Reviewerly/exHarmony huggingface dataset:
Dynamic sub-route-based self-adaptive beam search Q-learning algorithm for traveling salesman problem

In this paper, a dynamic sub-route-based self-adaptive beam search Q-learning (DSRABSQL) algorithm is proposed that provides a reinforcement learning (RL) framework combined with local search to solve the traveling salesman problem (TSP). DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive ε-beam search strategy, and a new Q-value update formula. Then, a self-adaptive beam search Q-learning (ABSQL) algorithm is designed. To solve the problem that the sub-route is not fully optimized in the ABSQL algorithm, a dynamic sub-route optimization strategy is introduced outside the QL framework, and then the DSRABSQL algorithm is designed. Experiments are conducted to compare QL, ABSQL, DSRABSQL, our previously proposed variable neighborhood discrete whale optimization algorithm, and two advanced reinforcement learning algorithms. The experimental results show that DSRABSQL significantly outperforms the other algorithms. In addition, two groups of algorithms are designed based on the QL and DSRABSQL algorithms to test the effectiveness of the five strategies. From the experimental results, it can be found that the dynamic sub-route optimization strategy and self-adaptive ε-beam search strategy contribute the most for small-, medium-, and large-scale instances. At the same time, collaboration exists between the four strategies within the QL framework, which increases with the expansion of the instance scale.
Neutron spectrum unfolding using two architectures of convolutional neural networks

We deploy artificial neural networks to unfold neutron spectra from measured energy-integrated quantities. These neutron spectra represent an important parameter allowing to compute the absorbed dose and the kerma to serve radiation protection in addition to nuclear safety. The built architectures are inspired from convolutional neural networks. The first architecture is made up of residual transposed convolution's blocks while the second is a modified version of the U-net architecture. A large and balanced dataset is simulated following “realistic” physical constraints to train the architectures in an efficient way. Results show a high accuracy prediction of neutron spectra ranging from thermal up to fast spectrum. The dataset processing, the attention paid to performances' metrics and the hyper-optimization are behind the architectures' robustness.
A plan to develop Korean language textbooks for university liberal arts to attract international students

Recently, as the number of international students in Korea continued to surge, liberal arts Korean subjects were opened for foreign undergraduates in universities and the importance of Korean language education was further emphasized. Accordingly, discussions on the development of a curriculum that can improve international students' Korean language skills are also being actively conducted. However, the development of liberal arts Korean textbooks, especially at local university institutions, which can be used in liberal arts Korean subjects, has not yet begun in earnest. Although foreign students have completed a predetermined Korean language curriculum at a university-affiliated language education institution, they are complaining of difficulties in taking major classes due to the lack of Korean language skills of international students who have entered the undergraduate department. This can also be a reason for giving up studying because they cannot adapt to college life. Therefore, the need for Korean liberal arts courses to improve their Korean language skills and improve their studies is emerging. To this end, this study examines the current status of liberal arts Korean curriculum at local university A and the requirements for the development of liberal arts Korean textbooks. Based on the topic, it presents a textbook development plan that can be used in actual education.
Research on path planning of automated guided vehicle based on improved RRT* algorithm

An improved RRT* algorithm is proposed to solve the problems of poor goal orientation, low space utilization and many inflection points in the path planning of Automated Guided Vehicle (AGV) transportation. The algorithm adopts the approach sampling and restricted sampling strategies. First, the approach sampling method improves the sampling efficiency and reduces the path search time; then, the sampling area is reduced by limiting sampling, so as to improve the probability of sampling points falling in the path neighborhood and shorten the path length. On this basis, the redundant points of the path are trimmed according to the greedy algorithm, and the curvature of the path is optimized using the cubic B-spline smoothing method to obtain the driving track that meets the requirements of AGV running stability. Finally, through MATLAB simulation and comparative analysis, in the same environment, compared with the RRT* algorithm, the improved RRT* algorithm reduces the time of path search by 61.59% and the path length by 8.6%, which verifies the effectiveness of the improved RRT* algorithm.
The Parallel Reality Technology

Parallel Reality (PR) is a technology introduced by Misapplied Science at the CES consumer technology fair held in the United States in 2020. This technology makes it possible to simultaneously show different contents for each of the dozens of people in the same place on a single screen. Unlike augmented or virtual reality, this reality does not require people to have extra equipment such as cell phones or special glasses. The pixels can transmit contents according to the viewing angle of the people in front of the screen. In addition, contents can follow the location of people through a sensor integrated into the system. Thanks to this sensor, the image for each person is displayed in such a way that they can only see it from the angle they are in. Thus, unique and personalized content can simultaneously be displayed for each person in crowded environments. As with augmented, virtual, and mixed reality, the parallel reality is a product of high technology. Therefore, this study compares the concept of parallel reality with other types of realities in various contexts, explains its working principles in detail, and provides examples of its application areas. This technology can be used effectively, especially for educational purposes. In addition, in the last part of the research, the researcher proposed a new value term to be used in parallel reality. It is thought that this article will make valuable contributions to the field, as it will be the first study in the field of education and educational technologies, and this type of reality will affect many areas.
Soft Computing for Music Generation using Genetic Algorithm

