Reviewerly/exHarmony huggingface dataset:
Discovery of multivariable algebraic expressions using evolutionary optimization

Machine learning interpretation has a well-established discussion in various areas. Whereas most interpretation is made after the modelling, one may try to obtain the informative by itself model. Mainly such models are represented by time-series models such as AR(auto-regression). For time series, we may also obtain the expression in closed form, which has the advantage of being interpretable. Transferring from single to multiple dimensions increases the search space drastically. Thus, we must reduce it for the initial assumption search. The paper proposes the algorithm of algebraic expression discovery using evolutionary optimization. The initial assumption is made using the Fourier series decomposition. Several examples of the algorithm's work based on known functions and arctic ocean ice concentration data are shown.
Diagnostics of motion sickness (kinetosis) and training of resistance to it in VR simulators

Approximately 5-15% of adults suffer from motion sickness (ICD-10: T75.3, ICD-9: 994.6), also known as kinetosis, in one way or another. Being prone to it may negatively affect not only the daily activities of an average person, but also maritime specialists, whose professional activity is directly related to sailing in sea. The study of motion sickness, as well as the development of simulators that would allow training a resistance and adaptation to it and its symptomatology is an urgent task for the maritime industry. Various approaches for the physiological motion sickness resistance and adaptation training exist, among which the use of virtual reality (hereinafter referred to as VR) technologies is of high importance. Key advantage of using VR is the ability to simulate any visual environment, limited only by the task, hardware and software capabilities of the researcher. Currently, VR is already being used in the study of motion sickness [1]. We investigated not only the possibility of motion sickness, but also methods of reducing manifestations and symptoms by dynamically changing the viewing angles, as well as assessing the influence of peripheral and stereoscopic vision, including their various variations. The primary goal of the study is to investigate the direct effect of a visual channel on the symptoms of motion sickness, including changes of channel parameters (e.g., changes in viewing angles), and to assess the possibility of training a control group of users with vivid symptoms to reduce them. This paper considers 50 participants to be involved in an experiment, which is based on the developed research methodology for physiological motion sickness. Series of tests is carried out to identify symptoms and conduct a training, after which the research methodology has been evaluated.
Approach of variable clustering and compression for learning large Bayesian networks

This paper describes a new approach for learning structures of large Bayesian networks based on blocks resulting from feature space clustering. This clustering is obtained using normalized mutual information. And the subsequent aggregation of blocks is done using classical learning methods except that they are input with compressed information about combinations of feature values for each block. Validation of this approach is done for Hill-Climbing as a graph enumeration algorithm for two score functions: BIC and MI. In this way, potentially parallelizable block learning can be implemented even for those score functions that are considered unsuitable for parallelizable learning. The advantage of the approach is evaluated in terms of speed of work as well as the accuracy of the found structures.
Dismounting the so-called Condorcet Drivers Paradox

In a multi-agent dynamic common-policy decision-making environment, by asking questions, an agent can generate an agreement on dynamically changing (on the fly, during the decision-making process) the common policy shared by the agents, and therefore, it can change the current list of possible options from which the decision is made. Important efforts were previously undertaken to define the Logic of Preferential Choice (LPC) as a model in which the so-called Condorcet Driver Paradox (CDP) and, more general, other apparently paradoxical decisions are legal syntaxes and valid formulas. On the contrary, we prove here that CDP can be easily dismounted in many formal languages – such as English, Arithmetic, SQL, Binary Logic, and Probabilities) - languages that are not only appropriate for modeling the process of question asking, but they are also proved as productive perspectives in decision-making, much popular in the community, much simpler and much largely accepted than the Logic of Preferential Choice.
Exploring the Relationship Between Error and Interpretation of the Segmentation Model's Prediction

The rapid adoption of artificial intelligence methods in the medical industry opens up more and more questions for researchers about the control of their predictions and the explanations for those predictions. One of such medical tasks is the detection of functional tissue units in microscope kidney images. In this paper we study the XAI (eXplainable Artificial Intelligence) methods related to the segmentation task, explore the techniques of analysing the model's prediction and analyse the obtained errors. During the work we studied the hypothesis of filtering errors from model's prediction using insights from it's interpretation and came up with the method of improving the dataset labeling reliability.
Learning using rebalanced statistical invariants for imbalanced classification

