Reviewerly/exHarmony huggingface dataset:
TextDC: Exploring Multidimensional Text Detection via a New Benchmark and Solution

Text detection has been significantly boosted by the development of deep neural networks but most existing methods focus on a single kind of text instance (i.e., overlaid text, layered text, scene text). In this paper, we expand the text detection task from a single dimension to multiple dimensions, thus providing multi-type text descriptions for the scene and content analysis of videos. Specifically, we establish a new task to detect and classify text instances simultaneously, termed TextDC. As far as we know, existing benchmarks cannot meet the requirements of the proposed task. To this end, we collect a large-scale text detection and classification dataset, named Text3C, which is annotated using multilingual labels, location information, and text categories. Together with the collected dataset, we introduce a multi-stage and strict evaluation metric, which penalizes detection approaches for missing text instances, false positive detection, inaccurate location boxes, and error text categories, developing a new benchmark for the proposed TextDC task. In addition, we extend several state-of-the-art detectors by modifying the prediction head to solve the new task. Then, a generalized text detection and classification framework is designed and formulated. Extensive experiments using the updated methods are conducted on the established benchmark to verify the solvability of the proposed task, the challenges of the dataset, and the effectiveness of the solution.
Learning invariant representation using synthetic imagery for object detection

Recent years have witnessed a rapid advance in training and testing synthetic data through deep learning networks for the annotation of synthetic data that can be automatically marked. However, a domain discrepancy still exists between synthetic data and real data. In this paper, we address the domain discrepancy issue from three aspects: 1) We design a synthetic image generator with automatically labeled based on 3D scenes. 2) A novel adversarial domain adaptation model is proposed to learn robust intermediate representation free of distractors to improve the transfer performance. 3) We construct a distractor-invariant network and adopt the sample transferability strategy on global-local levels, respectively, to mitigate the cross-domain gap. Additional exploratory experiments demonstrate that the proposed model achieves large performance margins, which show significant advance over the other state-of-the-art models, performing a promotion of 10%–15% mAP on various domain adaptation scenarios.
Characteristic Estimator of Interval-Censored Binomial Data and Its Application

This study aims to determine the estimation of interval-censored data with a special distribution, namely the binomial distribution. This research is using quantitative methods, the steps are estimating parameters on the interval-censored binomial distribution using the Maximum Likelihood Estimation method. The second step shows the properties of the estimator on the interval-censored binomial distribution. The last is to determine the parameter estimation of interval-censored data from the binomial distribution in survival analysis and provide an example of research containing interval-censored observations which will then be used as a case study. The results showed that the estimator is a sufficient statistic, meaning that it is unbiased. The case study was conducted using interval-censored data regarding the study of ninety-four breast cancer patients to see which group survived longer (survival value) of the two treatments, namely patients who underwent radiotherapy alone and patients who underwent radiotherapy followed by adjuvant chemotherapy.
Improved characters distance sampling for online and offline text searching

Sampled string matching is a very effective technique to reduce the search time for a pattern within a text at the cost of a small amount of additional memory, used for storing a partial index of the text. This approach has recently received some interest and has been applied to improve both online and offline string matching solutions, improving standard solutions by more than 50%. However, this improvement is currently only achievable in the case of texts on large-sized alphabets, and remains small (or absent) in the case of small-sized alphabets. In this article we propose an extension of the approach to text-sampling, known as Character Distance Sampling, to the case of small alphabets, obtaining an improvement of up to 98% compared to standard solutions in the case of online string matching. We also extend this approach to the case of offline string matching, introducing a sampled version of the suffix array, obtaining performances up to 5 times higher than the search obtained on the standard suffix array. Differently from what has been done by previous solutions, our idea is not based on the reduction of the number of indexed suffixes, but on the construction of the index directly on the sampled text.
A reference material for evaluating full-field out-of-plane deformation measurement system

