Reviewerly/exHarmony huggingface dataset:
Improved Attacks on LowMC with Algebraic Techniques

The LowMC family of SPN block cipher proposed by Albrecht et al. was designed specifically for MPC-/FHE-/ZKP-friendly use cases. It is especially used as the underlying block cipher of PICNIC, one of the alternate third-round candidate digital signature algorithms for NIST post-quantum cryptography standardization. The security of PICNIC is highly related to the difficulty of recovering the secret key of LowMC from a given plaintext/ciphertext pair, which raises new challenges for security evaluation under extremely low data complexity.In this paper, we improve the attacks on LowMC under low data complexity, i.e. 1 or 2 chosen plaintext/ciphertext pairs. For the difference enumeration attack with 2 chosen plaintexts, we propose new algebraic methods to better exploit the nonlinear relation inside the introduced variables based on the attack framework proposed by Liu et al. at ASIACRYPT 2022. With this technique, we significantly extend the number of attack rounds for LowMC with partial nonlinear layers and improve the success probability from around 0.5 to over 0.9. The security margin of some instances can be reduced to only 3/4 rounds. For the key-recovery attack using a single plaintext, we adopt a different linearization strategy to reduce the huge memory consumption caused by the polynomial methods for solving multivariate equation systems. The memory complexity reduces drastically for all 5-/6-round LowMC instances with full nonlinear layers at the sacrifice of a small factor of time complexity. For 5-round LowMC instances with a block size of 129, the memory complexity decreases from 286.46 bits to 248.18 bits while the time complexity even slightly reduces. Our results indicate that the security for different instances of LowMC under extremely low data complexity still needs further exploration.
Committing Security of Ascon: Cryptanalysis on Primitive and Proof on Mode

Context-committing security of authenticated encryption (AE) that prevents ciphertexts from being decrypted with distinct decryption contexts, (K,N,A) comprising a key K, a nonce N, and associate data A is an active research field motivated by several real-world attacks. In this paper, we study the context-committing security of Ascon, the lightweight permutation-based AE selected by the NIST LWC in 2023, for cryptanalysis on primitive and proof on mode. The attacker’s goal is to find a collision of a ciphertext and a tag with distinct decryption contexts in which an attacker can control all the parameters including the key. First, we propose new attacks with primitives that inject differences in N and A. The new attack on Ascon-128 improves the number of rounds from 2 to 3 and practically generates distinct decryption contexts. The new attack also works in a practical complexity on 3 rounds of Ascon-128a. Second, we prove the context-committing security of Ascon with zero padding, namely Ascon-zp, in the random permutation model. Ascon-zp achieves min {t+z/2 , n+t−k−ν/2 , c/2}-bit security with a t-bit tag, a z-bit padding, an n-bit state, a ν-bit nonce, and a c-bit inner part. This bound corresponds to min {64 + z/2 , 96} with Ascon-128 and Ascon-128a, and min {64 + z/2 , 80} with Ascon-80pq. The original Ascon (z = 0) achieves 64-bit security bounded by a generic birthday attack. By appending zeroes to the plaintext, the security can be enhanced up to 96 bits for Ascon-128 and Ascon-128a and 80 bits for Ascon-80pq.
ANALYSIS OF ERROR IN INTERPOLATION OF CONTINUOUS FUNCTIONS WITH UNKNOWN CONTINUOUS DERIVATIVES USING CUBIC SPLINES

Methodology of determination of error of sk(d) interpolation of function is worked out by a cube spline in case of unknown fourth derivative. It is shown that value of sk(d) on an interval under a number k equal h|dk|/6, where h is a size of interval, dk are coefficients of polynomials of spline at a variable in the third degree. The algorithm of calculation of coefficients of spline supposes implementation of next terms: a function is continuous and possesses continuous the first and second derivatives, on the borders of function the first derivatives are set, second derivative on a right border is equal to the zero. In case of the unknown first derivatives on borders possibility of their calculation is envisaged with the use of values of function in key points. The proposed method was tested on monotone functions sin(πх/2), (1-exp(-x))/(1-exp(-1)), log(1+х)/log(2), (2x+x2+x3+x4)/5, which do not have inflections and extreme points on the segment [0,1]. The standard deviation of the values of the reconstructed function sin(πх/2) from the values of a spline of three polynomials, equal to s=0.92·10-2, is in good agreement with the error calculated with the proposed method and equal to sk(d)=1.3·10-2. Increase of amount of knots to 103 and more provides exactness of interpolation to 10-7 and higher. In all cases the size of sk(d) well comports with the exact meaning of s. An analogical situation is observed for many droningly decreasing or increasing functions, not having bends and extreme points. At renewal of any functional dependences it is recommended to break up the investigated segment on such intervals, wherever functions have bends and extreme points. Keywords: renewal of function, interpolation polynomial, interpolation, cube spline, error of interpolation, function.
CI3D: Context Interaction for Dynamic Objects and Static Map Elements in 3D Driving Scenes

