Reviewerly/exHarmony huggingface dataset:
Artificial Neural Network Modeling of Industrial Liquid Level Control

System modeling is a scientific method that combines theory with experimental studies and has an important place in research activities. With the system model, the data to be obtained through real tests and experiments are provided more economically in terms of cost and the critical points of the system are provided with time savings. Some system models are very difficult to obtain using only analytical equations and methods. At this point, artificial neural networks are an alternative way to model complex, uncertain, nonlinear systems. Artificial neural network is an artificial intelligence system that takes the human brain as an example, learns from existing examples, can produce results with noisy, incomplete, non-linear data, and can make predictions and generalizations with high speed and accuracy after learning once. In this study, RT 512 liquid level control system produced by GUNT Hamburg, an experimental process control system for educational purposes, was modeled with an artificial neural network. In order to create the dynamic model, an input-output data set was created by operating the system in open-loop mode. In this set, the level change seen in the liquid level tube against the given control sign has been taken into account. For this process, a certain number of output data was obtained for a certain number of input data by using computer, Arduino, MCP4725 DAC, current/voltage, voltage/current converters. In the developed ANN model, the relationship between the regression curves and the model output and the test data taken from the system was observed and high accuracy was obtained.
A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder

Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-encoder and convolutional auto-encoder (CAE) is improved, supervised learning auto-encoder and improved convolutional auto-encoder are proposed, and a hybrid convolutional auto-encoder model is constructed by combining them. It can extract low-dimensional abstract features of road environment images by using convolution layers and pooling layers in front of the network, and then supervised learning auto-encoder are used to enhance and express semantic segmentation features, and finally de-convolution layers and un-pooling layers are used to generate semantic segmentation results. The hybrid convolutional auto-encoder model proposed in this paper not only contains encoding and decoding parts which are used in the common semantic segmentation models, but also adds semantic feature enhancing and representing parts, so that the network which has fewer convolutional and pooling layers can still achieve better semantic segmentation effects. Compared to the semantic segmentation based on convolutional neural networks, the hybrid convolutional auto-encoder has fewer network layers, fewer network parameters, and simpler network training. We evaluated our proposed method on Camvid and Cityscapes, which are standard benchmarks for semantic segmentation, and it proved to have a better semantic segmentation effect and good real-time performance.
The Correlation between Reading and Speaking Skills on Students’ English Learning as Foreign Language

Reading is the action of extracting meaning from a text or passage and obtaining information from the author. Speaking is the practice that using words to interact with others. Than English language learning is a behavior that tends to have relative provisions and produces strong actions. The problem that discussed are focused on reading and speaking skills because students felt unconfident to read and speak English and them afraid of making mistakes in English learning.&#x0D; This research used quantitative correlation design to approach the data. The population was 642 students’ at the first grade in SMA Ibrahimy sukorejo. The sample selected as many as 62 students in the study was carried out using a side-by-side purposive technique while data collection was carried out by tests and observations the data analysis used Pearson product moments to analyze the relationship between reading and speaking skills with learning English in class 1 socio IV at SMA Ibrahimy Sukorejo of Situbondo.&#x0D; The research's findings indicate that students' reading and speaking skills have a positive and correlation significant of English learning and alternative hypothesis is accepted. Based on The score sig 1.000 ≤ 0.05. The Alternative Hypothesis (Ha) is accepted based on the results of the hypothesis testing, and based on the output of the analysis of the Pearson formula in SPSS, it has a significant score (Sig) of 0.24 and 1.000. Score Sig 0.05 indicates that the independent variable has an impact on the dependent variable. It shows there is a correlation between reading and speaking skills in the first grade of social 4 at SMA Ibrahimy Sukorejo students learning English as a foreign language.
The Implementation of Audio Visual as Media to Increase Speaking Skill

