Reviewerly/exHarmony huggingface dataset:
Transformer Models for Question Answering on Autism Spectrum Disorder QA Dataset

Question answering (QA) Transformer-based models might become efficient in inclusive education. For example, one can test and tune such models with small closed-domain datasets before the implementation of a new system in an inclusive organization. However, studies in the sociomedical domain show that such models can be unpredictable. They can mislead a user or evoke aversive emotional states. The paper addresses the problem of investigating safety-first QA models that would generate user-friendly outputs. The study aims to analyze the performance of SOTA Transformer-based QA models on a custom dataset collected by the author of the paper. The dataset contains 1 134 question-answer pairs about autism spectrum disorders (ASD) in Russian. The study presents the validation and evaluation of extractive and generative QA models. The author used transfer learning techniques to investigate domain-specific QA properties and suggest solutions that might provide higher QA efficiency in the inclusion. The study shows that although generative QA models can misrepresent facts and generate false tokens, they might bring diversity in the system outputs and make the automated QA more user-friendly for younger people. Although extractive QA is more reliable, according to the metric scores presented in this study, such models might be less efficient than generative ones. The principal conclusion of the study is that a combination of generative and extractive approaches might lead to higher efficiency in building QA systems for inclusion. However, the performance of such combined systems in the inclusion is yet to be investigated.
Modulated Signal Recognition based on Feature-multiplexed Convolutional Neural Networks

Modulated signal identification plays a crucial role in both military reconnaissance and civilian signal regulation. Traditionally, modulated signal identification is based on high-order statistics, but this approach has many drawbacks. With the development of deep learning, its advantages are fully exploited by combining it with modulated signals to avoid the complex process of computing a priori knowledge while having good fault tolerance. In this paper, ten digital modulated signals are classified and recognized, and improvements are made on the basis of convolutional neural networks, using feature reuse to increase the depth of the convolutional layer and extract signal features with better results. After experimental analysis, the recognition accuracy increases with the rise of the signal-to-noise ratio, and can reach 90% and above when the signal-to-noise ratio is 30dB.
Requirements for Operation Procedure and Plan for the Korean Aviation Safety Big-Data Platform based on the Case of FAA ASIAS

The importance of a systematic approach to collect, process, analyze, and share safety data in aviation safety management is continuously increasing. Accordingly, the domestic aviation industry has been making efforts to build a Big-data platform that can utilize multi-field safety data generated and managed by various stakeholders and to develop safety management technology based on them. Big data platforms not only must meet appropriate technical requirements, but also need to establish a management system for effective operation. The purpose of this study is to suggest the requirements of the aviation safety big data platform operation procedure and plan by reviewing the advanced overseas cases (FAA ASIAS). This study can provide overall framework and managerial direction for the operation of the aviation safety big data platform.
Few-shot Learning Named Entity Recognition of Pressure Sensor Patent Text Based on MLM

Abstract of patent text, as an important support for intellectual property protection, is an ideal data source for technology mining. Named entity recognition of patent text can reduce the workload of patent analysis, improve work efficiency, and provide effective technical means for patent discovery, patent promotion, patent infringement and other aspects. However, the technical terms of patent texts are difficult to be mined, extracted and labeled. Therefore, this paper proposes a few-shot learning named entity recognition method to solve the problem that the named entity recognition of pressure sensor patent text lacks sufficient annotation data.This method uses MLM (Masked Language Model) pretraining method of BERT Model, selects a small part of token to mask each time, and then repeatedly trains on the same sample, finally obtains the training embedding of bidirectional fusion information on massive continuous corpus. Then the CRF layer is used to decode and finally the prediction tag sequence is obtained. Experiments on 55 patent abstracts and 34 patent abstracts in the field of pressure sensor preparation, the simulation results show that the proposed method can improve the recognition accuracy by about 10% compared with the traditional machine learning model (HMM, CRF) in the case of small samples. Compared with the deep learning model (BI-LSTM and BiLSTM+CRF), the accuracy of the model is improved by about 30%, and the accuracy of the model is 93%.
Two-Level Planning System for Location Selection of Logistics Distribution Center Based on Internet of Things Technology

