Reviewerly/exHarmony huggingface dataset:
Leveraging Unlabeled Data Using Semi-Supervised Generative Adversarial Network for Medical Image Segmentation

Medical image analysis has experienced different stages of development, especially with the emergence of deep learning. However, it is difficult to acquire large-scale, highquality labeled data to train the model when using deep learning. This paper proposes a semi-supervised learning method to achieve medical image segmentation using limited labeled data and large-scale unlabeled data. Inspired by the classic Generative Adversarial Network (GAN), we proposed semi-supervised learning based on GAN (semi-GAN) to implement medical image segmentation. In the proposed semiGAN, adversarial training between the generator and discriminator has achieved higher segmentation accuracy. The dataset used was hippocampus data in Medical Segmentation Decathlon (MSD), and there are four training data settings: 25 labeled slices/3,374 unlabeled slices; 50 labeled slices/3,349 unlabeled slices; 100 labeled slices/3,299 unlabeled slices; 200 labeled slices/3,199 unlabeled slices. For each data setting, there are two experiments conducted: fully-supervised learning based on a generator network using only labeled data (F-Generator), and semi-GAN. The experiments showed that semi-GAN can improve segmentation accuracy by an average of 0.4% using unlabeled data compared to F-Generator using labeled data. Further study will be conducted to improve the semi-GAN architecture.
Reinforcement Learning for UAV control with Policy and Reward Shaping

In recent years, unmanned aerial vehicle (UAV) related technology has expanded knowledge in the area, bringing to light new problems and challenges that require solutions. Furthermore, because the technology allows processes usually carried out by people to be automated, it is in great demand in industrial sectors. The automation of these vehicles has been addressed in the literature, applying different machine learning strategies. Reinforcement learning (RL) is an automation framework that is frequently used to train autonomous agents. RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task. However, learning autonomously can be time consuming, computationally expensive, and may not be practical in highly-complex scenarios. Interactive reinforcement learning allows an external trainer to provide advice to an agent while it is learning a task. In this study, we set out to teach an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. Two simulated scenarios were proposed for the training; one without obstacles and one with obstacles. We also studied the influence of each technique. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach. Nevertheless, the agent achieves lower execution times and less dispersion during training.
DeflickerCycleGAN: Learning to Detect and Remove Flickers in a Single Image

Eliminating the flickers in digital images captured by rolling shutter cameras is a fundamental and important task in computer vision applications. The flickering effect in a single image stems from the mechanism of asynchronous exposure of rolling shutters employed by cameras equipped with CMOS sensors. In an artificial lighting environment, the light intensity captured at different time intervals varies due to the fluctuation of the AC-powered grid, ultimately leading to the flickering artifact in the image. Up to date, there are few studies related to single image deflickering. Further, it is even more challenging to remove flickers without a priori information, e.g., camera parameters or paired images. To address these challenges, we propose an unsupervised framework termed DeflickerCycleGAN, which is trained on unpaired images for end-to-end single image deflickering. Besides the cycle-consistency loss to maintain the similarity of image contents, we meticulously design another two novel loss functions, i.e., gradient loss and flicker loss, to reduce the risk of edge blurring and color distortion. Moreover, we provide a strategy to determine whether an image contains flickers or not without extra training, which leverages an ensemble methodology based on the output of two previously trained markovian discriminators. Extensive experiments on both synthetic and real datasets show that our proposed DeflickerCycleGAN not only achieves excellent performance on flicker removal in a single image but also shows high accuracy and competitive generalization ability on flicker detection, compared to that of a well-trained classifier based on ResNet50.
Steganographic Method of Embedding Data in Images for Transmission over a Noisy Communication Channel

A method for embedding a visually imperceptible message into an object of a digital image for transmission through a noisy communication channel is proposed. The embedding method is based on inverting some of the pixels of the secret image according to a certain rule and adding them to the selected positions of the bit plane of the least significant bit according to the mask generated by the pseudo-random number generator (PRNG). The authors use a two-dimensional nonlinear filtering algorithm in order to ensure the integrity of the data transmitted over the communication channel at low signal-to-noise ratios. The article presents the results of experimental estimates of the invisibility of the embedded image and the integrity of the original and secret images after transmission over a noisy communication channel for signal-to-noise ratios up to -6dB.
Functional Modeling as a Basis for Classifying Security Threats

