Reviewerly/exHarmony huggingface dataset:
DESIGN OF ACEH LANGUAGE LEARNING ANDROID BASED APPLICATION FOR PAUD TEACHERS USING USER CENTERED DESIGN (UCD) METHOD

This research is purpose to develop a android application that can be a Aceh language learning media based on PAUD curriculum. Backgrounds of this research is a requirement of PAUD teacher media for learning Aceh language based on Aceh curriculum. This research using User Centered Design (UCD) for the method and system usability scale (SUS) method for apps usability test, the final result for this apps who tested on 20 respondent consist of 14 respondent from PAUD teacher, 3 respondent from lecture of PTI and 3 respondent from PAUD lecture is79 score point, in the percentile rank graphic this application get B grade and based on adjective rank this apps get good rating, so thi]s app categorized as good apps cause already according to users need and easy to use for users
Ship-to-ship Dialogs Using A Finite State Machine

Section 4 describes the structure of states and transitions in the developed program and the possible evolutions through the graph.
Improved Offset Handling in Hand-centered Object Manipulation Extending Ray-casting

As Virtual Reality (VR) is becoming a readily available tool in an increasing number of fields, we see also an increasing number of non-technical users entering virtual environments (VEs). One pose manipulation technique that has the potential to be both fast and intuitive to use, is the Hand-centered Object Manipulation Extending Ray-casting (HOMER), presented first by Bowman and Hodges [1]. In this paper we take a look at how a naive implementation of HO-MER can lead to unintuitive interaction and present mathematics for avoiding this problem, by using a rotational offset of the grab vector instead of a static offset vector. Subsequently we compare the naive and rotation-based versions with respect to task completion time and number of re-grabs, and measure their respective System Usability Scale (SUS) score and Raw NASA Task Load Index (RTLX)
[The Logic of Scientific Reasoning in Peer Review Process].

We outlined the logical argumentation of scientific reasoning, focusing on peer-reviewed rebuttal letters. Initially, we compared the argumentation in the peer review process with that in writing papers, and distinguished between commonly used argumentation and those that are characteristic of rebuttal letters. First, commonly used forms of logical argumentation, such as deductive and inductive argumentation, are introduced, followed by examples of typical errors in reasoning. Subsequently, we explain the forms of logical argumentation characteristic of the peer review process, including the issue of incommensurability and the error of double standards. This article aims to improve our skills in responding to peer-review comments by being more aware of the logical argumentation used in peer review.
Personalized Medicine

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
A Novel Functional Machine Learning Approaches on Prostate Cancer

Abstract: Cancer registries are collections of curated data about malignant tumor diseases. The amount of data processed by cancer registries increases every year, making manual registration more and more tedious.This research work finds Bayes Net classifier gives an optimal results. The Sequential Minimal Optimization of functional machine learning approach is having highest accuracy level which is 85% of accuracy level. The Sequential Minimal Optimization of functional machine learning approach is having highest precision level which is 0.85 of precision level. The least precision value is 0.80 of precision value which is having Quadratic Discriminant Analysis of functional machine learning classifier approach. The Sequential Minimal Optimization of functional machine learning approach is having highest recall level which is 0.85 of recall level. The least recall value is 0.79 which is produced by Quadratic Discriminant Analysis functional machine learning classification approach. The Sequential Minimal Optimization of functional machine learning approach is having highest FMeasure level which is 0.85 of F-Measure level. The Fisher’s Discriminant Analysis algorithm of functional machine learning classifier and Linear Discriminant Analysis classification algorithm of functional machine learning classifier are having same receiver operating characteristic curve value which is 0.90 of receiver operating characteristic curve value.The maximum precision recall curve value is 0.90 of precision recall curve value which is produced by Linear Discriminant Analysis of functional machine learning classifier. This system recommends that the Sequential Minimal Optimization of functional machine learning approach produces optimal results compare with other models. Keywords: SMO, functional learning, LDA, QDA, and SDG
Rancang Bangun Sistem Informasi Administrasi Tamu Menggunakan RUP (Rational Unified Process) Pada Losmen Wedy Jaya

