Reviewerly/exHarmony huggingface dataset:
Sentiment analysis of food safety internet public opinion based on XLNet

Internet public opinion sentiment analysis is significant for managing and controlling food safety events. Since emotions can play a decisive role in behavior, netizens’ emotions towards the food safety events will influence their expressions of opinions on the Internet, thereby influencing the development of public opinion on the events. However, few scholars have analyzed the sentiment of Internet public opinion regarding food safety. We employ XLNet, a dynamic text representation method, to build context-dependent word vectors for the distributed representation of Internet public opinion in order to better analyze Internet public opinion on food safety events according to its characteristics. Then, we input the word vectors into Convolutional Neural Networks (CNN) and Bi-directional Long Short-Term Memory (BiLSTM) layers for local semantic features and contextual semantic extraction. Additionally, we introduce an attention mechanism to assign different weights to the features based on their importance before conducting sentiment tendency analysis. The experimental results showed that the average accuracy and Fl values of the sentiment analysis model proposed in this study for Internet public opinion regarding food safety reached 94.12% and 94.61%, respectively, which achieved better results than comparable sentiment analysis models.
Encryption method of electronic conference image based on mixed chaotic sequences

Because the image has the characteristics of large amount of information and high redundancy, the traditional AES, DES and other encryption methods applied to text and numbers can not be effective. Chaotic sequence algorithm has natural similarity with cryptography, such as extreme sensitivity to initial value, ergodicity, non-periodicity, pseudo-randomness and very high iterative rate. as a result, the encryption of chaotic sequence algorithm in the field of electronic conference image will be better than the traditional encryption method, and it has become a new direction to solve the problem of electronic conference image encryption. However, the limited computer accuracy will also reduce the randomness of chaotic sequences. in order to make up for this defect, this paper uses the idea of hybrid encryption to design a more secure and efficient hybrid chaotic sequence encryption algorithm.
Uniform Restricted Chase Termination

The chase procedure is a fundamental algorithmic tool in database theory with a variety of applications. A central problem concerning the chase procedure is uniform (a.k.a. all-instances) chase termination: for a given set of tuple-generating dependencies (TGDs), is it the case that the chase terminates for every input database? In view of the fact that this problem is, in general, undecidable, it is natural to ask whether known well-behaved classes of TGDs ensure decidability. We focus on the main paradigms that led to robust TGD-based formalisms, namely guardedness and stickiness, that have been introduced in the context of knowledge-enriched databases. Although uniform chase termination is well understood for the oblivious version of the chase (2EXPTIME-complete for guarded, and PSPACE-complete for sticky TGDs), the more subtle case of the restricted (a.k.a. the standard) chase is rather unexplored. We show that uniform restricted chase termination under guarded single-head TGDs and sticky single-head TGDs is decidable in elementary time. In the case of guardedness, we provide a reduction to the satisfiability problem of monadic second-order logic over infinite trees of bounded degree, while for stickiness we provide a reduction to the emptiness problem of deterministic Büchi automata. Those reductions build on a series of technical results of independent interest related to the notion of fairness of the restricted chase, and the existence of critical databases that characterize nontermination of the restricted chase via databases of a certain form.
Many topological regions on the Bloch sphere of the spin-1/2 double-kicked top

Floquet topological systems have been shown to exhibit features not commonly found in conventional topological systems such as topological phases characterized by arbitrarily large winding numbers. This is clearly highlighted in the quantum double kicked rotor coupled to spin-1/2 degrees of freedom [Phys. Rev. A 97, 063603 (2018)] where large winding numbers are achieved by tuning the kick strengths. Here, we extend the results to the spin-1/2 quantum double kicked top and find not only does the system exhibit topological regions with large winding numbers, but a large number of them are needed to fully characterize the topology of the Bloch sphere of the top for general kick strengths. Due to the geometry of the Bloch sphere it is partitioned into regions with different topology and the boundaries separating them are home to 0 and $\ensuremath{\pi}$ quasienergy bound states. We characterize the regions by comparing local versions of the mean field, quantum, and mean chiral displacement winding numbers. We also use a probe state to locate the boundaries by observing localization as the state evolves when it has a large initial overlap with bound states. Finally, we briefly discuss the connections between the spin-1/2 quantum double kicked top and multistep quantum walks, putting the system in the context of some current experiments in the exploration of topological phases.
An analysis of student decision making for educational recommender systems

