Reviewerly/exHarmony huggingface dataset:
Leonardo Drone Contest Autonomous Drone Competition: Overview, Results, and Lessons Learned from Politecnico di Milano Team

Abstract In this paper, the Politecnico di Milano solutions proposed for the Leonardo Drone Contest (LDC) are presented. The Leonardo Drone Contest is an annual autonomous drone competition among universities, which has already seen the conclusion of its second edition. In each edition, the participating teams were asked to design and build an autonomous multicopter, capable of accomplishing complex tasks in an indoor urban-like environment. To reach this goal, the designed systems should be capable of navigating in a Global Navigation Satellite System (GNSS)-denied environment with autonomous decision making, online planning and collision avoidance capabilities. In this light, the authors describe the first two editions of the competition, i.e., their rules, objectives and overview of the proposed solutions. While the first edition is presented as relevant for the experience and takeaways acquired from it, the second edition solution is analyzed in detail, providing both the simulation and experimental results obtained.
Face recognition technology based on low-rank joint sparse representation algorithm

With the improvement of computer computing power and the development of artificial intelligence technology, face recognition technology has made a major breakthrough, and has been popularized and applied in all areas of life. However, different face structure and pose will affect the accuracy of face recognition. To overcome the problem, a low rank joint sparse representation algorithm for face recognition is proposed. The low rank features of images are extracted by structure independent and pairwise rank decomposition methods. The extracted low rank features of the first level image and the low rank features of the second level image are sparsely represented. Finally, the residual rate model is used to classify the images, and the final result of face recognition is obtained. The experimental results show that the proposed SRP algorithm has a recognition accuracy of more than 92% in two different face recognition tests. In the mixed multi face pose test, PRS algorithm performs best in the recognition of 1, 2, 3, 4, and 5 multi face pose types, with recognition rates of 95%, 94%, 93%, 91%, and 90% respectively. The algorithm also has excellent recognition performance and robustness in identifying harsh environments such as fuzzy environments. The research content focuses on complex face recognition scenes, innovatively uses low rank to complete the extraction of face feature data, and combines sparse selection of classification features to improve the overall effect of face recognition. It has important reference value for improving the overall security and recognition rate of face recognition.
LSPeval—An Educational Decision Support Tool Based on Soft Computing Logic Aggregation

LSPeval is a free Internet-based soft computing educational software tool. It is successfully tested in project-oriented graduate and advanced undergraduate courses that introduce students to the design and use of decision support systems. Instructors can use LSPeval to expose students to mathematical models for decision making, based on soft computing, fuzzy reasoning, and graded logic implemented in the LSP method. Students use LSPeval to solve professional decision problems of medium complexity. Such problems include both functional and financial inputs. Students are also exposed to graded logic and become familiar with hard and soft models of graded simultaneity and substitutability, conjunctive mandatory-optional criteria, financial aspects of decision making, and interpretability and explainability of proposed decisions. LSPeval is designed to provide professional evaluation experiences with minimum mathematical prerequisites. Consequently, it can be used in initial segments of soft computing and decision support courses. This paper includes all information necessary for instructors and students to use the LSP evaluation methodology and to experience realistic evaluation projects. An educational example and a sample project are presented in detail. The contributions of this paper include the structure and functionality of the LSPeval tool, the project-oriented stepwise refinement approach to presenting soft computing logic and decision problems, and the pedagogical methodology and software for including evaluation projects in computational intelligence courses.
The Use of Cognitive Modeling in Solving Problems of Ecological Sustainability of the Region

The paper shows the prospects for combining two modeling platforms in a hybrid system of difficult to formalize tasks, the description of which is possible both at the cognitive and functional levels. One of the most difficult problems for the management involved in developing a strategy for the sustainable development of a region is understanding the complex causal chains that determine the impact of external and internal conditions on the environment. Today, this problem is compounded by the growing complexity and instability of the economic environment, leading to numerous uncertainties and risks.
Automatic Synthesis of Rule Bases of Fuzzy Control Systems Based on Genetic Algorithms

