Reviewerly/exHarmony huggingface dataset:
CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints

Finding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that can feasibly be included. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set identification. This reduces the search space substantially and can be applied to large-dimensional data. We use what we call 'generational orderings' based search for optimal networks, which is a novel way to efficiently search the space of possible networks given the possible parent sets. The algorithm supports both continuous and categorical data, as well as continuous, binary and survival outcomes.We demonstrate the efficacy of our algorithm on both synthetic and real data. In simulations, our algorithm performs better than three state-of-art algorithms that are currently used extensively. We then apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find an optimal network describing the disease pathway consisting of 6 genes leading to the outcome node in just 3.4 min on a personal computer with a 2.3 GHz Intel Core i9 processor with 16 GB RAM.Our generational orderings based search for optimal networks is both an efficient and highly scalable approach for finding optimal Bayesian Networks and can be applied to 1000 s of variables. Using specifiable parameters-correlation, FDR cutoffs, and in-degree-one can increase or decrease the number of nodes and density of the networks. Availability of two scoring option-BIC and Bge-and implementation for survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional data in a variety of fields.
‘Chinese virus’

Since the emergence of COVID-19, researchers have documented an increase in cases of anti-Asian racism and hate crimes. Research shows a possible connection between the ‘Chinese virus’ discourse of the Trump administration and violence in society (Arora and Kim 2020:387). Drawing from critical discourse studies we explore 2,071 comments from one YouTube video which documents anti-China rhetoric by the Trump administration in order to understand the underlying strategies commenters relied on in their reproduction and defence of this discourse. Findings show the trickle-down influence of Trump’s discourse on YouTube commenters, but also ways in which social media created a platform for building solidarity among racist groups, as well as sites of resistance. The authors conclude by suggesting more studies attend to this type of discourse and work to educate people on how to counter it.
Arguing in Direct Democracy: An Argument Scheme for Proposing Reasons in Debates Surrounding Public Votes

Abstract We develop a novel argument scheme tailored to debates surrounding public votes on a state action. It can be used to propose reasons for voting “yes” or “no” and allows for natural reconstructions of such debates. These reconstructions are of particular use to voters trying to weigh the pros and cons of the proposed state action. The scheme for proposing reasons helps answering two questions: What changes will the proposed state action bring with it? And are these changes good or not? Using the scheme, we derive a three-layered structure of public debates surrounding votes that aides in constructing systematic and comprehensive overviews. The scheme for proposing reasons also lends itself to concise summaries for voter information and can be incorporated into more general deliberation dialogues.
A fast analytical local path planning method with applications in parking scenarios

In this paper an analytical and geometry-based path planning method is proposed that provides non-holonomic and collision free paths for parking maneuvers. The proposed method is straightforward in the sense that it does not require any sampling or exploring of the configuration space, or relies on any optimization process. Furthermore, the calculation is fast enough to be used in real-time applications for guiding the vehicle in parking scenarios. For any pair of start-goal configurations, the planner presents an infinite set of solutions. These paths have minimum number of driving direction changes as this can be more important for a driver than the path length. They may have any curvature lower than or equal to the maximum specified curvature. Simulations show the superiority of the method in local path planning over conventional methods such as hybrid A* and optimization based approaches in terms of computation time and path quality attributes such as the number of motion primitives and the number of direction changes. To compose a complete motion planning system, the proposed local planner is applied along with hybrid A* as global planner and a path tracking controller. The performance of the motion planning system is evaluated by simulating a valet parking scenario. MATLAB/Simulink is used to simulate the system, and a comprehensive vehicle model in CARSIM is used to represent the vehicle. Simulation results shows the capability of the proposed motion planner to implement automated valet parking tasks while respecting the physical limitations of the vehicle subsystems and driving comfort requirements.
Correlating Edge with Parsing for Human Parsing