Recommender systems in education aim to help students make good decisions about the direction of their learning.The design of such systems in conventional research has treated the decision making process of students as a black box and assumes the best recommendations to be those that accurately predict student choices.Such an approach overlooks potentially valuable use cases for supporting optimal decision making, especially in self-directed learning contexts which present such challenges as identifying all available options, accurately evaluating the options against selection criteria, and selecting the best choice.This qualitative study aims to understand the areas where students struggle in the context of planning an open-ended project in order to inform the design of educational recommender systems.Data from interviews with 7 students at an international engineering school in Japan are analyzed to examine choice behaviors, influences on choice, and difficulty to choose in a self-directed learning context.The results illustrate considerations for designing educational recommender systems that can support the divergent thinking and convergent thinking demands of decision making.We provide case-based examples where the use of different recommender metrics, such as novelty and diversity, may provide value to users with different approaches to the decisionmaking process.
Tipping Points (Reflection Component)

The Ph.D. project in Artistic Research, Tipping Points, conducted by Tijs Ham ('81), is situated in the field of live electronics and focuses on the exploration of chaotic processes within instrument design, compositional strategies, and performance. The unpredictable nature of chaos impacts many aspects of musicking. Artistic works emerge from the interferences between processes that are set in motion. Instruments are influenced and in turn influence the performer in return. The reflections turn to the notion of wondering as the performer and audiences alike encounter unforeseen sonic behaviors that are strangely musical despite their volatile and fragile chaotic origins.
Improving trapped-ion-qubit memories via code-mediated error-channel balancing

The authors propose a protocol for improving the performance of a noise-biased trapped-ion qubit memory by rebalancing the noise bias in the logical qubit. The authors demonstrate that the protocol can reduce the net logical error rate by up to two orders of magnitude for realistic parameters.
A Visual Question-Answering System for CCTV Surveillance using a Neuro-symbolic Method

모니터링에 사용되는 장비가 저렴해지고 정교해짐에 따라 분석할 수 있는 CCTV 영상 데이터의 양이 급속히 증가하고 있다. 이상 상황이 발생 시 관리자가 시스템과 시각적 질의응답을 수행하기 위해서는 객체 및 관계의 인식 기능과 언어로 표현된 질의를 처리하는 기능이 동시에 필요하다. 본 논문에서는 실제 도로에서 촬영되는 행인, 차량, 객체들의 관계를 인식하는 고성능 신경망과 자연어 기반의 복잡한 질의 응답을 위한 추론 알고리즘을 결합한 뉴로-심볼릭 질의응답 방법을 제안한다. 이상상황의 판단과 객체인식을 위해 컨볼루션 신경망을 사용하였고, 인식된 객체와 질의의 맵핑 및 응답의 추론을 위해 심볼릭 연산을 수행하다. 제안하는 방법은 실제 CCTV 환경에서 다섯가지 종류의 질의에 대해 99.71%의 응답 정확도를 달성하였다. 추가적으로, 학습된 질의응답 모형을 웹 기반 데모시스템으로 구축하여 실용성을 평가하였다. As monitoring equipment becomes cheaper and more sophisticated, the volume of CCTV video data that can be analyzed will rapidly increase. For an administrator to perform visual question-and-answering with the system when an abnormal situation occurs, recognizing relationships between objects and processing queries expressed in language need to occur simultaneously. This paper proposes a neuro-symbolic question and answer by combining a high-performance neural network that recognizes the relationship between observed objects and an inference algorithm for question and answer based on natural language. A convolutional neural network was used to determine anomalies and object recognition, and a sym-bolic operation was performed for mapping objects and queries to infer responses. The proposed method achieved a 99.71% response accuracy for five queries in the real-world CCTV environment. We also demonstrated the practicality of the pro-posed method by developing a web-based real-time monitoring system.
The Relationship Between Achievement in and Attitude toward Mathematics in the Digital Age

The goal of this paper is to investigate the relationship between students' attitude toward the subject of mathematics (ATM) and their achievement in mathematics (AIM) in the context of online schooling.Further, this project seeks to compare the AIM-ATM relationship of students who take a math course online with that of students who take math in a physical classroom setting.While previous studies have explored the AIM-ATM relationship, the results have been inconsistent, and none have been performed in the age of digital learning.In this project, the data were collected from 144 high school respondents through a Google Form that was shared on social media and several forums.Spearman Rank Correlation Coefficient (SRCC) tests were conducted on the AIM and ATM data provided by respondents, and further testing was done on the demographic characteristics of the respondents in order to control for confounding variables.The results suggest that there is a positive, monotonic relationship between AIM and ATM for students taking math online, but there is no AIM-ATM relationship for students taking math in-person.No demographic characteristics appeared to influence AIM except for sex.These findings have the potential to affect future pedagogical methods for mathematics.
Research on atmospheric turbulence-degraded image restoration based on generative adversarial networks