Optical techniques such as Digital Speckle Pattern Interferometry(DSPI) and Digital Image Correlation (DIC) have been widely used in deformation measurement for their advantages of full-field, non-contact and high accuracy. To exploit the advantages of those optical techniques and enhance the confidence in the data from the optical system, a physical reference material (PRM) that can generate a deformation field with submicron uncertainty is proposed. The PRM is a circular plate of composite structure including a central rod and the peripheral annular plate clamped at the edge, , the central rod of which has merely displacement and no deformation under displacement loading. The displacement of the central area of the PRM is used for calibration, which provides connection to the national standard for length. Through traceability comparison, the expanded uncertainty of full field deformation of the PRM is 0.03μm, with a coverage factor of K=2. The PRM is used to calibrate a digital speckle pattern interferometry system. The combined standard uncertainty (CSU) of the full-field out-of-plane deformation measurement is 0.090μm. Both theoretical analysis and experiments results show that the proposed device can be applied as a PRM to calibrate and evaluate optical full-field out-of-plane deformation measurement system to a submicron level of uncertainty.
Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Credit cards are increasingly being used in real life for a wide variety of purposes.Because of the growing number of users, the number of scammers is also growing at an accelerating rate.E-commerce fraud detection methods are critical for reducing losses.Models developed in the past using unbalanced datasets show a high degree of accuracy.The precision, recall, and weighted average precision and recall are all quite low for the models.As a result of this research, techniques such as logistic regression (LR) and random forest (RF), along with SMOTE, were developed to increase the model's performance with imbalanced datasets.SMOTE techniques are used to balance the datasets because they are so unbalanced.SMOTE analysis has revealed that the RF with SMOTE is the best model for detecting credit card fraud, with accuracy, precision, and recall scores of 99.95%, 85.40%, 86.02%, and 85.71%, respectively.
Basic Experiments Toward Mixed Reality Dynamic Navigation for Laparoscopic Surgery

Laparoscopic surgery is a minimally invasive procedure that is performed by viewing endoscopic camera images. However, the limited field of view of endoscopic cameras makes laparoscopic surgery difficult. To provide more visual information during laparoscopic surgeries, augmented reality (AR) surgical navigation systems have been developed to visualize the positional relationship between the surgical field and organs based on preoperative medical images of a patient. However, since earlier studies used preoperative medical images, the navigation became inaccurate as the surgery progressed because the organs were displaced and deformed during surgery. To solve this problem, we propose a mixed reality (MR) surgery navigation system in which surgical instruments are tracked by a motion capture (Mocap) system; we also evaluated the contact between the instruments and organs and simulated and visualized the deformation of the organ caused by the contact. This paper describes a method for the numerical calculation of the deformation of a soft body. Then, the basic technology of MR and projection mapping is presented for MR surgical navigation. The accuracy of the simulated and visualized deformations is evaluated through basic experiments using a soft rectangular cuboid object.
Sentiment Analysis of Stocks Based on News Headlines Using NLP

In todays' world everyone starting from a child to an adult is studying stocks and is finding ways to earn more by studying the patterns of the market.Stock market is a compound interrelated system of various investors.It fluctuates frequently and hence is hard to predict what is yet to come.All the companies worldwide rely on these forecasts and speculations so that they can increase their profits.Everyday news on the economic front plays a vital role in defining the jump or drop in the prices of stocks.The market news helps the investor excessively in determining his bidding as it is immensely rich in information.In this study, we extract useful information from news headlines of a particular company to investigate the immediate impact of it on the company's stock growth.Having the text based dataset we use NLP and compare two approaches using two different algorithms which both collectively determine the sentiment of the news headline (whether it is positive/negative/neutral) in lieu with the company stocks.
The Analysis of the Influencing Factors of Virtual Currency Price Based on Multiple Regression Method

In view of the influencing factors of bitcoin price, this paper firstly summarizes the relevant data of bitcoin price and annual cumulative supply in the past ten years from the Block Chain website, and selects several explanatory variables including actual bitcoin supply, market macroeconomic level and the number of users. Then, Eviews9 software is used to establish a multiple linear regression model. The econometric test methods such as multicollinearity, heteroscedasticity and autocorrelation were carried out to test and modify the model. The results show that the supply and demand factors of bitcoin itself, internal factors and macroeconomic factors all have a certain impact on the price of bitcoin. The results show that the price of bitcoin is mainly affected by three factors: the supply quantity, the global average annual gross product and the cumulative number of users, among which the global average annual gross product is the largest.
Virtual Reality Technology and Its Implications for the Future of Education

This chapter looks into the future of education to see if there are chances of virtual reality (VR) technologies becoming a driving force in future teaching and learning. The chapter provides an explanation of education with focus on the purposes and ends they are designed to achieve. The chapter examines how some institutions are currently using VR technologies and the impact they are having on institutional learning, efficiency, and cost. The authors use these discussions to project that education could have a very productive future with the use of VR technologies. They make this argument while staying conscious of cost as a factor that could impede the speedy and effective adoption of this technology unless ways are found to navigate these factors.
Riding the Digital Wave of Change in Academia