Human parsing has great application prospects in the field of computer vision, but there are still many problems. In the existing algorithms, the problems of small-scale target location and the problem of background occlusion have not been fully resolved, which will lead to wrong segmentation or incomplete segmentation. Compared with the existing practice of feature concatenation, using the correlation between two factors can make full use of edge information for refined parsing. This paper proposes the mechanism of correlation edge and parsing network (MCEP), which uses the spatial aware and two max-pooling (SMP) module to capture the correlation. The structure mainly includes two steps, namely (1) collection operation, where, through the mutual promotion of edge features and parsing features, more attention is paid to the region of interest around the edge of the human body, and the spatial clues of the human body are collected adaptively, and (2) filtering operation, where parallel max-pooling is adopted to solve the background occlusion problem. Meanwhile, semantic context feature extraction capability is endowed to enhance feature extraction capability and prevent small target detail loss. Through a large number of experiments on multiple single-person and multi-person datasets, this method has greater advantages.
An algebraic approach to symmetric linear layers in cryptographic primitives

Abstract Subterranean 2.0 is a permutation-based cipher suite which works with a 257 bit-state. It is designed for lightweight cryptography, and it scores very well with respect to energy consumption. Its security has been investigated by the designers against well-known attack vectors. A possible point of concern is the relatively low order of its linear layer, which equals 256. In the past, such properties have been exploited by invariant subspace attacks. We define linear mappings with a similar structure as the linear layer of Subterranean as SC-compositions. In this work, we explore finding SC-compositions with a higher order than 256. We rely on concepts from abstract algebra and number theory to understand the relation between the order and the bit-states of SC-compositions. Using a 257 bit-state as done in Subterranean is an unfortunate choice for designing such SC-compositions with a high order. We present two examples with different bit-states, each having a significantly higher order than 256.
Appliance of Deep Learning on hate speech detection: a systematic review

Hate speech is defined as offensive viewpoints toward individuals or groups divided by ethnicity, religion, or gender. Hate speech is not restricted by law in many countries, and may be seen as the protection of free speech since whether a statement is hate speech is highly related to the individual. Hate speech is widely recognized and has been researched extensively in recent years due to its nebulous definition and quick spread. With deep learning's ongoing growth, some researchers have started to apply deep neural networks to hate speech detection (HSD). Although a lot of progress has been made in research-based work around the task, a comprehensive overview of the progress of the task is lacking. Therefore, an overview of the progress that using deep neural networks to address hate speech problems is necessary. The overview presents what methods have been employed recently to enhance the task's performance, and also analyzes the potential problems of the task.
Exploration of the distillability problem

Quantum mechanics has a history more than 100 years, and there are still many challenging open-ended questions. The problems encountered in modern physics such as matrix mechanics and quantum information often involve both quantum theory and open-ended problems in modern mathematics such as linear algebra. Distillability problem is an important open-ended problem in the field of quantum information and linear algebra. This article recalls the development history and application of linear algebra, summarizes the background and progress of distillability conjecture, and attempts to explore this problem by summarizing and using the knowledge of linear algebra and matrix theory learned in the course.
Open AI Codex: An Inevitable Future?

Abstract: OpenAI Codex is an AI system that converts natural language into code, OpenAI shows how the software can be used to build simple websites and rudimentary natural language games, translate between different programming languages, and answer data science queries. The user enters English commands into the software. For example, Create a web page with a menu on the side and a title at the top and Codex will translate this into code. Codex is the model behind GitHub Copilot, developed and launched in partnership with GitHub. Codex is proficient in over 10 programming languages and can interpret and execute simple natural language commands on your behalf, allowing you to create natural language interfaces to your existing applications.
Application of Random Walks in Data Processing

A random walk is known as a process that a random walker makes consecutive steps in space at equal intervals of time and the length and direction of each step is determined independently. It is an example of Markov processes, meaning that future movements of the random walker are independent of the past. The applications of random walks are quite popular in the field of mathematics, probability and computer science. Random walk related models can be used in different areas such as prediction, recommendation algorithm to recent supervised learning and networks. It is noticeable that there are few reviews about randoms for the beginners and how random walks are used nowadays in distinctive areas. Hence, the aim of the article is to provide a brief review of classical random walks, including basic concepts and models of the algorithm and then some applications in the field of computer science for the beginners to understand the significance and future of random walks.
Recent Deep Neural Networks for Object Detection

