Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Diagnosis Masalah Kulit Yang Berbasis Web Dengan Metode Naive Bayes Classifier

As we all know, the world of technology and information has become very advanced in recent years. This has led to numerous new ideas that make it easier for a person or organization to do their work and achieve their goals. Progress in technology and information greatly affects all areas, from culture and education to the economy, health, and even beauty. So, in this study, the researcher brought up a problem at a beauty clinic in Maguwoharjo, DIY, called Emila Aesthetic Solution. The problem is that consulting with patients is still done face-to-face. In other words, the clinic does not have a computerized system to help with consultations so that patients do not have to wait in line and can get treatment right away based on the results of their consultation. As a result, this study focused on developing an expert system to diagnose skin problems using the naïve Bayes classifier method.In this study, the system was made using the naïve Bayes classifier and the waterfall model for system development. The model was then coded into the system to be used. The goal of making this expert system was to make it easier for patients to get advice and for the clinic to handle patients.Based on the patient history data obtained, namely 20 patient data that experts have tested, this expert system, by implementing the naïve Bayes classifier method, yielded results indicating that the system's accuracy was 100 percent.
Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List Scheduling

This paper investigates the problem of dynamic workflow scheduling in cloud computing. In a real-time scenario, the only available information is input data size, and the other task execution requirements, such as execution time, memory consumption, and output data size, must be estimated. In this study, we ask whether a more accurate estimation of task execution requirements can be obtained if workflow structure is taken into account explicitly and whether such estimations can result in more efficient task resource allocations and better computing resource utilization. We compare the estimation accuracy of a graph learning neural network, e.g., Recursive Neural Network (RecNN), with two standard prediction models (that do not consider the workflow structure), e.g., a linear and non-linear regression. We used two types of scientific workflows, Montage and LIGO, to train the prediction models. The execution time (makespan) comparison of the newly generated workflows with the original set of workflows shows that the RecNN model estimates the task information more accurately than linear and non-linear regression models, and the makespan of the workflow generated by the estimated values by RecNN is closer to the makespan of the original workflows. To the best of our knowledge, we are the first to consider the overall workflow topological structure in real-time workflow scheduling scenarios. The result shows that explicitly considering the workflow structure through structure learning models such as RecNN can considerably improve workflow scheduling in the cloud.
The Analysis of Hybrid Brain Storm Optimisation Approaches in Feature Selection

The volume of data available has risen significantly in recent years due to advancements in data gathering techniques in different fields. The collected data in many domains are typically of high dimensionality, making it impossible to select an optimum range of features. There are many existing research papers that discuss feature selection process used by metaheuristic algorithm. One of them is brain storm optimisation (BSO) algorithm, which is relatively new swarm intelligence algorithm that mimics the brainstorming process in which a group of people solve a problem together. The aim of this paper is to present and analyse hybrid BSO algorithm solutions combined with other metaheuristic algorithms in feature selection process. The hybrid BSO algorithm overcomes the lack of exploitation in the original BSO algorithm; and simultaneously, the obtained statistical results prove the efficiency and robustness over other state-of-the-art approaches.
Generalizing an Improved GrowCut Algorithm for Mammography Lesion Detection

In the past five years, 7.8 million women were diagnosed with breast cancer. Breast cancer is curable if it is discovered in early stages. Therefore, mammography screening is essential. But, since interpretation can prove difficult, various automated interpretation systems have been proposed so far. A crucial step of the interpretation process is segmentation: identifying the region of interest. In this paper we aim to evaluate an improved version of the GrowCut algorithm - which reduces both the human intervention and the computational time, while preserving a high level of accuracy, and analyzes three possibilities of avoiding the need for initial background seeds: (1) automatically generating seeds inside the breast, (2) using the mammogram’s black pixels and (3) not using background seeds - on a different, much larger dataset than the one initially used for experiments, in order to analyze the impact of a dataset’s particularities and the influence of different seed types on the segmentation results, and to validate the initial conclusions. However, the experimental results presented in this paper do not only validate the premises, but also demonstrate that the improved version is a generic algorithm which can be used on any dataset with different types of background seeds.
RACISM IN TONI MORRISON’S THE BLUEST EYE

