Reviewerly/exHarmony huggingface dataset:
Research on Interface Specification of Distributed Business Interaction and Data Sharing

In order to construct the cross-level data through-channel of infrastructure platform, the data sharing interface constraints are established according to the standard API interface, and the data sharing specification sorting method is adopted to clarify the data transmission mode and interface details, and the interface specification of business interaction and data sharing is defined. Based on the open architecture system, the micro-service component development technology is adopted to design and implement the data sharing interface specification, which effectively solves the problems of frequent business changes and diverse types. The results show that the specification reduces the intensity of manual work, improves the efficiency of evaluation, and ensures the integrity and accuracy of data sharing. It can be seen that this specification solves the problems encountered in the data sharing work of infrastructure platform, promotes the data sharing of various specialties, promotes the mutual integration of professional businesses and the data integration of key links.
FLC: A New Secure and Efficient SPN-Based Scheme for Block Ciphers

The FLC scheme was proposed and analyzed the theoretic security in [1]. In this paper, We add the practically secure proofs of a structure based on FLC against the linear and differential cryptanalysis. Then, we propose the MDS matrices to implement this structure with two 128-bit/256-bit block sizes. Finally, we evaluate the above experimental results and compare them with several implementations of the well-known block ciphers. These results show that FLC is indeed a prospective scheme for post-quantum cryptography.
Improving Automatic Speech Recognition for Low-Resource Language by Data Augmentation

Automatic speech recognition (ASR) is one of the emergency tasks in human-computer interaction. There are many studies work in the field of building network architecture to deal with this task. While data augmentation was deeply discovered in computer vision, it is a big lag behind in the field of speech. Large data collection is not trivial, and in some cases it is impossible. The problem with data size is even more serious in some low-resource languages, such as Vietnamese. This study focuses on the data augmentation approach to deal with the small-size datasets to help the deep learning network better coverage in the ASR task. The experiment results on various configures of the VIVOS dataset, and two variations of the Conformer network architecture show that our proposed method gets promising improvement.
Design and Development Indoors Autonomous Ping-Pong Collection Robot with Vision System

In this paper, we have solved the four key problems of autonomous ping-pong ball collecting robots: ball detection, distance estimation, path planning, and avoiding objects in the path. The proposed path planning and obstacle avoidance method are the most effective compared to current practices in simulation. Practical experiments also give promising results.
Vietnamese Electronic Medical Record Management with Text Preprocessing for Spelling Errors

With an important development of information technology, electronic medical records (EMRs) play a crucial role in medicine and its related fields such as pharmacology, bioinformatics, and healthcare research. An EMR management system is thus significant so that EMRs can be manipulated conveniently by their key users, who are doctors, nurses, and patients, and further exploited by other researchers. Nonetheless, there is no consideration on the EMRs when they are committed to the system. Their clinical texts are especially left behind upon their entry. Such a circumstance is understandable because they are created in a high-pressure working environment and normally late examined as needed in other tasks. In order to fill this gap, we propose an EMR management system with text preprocessing for spelling error correction on clinical texts. Our system is dedicated to Vietnamese EMRs with a practical demonstration confirmed by doctors and medical students. In addition, the spelling error correction method is defined to cover a wide diversity of spelling errors in a novel hybrid manner, by effectively combining a rule-based approach, dictionaries, n-grams, and a pre-trained monolingual sequence-to-sequence model. Indeed, the experimental results show that the method has better performance on real EMRs as compared to one of the most recent existing ones. As a result, it is promising that our work can enhance and make Vietnamese EMRs more utilizable.
Generating Test Paths to Detect XSS Vulnerabilities of Web Applications

Web technologies have developed rapidly because web applications are currently leading the trends in software development. In the face of emerging security issues, preventing software security vulnerabilities is a great concern for developers, vendors, and customers. In fact, the cross-site scripting (XSS) attack is a very popular type of attack that causes security vulnerabilities in web systems. However, when testing to detect XSS attacks for web applications, optimizing the test paths still has some problems with the time or space of the test paths. Therefore, in this paper, we propose a method to solve this problem. Our approach uses Q-learning in generating automated test paths to test XSS vulnerabilities for web applications. The proposed method consists of generating the graph of the web application, setting the weight for the graph, building the memory matrix, and generating test paths. We have experimented proposed approach with the online learning website system. The experimental results show a significant reduction in the number of test paths and this helps to reduce the test case space and test time to detect XSS vulnerabilities of web applications.
A Study on Information Extraction: Application to Administrative Document Images

