exHarmony huggingface dataset:
On the structure of 1-generator quasi-polycyclic codes over finite chain rings

Quasi-polycyclic (QP for short) codes over a finite chain ring R are a generalization of quasi-cyclic codes, and these codes can be viewed as an R[x]-submodule of $${\mathcal {R}}_m^{\ell }$$ , where $${\mathcal {R}}_m:= R[x]/\langle f\rangle $$ , and f is a monic polynomial of degree m over R. If f factors uniquely into monic and coprime basic irreducibles, then their algebraic structure allow us to characterize the generator polynomials and the minimal generating sets of 1-generator QP codes as R-modules. In addition, we also determine the parity check polynomials for these codes by using the strong Gröbner bases. In particular, via Magma system, some quaternary codes with new parameters are derived from these 1-generator QP codes.
A Newton Frank–Wolfe method for constrained self-concordant minimization

We develop a new Newton Frank–Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds globally, the class of self-concordant functions only has local bounds, making it difficult to estimate the number of linear minimization oracle (LMO) calls for the underlying optimization algorithm. Fortunately, we can still prove that the number of LMO calls of our method is nearly the same as that of the standard Frank-Wolfe method in the L-smooth case. Specifically, our method requires at most $${\mathcal {O}}\big (\varepsilon ^{-(1 + \nu )}\big )$$ LMO’s, where $$\varepsilon $$ is the desired accuracy, and $$\nu \in (0, 0.139)$$ is a given constant depending on the chosen initial point of the proposed algorithm. Our intensive numerical experiments on three applications: portfolio design with the competitive ratio, D-optimal experimental design, and logistic regression with elastic-net regularizer, show that the proposed Newton Frank–Wolfe method outperforms different state-of-the-art competitors.
Generative convolution layer for image generation

This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) performance. Unlike the standard convolution, GConv first selects useful kernels compatible with the given latent vector, and then linearly combines the selected kernels to make latent-specific kernels. Using the latent-specific kernels, the proposed method produces the latent-specific features which encourage the generator to produce high-quality images. This approach is simple but surprisingly effective. First, the GAN performance is significantly improved with a little additional hardware cost. Second, GConv can be employed to the existing state-of-the-art generators without modifying the network architecture. To reveal the superiority of GConv, this paper provides extensive experiments using various standard datasets including CIFAR-10, CIFAR-100, LSUN-Church, CelebA, and tiny-ImageNet. Quantitative evaluations prove that GConv significantly boosts the performances of the unconditional and conditional GANs in terms of Frechet inception distance (FID) and Inception score (IS). For example, the proposed method improves both FID and IS scores on the tiny-ImageNet dataset from 35.13 to 29.76 and 20.23 to 22.64, respectively.
Social Science Education In The Context Of Functional And Literate Personality

One of the current trends in the development of education is its focus on metasubject results and the formation of a functional-literate personality.In this context, the problem of the development of subjectbased education is seen as the search for conceptual solutions for realizing the metasubject potential of educational disciplines and understanding the importance of some school courses in the formation of new literacy.The focus of the described study includes the results of social science education in the context of metadisciplinarity.The purpose of the study is to determine methodological guidelines for preparing a teacher to plan and diagnose metasubject results of social science education.The achievement of the purpose of the study involved two interrelated stages: approaches to planning and diagnosing results in the context of a functional person and methodological support for preparing a future teacher for the implementation of these approaches.According to the identified stages of the study, the materials presented in the paper include the following: a description of an integrated approach to the diagnosis of metasubject results of social science education in the context of civic literacy; substantiation of the structure and content of the methodological module aimed at preparing a future teacher for the development of metasubject results, characterizing the results of its development and providing examples of test tasks.The conclusions address the peculiarities of organizing the methodological training of a future teacher in the context of a wider understanding of the functional significance of social science education.
The Effect of Countermeasure Readability on Security Intentions

Human failure is a primary contributor to successful cyber attacks. For any cybersecurity initiative, it is therefore vital to motivate individuals to implement secure behavior. Research using protection motivation theory (PMT) has given insights into what motivates people to safeguard themselves in cyberspace. Recent PMT results have highlighted the central role of the coping appraisal in the cybersecurity context. In cybersecurity, we cope with threats using countermeasures. Research has shown that countermeasure awareness is a significant antecedent to all coping appraisal elements. Yet, although awareness plays a key role within the PMT framework, it is generally challenging to influence. A factor that is easy to influence is countermeasure readability. Earlier work has shown the impact of readability on understanding and that readability metrics make measuring and improving readability simple. Therefore, our research aims to clarify the relationship between countermeasure readability and security intentions. We propose an extended theoretical framework and investigate its implications using a survey. In line with related studies, results indicate that people are more likely to have favorable security intentions if they are aware of countermeasures and are confident in their ability to implement them. Crucially, the data show that countermeasure readability influences security intentions. Our results imply that cybersecurity professionals can utilize readability metrics to assess and improve the readability of countermeasure texts, providing an actionable avenue towards influencing security intentions.
Relating measurement disturbance, information, and orthogonality