The imaging equipment working in the atmosphere will not only be limited by the performance of the imaging system, but also be affected by turbulence. In the fields of astronomical observation, ground-based remote sensing and remote monitoring, there is an urgent need for corresponding methods and technologies to eliminate the impact of atmospheric turbulence and obtain clear images. With the development of computer technology, atmospheric optics theory and image processing technology, more and more researchers hope to combine deep learning technology with atmospheric turbulence theory to reduce the impact of turbulence on imaging and obtain clear and stable images. In this paper, a turbulence image restoration technique based on Generative Adversarial Networks (GAN) is proposed, which is divided into generator network and discriminator network. The generator network is used to convert blurred images affected by turbulence into clear images. The discriminator network is used to compare the converted image with the real clear image to determine whether the image is real or generated. After the whole GAN is optimized and trained, the image transformed by the generator and the real and clear image cannot be distinguished from each other. Because the training of the GAN requires a large number of corresponding samples, it is difficult to obtain the images affected and unaffected by turbulence at the same time in real life, so this paper uses the statistical characteristics of turbulence to simulate a large number of images affected by turbulence. We used the trained GAN model to simulate turbulence image restoration and got some achievements.
YOGA POSE DETECTION USING MACHINE LEARNING

Yoga has become popular for physical and mental well-being.Machine learning can automate and enhance yoga practice.Our research proposes a novel approach using computer vision and machine learning to detect yoga poses.We estimate poses, extract features, and classify them.Training on a diverse dataset using supervised learning techniques achieves high accuracy.Real-time experiments demonstrate its potential for interactive training, posture correction, and feedback generation.Our research enhances the yoga experience by automating pose identification.
DIALEXA AN ONLINE DIALECT CLASSIFIER USING NAÏVE BAYES ALGORITHM

Dialects are evolving through the years, and there has been a slight variation between dialects of the same language, such as the Visayan language.Because of dialectal variation, this led to dialect misclassification.This paper aimed to classify the dialect of a particular word or sentence using the Naive Bayes algorithm.This algorithm was used to classify the dialect by getting and comparing the probability of every dialect.The choices used for classification were Cebuano, Hiligaynon, and Waray.This paper also provided a repository for each dialect in which to preserve and evade dialect extinction.Consequently, using the Naive Bayes algorithm for classifying gave a high accuracy for dialect classification.
WEB BASED AR LOCATION IDENTIFIER

This study describes a web-based augmented reality (AR) detector that allows users to use their cell phones to identify and find locations of interest.This technology determines the user's location and overlays relevant information on the user's screen using a combination of GPS data and image recognition algorithms.The AR interface offers a straightforward and pleasurable way to interact with the world, making it perfect for use in public spaces such as parks, tourist attractions, and historic sites.The system will be simple to use and can be quickly integrated into existing websites or mobile applications, regardless of technical expertise.According to the poll results, the method is useful in terms of improving the user's ability to navigate and identify local points of interest.
PROXY RE ENCRYPTION SCHEME FOR SECURE DATA STORING IN CLOUD USING BLOCKCHAIN TECHNOLOGY

Data sharing has been identified as one of the Internet of Things' most useful uses in cloud computing.Data security continues to be one of the challenges this technology has, even though it has been visually appealing, as improper usage of data results in a number of negative effects.We suggest a proxy re-encryption strategy to protect data sharing in cloud contexts in this study.Data owners can utilize identity-based encryption to outsource their encrypted data to the cloud, while proxy re-encryption construction will enable legitimate users access to the data.Due to the limited resources of Internet of Things devices, an edge device operates as a proxy server to conduct demanding computations.Furthermore, we use information-centric networking features to effectively deliver cached content in the proxy, thereby improving performance making efficient use of the network capacity and raising the quality of the service.Our system concept is also built on the blockchain, a ground-breaking technology that enables decentralized data sharing.It accomplishes fine-grained data access control while reducing bottlenecks in centralized systems.The results of our plan's security analysis and evaluation demonstrate its potential for maintaining the security, confidentiality, and integrity of data.
Blockchain Technology Evaluation with Linguistic Variables: A Case for Smart Agriculture