This paper presents a study on the problem of information extraction and its application in building an information extraction system for administrative documents. The proposed end-to-end system contains three significant modules, including Text detection (TD), Optical character recognition (OCR), and Information extraction (IE). We developed the IE module by us based on two platforms, GraphSAGE and GATs. We have made many changes and improvements, such as redesigning graph modeling and node representation to match the goals and problems posed. We also elaborately studied to establish a complete information extraction system and dived into the information extraction module instead of all modules in the system. Besides that, we also built and evaluated our dataset of Vietnamese Administrative Documents Images (VADI2021).
Design of an SoC Based on 32-bit RISC-V CPU and Lightweight Block Cipher PRINCE on FPGA

This paper discusses an SoC consisting of the algorithm of PRINCE, a block cipher used in lightweight cryptography, and a 32-bit RISC-V CPU. The system was implemented successfully on an Intel DE2-115 FPGA board. The PRINCE core was functionally validated using a simulation waveform on ModelSim and an embedded Nios II processor on Intel's FPGA. The PRINCE core was integrated with 32-bit RISC-V to form a custom SoC on an FPGA. The SoC utilizes 8,127 logic elements, 2,983 registers, 391,872 memory bits, eight multipliers, and one PLL block. The proposed SoC based on the open-source 32-bit RISC-V CPU and the lightweight cryptography core, which were implemented on FPGA, consumed fewer logic resources. It can be used to design a secure SoC system or a compact Trusted Execution Environment suitable for Internet of Things security systems.
Multi-Task Learning with Convolutional Neural Network Approach for Packet Collision Avoidance in 802.11 WLAN

Packet collision can degrade wireless network performance. The IEEE 802.11 Wireless Local Area Network (WLAN) uses the Clear Channel Assessment (CCA) mechanism to monitor channel availability to avoid interference of the presence signal. CCA successfully detects 802.11 signals if it obtains the packet preamble information or detects the threshold ambient power on the channel to determine the channel state. This paper proposes multi-task learning (MTL) with convolutional neural network (CNN) approach to detect WLAN packet formats and modulation types without preamble part information as a supplement to enhance CCA sensitivity. The main advantages of this method over single-task training are high classification accuracy and rapid learning with a lightweight neural network model. Shared knowledge of representation layers, such as model weights or gradients, improves the efficiency of training data and reduces redundancy. WLAN signals generated by the Matlab waveform simulator are used to verify the accuracy of the proposed method, which is then implemented on a real-time SDR-based hardware testbed. Although different time offsets affect the classifications, the proposed method proves superior in classifying the packet format and modulation of WLAN signals with an accuracy of 98.93% and 88.53% at SNR = 24 dB, respectively. The proposed method improves channel utilization and throughput of the WLAN network, as demonstrated by an NS-3 simulation.
Foggy-DOTA: An adverse weather dataset for object detection in aerial images

Nowadays, object detection in aerial images in adverse weather, especially in foggy scenes becoming very challenging and incredibly practical. Furthermore, fog and clouds usually appear in the majority of aerial images captured via drones everywhere on Earth, especially in the early morning. Understanding the need for qualified deep learning approaches, we propose a Foggy-DOTA dataset inheriting from the original DOTA dataset and then empirically evaluate it on multiple State-of-the-art methods. After having conducted lots of experiments on some well-known baselines, ReDet is the highest method achieving 76.680 mAP on the original DOTA, only 74.194 mAP on our Foggy-DOTA dataset (60.706 mAP if trained on DOTA). On the other hand, S2ANet and RoI Transformer achieve 74.190, 76,09 mAP on the original DOTA, only yield 71.629 and 73.381 mAP on our Foggy-DOTA dataset (46.503 and 40.297 mAP - significantly low if trained on DOTA), respectively. Our work provides comprehensive statistical evaluation being an essential baseline for future object detection research.
Development of Homogenous Cross-Project Defect Prediction Model Using Artificial Neural Network

Defect prediction is an extremely new software quality assurance study field. A project team’s goal is to provide a high-quality product with no or few flaws. The quantity of flaws in a product is connected to its quality, which is also restricted by time and money. As a result, defect prediction is critical in the field of software quality. This study provides an in-depth look of the software defect/fault prediction. It covers important aspects of software defect prediction. It emphasizes several significant outstanding concerns for the future and explains the key areas of software defect prediction practice. This paper discusses methods for homogenous defect prediction (HDP), which compares metrics within projects. Our aim is to improve the quality of the software by removing the defects so that the working could be more efficient and the results would be more optimum. Our model will help in identifying the problematic modules, which would save a lot of resources that are required for the assurance of the software quality. All the algorithms and techniques of an HDP model will be Universal, but depending on the type of dataset used, the model can be used in multiple industries, for instance food, healthcare, business, and many more. We have built a HDP model using multiple activation functions. HDP models will only be applicable to balanced datasets but we have taken an unbalanced dataset for the model in order to show how to balance a dataset using multiple balancing techniques before moving to the actual development of the HDP model.
Theft Detection: An Optimized Approach Using cGAN and YOLO