This research was classroom action research (CAR) which focuses on the implementation of audio-visual media in the classroom. The method in this study was qualitative descriptive that used by the Kemmis and Mc Taggart method which involves four components, such as: (1) Planning (2) Action (3) Observation and (4) Reflection. The object of this research were students collage at the second semester. Total participants in this study were 11 students. The researcher was a subject (researcher and actor in the study). The implementation of Audio-visual media were carrying out for 3 cycles. The result of this application were increasing English language skill with the right language rule. By using Audio Visual, the students directly imitated the speaker's language style, such as; conversation, pronunciation, accent, mimicking, gestures, intonation, the use of words and sentences that used on communication. This result support with previous research that reading, listening, seeing and practicing will be easier to remember and learn. By using audio-visual media, it can mediate learning to master the four components in scientific understanding in learning English language.
The Effectiveness of Game Quizizz Using Learning Media On Students' Outcomes

In this neo culture technology, the role of technology and media, then becomes very large in the 21st century learning process and its various implications. In learning process may vary between teachers, technology, and media. This study aims to determine the influence of Quizizz learning material-based educational games on the performance of junior high school students in foreign language dormitories at Language Boarding House (LBH) Salafiyah Syafi’iyah Sukorejo. This research used a non-equivalent group, a post-trial semi-experimental research design, it is an examination of learning activities in the form of actions that deliberately appeared and occur in the class simultaneously. Collection of the data techniques were done by tests and questionnaire. The use of Quizizz educational game-based learning materials is effective in improving the academic achievement of Intermediate Foreign Language Boarding (LBH) students. It is proven by the t count’s value (3.373) &gt; t table (2.160). The results of the effective use of Quizizz educational game-based learning media are high/good on the learning outcomes of Intermediate Language Boarding House (LBH) students. It is indicated by the percentage of students with scores greater than or equal to 80 is 43% percent and the percentage of students with scores greater than or equal or equal to 70 is 86%. And the percentage of student response questionnaires shows that 71% of student responses are very good and 29% are good.
Financial information security using hybrid encryption technique on multi-cloud architecture

One of the most severe issues in the cloud is security. In comparison to financial data, so it is extremely sensitive and must be safeguarded against unwanted access. We have developed a proposed system based on three different keys. We divided the data into insensitive, sensitive, and highly sensitive data. The data will be saved on a separate cloud server. The proposed system used different keys for encryption and decryption purposes. The elliptic curve cryptography (ECC) based distributed cloud-based secure data storage (DDSPE) approach was proposed to provide secure large data based data protection across the different clouds. With DDSPE technology, the ECC scheme has been used for encryption and decryption purposes. The cloud is used for simulation. The results of the tests reveal that the suggested DDSPE system is safe and saves time regarding data retention and retrieval. To analyze performance, we compared the DDSPE method with advance encryption standard (AES), blowfish, rivest shamir adleman (RSA), security-aware efficient distributed storage (SA-EDS), and attribute based encryption (ABE) based secure distributed storage (ASDSS). In terms of information retention and recovery, our methodology is quite effective because it requires less amount of time as compared to other strategies.
SnapUnlock: A Contrastive Learning-Based Contactless Authentication via Heterogeneous Sensors

Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.
A MOUSE’S COURAGE: THE CHARACTERIZATIONS OF THE BOY IN ROALD DAHL'S THE WITCHES

This study aims to analyze the character of The Boy as the main protagonist character in Roald Dahl's The Witches. The researcher collected the data by using the method of close reading and note-taking. As the main protagonist in the novel, The Boy was described as cheerful, curious, brave, and intelligent. He was described to be optimistically move on with his life after his parents’ deaths and after he was turned into a mouse. He was also a curious child, which lead him to investigate the witches' activity at the resort. As a brave and intelligent mouse, The Boy found tricks to tackle the Grand Witch and her army of witches. As a child who was turned into a mouse The Boy’s characterizations enabled him to save other children from being turned into a mouse. These characterizations could inspire children and readers in general to be optimistic in facing life.
Optimized Demand Information Sharing Model of Dual-Channel Supply Chain in E-Commerce

