Reviewerly/exHarmony huggingface dataset:
On the Probabilistic Coupling Between Rotation and Translation in State Estimation

Object tracking is an essential part of autonomous systems and deals with the estimation of the states of all dynamic objects within the area of surveillance. An appropriate state representation is highly important to capture the associated uncertainties correctly and Lie groups have been investigated with respect to this aspect over the last years. In this paper, we evaluate the performance of an extended Kalman filter on Lie groups employing two different state space representations: the direct and the semi-direct product of the two-dimensional special orthogonal group and a two-dimensional Euclidean vector capturing the rotational and translational component, respectively. Focus of our study is to investigate if the intrinsic relationship between rotation and translation is appropriately captured by means of the covariance matrix or if an encapsulation into the state space is beneficial. Statistical evaluation shows that a representation using the semi-direct product of the two components yields a better performance in terms of the Wasserstein distance and the root-mean-square errors of position and orientation.
Survey on Centric Data Protection Method for Cloud Storage Application

IT personnel are being reassigned the ability to alter resources to suit business shifting needs as businesses compete to get their applications up and operating quicker with increased manageability and less preservation. The Cloud Computing Mechanism, which is distinguished by a "pay as you go" pricing model, can help with this. An on-demand pool of adjustable computer resources is provided as part of the paradigm, along with storage options. There are security risks and weaknesses with Cloud Computing, just like with any new technology. These risks and vulnerabilities include illegal eavesdropping and data tampering, deliberate malicious data mutations, data loss and identity theft. Because of these security flaws, cloud computing is now a hot topic for researchers. An indepth look into cloud security research and mitigation methods is presented in this article. Among organizations, cloud storage and cloud services are the most commonly used applications. These two areas, however, present the greatest number of risks and security issues. Outsourcing sensitive data to cloud service providers (CSPs) creates trust difficulties within enterprises as a result of this arrangement. A comparative study of cloud storage and cloud service security frameworks is presented in this paper, which sheds light on the need for additional research.
The Importance of Emotional Education in the Era of the 4th Industrial Revolution: Focusing on Plato’s Tripartite Soul

The purpose of this paper is to reveal the importance of personality education in the era of the 4th revolution. Especially, what I want to focus on in this thesis is emotional education among personality education. However, recently experimental psychology and experimental philosophy argue that not only reason but also emotions participate in the process of moral judgment. Through the verification process using actual scientific tools, these facts have been clearly proven. This can be said to imply the importance of emotional education beyond education that emphasizes only rational activities in personality education. In other words, it means that not only reason but also emotions are necessary for right and rational judgment. Furthermore, it can be said that education on emotions is a necessary condition for cultivating right personality, which is the basic virtue for right and rational judgment. Therefore, this paper will reveal the importance of emotional education through fMRI (functional magnetic resonance imaging), a research method used by experimental philosophers or experimental psychologists. Also, I will examine why emotional education is important in Plato’s the tripartite soul and discuss the use of virtue ethics as a methodology for emotional education.
Estimation and Assessment of Direct Normal Irradiance from Meteorological Data: Complete Methodology and Application to Portugal

Engerer model was used to estimate hourly values of direct normal irradiance out of global horizontal irradiance ground measurements for Portugal. Model parameters were calibrated for Évora station (southern Portugal) and applied to a network of 90 stations in mainland Portugal, part of the Instituto Português do Mar e da Atmosfera. From modelled direct normal irradiance time-series, and for stations that comprise 20 years of data, typical meteorological years were determined. A comparison analysis to all stations showed annual availabilities up to ~2307 kWh/m2 in Castro Marim, deviating ~6.81 % from the estimation made by the Photovoltaic Geographical Information System (~2160 kWh/m2). Moreover, to identify locations of interest for possible installations of solar power systems, annual direct normal irradiance availabilities were mapped. This work aims to support decision making at energy policy and management levels by providing reliable estimations of direct normal irradiance.
Adaptive brokerage framework for the cloud with functional testing

In this paper, we present an Adaptive Brokerage for the Cloud (ABC) that can be used to simplify application deployment, monitoring and management processes in the cloud. The broker uses modern cloud infrastructure automation tools to test, deploy, monitor and optimise cloud resources. We used an e-commerce application to evaluate the entire functionality of the broker, we found out that different deployment options such as single-tier vs two-tier lead to interesting hardware and application performance insights. These insights are used to make effective infrastructure optimisation decisions.
Voice Conversion of Tibetan Amdo Dialect Based on Cycle-Consistent Adversarial Network