The purpose of this study is to reveal the form of racism and its impact on the mental health of African American in Toni Morrison's The Bluest Eye. The design of this study is qualitative research because the data that support this research are collected from books, journals, articles, and internet. The researchers use two kinds of data collection, namely the primary source that is the novel itself and the secondary sources are books, articles, the internet, and journals. The approach needed in research ismimetic approach, because this topic is related to the environment and as a reflection of human life. In analysing the data, researchers use three techniques called data reducing, data display, and conclusion. Researchers have read the novel and then decided the topic. From the amount of data collected from various sources researchers use text or jotting to compares, contrasts, and sorts the required data, from the notes written by the researchers, then categorize the relevant data. The results of the analysis are racism experienced by African Americans in the form of rejection, insults and violence. Racism in the novel is not only practiced by white people but also among black people.
Online/offline and History Indexing Identity-based Fuzzy Message Detection

Fuzzy message detection is a novel cryptographic primitive in which the remote storage cloud can help the client carry out fuzzy detections with some false-positive rate. This primitive protects the privacy of clients and does not reveal exactly the matching messages to the untrusted cloud. However, the existing public-key-based schemes require many public keys to generate a single flag ciphertext. This introduces heavy costs in terms of public-key certificate management. In this paper, we propose an efficient identity-based fuzzy message detection method based on a novel identity-based online/offline encryption scheme. All heavy cost computation operations are carried out in the offline phase without the knowledge of the identities for each message, and the single flag ciphertext can be generated quickly with the message’s identities since only light cost computations are required in the online phase. We apply our scheme to the blockchain-based fuzzy message detection systems. Additionally, we design a new history indexing scheme based on Dodis’ verifiable random function and Schnorr’s signature. The transactions history and order of accessing storage cloud (storing and detecting messages) are indexed, and signed by each client. We provide the analysis of privacy guarantees and differential privacy requirements for our online/offline ID-FMD scheme, and implement our FMD system over the Fibos platform and the Huawei Elastic Cloud.
Robust and verifiable privacy federated learning

Federated Learning (FL) safeguards user privacy by uploading gradients instead of raw data. However, inference attacks can reconstruct raw data using gradients uploaded by users in FL. To mitigate this issue, researchers have combined privacy computing techniques with FL. However, these tech-niques may not ensure the Byzantine robustness of aggregation or the integrity of the aggregated outcomes. Most current robust privacy FL methods assess differences between gradients and benchmarks in the direction, allowing adversaries to poison the aggregation against the magnitude. Furthermore, these methods cannot ensure the integrity of the aggregation results. To over-come these challenges, this study proposes a novel algorithm, Robust and Verifiable Privacy Federated Learning(RVPFL), which can more effectively eliminate the poisoning attack of the opponent by measuring the direction and magnitude of the gradient in the ciphertext state. The proposed algorithm guarantees the integrity of server aggregation results while safe-guarding user privacy. In this study, comprehensive theoretical analysis and experimental validation of RVPFL are conducted to demonstrate its superiority. The proposed RVPFL algorithm solves the Byzantine robustness problem of aggregation and the integrity problem of aggregation results, which helps to research and develop more robust and effective privacy-preserving federal learning techniques.
THE DETERMINATION OF OCEANOGRAPHIC PHYSICAL PROPERTIES AT THE COASTAL AREA OF TEGAL CITY

Throughout our observations, the coastal area of Tegal has not been equipped with geological data and oceanographic data, especially the distribution of ocean current patterns around the coastal area of Tegal City. It is very important to know the nature of the pattern of ocean currents near the coast because the coastal area is for recreational activities and shipping activities to and from the port. The speed of ocean currents and where they are heading needs to be described so that the users of the coastal area can monitor well about the danger or not. In the study of the current pattern at the coastal area of Tegal City, the tool used was designed based on the Lagrangian method and the Helmholtz method to obtain the direction and magnitude of the ocean current's velocity. The problem in determining the current pattern is that the current signal is the result of various signals having a certain frequency generated by several different forces. The next problem in determining the height of the waves, the initial coordinate point is needed as the zero point. From this problem, researchers are interested in the current pattern that is formed. Ocean currents are the mass movement of seawater from one place to another, either vertically (upward motion) or horizontally. The purpose of this study finds the energy of ocean currents and the physical properties of oceanography near the coast, to get the level of danger if used for recreational areas, shipping activities, and research activities.
On Density of Grid Points in l∞-Balls