Theft detection is one of the ways to reduce crime in today's chaotic society. Lot of the researchers have worked on this topic. Most of the thefts happen in low-lighting conditions. Hence, the main challenge is to increase the accuracy of the theft detection systems using an optimization technique. In the past, such systems were based on IoT resulting in false alarms in most of the cases. This work suggests a theft detection system build on You Only Looks Once (YOLO) for detection of weapons and a technique based on conditional generative adversarial network (cGAN) which is used for optimization of the results. Proposed work compares the YOLOv3 and YOLOv5 model for the detection of the weapons on a custom dataset. The accuracy for cGAN, YOLOv3 and YOLOv5 are 97.8%, 89.9% and 87.5% respectively. Also, this work presents a comparative study between the results before and after using the proposed optimization. Proposed work can be deployed at high risk areas which are highly prone to theft.
Detection of Offensive Comments for Textual Data Using Machine Learning

Social Media is an essential part of our lives today that provides us to connect with others all around the world. Social Media facilitates the sharing and spreading of information, thoughts, and ideas. However, just like any other innovation, it influences people in a harsh or another way. They have become a platform for spreading hatred, negative comments, and cyberbullying. Cyberbullying is bullying that occurs via digital technologies for example social media, messaging platforms, gaming platforms, and mobile phones. Cyberbullying includes posting, sending or sharing negative, mean, and harmful content. A lot of efforts are being made by researchers for the detection of cyberbullying on social networking sites. The research in this paper focuses on detecting offensive comments for textual data. The data set has been taken from Kaggle, containing 35,000 comments. A rigorous testing has been performed using various Machine learning techniques, among them SVM outperforms with an accuracy of 92.2%.
Big Data Visualization of Association Rules and Frequent Patterns

Big data and machine learning are driving Industry 4.0. In the current era of big data, numerous rich data sources are generating huge volumes of a wide variety of valuable data at a high velocity. Embedded in these big data are implicit, previously unknown, and potentially useful information and knowledge. This calls for data science, which makes good use of big data mining and analytics, machine learning, visualization, and related techniques to discover and visualize hidden gems. This may maximize the citizens' wealth and/or promote all society's health. As an important big data mining and analytics task, frequent pattern mining aims to discover interesting knowledge in the forms of frequently occurring sets of merchandise items or events. To enable users to get better understanding of the discovered patterns in a comprehensive manner, several data visualization and visual analytics tools have been proposed. This encyclopedia article focuses on visualization of big data, as well as association rules and frequent patterns discovered from these big data.
A two-stream deep neural network for infrared image enhancement

Aiming at the problems of low contrast and blurry details of infrared images, a novel two-stream deep full convolutional neural network is proposed for low-quality infrared image enhancement. An infrared detail enhancement sub-network and a global content-invariant sub-network are designed to achieve adaptive enhancement of infrared features. First, the detail enhancement network composed of mixed attention block with multi-convolutions, residual learning and up-sampling unit is used to extract deep features from inputs, learn meaningful thermal radiation target information, restrain unnecessary background, and then perform the separation of target and background. Second, the content-invariant network mainly consisting of dilated convolutions and multi-scale convolutions captures rich contextual information to focus on the overall content, maintain the spatial structure, and avoid over-enhancement of local regions. Finally, the fine-tuning unit fuses the features extracted by the two-stream to complete the element complementation between different mappings and generate high-quality infrared images. Furthermore, experiments on public datasets and self-collected infrared datasets demonstrate that the proposed method outperforms other image enhancement methods not only for image quality on PSNR and SSIM, but also that has better visual quality with less artifact and noise.
Data Science in the Database

An important part of the data lifecycle is preparing the data for analysis. This includes exploratory data analysis to find problems, data cleaning to solve problems related to 'dirty' data, and data transformation to achieve the format required by data analytics tools. Much data resides in databases, and it is beneficial for the data scientist to be able to carry out as much of this work as possible inside the database. This can be achieved using SQL. This article overviews some of the basic approaches to data exploration, cleaning and wrangling using SQL, including methods to deal with missing data, outliers, and bad values.
Forecasting Short-Term Solar PV Using Hierarchical Clustering and Cascade Model