This paper proposes a Voice Conversion (VC) method of Tibetan Amdo Dialect based on Cycle-Consistent Adversarial Network (CycleGAN). Non-parallel VC performance based on CycleGAN is almost similar to parallel VC. The model used in this paper is a improved version of CycleGAN, which has been improved in terms of network structure and adversarial losses. In terms of network structures, the model uses a 2D Convolutional Neural Network (CNN) as a generator and uses PathGAN instead of FullGAN as an discriminator. In order to reduce the negative impact of excessive smoothing, a new discriminator is introduced to improve the adversarial losses. The improved VC model used in this paper shows a good effect than the Traditional CycleGAN model. Objective evaluation shows that VC of Tibetan Amdo Dialect based on CycleGAN is close to the spectrogram of target speakers. Subjective assessments mainly include naturalness and similarity. In terms of naturalness, the MOS score of the converted voice quality reaches 3.33. In terms of similarities, the converted voice is closer to the voice of the target speaker.
Hough Transform Sensitivity Factor Calculation Model Applied to the Analysis of Acne Vulgaris Skin Lesions

The number of infectious spots or pathological structures recorded on dermatological images is a tool to aid in the diagnosis and monitoring of disease progression. Dermatological images for the detection and monitoring of the evolution of acne infections are evaluated globally, comparing whether the increase or decrease in infectious lesions appearing on an image is significant. This evaluation method is only indicative since its accuracy is low. The accuracy problem could be improved by an exact count of the number of structures and spots appearing on the image. The mathematical function circular Hough transform (CHT) function implemented in MATLAB is here applied to develop a procedure for counting these structures. CHT has been used in the recognition of benign and distorted red blood cells, in the detection of pellet sizes in industrial processes and in the automated detection and morphological characterization of breast tumor masses from infrared images, as well as for the detection of brain aneurysms and use in magnetic resonance imaging. The sensitivity factor is one of the many parameters required to feed the CHT algorithm. Its choice is unclear as there is no proper methodology to select an optimum value suitable for each image. In this work, a procedure for determining the optimal value of the sensitivity factor is proposed The approach is validated by comparison with the results of the manual counting of the points (ground truth).
Convolutional Neural Networks for Enhanced Compression Techniques

This paper explores a compression framework combining convolutional neural networks (CNNs) with traditional image codecs. Various activation functions, codecs, optimizers, and upscaling techniques are tested to obtain the best image quality. While it was originally applied to grayscale images, the framework's performance with 8-bit IR images is also explored.
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Mechanical structure design of compact intelligent dustbin

Aiming at the low efficiency of manual garbage classification, a compact four-classification intelligent garbage bin is designed. The functions of the garbage bin is composed of a rotating wheel structure, a turntable structure and steering gear transmission structure. According to the size of the garbage that can be contacted in the current life, the data parameters inside the garbage bin are determined, the space is used reasonably, and the compactness of the garbage bin is improved. The turntable structure and the rotating plate complete the motion segmentation through the independent rotation of the inner and outer shafts of the roller, thus realizing the automatic garbage classification and greatly improving the throwing speed of garbage classification.
Solution Density and Search Strategy in Narrative Generation

Intelligent interactive narrative systems coordinate a cast of nonplayer characters to make the overall story experience meaningful for the player. Narrative generation involves a tradeoff between plot-structure requirements and quality of character behavior, as well as computational efficiency. We study this tradeoff using the example of benchmark problems for narrative planning algorithms. A typical narrative planning problem calls for a sequence of actions that leads to an overall plot goal being met, while also requiring each action to respect constraints that create the appearance of character autonomy. We consider simplified solution definitions that enforce only plot requirements or only character requirements, and we measure how often each of these definitions leads to a solution that happens to meet <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> types of requirements—i.e., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">density</i> with which narrative plans occur among plot- or character-requirement-satisfying sequences. We then investigate whether solution densities can guide the selection of narrative planning algorithms. We compare the performance of two search strategies: one that satisfies plot requirements first and checks character requirements afterward, and one that continuously verifies character requirements. Our results show that comparing solution densities does not by itself predict which of these search strategies will be more efficient in terms of search nodes visited, suggesting that other important factors exist. We discuss what some of these factors could be. Our work opens further investigation into characterizing narrative planning algorithms and how they interact with specific domains. The results also highlight the diversity and difficulty of solving narrative planning problems.
Mapping Heterogeneous Interfaces for System Integration