Object recognition is a basic and difficult task in computer vision. Its purpose is to identify the object and give its exact position in the picture. In recent years, it has attracted extensive attention and gradually become a research hotspot. With the continuous development of object detection, some investigators have been solving existing problems and started to apply deep neural networks to the task. Despite the great progress in the research work surrounding the mission, there are few reviews of the mission, lacking a comprehensive review of its development in recent years. Given this period of rapid development, I will give an overview of object detection and present what methods have been used in object detection in recent years to improve task performance. This paper's goal is to give an exhaustive overview of the most latest developments in the area brought about by deep learning technology. At the same time, the possible problems in the task are analyzed, and the potential problems in the task are analyzed.
Research on Intelligent System of Computer Aided Ideological and Political Course Teaching

This paper focuses on the design scheme of the auxiliary teaching system for ideological and political theory courses using J2EE architecture design. In this paper, the overall planning of the system's architecture, work flow, system function, data structure, etc. is carried out, and it is designed and implemented in detail. The system adopts B/S three-tier structure and uses JSP technology to design dynamic pages. Consider using JavaBean to encapsulate the key code of the program from the aspects of system security and code reusability. Use CASE tools and object-oriented modeling language in system design. Through UML modeling language, use VISIO, ROSE and other modeling tools to analyze and design the system to generate conceptual models and entities of the system. Through requirement analysis and detailed design, the user login module, network course module, online examination module, learning resource module and exchange forum module are finally realized. Finally, the thesis assists the realization of the functions of each module of the teaching system, and carries out corresponding tests and verifications, and the system is in the testing stage and plays a great role in practice.
Fast thresholded concordance probability for evolutionary optimization

The concordance probability is an extension of the popular area under the curve (AUC) which is commonly used to measure the accuracy of a predictive model. It can be extended to the thresholded and weighted concordance probability which are more appropriate for some applications. The naive way of estimating this measure requires a quadratic computation time, which is prohibitive for large data sets. We propose a new algorithm that computes the weighted thresholded concordance probability in linearithmic time, which is proven and empirically confirmed. This unlocks the possibility of calculating the thresholded concordance probability in a big data world, and makes it possible to base the fitness function of a machine learning algorithm on the concordance probability. These applications are successfully illustrated by two real examples from the insurance sector. The first one focuses on feature selection based on the concordance probability using a binary particle swarm optimization. In the second application, we use a genetic algorithm to optimize a loss function based on the concordance probability. Since both of these applications require evaluating the concordance probability a very high number of times, a huge decrease in computation time is obtained using our fast algorithm. Moreover, it is shown that the neural network optimized for the concordance probability with the genetic algorithm outperforms the traditional benchmark methodology, i.e. a classical neural network optimized for the deviance. The applicability of our fast algorithm extends beyond these illustrations and unlocks various new uses of the thresholded and weighted concordance probability.
Rethinking security with cloud native in mind

The cloud native application protection platform (CNAPP) is a response to growing threats against cloud-native architectures. But it's not a simple matter of picking an off-the-shelf solution. Organisations looking to reap the benefits of cloud-native applications need to carefully consider their security requirements too.
Differential Privacy Preservation for Location Semantics

With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users' lives. LBS providers upload and share the collected users' location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.
Light Field View Synthesis Using the Focal Stack and All-in-Focus Image