Guest data processing at Wedy Jaya Inn is not in accordance with today's technological developments. In processing guest data, the receptionist still uses a notebook to record guest reservation data and to perform check-in and check-out transactions for inn guests. This research was conducted using the RUP (Rational Unified Process) model research method. Data collection techniques were carried out by means of observation and literature study. The problem is that the guest administration process and data processing are still manual, starting from guest data to storing other data related to transactions to making reports. So because it is possible during the process there is an error in recording, less accurate reports are made and delays in the required search. Then there is no online room availability checking system which makes guests have to come directly to the Wedy Jaya inn to check if there are still rooms available and this can take a lot of time. The solution is to create a website-based system for inn guest data processing that will make it easier for receptionists to collect reservation data, input check-in and check-out of inn guests. Designing a database that will accommodate all data related to reservations, check-in and check-out of inn guests. A website-based system for inn guest data processing can simplify and speed up the tasks of the receptionist in data collection of reservations, check-in and check-out of inn guests as well as in making reports needed by inn owners and employees as well as providing a system that can be used to check room availability using the RUP method. Rational Unified Process)
Deep Learning Approach for Real-time Video Streaming Traffic Classification

Video streaming services such as Amazon Prime Video, Netflix and YouTube continue to be of enormous demand in everyday peoples&#x0027; lives. This enticed research into new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3&#x0025; when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach&#x0027;s effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4 &#x0025; overall accuracy.
Antecedents of QR code acceptance during COVID-19: Towards sustainability

This study’s primary goal is to determine that investment in entrepreneurship education is vital as it will help in new venture creation and identify future leaders with an entrepreneurial mindset. The research aims to examine whether there is an interest and awareness in the entrepreneurial intentions among female Emirati students studying in the UAE. The study also tries to find any differences in the different curricula that impact their preferences in the UAE. This study’s conceptual framework primarily relies on the existing Theory of Planned Behavior (TPB). This research is an empirical study the results and analysis are from the survey data of 490 Emirati female students in HCT higher education in the UAE. This study and the research analysis show the various factors: Attitude, Subjective Nom, PerceivedBehavioral Control, Family Background, Level of Awareness, and Knowledge of the student, have the most significant impact on Entrepreneurship Intention to become future Entrepreneurs. All three core constructs of the Theory of Planned Behavior substantially affect entrepreneurship intention. The Subjective Norm has an inverse relationship (negatively significant) with Behavioral Intention, which indicates that the respondents’ peers in this study are against entrepreneurship. This paper is unique because it gives a glimpse of the new innovative ways the next generation of students will think about business models during and after the COVID-19 pandemic. These new business models and approaches will have a more significant impact. Entrepreneurship intentions will be a deciding factor for the next generation of leaders in the UAE.
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks

Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today's NLU models still struggle to capture their semantics.We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models.We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops.Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages.To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.
Long Movie Clip Classification with State-Space Video Models

Most modern video recognition models are designed to operate on short video clips (e.g., 5-10s in length). Thus, it is challenging to apply such models to long movie understanding tasks, which typically require sophisticated long-range temporal reasoning. The recently introduced video transformers partially address this issue by using long-range temporal self-attention. However, due to the quadratic cost of self-attention, such models are often costly and impractical to use. Instead, we propose ViS4mer, an efficient long-range video model that combines the strengths of self-attention and the recently introduced structured state-space sequence (S4) layer. Our model uses a standard Transformer encoder for short-range spatiotemporal feature extraction, and a multi-scale temporal S4 decoder for subsequent long-range temporal reasoning. By progressively reducing the spatiotemporal feature resolution and channel dimension at each decoder layer, ViS4mer learns complex long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer is $2.63\times$ faster and requires $8\times$ less GPU memory than the corresponding pure self-attention-based model. Additionally, ViS4mer achieves state-of-the-art results in $6$ out of $9$ long-form movie video classification tasks on the Long Video Understanding (LVU) benchmark. Furthermore, we show that our approach successfully generalizes to other domains, achieving competitive results on the Breakfast and the COIN procedural activity datasets. The code is publicly available at: https://github.com/md-mohaiminul/ViS4mer.
Ketersediaan Rekam Medis di Rumah Sakit Islam Jakarta Sukapura