In the general theory of quantum measurement, one associates a positive semidefinite operator on a $d$-dimensional Hilbert space to each of the $n$ possible outcomes of an arbitrary measurement. In the special case of a projective measurement, these operators are pairwise Hilbert--Schmidt orthogonal, but when $n>d$, orthogonality is restricted by positivity. This restriction allows us to more precisely state the quantum adage: information gain of a system is always accompanied by unavoidable disturbance. Specifically, we investigate three properties of a measurement with L\"uders rule updating: its disturbance, a measure of how the expected post-measurement state deviates from the input; its measurement strength, a measure of the intrinsic information producing capacity of the measurement; and its orthogonality, a measure of the degree to which the measurement operators differ from an orthonormal set. These quantities satisfy an information-disturbance trade-off relation that highlights the additional role played by orthogonality. Finally, we assess several classes of measurements on these grounds and identify symmetric informationally complete quantum measurements as the unique quantum analogs of a perfectly informative and nondisturbing classical ideal measurement.
Science Serving Justice: Opportunities for Enhancing Integrity in Forensic Science in Africa

Keywords: forensic scienceforensic science laboratoryresearch integrity Africa; biasoversightnetworksmentoring
Penerapan Metode Prototype Pada Perancangan Sistem Administrasi Pembayaran Karate Berbasis Website: Studi Kasus Lemkari Prabumulih

The development of information technology, especially the internet, is certainly welcomed by all circles, one of which has even penetrated the world of organizations. Lemkari Kota Prabumulih is a sports organization located in Prabumulih City which is engaged in the sport of karate. The system applied in several lemkari karate training locations in Prabumulih is still done manually. If a payment transaction occurs, management records the payment into a manual written ledger and only recapitulates the records in Microsoft Office Excel. So that sometimes there are often errors in recording data, errors in recording data for those who have made payments because the participants are mostly small children, the time is reduced due to slow manual data recording so that it is not effective in training due to the slow service of trainers to participants who make payments. Therefore, it is necessary to have a website-based system for every administrative service in order to facilitate the process of good data management and data transparency for trainers, participants, and participants' parents. This website was created using the PHP (Personal Hypertext Preprocessor) programming language with MySQL Database storage and using UML (Unifield Modeling Language) as a design method.
Designing a Trusted Data Brokerage Framework in the Aviation Domain

In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.
The elective course "Basics of artificial intelligence" for high school students

The article emphasizes the relevance of studying topics on artificial intelligence in a modern school, and points out the corresponding problems. The degree of representation of these topics in the school informatics course is analyzed. The author's elective course "Fundamentals of Artificial Intelligence" is described, the features of teaching high school students in the framework of this course are considered. The course is electronic and presented on the educational portal of the Omsk State Pedagogical University. Particular attention in the course is paid to the use of interactive elements that make it possible to organize interaction a teacher with schoolchildren both in distance and in blended learning. The article draws conclusions about possible improvements to the course, depending on the initial level of training of students in the field of informatics and information technology.
Intriguing Findings of Frequency Selection for Image Deblurring

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to leverage kernel-level information for image deblurring networks by inserting Fourier transform, ReLU operation, and inverse Fourier transform to the standard ResBlock. 1x1 convolution is further added to let the network modulate flexible thresholds for frequency selection. We term our newly built block as Res FFT-ReLU Block, which takes advantages of both kernel-level and pixel-level features via learning frequency-spatial dual-domain representations. Extensive experiments are conducted to acquire a thorough analysis on the insights of the method. Moreover, after plugging the proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset. Our method noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity. Code is available at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.
Implementation of Face Recognition Method for Attendance in Class

Face recognition has become one of the key aspects of computer vision. In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in a face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. It is a computer application for automatically identifying a person from a still image or video frame. In this paper, we proposed an automated attendance management system. In this research, our effort is to develop a system that allows easy attendance marking using real-time face recognition. The system is based on the Viola-Jones algorithm, this research provides a more efficient and secure Attendance for students at Binus University.
LSPP: A Novel Path Planning Algorithm Based on Perceiving Line Segment Feature