Due to the different degrees of dispersion of the distribution targets in the region, the time between the logistics distribution center and different destinations is quite different. For this reason, a two-level planning system design for the location of the logistics distribution center based on Internet of Things technology is proposed. The ZD53001 radar microwave detector is used to collect the traffic flow of all roads in the target area, and the data are counted to the server. After the fixed cost of the distribution center, the freight distribution cost and the total income of the logistics distribution service are used as the distribution center location target, the logistics distribution is constructed Center positioning model, and finally calculate the best distribution center. The test results show that the time from the center of the system to the surrounding distribution warehouses is basically stable within 20 minutes.
Few-Shot Classification for Human Context Recognition Using Smartphone Data Traces

Human Context Recognition (HCR) is an important field at the intersection of personalized healthcare and ubiquitous computing that aims to describe the current state of a person given mobile sensor data. Previous work has demonstrated the ability to detect various ailments through context patterns and deviations from those patterns, illustrating an important application of HCR. Contexts captured by mobile sensor data are often associated with high intra-subject and inter-subject variability, which hinders the process of effectively learning representations for each class. Obtaining a sufficient quantity of labeled context data for reliable predictions becomes prohibitively expensive as the number of users and targeted contexts increases. Few-Shot Classification is a family of methods wherein models are trained with limited amounts of labeled data such that their latent representations can be used for transferring information from one context to another. In this paper, we provide the first comprehensive study of state-of-the-art Few-Shot classifiers on HCR data. We find that methods that fine-tune classifiers using a general feature extractor on limited labeled samples outperform more sophisticated Few Shot techniques in the HCR setting by up to 50%.
Topical issues of scientific and methodological support for the investigation of cyber incidents associated with the use of malicious software

The article is devoted to the problems of information security and the fight against cybercrime. Attention is focused on the use of software specially designed or modified for implementation of cyberattacks, which, according to the formulations of the current legislation, falls under the definition of «malicious program». The article examines the most well-known cases of large-scale cyberattacks in Ukraine, analyzes the course of these attacks and the destructive effectiveness of malicious software. The importance of forensic expertise support of the investigation of cyber incidents in terms of research of malicious software is substantiated. The need for proper scientific and methodological support of the above forensic expertise research is noted. The state of scientific and methodological support of forensic expertise researches of malicious software is analyzed, chronological description of the development and implementation of scientific and methodological materials for expertise during the investigation of cyber incidents related to the use of malicious software is presented. Problematic points in the study of malicious software are indicated. The limits of the forensic expert's competence are outlined and the impossibility to determine the program's affiliation to malicious software by a purely expert basis is substantiated. The classification of malicious software proposed in the methodical recommendations is given in an abbreviated form. A comparative analysis is conducted and the advantages of the methodological recommendations of the Security Service of Ukraine are specified, both in training and certification of forensic experts, and actually during conducting of malicious software expert researches.
Design of dynamic obstacle avoidance system for self-driving sweeper based on lattice-planner

In this paper, a dynamic obstacle avoidance control system of self-driving sweeper is designed based on lattice-planner algorithm in robot operating system (ROS). On the basis traditional manual sweeper, an obstacle avoidance method in line with the driving characteristics of sweeper has been proposed. Firstly, the planning starting point of this frame is calculated according to the mileage points of the previous one. The summary information of static and dynamic obstacles is obtained according to the point cloud data of lidar. And then the horizontal and vertical trajectories are generated in accordance of ST and SL diagrams. After the feasibility analysis of the trajectory, a large number of trajectory bundles are eliminated. The cost detection is carried out for the trajectory bundles that meet the rules, and the collision detection is carried out for the only few remaining trajectories. Finally, through real vehicle debugging, the scientificity and safety of the algorithm in application are verified.
Long-short Term Prediction for Occluded Multiple Object Tracking

Online multiple object tracking (MOT) is a challenging problem in complex scenes due to frequent occlusions. Most of the existing MOT methods tend to focus on addressing an individual type of occlusion, which cannot meet the requirements of real complex scenes. In this paper, we propose a unified MOT framework that combines long- and short-term prediction models for online multiple object tracking. Basically, The short-term prediction model consists of an appearance-based model and a motion-based model, aiming at exploiting the appearance and motion of objects to handle different types of occlusions jointly. Furthermore, we adopt a cubic spline interpolation as a long-term prediction model to estimate the trajectory of the target in occluded frames. To handle different lengths of occlusions, an adaptive weighted fusion model is proposed to combine the short-term prediction model, and the long-term prediction model. Experimental results on several challenging datasets demonstrate that the proposed method outperforms state-of-the-art methods.
Human-Agent Collaboration Strategies for Vision-Grounded Instruction Following

