Reviewerly/exHarmony huggingface dataset:
Research on Nonlinear Correlation Tracking Technology of Financial Data Mining Based on Cloud Computing

Cloud computing is a commercial computing model that distributes computing tasks on a resource pool composed of a large number of computers, and can provide users with on-demand computing capabilities, storage capabilities, and application service capabilities; cloud computing provides storage and analysis of massive data Cheap and efficient solution. Financial data mining is a challenging research direction in the information society. The random nature of financial data makes it difficult to find the inherent rules hidden in the data. Furthermore, the properties of high-order correlation coefficients are discussed, and it is proved that high-order correlation can not only describe hidden nonlinear correlation information, but also describe the gap between linear correlation and independence. Therefore, the computational simplicity of high-order correlation can be used to track the time-varying nonlinear correlation characteristics in financial data in real time.
DepMiner: Automatic Recommendation of Transformation Rules for Method Deprecation

Software applications often depend on external libraries and must be updated when one of those libraries releases a new version. To make this process easier, library developers try to reduce the negative effect of breaking changes by deprecating the API elements before removing them and suggesting replacements to the clients. Modern programming languages and IDEs provide powerful tools for deprecations that can reference the replacement or incorporate the rules written by library developers and use them to automatically update the client code. However, in practice library developers often miss the deprecation opportunities and fail to document the deprecations. In this work, we propose to help library developers support their clients with better deprecations. We rely on the transforming deprecations offered by Pharo and use data mining to detect the missing deprecation opportunities and generate the transformation rules. We implemented our approach for Pharo in a prototype tool called DepMiner. We have applied our tool to five open-source projects and proposed the generated deprecations to core developers of those projects. 63 recommended deprecations were accepted as pull requests.
Waveguide-based swappable hyperentangled photon states in a multi-period, postselection-free device

We explore the generation of four postselection-free, photonic hyperentangled states with the help of a multi-period waveguide device based on potassium titanyl phosphate. The output photons are emitted in the much-in-demand telecommunication wavelengths of 1550-nm and 1310-nm. The device geometry and design enables hyperentanglement in the spatial mode and polarization bases, through multi-period quasi-phase-matching involved in different spontaneous parametric downconversion processes. The spatially entangled part of these hyperentangled states are rendered swappable with the help of two embedded electro-optic phase modulators. A two-path design provides the flexibility of such a device in the choice of polarization-entangled states which is important in relevant experiments. Through the biphoton joint spectral amplitude, we decipher the parameters that make frequency correlation and further entanglement of the photon pairs feasible. Such maximally-hyperentangled biphoton states are useful in quantum information applications.
Deep Learning for Prawn Farming

We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-h forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model, and the first to apply anomaly detection in general to this aquaculture problem. Our technical contributions include adapting ForecastNet for multivariate data and adapting Transformer and the Attention model to incorporate weather forecast data into their decoders. We attain an average mean absolute percentage error of 12% for dissolved oxygen forecasts and we demonstrate two anomaly detection case studies. The system is successfully running in its second year of deployment on a commercial prawn farm.
Virtual Assistants and Storytelling

As the proliferation of home virtual assistants (“VAs”) grows, so too do customers’ needs. But their expectations have far surpassed what the technology can currently provide, be it tracking a package, asking for the weather report, or playing a song. Greater content demands are emerging and soon story will be one of them. But is the VA an appropriate venue for story? Or are there other components of the story experience that can fulfill consumers’ needs through this technology? The authors of this article break down past mistakes and successes of the interactive ancestors of VAs and recommend what VA technology needs to adopt and avoid in order to thrive as a viable storytelling platform.
An Evaluation Study of Korean Speaking Acquisition by Academic Purpose Test to English and Chinese Learners of Korean : Focused on Task Types.

This study examined the effect of task types on the performance of academic Korean speaking tests conducted by both of English and Chinese learners of Korean. The major research questions explored in this study include: (1) Will the variable of L2 test-takers’ background language represent smilar or different features depending on task types of the test? (2) How do we apply the results to the design of the speaking tests? For the purposes, sixty eight foreign test-takers of English and Chinese natives who have learned Korean classified into three levels(4th, 5th, 6th) took part in the experiment. Twelve Korean raters were invited to determine to what extent subjects’ levels contributed to the overall proficiency ratings. Results indicated that there was no effect on test-takes’ background language, but the variable of proficiency level had an effect on ratings depending on test types; the subjects of the 4th level showed the wide range of the logit across the tasks, while those of the advanced(5th and 6th) levels did not. Also difficulty degree in all levels could be ordered as follows: independent task (speaking) < double integrated task (reading and speaking, or listening and speaking) < triple integrated task (reading, listening, and speaking). The most difficulty task, however, was differently chosen depending on the levels: the reading-based-double tasks in the 5th level as the middle class and triple-tasks in the most advanced level of 6th. The study implied that the speaking acquisiiton test for the academic purpose should include multiple integrated tasks.
A Mechanized Proof of Bounded Convergence Time for the Distributed Perimeter Surveillance System (DPSS) Algorithm A