Multi-view 3D visual perception including 3D object detection and Birds’-eye-view (BEV) map segmentation is essential for autonomous driving. However, there has been little discussion about 3D context attention between dynamic objects and static elements with multi-view camera inputs, due to the challenging nature of recovering the 3D spatial information from images and performing effective 3D context interaction. 3D context information is expected to provide more cues to enhance 3D visual perception for autonomous driving. We thus propose a new transformer-based framework named CI3D in an attempt to implicitly model 3D context interaction between dynamic objects and static map elements. To achieve this, we use dynamic object queries and static map queries to gather information from multi-view image features, which are represented sparsely in 3D space. Moreover, a dynamic 3D position encoder is utilized to precisely generate queries’ positional embeddings. With accurate positional embeddings, the queries effectively aggregate 3D context information via a multi-head attention mechanism to model 3D context interaction. We further reveal that sparse supervision signals from the limited number of queries result in the issue of rough and vague image features. To overcome this challenge, we introduce a panoptic segmentation head as an auxiliary task and a 3D-to-2D deformable cross-attention module, greatly enhancing the robustness of spatial feature learning and sampling. Our approach has been extensively evaluated on two large-scale datasets, nuScenes and Waymo, and significantly outperforms the baseline method on both benchmarks.
Smoothing Methods for Automatic Differentiation Across Conditional Branches

Programs involving discontinuities introduced by control flow constructs such as conditional branches pose challenges to mathematical optimization methods that assume a degree of smoothness in the objective function’s response surface. Smooth interpretation (SI) is a form of abstract interpretation that approximates the convolution of a program’s output with a Gaussian kernel, thus smoothing its output in a principled manner. Here, we combine SI with automatic differentiation (AD) to efficiently compute gradients of smoothed programs. In contrast to AD across a regular program execution, these gradients also capture the effects of alternative control flow paths. The combination of SI with AD enables the direct gradient-based parameter synthesis for branching programs, allowing for instance the calibration of simulation models or their combination with neural network models in machine learning pipelines. We detail the effects of the approximations made for tractability in SI and propose a novel Monte Carlo estimator that avoids the underlying assumptions by estimating the smoothed programs’ gradients through a combination of AD and sampling. Using DiscoGrad, our tool for automatically translating simple C++ programs to a smooth differentiable form, we perform an extensive evaluation. We compare the combination of SI with AD and our Monte Carlo estimator to existing gradient-free and stochastic methods on four non-trivial and originally discontinuous problems ranging from classical simulation-based optimization to neural network-driven control. While the optimization progress with the SI-based estimator depends on the complexity of the programs’ control flow, our Monte Carlo estimator is competitive in all problems, exhibiting the fastest convergence by a substantial margin in our highest-dimensional problem.
IF-Based Trajectory Planning and Cooperative Control for Transportation System of Cable Suspended Payload With Multi UAVs

In this paper, we tackle the control and trajectory planning problems for the cooperative transportation system of cable-suspended payload with multi Unmanned Aerial Vehicles (UAVs). Firstly, a payload controller is presented considering the dynamic coupling between the UAV and the payload to accomplish the active suppression of payload swing and the complex payload trajectory tracking. Secondly, different from the simplification of obstacles in most approaches, we propose three Insetting Formation (IF) algorithms for the complete obstacle shape to generate collision-free waypoints for the cooperative transportation system. An IF strategy is proposed by integrating three IF algorithms to improve the success rate of obstacle avoidance and reduce the algorithm complexity for performing the aggressive flight. Finally, we verify the robustness and high performance of the proposed algorithm through benchmark comparison and real-world experiments. Moreover, our source code is released as an open-source ros package.
Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadrotor with Unicycle-Driven and Rotor-Assisted Turning

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.
Comparison of Neural Deblurring Methods for Improving SPECT Reconstruction Quality