The utility of modern software systems is dependent on their ability to change. Driven by the need to remain relevant, software systems are subject to continuous and incremental integration. This need blurs the traditional separation between development and sustainment, and favors modularity as a highly desired architectural quality. Modularity in software systems partitions system functions into individually manageable services. Individual services can be tested, updated, scaled, and eventually replaced. A change in a service, however, often ripples through a system in the way it interacts with other services. The integration of a new service can be a non-trivial engineering activity that often requires interface changes. Such changes require mapping between two or more heterogeneous software interfaces. In this paper we examine the problem of interface mapping as it pertains to service-oriented architectures and provide system engineering considerations for design and implementation of interface-mapping software. Our considerations seek to inform the system engineering process and present practical guidance for reference. We demonstrate our engineering considerations as applied to an exemplar wealth aggregation service.
Long-range medical image registration through generalized mutual information (GMI): towards a fully automatic volumetric alignment

Objective.Mutual information (MI) is consolidated as a robust similarity metric often used for medical image registration. Although MI provides a robust registration, it usually fails when the transform needed to register an image is too large due to MI local minima traps. This paper proposes and evaluates Generalized MI (GMI), using Tsallis entropy, to improve affine registration.Approach.We assessed the GMI metric output space using separable affine transforms to seek a better gradient space. The range used was 150 mm for translations, 360° for rotations, [0.5, 2] for scaling, and [-1, 1] for skewness. The data were evaluated using 3D visualization of gradient and contour curves. A simulated gradient descent algorithm was also used to calculate the registration capability. The improvements detected were then tested through Monte Carlo simulation of actual registrations with brain T1 and T2 MRI from the HCP dataset.Main results.Results show significantly prolonged registration ranges, without local minima in the metric space, with a registration capability of 100% for translations, 88.2% for rotations, 100% for scaling and 100% for skewness. Tsallis entropy obtained 99.75% success in the Monte Carlo simulation of 2000 translation registrations with 1113 double randomized subjects T1 and T2 brain MRI against 56.5% success for the Shannon entropy.Significance.Tsallis entropy can improve brain MRI MI affine registration with long-range translation registration, lower-cost interpolation, and faster registrations through a better gradient space.
Enhancing Content Based Image Retrieval Technique by Observing Image Feature Extraction Methods

From the last few years, database of digital images has advanced substantially along with the techniques for image processing. Today, the databases of digital image are found in an increasing number, that provide useable and effective access to image collections. Image databases are becoming larger and more prevalent as a result of the Internet&#x2019;s spread and the accessibility of optical imaging technologies like digital camera systems and scanning of images, necessitating the development of image retrieval methods that are more productive and useful. The research focuses on feature&#x2019;s selection for extracting them in view to enhance the result of content-based image retrieval system. Identification of image features, corelating them on the basis of their effects, and the influence of these factors on retrieval are all part of this process. Low-level visual features that address more detailed perceptual components of visual data are observed along with high-level features that underpin in image retrieval techniques. As a result, the research is attempting to review these elements for improving the efficiency of CBIR search results. Further, in order to recognize the wider conceptual features of visual data, various features can be integrated with one another.
Research on the Prediction Method of the College Professional Admission Scores

This article is based on the web crawler technology to obtain the admission scores of some colleges and universities in various provinces, and integrate it with the existing college professional admission scores. The edit distance algorithm and the mean difference interpolation method are used to clean the data of similar duplicate records and missing records respectively. Using BP neural network theory and support vector machine algorithm to construct a network model with 9 characteristics of school, major, province, batch, discipline, average score, highest score, provincial control line and year as input, and the lowest score as output. Using labeling coding to encode non-numerical data to normalize the data. Preset 1% and 2% minimum score error bands and compare the pass rates predicted by two models in different preset error bands. Obtained that both models meet the preset 2% error, But within the 1% error band, the network model constructed based on the SVM algorithm has higher accuracy and the forecasting effect is better.
Performance Evaluation of Secure Web Usage Mining Technique to Predict Consumer Behaviour (SWUM-PCB)

