Reviewerly/exHarmony huggingface dataset:
BRB U-Net: Bottleneck Residual Blocks in U-Net for Light-Weight Semantic Segmentation

Cell is the fundamental entity of all living organisms.Understanding cell behaviour is improtant to study the biological processes in living organisms.In this work semantic segmentation of cells in microscopy images is studied.Specifically a novel deep learning architechture, BRB U-Net is proposed for the semantic segmentation of cells in microscopy.Bottleneck residual blocks are incorporated in U-Net architechture to achieve a light weight semantic segmentation model.The proposed method is evaluated with Phc-C2DH-U373 dataset of cell tracking challenge and achieves 0.9430 and 0.8383 dice similarity coefficient and intersection over union respectively.BRB U-Net achieved 7.68 times less number of parameters and model size is 7.35 times lesser than U-Net.
Frictionless Data Standards

Getting insight from data is not always a straightforward process. Data is often hard to find, archived in difficult to use formats, poorly structured and/or incomplete. These issues create friction and make it difficult-to-use, publish and share data. The Frictionless Data initiative at Open Knowledge Foundation aims to reduce friction in working with data, with a goal to make it effortless to transport data among different tools and platforms for further analysis. The corresponding toolkit consists of a set of standards for data and metadata interoperability, accompanied by a collection of open source softwareÂ that implement these standards, and a range of best practices for data management.Â In this article we are going to focus on data standards, why they are important, and, using the Frictionless Data standards as a base for the reflection, we will try to understand what makes - in our opinion- a standard successful.
Comparison of Freeze Software in Anti Forensic using National Institute of Standard and Technology Method

The development of technology brings rapid changes in the fields of software, hardware and brain ware.Solid State Drive (SSD) is generally used as an operating system container because it is considered more efficient.To reduce maintenance costs for frequently used software such as Deep Freeze, Shadow Defender and Reboot Restore Rx.But software like this can also be exploited for computer crimes, such as removing original data so that an investigator can't find the evidence, he's looking for a trial.it is commonly called Anti-forensic.Previous research on one of the software freezes turned out to be able to inhibit investigators from working.However, there is no definite comparison for each software freeze.To reveal these facts, research was made that is able to display the results of the analysis of the scenarios that have been designed using digital forensic science.The objects of this research are three virtual OS that have been manipulated in the form of steganography in freeze mode Deep Freeze, Shadow Defender and Reboot Restore RX.Methods in collecting data are literature study, experimental and simulation techniques.The stages of the research carried out were starting from looking for literature references, designing case simulations, analyzing simulation and research needs, running case simulations.Next, look for digital evidence to find the third difference in the success or effectiveness of software freezing to become anti-forensic using the National Institute of Standards and Technology method with stages in the form of collection, examination, analysis, and reporting.The results obtained were, in the search for digital evidence, the examination process did not go well.The success rate of software freeze in inhibiting the examination process on a virtual operating system is very high.The operating system installed with the Deep Freeze application has an effectiveness of 93.23%, the Shadow Defender application is 90%, and the Reboot Restore RX application is 100%.Shows software freezes are proven to be effective for investigations on storage forensics cases
Web Server Security Analysis Against Cross Site Scripting (XSS) Attacks using Penetration Testing

A web application is a program that can be accessed online via an intranet or the internet.This web app is a digital donation service available on mobile and on the website.Web apps that have not undergone security testing are vulnerable to hacker attacks.Web application performance will decrease due to vulnerabilities caused by hackers.The problem with implementing web apps security is that they have never tested the security of web apps, have not implemented a good standard of security analysis, especially in terms of dealing with Cross Site Scripting (XSS) attacks, and indeed needs to be tested because to avoid the risks that will occur.Penetration testing is carried out to secure web apps which are used as recommendations for follow-up repair solutions in securing web apps.Penetration testing is a popular technique, by actively evaluating defenses and web servers through the preparation and execution of all feasible attacks to find and exploit existing vulnerabilities.In this study, security testing was carried out using penetration testing with the zap and acunetix tools .This penetration testing consists of seven stages, namely: pre-engagement, information gathering, threat modeling, vulnerability analysis, exploitation, post exploitation, and reporting .The test results with Acunetix found a medium level Cross site Scripting (XSS) vulnerability, while the ZAP tool testing that has been carried out has identified 11 vulnerabilities, 2 medium level vulnerabilities, 7 low level vulnerabilities, and 2 informational vulnerabilities.The results of the recommendations are in accordance with the results of the analysis, so web apps need to use input validation for acceptable input that is truly in accordance with the specifications.
Arabic Minimal Pairs Word Detection and Disambiguation