Single photon emission computed tomography (SPECT) is a nuclear imaging technique which has numerous applications in the clinical and medical research domain. However, SPECT images suffer from various types of image degradation which may directly influence the quality of the final resulting image. In recent years, due to the fast development of machine learning, many studies have emerged that utilize neural networks (NNs) to improve the image quality of SPECT. Yet, many of of these developing techniques require a significant amount of time and data to train the NN in order to achieve acceptable performance. In this work, we investigated several NN deblurring models which have been pretrained but were never previously trained for medical imaging tasks or with SPECT reconstruction. These methods provide a fast approach for deblurring since there is no extra training work required. Visually inspecting all the methods demonstrated each method's ability to effectively reduce the amount of blur. We also used a quantitative measurement called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) to evaluate the output image quality of those four methods. The result shows all the neural deblurring methods such as SwinIR and ESRGAN are able to effectively produce a clearer and more readable image from the blurry SPECT reconstructions.
ETAUS: An Edge and Trustworthy AI UAV System with Self-Adaptivity for Air Quality Monitoring

This work presents the ETAUS, an Edge and Trustworthy AI UAV System, as a mobile sensing platform for air quality monitoring. ETAUS employs an FPGA device as the main hardware computing architecture rather than relying solely on a microprocessor or integrating with GPUs to meet real-time processing demands and achieve adaptivity and scalability. ETAUS contains a neural engine that can execute our customized AI model for air quality index (AQI) level classification and a pre-trained model for detecting objects containing private information. ETAUS also incorporates a de-identification process, cryptographic functions, and protection matrices to safeguard information and individuals' privacy. Furthermore, cryptographic functions and protection matrices are implemented as reconfigurable modules, which can accelerate processing, protect data privacy, and be reconfigured as needed. Experiments have demonstrated ETAUS can achieve a speedup of 3.15x to 72.46x for AQI level classification compared to microprocessor-based and GPU-based designs. ETAUS can also enhance energy efficiency by 5.02x compared to embedded GPU solutions such as NVIDIA Jetson Nano. To support all the cryptographic functions and protection matrices, system adaptivity in ETAUS can significantly increase resource utilization while decreasing power consumption by up to 2.79%.
Shielded Reinforcement Learning for Hybrid Systems

Safe and optimal controller synthesis for switched-controlled hybrid systems, which combine differential equations and discrete changes of the system’s state, is known to be intricately hard. Reinforcement learning has been leveraged to construct near-optimal controllers, but their behavior is not guaranteed to be safe, even when it is encouraged by reward engineering. One way of imposing safety to a learned controller is to use a shield, which is correct by design. However, obtaining a shield for non-linear and hybrid environments is itself intractable. In this paper, we propose the construction of a shield using the so-called barbaric method, where an approximate finite representation of an underlying partition-based two-player safety game is extracted via systematically picked samples of the true transition function. While hard safety guarantees are out of reach, we experimentally demonstrate strong statistical safety guarantees with a prototype implementation and Uppaal Stratego. Furthermore, we study the impact of the synthesized shield when applied as either a pre-shield (applied before learning a controller) or a post-shield (only applied after learning a controller). We experimentally demonstrate superiority of the pre-shielding approach. We apply our technique on a range of case studies, including two industrial examples, and further study post-optimization of the post-shielding approach.
Implementation of Academic Management Information System Based on Website as an Effort to Improve Academic Management Effectiveness at SMA Negeri 9 Banda Aceh

The success of an educational organization depends on access to relevant and up-to-date information. The administration management of an institution will determine the quality of education, where the available information is obtained from good academic activities. This research aims to improve th
Assessment of university students’ need to acquire knowledge about ecology

The article discusses the necessity and possibility of including an integrated module of environmental education and foreign language in the training of bachelors at the university. It is noted that the inclusion of environmental education in a foreign language course contributes to the expansion of knowledge acquired earlier, reduces the burden on students, meets the needs of society and the individual. To design an integrated module, it is necessary to determine the readiness and desire of students for environmental education. The authors developed a questionnaire consisting of four blocks aimed at determining previous environmental education in various forms, students’ involvement in personal environmental practices, knowledge and attitude towards measures to improve the environment; students’ needs in environmental education. It is established that a minority of students are involved in personal practices of pollution reduction, students show pessimism in the use of modern technologies, are focused on state measures and control in the field of environmental protection. On the one hand, respondents note the need for knowledge on environmental issues; on the other hand, they believe that only special disciplines are able to provide this data, given that such disciplines are not included in the curriculum at non-core faculties. The data obtained confirm the relevance of the integrated course, allow us to identify topics that are designed to provide students with new knowledge and strengthen confidence in the technological solution of many environmental problems, as well as support students in applying personal practices that reduce environmental harm.
PENGENALAN E-LEARNING PEMBELAJARAN GURU-GURU MADRASAH IFTIDAIYAH SWASTA BONTOA