This chapter is dedicated to the method of the rule base (RB) automatic synthesis for the fuzzy automatic control systems (FACS) of Mamdani-type with the determination of optimal consequents based on the genetic algorithms. The obtained method allows to effectively synthesize the rule bases with optimal consequents for Mamdani-type FACSs at insufficient initial information (in conditions of a high degree of information uncertainty), at a sufficiently large number of rules when the compilation of the FACS rule base on the basis of expert knowledge is not always effective, and at the various levels of experts qualifications. The study of the efficiency of the proposed method is carried out at the development of the rule base of the fuzzy controller of Mamdani-type for the automation system of the multipurpose caterpillar mobile robot (MR) with the ability of moving on vertical and inclined ferromagnetic surfaces. The results of computer simulation confirm the high effectiveness of the designed method.
Clustering of the State of Plant Species in the Urban Environment Under Z-Information

The paper developed a model for clustering the state of plant species in an urban environment using Z-information. The purpose of the paper is to analyze and identify groups of plant species that are most adapted to the harmful effects of large cities. This approach allows you to plan urban greening activities and reduce the costs associated with re-landscaping due to plant death. The state of plant species is assessed by experts with a certain degree of reliability, which must be taken into account in order to avoid the risk of expert errors. To formalize expert assessments of plants, the authors use Z-numbers, the components of which are the values of semantic spaces. The assessment of the state of each plant species is presented in the form of a set of Z-numbers. For a comparative analysis of the states of different plant species, indicators of difference and similarity are determined based on the distances between sets of Z-numbers. Pairwise similarity indicators are used to construct a fuzzy binary similarity relation on the set of states of plant species, which is transitively closed. The resulting fuzzy binary similarity relation is decomposed into equivalence relations and breaks down the states of plant species into clusters of similar ones with a certain level of reliability. A cluster analysis of the state of eight plant species growing in Moscow was carried out.
Comparison of Artificial and Convolutional Neural Networks in Recognition of Handwritten Letters of Azerbaijani Alphabet

Industrial and business life requires development of intelligent systems. Implementation of such systems and performing some automation tasks dictates building effective models for entering information. In the case the information is graphical, image recognition has a great potential to accelerate the increase of intelligence of machines. Known approaches for image recognition definitely include using Artificial Neural Networks with feature extraction and Convolutional Neural Networks which extracts a feature based on the training set. In the study these two models are compared on the problem of recognizing handwritten letters of Azerbaijani alphabet.
Advanced Trainings in the System of Teachers’ Professional Development: The Empirical Study

This article presents a method for solving the algorithm of the model of recognition of visual and audio information of the environment by constructing an algorithmic model that simulates the human abilities of the motor (unconscious or non-intellectual) behavior. The article is devoted to the development of a machine intelligence system with a gradual increase in the complexity of the behavioral model of an artificial personality (IL) in order to experimentally study the issues of artificial intelligence.
Dynamic Programming on a Quantum Annealer: Solving the RBC Model

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.
Modern Information and Communication Technologies in the Project and Research Activity of Bachelor Students

The article presents a description of ICT in the project activities of students. The design is based on research work. Three stages of the project: preparatory, main, final. The goal of the project is to create professional electronic Russian and English-Russian dictionaries. The macrostructure of dictionaries contains more than 1231 items. The microstructure is represented by a lemma, grammatical, functional-stylistic label, semantization, illustration, etymological label. The dictionaries are hosted on the Google Drive file storage and editing service. It is confirmed that the introduction of ICT in project activities contributes to the enrichment of the content of education, gives it a logical and exploratory character, and contributes to the activation of the cognitive interest of students.
Deep Active Learning for Left Ventricle Segmentation in Echocardiography

The training of advanced deep learning algorithms for medical image interpretation requires precisely annotated datasets, which is laborious and expensive. Therefore, this research investigates state-of-the-art active learning methods for utilising limited annotations when performing automated left ventricle segmentation in echocardiography. Our experiments reveal that the performance of different sampling strategies varies between datasets from the same domain. Further, an optimised method for representativeness sampling is introduced, combining images from feature-based outliers to the most representative samples for label acquisition. The proposed method significantly outperforms the current literature and demonstrates convergence with minimal annotations. We demonstrate that careful selection of images can reduce the number of images needed to be annotated by up to 70%. This research can therefore present a cost-effective approach to handling datasets with limited expert annotations in echocardiography.
Non-binary evaluation of next-basket food recommendation