Finding the minimum and the maximum densities for axes-parallel squares, cubes, and hypercubes, cast in the integer space, is an important problem in the domain of digital geometry. In this work, we study different variations of this problem and solve a number of them. Interestingly, the extremum values for integer sizes sometimes differ from those for real sizes, and hence, we have studied and analyzed them separately. Further, the results and proofs in 2D readily extend to higher dimensions, and hence we could get simple-yet-novel theoretical results for the extremum densities for l∞-balls in general. As ‘density’ provides a measure of how a set of points bounded by a region is relatively more concentrated or sparse, it has applications in image analysis, social networking, complex networks and related areas, apart from different branches of physical science. Hence, our results are fundamental in the understanding of locating the density minima and maxima in a discrete space of an arbitrarily large dimension.
Development of Mobile Information Collection System Based on IServer

Data is the "blood" of GIS. GIS focuses on data input, analysis and output, so data collection is particularly important. Traditional surveying and mapping has some shortcomings, such as backward equipment, low accuracy of data acquisition, low acquisition efficiency, and high cost of manpower and material resources. In the information collection of modern urban planning, traditional surveying and mapping has been difficult to meet the needs of data collection. At the same time, the emergence of various mobile intelligent devices and the increasingly mature development of related software and hardware make mobile terminal information acquisition technology gradually replace traditional surveying and mapping in field operations. This paper combines traditional surveying and mapping and modern information technology to develop the mobile terminal information acquisition system based on iServer. First, learn about the development of information collection technology in mobile GIS at home and abroad by consulting a large number of relevant literature, and define the value and significance of developing mobile terminal information collection system. Then, analyze the shortcomings of traditional surveying and mapping in field survey and the problems existing in mobile GIS collection information at present, combine available resource tools to form a project demand analysis, and conceive a project outline design according to the demand analysis, Finally, the information collection system based on iServer and Android is designed in detail, including software functional framework, database design and server side functional design. Theoretical analysis and practical results show that the system can effectively improve the accuracy and speed of data acquisition while reducing the cost of data acquisition.
High-frame-rate reconfigurable diffractive neural network based on superpixels

The existing implementations of reconfigurable diffractive neural networks rely on both a liquid-crystal spatial light modulator and a digital micromirror device, which results in complexity in the alignment of the optical system and a constrained computational speed. Here, we propose a superpixel diffractive neural network that leverages solely a digital micromirror device to control the neuron bias and connection. This approach considerably simplifies the optical system and achieves a computational speed of 326 Hz per neural layer. We validate our method through experiments in digit classification, achieving an accuracy of 82.6%, and action recognition, attaining a perfect accuracy of 100%. Our findings demonstrate the effectiveness of the superpixel diffractive neural network in simplifying the optical system and enhancing computational speed, opening up new possibilities for real-time optical information processing applications.
GAZE - a benchmark sample of free gaze behaviour towards complex scenes

Observers free-viewing scenes reliably vary along oculomotor metrics as well as their tendency to fixate objects with specific semantic attributes, like faces and text. Other findings suggest some of these gaze biases are heritable and associated with neurodevelopmental disorders. However, most studies examining free gaze behaviour used small samples, unlikely to reflect the breadth of human variability. Here, we moved towards a more comprehensive understanding of individual gaze by collecting a massive dataset of scene viewing behaviour outside the lab. We collaborated with a science museum with 150k visitors per year to build a fully automated eye-tracking exhibit. During participation, subjects pass through a survey and calibration routine before freely viewing a video and 40 natural scenes - a short test of individual gaze, which we previously validated. So far, we collected > 2 million fixations from > 6,000 female (54%), male (42%) and diverse (4%) observers, ranging from 4-85 years of age. We find large variability for oculomotor measures like avg. fixation duration (3-fold range: 239ms-771ms) and saccadic frequency (4-fold range: 0.7-3.3 Hz), as well as for individual salience biases, like the central-bias (4-fold range), the proportion of dwell time on text (0% - 53%) and faces (9-fold range: 9% - 86%). The large and diverse nature of our sample allows us to tackle novel questions regarding the development and diagnostic potential of individual gaze. A first analysis revealed surprisingly late plasticity, showing a 2-fold, linear increase in text salience from age 4 to 24 (R^2 = 93% for age-wise bins), which was complemented by a 14% decrease in face salience; as well as a U-shaped central-bias development across the lifespan. In ongoing clinical collaborations, we collect data from children with ASD and ADHD, allowing us to probe the diagnostic potential of our paradigm against a massive benchmark sample.
Reward Maps Predict Target-present and Target-absent Visual Search

