Reviewerly/exHarmony huggingface dataset:
Clustering Approach for Detecting Multiple Types of Adversarial Examples

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture. Especially, defense methods using adversarial example detection architecture have been actively studied. This is because defense methods using adversarial example detection architecture do not make wrong decisions for the legitimate input data while others do. In this paper, we note that current defense methods using adversarial example detection architecture can classify the input data into only either a legitimate one or an adversarial one. That is, the current defense methods using adversarial example detection architecture can only detect the adversarial examples and cannot classify the input data into multiple classes of data, i.e., legitimate input data and various types of adversarial examples. To classify the input data into multiple classes of data while increasing the accuracy of the clustering model, we propose an advanced defense method using adversarial example detection architecture, which extracts the key features from the input data and feeds the extracted features into a clustering model. From the experimental results under various application datasets, we show that the proposed method can detect the adversarial examples while classifying the types of adversarial examples. We also show that the accuracy of the proposed method outperforms the accuracy of recent defense methods using adversarial example detection architecture.
Construction and application of financial fraud early warning model based on data mining

Financial fraud seriously destroys the principle of truthfulness and accuracy of information disclosure in the securities market. False information misleads stakeholders, brings them huge losses, and has a highly negative impact on the capital market. Therefore, exposing and curbing corporate financial fraud has always been the research focus of academic and practical circles. The application of data mining technology to the financial fraud early warning system of listed companies can enable enterprises to find, avoid and effectively prevent financial risks in time, to identify and curb the phenomenon of financial fraud effectively. Through the research, the overall recognition rates of the three fraud identification models for fraud examples and non-fraud examples are more than 80%. Through the comparison and improvement of three financial fraud recognition models, a comprehensive recognition model with high recognition rate and reliability is constructed.
IRePf: An Instruction Reorganization Virtual Platform for Kernel Stack Overflow Detection

Stack overflow vulnerabilities are among the most common security issues. However, the existing stack overflow detection solutions only protect the return address and ignore the imbalance between function calls and returns in the system, which will lead to a higher false-positive rate. In this paper, we propose an instruction reorganization virtual platform technique for kernel stack overflow detection, named IRePf. It can dynamically monitor the kernel stack when the system is running through dynamic reorganization instructions, thread creation and termination, call instructions, and RET instructions. IRePf uses backup stack creation and destruction technology to back up the return address and the address of the return address at the same time. IRePf determines whether the stack overflow occurs in the system when the function call and return are unbalanced to realize the kernel stack overflow detection. The experimental results show that IRePf can effectively detect stack overflow attacks, has low system resource occupancy and high real-time performance, and effectively improves the ability and security of defense stack attacks.
Combining error oversampling and multi-task learning for Chinese meteorological alert text correction

Chinese meteorological alert text issued by meteorological authorities needs to be free of spelling errors. Automatic spelling error correction can find text errors and give correction suggestion. Most existing studies focus on open domain text correction such as news etc., however, the methods for vertical domain text correction such as meteorological alert text has not been well studied. In this work, we utilize the template feature of meteorological alert text and propose error oversampling strategy to enhance the correction model training. As for the correction model, we use multi-task learning to train the correction model by accounting error detection and error correction simultaneously. Experimental results on real-world alert texts show that our proposed method is significantly better than the baseline, exceeding the baseline by 3% on F1 measure.
BGAT: Aspect-based sentiment analysis based on bidirectional GRU and graph attention network

In today’s social media and various frequently used lifestyle applications, the phenomenon that people express their sentiment via comments or instant barrage is common. People not only show their joys and sorrows in the process of expression but also present their opinions to one thing in many aspects which include. Nowadays, aspect-based sentiment analysis has become a mature and wildly-used technology. There are many public datasets considered as a benchmark to test model performance, such as Laptop2014, Restaurant2014, Twitter, etc. In our work, we also use these public datasets as the test criteria. Current mainstream models generally use the methods of stacking multi-RNNs layers or combining neural networks and BERT or other pre-trained models. On account of the importance displayed by the dependence between aspect words and sentiment words, we investigate a novel model (BGAT) blending bidirectional gated recurrent unit (BiGRU) and relational graph attention network (RGAT) to learn dependencies information. Extensive experiments have been conducted on five datasets, the results demonstrate the great capability of our model.
Review of gesture recognition gloves and its applications in thermal insulation and heat resistant