Building a threat model plays one of the key roles in assessing the security of information systems. In this case, a rather important point is to establish a connection between the description of the protected object (functions of processing the protected information) and the list of threats directed at the protected object. One of the possible ways to describe the protected processes in information systems is to build a functional model. The proposed study examines the relationship between the IDEF0 functional modeling methodology and various classes of threats. The basic idea is as follows: different types of arrows in the IDEF0 notation correspond to different types of threats. The horizontal arrows correspond to threats of confidentiality and integrity violation of data, storage media, and other objects. Mechanism arrows correspond to threats and attacks directed at components of information systems (disabling components, introducing malware, etc.). Control arrows correspond to threats related to system trust (errors during development, implementation of undocumented features). The article provides a classification of threats based on the typing of arrows in the IDEF0 notation, and examples of threats for typical functions of processing protected information.
A CPU-Parallel Implementation of Pattern Matching for the Hybrid Index

Pattern Matching (PM) is one of the main problems in the area of text analysis, which can be solved directly by means of some online algorithms that operate directly on the text <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> or using a data structure previously built on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> . However, one of the most attractive alternatives to solve PM is the use of compressed indexes, which offer faster searches in a smaller space than online algorithms or explicit indexes. This work proposes a CPU-parallel algorithm that allows solving PM using the Hybrid Index, a set of highly compressed data structures that stores the LZ77 factorization of the input text. The implemented parallelization aims to improve search times compared to the original algorithm for both conventional texts and highly repetitive sequences. Experimental results indicate that, depending on the features of the text, the length of the pattern to be searched and the number of threads to be used, execution times between ~ 1.3 x and ~ 3.7 x faster than the original version can be obtained.
RDFS<sub>(c)</sub> Schema Inconsistency Checking Based on a Key Instance and Its Query Rewriting

The introduction of a constraint mechanism to the resource description framework schema (RDFS) poses challenges for keeping RDFS ontologies consistent, particularly in the presence of reasoning rules (RRs). This study presents a method to detect inconsistencies in a given constraint enhanced RDFS, termed RDFS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathrm {(c)}}$ </tex-math></inline-formula> , under a given set of RRs. This method can be used to efficiently infer the type qualifications and constraints that may be violated when the RRs are applied to the graph instances of the schema. First, the concept of an internal schema is developed, and the conversion of an RDFS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathrm {(c)}}$ </tex-math></inline-formula> to an internal schema is examined. An internal schema is more versatile than the corresponding original schema language. Second, the concept of key instances of an internal schema is established as a basis for detecting schema inconsistencies. The number of key instances is considerably smaller than that of original instances, resulting in a significantly higher detection efficiency. Moreover, a rigorous formal proof is given to ensure that the schema generated based on the key instances is semantically equivalent to the original schema. Finally, an algorithm is provided to detect schema inconsistencies in the key instances of the corresponding internal schema. This algorithm extends the key instances by query rewriting and constructs potential instances that violate the constraints. The minimality of rewriting ensures that a minimum number of instances are constructed, thereby further improving the efficiency for inferring constraint violations. Test results show that key instances and query rewriting can be used to significantly enhance the efficiency of detecting inconsistencies and that the proposed method is superior to relevant methods in terms of time performance.
Research on Trajectory Planning of Multi-DOF Manipulator Based on Arduino

With the development of industry and manufactory, the high efficiency of production process becomes more and more important. As a most commonly used operating platform, the research of the manipulator controlling and trajectory planning still remains popularity. This paper designed a 6-DOF manipulator grasping platform based on Arduino with a MATLAB calculation core. First, a PCB was designed to control the manipulator. Then, the modelling and servo angles calculating were finished by kinematics analysis. The trajectory planning was realized by quintic polynomial interpolation. At last, the comparison experiments between the simulation and the real manipulator showed that the manipulator was able to real-time track the whole path according to the results of trajectory planning. The manipulator grasping platform realized the expected function and had a robust performance.
Hash-based Associative Memory-like Pattern Classifier

