Reviewerly/exHarmony huggingface dataset:
P‐29: Noise reduction and defect detection algorithm in white‐light scanning interferometry for display panel inspection

White‐light scanning interferometry (WSI) has been widely used for the inspection of various display panels by 3D surface imaging from the micrometer scale to the nanometer scale. However, WSI often fails to sense light reflected from towering defects due to diffused reflection, represented by near‐zero heights, called black spot (b‐spot). It is challenging to detect the b‐spot defect especially in display pad area with varying heights. In addition, vibration of WSI facility hinders a defect spot due to harsh noise amplification. To resolve the issues, we proposed a novel and efficient image processing algorithm which consists of five steps. The first step is image denoising by median filtering with rectangle kernels. The second is pad area preprocessing to avoid misdetection. The third is to identify the presence of b‐spot defect using curvature of 1D projection array of an image. The fourth is defect detection through minimum value finding method. The last is Laplace fitting to infer the height of towering b‐spot defect. We conducted an experimental study using WSI raw data of defective display panels to evaluate the defect detection rate and the reliability of inferred height. We also calculated the correlation between the inferred height by Laplace fitting and measured height by atomic force microscopy (AFM) as a gold standard. We have shown that the proposed method can effectively detect a towering b‐spot defect position with much improved detection rate and infer the height with high precision.
Students’ preferences for attributes of a B1 English level course: A conjoint analysis

The increasing demand and requirements of the recruitment market towards foreign languages, especially English, has prompted many universities in Vietnam to consider English certificate as one of the mandatory conditions for students to be graduated. This has increased the demand for English courses. This study aims to ascertain students’ preferences and willingness to pay for attributes of a B1 English level course. Through an experimental approach, we conducted a survey among students enrolled at An Giang University (Vietnam National University, Ho Chi Minh City). The findings indicate that the most significant attribute influencing student preferences for an English course is the reputation of the foreign language centres offering the course, followed by the tuition fee, assurance policies for learning outcomes, and foreign teachers. Additionally, the course duration, availability of free materials, and proximity of the language centre to the student’s residence are also important factors affecting student interests. Based on these results, several implications are suggested for foreign language centres to develop effective marketing strategies aimed at attracting learners.
Exploring the Effectiveness of Deep Learning in Analyzing Review Sentiment

This study aimed to analyze sentiment in office product reviews by using word embedding with three neural network modeling approaches: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Office product review data is taken from Amazon's reviews of office products covering a wide range of sentiments. Word embedding converts text into a numerical vector representation for neural network processing. Experimental comparison of this model reveals that CNN achieves the highest accuracy, 77.99%. The CNN model effectively extracts significant features from review text, improving sentiment classification performance. Although the LSTM and GRU models show satisfactory results, they do not match CNN performance. These findings demonstrate the effectiveness of word embedding and neural networks for sentiment analysis in office product reviews. This provides valuable insights for companies to improve their products based on user feedback from online reviews. Additionally, this research serves as a foundation for further advances in sentiment analysis across a wide range of other products and services
Multiclass Email Classification by Using Ensemble Bagging and Ensemble Voting

Email is a common communication technology in modern life. The more emails we receive, the more difficult and time consuming it is to sort them out. One solution to overcome this problem is to create a system using machine learning to sort emails. Each method of machine learning and data sampling result in different performance. Ensemble learning is a method of combining several learning models into one model to get better performance. In this study we tried to create a multiclass email classification system by combining learning models, data sampling, and several data classes to obtain the effect of Ensemble Bagging and Ensemble Voting methods on the performance of the macro average f1 score, and compare it with non-ensemble models. The results of this study show that the sensitivity of Naïve Bayes to imbalance data is helped by the Ensemble Bagging and Ensemble Voting method with ∆P (delta performance) of range 0.0001 – 0.0018. Logistic Regression has performance with Ensemble Bagging and Ensemble Voting by ∆P of range 0.0001-0.00015. Decision Tree has lowest performance compared to others with ∆P of -0.01
Penggunaan Bahasa Pada Anak Usia Dini di Kelurahan Bulukunyi Kecamatan Polongbangkeng Selatan Kabupaten Takalar

This study aims to describe the use of language in early childhood in the home and school environment in Bulukunyi Village, South Polongbangkeng District, Takalar Regency. This type of research is classified as qualitative research, which is analysed descriptively. The data in this study are the utterances used by early childhood while playing in the home and school environment. The key instrument and used tools in the form of videos and cameras. The source of data in this study is class B students of Hilmawan Kindergarten, Takalar Regency with 23 students. This research data is data obtained from the results of observations and interviews of researchers on students of Hilmawan Kindergarten, Takalar Regency. The results show that the use of language in early childhood in Bulukunyi Village, Polongbangkengeng Selatan District, Takalar Regency uses two languages, namely Makassar language and Indonesian language which is called code mix. The results obtained, early childhood is more dominant in using non-standard Indonesian, but the position of the use of Makassar language can still be declared not extinct because early childhood when communicating still uses code mix.
Managing cyber risk in the financial sector: Insights from a case study