Will digital instruction be the wave of teaching and learning in institutions of higher education? Will this new way of delivery continue to evolve into more collaborative and interactive assessments vs. traditional testing? Are we willing to be disrupted and challenged in academia? We (educators) know that education, especially institutions of higher learning, has been the great equalizer for many decades and continues to be an evolving landscape of creativity and visionary concepts, through virtual and augmented realities; thus, the metaverse emerges. Soon it will be as omnipresent as TikTok, Instagram, and Facebook (now Meta). As technology advances to bring us new immersive and imaginary worlds, how we educate children and prepare teachers must also advance to meet these new opportunities. To leverage the potential of the metaverse as a 3D, global, interconnected, immersive, and real-time online space, we need new ways to connect the physical world with augmented and virtual reality (VR) experiences.
Millimeter-Wave Display-Mounted Antenna for Hand Gesture Sensing on Mobile devices

In this paper, we propose a millimeter-wave (mmWave) printed-type antenna that can be mounted on display panel of mobile devices for hand gesture sensing. To be mounted on a narrow dead-zone in the display, the antenna has a thin strip. The antenna resonates between the adjacent strip and the ground slot, and it has a fractional impedance bandwidth of 12.3%. The simulated and measured results show that the proposed antenna can be utilized for hand gesture sensing by interfacing the antenna with a mmWave commercial radar module.
Keyword analysis as a means of identifying dominant topics in news discourse

This article is devoted to the identification of dominant topics in English online news discourse in 2022 based on the analysis of keywords and their collocations. A corpus-driven methodology is employed to extract keywords in the virtual corpus of English news articles of 2022 created within the NOW corpus. #LancsBox tools are used to build collocation (association) networks of the keywords.
StarGAN Improvements: Automatically Generated Labels

Recent studies have shown that image-to-image translation methods have achieved remarkable success in two domains. In the context of the increasingly hot face generation technology, the StarGAN is one of the most effect generation methods. The existing StarGAN can achieve multi-domain learning through the mapping relationship between image generators, it solves the limited scalability and robustness of existing methods when dealing with more than two domains. However, because StarGAN uses one-hot encoding and latent code to input to the model, that is, the label is a fixed tensor, which greatly increases the burden of manpower and time. In this paper, to get around this limitation of traditional StarGAN, a ResNet based multi-label method is designed to automatically generate labels for StarGAN. Our method can effectively augment the training set without adding labor.
Simulation Design of Intelligent Logistics Control System Based on Programmablelogic Controller

This paper focuses on the simulation design of intelligent logistics control system of PLC. The application of small Mitsubishi PLC, and the research and design of the requirements and characteristics of the intelligent logistics control system, can complete the entire intelligent logistics control system of the specified location pick-up, item weighing and delivery to the designated location. This project uses the Mitsubishi FX3U-48MR PLC as the main controller for control, and uses the FX3U-4AD analog module to make the classification of items more accurate. The operation is more convenient and efficient, reducing the labor intensity of labor, saving transportation time and cost.
Hierarchical IP Core Generator for Quantum Fourier Transform Implementation in FPGA

Quantum computers consist of two parts - a classical computer and its quantum coprocessor or accelerator. The functioning of the main elements of a quantum coprocessor is described by a wave function. You can create digital elements in FPGAs, the operation of which will be described by the wave function, so called digital qubits. Due to the high computational complexity of quantum computing such as Shor’s algorithm of factorization VHDL-descriptions of digital quantum units must be created for their implementation in FPGA. The article estimates the computational complexity of operations that must be performed according to the Shor’s algorithm. Also the article describes a multilevel generator of VHDL-descriptions for quantum Fourier transform implementation. The generator creates descriptions of both homogeneous and heterogeneous coprocessors. In addition, the generator creates test circuits to check the generated coprocessors. This makes it possible to study the created coprocessors, compare results of their functioning with ones of real quantum coprocessors and analyze them.
Synthetic data generation for medical by generative adversarial network

As the world develops, medical data privacy is becoming an important issue. Differential privacy can better ensure accuracy and reduce the reflection of personal privacy, while generative adversarial networks can generate data sets. Obviously, the combination of differential privacy and generative adversarial network (GAN) can synthesize sensitive medical data to form new data sets, which not only protects personal privacy but also does not affect medical research. We summarize the current difficulties in medical data, compare the common medical data types with typical DP methods of different GAN, analyze the advantages and disadvantages of each method, and discuss future development. At the same time, we also introduce GAN applied in the image data field. Because this approach is very mature, we only introduce some commonly used GANs, compare their improvements, and introduce their applications in data generation. This may help in the future development of medical data privacy algorithms.
Weighted Apriori Algorithm Based on Spark