Light field reconstruction and synthesis algorithms are essential for improving the lower spatial resolution for hand-held plenoptic cameras. Previous light field synthesis algorithms produce blurred regions around depth discontinuities, especially for stereo-based algorithms, where no information is available to fill the occluded areas in the light field image. In this paper, we propose a light field synthesis algorithm that uses the focal stack images and the all-in-focus image to synthesize a 9 × 9 sub-aperture view light field image. Our approach uses depth from defocus to estimate a depth map. Then, we use the depth map and the all-in-focus image to synthesize the sub-aperture views, and their corresponding depth maps by mimicking the apparent shifting of the central image according to the depth values. We handle the occluded regions in the synthesized sub-aperture views by filling them with the information recovered from the focal stack images. We also show that, if the depth levels in the image are known, we can synthesize a high-accuracy light field image with just five focal stack images. The accuracy of our approach is compared with three state-of-the-art algorithms: one non-learning and two CNN-based approaches, and the results show that our algorithm outperforms all three in terms of PSNR and SSIM metrics.
INTERNATIONAL ARMED CONFLICT AND ITS IMPACT ON EDUCATION

Violent conflict represents one of the greatest development challenges facing the international community. If we look beyond the immediate human suffering in these cases, we find that international armed conflict is one of the underlying causes of poverty, inequality and economic stagnation. Children and education systems are often the targets of violent conflict. It is necessary to liberate all the energies and capabilities of education in order to become a force for peace, therefore the Preamble to the Constitution of UNESCO declares that “since wars begin in the minds of men, it is in the minds of men that the defences of peace must be constructed.” (UNESCO,2011) There are no more immune bulwarks than public behaviours and attitudes based on tolerance, mutual respect and a commitment to dialogue, which must be developed and refined every day in classrooms around the world. If schools are used in another way, i.e., as a tool for the development of a spirit of intolerance, chauvinism and disrespect for others, this not only leads to ignorance but also paves the way for violence. Official and non-official stakeholders involved in the United Nations system should take an active role in rebuilding education systems in conflict-affected countries, drawing on work in the areas of intercultural dialogue in order to spread peace, curriculum development, teacher preparation and textbook reform, passed Many years since the establishment of the United Nations, the nature of the challenges posed by the militants has changed and the methodology of conflicts has changed, but the principles, values and institutions on which the regime is based have not changed, though the United Nations has retained its credibility and remained in force as always. In his article, the author refers to the necessity of education in building societies, drawing on examples from the history of nations to inform the public of the need to support educational institutions by states, governments and even at the level of individuals and civil society activists because of their profound impact on the sustainability, goodness and prosperity of societies. It has presented some legal texts that have been enacted to protect education at all levels and the rights of learners in times of armed conflict and insecurity.
Optimizing the Digital Education Technology in Learning Management System Design During and Post-Covid-19 Pandemic in Society 5.0

The use of digital devices has increased since the global pandemic was officially declared by the world health agency on March 11, 2020.The increase in the use of digital devices is marked by an increase in internet use by up to 40% where virtual services and activities are urgently needed during the Covid-19 pandemic.The community's economic is also affected by the pandemic, but on the other hand all needs must be fulfill including the use of digital devices, smartphones which have become ubiquitios in the current digital era.Basic knowledge of the use of digital devices is needed to maximize the use of digital devices in daily use.Digital devices such as mobile phones, tablets, laptops, personal computers became ubiqutos in a short time which also became part of the technology driven force.As ubiquitos in life, the current digital device of society must be optimized than change with new one while the impact of pandemic affect the economic's society.In this case, the society 5.0 define that human and technolgy will live side by side to improve the human life quality on sustainable based.The specifications of the processor, memory, input and output devices, data transmission technology in terms of hardware, software updates used need to be understood including developments and updates of devices that needed, so all digital activities can run well.This digital interaction occurs in almost all human life activities, include the essensial sector like education, health, work, business, banking, shopping and many more.To fulfill the need for using digital devices in the pandemic era, analyzes the needs and specifications for usage requirements, including interactions the using of icon on digital devices, especially in education interaction.Learning management need to apply at all level of education during the complex obstacles such as digital devides, human resources that related to computer and internet literacy.The optimization use of digital device could give many advantages to community, especially in education as one of priority target.During and post covid-19 pandemic, education could be one of the best way to recover the community health and prepare entering the new normal which many field dominated by digitalization.