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the offline evaluation experiments and binary relevance paradigm. Specifically, we argue that recommended baskets which are more similar to ground truth baskets are better recommendations than those that share little resemblance to the ground truth, and therefore, they should be granted some partial credits. Based on this notion of non-binary relevance assessment, we propose new evaluation metrics for NBR by adapting and extending similarity metrics from natural language processing (NLP) and text classification research. To validate the proposed metrics, we conducted two user studies on the next-meal food recommendation using numerous state-of-the-art NBR methods in both online and offline evaluation settings. Our findings show that the offline performance assessment based on the proposed non-binary evaluation metrics is more representative of the online evaluation performance than that of the standard evaluation metrics.
Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method.
Statistically Consistent Broadcast Authenticated Encryption with Keyword Search

Searchable Encryption (SE) allows users to perform a keyword search over encrypted documents. In Eurocrypt’04, Boneh et al. introduced Public-key Encryption with Keyword Search (PEKS). Broadcast Encryption with Keyword Search (BEKS) is a natural progression to allow some amount of access control. Unfortunately, PEKS and BEKS suffer from keyword-guessing attacks (KGA). In the case of KGA, an adversary guesses the keyword encoded in a trapdoor by creating a ciphertext on a sequence of keywords of its choice and testing them against the trapdoor. In ACISP’21, Liu et al. introduced a variant of BEKS called Broadcast Authenticated Encryption with Keyword Search (BAEKS), which tried to mitigate KGA in BEKS. This construction did not argue consistency and achieved weaker security in the random oracle model. In this work, we first introduce the notion of consistency for BAEKS and introduce security models much stronger than those of Liu et al. We propose a new statistically-consistent construction of BAEKS in the standard model that achieves security in the newly introduced models. Our proposal is proven adaptively secure under the well-studied bilateral Matrix Diffie-Hellman Assumption and still achieves asymptotic efficiency similar to that of Liu et al.
Adaptively Secure Identity-Based Encryption from Middle-Product Learning with Errors

Introduced in 2017, Middle-Product Learning with Errors (MPLWE) and its variants offer a way to construct cryptographic primitives which preserve the efficiency of those based on Polynomial-LWE (PLWE) while being at least as secure as them over a broad choice of number fields. Based on MPLWE, a series of cryptographic schemes have been proposed, including public key encryption (PKE), digital signature, and identity-based encryption (IBE). In this paper, we extend this line of research and propose a new IBE scheme that is adaptively secure in the standard model from MPLWE. Existing IBE schemes from MPLWE only offer selective security or rely on the random oracle model. We follow the blueprint of Agrawal et al. at EUROCRYPT2010 and adapt the well-known partitioning technique to the MPLWE setting. The resulting scheme offers similar efficiency to schemes based on PLWE under a milder assumption.
Practical Verifiable Random Function with RKA Security

A verifiable random function (VRF) allows the generation of a random number with publicly verifiable proof, showing that the random number is honestly generated. The practical VRF used in real-world applications considers the security of uniqueness, pseudorandomness, and unpredictability under malicious key generation. In this paper, we propose the security model of related-key attack to VRF for capturing attacks like tampering attacks. We propose a new construction of VRF that satisfies the RKA security together with the existing security requirements. We implement our VRF construction and demonstrate that our scheme is practical for real-world applications.
Key Recovery Attacks on Grain-Like Keystream Generators with Key Injection

A common structure in stream ciphers makes use of linear and nonlinear shift registers with a nonlinear output function drawing from both registers. We refer to these as Grain-like keystream generators. A recent development in lightweight ciphers is a modification of this structure to include a non-volatile key register, which allows key bits to be fed into the state update of the nonlinear register. Sprout and Plantlet are examples of this modified structure. The authors of these ciphers argue that including these key bits in the internal state update provides increased security, enabling the use of reduced register sizes below the commonly accepted rule of thumb that the state size should be at least twice the key size. In this paper, we analyse Plantlet and show that the security of this design depends entirely on the choice of the output function. Specifically, the contribution from the nonlinear register to the output function determines whether a key recovery attack is possible. We make a minor modification to Plantlet’s output function which allows the contents of the linear register to be recovered using an algebraic attack during keystream generation. This information then allows partial recovery of the contents of the nonlinear register, after which the key bits and the remaining register contents can be obtained using a guess and check approach, with a complexity significantly lower than exhaustive key search. Note that our attack is not successful on the existing version of Plantlet, though it only requires minor modifications to the filter function in order for the attack to succeed. However, our results clearly demonstrate that including the key in the state update during keystream generation does not increase the security of Plantlet. In fact, this feature was exploited to recover the key during keystream generation without the need to consider the initialisation process. This paper provides design guidelines for choosing both suitable output functions and the register stages used for inputs to these functions in order to resist the attacks we applied.
Identity-Based Encryption from Lattices Using Approximate Trapdoors