A pattern classifier named Hash-based Associative Memory-like Pattern Classifier (HAPC) based on hashing technique, associative memory, and human deep-thinking logic is proposed in this paper to solve pattern classification problems without complicated mathematical computation. In the hashing layer of the designed system, one of the similarity-preserve hashing is used to convert the input samples to hashed data which dramatically reduces the dimensionality of the data but keeps the similarity information. In the clustering layer, hashed samples are divided into different clusters, which can be considered a feature extraction process based on the similarity of the hash value. In the associate memory layer, each cluster will be trained as an associate memory to store the hashed data stably. In the logical layer, Bayesian inference is used to perform the decision-making process to classify the testing sample without complicated mathematical computation. In the simulation, we are able to achieve a good result, especially on small numbers of training data.
Application of Boolean Algebra Methods to the Problem of Measurement Data Compression

The article describes an approach to reversible compression of measurement data frames. It is based on the representation of the bit-difference type of frames as a truth table of some logical function of several variables, with its subsequent minimization. Based on this approach, a reversible compression algorithm has been developed that allows one to switch from direct data compression to encoding the minimal disjunctive normal form obtained in the process of minimization using adaptive prefix coding. The efficiency of the algorithm was studied on data obtained from a number of energy facilities in the Far East. Which showed that this approach to reversible data compression turned out to be quite effective, regardless of their statistical properties and, in particular, stationarity.
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario

Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.
Remarks on a Feedforward Feedback Controller Using an Echo State Network for Controlling Dynamic Systems

This study explored the possibility of using reservoir computing in control system applications. An echo state network was first applied to design a servo control system. In the designed system, the network input comprised d–step ahead reference and current outputs of the object plant, whereas the network output was combined with a feedback controller output to synthesise the control input of the plant. This design was chosen because feedback error learning performed in real time could be used to train the network and compensate for the identification error of the inverse transfer function of the plant attained through a linear regression approach performed offline. Then we conducted computational experiments on controlling a discrete–time nonlinear plant to evaluate the effectiveness of the proposed control system. Simulation results showed this task’s feasibility and echo state network–based controller characteristics.
Computer Attacks Simulation in IIoT Systems

Modern IIoT systems include a huge number of sensors and actuators designed to control the processes of technological production. Trusted interaction, which involves the guaranteed transmission of data within such systems, is a key aspect in the operation of such systems. However, the currently available industrial protocols contain a certain number of vulnerabilities, which can destabilize the operations of IIoT systems. That’swhy, when designing such systems, additional attention should be paid to the protection of transmitted data. The current article presents the modeling of computer attacks on the IIoT system, using the industrial Modbus TCP protocol. This protocol is widely used in the design of IIoT systems, also has a number of vulnerabilities
An Improved Cross-Ratio Based Gaze Estimation Method Using Weighted Average and Polynomial Compensation

The cross-ratio (CR)-based method exploits the invariance property of CRs in projective transformation to determine a screen point corresponding to the pupil center. However, this point is essentially the intersection of the eyeball optical axis (OA) and the screen, rather than the actual point-of-regard (POR). In addition, the premise of CR calculation is that the corneal reflection points of four on-screen light sources are coplanar with the 3D pupil center, but they are only assumed to be coplanar. To solve these issues, this paper proposes an improved CR-based gaze estimation method using weighted average and polynomial compensation. Under the configuration of a single camera and two light sources, the 3D corneal center and the normal vector of virtual pupil plane are first estimated using the eyeball imaging model, and then four reference planes parallel to the virtual pupil plane are determined based on the geometric model of pupil and screen corner points. The screen point corresponding to the intersection of each reference plane and the line connecting the camera optical center and the imaging pupil center is calculated using the conventional CR-based method. Thus, the point where the OA intersects the screen is determined by the weighted average of these four points. Finally, a polynomial is learned to compensate it to the POR. The experimental results show that the gaze accuracy can reach 1.33° when the more accurate eye is selected, and it would be improved by 24% by calculating the joint POR, which is competitive with the state-of-the-art methods using more complex systems. On the basis of simplifying the system configuration of CR-based methods, the proposed method avoids the non-coplanarity of 3D pupil center and corneal reflection plane, and improves the gaze estimation performance.
Application of Blockchain Technology for Verification of State Documents in the Russian Federation