The medical record is an administration system that records all diagnoses and actions followed by the storage of medical records. Medical record retrieval is an important part to support the effectiveness of services in providing medical records for patients who return to the hospital. This research method uses quantitative descriptive and data collection techniques by observation, interviews and literature study. Based on the results of the research, the filing officer of the Islamic Hospital of Jakarta Sukapura often faced problems during retrieval, the results of the study found that 17 (3.4%) medical records were not found and 26 (5.2%) medical records were misplaced. Factors inhibiting the implementation of medical record retrieval include man factors such as the educational background of officers and the habitual factor of officers who do not use tracers when carrying out medical record retrieval that is not in accordance with SPO at the Islamic Hospital of Jakarta Sukapura. The money factor does not affect the implementation of medical record retrieval. The machine factor is the SMART system for medical record data entry that comes off the shelf. The method factor is that the standard operating procedure for retrieval of medical records is not fully appropriate. The material factor is the absence of loan receipts.
Does aggregating results of AI system for mammography with ML meta-model improve quality of malignancy detection?

.
A Study on the Korean Variant Characters in the Joseon Dynasty in Korea With Hakbongjip(《鶴峰集》) as the Breakthrough Point

In the Joseon Dynasty, printing technology made great progress. Korean literati and officials were keen on writing books, and their written works were compiled and printed by themselves or their disciples and descendants. The Korean Literary Collections in Classical Chinese (한국 문집 총간), compiled by the Institute of Classical Translation of Korea, is a collection of anthologies written by many writers of the Joseon Dynasty. In the middle and late Period of Korea, a large number of Korean literary anthologies began to emerge, in which there are a large number of variant characters worth studying. In particular, these different characters can show the characteristics of foreign spread and local change of Chinese characters in the Joseon Dynasty. In this paper, the representative anthology Hakbongjip is selected as the research object. After determining the definition of heteromorphic characters in this paper as “characters with identical and interchangeable meanings that can all be used in the same word”, Select the “遡”, “飜”, “摠”, “脣”, “椀”, “覩”, “筭”, “鰌”, “ ”, “窻”, “?”, “?”, “㗳”, “疎”, “?” a total of 15 words.According to Wang Ning
Encrypting Image by Lossless and Reversible Data Hiding using Cryptography

This paper proposes a lossless and reversible data hiding scheme for images by using chaotic system with the help of public key cryptography. In this system original grey scale image will be encrypted using shuffling and scrambling using double encryption via chaotic system. And encryption will not affect original grayscale image. With the help of this technique, a receiver may extract a part of embedded data before decryption, and extract another part of embedded data and recover the original plaintext image after decryption.
An Improved Encryption Technique for Digital Imaging and Communications in Medicine (DICOM)

Secured transmission is the most important aspect of transmission in today’s world as existing encryption techniques are facing different issues in the last few years. There is a high need for efficient and highly secured encryption techniques for medical data. In this work, various studies have been done so far but due to highly complex data. It limits the security high so here the present study proposes hybrid methods combining segmentation technique with scan pattern approach, which uses qualities of Segmentation, Scan pattern, and XORing with keys for medical data. The proposed hybrid method is prepared more strongly and novelly by smearing various operations on the pixels of the image provisional on the outcome of the calculation of the segmentation. The results analysis of various security parameters with previously existing techniques is significant.
Efficient Chatbot Designing