As an effort to prevent the Covid-19 pandemic, the government and its staff issued a policy for schools to carry out learning at home. As of March 16, 2020, the school has implemented the online student learning method. The online learning system (on the network) is a learning system without face to face directly between teachers and students but is carried out online using the internet network. Teachers must ensure that teaching and learning activities continue, even though students are at home. The solution is that teachers are required to be able to design learning media as innovations by utilizing online media (online). Due to the diversity of teacher competency levels in carrying out online learning, it is necessary to socialize the introduction of the learning system. Therefore, in this community service, lecturers who carry out community service try to introduce e-learning learning media by providing examples of how to use it, for example using Google Classroom, and making learning videos uploaded on YouTube. In addition to teachers, lecturers also provide direction, views and motivation to parents because students' parents are considered to have an important role and cooperation in educating children during online learning at home. The result of this community service is that teac hers already know, understand and can manage online classes well e-learning
Nuances of Remuneration of Pedagogical Workers as a Multidimensional Object, Based on Rationalization Factors

A teacher’s remuneration depends on a number of factors and can be viewed as a multidimensional object influenced by several constituent elements. Thus, it is relevant to determine these constituent elements, namely the pay rate for normal work hours, number of extra work hours per teacher, number of students in the class. The purpose of this article is to determine and calculate how these factors influence the teacher's rate of pay. The article studies correlation between teachers’ wages and the constituent factors based on a city school in the Trans-Baikal Region, which is a typical school with typical pay data. In the course of the study, the following methods were used: studying various sources of information, summary and interpretation, analysis of information obtained from various sources, and a study of teachers’ remuneration through modeling methods. The results of the study revealed each of the influencing factors’ percentage of significance, as well as, based on the data obtained, possibilities of making some managerial decision to make sure that the factors reach their rational value. A possible area of application of the results of the study is the economy of education; in particular, they could be applied in general educational institutions.
Orthonormal Bases on \(\mathit{L}^2\)(\(\mathbb{R}\)\(^+\))

In addition to orthogonal polynomials, orthogonal functions also play an important role. Their applications are, among others, in the fields of signal and data analysis, dynamic modeling. They are related to the solution of differential equations. In this paper we derive the explicit form of one parameter family of orthonormal bases on space \(\mathit{L}^2\)(\(\mathbb{R}\)\(^+\)). The bases are formed by eigenvectors of the self-adjoint extension \(\mathit{H}_\xi\), parametrized by \(\xi\) \(\in\) \(\small\langle\)0,\(\pi\)), of differential expression \(\mathit{H} = -{ \mathit{d}^2 \over \mathit{d}^{x2}} +{ \mathit{x}^2 \over 4} \) together with the spectrum \(\sigma\)(\(\mathit{H}_\xi\)) on the space \(\mathit{L}^2\)(\(\mathbb{R}^+\)). For each \(\xi\) the set of eigenvectors form an orthonormal basis of \(\mathit{L}^2\)(\(\mathbb{R}\)\(^+\)). From the physical point of view, it is a solution of the Schrodinger equation of a harmonic oscillator on a semi-straight line. To correlate platelet count, splenic index (SI), platelet count/spleen diameter ratio and portal-systemic venous collaterals with the presence of esophageal varices in advanced liver disease to validate other screening parameters.
Software para recolección de evidencias digitales rápidas en sistemas Windows

In the field of Digital Forensics, there are numerous criteria to consider when determining which elements are relevant for obtaining useful information to clarify a case. During a search and seizure procedure, when there are numerous technological items that could be candidates for confiscation for in-depth analysis in the laboratory, it becomes necessary to discern which elements would be of interest, and which would not provide useful information. This is because the analysis times grow exponentially as more equipment and mass storage devices are added to the list of items to be seized. In this paper, we present an "on-site" application software for obtaining digital forensic evidence during search and seizure procedures, following the current protocol of action. This software will also be used as an "indicator" of the content of the equipment under analysis, allowing the acting expert to make decisions (such as whether to confiscate a specific piece of equipment or not) at the location where the procedure is being carried out.
Latest Technologies in Full Stack Web Development

This study delves into the core of modern web development, unraveling the intricacies of technologies that empower the creation of dynamic and interactive web applications. By exploring an array of front-end frameworks such as React , Angular and Vue.JS, alongside back-end technologies like Node.js, Django, and Ruby on Rails, the paper offers a nuanced understanding of their applications, advantages, and real-world implementations.