The article provides an analysis of the possibilities of using Blockchain technology in the framework of programs for economic digitalization and development of the educational sector. Three main areas of blockchain use are highlighted: cryptocurrencies, tracking the uniqueness of items and smart-contracts. A classification of blockchain-based systems dividing existing systems by scope has also been proposed. The article discusses the current methods of using this technology for the implementation of various tasks within the framework of individual enterprises, states and the world. A promising solution in accordance with the current trends and directions of development has also been proposed. The problems associated with the implementation of blockchain systems are also described.
Schema dependency-enhanced curriculum pre-training for table semantic parsing

Large pre-trained models exhibit improved table-semantic-parsing performances by leveraging large-scale corpora to enhance the representation learning ability of semantic parsers. However, existing table pre-training methods do not sufficiently consider the explicit interactions among natural language (NL) questions, SQL queries, and the corresponding database schemas, which are essential for table semantic parsing. To overcome the aforementioned limitation, this study designs a novel schema dependency prediction (SDP) objective to incorporate SQL-aware schema linking information into table pre-training. Specifically, SDP aims to predict the schema dependency connecting the NL question, table schema, and triggered SQL operations. We further propose a schema-aware curriculum learning approach (SAC) to mitigate the impact of noise present in the pre-training data and train the table pre-training model in an easy-to-hard manner. We evaluate the effectiveness of our pre-training framework by fine-tuning it on three downstream benchmarks (Spider, Spider-DK, and SQUALL). Experimental results demonstrate that the proposed pre-training method outperforms existing methods considered for comparison.
Reliability of power supply to facilities of a local hybrid energy complex with generation based on renewable energy sources

Ensuring guaranteed power supply to consumers in isolated and distributed energy systems is achieved either by connecting the system to the electrical grid, by introducing energy storage devices on the generation side or by creating conditions for managing the amount of consumption, also including the use of energy storage devices on the consumer side. The article discusses several ways to increase the reliability of power supply when using installations based on renewable energy sources (RES) to power consumers in a hybrid energy complex. The mathematical model and algorithm proposed for optimizing the operation of the hybrid energy complex are implemented in the form of a digital twin, taking into account the influence of numerous factors, such as weather conditions and specific consumer characteristics. The digital twin of the hybrid energy complex was tested through a series of calculations that confirmed its operability and adequacy. The proposed method and the results obtained can be used in solving design tasks to validate the parameters of power supply systems for consumers in isolated and hard-to-reach areas, as well as in nodes of distributed energy systems.
Coverage-based Greybox Fuzzing with Pointer Monitoring for C Programs

C has been regarded as a dominant programming language for system software implementation. Meanwhile, it often suffers from various memory vulnerabilities due to its low-level memory control. Quite massive approaches are proposed to enhance memory security, among which Coverage-based Greybox Fuzzing (CGF) is very popular because of its practicality and satisfactory effectiveness. However, CGF identifies vulnerabilities based on the catched crashes, thus cannot detect vulnerabilities with non-crash. In this paper, we consider to trace pointer metadata (status, bounds and referents) to detect more various vulnerabilities. Additionally, since pointers in C are often directly related to memory operations, we design two standards to further use pointer metadata as the guidance of CGF, making fuzzing process target to the vulnerable part of programs.
Extraction and Management of Rationale

Software developers often have to make many design decisions. The underlying logic behind these decisions, also called design rationale, represents beneficial and valuable information. In the past, researchers have tried to automatically extract and exploit this information, however, prior techniques are only applicable to specific contexts and there is insufficient progress on an automated end-to-end rationale extraction and management system. In this research project, we propose to use Natural Language Processing (NLP) and Machine Learning (ML) techniques to create a system for the automated extraction, structuring and management of design rationale. This system would support and ensure the consistency and the coherence of the development process.