Reviewerly/exHarmony huggingface dataset:
Research on Curriculum Construction of Big Data and Accounting under the Background of Big Data

In the era of big data and artificial intelligence, society has put forward new requirements for the quality structure, ability structure, knowledge structure, skill structure and learning ability of accounting talents. In response to the new needs of the society for the skills, structure and learning ability of accounting professionals in the big data era, this article analyzes the reconstruction of the accounting professional curriculum system from the perspective of big data technology and management accounting, and analyzes the accounting professional curriculum system in the big data era Based on the core courses of engineering and management, it puts forward suggestions and countermeasures for the cross-border integration of big data and accounting curriculum system, hoping to help the accounting professional teaching in colleges and universities to achieve better development.
Pedestrian Detection and Tracking with Deep Mutual Learning

In the last decade, the application of pedestrian detection in computer vision has gradually increased, such as social distance detection in the epidemic era. In this paper, we improve the newly proposed YOLOv5 model, use the idea of deep mutual learning for training, compare the performance and accuracy of different parameters, and select a relatively good model. As for the application, after detecting an abnormal pedestrian or a designated pedestrian, we use the Deep SORT method to track the pedestrian via the pedestrian's ID. Experimental analysis shows that our model performs well in terms of mean average precision (mAP), total loss (TL), and frames per second (FPS).
Outdoor Autonomous Wheeled Robot

In this paper, a VWB-based Outdoor autonomous wheeled robot is designed to replace manual material handling in a special dangerous environment. The system uses STM32F103zet6 as the main control MCU, uses accelerometer to sense the direction of the car, UWB positioning system to obtain the robot position, PID control algorithm to control the motor and steering gear rotation, Dijkstra shortest path algorithm calculates the best path of robot motion, and finally through the path driving algorithm control the robot follows the planned path and reaches the designated destination.
Expert Recommendations with Temporal Dynamics of User Interest in CQA

Community question answering (CQA) has become an essential service of promoting knowledge sharing in social platforms. To make question answering more efficient, several expert recommendation methods for CQA have been proposed, but most of them focus on the similarity matching between user interest and question content while ignoring the temporal dynamics of user interest, whose changes may decrease the quality of recommendation results. In this paper, a long and short term-based expert recommendation model (LSTERM) via attention mechanism-based CNN and Bi-GRU, which considers not only user interest but also user expertise, is proposed. The model can learn the embedded user/question feature representation from various content information by using attention mechanism-based CNN and then track the change of user interest and expertise over time by using Bi-GRU. Experiment results on real data demonstrate that with temporal dynamics, the recommendation accuracy is substantially improved compared with other state-of-the-art methods.
Learners based on transducers

The learners considered here process data in cycles and maintain as a long term memory a string which provides all internal data the learner can use in the next cycle. Updating of these strings is usually done by either recursive or automatic learners. The present work looks at transduced learners, which sit in-between. The results include that transduced learners can learn all learnable automatic families with memory exponential in the size of the longest input seen so far. Furthermore, there is a hierarchy based on the memory-allowance: if n is the size of the largest datum seen so far, then for all k≥1, memory nk+1 allows one to learn more automatic families than memory nk. Further results shed light on when it can be imposed that transduced learners be consistent, conservative or iterative. The main result of this kind is that all learnable automatic families have a consistent and conservative transduced learner.
k-Winners-Take-All Ensemble Neural Network

Ensembling is one approach that improves the performance of a neural network by combining a number of independent neural networks, usually by either averaging or summing up their individual outputs. We modify this ensembling approach by training the sub-networks concurrently instead of independently. This concurrent training of sub-networks leads them to cooperate with each other, and we refer to them as "cooperative ensemble". Meanwhile, the mixture-of-experts approach improves a neural network performance by dividing up a given dataset to its sub-networks. It then uses a gating network that assigns a specialization to each of its sub-networks called "experts". We improve on these aforementioned ways for combining a group of neural networks by using a k-Winners-Take-All (kWTA) activation function, that acts as the combination method for the outputs of each sub-network in the ensemble. We refer to this proposed model as "kWTA ensemble neural networks" (kWTA-ENN). With the kWTA activation function, the losing neurons of the sub-networks are inhibited while the winning neurons are retained. This results in sub-networks having some form of specialization but also sharing knowledge with one another. We compare our approach with the cooperative ensemble and mixture-of-experts, where we used a feed-forward neural network with one hidden layer having 100 neurons as the sub-network architecture. Our approach yields a better performance compared to the baseline models, reaching the following test accuracies on benchmark datasets: 98.34% on MNIST, 88.06% on Fashion-MNIST, 91.56% on KMNIST, and 95.97% on WDBC.
Meta-Heuristical Constrained Optimization Based on a Mechanical Design Problem