In the changed scenario of e-commerce, not only the number of visitors but also the frequency of their visiting the site, browsing information is increasing day by day. So investigating client's conduct is a significant piece of web architecture which is helpful for decision making, planning sites as indicated by the client's needs. To understand and handle consumers’ web behaviour, web usage mining (WUM) is used which includes data preprocessing and pattern discovery. For pattern discovery, Apriori algorithm is selected as standard of the study. Apriori algorithm isn't just the primary utilized affiliation rule mining procedure yet also the most well-known one. After threadbare analysis, it came to the fore that the conventional Apriori algorithm suffered from two serious flaws. Firstly, it keeps filtering the database again and again and thereby generating, unnecessarily, enormous number of rows. Secondly, it lacks the foolproof system of security of data. The proposed algorithm (SWUM-PCB), which has been accomplished on genuine information of website by utilizing Hash table, Hashing Encryption (Message Digest algorithm MD5), association rule mining has a marked edge over the conventional Apriori algorithm. Not only its effectiveness and security of information is unquestionable but also its accuracy in predicting the consumer’s future conduct is found to be unparalleled. Experimental results to find a performance evaluation of the proposed approach will present better results on real weblogs (raw data) of a website in less execution time, memory usage, less number of rows, calculation cost and less searching time to predict the result.
Detecting Resource Release Bugs with Analogical Reasoning

The resource release bugs are a common type of serious programming bug. However, it is hard to catch them by using static detection for the lacking of comprehensive prior knowledge about the release functions. In this paper, a resource release bug detection method is proposed by introducing analogical reasoning on word vectors. First, the functions of the target source code are encoded into word vectors by the word embedding technique in natural language processing. Second, a two-stage reasoning method is developed for automatically identifying unknown resource release functions according to a few well-known seed functions. 3CosAvg algorithm is employed for the first stage, and a new algorithm is designed for the latter, called 3CosAddExchange. Finally, the identified release functions are translated into static analysis rules to detect potential bugs. The experiment shows that the proposed method is effective and efficient for the large-scale software project. Five unknown resource release bugs are successfully detected in the Linux kernel and confirmed by kernel developers.
Image Retrieval with Text Feedback based on Transformer Deep Model

Image retrieval with text feedback has many potentials when applied in product retrieval for e-commerce platforms. Given an input image and text feedback, the system needs to retrieve images that not only look visually similar to the input image but also have some modified details mentioned in the text feedback. This is a tricky task as it requires a good understanding of image, text, and also their combination. In this paper, we propose a novel framework called Image-Text Modify Attention (ITMA) and a Transformer-based combining function that performs preservation and transformation features of the input image based on the text feedback and captures important features of database images. By using multiple image features at different Convolution Neural Network (CNN) depths, the combining function can have multi-level visual information to achieve an impressive representation that satisfies for effective image retrieval. We conduct quantitative and qualitative experiments on two datasets: CSS and FashionIQ. ITMA outperforms existing approaches on these datasets and can deal with many types of text feedback such as object attributes and natural language. We are also the first ones to discover the exceptional behavior of the attention mechanism in this task which ignores input image regions where text feedback wants to remove or change.
Management Information System in context of Food grains: An Empirical Study at Eastern Maharashtra

The research article aims to know the role of Management Information System in Food grains (Soyabean and Tuwar) in Eastern Maharashtra. Indian Government in its market liberalization plan emphasized on the priority to the development of a market information system (MIS) which could be utilized by traders as well as to deliver frequent information by media on current market price and availability.In collaboration with NIC, the IT project department has created several vital software programs to assist farmers. This study aims at management information systems in the context of food grains (soyabean and tuwar) in Eastern Maharashtra. To ensure that the research design aligns with the research objectives, the researcher has made sure that the instruments used in the study are objective oriented such as Measure of central tendency and Z statistic. The result of the study suggests that because of technical complexity, end-users underestimate the agricultural information system's utility. Because of lack of agricultural knowledge, assistance for people information financing as a key priority in cultivation may dwindle. Farmers should have easier access to public information by increased funding for public information. More interactive information sources might persuade traditional farmers to embrace more modern farming techniques.
Aleksandr Bogdanov, ‘Science and the Working Class’

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