This work is an attempt to solve a common writing problem and pitfall for Arabic language.The problem involves words that contain letters such as (among many others) ‫ظ‬ THA and ‫ع‬ DHA.The problem involves terms that are formally minimal pairs (more precisely near minimal pairs), near homographs (homophones), it requires determination of the right term and resolutions of created ambiguities.It is not just embarrassing to the authors, but in many situations, it results in wrong usage of words and consequently can lead to an ambiguous sentence(s).It becomes difficult to interpret such words or sentences, especially by computer involved in applications such as information retrieval, language translations and summarizations.A very amalgamated determination process was suggested that is comprised of multiple stages of feature selection, classifier selection and classification.A sample set of terms selected with a reasonable success rate making classifiers accuracies vary, but overall, all terms are reasonably accurate and close in values.MCC values are also variable with some reasonable good ranges.It is notable that some classifiers did not converge and the MCC is set to zero.Considering results obtained from classifiers with highest training rates and those with highest MCC, It can be easily concluded that Random Forest algorithm is the champion with high accuracy in most of the terms, and many times very close to the highest rate, classifiers were close.It also scored the highest for the mean values calculation across all terms.We can easily say that a combination of extracted features from a corpus along with machine learning classification techniques, the problem can be solved with high accuracy.
Implementation of Wireless Sensor Network (WSN) for Earthquake Detection

The current earthquake monitoring system uses a seismometer that can capture seismic vibrations very well but is expensive, heavy, and difficult to launch. Therefore, earthquake monitoring stations can only be launched in a few places in small numbers. This study aims to implement a Wireless Sensor Network (WSN) system for earthquake monitoring. The WSN system has advantages in cost, size, and ease of launch, so it is very appropriate to be used for this purpose. An earthquake detection sensor system has been designed in this study using a vibration sensor and a piezoelectric sensor. When an earthquake occurs, the resulting shock will trigger the vibration sensor and activate the sensor node. The shock data is then captured by the piezo sensor and processed by the microcontroller using Fast Fourier Transform (FFT) to determine the frequency value of the shock. The data is then sent to a gateway via a sensor network and uploaded to the Cayenne monitoring website. Operators can then view the data on the website. Three sensor nodes are implemented in this study. The test is done by placing those sensor nodes together in random positions. A shock is then given to the three sensor nodes, and the resulting data is then observed. The results show that the three sensors can detect, retrieve, process, and send shock data to the Cayenne monitoring website.
Multi Sensor-Based Obstacle Avoidance Algorithm in Visual Engineering Environment

Obstacle avoidance is an essential problem for applications involving multiple wheeled mobile robots. This research proposes a simple obstacle avoidance rule utilizing only one type of sensor, i.e., infrared sensor. In this research, multiple infrared sensors are placed on a mobile robot, arranged 45° radially equidistance. By using a low-cost and easily available infrared sensor, the cost and time consumed to build and repair a wheeled mobile robot are considerably reduced. Avoiding rules, based on simple behavior, such as “turn”, “stop”, “follow”, and “ignore” are developed. By applying these rules, each robot can refer to the motion of other robot or stationary object to avoid collision. Simulation results show that the proposed algorithm performs well, at 66.7% chance of avoiding a moving object and at 93% chance of avoiding a stationary object.
Tracking the time course of phonological neighborhood clustering effects in spoken word recognition

This study used visual-world eye tracking to examine the effect—first observed in Chan and Vitevitch (2009)—of the phonological neighborhood clustering coefficient on the time course of lexical access in spoken word recognition. Target words from neighborhoods with relatively high clustering (i.e., neighbors of the target word are also neighbors of each other) showed a significant lag in eye fixations relative to words from less clustered neighborhoods after controlling for neighborhood density, target frequency, neighborhood frequency, and multiple phonotactic probability measures. This effect was also influenced by lexical frequency, neighborhood density, and neighborhood frequency, suggesting that neighborhood clustering influences spoken word recognition, and should be included in models of this process.
Voice Command Recognition for Movement Control of a 4-DoF Robot Arm