Path planning in the automatic driving system generally uses point features to evaluate the influence of obstacles on the robot, leading to missing information or increasing computational burden. This paper proposes a novel path planning with line segment (LSPP) algorithm from the viewpoint of line segment, which is easy to be obtained directly from sensors. In LSPP, an artificial potential field (APF) based on the azimuth and distance (ADAPF) from obstacles to the robot is first constructed for implementation of integral calculation. Unlike the traditional APF which only considers the nearest distance from obstacles to the robot, ADAPF can effectively assess the influence of boundary points on the robot by fully utilizing the information of obstacles. Based on ADAPF, boundary points are integrated into lines as edges of the drivable area in a specialized coordinate system. Then, a multi-loss function is designed to guarantee that the robot is far away from obstacles to the optimized path, and thus solved by a modified Dogleg (MDL) algorithm combined with symmetric rank-1 (SR1) method based on adaptive initial trust-region radii. Finally, a smooth and safe path is provided in LSPP with the requirements of speed and quality being satisfied simultaneously. Real-world experimental results validate the robustness and the effectiveness of LSPP.
The Train of Thought and Strategy of Chinese School Physical Education to Cultivate Unique Minority Students’ Motor Quotient in Yunnan

Yunnan is one of the most typical regions of ethnic cultural diversity in China.There are more than 15 unique ethnic minorities living in Yunnan for generations.To awaken the Motor Quotient of Yunnan minority students is a subject that should not be ignored in the reform of Yunnan school physical education.At the same time, the research of Motor Quotient of Yunnan minority students is of great significance to the physical education work of Yunnan schools.The Motor Quotient training of Yunnan minority students in school physical education should start with solving the outstanding problems of Yunnan minority students in school physical education, take the Motor Quotient theory as the breakthrough point, and take the curriculum standard as the basis to cultivate the Core sports literacy of Yunnan minority students.By cultivating physical education teachers with high Motor Quotient team, the construction of system of physical education curriculum, make with characteristic of ethnic sports activities, will move Motor Quotient and the integration of sports wisdom, build the campus sports culture with unique ethnic minorities in Yunnan characteristics such as strategy, develop unique ethnic minority areas in Yunnan students Motor Quotient, enhance Yunnan unique minority students physical health, Exercise the conscious consciousness and behavior habits of Yunnan minority students, and promote their all-round development.
An annotated video dataset for computing video memorability

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.
Drone Obstacle Avoidance Algorithm using Camera-based Reinforcement Learning

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger.Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information.Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information.Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed.Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera.Based on this distance, the presence or absence of obstacles is determined.Penalties are set if they are obstacles and rewards are given if they are target points.The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.
Non-inverse kinematics of free-floating space robot based on motion planning of sampling

Motion planning is one of the fundamental technologies for robots to achieve autonomy. Free-floating space robots composed manipulators and base satellite that do not actively control its position and attitude has nonholonomic characteristics, and there is a first-order differential relationship between its joint angle and the base attitude. In addition, the planning framework which first converts the goal end-effector pose to its corresponding target configuration, and then plan the trajectory from the initial configuration to the goal configuration still has the following problems: the goal configuration and the initial configuration may not be in the same connected domain. Based on the RRT framework, the motion planning of a free-floating space robot from the initial configuration to the goal end-effector pose is studied. In the algorithm design, in order to deal with the differential constraints of the free-floating space robot, and the requirement that the attitude disturbance of its base cannot exceed its limit, a control-based local planner for random configuration guiding growth of the tree and a control-based local planner for goal end-effector pose guiding growth of the tree that can adjust the attitude of the base when necessary are proposed. The former can ensure the effective exploration of the configuration space, and the latter can avoid the occurrence of singularity while ensuring that the algorithm converges quickly and the base attitude disturbance meets the constraints. The present algorithm does not need to solve the inverse kinematics, can successfully complete the planning task, and ensure that the base attitude disturbance meets the requirements. The simulation verifies the effectiveness of the algorithm.
Fast Retrieval Algorithm of English Sentences Based on Artificial Intelligence Machine Translation

With the continuous development of artificial intelligence technology, people all over the world are no longer bound by distance, accompanied by obstacles to group communication between different populations. In order to allow people to better communicate and talk, machine translation. It came into being, and this brand-new technology has become a hot research topic at home and abroad under the artificial intelligence model. This article first summarizes the basic theory of artificial intelligence technology, and then extends the core technology of artificial intelligence. Based on the current status of machine translation English sentences on the Internet, artificial intelligence technology is used to quickly retrieve machine translated English sentences. This research systematically expounds the rules and corpus of the machine translation system, as well as the principle, calculation process and model building of related algorithms. Through experimental analysis, the effect of rapid retrieval of English sentences based on machine translation under artificial intelligence is studied. This experiment carried out research on the theme of this article by the express questionnaire survey method and the analytic hierarchy process. The experimental research shows that the Simhash algorithm reduces the impact of the synonym processing stage on the overall performance, while retaining the advantages of high precision and high accuracy of the calculation results.