Traditional classification algorithms can be limited in their performance on highly imbalanced data sets, resulting in a drop in the classification performance on the minority class. The recent proposed learning using statistical invariants (LUSI) provides a new learning paradigm to the problem of classification. In order to resolve the issues of class imbalance, inspired by the idea of the prior data information, we compute the appropriate predicates and construct the corresponding invariants in LUSI. Additionally, we incorporated different ”rebalance” heuristics in LUSI modeling, which give sufficient consideration to the importance of the probability label information in classification and combine with the invariants to construct rebalanced statistical invariants in rebalancing environment, that effectively solves the imbalanced block of V-Matrix in LUSI. Experimental results signify the effectiveness of appropriate predicate for imbalanced data, in addition, one can see that the proposed rLUSI demonstrated superior performance in comparison to LUSI in dealing with imbalance problem.
Stixel-based obstacle recognization integrated with learning method

Obstacle detection and recognition are core problems in the field of autonomous driving. For urban road surfaces, this paper proposes a complete obstacle recognition method. Firstly, this paper differs from semantic segmentation and target detection frameworks but represents the region of interest based on stixel and improves the traditional scheme of generating stixel. Then this paper classifies the different obstacles in the region of interest based on the improved deep learning network DenseNet. Finally, recognition of individual obstacles is performed. The experimental results show that the method in this paper can effectively represent the region of interest consisting of the first layer of obstacles and achieve obstacle recognition more accurately.
A Novel Electromagnetic Radiation Source Localization Method Based on Dynamic Data Driven Simulations

Locating enemy targets via their electromagnetic radiation signal is vital to block and attack the enemy targets at an earlier stage. Traditional electromagnetic radiation source localization methods in literature are essentially geometric methods. Although they are simple and intuitive, they might fail to locate the source due to measurement noise. This paper proposes a novel electromagnetic radiation source localization method based on dynamic data driven simulations. In the proposed approach, we first model the spatial propagation process of the electromagnetic radiation signal emitted from the target, and then we assume a proper model for the noisy measurements. Based on the signal propagation model and the measurement model, the particle filter is employed to estimate the target position, and in the process addresses measurement and modeling errors. Identical-twin experiment is conducted to test and validate the proposed approach. The simulation results show that the proposed method can accurately locate the electromagnetic radiation source, and is robust to errors both in the model and in the data.
Pixelated Image Abstraction via Power Diagram

Image abstraction is increasingly used in the field of computer graphics and computer vision. Existing methods divide the input images into disjoint regions and generate the abstract images with a reduced color palette. However, the generated images are not satisfactory due to the presence of blurred and unsmooth boundaries, etc. To this end, a pixelated image abstraction method via power diagrams is introduced in this paper to generate high-quality abstract images. Firstly, a pre-processing step is used to capture the significant information in the input image, which is divided into core and non-core parts according to the importance of each pixel. Then, a density map is calculated on basis of the division and grayscale value of each pixel. Furthermore, the primal image is partitioned into polygonal subregions with equal area constraint based on power diagrams. Based on the average RGB color of the $$3\times 3$$ grids centered at its centroid, the pixels in each polygonal subregion are colored to produce the output abstract image. Experimental results demonstrate that the proposed method could generate high-quality images with abstraction controllable and smooth boundaries.
A robust adversarial attack against speech recognition with UAP

Speech recognition (SR) systems based on deep neural networks are increasingly widespread in smart devices. However, they are vulnerable to human-imperceptible adversarial attacks, which cause the SR to generate incorrect or targeted adversarial commands. Meanwhile, audio adversarial attacks are particularly susceptible to various factors, e.g., ambient noise, after applying them to a real-world attack. To circumvent this issue, we develop a universal adversarial perturbation (UAP) generation method to construct robust real-world UAP by integrating ambient noise into the generation process. The proposed UAP can work well in the case of input-agnostic and independent sources. We validate the effectiveness of our method on two different SRs in different real-world scenarios and parameters, the results demonstrate that our method yields state-of-the-art performance, i.e. given any audio waveform, the word error rate can be up to 80%. Extensive experiments investigate the impact of different parameters (e.g, signal-to-noise ratio, distance, and attack angle) on the attack success rate.
Optimized Transaction Processing in Lightweight Distributed Ledger Networks for Internet of Things