In the dual-channel supply chain, the relationship between the electronic direct selling channel and the traditional selling channel is situated in an antagonistic game. The present study used the master-slave game model to analyze the dual-channel supply chain structure, where the manufacturer is the leader of the game, and the seller is the follower of the game. Based on the game analysis theory, the game order of manufacturer and seller is analyzed in the state of demand information sharing. The decision model of each member in the dual-channel supply chain is constructed, and the optimal production decision and product price decision under the demand information sharing between the seller and the manufacturer are determined by the reverse induction method. The expected economic benefits of both are calculated, respectively. The experimental results showed that the economic benefit of the model is higher than that of the traditional demand information nonsharing model, which could enhance the economic benefit of the manufacturer and the seller at the same time and alleviate the contradiction between the electronic direct selling channel and the traditional selling channel.
A Systematic Literature Review on Leukemia Prediction Using Machine Learning

Blood cancer is one of the most dangerous diseases in kids because it spreads throughout the body, damages healthy cells, and causes uncontrolled white blood cell growth. If it is not promptly treated, blood cancer can kill a person. Leukemia, commonly known as "White Blood Cancer," is the most common type of blood cancer and it has a devastating effect on many people. In essence, it develops in the marrow of bones. Uncontrolled white blood cell proliferation in leukemia harms the body and creates malignant cells. However, throughout the past few years, numerous computer-aided technologies have been employed to manage, identify, treat, and detect leukaemia. Many researchers presented algorithms, methods, and approaches to treat this disease, and after applying them with traditional dataset samples and some of their own created ones, they produced many positive outcomes. Because imaged data sets are utilized in medical examinations, big data plays a significant part in the data collecting for datasets. Large datasets produced positive results for machine learning algorithms such as convolutional neural networks, which are regarded as the best method for classifying images. Hematologic malignancies, which include blood-related diseases like anemia, are cancers that are connected to the blood. Leukemia and multiple myeloma, Hodgkin’s lymphoma, non-Hodgkin’s lymphoma and encompasses acute AML( Acute Myeloid leukemia ), ALL (Acute Lymphoblastic Leukemia), CML( Chronic Myeloid Leukemia ), and CLL(Chronic Lymphocytic Leukemia) and affect adolescents, young grownups and the aged, and their prevalence increases with age. The main objective of this research is to present a systematic literature review associated with survey questions on leukemia detection by using machine learning algorithms, techniques, and methods recently. In this study, we outline some past and present uses of machine learning in leukemia prediction, identify manual diagnostic issues, and then discuss the historical background of leukemia discovery. We have also covered leukemia recognition, research motivation, research limits, the composition of white blood cells, and leukemia detection techniques. In order to do our review methodically, the methodology was written with the classification of papers according to their quality.
Making the most of what you pay for by delaying tasks to improve overall cloud instance performance

Resource elasticity and server consolidation have long been among two of cloud computing’s most relevant management tools. Yet, exemplified with a scientific application use case, this paper highlights how judicious scheduling of tasks can help maximize resource utilization and improve performance and costs for both users and cloud providers. Developing an efficient cloud service for DNA sequence comparisons is adopted as a motivating use case. Using the bioinformatics tool MASA that finds an optimal pair-wise sequence alignment, we propose a model for co-scheduling multiple alignments on a single cloud instance. The resulting, practically optimal, non-preemptive schedule can effectively double the throughput of MASA-based sequence alignment workflows.
Modal analysis of beam-like structures using multipoint dynamic testing vision system based on composite fringe pattern

Abstract A multipoint dynamic testing vision-based system for beam-like structures based on composite fringe pattern (CFP) was proposed. The CFP was decorated on the surface of a beam as a sensor, whose image sequences were captured by a camera for dynamic identification. The CFP consists of two cosine fringe patterns. The fringes on both sides of the CFP are used to locate the measuring positions and the middle one is used to measure the spatial displacement of structures. The key advantage of the proposed method is that it can measure multipoint vibration information along the length direction of the beam at one measurement without point-by-point scanning. Experiments on two different beams were carried out, and the results show that the method can get multipoint vibration information and the modal shapes of the beam. Therefore, the vision and CFP-based measurement method is suitable for vibration monitoring and modal analysis of beam-like structures.
Stereo vision-based 3D pointer for virtual object interaction