With the large-scale deployment of solar PV installations, managing the efficiency of the generation system became essential. Generally, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system, estimating revenue, sustaining profits, and ensuring the quality of service. In this paper, the authors propose a solar PV forecasting model using multiple blocks of GRUs and RNN in a cascade model combined with hierarchical clustering to improve the overall prediction accuracy of solar PV forecast. This proposed model is a combination of hierarchical clustering, the Pearson correlation coefficient for feature selection, and the cascade model with GRU layer from k-means clustering and hierarchical clustering. These results, which are evaluated using NRMSE, show that hierarchical clustering is more suitable for solar PV forecast than k-means clustering.
Discriminative feature fusion for RGB-D salient object detection

RGB-D salient object detection aims to separate salient object from an image aided by depth. While a number of effective approaches have been proposed, difficulties still exist, which is due to two challenges: (1) It is difficult to fully and effectively fuse RGB and depth features especially in challenging scenes; (2) How to enhance the semantic information of low-level feature and enrich the spatial information of high-level feature. Most of the existing approaches design separate modules to address them. In this paper, a unified discriminative feature fusion module is proposed to be used for both multimodal and multiscale feature fusion. The module can also increase the semantic information in low-level features and enrich the spatial information in high-level features. A multi-scale contextual perception module is embedded in the network to accurately localize objects at different scales. Unlike other methods, the depth branch in the network uses pure convolution for complementary feature extraction. This paper conducted a comparison with 14 state-of-the-art methods on 8 datasets, and the experimental results suggest that the proposed approach is more effective and superior.
Application of Ramsey theory to localization of set of product states via multicopies

It is well known that any N orthogonal pure states can always be perfectly distinguished under local operation and classical communications (LOCC) if $$(N-1)$$ copies of the state are available (Walgate et al. Phys. Rev. Lett. 85, 4972 (2000)). It is important to reduce the number of quantum state copies that ensures the LOCC distinguishability in terms of resource saving and nonlocality strength characterization. Denote $$f_r(N)$$ the least number of copies needed to LOCC distinguish any N orthogonal r-partite product states. This work will be devoted to the estimation of the upper bound of $$f_r(N)$$ . In fact, we first relate this problem with Ramsey theory, a branch of combinatorics dedicated to studying the conditions under which orders must appear. Subsequently, we prove $$f_2(N)\le \lceil \frac{N}{6}\rceil +2$$ , which is better than $$f_2(N)\le \lceil \frac{N}{4}\rceil$$ obtained in Shu (Eur. Phys. J. Plus 136, 1172 (2021)) when $$N>32$$ . We further exhibit that for arbitrary $$\epsilon >0$$ , $$f_r(N)\le \lceil \epsilon N\rceil$$ always holds for sufficiently large N.
On-board implementation using Julia precompiler for rendezvous path planning

This paper discusses the use of Julia programming language for on-board planning. A previously published Bezier time-of-arrival path planner was implemented in Julia and used for on-board planning on a fixed wing UAV. This planner served as a pedagogical tool to develop a process for using Julia on-board for real-time computation and planning. Using Julia's new PackageCompiler, we were able to overcome the limitations of Julia's just-in-time compilation when applied to real-time planning on embedded processors. We discovered that the trace script used by PackageCompiler needed to sufficiently sample starting conditions so that the resulting compiled image was capable of handling real-world inputs. The process described in this paper can help practitioners use Julia for rapid code development and testing.
Dynamics and Stability Analysis of a Tethered Unmanned Rotorcraft

Naval aviators use tethers to assist rotorcraft during ship board landing because of the ability to increase robustness against wind turbulence and to center the rotorcraft on the landing spot. Autonomous tethered landing with unmanned rotorcraft and automatic winch device for applying the tether force requires the knowledge of how the tether force impacts the wind affected dynamics of the rotorcraft. To this day, the influences of applied tether force and wind velocity on robustness and stability of the rotorcraft have been little explored. In this paper, we present an analysis of the dynamics and stability of a nonlinear model of a small-scale tethered rotorcraft. We develop a simplified model of a rotorcraft's longitudinal dynamics and vary model parameters including tether force, trim conditions, and the horizontal wind in order to study the interdependence of those parameters and their impact on the model's equilibrium points and stability. This is a preliminary step towards design of an automatic control of an unmanned rotorcraft capable of autonomous tethered landing and development of tether force control laws for the winch device.