The last decade has witnessed proliferation of Internet of Things (IoT) devices ranging from smart-watches to smart-cities. Concurrently, first distributed ledger technology was introduced and gained significant traction. Blockchain, integrated into the IoT world presents variety of opportunities such as, machine-to-machine automated micro-transactions, trusted dissemination of information in trust-less network environments. There are many potential applications of blockchain technology in Smart City context, such as smart-homes, and smart-grid. However, the obstacles encountered when merging these two emerging technologies are many-fold. IoT devices are ubiquitous, but they have limited resources, while most popular blockchain technologies require a significant amount of computational resources to participate. Recently, there have been novel proposals which adopt lightweight distributed ledger technologies, that would let IoT devices to participate. One such ledger, IOTA’s Tangle, uses a different underlying ledger data-structure, which is a directed-acyclic graph instead of a linked-list as in traditional blockchains. In this work, we have set-up a private, permissioned Tangle network, comprising of Raspberry-Pi devices as full-nodes and as transacting clients. Our previously proposed transaction optimization algorithm is implemented as an Nginx module.
Real Time Traffic Sign Recognition Algorithm Based on SG-YOLO

Precise and rapid recognition of road traffic signs can enhance the environment perception of autonomous vehicles, which is an essential component for the safe driving. Aiming at addressing the problem that the recognition of traffic signs performs poorly in real-time and generally with accuracy, this paper proposes a real-time traffic sign recognition algorithm based on SG-YOLO. Combining the light weight of Ghost convolution and the perceptual capability of SE attention, a new SG-Bottleneck module is proposed and introduced into the backbone network of YOLOv5, which decreases the model parameters of the algorithm while speeding up the detection. Based on the characteristics of the target shape of the traffic signs, this paper modify the loss function to Distance Intersection over Union to obtain higher accuracy. The experimental results show that the mean average precision can reach 74.95% and detection speed can reach 41.7 FPS. When compared with other traffic sign recognition methods, SG-YOLO demonstrates better real-time performance and guarantees accuracy.
A Deep Learning approach for automated Cytoplasm and Nuclei cervical segmentation

Cervical cancer is a severe disease that affects many women in developing countries. Increasing screening capabilities is the best tool to reduce cancer incidence and save lives. Segmentation is an essential task in screening because it can lead to a better understanding of the morphological characteristics of cells. This paper presents a method for multi-class cell segmentation into Nuclei and Cytoplasm regions. Using the Herlev dataset for evaluation, this work achieved good performance by using state-of-the-art classification architectures such as EfficientNet combined with Feature Pyramid Networks to complete the segmentation task. Performance metrics for both classes show that the approach is robust enough to complete the task. The model achieved a 0.91 F1 score, 0.85 IoU, 0.91 Precision, 0.92 Recall, and 0.96 Specificity as class average with a very low standard deviation, validated with a 5-fold cross-validation. The proposal can help experts correctly asset cervical cell lesions and provide better healthcare for the patients.
Research on Interactive Electronic Manual Based on VR Technology

Based on traditional IETM and VR technology, the concept of “VR + Manual” is proposed, and the main function design and four-layer architecture design of “VR + Manual” are given. The digital mapping data model is established between the IETM technical data and the digital prototype through the ontology data modeling method, and the seamless connection between the data model and the “digital prototype modeling and simulation engine” is realized. By using the integrating multi-class information visualization method, the visualization of multi-dimensional equipment technical status based on the digital prototype is realized. A data-driven interactive interface generation engine is proposed, which is oriented to five types of IETM data, such as description, procedure, fault, illustrated Parts, and maintenance plan. It realizes the rapid generation of interactive interfaces and the release of the application.
From Business-Level Specifications to Smart Contracts for Blockchain-Based Resource-Exchange Systems

Blockchain technology allows to store data in a secure and decentralized manner and can provide true ownership to the owners of the data. Therefore, it could be a good solution for applications where this is required. Resource-exchange systems are such a type of application. To allow to easily set up such systems using blockchain technology, we have introduced a framework that allows to generate such applications. The use of the framework does not require programming or blockchain knowledge. The range of applications that can be generated with the framework are applications for the management and exchange of resources across organizations and their customers. In this paper, we present the generation of the smart contracts from high-level specifications. We explain the mapping from the high-level user concepts used to specify a use case onto the technical concepts used in blockchain and smart contracts technology. We explain the different types of contracts generated as well as their role and functionality. The exchange of resources by means of these smart contracts is illustrated with some examples. We also discuss the limitations and further work.
A Campus Scene Navigation Scheme Based on MPCC Dynamic Obstacle Avoidance Method