The decentralized perimeter surveillance system (DPSS) seeks to provide a decentralized protocol for evenly distributing surveillance of a perimeter over time across an ensemble of unmanned aerial vehicles (UAVs) whose members may communicate only when in close proximity to each other.The protocol must also converge to an even distribution of the perimeter in bounded time.Two versions of the DPSS protocol presented in [5] seem to converge in bounded time but only informal proofs and arguments are given.A later application of model checking to these protocols found an error in one of the key lemmas, invalidating the informal proof for one and casting doubt on the other [2].Therefore, a new hand proof of the convergence time for the simpler version of the DPSS protocol or algorithm, Algorithm A or DPSS-A, was developed by Jeremy Avigad and Floris van Doorn [1].This paper describes a mechanization of that hand proof in the logic of ACL2 and discusses three specific ACL2 utilities that proved useful for expressing and reasoning about the DPSS model.
Критерий сходимости квантовой относительной энтропии и его использование

A criterion and necessary conditions for convergence (local continuity) of the quantum relative entropy are obtained. Some applications of these results are considered. In particular, the preservation of local continuity of the quantum relative entropy under completely positive linear maps is established.
Server-Side Browsers

As websites grow ever more dynamic and load more of their content on the fly, automatically interacting with them via simple tools like curl is getting less of an option. Instead, headless browsers with JavaScript support, such as PhantomJS and Puppeteer, have gained traction on the Web over the last few years. For various use cases like messengers and social networks that display link previews, these browsers visit arbitrary, user-controlled URLs. To avoid compromise through known vulnerabilities, these browsers need to be diligently kept up-to-date. In this paper, we investigate the phenomenon of what we coin server-side browsers at scale and find that many websites are running severely outdated browsers on the server-side. Remarkably, the majority of them had not been updated for more than 6 months and over 60% of the discovered implementations were found to be vulnerable to publicly available proof-of-concept exploits.
Quantum blockchain

The expansion of quantum computation initiated more researchers to possess an interest in hands-on experience within the exploration of the quantum blockchain (QB). The chapter tends to survey QB and presents its merits along with algorithms being used in the development of blockchain. The organization of this chapter includes a brief introduction of QB technology along with its attributes. Furthermore, we briefly review the classical blockchain including the vital technologies utilized in the blockchain. Specifically, the development of secure QB with Grover’s Algorithm and Shor’s Algorithm is discussed. The study also covers the aspects of blockchain security using QB. The process of quantum cryptography is also explored.
An Investigation of the Factors that Influence Job Performance During Extreme Events: The Role of Information Security Policies

Diligent compliance with Information security Policies (ISP) can effectively deter threats but can also adversely impact organizational productivity, impeding organizational task completion during extreme events. This paper examines employees’ job performance during extreme events. We use the conservation of resources (COR) theory to examine how psychological resources (individual resilience, job meaningfulness, self-efficacy) and organizational resources (incident command leadership, information availability, and perceived effectiveness of security and privacy controls) influence ISP compliance decisions and job performance during extreme events. The results show that a one-size-fits-all approach to ISP is not ideal during extreme events; ISP can distract employees from critical job tasks. We also observed that under certain conditions, psychological resources, such as individual resilience, are reserved for job performance, while others, such as self-efficacy, are reserved for ISP compliance. A post hoc analysis of data from respondents who experienced strain during a real extreme event while at work was conducted. Our discussion provides recommendations on how security and privacy policies can be designed to reflect disaster conditions by relaxing some policy provisions.
Application of adversarial machine learning in protocol and modulation misclassification