The traditional Apriori algorithm has the problem of low efficiency. It repeatedly scans the transaction database, generates candidate item sets, and mines massive redundant and worthless rules. This paper first aims at the problem that the Apriori algorithm ignores the importance of different items. Introducing weighting rules reflects the important difference between different items in terms of frequency and weight, secondly, when scanning the input dataset, Generating candidate sets and counting support values at the same time, and it does not use the raw input in every iteration data set, but to calculate the updated input data set with the removal of unnecessary items and transactions, combining the optimized Apriori with the parallel computing processing framework Apache Spark, a Spark-based weighted Apriori algorithm WABS(weighted Apriori algorithm based on Spark) is proposed. Compared with the latest similar algorithms, the experimental results show that the algorithm can effectively shorten the mining time, which is beneficial for discovering more valuable information.
Web-Based Service Development for Intellectual Maintenance of the Personalized Educational and Professional Program : Adaptive provision of educational services in academic mobility context through automated improvement of quality and relevance of tutorial content

The results of empirical analysis of conceptual sources in the direction of educational methods comparison for optimizing the teaching loads for engineering education applicants are presented. Disadvantages and potential conflicts of trainee when mastering of educational and professional program content are formalized, in particular within academic mobility frameworks, as well as in view of the rapid increase in the number of academic refugees. Among the recommended ways of solving problems with academic arrears accumulation, is the information and communication technology of matching the optimally combined educational content to soft compensate for the knowledge and skills that student’s lacks. The relevance of automated quality improvement and propriety of tutorial content is substantiated, and sets of software functionalities of web-based service for teaching loads personalization are determined, which were localized by departments of academic information space for purpose of integration into a united cross-academic communication system. Proposals regarding the reorganization of the structure of digitized methodical and information supports of the educational process according to the mosaic principle have been formulated. On basis of the developed analytical apparatus, a communication model of the network infrastructure of teaching loads optimization service was built, which provides a targeted selection of tutorial media content from problematic domain of subject in file storages of university library collections and confidential faculty funds and distributes according to the forms of educational process on the end terminal of the authorized user.
A Novel Approach to Secure Smartwatch Authentication: Structure-Borne Sound Identification and Gesture Recognition

With the need to conveniently secure devices, manufacturers have pushed to explore new methods of authentication. We propose a smartwatch authentication system based on structure-borne sound emitted from the contact between a user’s wrist and smartwatch. Audio recordings from users in loud and quiet settings, with and without hand movements (‘gestures’) were collected. After extracting relevant features from the data, numerous machine learning models, including Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and linear discriminants, were tested for authentication accuracy. Among these models, the linear discriminant model had the highest identification accuracy for recordings without gestures, and the K-Nearest Neighbors model performed the best for gesture-based authentication. Unlike more complex architectures, the relative simplicity and accuracy of linear discriminant models demonstrated the computational efficiency of structure-borne sound authentication.
A Secure and Reliable Mobile Authentication Alternative Utilizing Hand Structure

As technology advances, methods to protect information on personal devices are shifting towards forms of biometric user authentication. When assessing these new modes of authentication, it is pertinent to consider their implementation of security, reliability, privacy, and convenience. Current facial and fingerprint identification technologies compromise privacy since they require the collection of personal data. On the other hand, non-biometric mobile authentication lacks security due to a preference for convenient and reliable techniques such as short passwords. Hand structure-based authentication is a solution that incorporates all of these criteria. With privacy and convenience inherent to this mode of authentication, the proposed experimental procedure leverages hand biometrics and the K-Nearest Neighbors, Support Vector Machines, and Bagged Decision Trees machine learning algorithms to examine the security-reliability trade-off. The results show that there are statistically significant properties of structure-borne signals that enable differentiation between individuals and hand positions. These properties, along with manipulation of algorithmic learning parameters, are used to enable the model to specialize either in security or reliability. While all of the developed models excelled in security, the Bagged Decision Trees model outperformed the others by rejecting false users at a peak accuracy of 99.9% and rejecting the wrong hand positions with a peak accuracy of 99.4%.