The navigation system is a key module in the future application of mobile robots and is essential for the safety and robustness of mobile robot motion. Available navigation systems can already perform reasonable path planning and motion planning processes in specific scenarios. However, with the development of mobile robotics, there are higher requirements for the scenarios in which the robots operate and the response efficiency requirements. In order to solve the problem of motion planning and obstacle avoidance between path points in dynamic campus scenes, a combination of static obstacle avoidance based on voxel grid and dynamic obstacle avoidance based on MPCC is proposed on Ackermann kinematic model as well as motion control. The organic combination of static obstacle avoidance and dynamic obstacle avoidance solves the problem of quickly performing path planning and obstacle avoidance for unmanned vehicles in complex environments. The work has been experimented on simulation conditions and actual robots, and the robots have been placed in campus scenarios for validation.
Belief State Monte Carlo Planning for Multi-agent Visibility-Based Pursuit-Evasion

Planning for a team of agents to pursue an evader is a challenging issue. The setting of partial observability in many real-world applications further increases the complexity. This paper presents a belief state based Monte Carlo Tree Search (MCTS) algorithm which is capable of generating online pursuing policy in the multi-agent visibility-based pursuit evasion scenario. The algorithm, Belief State UCT Consider Durations (BS-UCTCD) search, extends the basic MCTS on two main aspects: 1) it forms the tree model considering the uncertainty of forward transitions as well as the ability to reason concurrent and durative moves of involved agents; 2) it employs particle filtering to approximate the belief and combine the tree search to track the belief changes. We evaluate BS-UCTCD in a scenario where the algorithm is used to plan for two patrollers to search and capture one intruder. The comparative results demonstrate that our approach is effective and can perform better than fixed strategies and searching without belief tracking. Some domain specific configurations also enable its use under real-time constraints.
Multi-Label Classification using Q-Learning

Multi-label classification is an important but difficult topic that involves assigning the most appropriate subset of class labels to each document from a large label collection. The enormous label space presents a number of research obstacles, including data sparsity and scalability. In recent years, breakthrough machine learning algorithms such as tree induction using large margin partitions of the instance spaces and label vector embedding in the target space have resulted in substantial progress. Example: The input text may be a narrative document from chinastory.cn, with the labels representing storey categories that infer the possible meaning of the content. However, applying standard neural network models to the Multi-label classification problem in a haphazard manner results in sub-optimal performance because to the wide output space as well as the label sparsity problem. Despite its widespread success in other fields, Q-learning has not been investigated for multi-label classification. This paper presents the Q-learning algorithm to Multi-label classification, which was the first attempt of applying to Multi-label classification.
Query Expansion for Information Retrieval using Word Embeddings: A Comparative Study

Internet in today's times is the daily need of people. To retrieve right information efficiently is the constant desire. Expanding user queries by transforming some keywords to retrieve specific domain keywords has been a probable solution for information retrieval. Various methods have been combined with query expansion from time to time to improve the information retrieval results right from Classical IR methods to semantic methods or to natural language processing methods. All the methods have eventually minimized the mismatch problems and gave better retrieval results. This paper discusses the performance of various such methods that can be implemented to expand user query such that it gives high precision search results. The paper mainly focuses on word embeddings methods like Word2Vec - CBOW or Skip gram and Glove that are trained on real estate related legal datasets over classical methods. Experimental results show that word embeddings give better results with 87% mean average precision (mAP) values on all datasets.
Patterns of Vowel Production in the Speakers of Sanskrit Language

Sanskrit is a lexically productive language that undergoes euphonic assimilation of phones at word boundaries. It exhibits variations in pronunciations and spelling conventions. This paper introduces the Vowel Triangle plot of Sanskrit vowel-sounds pronunciations using their F1-F2 values generated from PRAAT software. The work was conducted on spoken word utterances (single utterance) of eight male and eight female subjects, fluent in speaking Sanskrit Shlokas. The paper also presents visible and notable similarities and differences in the spoken Sanskrit and Hindi vowel systems through their Spectrogram-based Vowel Triangle System analysis. Since there has been a developing cultural trend of treating Hindi as the sole inheritor of Sanskrit because of religious ideologies, it is often assumed that the language structure of both the Sanskrit and Hindi languages is the same or similar in most cases. However, in the context of a Speech community, linguistic research has shown considerable differences in the phonetic and phonological views of the languages, even between spoken Hindi and Sanskrit. This study can further help in future studies of under-resourced language and speech recognition approaches, considering their phonetic and phonological insights. These inferences demand an extensive study of phonemic and intonational Sanskrit language due to their originating influence in the realistic environment.