Creating good music is truly a laborious task since it requires lots of effort, extended time, and many instruments. When the tune of music does not sound good after being composed with much toil, then composers must discard it, which is a hectic job. Hence, an easy way to compose the music which will require less time and less effort is required. Genetic algorithm is a possible way of searching the solution to the problem in large dimension search space. Genetic algorithm (GA), a part of soft computing in the field of music composition can be used to solve this issue. This paper proposes the use of GA for composing music and the use of fitness function to select more melodious music. In GA, for music creation, two musical segments will act as parent nodes for creating new music, and by applying genetic operators, there is a change in the music such that breaks are modified between the tunes. Music which sounds pleasing is chosen with the user’s help using fitness function, and if the user is satisfied with the generated tune, then the process of generating the music is terminated; otherwise, the selected musical tune by the fitness function will act as the parent node for the next generation of musical tune. Moreover, this work explains which fitness function to be applied on the specific problem.
Valuation and Forecasting of Cryptocurrency: Analysis of Bitcoin, Ethereum and Dogecoin

Currently, cryptocurrency has become a research and investment topic of great concern and attracted considerable attentions in a wide range of fields. Stocks are well known as a form of investment, and there are countless studies on how its price trends. Cryptocurrencies, however, are not easily predicted due to their extremely high volatility. This paper implements the forecasting results by taking three major cryptocurrencies (i.e., Bitcoin, Ethereum, Dogecoin) as examples for the period starting from January 1st, 2020 to May 31st 2022. Four popular prediction models (XGBoost, LightGBM, GARCH, ARIMA) are applied in Python by training models and testing the prediction results. According to the analysis, XGBoost and LightGBM can forecast the future prices for all three cryptocurrencies exactly apart from the turning points when prices rise and drop suddenly. Although the predicting trends of GARCH, ARIMA have differences from the real price, they can forecast well except the unexpected situations such as COVID-19. Overall, relatively reliable and accurate prediction models can be provided for investors to apply and make wise decisions in investments based on the results of this study.
<i>Qi and Southern Appalachian Digital Collections (SADC): Implementing a New CMS for a New Digital Collections Collaboration </i>

"Qi and Southern Appalachian Digital Collections (SADC): Implementing a New CMS for a New Digital Collections Collaboration ." Journal of Electronic Resources Librarianship, 35(1), pp. 74–75
Efficient Search of Active Inference Policy Spaces Using k-Means

We develop an approach to policy selection in active inference that allows us to efficiently search large policy spaces by mapping each policy to its embedding in a vector space. We sample the expected free energy of representative points in the space, then perform a more thorough policy search around the most promising point in this initial sample. We consider various approaches to creating the policy embedding space, and propose using k-means clustering to select representative points. We apply our technique to a goal-oriented graph-traversal problem, for which naive policy selection is intractable for even moderately large graphs.
Naïve Bayes with Negation Handling for Sentiment Analysis of Twitter Data

This paper proposes an enhanced negation handling technique for sentiment analysis of Twitter data using the Naïve Bayes algorithm and Part-of-Speech (POS) tagging. Negation handling detects negated content in text and can thus improve sentiment prediction. The proposed technique focuses on the detection of direct negation words such as “not” and “no”, and implicitly negated content such as “could have been” and “should have been”. The paper compares the proposed negation handling technique with an existing negation handling technique. The Sentiment140 dataset is used in the experiments. Naïve Bayes with the proposed negation handling technique gave an accuracy of 77.57% while the accuracy of the Naïve Bayes with the existing negation handling was 76.93% and the accuracy of the standard Naïve Bayes was 76.12 % for a dataset of 1,000,000 tweets. Of these 1,000,000 tweets 197,381 contained one or more negations. Taking these negated tweets alone, the proposed technique showed an improvement over the existing technique and standard Naïve Bayes with accuracies respectively of 76.51%, 75.98%, and 75.09%. The improvements and shortcomings of the proposed technique are discussed.
Applied Cyber Law in Architecture Model for Medical Gasses Cylinder Management