Communication is derived from a Latin word meaning to share information. It plays a crucial role in each and every individual life. This tool is common for everyone in the world even for hard-hearing and speech impaired people. So the same tool is intimated by them through their sign language. Sign language is defined as a language to convey messages with their hand movements and facial narration. This movement needs a recognition system to connect with the society. Gestures have been conquered using various types of gloves. An electronic device which transforms motion of sign language symbols into text or voice outputs is a data glove. In reality there exists a multitude variety of gloves like LED, Digital, String, coloured, sensor fixed gloves etc. The cyber glove acts as an input device between human to processing device. Gloves possess huge areas of usage except gesture posing are namely robotics, surgery, gaming, imaging, motion. In this paper, a review of multiple gloves employed in the gesture recognition for different applications have been layered out.
A method of real-temporal object tracking combined the temporal information and spatial information

The purpose of single target tracking is to accurately and continuously locate a specific object when it is moving. However, when the objects encounter with fast movement, severe occlusion, too small size, and the same local features, the tracking algorithm which based on correlation filter or convolutional neural network will appear the positioning error phenomenon. Aiming at the above problems, this paper designs a single target tracking algorithm: relative temporal spatial network (RTSnet). RTSnet is a multi-thread network that composed of Relative temporal Information Network (RTInet) and Relative Spatial Information Network (RSInet). RTInet is designed on the basis of LSTM, and it has the predictable characteristics of temporal. It mainly obtains the relative temporal information between the frames before and after the target. RSInet, an improved twin network based on the Triplet Network, has the effect of similarity determination which can to obtain the spatial information between the frames before and after the target. In the experiments, the RTSnet is trained by using LASOT data set and verified by using the LASOT test set and the OTB100 data set. In the test set of LASOT, the accuracy of RTSnet reaches 85.5%, Trans-T reaches 62.3% and STMTrack reaches 57.4%. Meanwhile, its tracking speed reaches 117.3fps due to the RTSnet adopts dual-thread operation. On the OTB100 data-set, the accuracy of RTSnet is 81.1%.
Towards designing a generic and comprehensive deep reinforcement learning framework

Abstract Reinforcement learning (RL) has emerged as an effective approach for building an intelligent system, which involves multiple self-operated agents to collectively accomplish a designated task. More importantly, there has been a renewed focus on RL since the introduction of deep learning that essentially makes RL feasible to operate in high-dimensional environments. However, there are many diversified research directions in the current literature, such as multi-agent and multi-objective learning, and human-machine interactions. Therefore, in this paper, we propose a comprehensive software architecture that not only plays a vital role in designing a connect-the-dots deep RL architecture but also provides a guideline to develop a realistic RL application in a short time span. By inheriting the proposed architecture, software managers can foresee any challenges when designing a deep RL-based system. As a result, they can expedite the design process and actively control every stage of software development, which is especially critical in agile development environments. For this reason, we design a deep RL-based framework that strictly ensures flexibility, robustness, and scalability. To enforce generalization, the proposed architecture also does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents.
A Study of University Students’ Perspective on Intercollegiate Experiences －A Case of Intercollegiate Extracurricular Program

The purpose of this study was to explore the university students’ perspectives regarding intercollegiate experiences. In order to achieve the research purpose, the intercollegiate program(University A in Chungbuk, University B and C in Seoul) was set as the research case. A qualitative case study method using an in-depth interview method was applied to 13 students from 5 teams engaging in active teamwork. The research results were largely presented in three areas: motive, experience of participation, and support needs. Students had a demand for interactions with other students, and the intercollegiate program was recognized as an environmental factor for such interactions. The intercollegiate experience became an opportunity for students to break stereotypes about the pecking order, reflect on oneself, and to discover new aspects of themselves. Lastly, in order for the intercollegiate experience to be a significant learning experience, an opportunity to form intimacy among participating students should be provided, and it should be operated in the form of a convergent exchange that forms a team by mixing schools. Based on these results, policy and institutional implications for maximizing the strengths of intercollegiate experiences are presented.
Social media monitoring using ELK Stack

