Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative weighted bounds for composite operators on spaces of homogeneous type

Let T1,T2 be Calderón-Zygmund operators, T1,b denotes the commutator of T1 and the BMO function b, by establishing bi-sublinear sparse dominations, we obtain the quantitative weighted bounds for composite operators T1T2 and T1,bT2 on Lp(X ,ω) with ω ∈ Ap(X).
Research on A Low Computational Cost Vision-aided Inertial Navigation Method for Precision Landing on Asteroid

Asteroid exploration missions demand more autonomous navigation system that are precise and in real-time. Visual-aided inertial navigation systems combine the advantages of autonomy of inertial navigation systems and accuracy of visual navigation systems. However, both increase the computational burden for the information processing. With the goal of decreasing the computational load, this paper proposes a low cost vision-aided inertial navigation method which can calculate increment position, velocity and attitude directly in inertial frame by pre-integrating IMU measurements. For visual information, specific combinations of the features are selected according to information entropy theory to reduce computation. Simulation results show that the standard deviations of the spacecraft’s estimated position error is less than 1 m, the velocity less than 0.002 m/s and the attitude angle less than 0.005 deg, compatible with precision landing on asteroids.
A Simple Data Privacy and Security Protection Algorithm Based on Federated Learning

Although federated learning can break the data island and enhance privacy, it is not absolutely secure. External eavesdroppers can intercept the model when clients upload their models to the server, which might be used to infer the data information. A natural solution is to add noise to the uploaded model. However, this solution has its drawback, because the added noise cannot be eliminated when the models are aggregated at the server, which leads to poor performance. In order to tackle this problem, we propose a simple but effective algorithm, FedNoise. In the vanilla federated learning algorithm, a gradually decreasing learning rate is applied at the client side. While in our algorithm, we divide the parameter update into client-side and server-side. The stochastic gradient descent update with constant learning rate is performed at the client side and a gradually decreasing learning rate is performed at the server. Our algorithm can not only ensure the data security, but also maintain comparable system performance. Simulation results validate our conclusion.
Design of an Improved Impedance Controller for Powered Transtibial Prosthesis

With the rapid development of powered transtibial prostheses, the control of motorized actuators is a research hotspot. This study designs a double-loop impedance controller to improve the performance of the traditional controller. Including time-varying parameters, the designed controller is implemented on a novel elastic actuator. Under different loads of users’ weights, simulation tests are conducted in a working condition of walking, and the results show that the designed control system has outstanding performances in the control of an entire gait cycle. The error between output and reference of ankle joint position during walking is less than 0.013rad in each sample time. A position disturbance test demonstrates that the control also has a high tolerance of any position disturbance at the ankle joint. The double-loop impedance controller can replace traditional finite-state impedance control in the field of the powered transtibial prosthesis and has the potential to be applied in other relative fields.
An Optimal Evader-Pursuer Strategy based on Machine Learning and Differential Game

Based on the special differential game model of evader and pursuers, in this paper, we propose an optimal escape and containment strategies of evader and pursuers, and gives the escape boundary conditions of evader. Through the training of DDPG and A3C algorithms, the evader have the ability to escape autonomously, and the evaluation system is established with the reward value, the number of rounds, the success rate of escape and the ratio of escape speed as the main indicators. Taking different pursuer-evader speed ratios under DDPG and A3C as key indicators, a quantitative evaluation system was established, and the evaluation results verify the effectiveness of the proposed approach.
Improving the Transferability of Adversarial Examples by Using Generative Adversarial Networks and Data Enhancement

Deep neural networks (DNNs) can be attacked by adversarial examples that are undetectable by humans. Generation-based approaches have recently gained popularity because they directly translate the input distribution to the distribution of adversarial instances, making them more effective and efficient. However, existing techniques are susceptible to overfitting on the substitute model, limiting the transferability of adversarial examples. In this paper, we introduce data augmentation into AdvGAN, called AdvGAN-DE, which can dramatically improve the transferability of adversarial examples. Experiments demonstrate that we enhance the success rate by 31.09% for defense models and 10.23% for typically trained models on average when compared to AdvGAN.
Simulation and Implementation of Robot Obstacle Avoidance Algorithm on ROS

