Reviewerly/exHarmony huggingface dataset:
Naimark dilations of qubit POVMs and joint measurements

Abstract Measurement incompatibility is one of the cornerstones of quantum theory. This phenomenon appears in many forms, of which the concept of non-joint measurability has received considerable attention in the recent years. In order to characterise this non-classical phenomenon, various analytical and numerical methods have been developed. The analytical approaches have mostly concentrated on the qubit case, as well as to scenarios involving sets of measurements with symmetries, such as position and momentum or sets of mutually unbiased bases. The numerical methods can, in principle, decide any finite-dimensional and discrete joint measurability problem, but they naturally have practical limitations in terms of computational power. These methods exclusively start from a given set of measurements and ask whether the set possesses incompatibility. Here, we take a complementary approach by asking which measurements are compatible with a given measurement. It turns out, that this question can be answered in full generality through a minimal Naimark dilation of the given measurement: the set of interest is exactly those measurements that have a block-diagonal representation in such dilation. We demonstrate the use of the technique through various qubit examples, leading to an alternative characterisation of all compatible pairs of binary qubit measurements, which retrieves the celebrated Busch criterion. We further apply the technique to special examples of trinary and continuous qubit measurements.
Learning Performance of Reinforcement Learning According to the Tensor Dimensions Representing States

In reinforcement learning, an agent takes actions in an environment, which is interpreted into a reward and a “representation of the state”. It is well known that the performance of the reinforcement learning is dependent on the “data model representing the state” of a given environment. This paper proposes a data model representing the state which is suitable for a FPS (First Person Shooting) game, a military tactics simulator that changes state extremely and needs decision making quickly. The proposed data model consists of matrix (multi-dimensional tensors) for spatial features and vectors for non-spatial features. To prove the usefulness of the proposed data model, this paper shows experimental results for a FPS game.
Sparse Adversarial Attack via Bi-objective Optimization

Neural classifiers have achieved near human level performances when applied to several real-world tasks. Despite their successes, recent works have demonstrated their vulnerability to adversarial attacks. In particular, image classifiers have shown to be vulnerable to fine-tuned noise that perturb a small number of pixels, known as sparse attacks. To generate such perturbations current works either prioritise query efficiency by allowing the size of the perturbation to be unbounded or the minimization of its size by allowing a large number of pixels to be perturbed. Addressing the drawbacks of both approaches we propose a method of conducting query efficient sparse adversarial attacks that minimizes the number of perturbed pixels by formulating the attack as a constrained bi-objective optimization problem. Within the single objective unbounded query-efficient scenario our method is able to outperform state-of-the-art sparse attack algorithms in terms of success rate and query efficiency. When also minimizing the number of perturbed pixels in the bi-objective setting, the proposed method is able to generate adversarial perturbations that impact a fewer number of pixels than its state-of-the-art competitors.
Hybrid Method Implementation Fuzzy K-Nearest Neighbor (FK-NN) And Particle Swarm Optimization (PSO) for Classification of Liver Disease

Liver disease is a disease that attacks the liver or liver where this disease is caused by viral infections, toxic materials and bacteria, causing inflammation of the liver and causing the liver to not function properly. Therefore, the author will conduct research to make a Liver Disease Classification program. This research will use Fuzzy K-Nearest Neighbor (FK-NN) and Particle Swarm Optimization (PSO) methods. Fuzzy K-Nearest Neighbor is a classification method that combines fuzzy and k-nearest neighbor algorithms. Particle Swarm Optimization is a simple optimization technique to apply and modify several parameters. This research will implement the application design to the lines of web-based program code using the Python language and the Django framework. This study resulted in the value of the accuracy range obtained by the PSO-FKNN hybrid method is 66 to 74 (in percent) compared to the range of accuracy values ??of FKNN without the hybrid method is 64.90% to 68.29% (in percent), the difference in the accuracy values ??obtained by PSO-FKNN FKNN is affected by changes in the position of the training and testing data in each test
A Practical Approach for Vehicle Speed Estimation in Smart Cities