Predictions of attention control are typically formalized as priority maps, and many types have been proposed (e.g., saliency maps). We add to this list by introducing a reward map, a map of the reward expected by fixating different locations in an image. In the context of goal-directed attention, our premise is that achieving a goal is rewarding and that goal-directed fixations are controlled by computing expected reward and attempting to maximize its receipt. We obtained reward maps for the COCO-Search18 target-object categories using Inverse-Reinforcement-Learning (IRL), which learns from observations of search fixations (in the training data) a reward function for predicting the scanpaths of fixations made by people searching scenes for categories of target objects. Reward maps are therefore priority maps that are reverse-engineered from the training fixations using IRL. From this purely data-driven approach, we found that reward maps explained the combined variability of saliency maps, target maps, object maps, and meaning maps in predicting target-present and target-absent search behavior, thus supporting our hypothesis that the pursuit of expected reward is the common thread stitching together these other attention biases. Moreover, scanpath predictions from reward maps approached the noise ceiling imposed by agreement in participant behavior (meaning there is little room for improvement) and came close to achieving state-of-the-art (SOTA) against scanpath-prediction models in computer vision. This SOTA, however, is a black box, whereas predictions from reward maps are highly interpretable as reward. We conclude that goal-directed attention control can be understood as seeking out expected goal-related reward, and that a reward map may be THE priority map—the common priority representation into which other bottom-up and top-down biases collectively exert their control over behavior. Our work also enables scanpath prediction researchers to weigh interpretability benefits against the (often negligible) performance costs incurred by making biologically plausible modeling decisions.
Stop pretending your trials are independent: Learn more from your data with asymptotic regression

Typical experiments in vision science include a brief set of practise trials to ensure participants understand the task, and then a series of trials that repeat a set of conditions multiple times. The unit of analysis is typically a measure of central tendency taken from all the trials within a given condition/participant, which represents an estimate of performance in that condition that can be compared to other conditions. But inherent in any set of repeated trials is the way performance changes; for example, stimulus-response mappings get faster and less error-prone, and participants tune their attention to pick up information from the appropriate locations and start to anticipate sequences of events and their timing with more precision. We throw all this information away when we express performance as an average. Multi-level models (LMM) are an increasingly popular approach, but these assume that all trials are statistically independent and identically distributed. Here we advocate for applying a straightforward asymptotic regression (Stevens, 1951) to repeated-measures performance data and show how it provides a richer and more accurate measure of the effects of different manipulations on performance. Methods based on "average performance" (whether using aggregate statistics or a multi-level framework) tend to result in statistics that are biased by the early trials. We demonstrate the utility of asymptotic regression using data from classic paradigms such as visual search and show the extent to which established effects are driven by a) performance in early trials b) differences in the stable performance asymptote and c) how the learning rate varies between conditions. We think asymptotic regression should become a standard tool applied to repeated measures data to provide a richer picture of the dynamic changes in performance inherent in experiments with repeated measures.
Directional effects on saccadic sequence and post-saccadic oscillations

Video-based eye tracking is widely used in current vision research, which tracks the pupil center as a proxy to the movement of the eyeball. It has been shown that saccades recorded by such eye trackers may contain post-saccadic oscillations (PSOs), which are positively correlated with the deceleration of the main saccades. However, most research into saccades and PSOs using video-based eye trackers are recorded in cardinal directions only. Oblique direction saccades have been studied mainly by other eye tracking devices, such as scleral search coils. As saccades in cardinal directions are mainly driven in isolation by one of the two pairs of ocular muscles, but oblique saccades are driven by a combination thereof, it stands to reason that different force profiles between cardinal and oblique saccades may lead to different saccadic sequence profiles. We compared the effect of saccade direction on both the main saccadic sequence and PSOs with a video-based eye tracker. Eye movement data was recorded in a dim environment. Participants were asked to make repetitive saccades following a small annulus on a computer screen in eight directions, including horizontal, vertical and obliques. Peak velocity, peak acceleration and peak deceleration of main saccades were compared and PSOs amplitudes were further analyzed and compared with the main sequence parameters. For the main saccades, we find that compared with horizontal and vertical directions, acceleration and deceleration are significantly higher in the oblique directions, although peak velocity is approximately the same. Meanwhile, PSOs amplitude is significantly smaller in the oblique than cardinal directions, even though the deceleration of the main saccadic sequence is higher. Our results show that the movement profiles for oblique saccades may differ from cardinal directions. The relationship for the parameters of the main saccade and the PSOs should be reconsidered in oblique directions.