Practical implementations of advanced lattice-based constructions have received much attention since the first practical scheme instantiated over $$\textsf{NTRU}$$ lattices, proposed by Prest et al. (Asiacrypt 2014). They are using powerful lattice-based building blocks which allow to build Gaussian preimage sampling and trapdoor generation efficiently. In this paper, we propose two different constructions and implementations of identity-based encryption schemes (IBE) using approximate variants of “gadget-based” trapdoors introduced by Chen et al. (Asiacrypt 2019). Both constructions are proven secure. Our first IBE scheme is an adaptation of the Bert et al. scheme (PQCrypto 2021) to the approximate setting, relying on the $$\textsf{Module}\text {-}\textsf{LWE}$$ hardness assumption and making use of the Micciancio-Peikert paradigm with approximate trapdoors. The second IBE relies on a variant of the $$\textsf{NTRU}$$ hardness assumption. We provide several timings and a comparison analysis to explain our results. The two different instantiations give interesting trade-offs in terms of security and efficiency and both benefit from the use of approximate trapdoors. Though our second IBE construction is less efficient than other $$\textsf{NTRU}$$ -based IBEs, we believe our work provides useful insights into efficient advanced lattice-based constructions.
ScaRLib: A Framework for Cooperative Many Agent Deep Reinforcement Learning in Scala

Multi Agent Reinforcement Learning (MARL) is an emerging field in machine learning where multiple agents learn, simultaneously and in a shared environment, how to optimise a global or local reward signal. MARL has gained significant interest in recent years due to its successful applications in various domains, such as robotics, IoT, and traffic control. Cooperative Many Agent Reinforcement Learning (CMARL) is a relevant subclass of MARL, where thousands of agents work together to achieve a common coordination goal. In this paper, we introduce ScaRLib, a Scala framework relying on state-of-the-art deep learning libraries to support the development of CMARL systems. The framework supports the specification of centralised training and decentralised execution, and it is designed to be easily extensible, allowing to add new algorithms, new types of environments, and new coordination toolchains. This paper describes the main structure and features of ScaRLib and includes basic demonstrations that showcase binding with one such toolchain: ScaFi programming framework and Alchemist simulator can be exploited to enable learning of field-based coordination policies for large-scale systems.
Related-Cipher Attacks: Applications to Ballet and ANT

Quite a lot of block ciphers proposed in recent years are families of ciphers that conveniently support multiple block lengths and key lengths. The essential security requirements for a family of block ciphers are: (1) Each cipher instance from family is secure; (2) Cipher instances do not endanger each other’s security, namely, by one or more cipher instances, other instances cannot be predicted. However, traditional cryptanalysis methods always assess the security of a special member of the family cipher, such as differential cryptanalysis, linear cryptanalysis, integral cryptanalysis. Related-cipher attacks focus on the security between cipher instances. This paper researches the security of Ballet-128 and ANT-128 against related-cipher attacks. Since their key schedules do not rely on the round number of encryption, we consider the related-cipher attack with equivalent keys by limiting the 256-bit key space. As a result, we recover the secret key of the full Ballet-128/128 with just one chosen plaintext pairs and one call of Ballet-128/128 and Ballet-128/256, which means Ballet-128 is insecure against related-cipher attack. For ANT-128, we show that there exist at most 6-round related-cipher distinguishers between ANT-128/128 and ANT-128/256, and launch a 9-round key-recovery attack on ANT-128/128 based on a 6-round related-cipher distinguisher with the time complexity about $$2^{60.9}$$ .