Robots are widely used in industry. Robots generally have a control system or intelligence embedded in the processor. The robots consist of mobile mode, manipulator, and their combination. Mobile robots usually use wheels, and manipulator robots have limited degrees of freedom. Both have their respective advantages. Mobile robots are widely applied to environments with flat floor surfaces. The manipulator robots are applied to a static environment to produce, print, and cut material. In this study, the robot arm 4 Degree of Freedom (DoF) is integrated with a computer. The computer controls the whole system, where the operator can control the Robot based on voice commands. The operator's voice is one person only with different intonations. Voice command recognition uses the Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Networks (ANN) methods. The MFCC and ANN programs are processed in the computer, and the program output is sent to the Robot via serial communication. There are nine types of voice commands with different MFCC patterns. ANN training data for each command is 10 data, so the total becomes 90. In this experiment, the Robot can move according to voice commands given by the operator. Tests for each voice command are ten experiments, so the total experiment is 90 times with a success rate of 94%. There is only one operator, and experiments have not yet been carried out with the voices of several operators. The error occurred because there were several similar patterns during system testing.
Identification of Land Fire Risk Areas with Random Forest Using Landsat Image Data 8 OLI

Land fire is a complex and serious problem affecting several human life sectors. Stimulating the occurrence of land fires caused by long dry seasons and erratic rainy seasons causes plants to dry up and die. Illegal land burning by irresponsible people for expansion and other interests. This study uses quantitative methods. The variables used are vegetation index values ​​obtained from Landsat 8 OLI images. The result of this research is that data classified using the random forest algorithm at RStudio produces a default number of 500 trees, and the number of variables tested in each split is 2. So, the estimated error rate of 2 out of the bag is 0.47%. Prediction and confusion matrix - train 403 data are less risky "0" (middle risk) of a fire, and there are 22 data of high risk of "1" (high risk) of a fire, with an accuracy value of 0.9914, 1. While the second prediction of the confusion matrix - test data, there are 167 data less risky "0" (middle risk) of fire. There is 1 data with a high risk of “1” (high risk) of a fire. There are seven high-risk data "1" (high risk) of fire, with an accuracy value of 0.9943, indicating a more accurate assessment of model accuracy, and the range is still good, around 96% to 99%. There are several incorrect classifications in the prediction and confusion matrix – train, which is slightly higher than the predictions of the two-confusion matrix – test, and sensitivity: 1.0000, which is the same in both prediction and confusion matrix – train &amp; tes.
On The Modification of Chaos Game Rules on A Square

Fractal is a collection of geometric patterns found in nature and can also be a mathematical model visualization in which the pattern is repeated on a different scale. The formation of a fractal object can be done with a rule called chaos games. Chaos games explain a dot that moves erratically. On this research there will be random and non-random modification of the chaos game rules on a square. The purpose of this research is to make modifications and get visual results from modifications of the rules random and non-random chaos game. Depictions of random and non-random chaos game are carried out using MATLAB programs. Visualization of the random chaos game rule modification is a new fractal object that has self-similarity. Whereas modifications of the non-random rules by giving a particular sequence in selection a square point result in convergent points at specific coordinates. This is demonstrated by showing the value of the limit from the distance between points that produced by non-random chaos game is zero.
Task dependent model complexity

Deeper network models have opened a world unknown to many before. With the help of deeper networks, high dimensional data problems are solved. Yet the need for compact networks is growing as the end devices are still trying to harness the computational power in a small form factor. With the current state, finding the optimal architecture for a problem is one of the primary concern for edge deployment. Deeper networks come with immense power but a vast amount of it remain unused for simple problems. In this study we tried to show whether task dependencies have any correlation with the need of deeper or shallower network and how the network compression behaves with the evolving parameters. For the task dependency, we tried to restrict a DCNN to recognize a fixed scale feature and discard the others. On the way of building the experiments we have seen some intriguing phenomena such as how and where the filters responsible for recognizing a certain scale of the dataset grows. We used unstructured pruning without retraining for the deeper networks that uniformly discarded the filters having rank lower than a set threshold. All the experiments were performed on a binary classifier and the best result from a deeper network is 97.31% while the accuracy of the shallow network is 94.59% on the scale dependent task with a computable parameter ratio of 119:1.
A dynamic mutation particle swarm optimization algorithm

In view of the drawbacks of the conventional particle swarm optimization algorithm, such as the ease of falling into local extreme values, poorer diversity, and slower convergence speed in subsequent optimization, a dynamic mutation particle swarm optimization algorithm (DMPSO) is proposed. On the basis of the characteristics of the particle swarm optimization algorithm, such as a fast convergence speed and simple mechanism, the mutation mechanism of the genetic algorithm is introduced. We redesigned the dynamic inertia weight and mutation rate, and we added a ratio adjustment formula for speed, which accelerates the convergence speed of the algorithm and improves its optimization performance. In an experiment, six benchmark functions were selected for an optimization test, and the results show that the proposed method has a faster convergence speed and better optimization performance.
PENGARUH ADAPTASI NEW NORMAL TERHADAP RENDAHNYA MINAT BELAJAR SISWA DI SMAN 2 LAMBU KABUPATEN BIMA