Analyzing data in real-time and processing them online has been an important aspect when coming into social media monitoring. This helps to understand the current trends, political scenarios all over the world and most importantly for the purpose of data collection. When viewing from an organizational perspective, offline data analysis can seem to be more productive when data collection happens to be a onetime process. But the current researches require real-time data analysis but still faces various challenges when continuous data fetching disrupts and affects the overall efficiency of the process. This paper demonstrates a solution to effectively address the challenges of real-time analysis using an ELK (Elastic search, Log stash and Kibana) stack which can be used as a SIEM for small scale organization (with various limitations) where logs are taken from Social media platforms (Twitter), analyzed, processed and conclusions are derived from them.
Artificial Intelligence for Unidentified Mode S Registers Decoding

Aeronautical surveillance is primarily based on cooperative sensors that perform an exchange of messages between aircraft and surveillance ground infrastructure. The aircraft electronic equipment in charge of the messages exchange is called transponder. In addition to basic surveillance information, the transponder also holds registers with avionic related data such as the selected altitude, the magnetic heading, the ground speed, etc. However, messages containing data from transponder registers do not always include an identification number, making the decoding of such messages impractical for an external observer.In this paper, a new method to identify a transponder register without prior knowledge of its identification number is presented, allowing an external observer to decode correctly registers passively received.The proposed method is based on an innovative approach using Artificial Intelligence and more specifically Natural Language Processing (NLP) techniques applied to surveillance messages. Each message is considered as a sentence and is evenly split into words. First, a tokenization layer identifies each word, then the embedding layer attaches to them a fixed number of properties, and finally a recurrent neural network recomposes the sentence and ensures the message identification.The implemented method achieves a very good performance, showing an accuracy in the registers identification of 0.999987, which is significantly higher than any other existing solutions.
Sparse Gaussian Processes for Solving Nonlinear Pdes

In this article, we propose a numerical method based on sparse Gaussian processes (SGPs) to solve nonlinear partial differential equations (PDEs). The SGP algorithm is based on a Gaussian process (GP) method, which approximates the solution of a PDE with the maximum a posteriori probability estimator of a GP conditioned on the PDE evaluated at a finite number of sample points. The main bottleneck of the GP method lies in the inversion of a covariance matrix, whose cost grows cubically with respect to the size of samples. To improve the scalability of the GP method while retaining desirable accuracy, we draw inspiration from SGP approximations, where inducing points are introduced to summarize the information of samples. More precisely, our SGP method uses a Gaussian prior associated with a low-rank kernel generated by inducing points randomly selected from samples. In the SGP method, the size of the matrix to be inverted is proportional to the number of inducing points, which is much less than the size of the samples. The numerical experiments show that the SGP method using less than half of the uniform samples as inducing points achieves comparable accuracy to the GP method using the same number of uniform samples, which significantly reduces the computational cost. We give the existence proof for the approximation to the solution of a PDE and provide rigorous error analysis.
Automated Dubbing and Facial Synchronization using Deep Learning

With the recent global boom in video content creation and consumption during the pandemic, linguistics remains the only barrier in producing im-mersive content for global communities. To solve this, content creators use a manual dubbing process, where voice actors are hired to produce a “voiceover” over the video. We aim to break down the language barrier and thus make “videos for everyone”. We propose an end-to-end architecture that automatically translates videos and produces synchronized dubbed voices using deep learning models, in a specified target language. Our architecture takes a modular approach, allowing the user to tweak each component or replace it with a better one. We present our results from said architecture, and describe possible future motivations to scale this to accommodate multiple languages and multiple use cases. A sample of our results can be found here: https://youtu.be/eGB-gL6bDr4
Analysis of University Student Cadres' Retreat Actions in China Based on K-Core Algorithm and FCM Model

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students’ autonomy and using the “K-core” algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance.
Research on Robot-Based Gesture Interactive Decoration Design