Modern advances in optical metrology and computer vision have provided an unprecedented ability to generate a wide variety of 3D digital content. The mouse, trackpad, and touch screens are typical 2D interactive interfaces of digital content. However, such interfaces are restrictive to manipulate 3D content such as models, object scans, and environments. In this work, a 3D pointer based on stereo vision to interact virtually with digital 3D objects is proposed. The theoretical principles and the experimental calibration procedure are provided. The proposed 3D pointer is evaluated experimentally by simple interaction routines with objects reconstructed by fringe projection profilometry.
Multi-baseline stereo vision for three-dimensional object reconstruction

Three-dimensional object reconstruction is an essential task in many computer applications. In essence, it consists of firstly estimating the disparity of all corresponding points of an observed scene from a pair of stereo images and then determining the depth map of the scene by triangulation from the estimated disparity. Conventionally, the baseline is fixed in general-purpose stereo cameras. This can limit the resolution and robustness of the three-dimensional reconstruction. In this work, a multi-baseline stereo vision approach for three-dimensional object reconstruction is presented. The mathematical principles of multi-baseline stereo vision are provided. Additionally, experimental results of three-dimensional object reconstruction are presented and discussed in terms of objective measures.
Camera pose estimation based on local image correlation

Camera pose estimation is an essential task in many computer vision applications. A widely used approach for this task is given by the specification of several corresponding points in a pair of captured input and reference images. The effectiveness of these methods depends on the accuracy of the specified points and is very sensitive to outliers. This work presents an iterative method for camera pose estimation based on local image correlation. The pose of the camera is estimated by finding a homography matrix that produces the best match between local fragments of the reference image constructed around the specified points and their corresponding projective transformed fragments of the input image when using the estimated homography. The performance of the proposed method is tested by processing synthetic and experimental images.
Action Recognition using Time-series Heat Maps of Joint Positions from Volleyball Match Videos

Data analysis in sports is becoming increasingly important, and one of the sports in which sports analysts play an active role is volleyball. Volleyball analysts have the task of annotating match videos, a time-consuming and technically challenging task that makes use of data difficult. In this paper, we propose a method for recognizing players' actions from volleyball game videos using time-series heat maps of joint positions to automate the analysis of volleyball match videos. In experiments to verify the effectiveness of the proposed method, we confirmed that the use of time-series heat maps of joint positions improves both the accuracy and F1 score compared to the baseline method using only RGB images as an input. We also confirmed the effectiveness of the proposed method in recognizing players' actions from volleyball match videos, which were not included in the dataset.
Exploiting Social Capital for Recommendation in Social Networks

Many computational techniques have been proposed by social networks to analyze the users' behaviors to recommend relevant content for them.Social networks generate a huge volume of information, which users cannot consume, generating a problem known as information overload.This way, filtering relevant information to help users with this problem becomes necessary.Social networks have many available features, such as relationships and interactions, which can be used to investigate the users' behaviors regarding news on their feed.The value of news can be defined as Social Capital, which is used by this work to model the user's preferences.This paper aims to investigate, model, and quantify interactions on social networks by exploiting social capital to develop a recommender system.Hence, in order to evaluate recommendations, an experiment was conducted with real users.Results show that our proposal was able to generate relevant recommendations on at least 62% of the scenarios.
Case Study of Designing Interface of the AGH Students Information Bulletin Work Support System

In this paper, we present a case study research on designing the system interface for handling organisational processes implemented in the editorial office of the AGH-UST Students Information Bulletin.A study of the basic formal and legal sources, such as regulations and statutes of the organisation, was also carried out for the proper identification of goals, responsibilities, and organisational structure.To deepen this knowledge, social research was carried out, which examined how the users use the current solutions and what their needs are related to the designed system.Based on the research results and the formulated conclusions, the diagrams of business processes carried out in the organisation were created.In the discovered process models, we defined reusable sub-processes using Justified Aggregation of Neighboring Activities.Finally, the information architecture of the target system solution, followed by a mediumfidelity interface were designed.