This paper explores the application of adversarial machine learning (AML) in RF communications, and more specifically the impact of intelligently crafted AML perturbations on the accuracy of deep neural network (DNN) based technology (protocol) and modulation-scheme classifiers. For protocol classification, we consider multiple heterogeneous wireless technologies that operate over shared spectrum, exemplified by the coexistence of Wi-Fi, LTE LAA (Licensed Assisted Access), and 5G NR-Unlicensed (5G NR-U) devices in the unlicensed 5 GHz bands. Time-interleaving-based spectrum sharing is assumed. Given a window of received I/Q samples, a legitimate DNN-based classifier (called the <i>defender’s classifier</i> ) is often used to identify the underlying protocol/technology. Similarly, DNN classifiers are often used to discern the underlying modulation scheme. For both types of classifiers, we study an attack model in which an adversarial device eavesdrops on ongoing transmissions and uses its own attacker’s classifier to generate low-power AML perturbations that significantly degrade the accuracy of the defender’s classifier. We consider several DNN architectures for protocol and modulation classification (based on recurrent and convolutional neural networks) that normally exhibit high classification accuracy under random noise (i.e., AWGN). By applying AML-generated perturbations, we show how the accuracy of these classifiers degrades significantly, even when the signal-to-perturbation ratio (SPR) is high. Several attack vectors are formulated, depending on how much knowledge the attacker has of the defender’s classifier. On the one extreme, we study a “white-box” attack, whereby the attacker has complete knowledge of the defender’s classifier and its training dataset. We gradually relax this assuming, ultimately considering an almost “black-box” attack. Mitigation techniques based on AML training are presented and are shown to help in countering AML attacks.
The Rules of the Game

This chapter describes AI algorithms divided into supervised and unsupervised learning schemes. In supervised learning, an ML model is trained to map input to output data based on a set of examples. Discrete or continuous outputs are related to classification or regression tasks, respectively. Most supervised learning methods are based on finding the minima of loss functions in a high-dimensional space via steepest descent. The number of parameters is chosen such as to reach a chosen trade-off between bias and variance in the output. The mapping of input to output data can be implemented via support-vector machines, kernel methods, decision trees, or artificial neural networks (NNs). NNs with hidden layers are associated with deep learning. NNs include fully connected feed-forward NNs, convolutional NNs, and recurrent NNs. While minimizing loss functions corresponds to a frequentist approach, the latter is increasingly replaced by Bayesian inference enabling the treatment of prior knowledge and uncertainty. Unsupervised learning is used to find patterns and to reduce the dimensionality of data. Simple algorithms are clustering and principal component analysis. More complex methods are based on NNs in the form of autoencoders, physics-inspired restricted Boltzmann machines, or generative adversarial networks which are pairs of NNs playing zero-sum games.
Designing for Meaningful Interactions and Digital Wellbeing

In the contemporary attention economy, tech companies design the interfaces of their digital platforms by adopting attention-capture dark patterns to drive their behavior and maximize time spent and daily visits. Two popular examples are viral recommendations and content autoplay on social networks. As these patterns exploit people’s psychological vulnerabilities and may contribute to technology overuse and problematic behaviors, there is the need of promoting the design of technology that better align with people’s digital wellbeing. This workshop seeks to advance this timely and urgent need, by inviting researchers and practitioners in interdisciplinary domains to engage in conversation around the design of interfaces that allow people to take advantage of digital platforms in a meaningful and conscious way.
Developing the guided inquiry-based module on the circulatory system to improve student's critical thinking skills

The limitations of inquiry-based teaching modules impact the low critical thinking skills of students. The research objective is to develop an inquiry-based module to improve students' critical thinking skills. This development research uses the Borg and Gall model in designing and testing the module. The development was carried out in February - November 2020 at Batik Senior High School 1 of Surakarta, Central Java. The subjects of this study were students of class XI-Science Batik Senior High School 1 of Surakarta. The assessment of the feasibility and effectiveness of the module uses a validation instrument by linguists, material experts, and learning experts. Assessment of students' critical thinking skills using HOTS questions. The effectiveness of the module was tested using the Z test. The results concluded that the module was feasible to use in learning with a feasibility score of 3.83 (linguistic aspect), 3.92 (material aspect), and 3.80 (learning aspect). Furthermore, this module effectively improves students' critical thinking skills as indicated by the results of the Z test (α&lt;0.05). This study recommends the development of inquiry-based modules in improving students' critical thinking skills.
Sentiment Analysis of Covid-19 Vaccination on Twitter Using Classification Algorithms based on PSO