Recently, automata have become a research hott spot in various countries for a wide range of applications in areas such as security, entertainment, and industry. We designed a mobile robot which is based on gesture control with the focus on gesture interaction control which includes: gesture definition, hand gesture acquisition, hand gesture decoration, hand gesture pre-processing and hand pointer recognition in this paper.This paper uses a combination of qualitative analysis and quantitative research to study the mapping characteristics of gesture interactive decoration and its internal connection with user interaction behaviors by using a combination of theoretical analysis and situational experiments, which not only helps to understand the human-computer interaction decoration Complexity, it has important positive significance in theoretical discussion, research methods and experimental methods. The experiments show that the average recognition rate of the five gestures obtained in this paper reaches 92.5%. The robot can accurately recognize gestures and make corresponding actions
Research on cooperative collimating and locating model of air defense based on extended Kalman filter

The existing antiaircraft fire system is a multi-level system, including high-precision long-range missiles and low-precision short-range antiaircraft artilleries. When firepower nodes are destroyed by the enemy or locked by missiles, it is necessary to position the target with other available collimating equipment. Therefore, it is of great significance to study how to achieve high-precision cooperative collimating and locating effect with direction finding and ranging data of lower precision. The problem can be solved by the principle of extended Kalman filter, and improved by an introduced feedback link in the simulation process. It has proved that this method is conducive to gradually narrowing the data error to stability in different time spans, which is applicable to this coordinative problem and promotes the effectiveness of air defense combat.
tweedledum: A Compiler Companion for Quantum Computing

This work presents tweedledum-an extensible open-source library aiming at narrowing the gap between high-level algorithms and physical devices by enhancing the expressive power of existing frameworks. For example, it allows designers to insert classical logic (defined at a high abstraction level, e.g., a Python function) directly into quantum circuits. We describe its design principles, concrete implementation, and, in particular, the library's core: An intuitive and flexible intermediate representation (IR) that supports different abstraction levels across the same circuit structure.
The scheme of quantum dense coding in three-dimensional spaces

Abstract Quantum communication is a new emerging inter-discipline in which classical communication theory and quantum mechanics are intertwined. Among them, the utilization of quantum entangled state properties is fundamental to the realization of quantum communication. In this way, we can ensure the security and confidentiality of information during transmission based on a new communication scheme, i.e., a quantum dense coding scheme. It enables the transmission of two bits of classical information by transmitting only one qubit. In the past decades, the scheme has attracted the attention of many researchers because of its promising development. In this paper, on top of quantum mechanics, we first review the two-dimensional dense coding scheme and analyze the process implemented by the above scheme. Then it is successfully extended to higher dimensions (three dimensions), predicting the possibility of implementation in more than two dimensions. Finally, the importance of the maximally entangled state for the transmission of this scheme is briefly explained.
Mobile Assessment Applications and Training for Professional Examinations

Mobile-assessment applications provide various conveniences to working professionals who prepare for a certification exam. The purposes of this chapter are highlighting the usefulness of m-assessment apps to learners and informing the field about the needs in this area. The chapter focuses on the m-assessment apps in terms of preparing for project management certification examinations. The authors developed nine criteria based on learning theories and literature on m-assessment. These criteria organized important features of m-apps under three categories: 1) general user experience, 2) learning experience, and 3) practice test-experience. The authors also evaluate the features of m-assessment apps found in the app spaces using the criteria. Results communicate how the examined apps perform against the criteria and emphasize different ways that these features can help learners. The chapter concludes with future directions based on the updated requirements of the project management certification exam and the need for more apps that implement theoretically supported features.
Learnability evaluation of the markup language for designing applications controlled by gaze

We present the Gaze Interaction Markup Language (GIML) and its interpreter, the Gaze-Controlled Application Framework, making the development of personalized gaze-based software available to people with no programming background. This software can be used by caregivers to find new methods of communicating with people who suffer from various physical disabilities, such as designing personalized alternative communication boards. It also allows psychologists and cognitive scientists to easily develop experiments using eye trackers. In this paper, we present a study assessing the learnability of GIML. The aim is to check whether it is possible to learn the basics of the language during a one-hour hands-on-keyboard session. To confirm this, two groups of novices with no prior experience of GIML were asked to learn about GIML and complete a tutorial. Then we asked them to perform a set of tasks in order to test that they had mastered the basics of GIML. The results show that even people who have no programming experience can learn the basics of the proposed language.