Abstract: A conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence (AI). Indeed, ever since the birth of AI, creating a good chatbot remains one of the field’s hardest challenges. While chatbots can be used for various tasks, in general they have to understand users’ utterances and provide responses that are relevant to the problem at hand. In the past, methods for constructing chatbot architectures have relied on hand-written rules and templates or simple statistical methods. With the rise of deep learning these models were quickly replaced by end-to-end trainable neural networks around 2015. More specifically, the recurrent encoder-decoder model [Cho et al., 2014] dominates the task of conversational modeling. This architecture was adapted from the neural machine translation domain, where it performs extremely well. Since then a multitude of variations and features were presented that augment the quality of the conversation that chatbots are capable of the next section of my paper focuses on adapting the very recent Tranformer [Vaswani et al., 2017] model to the chatbot domain, which is currently the state-of-the-art in neural machine translation. I first present my experiments with the vanilla model, using conversations extracted from the Cornell Movie-Dialog Corpus [Danescu-Niculescu-Mizil and Lee, 2011]. Secondly, I augment the model with some of my ideas regarding the issues of encoder-decoder architectures. More specifically, I feed additional features into the model like mood or persona together with the raw conversation data. Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features, affect the quality of the generated responses
Optimal Recognition of Volleyball Player’s Arm Movement Track Based on Embedded Microprocessor

An embedded microprocessor is the core part of the integrated circuit system, and it represents one of the highest levels of a digital integrated circuit design. Therefore, the design of low-power embedded microprocessors has become an important research direction in the integrated circuit design. Volleyball has always been a very important sport in our country, and it is a sport that the masses of people like to see. The basic techniques of volleyball include ready posture and movement, serving, hot ball, passing, smashing, and netting. The research content of this article is a simulation study of volleyball players’ arm trajectory optimization recognition. In response to the above problems, this paper proposes an optimized recognition method based on the volleyball player’s arm motion trajectory. The simulation results show that the method has high recognition accuracy and provides a strong scientific basis for improving the volleyball players’ spike skills. Without interference from the right arm, the controller input and position tracking error will not fluctuate in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>1</mn> <mo>≤</mo> <mi>T</mi> <mo>≤</mo> <mn>3</mn> </math> , the controller input is stable, and the output error is zero. Based on the above simulation analysis, it can be seen that the control method does not require the robot kinematics and dynamics model to generate any regression matrix when designing the controller. The controller is suitable for the performance tracking of humanoid robot arms.
Predicting sales during COVID using Machine Learning Techniques

Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.
Prototype Sistem Keamanan Pintu Kandang Dan Pemberian Pakan Ternak Puyuh Otomatis Berbasis Mikrokontroler

This research aims to build a system that can maintain the security of the cage, give feed quail automatically and weigh the weight of the feed by the needs of quail based on the time of eating quail per day. The system consisted of several components, namely fingerprint, SW- 420, buzzer, LED, relay, Arduino Uno, solenoid key, servo, RTC, and Load cell. Access is restricted by examining the authentication through a fingerprint on the fingerprint sensor. If access is granted, then the cage door will open. Otherwise, the door remain close, and if the door is tampered with or opened by force, the SW-420 will automatically detect vibration on the door and turn on the buzzer as a notification. On the part feeding, a separate container-shaped tube as feed storage will be mounted to the motor servo, and the load cell as a tool weighing the weight of the feed is mounted on the container. The servo spin (open) duration is adjusted by the load cell when weighing the feed coming out of the servo with the amount of feed out by the requirements set out in the range of 14-16 gr/session time. Feed that has weighed on the container is ready to be consumed by the quail.
Efficient Composition Algorithm Strategies on Semantic Web Services Using Nature Inspired Approach

Web services are used all around the world for commercial purposes. It works majorly by Universal Resource Locators, which are stored in the repository UDDI. The different web service has to be composed for creating new business services. It is time dependent for composing more web services, which is massive. A semantically described WSDL description solves automatic composition of web services. Also with ontological models and languages, such as RDF, OWL, and SWRL, the better automatic discovery, selecting, integration, and composition of services have been done. Service requirements, such as connectivity, quality-of-service properties, correctness, and scalability, need to be satisfied. For achieving these, many evolutionary algorithms will be used for improving quality of service parameters. This paper describes many optimizations algorithms which will be used efficiently in optimizing composition techniques in semantic web.