Mechanical design optimization is a crucial part of engineering and manufacturing problems, according to the literature. Dealing with constraints is amongst the most challenging parts of this form of problem. Many studies have been presented to concern with infeasible solutions in process optimization, especially those based on genetic algorithms. The application step is carried out in this study with a chosen problem that is investigated by a Group Search Optimizer (iGSO) for resolving mechanical design optimization problems and an algorithm that uses both subsets and cooperative evolutionary strategies to enhance swarm intelligence capabilities and optimization accuracy. The selected pressure vessel design from this study aims to minimize the total cost of materials, forming and welding of the pressure vessel. By this aim, the pressure vessel design optimization problem was implemented with Simulated Annealing and Great Deluge Algorithms via MATLAB R2020b software. The parameters and constraints were specified in two distinct functions to compute the fitness function using an inverse tangent approach and even to obtain a penalty function that decreases the fitness values of infeasible solutions in accordance with the degree of constraint violation. Since it has a tiny area and performs in a restricted period, the Simulated Annealing Algorithm offered a reasonable result despite violating any constraints, according to MATLAB outputs.
A High Payload Double Secured Video Steganography based on AES Encryption and BCH code

Because of advancements in information and communication technologies, the science of encryption has risen to prominence in recent years. This method is designed to conceal hidden text and images. Any steganographic algorithm's performance is determined by the attackers' embedding efficiency, embedded payload, and durability. Many current steganographic techniques have significant flaws in terms of coverage, security, and video quality. We present a novel technique of video steganography based on the discrete Wavelet transform (DWT) and error correction codes in this article (ECC). A secret message is first encrypted using AES and then encoded with BCH codes to enhance the security of the proposed method. After that, it's incorporated in video frames' DWT coefficients. Each Y, U, and V plane's DWT coefficients contain the encrypted message. The suggested algorithm is put to the test on a variety of videos. The suggested algorithm's resilience is shown by its experimental findings. The video quality of stego is excellent.
A Reinforcement Learning Approach for Shortest Path Navigation in Automated Guided Vehicles for Medical Assistance

With the advent of tremendous innovation in the field of autonomous navigation, the day-to-day chores of humans has been simplified. Autonomous navigation is used in all types of vehicles including aerial, ground and underwater vehicles. They are being used in vast domains like surveillance, delivery, self driving cars, etc. In addition to this, they also find application in the defence sector where they play a crucial role through surveillance in the border. The real challenge for the autonomous vehicles lies in obstacle detection and avoiding it intelligently. Another challenge for experts in the autonomous navigation field is identifying the shortest path. Hence, we propose a novel framework to tackle the obstacle detection problem and to identify the shortest path using a reinforcement learning approach that could be helpful in delivering food and medicines to the old and the special need people. The autonomous vehicle is trained and tested in the grid environment on both Q learning and Double Q learning approach. The performance of the algorithms is evaluated, and the metrics are discussed in the chapter.
Top Reported Data Security Risks in the Age of COVID-19

Data has been collected and stored for thousands of years. Securing data during the digital age has remained difficult. Research shows that in 2018 there was over 33 zettabytes of data, which is approximately an equivalent to 129 billion 256GB mobile devices of data. Risk management in recent years has made attempts at balancing data security risks with organizational business and budgetary requirements. This research examines high probability data security threats and mitigations. It then reports on the threats in connection with the top United States healthcare data breaches reported during the COVID outbreak to the Health and Human Services (HHS) between June 11, 2020 and June 11, 2021. The data analysis shows that there were nine breaches of over a million affected individuals reported to HHS affecting 15,936,679 individuals in total. Five-million individuals is approximately larger than the populations of Los Angeles, New York, and Chicago combined. We connect the common security risks with the reports of these incidents to gain insights into common network security concerns and inform future network architectures and risk mitigations.
Automated Nanoparticle Count via Modified BlendMask Instance Segmentation on SEM Images