Blockchain (BC) technology is developing in every sector, and it guarantees to solve many problems concerning security, privacy, and trust.BC is not a separate technology; it combines technologies in computer science and financial applications.When BC technology is assessed concerning agri-food processes, it is stated that it can be a quality solution to support transparency and help to build trust in the agri-food sector.Concerning all the benefits and paradigm changes in the agri-food chain, understanding the BC technology is the first step before including it in the existing environments.Knowing the key factors affecting the BC efficiency and application may be an excellent way to start and investigate the developing technology.Accordingly, this paper aims to create an assessment methodology for the BC technology from the smart agriculture (SA) perspective.The 2-Tuple linguistic (2-TL) model is used with multi-criteria decision-making (MCDM) tools.The 2-TL model is a linguistic-based analytical approach that can eliminate information loss during the translation stage.While evaluating emerging technology, a linguistic-based methodology could be more understandable for practitioners and researchers.The DEMATEL method is utilized in this study's framework thanks to its ability to check the interdependence among the proposed criteria and extract their interrelationships.Evaluating these relationships can help companies increase the efficiency of their own evaluation processes.A case study to test the plausibility of the suggested framework is also given with detailed results and discussions.
Customer Clustering and Classification for CRM Loyalty Program in Milk Powder Industry

Changes in lifestyle during the pandemic affect people's purchasing power and habits towards the need for baby food, especially dairy products, so that increasing profits and business competitiveness becomes an important part for companies which are engaged in procuring powdered milk for toddlers.One of the strategies to increase profitability and competitiveness is to increase customer retention by predicting customer loyalty and classifying customer profiles in the Customer Relationship Management (CRM) which is often also known as the Customer Loyalty Program.This discussion tries to classify customer loyalty through customer profiles (demography, transactions, etc.) using techniques from data mining methods.Beforehand, to classify the loyal and non-loyal customers as the objective of customer loyalty program, this study used cluster analysis to map the customers based on their behavior recorded in the transactions they made and the information they provided.The objective of this study is to answer several questions such as: is the predicted performance value of loyal customers accurate?And what variables are most important in classifying loyal customers?In addition, this study also aims to understand the use of data mining methods and how to interpret the results of the techniques that have been used.The dataset contains 655,625 observations and 20 variables while for cluster analysis, there are only 7 variables are being used whereby using this method, the optimal clusters obtained are 3 clusters.With the new labels for targeted variable, labels 0 and 1 are labeled as non-loyal customer and label 3 is the loyal customer where the proportion of each label are 76%, 15.18% and 8.82% then the proportion of non-loyal and loyal customers are 91.18% and 8.82%.Using logistic regression method to classify loyal and non-loyal customers, the result showed that the model accuracy is about 96.6% where the misclassification error for the model is 3.4%.With this accuracy value, it can be recommended that this model can be used to classify customers in the customer loyalty program based on the dataset and variables that build the model.
Analysis of MetaMed Web Application to determine strategies

Healthcare is an essential service to everyone and as such, it is important that the policies and systems which are implemented in this area be adequate and flexible.Several issues have been raised concerning the capability of modern healthcare information systems in terms of the privacy, security, and availability of the data which it holds.MetaMed is a web application which seeks to remedy the problems of modern healthcare information systems through the provision of a different backend implementing by using blockchain technology.The objective of this paper was to analyze MetaMed using multiple analytical tools and methodologies to develop potential improvements for the product.A survey is deployed for such purposes.It consists of questions from varying perspectives which are constructed using the six thinking hats methodology.Once the survey results are ready, a SWOT analysis which analyzed the product's internal and external factors was conducted.Finally, a TOWS matrix analysis referenced the findings of the SWOT analysis to develop strategies to capitalize on the product's advantages and mitigate its weaknesses.Conclusively, results were produced through these analytical tools and methods supplying potential improvements on the product's features, interface, functionalities, and deployment.
Social Engineering Attack Awareness and Preparation Training Program Design: A Case study

The case study entitled "Social Engineering Attack Awareness and Prevention Training" is focused on improving the security for a diving business.In recognizing the importance of tourism to the economy of the Philippines, the business was chosen with the goal of proposing an information security plan to help the business better their current state of security.The proponents of the study conducted an online interview with a representative of the company for the main purpose of acquiring information regarding the nature of the business and their current information security system.In this study, it was determined that the business greatly lacked the appropriate security measures and knowledge when it came to social engineering threats.Thus, it was decided that a social engineering security training program was the optimal route in improving the overall security of the company.After gathering information from various professional organizations, individuals, and websites on the Internet, the training program -sharing the same name as the case study -was designed to take place over the course of three days.In those three days, the training program introduces what social engineering attacks are, how to identify and prevent them, and how to develop a business recovery and continuity plan.Through this, the company should be knowledgeable and prepared for threat actors seeking to harm the business with the use of social engineering attacks.