The last few decades have witnessed the increasing deployment of digital technologies in the urban environment with the goal of creating improved services to citizens especially related to their safety. This motivation, enabled by the widespread evolution of cutting edge technologies within the Artificial Intelligence, Internet of Things, and Computer Vision, has led to the creation of smart cities. One example of services that different cities are trying to provide to their citizens is represented by evolved video surveillance systems that are able to identify perpetrators of unlawful acts of vandalism against public property, or any other kind of illegal behaviour. Following this direction, in this paper, we present an approach that exploits existing video surveillance systems to detect and estimate vehicle speed. The system is currently being used by a municipality of Sardinia, an Italian region. An existing system leveraging Convolutional Neural Networks has been employed to tackle object detection and tracking tasks. An extensive experimental evaluation has been carried out on the Brno dataset and against state-of-the-art competitors showing excellent results of our approach in terms of flexibility and speed detection accuracy.
Requirements Gathering Techniques and Issues

Now a day’s project management is play very important part in our software industries. It is a very big deal to handle the project to became a success. There are some phases in the project management process. Number one is requirement gathering it is to get the right the requirements, as so far the most important one to done our projects successfully. Requirements is describe in planned phases then these requirements are used in the projects when it doesn’t end. Here is few technique for getting the requirement. Interview, questionnaires, prototyping, brainstorming and storyboard. When we gathering requirements, many problems are going to faced which are not good or better for completing the project. Will discuss in this paper about these techniques and problems and also find our there proper solution.
REQUIREMNET TRACEABILITY IN GREAT DETAIL

As we all know that the requirement traceability is a bigger and important part of software engineering. We here are going to try our best to explain the importance of requirement traceability in the respective field. Our paper is basically consisting on the reasons by which traceability is considered as a very first step of any kind of project. There is a specific team to trace the requirements of any project before the implementation is started. If there is a problem in this team or the team is not working according to the main and important principles of this very field, there will be errors in the project. A traceability matrix allows a requirement engineer to represent the relationship between requirement and work product. Requirement traceability increases with the increase of the steps, working and complexity of the project. Mostly it is the part in which organizations invest most to get the requirement crystal clear. All of the main steps and methods of traceability are discussed in this paper.
Requirement Change Management with respect to Different Models

Changes in the requirements can be proposed by the clients continuously during the whole development cycle of the software. Those changes in requirements causes very major errors on development, cost and time etc. Because at very initial level, requirements are engineers before actual implementation. The effective approach is to handle these changes very efficiently by keeping all things and effects of that change in mind. There are different process models that tells how to handle these changes. This paper suggests different process models to handle those requirements changes. The difference between the process models and also suggest the good approach and good model to handle all these changes. Because its very important to understand, thousands of projects are flopped due to changes. In every system, a stage must come, at which you have to update your software. Some changes are come with the passage of time, and some will come immediately by the customer.
Use of Artificial Intelligence in Software Development Life Cycle

Current automated software development life cycle was mainly allotting with the automated generation code. That kind of method mostly include in the software combination from model’s design, predetermined polices or the fixed rules. In this paper we discuss about the propose an approach that should went above the SDL automated that should be presenting direct method which is able to make decision and take decision to generate code and software. The ontology do an important part of developer by fulfill the development of software affair freely. [2]Thus behavior catch a database of ontological permit highest level judgement to explain design and incorporate the logic of system depend on the configuration system which is physical system . This kind of method implement in graphical tools. This paper discuss the autonomated ontological form of methodology create the code of according to artificial intelligence
REQUIREMENTS GATHERINGS AND IMPLEMENT ISSUES

Summary of the Research Research goal is better understanding and learning by using old and new techniques for resolve problems and empty blank spaces which are mostly facing during requirement gathering phase to development phase. This paper mainly purpose gathering research on Software requirements gathering and their effect on all incoming challenges and cost, benefits. Requirements is one in technology application style and development. . Most of the projects are fail due to gathering the poor requirement gathering. Many of the software based projects acquired by one to one the interview for requirement gathering. During last few years new software development approaches were introduced for increase software product which are helpful for reduce cost, time, and effort. For all procedure Software Requirement gathering is the one of the special key. Requirement gathering is the most important area in Software Engineering and project management.
Using Questionnaire for Avoid Confusion in Requirement Elicitation Technique