In this paper, we explore two human-agent collaboration strate-gies (human-designed curriculum learning and human-agent dialogue) to boost Reinforcement Learning agent's training in the context of vision-grounded instruction following. The agent is given a text instruction to fetch the described object or switch an appliance. The agent needs to navigate in a simulated multi-room environment, interacts with the objects, furniture, appliances to follow the instruction. Our first strategy is training the agent with a human-designed curriculum. We create a series of subtasks and let the human advisor select several subtasks and design the curriculum composed of the selected tasks in increasing order of complexity. Then the agent is first trained on the human-designed curriculum before training on the target task. Secondly, we enhance the agent with a dialogue module, which allows the agent to query a human collaborator (with a limit number of dialogue exchanges) and obtain three types of hints: action selection, navigation and recognition. Experiments show that the training with a properly designed subtask curriculum can accelerate the performance growth on the target task compared with directly training on the target task, and incorporating a human collaborator dialogue mod-ule further improves the agent's task success rate and route efficiency.
Teaching English and Science to the Deaf Students

Deaf students need special classroom settings where a teacher is supported by an interpreter who performs the gestures for the students. This paper presents the role of technologies and techniques in the teaching English and Science to the deaf students. Furthermore, it highlights the usage and importance of avatar technology in improving the teaching process of deaf students. Different researchers have presented many techniques for teaching English and Science to deaf students. For example, AR Book, E-Gloves, animated software, and interactive software, ICT based technology. The Avatar approach also plays an important role. An Avatar approach provides a platform where teachers teach easily English words, phrases and sentences more accurately, more efficiently and in a low cost. The basic purpose of avatar approach is to minimize the workload of teachers as an important instructing help.
Distilling Financial Models by Symbolic Regression

Symbolic Regression has been widely used during the last decades for inferring complex models. The foundation of its success is due to the ability to recognize data correlations, defining non-trivial and interpretable models. In this paper, we apply Symbolic Regression to explore possible uses and obstacles for describing stochastic financial processes. Symbolic Regression (SR) with Genetic Programming (GP) is used to extract financial formulas, inspired by the theory of financial stochastic processes and Itô Lemma. For this purpose, we introduce in the model two operators: the derivative and the integral. The experiments are conducted on five market indices that are reliable at defining the evolution of the processes in time: Tokyo Stock Price Index (TOPIX), Standard & Poors 500 Index (SPX), Dow Jones (DJI), FTSE 100 (FTSE) and Nasdaq Composite (NAS). To avoid both trivial and not interpretable results, an error-complexity optimization is accomplished. We perform computational experiments to obtain and investigate simple and accurate financial models. The Pareto Front is used to select between multiple candidates removing the over specified ones. We also test Eureqa as a benchmark to extract invariant equations. The results we obtain highlight the limitations and some pursuable paths in the study of financial processes with SR and GP techniques.
A Multi-view 3D Vehicle Detection Method Based On Novel 3D Proposal Generation Method

This paper introduces a multi-level fusion 3D vehicle detection method based on novel 3D proposal generation method for autonomous driving scenes. The entire detection network is composed of two subnets: a region proposal network (RPN) and a final detector network. The RPN is designed to generate reliable 3D object proposals for multiple targets using pixel-level fusion of depth map from lidar point cloud and images from camera processed by a 2D detection structure. To further improve the accuracy of 3D object proposals, a novel multi-source fusion based depth prediction method is proposed, which outputs the depth from the target while detecting 2D bounding boxes. The final detector network processes the 3D proposals generated by RPN to extract multi-view features, which serve as input to predict the 3D location, orientation and classification of targets. The whole detection method cleverly realizes the information fusion of heterogeneous data from multiple sensors at multiple levels. The experimental results on the challenging KITTI dataset prove that our method has certain advantages compared with the existing multi-view fusion detection methods.
Cost-Effective Updating of Distributed Reordered Indexes

Index reordering techniques allow document collections to be renumbered, with the goal of developing a permutation of the initial document ordinal identifiers that places documents that are (somehow) like each other into positions near each other in the permuted ordering. The clustering that results allows inverted index size to be reduced, since each term’s posting list is more likely to contain a non-uniform set of inter-document integer gaps. Reordering is normally performed once, at the time the index is created.
Analysis of enterprise management technology and innovation based on multilinear regression model