Robots often collide in the process of simulation soccer match. The collision will block the robot’s own motion trajectory, which is not conducive to the robot’s ball grasping and defense. Considering that the obstacles have the characteristics of dynamics, antagonism and uncertainty, the robot’s motion trajectory, path selection and obstacle avoidance are very important. Taking ROS as the research platform, the study focuses on the path planning of simulated soccer robot. Meanwhile, we have applied some obstacle avoidance algorithms to the Middle Size Simulation League of soccer.
Positioning in Urban Scenarios Based on Smartphone Camera/IMU and Digital TV Signal

The built-in sensors of smartphone, such as cameras, accelerometers and gyroscopes, provide abundant environmental information for positioning. VIO (visual-inertial odometry) can be derived from the embedded camera and inertial sensors to provide the estimation of a continuous and smooth trajectory of the pedestrian. However, the trajectory will drift due to the accumulation of system errors. Currently, DTV (digital TV) signal has been verified to be promising for precise ranging in urban scenarios. Therefore, this paper investigates a method of fusing the VIO with the ranging information from DTV signal to improve the positioning accuracy. An Extended Kalman filter is presented to estimate pedestrian mobile state. Field tests have been carried out and the fusion results can effectively reduce trajectory drift errors and improves positioning performance.
Survey of Image Augmentation Based on Generative Adversarial Network

On the visual side of computer science, image data is very important in the training of neural network models. Sufficient training data can alleviate the over-fitting problem of the model during training and help the model obtain the optimal solution. However, in many computer vision assignments, it is not easy and costly to obtain sufficient training samples. Therefore, image augmentation has become a commonly used method to increase training samples. Generative Adversarial Network (GAN) is a generative method of machine learning that can generate realistic images and provide a new solution for image augmentation. This article first introduces image augmentation and its commonly used four types of methods. Secondly, the basic principles of GAN and its direct and integrated methods in image augmentation are introduced, and the typical methods used to calculate whether the images from the networks meets the requirements; then the research status of GAN in image augmentation is analyzed. Finally, the problems and development trends of GAN model in image augmentation are summarized and prospected.
Intelligent Scheduling System for Production Line Automatic Matching Based on DSSM-XGBoost

Intelligent production scheduling is an important part of intelligent production, and its production time and intensity are reasonably arranged, which can fully improve production efficiency. This article provides an intelligent model for engine manufacturing and scheduling. After the model decomposes the order into processes, the DSSM algorithm solves the semantic similarity of the text in the production content of the process, and generates a semantic vector to convert unstructured data into structured data. Based on XGBoost algorithm, the required production line is marked to establish the association between the production content and the production line, and realize automatic matching. After classification, the average working hours and other information are estimated according to the established logic. Finally, schedule production based on the principle of maximum utilization of the production line. Through testing and evaluation, the scheduling results of this smart scheduling model can be completed in just a few seconds, which saves time and costs in the scheduling process.
IRNet: A Real-time Intersection Recognition Network Based on Graph Classification and 3D LiDAR Point Cloud for UGVs

The intersections are junctions of roads. Recognizing intersections based on LiDAR accurately and quickly is a key task for UGVs. In this paper, we propose an end-to-end real-time intersections recognition network (IRNet) with graph attention convolution based on graph classification and 3D LiDAR point cloud. Multiple evaluations on KITTI and Tunnel dataset demonstrate that our model performs better than other competitive models and meets the real-time recognition. We research the performance of our model under different number of input points, and certify that neither of two spatial transformation networks is effective for our model. Ablation experiments certify effectiveness of the proposed features skip connection.
A noniterative solution to the inverse Ising problem using a convex upper bound on the partition function

The inverse Ising problem, or the learning of Ising models, is notoriously difficult, as evaluating the partition function has a large computational cost. To quickly solve this problem, inverse formulas using approximation methods such as the Bethe approximation have been developed. In this paper, we employ the tree-reweighted (TRW) approximation to construct a new inverse formula. An advantage of using the TRW approximation is that it provides a rigorous upper bound on the partition function, allowing us to optimize a lower bound for the learning objective function. We show that the moment-matching and self-consistency conditions can be solved analytically, and we obtain an analytic form of the approximate interaction matrix as a function of the given data statistics. Using this solution, we can compute the interaction matrix that is optimal to the approximate objective function without iterative computation. To evaluate the accuracy of the derived learning formula, we compared our formula to those obtained by other approximations. From our experiments on reconstructing interaction matrices, we found that the proposed formula gives the best estimates in models with strongly attractive interactions on various graphs.
Mach: A Pluggable Metrics Storage Engine for the Age of Observability