As digitalization is conveniently managing and controlling our daily activities. The goal of the research is to explore and provide a capability architecture model that may assist a transformation digital platform in the medical sector should have a barrier, such as risk of cybercrime. Method implemented is using TOGAF, and mapping risk analysis potentially related to cybercrimes. Results finding cyberlaws help to control disobedience toward compliance in the digital platform. This research focus on transforming traditional medical gasses for hospital future needs and planning toward digital platform. The impact of the research then identified cybercriminals, like data offenses, fraud, repudiation, false/defamatory statement, and unauthorized access, which are useful for law enforcement facing cybercrimes’ trends.
An RF modulation recognition method using machine learning

In recent few years, deep learning has been successfully applied in many fields to optimize decision making including self-driving cars, health care, machine translation, image recognition, etc. In wireless communication, deep learning has been used in channel estimation, signal classification, massive MIMOs, heterogeneous networks, energy harvesting, device-to-device (D2D) communications, and so on. In this paper, we applied machine learning (ML) and deep learning (DL) neural networks to RF signal recognition. Specifically, we built, trained, and tested two ML models, SVM and XGBoost, and two DL models, ConvNet and ResNet. We utilized the online dataset at radioml.com. Our goals are to learn how to scientifically apply ML/DL in terms of dataset processing, deep neural network constructing, training, testing, fine-tuning, and results analyzing and reporting.
Color performance of 0.8 um CMOS image sensor with CMY color filters

Modern digital cameras capture images with a subsampling method via mosaic color filter array (CFA). Of particular interest is the CFA with Cyan-Magenta-Yellow (CMY) color filters. Despite the improvement of the sensitivity, images reconstructed from CMY CFA usually suffer from lower color fidelity compared to the conventional Red-Green-Blue (RGB) color filters. In this paper, we proposed a CMY CFA with novel spectral sensitivities (CMY2.0), which were carefully designed to overcome the shortcomings of previous CMY CFA (CMY1.0). A CMY CMOS image sensor (CIS) with such optimized spectral sensitivities is then realized in order to evaluate the color performance and signal-to-noise ratio (SNR). As a result, the camera equipped with the CMY CFA with proposed spectral sensitivities (CMY2.0) features both an improved sensitivity and a high color fidelity, which is suitable for a wide range of applications, such as low-light photography, under-screen cameras and automotive cameras.
Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer

Introduction: Breast cancer is the object of thousands of studies worldwide. Nevertheless, few tools are available to corroborate prediction of response to neoadjuvant chemotherapy. Artificial intelligence is being researched for its potential utility in several fields of knowledge, including oncology. The development of a standardized Artificial intelligence-based predictive model for patients with breast cancer may help make clinical management more personalized and effective. We aimed to apply Artificial intelligence models to predict the response to neoadjuvant chemotherapy based solely on clinical and pathological data. Methods: Medical records of 130 patients treated with neoadjuvant chemotherapy were reviewed and divided into two groups: 90 samples to train the network and 40 samples to perform prospective testing and validate the results obtained by the Artificial intelligence method. Results: Using clinicopathologic data alone, the artificial neural network was able to correctly predict pathologic complete response in 83.3% of the cases. It also correctly predicted 95.6% of locoregional recurrence, as well as correctly determined whether patients were alive or dead at a given time point in 90% of the time. To date, no published research has used clinicopathologic data to predict the response to neoadjuvant chemotherapy in patients with breast cancer, thus highlighting the importance of the present study. Conclusions: Artificial neural network may become an interesting tool for predicting response to neoadjuvant chemotherapy, locoregional recurrence, systemic disease progression, and survival in patients with breast cancer.
Hypothesis Testing

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Differentiated Learning and Proposed Strategies for Teaching a Foreign Language

In the classrooms coexist children who have different reflecting intercultural and cultural heterogeneity, with different backgrounds and family expectations, spiritual or educational level, but also changing foci of interest. Heterogeneity affects the degree of readiness of students and the way in which the teacher will differentiate their educational approach. Therefore, the teacher is called upon to adopt differentiated methodological approaches and strategies to ensure a learning environment with equal access of children to foreign language education. This paper suggests differentiated teaching strategies for material enrichment, active and enjoyable participation of all students and immediate feedback.
Patent exhaustion as a canon of expressive freedom

Although they are ostensibly directed only to utilitarian subject matter, patents regulate and impact freedom of expression. They may inhibit uses of technical conduits of communication, of methods of expression, or of expressive computer code. Consequently, patents constitute state-sponsored regulation of privately held communicative property. Moreover, unlike copyright, patent law entails few privileges or exemptions that might mediate the conflict between exclusive intellectual property rights and constitutional guarantees of expressive freedom. However, in the United States, recent Supreme Court cases dealing with exhaustion of patents may provide a vehicle for mediating this conflict. Exhaustion truncates the exclusive rights of a patent holder upon the authorized sale of a patented device. The Supreme Court has extended this doctrine to devices that implement patented processes, and to the exclusive right to use the claimed invention. This expansive view of exhaustion provides a potential buffer between patent rights and the US First Amendment.
An Efficient Code-Embedding-Based Vulnerability Detection Model for Ethereum Smart Contracts

Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.
Byzantine Attack Identification via Group Testing Approach in Coded Distributed Computing for Matrix Multiplication

여러 컴퓨팅 노드를 활용하는 분산 컴퓨팅 환경에서 분산 부호화 컴퓨팅은 낙오 효과 감소, 데이터 보안 보장 등을 위해 활용되어 왔다. 그러나 많은 컴퓨팅 노드를 활용하는 분산 컴퓨팅의 특성상 컴퓨팅 노드 중 잘못된 연산 결과를 보내 전체 연산을 망치고자 하는 악의적 공격을 수행하는 비잔틴 워커가 존재할 수 있다. 본 논문에서는 행렬 곱 연산을 위한 분산 부호화 컴퓨팅에서 악의적으로 잘못된 결과를 반환하는 비잔틴 워커가 존재할 때, 이를 효과적으로 탐지하고 올바른 전체 연산 결과를 복호화하는 기법을 소개한다. 우선 악의적 공격 탐지를 위한 새로운 분산 부호화 컴퓨팅 기법을 소개하고, 이에 적합한 두 가지 집단 검증 기법을 제안한다. 또한, 분산 컴퓨팅 환경을 제공하는 아마존 웹 서비스에서 비잔틴 워커의 존재 하에 행렬 곱 연산을 수행하는 실험을 통해 비잔틴 워커 탐지에 소요되는 시간을 밝힌다. In a distributed computing system utilizing multiple computing nodes, coded distributed computing has been proposed to reduce straggler effects, ensure data privacy, and so on. However, due to the characteristic of distributed computing systems where many computing nodes called workers are utilized, there may exist Byzantine workers that carry out malicious attacks by sending wrong computation results to contaminate the overall computation results. In this paper, for the case where some Byzantine workers that return wrong computation results exist in coded distributed computing for matrix multiplication tasks, we introduce a new coded distributed computing scheme for effective identifying Byzantine workers and decoding the overall computation results. First, we suggest a new coded distributed computing technique for identifying Byzantine workers, and propose two group testing approaches suitable for the proposed coded distributed computing scheme. In addition, we show the Byzantine worker identification time to identify Byzantine workers via experiments that perform matrix multiplication tasks in the presence of Byzantine workers in Amazon Web Service (AWS) distributed computing systems.
The Combination of Flappy Bird and Reinforcement Learning Using Q-learning

The development of artificial intelligence has expanded application fields gradually. In recent years, the combination of artificial intelligence and games attracted much attention. In this case, reinforcement learning is often chosen as an effective method to let the computer play the game by itself. In this study, the Q-learning algorithm from reinforcement learning was applied to Flappy Bird. There are the important factors of Q-learning, including state (S), action (A), reward (R), policy (π), time (t), Epsilon Greedy policy and Q-table. After that, a python class called “bot” was used, and it is used as an intelligent agent in the project. In order to implement the Q-learning algorithm, the state of each element of the game was adjusted continuously through the “mainGame” function of the Flappy Bird game. Finally, the survival reward was set to 1 and the death reward to -1000 to increase the survival rate. In addition, coins with different reward values were added to increase the difficulty of training. After training, the survival rate of the bird is improved, and it is clear that the reward value of gold coins will affect the agent's choice tendency. To combine artificial intelligence and games means that computers can be trained to deal with the complex and changing situations in games, and the progress will affect the application of artificial intelligence in real life more deeply.
A Novel Working Scheme for Robust Optical Coupler Unit for Programable Unitary Matrices

Programmable optical unitary matrices (POUMs) in photonic integrated circuits (PICs) technology play a critical role in modern information technology. With the expansion of their scale and integration density, POUMs have their performance limited by fabrication errors and tuning phase errors of their building blocks, i.e., tunable 2 × 2 optical couplers. Here, we provide an in-depth theoretical analysis of a prior robust equivalent 2 × 2 optical coupler and demonstrate a general requirement of its passive beam splitters' split ratios, under which an arbitrary tunable transmission ratio can always be achieved. More importantly, we analyze the impact of the phase shifters' tuning phase errors which is always caused by the limited voltage/current resolution of the electronic driving interface or thermal fluctuation. By the analyzed result, we propose a novel working scheme for the first time by discovering an unconventional configuration with large beam split ratio errors, under which robust POUMs against fabrication and tuning phase errors can be achieved.