Elicitation technique is very important part in the field of requirement engineering, which it is very necessary for software process. Questionnaire is lead to success in our projects. Questionnaire makes us how to avoid confusion in the project. Misguided is very big failure in the software process. Questionnaire makes the right way to successful in every field. Questionnaire is the basics need to correct the design. This paper helps us that using with questionnaire we reduce specially time and cost. Finally we present a article that tells us using questionnaire we reached to succeed in the field of software requirement engineering .
Requirements Gathering Techniques and Issues

Now a day’s project management is play very important part in our software industries. It is a very big deal to handle the project to became a success. There are some phases in the project management process. Number one is requirement gathering it is to get the right the requirements, as so far the most important one to done our projects successfully. Requirements is describe in planned phases then these requirements are used in the projects when it doesn’t end. Here is few technique for getting the requirement. Interview, questionnaires, prototyping, brainstorming and storyboard. When we gathering requirements, many problems are going to faced which are not good or better for completing the project. Will discuss in this paper about these techniques and problems and also find our there proper solution&#x0D;
A novel metaheuristic for solving LSGO problems

Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach.
Learning-based Light Field View Synthesis Using Multiplane Images

Light field camera acquires an image set by capturing a scene at several viewpoints. However, it has a trade-off issue between the number of views and the spatial image resolution. To address these problems, this paper presents a light field view synthesis using multiplane image generation. Since multiplane images consisting of color values and transparency of objects are projected onto some planes respectively representing disparity information, it is possible to synthesize and transform images to arbitrary viewpoints. The proposed method constructs the multiplane images using 3D convolutional neural network, and then synthesizes a novel view by blending the multiplane images. For the experiment, the proposed method demonstrates the quality is better than existing methods.
THE EFFECT OF WRITING ANXIETY TOWARD WRITING SELF-EFFICACY ON GRADE EIGHTH STUDENTS AT SMP NEGERI 7 BAUBAU

Problem Problem statement of this research was whether there was a significant effectof students' writing anxiety on the writing self-efficacyat SMP Negeri 7 Baubau. Objective of the research was to find out whether there was a significant effect of writing anxiety toward writing self-efficacyat SMP Negeri 7Baubau. Data collection in this research was carried out using a research instrument in the form of a questionnaire, to determine the effect of writing anxiety on writing self-efficacy in the use of English. The data obtained were analyzed by descriptive statistical analysis, prerequiset analysis, and inferential statistical analysis. Based on the results of the research, it was concluded that there was no significant effectbetween writing anxiety toward writingself-efficacy. This conclusion is based on statistical analysis. Because Fcount is 3.112.with a significant value of 0.183. the calculated Fcount is lower than Ftable and the significant value is greater than (0.05). This means that the null hypothesis is accepted and the alternative hypothesis is rejected.
ROLE OF EMF DETECTOR FOR THE INVESTIGATION OF LAMBI DEHAR MINES 10KM FAR FROM LIBRARY CHOWK MUSSOORIE

There are many unsolved questions which are haunting, these happen from many years. Actually many persons ask do ghosts really exist. In India there are many haunting place as in Kolkata Wipro Company where situated it's really haunted. In India 87% persons believe that ghost is exist all time. As that I visited the place Lambi Dehar Mines and it is infamous one of most haunted places in uttarakhand. This place is a ghost area and by EMF meter and I give rent two two ojhas and they went with me this time. By our research i faced many horror incidents. As I mentioned below
Social Media Usage and SME Firms in Kuwait

Various company ecosystems are gradually changing because of social media and its nearly infinite capacities. As a result, growing companies worldwide are using this innovation to streamline their processes. The results of previous studies have not been consistent, even though the literature on ”SMEs’ use of social media is still in its infancy. During this, little observed data has been presented on the ’ ’technology’s suitability for SMEs, notably Kuwait. This study aims to close this research vacuum by examining how social media use affects ”SMEs’ long-term viability, especially in Kuwait. Keywords: Social Media Usage, SME Firms DOI: 10.7176/EJBM/15-5-04 Publication date: February 28 th 2023
Effects of Class Participation on Class Satisfaction in Real-Time Interactive Classes: Mediating Effect of Task Value