In the high-throughput drug research, statistical analysis of nanoparticles has been one of the focuses in drug carrier systems. This can be completed via electronic microscopy imaging and image analysis such as image segmentation. For example, in some cases selecting and counting the nanoparticles in the field of view are important for drug screening. In order to minimize manual interactions and avoid extensive workloads, we present a pipeline that is featured by deep learning-based instance segmentation, with experiments implemented on both real and synthetic data. The proposed instance segmentation approach, namely Modified BlendMask, aims to improve the accuracy of nanoparticle detection and further refine the automated nanoparticle count. In this framework, we are devoted to address the problem of missing detection, i.e., false negative, introduced by overlap and blur, sparse particle distribution, and tiny particle occurrence in images. Sufficient experiments demonstrate the reasonableness and effectiveness of the proposed pipeline and instance segmentation method for automated nanoparticle count, with an overall count accuracy of 70.2% for 38670 particles on the 1141 test images.
Asynchronous Processing of Measurements with Uncertain Latencies in a Factorized, Consider-Neglect Navigation Filter

This paper presents an approach for asynchronous and out-of-sequence measurement processing within the square-root formulation of the consider/neglect Kalman filter. It is commonly assumed that sensor systems generate and communicate measurements instantaneously, synchronous with the navigation system's operational frequency. The complex interactions of true flight systems make this assumption difficult to realize. Within this paper, the impact of temporal uncertainties in the measurement acquisition and processing on navigation performance is assessed. Methods to handle uncertainties arising from the complex measurement processing chains are presented. The impact of the developed techniques on navigation performance is assessed through Monte Carlo simulation and analysis.
Progressive editing with stacked Generative Adversarial Network for multiple facial attribute editing

Generative Adversarial Network (GAN) based facial attribute editing has been successfully applied to many real world applications. However, most of existing methods suffer from semantic entanglement and imprecise editing when handling multiple facial attributes. The situation is worse when the samples with minority attribute values are insufficient, and majority attribute values dominate the learning easily. A stacked conditional GAN (cGAN) is proposed in this study aiming at solving these problems. Multiple attribute editing is broken down into several single attribute editing tasks which have been learned by base cGANs individually. Moreover, samples with a minority attribute value are paid more attention in learning. This proposed method not only reduces the difficulty of multiple attribute editing but also mitigates the imbalance problem. The residual image learning is applied to our model to reduce the difficulty of the image generation. The superiority of our model is demonstrated and compared with popular GAN-based facial attribute editing methods experimentally in terms of image quality, editing accuracy and training cost. The results confirm that our proposed model outperforms the other methods, especially in imbalance situations.
Audio-Based Hate Speech Classification from Online Short-Form Videos

In this study, we pioneer the development of an audio-based hate speech classifier from online, short-form TikTok videos using traditional machine learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machines. We scraped over 4746 videos using the TikTok API tool and extracted audio-based features such as MFCCs, Spectral Centroid, Rolloff, Bandwidth, Zero-Crossing Rate, and Chroma values as primary feature sets. Results show that using the extracted predictors for hate speech detection can obtain up to 78.5% accuracy on an optimized Random Forest model, crossing the 50% benchmark for models in this task. In addition, comparing the Information Gain scores and globally learned model weights identified that Spectral Rolloff and MFCCs are top predictors in discriminating hate speech for the Filipino language.
Deception in Epistemic Causal Logic

Deception is an act whereby one person causes another person to have a false belief. This paper formulates deception using causal relations between a speaker’s utterance and a hearer’s belief states in epistemic causal logic. Four different types of deception are considered: deception by lying, deception by bluffing, deception by truthful telling, and deception by omission, depending on whether a speaker believes what he/she says or not, and whether a speaker makes an utterance or not. Next several situations are considered where an act of deceiving happens. Intentional deception is accompanied by a speaker’s intent to deceive. Indirect deception happens when false information is carried over from person to person. Self-deception is an act of deceiving the self. The current study formally characterizes various aspects of deception that have been informally argued in philosophical literature.
A New Model of Software Network for Object-Oriented Software System

Software quality is critical in the current information age, we need to find effective modeling methods to evaluate the quality of increasingly large software. Complex network is an important tool for modelling software systems from the macro perspective, and it expresses software as a complex network by analyzing the dependencies between software components. In this paper, we propose a new model called weighted class dependency network (WCDN) based on complex network theory to describe the characteristics of object-oriented software systems. WCDN model is based on class granularity, which considers not only the difference of multiple dependencies caused by object-oriented programming languages, but also the disparity of class importance. We apply WCDN to a refactoring algorithm based on three modular metrics, and evaluate the rationality and effectiveness of the network through disturbing-recovering experiments. After the comparative experiments of 12 open-source software systems, the results prove that WCDN can represent the architecture of the object-oriented software system correctly.
THE USE OF INNOVATIVE TOOLS IN THE EDITORIAL PROCESS OF SCIENTIFIC JOURNALS OF UKRAINE