Observability is gaining traction as a key capability for understanding the internal behavior of large-scale system deployments. Instrumenting these systems to report quantitative telemetry data called metrics enables engineers to monitor and maintain services that operate at an enormous scale so they can respond rapidly to any issues that might arise. To be useful, metrics must be ingested, stored, and queryable in real time, but many existing solutions cannot keep up with the sheer volume of generated data. This paper describes Mach, a pluggable storage engine we are building specifically to handle high-volume metrics data. Similar to many popular libraries (e.g., Berkeley DB, LevelDB, RocksDB, WiredTiger), Mach provides a simple API to store and retrieve data. Mach’s lean, loosely coordinated architecture aggressively leverages the characteristics of metrics data and observability work-loads, yielding an order-of-magnitude improvement over existing approaches—especially those marketed as “time series database systems” (TSDBs). In fact, our preliminary results show that Mach can achieve nearly 10 × higher write throughput and 3 × higher read throughput compared to several widely used alternatives.
Statistical disclosure control for machine learning models.

AI models are trained on large datasets. Where the training data is sensitive, the data holders need to consider risks posed by access to the training data and risks posed by the models that are released. The first problem can be considered solved: there are multiple tested solutions delivering secure access to sensitive data for research purposes. These include robust ‘statistical disclosure control’ (SDC) procedures for checking the confidentiality risk in outputs released from the secure environment. However, these SDC procedures are designed for statistical outputs. It is not clear how they relate to AI model specification created within the secure environment. Similarly, there is a small but growing literature on re-identification and other risks from AI models trained on personal data. However, this does not consider the operational circumstances which might limit opportunities for misuse.
DETUROPE – THE CENTRAL EUROPEAN JOURNAL OF REGIONAL DEVELOPMENT AND TOURISM

The paper aims to reposition urban cemeteries within the tourism supply and to showcase their values as cultural tourism products that can enrich visitors’ experiences. Although urban cemeteries as ritual meeting points of life and death have become an integral part of city tourism, contemporary tourism literature mostly embeds them in dark tourism or thanatourism, neglecting the experience-rich potentials of cemeteries as cultural products. This paper rectifies this by arguing that cemetery tourism makes a fascinating cultural display for tourists, offering both nature-based and cultural activities, therefore, it can be rightfully placed in heritage and cultural tourism. The paper investigates foreign visitors’ attitude at the National Graveyard in Budapest. During the empirical research 52 questionnaires were correctly filled in, followed by the same number of mini-interviews. The research findings confirmed the initial hypotheses: 1. Visitors regarded cemeteries as complex attractions representing both natural and cultural values, which added to the positive experiences of a Budapest city break. 2. There was no reference to the so called “dark aspects” of cemeteries in the answers. 3. Although the satisfaction rate was high, the lack of visitors is a clear indication that the cemetery in Budapest has been so far undervalued as an urban attraction. On the negative side, respondents criticized the lack of information sources available prior to visit, the inefficient marketing and the undesirable neighbourhood. The paper ultimately aims to provide stakeholders solid, preliminary data that might serve as a launching pad for further larger-scale research.
Preempting Flaky Tests via Non-Idempotent-Outcome Tests

Regression testing can greatly help in software development, but it can be seriously undermined by flaky tests, which can both pass and fail, seemingly nondeterministically, on the same code commit. Flaky tests are an emerging topic in both research and industry. Prior work has identified multiple categories of flaky tests, developed techniques for detecting these flaky tests, and analyzed some detected flaky tests. To proactively detect, i.e., preempt, flaky tests, we propose to detect non-idempotent-outcome (NIO) tests, a novel category related to flaky tests. In particular, we run each test twice in the same test execution environment, e.g., run each Java test twice in the same Java Virtual Machine. A test is NIO if it passes in the first run but fails in the second. Each NIO test has side effects and “self-pollutes” the state shared among test runs. We perform experiments on both Java and Python open-source projects, detecting 223 NIO Java tests and 138 NIO Python tests. We have inspected all 361 detected tests and opened pull requests that fix 268 tests, with 192 already accepted, only 6 rejected, and the remaining 70 pending.
IDENTIFICATION OF THE INDICATORS OF THE HEALTH OF UKRAINE’S POPULATION ON THE BASIS OF MARKETING RESEARCHES (MODEL OF MENTALIZATION)