The purpose of this study is to examine factors influencing university students’ class satisfaction in the real-time interactive classes conducted due to COVID-19 and to draw suggestions for enriching and improving remote classes. To this end, a survey was conducted on students who performed all tasks in the teaching-learning process of the real-time interactive classes from the first semester in 2020 to the first semester in 2021. Structural Equation Model(SEM) was conducted for the causal relationship among class participation, task value and class satisfaction, and the approach and method proposed by Holmbeck(1997) were applied to verify the mediating effect. The findings are as follows: First, learners’ participation had a positive effect on the task value in the real-time interactive classes. The more active the learner’s participation, the stronger task value was perceived. Second, the task value perceived by learners had a positive effect on the class satisfaction in the real-time interactive classes. The stronger the learner perceived the task value, the better class satisfaction in the classes. Third, the perceived task value by learners variable fully mediated between class participation and class satisfaction. On the basis of the findings, it is significant for providing practical tasks for the real-time interactive remote classes to be established in the form of sustainable ones, in the field of education and operation strategy is suggested.
Storytelling and University Communication Education

This study explores the possibilities of storytelling as a theory and method of college writing education and explores its possibilities. As a core of narrative theory, storytelling has recently been noted by cultural contents and game industries. Although university writing education has some standardized forms, it varies greatly with the trends and the conditions of each university. It is due to the lack of theoretical foundation in writing education. As a basis of narrative theory, storytelling has a sophisticated and multi-dimensional characteristics. These characteristics provide a specific methodology for writing education, and help learners stand as active subjects. Besides, storytelling can be applied to speaking, teaching and learning education. In the future, in order to solidify its status and value, it is necessary to create the theoretical foundation, common ground, and essential values and ideas for college writing education. This study claims this necessity.
An Analysis of Trends in Korean Theatre from the Perspective of Cultural Diversity Policies

This study aimed to figure out the trends of Korean theatre from the perspective of cultural diversity policies. The cultural diversity policies have mainly focused on the social minorities' culture and art activities and participation in Korea, which means that there is a relative shortage of discussion about the diversity of art. The study analyzed the achievements and limitations of cultural diversity policies in South Korea, and distinguished the diversity of art according to intrinsic and extrinsic criteria. Also analyzed in the study were reviews published in Critiques of Theatre for the last six years(2015~2020) based on the criteria of artistic diversity. The analysis results show that the diversity of art in the field not only accepted extrinsic diversity including the nationality, gender, and regional diversity of those who participated and enjoyed, but also realized the values of diversity including the content and formal diversity of artistic works.
An adaptive LIC based geographic flow field visualization method by means of rotation distance

Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields. However, due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields, existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution, resulting in incomplete and inaccurate vortex information. To address this, we propose an adaptive Line Integral Convolution (LIC) based geographic flow field visualization method by means of rotation distance. Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively. We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution, with the integration step size adjusted. This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields. Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.
Obstacle Avoidance Strategy with Path-following for USV Based on Obstacle-guidance Model Using 2D-Lidar in Urban River Conditions

An obstacle detection and automatic avoidance algorithm using lidar based on the obstacle-guidance model is proposed for Unmanned Surface Vehicles (USV) in urban river conditions. The smallest bounding rectangles represent obstacles detected by 2d-lidar due to the clutter of urban river laser data and the shape of actual blocks during navigation. On this basis, an obstacle vortex guidance model is proposed. The obstacle avoidance algorithm takes the heading and speed obtained by the Line-Of-Sight (LOS) method as input. It combines the obstacle guidance model to give the speed and yaw required for collision avoidance, which achieves good real-time performance and robustness in practical applications. Finally, the actual experiment is completed based on the built USV platform, where the entire algorithm is implemented on the Robot Operating System (ROS). The experiment results verify the proposed obstacle-avoidance method’s feasibility and validity.