Rapid progress in the field of publishing scientific journals, on the one hand, facilitates all editorial processes, and on the other hand, increases the risks of losing the uniqueness of a scientific article. The growing need of scientific journals for supporting tools that would, on the one hand, protect journal editions from unscrupulous authors who resort to the practice of scientific plagiarism, and on the other hand, instill in authors a sense of responsibility for the texts they send. The purpose is to reveal the problems of using text similarity scanners - plagiarism checking services in the editorial process of scientific journals of Ukraine, to verify by empirical research the theoretical hypothesis about the existence of certain types of practices of academic plagiarism in the Ukrainian scientific environment. Survey of editors of professional editions of the Ministry of Education and Science of Ukraine has been conducted using the CAWI method with the help of the Google forms functionality. The sample consisted of 99 experts (editors of category “A” journals – 8%; editors of category “B” journals – 92%), who represented the general population on the basis of “journal category”, which ensured the validity of the results. The opinion of the editors of scientific journals on the use of text similarity scanners in the editorial process has been determined. The most widely used services are Unicheck and Antiplagiat, which, according to respondents, most simply and concretely solve the problem of plagiarism and reuse of text. It has been identified that publishing houses that publish journals with international distribution and those indexed by the scientometric platforms Scopus and WoS (category “A” according to the national classification) mostly use similarity scanners. Publishing houses operating only within Ukraine, the journals of which are not represented in prestigious scientometric platforms, often ignore plagiarism detection software altogether and rely solely on the opinion of reviewers and editors. It is shown that the practice of using text similarity scanners, although entrenched in the Ukrainian scientific and publishing space, is still not widespread enough and does not cover the vast majority of scientific journals that rely only on traditional forms of reviewing scientific texts.
A Technique for Extracting User Specified Information from Streaming Data

Users are often interested in a specific type of data (user-preferred data) from large volumes of data streaming into the system in real-time. An efficient system that only stores user-preferred data from these data can reduce storage space by storing only the user-preferred data and discarding the remaining data. It also reduces the search latency which allows the users to search for relevant information in a timely manner. The motivation behind this research is to devise a technique that filters streaming data and stores only the filtered data for post-processing, thereby saving storage space and reduce search latency. A proof-of-concept prototype for this technique has been built on Apache Spark by leveraging the parallel processing framework for streaming data and its machine learning library. The performance of the prototype subjected to synthetically generated streaming data is analyzed. The analysis of experimental results demonstrates the efficacy of this technique and provides insights into system behavior and performance.
Research on Athlete Behavior Recognition Technology in Sports Teaching Video Based on Deep Neural Network

In recent years, due to the simple design idea and good recognition effect, deep learning method has attracted more and more researchers' attention in computer vision tasks. Aiming at the problem of athlete behavior recognition in mass sports teaching video, this paper takes depth video as the research object and cuts the frame sequence as the input of depth neural network model, inspired by the successful application of depth neural network based on two-dimensional convolution in image detection and recognition. A depth neural network based on three-dimensional convolution is constructed to automatically learn the temporal and spatial characteristics of athletes' behavior. The training results on UTKinect-Action3D and MSR-Action3D public datasets show that the algorithm can correctly detect athletes' behaviors and actions and show stronger recognition ability to the algorithm compared with the images without clipping frames, which effectively improves the recognition effect of physical education teaching videos.
Viewpoint-Tolerant Semantic Segmentation for Aerial Logistics

Semantic segmentation is fundamental for enabling scene understanding in several robotics applications, such as aerial delivery and autonomous driving. While scenarios in autonomous driving mainly comprise roads and small viewpoint changes, imagery acquired from aerial platforms is usually characterized by extreme variations in viewpoint. In this paper, we focus on aerial delivery use cases, in which a drone visits the same places repeatedly from distinct viewpoints. Although such applications are already under investigation (e.g. transport of blood between hospitals), current approaches depend heavily on ground personnel assistance to ensure safe delivery. Aiming at enabling safer and more autonomous operation, in this work, we propose a novel deep-learning-based semantic segmentation approach capable of running on small aerial vehicles, as well as a practical dataset-capturing method and a network-training strategy that enables greater viewpoint tolerance in such scenarios. Our experiments show that the proposed method greatly outperforms a state-of-the-art network for embedded computers while maintaining similar inference speed and memory consumption. In addition, it achieves slightly better accuracy compared to a much larger and slower state-of-the-art network, which is unsuitable for small aerial vehicles, as considered in this work.