Abstract With the continuous innovation of scientific research, technological level and social and economic development, the technological innovation of enterprises in the continuous management is also facing a new breakthrough and upgrade. Especially for enterprises in the era of big data, in order to better respond to the new demands of the development of the new era, enterprise managers should make effective technological innovation from the global economic growth trend on the basis of clarifying their own development advantages. Therefore, on the basis of understanding the multiple linear regression model and its construction conditions, this paper analyses the influencing variables in the actual development according to the current management technology and innovation of enterprises, and obtains the clear results of the model research.
Design of a Financial Information Security Encryption System Based on a BP Neural Network

Aiming at the problem that the encryption precision of the existing security encryption system is small, a financial information security encryption system based on a BP neural network is designed. The hardware part designs the structure of the financial information data board and sets up the data acquisition circuit. In the software part, a BP neural network is used to update the data stream, and the output centralized control data stream is used as the whole data domain. After setting up the system security encryption environment, financial information data shall be collected, and the security encryption system based on big data technology, the security encryption system based on Internet of Things technology and the security encryption system designed shall be prepared to participate in the test. The test results show that the encryption precision parameters of the design information security encryption system are the largest and the security encryption effect is the best.
Measurement of polarization quantum states under chromatic aberration conditions

The wave plate is a basic device for transforming and measuring the polarization states of light. It is known that the transformation of light by means of two wave plates makes it possible to measure the state of polarization in an arbitrary basis. The finite spectral width of the light, however, leads to a chromatic aberration of the polarization quantum transformation caused by the parasitic dispersion of the birefringence of the plate material. This causes systematic errors in the tomography of quantum polarization states and significantly reduces its accuracy. This study is a development of our work<sup>1</sup>, in which an adequate model for quantum measurements of polarization qubits under chromatic aberration was first formulated. This work includes a generalization of the results obtained earlier for the cases of two-qubit states. Along with examples of random states those uniformly distributed over the Haar measure are considered. Using a matrix of complete information, it is quantitatively traced how the presence of chromatic aberrations under conditions of a finite spectral width of light leads to the loss of information in quantum measurements. It is shown that the use of the developed model of fuzzy measurements instead of the model of standard projection measurements makes it possible to suppress systematic errors of quantum tomography even when using high-order wave plates. It turns out that the fuzzy measurement model can give a significant increase in the reconstruction accuracy compared to the standard measurement model.
Implementation of a Method Using Image Sequentialization, Patch Embedding and ViT Encoder to Detect the Breast Cancer on RGBA Images and Binary Masks

This paper uses an approach where we will be training the unlabelled and labelled datasets into the system and these datasets comprises of: RGBA images and Binary masks. This will be a benefit in order to get a much better result and more accurate also as pre-training method always helps in getting excellent output. We will use a Transformer Model in this paper where we will apply an image sequentialization technique having 51 million parameters which will help us in attaining the smoother images without any noise. At times, this approach could be time consuming due to used datasets and its sizes, however it shows more accurate and efficient results. Further, we will try to figure out the pixel wise label map using patch embedding technique. Once these techniques are applied then CNN-Transformer Hybrid will come into the role which will encode and decode the images to high-level feature extractions and full spatial resolution respectively. This way of doing encoding and decoding is also known as forward pass and back propagation. Also, it will involve the cascaded upsampler where we will try to use self-attention processes into the design of encoder using the transformers. This entire mechanism will involve few of the best evaluation metrics and those are: Pixel accuracy, IoU, Mean-IoU and Recall/Precision/F1 Score giving the effective and best results.
Discrete Latent Variables Discovery and Structure Learning in Mixed Bayesian Networks

Latent variables pose a challenge for accurate modelling, experimental design, and inference, since they may cause non-adjustable bias in the estimation of effects. While most of the research regarding latent variables revolves around accounting for their presence and learning how they interact with other variables in the experiment, their bare existence is assumed to be deduced based on domain expertise. In this work we focus on the discovery of such latent variables, utilizing statistical hypothesis testing methods and Bayesian Networks learning. Specifically, we present a novel method for detecting discrete latent factors which affect continuous observed outcomes, in mixed discrete/continuous observed data, and device a structure learning algorithm that adds the detected latent factors to a fully observed Bayesian Network. Finally, we demonstrate the utility of our method with a set of experiments, in both controlled and real-life settings, one of which is a prediction for the outcome of COVID-19 test results.