Problems related to the adaptation of the new normal to the low learning interest of New Normal students is a new life where people continue to carry out various activities as usual but still apply the health protocols that have been set by the government so that the spread of the Covid 19 Virus can be overcome. In contrast, if people ignore health protocols, the implementation of the new standard will cause the number of Covid-19 cases to increase. The new normal's performance impacts the world of education, where students are currently studying at home by implementing the online learning process. This study aimed to determine the influence and level of adaptation of the new normal to the low interest in student learning at SMAN 2 Lambu, Bima Regency. The research approach used is quantitative. This research is located at SMAN 2 Lambu, Bima Regency. The research variable used is the new normal adaptation as an influence variable (X), while student interest in learning is an affected variable (Y). The research population is 206 students. The sample used was 103 students. The research subjects were students of classes X and XI. The research time is two (2) months, from June to July 2022. Data collection using a questionnaire or questionnaire. The data analysis technique used a simple linear regression formula and the determinant test. The results of this study indicate that based on the analysis of simple linear regression equations with a value of = 1830679 + 2281873 X = 2464940, while the value of Ftable is 3.94, therefore there is an effect of adapting the new normal as the X variable to the low interest in student learning as the Y variable in SMAN 2 Lambu, Bima Regency. The level of influence in the two research variables is 86.5%, while other variables determine 13.5%.
HUBUNGAN KEBIJAKAN SISTEM ZONASI TERHADAP MOTIVASI BELAJAR SISWA SEKOLAH DASAR

This research was motivated by enacting regulations regarding the zoning-based New Student Admission system in various schools in Indonesia. Meanwhile, the purpose of this research is to find out whether there is a relationship between the zoning system policy set by the government in PPDB on children's learning motivation and to find definite answers regarding the pros and cons that occur among the general public. In analyzing this, we use literature study as a research method, namely looking for various literature related to our research that is sourced from multiple media, both online and offline. Literature collection begins by searching for literature that discusses the zoning system and then on learning motivation in elementary schools, which includes definitions and characteristics. In searching for literature studies, prioritize the most recent year of publication. After collecting the literature, the relationship between zoning policies and the learning motivation of elementary school students. This study's results indicate a connection between the zoning policy in PPDB and the level of student motivation in elementary schools.
PROGRAM STUDENT OF THE MONTH SEBAGAI PROSES HABITUASI NILAI DISIPLIN SISWA

Student of the month is a program dedicated to giving appreciation and awards from the school to the best students with specific criteria. The selection of student of the month is based on the following criteria: (1) The percentage of student attendance in that month is above 90%, (2) Good character, (3) Obeys madrasa regulations, (4) Active in teaching and learning activities and extracurricular activities. This study aims to identify the process of implementing the student of the month program, determine the implementation of the student of the month program at MAN 2 Serang, and assess the impact of the student of the month program on students at MAN 2 Serang. This research is included in the descriptive analysis. This research is qualitative. The collection technique was done through interviews, documentation, and observation. Research subjects in this study were determined purposively, namely the selection of research subjects intentionally by researchers based on specific goals and criteria. The determination of research subjects is based on particular characteristics or characteristics that assess the researcher's subjectivity. The data analysis technique used is the inductive analysis technique. The study results concluded that the student of the month program led to a change in student habits, where there was a change in student behavior patterns, especially in increasing discipline. The sustainability of the program, which is carried out every month, has implications for forming a habitus for students.
DAMPAK PEMBERIAN LABEL MISKIN KEPADA KELUARGA PENERIMA BANTUAN PKH DI JORONG TANJUNG DURIAN KEC. SANGIR JUJUAN KAB. SOLOK SELATAN

This research examines about The Impact Of Labeling Poor On PKH Recipient Families In Jorong Tanjung Durian, Sangir Jujuan District South Solok Regency. This study aims to describe the impact of labeling poor in Jorong Tanjung Durian Nagari Padang Limau Sundai, South Solok Regency. The approach used in this study is a descriptive qualitative approach. Data collection techniques were carried out by observation, interviews, and documentation studies. The unit of analysis in this study is PKH recipient family. The data analysis technique used is the Miles and Huberman model, namely data collection, data reduction, data presentation, and drawing conclusions. The results of this study can be concluded that the impact of labeling poor on families receiving PKH assistance in Jorong Tanjung Durian, Sangir Jujuan District, South Solok Regency is (1). PKH recipient families feel awkward in society. (2). PKH recipient families become a byword in society. (3). PKH recipient families are belittled in society. (4). Have the opportunity for their children to receive scholarships at school. (5). Opportunity to receive assistance again in the future.