Twitter social media is widely used by internet users to provide opinions on an event. In 2021, the opinion about the Covid-19 vaccination was widely discussed by the Indonesian people on social media. Togetherness of opinion can be categorized as positive sentiment and negative sentiment. To categorize negative and positive sentiments, data mining processes can be used. This study discusses the sentiment of Covid-19 vaccination using classification. Data from Twitter is collected in a crawling process, then labeled into two classes, namely positive and negative sentiments. After labeling (polarity) the next stage is data preprocessing which consists of transform case, annotation removal, tokenizing and stemming. The classification algorithm used is Naïve Bayes (NB) and Support Vector Machine (SVM) and then compared with the classification algorithm using Particle Swarm Optimization (PSO). Tests were carried out using k-Fold Cross Validation to obtain accuracy values, Confusion Matrix tables and Area Under Curve. The test results on the classification using PSO are better than those without using PSO. The results of the accuracy of the NB and SVM algorithms are 64.04% and 72.55%, while the accuracy results after PSO on the SVM and NB algorithms are 70.43% and 76.38%, respectively.
CONTENT OF SOCIAL AND PEDAGOGICAL ASSISTANCE TO STUDENTS-VICTIMS OF BULLYING

The article considers the problem of social and pedagogical assistance to students who are victims of bullying. Its purpose is to analyze the content of social and pedagogical assistance to students who are victims of bullying in an educational institution. Research methods applied: systematization and analysis of scientific and pedagogical literature on the problem of social and pedagogical assistance to victims of bullying; classification of forms and methods of social and pedagogical work with students who are victims of bullying at different stages; generalizations to formulate research conclusions. It is determined that assistance to children victims of bullying is an important area of social and pedagogical work, should be comprehensive and multilevel, not limited to work with a child who has become a victim of school bullying. The stages of social and pedagogical assistance are analyzed: diagnostic; individual assistance to students who are victims of bullying; group work, which is based on the system of interaction of students, class teacher, teachers and class, parents of bullies. It was found that social and pedagogical work with students who are victims of bullying should be carried out using both traditional and innovative methods of work: conversations, individual consultations, correctional and developmental classes, group classes with elements of training, communication hours, etc. Because bullying is a deliberate, group, conscious, and organized process, it cannot be stopped without the intervention of professionals (educators, psychologists, social educators) and parents. This involves purposeful anti-bullying work, which requires the involvement of all available resources of the educational institution and the child's environment.
Analysis of Factors Affecting Freshmen’s on Adaptation to College Life and Dropout in the COVID-19 Situation - Focused on B University -

This study was conducted to explore various influencing factors for adaptation to college life and drop-out prevention of new students in the 2021 academic year of B University. College student Adaptability Test(CAT) was conducted for all first-year students, and the final 1,025 subjects were the final research subjects. Multiple regression analysis was used to analyze the relationship between college adaptability variables and college satisfaction, college adaptability variables and dropout tendency. As a result of the study, academic cohesion, social cohesion, career identity, relationship with professors, emotional support at home, and stability in academic continuity were analyzed as significant factors that increase college satisfaction. Factors affecting the risk of dropping out of freshmen were analyzed. As a result, it was found that the higher the academic integrity, emotional stability, satisfaction with college education, and stability of academic continuity, the other variables except the relationship with the professor, the lower the tendency to dropout. On the other hand, social cohesion, career identity, and educational service satisfaction did not have a significant effect. Based on these results, a university-level plan was proposed to increase the discussion and university satisfaction.
An AR-based DC Circuit Connection Simulator for Beginners

This paper proposes an immersive interaction for beginners to implement DC circuit connection simulation experiment based on AR. In this application, beginners conduct circuit connection simulation experiments by adding and deleting components and drawing wires. The application supports real-time calculation of voltage and current. The results of the user survey show that most beginners agree with the intuitiveness, interest and promoting effect of the application on learning. Compared with the laboratory experiment, most beginners prefer AR-based experiment. The experiment using 2D circuit simulator is also used as a comparative experiment. The results show that most beginners think that AR-based tool is more intuitive and realistic. Concerning gender, females are more satisfied with AR technology.
Enforcing Consistent Code Style in a Repository While Allowing Developer-Specific Preferences in Local Workspaces: An Experience Report

When developers collaborate on a software project, the style of the code should be consistent across the codebase. However, as developers do not always concur on code style practices, achieving a consensus can be burdensome. Even when code style guidelines are defined for a project, developers often have difficulties adhering to them. Additionally, when working on several projects with diverse guidelines, following different styles can become a frustrating, error-prone, and time-consuming task for a developer. This work presents experiences from adopting two development workflow patterns that automatically ensure that code’s layout and formatting are consistent in a project’s repository while enabling each developer to utilize their preferences locally. From our experience, using any of these two patterns provides a successful way for collaborative software development that allows developers to relocate their time and concerns from code formatting to more valuable matters.