Human health is the most important socio-economic value of any state. The condition of health of the population is one of the most important indicators of the well-being of the nation. The sphere of public health is one of the priority directions of state policy and national security in general. Important directions of modern health care reform are optimization of management, rational distribution of limited financial resources, efficient use of material resources, introduction of health insurance, restructuring of medical and preventive care to the population. The summation of socio-economic relations in the health care sector forms the market of medical services. In this area, marketing can be defined as a complex process of planning, economic substantiation and management of the process of provision of medical services, the formation of a pricing policy for the treatment and prevention process, ensuring effective communication with patients. Marketing studies are the most important components of the analysis of any market, including the market of medical services. In this market, their conduct is complicated in terms of the very methodology of conducting and analyzing the results, in particular with the aim of formulating proposals for the effective activity of the subjects on it, as well as identifying health indicators of the population of Ukraine. The purpose of the study is to identify the health status of the population and determine the factors of demand for medical services and their demand. The main task set before market research on identifying the health of the population based on the model of self-esteem is the formation and provision of benefits to consumers that meet their needs for qualified medical care and improve the quality of life of each patient in particular. The research methods used in the work are based on a probabilistic, stratified, quota, representative sample for the entire population in Ivano-Frankivsk, Ukraine. The obtained results form the realistic representation of the main tendencies in the field of Modern engineering and innovative technologies Issue 8 / Part 1 ISSN 2567-5273 www.moderntechno. de 17 medical services in Ukraine and allow identification of the indicators of the health status of the population based on the model of self-evaluation, assessment of the potential of socio-economic adaptation of the population in the context of the economic crisis in obtaining medical services.
Fischer-Tropsch Synthesis as Key for a Decentralized Sustaina- ble Kerosene Production

Synthetic fuels play an important role in the defossilization of future aviation transport. To reduce the ecological impact of remote airports due to long range transportation of kerosene, a decentralized on-site-production of synthetic paraffinic kerosene is applicable, preferably as neardrop-in fuel or alternatively as blend. One possible solution for such a production of synthetic kerosene is the Power-to-Liquid process. The basic development of a simplified plant layout addressing the specific challenges of a decentralized kerosene production which differ from most current approaches for infrastructural well-connected regions is described. The decisive influence of the Fischer-Tropsch synthesis on the PtL process is shown by means of a steady-state reactor model which was developed in Python and serves as basis for further development of a modular environment able to represent entire process chains. The reactor model is based on reaction kinetics according current literature. The effects of adjustments of the main operation parameters on the reactor behavior are evaluated and the impacts on upand downstream processes are described. The results prove the governing influence of the Fischer-Tropsch reactor on the PtL process and show its flexibility regarding the desired product fraction output, which makes it an appropriate solution for a decentralized kerosene production.
Commissioning and Independent Television Production: Power, Risk, and Creativity

Project development—the initial stage of television production—establishes the aesthetic and narrative parameters for the final on-screen representations. This article explores this important yet largely under-researched phase of production by investigating the structures and processes of project development in the British television industry with a focus on the conditions for the independent production sector in a broadcaster–publisher system. Its arguments are based on findings of an ethnographic production study in independent production companies in the United Kingdom, which are contextualized by statistical industry data and correlated to published research undertaken across the last two decades to assess changes and continuities over time. The article analyses the cultural and economic power relations between independent producers and commissioning editors and how they affect development practices. It evaluates the implications for creative autonomy and diversity, and shows how the existence of preferred suppliers and the demand-led nature of commissioning constrain producer creativity, leading to fewer opportunities for independent producers and a more homogenous television output.
BLENDED APPROACH TO LEARNING - ITS CHALLENGES AND FUTURE

COVID-19 has brought the trend of e-learning in education sector and one of the tools to implement this concept is through Blended Learning. Blended learning is deﬁned an approach to education which combines online educational materials and opportunities for interaction online with traditional place-based classroom methods This paper gives the overview of the concept of Blended Learning. In higher education system blended learning has been not a new term, but it has intensively attached the importance owing to rapid development of available digital and social media that have been utilized for adequate learning environments. Besides this, the paper also reveals the dimensions of the blended learning approach i.e., blending ofﬂine and online learning, self-paced blending and live blending, collaborative learning, and blending structured and unstructured learning. This paper also describes the areas where this technique is beneﬁcial, along with the challenges faced by this technique such as technical Challenges, organizational challenges and instructional design challenges. Then the Blended Learning process is described in terms of its various advantages. Factors that promote successful blended learning are also mentioned in this paper.