Purpose: This article focuses on cyber risk as an emerging issue within the risk management process and the internal control system in the financial sector. It in-vestigates whether cyber risk management (CRM) is (dis)integrated into traditional enterprise risk management (ERM) and analyzes the external dynamics affecting the CRM design. Design/methodology/approach: This article draws upon institutional theory and the concept of boundary objects. The research examines a listed Italian bank and gathers the data from semi-structured interviews, direct observations, meet-ings, and archival sources. Findings: The findings underline that cyber risk rationale plays a crucial role in the CRM process. The interplay between institutional complexity and the need to manage cyber risk is critical for a bank to have a stable and flexible infrastructure. The knowledge boundaries related to the cyber risk culture require further cyber risk talk. Originality/value: This research furthers the understanding of cyber risk and CRM as an integral part of the ERM and internal control systems in the financial sector, in which there is a shortage of case studies. The financial sector is highly regulated, and managing cyber risk has become crucial as banks usually deal with enormous amounts of personal and sensitive data stored on networks and in the cloud. Practical implications: This case study emphasizes the crucial role of CRM in the identification and reporting of cyber risk information in annual reports.
54‐1: <i>Invited Paper:</i> Through OLED‐Display Proximity Sensing

Integrating a proximity sensor behind an organic light‐emitting diode (OLED) display increases usable display area but introduces multiple design challenges. We discuss solutions that enable behind‐display proximity sensing in iPhone 14 Pro and iPhone 14 Pro Max.
DETECTION OF SPAM REVIEWS IN WEBSITES USING NET SPAM ALGORITHM

Now-a-days, people in general choose to purchase an online item depending on the reviews and feedback given in social media. The probability of leaving a scrutiny gives a glorious chance for person who writing a spam reviews regarding the product for individual reasons. Categorizing these kinds of spammers and the spam content create an interesting issue of analysis. Though a generous range of studies are done recently towards this, till date the methodologies used still hardly find the spam reviews. Here, propose an exclusive framework called Net-Spam that exploit spam options for creating review datasets as heterogeneous data networks to map spam detection procedure into further classifications. Maltreatment of spam options helps us to get the best output pertaining to various metrics experimented on realtime review datasets from Amazon and Yelp websites. The results show that Net-Spam outperforms the current ways from among the three classes of features namely detection of review, detection of user and detection of Spam Groupers.
Who Will Optimize The Optimizers?

While looking over papers from the past year, one theme in particular stood out to me: meta-optimization, or optimizing how we optimize things.A generic optimization (image credit to Georgios Balaouras). Meta-optimization has long been a focus of research in computer science, where new optimization algorithms can have an incredibly high impact (e.g. ADAM, one of the most commonly used optimizers for neural network training). More recently, the a
Morphism Equality in Theory Graphs

Theory graphs have theories as nodes and theory morphisms as edges. They can be seen as generators of categories with the nodes as the objects and the paths as the morphisms. But in contrast to generated categories, theory graphs do not allow for an equational theory on the morphisms. That blocks formalizing important aspects of theory graphs such as isomorphisms between theories. MMT is essentially a logic-independent language for theory graphs. It previously supported theories and morphisms, and we extend it with morphism equality as a third primitive. We show the importance of this feature in several elementary formalizations that critically require stating and proving certain non-trivial morphism equalities. The key difficulty of this approach is that important properties of theory graphs now become undecidable and require heuristic methods.
Techno-technologized world in the light of paradigmatic philosophical and methodological principles

Introduction. The human world is presented as an integrity — an organic unity of many inter- connected and interdependent centers (parts, sides, elements): natural and cultural, natural and artificial, animate and inanimate. When any center dominates over others (for example, technical and technological) and / or attempts to realize its claim to the status of a whole, the agreed and optimal ra- tio in the coexistence and synergistic development of all centers, and, consequently, of the whole, is violated. There arises an ecosystem problem. The purpose of the study is to present and analyze the techno-technologized world in the context of current philosophical and scientific methodologi- cal principles: polycentricity, complexity, network- centricity, and ecosystem; to designate their theoretical and practical significance, interdepend- ence; to substantiate the principle of ecosystems as more general and fundamental and to present its effectiveness in preserving the integrity of man, his world and the full value of human being-in-the- world. Methods. The author uses methods of dialectics, synergetics and systemic approach in the philo- sophical understanding of the specifics of the modern techno-technological world and a person’s existence in it. The author also uses the methods of extrapolation and interpretation, the ideas of B. Latour’s actor-network theory, and ecosystem approach. Scientific novelty of the research. The author projects the key attitudes and concepts of the actual conceptual and paradigm principles on the techno-technological world, and formulates the main advantages and disadvantages. The author substantially justifies that the principle of ecosys- tem is the most effective in solving the problem of preserving and reproducing the integrity of the human world, the person himself and his being. Results. The author carries out theoretical and methodological analysis of the techno-technolo- gized world and a person’s existence in it. It has been determined that when solving the problem of preserving and reproducing the integrity of a person and his world, the principle of ecosystems is the most effective and preferable. The ecosystem principle implies the synergistic unity of all centers of human existence, their interdependence and interconnection, ensures the priority of the whole for the benefit of each center, expressed in mutual strengthening and expansion of their potential. Conclusions. The author comes to the conclusion that the application of relevant paradigm principles in studying and analyzing the techno-technological world will make it possible to obtain a more mul- tidimensional and accurate interpretation of the ways and means of achieving a full-fledged human being, with a focus on preserving his identity, origi- nality, subjectivity and integrity.
Generators and Coroutines

This chapter introduces generators and coroutines. A generator is a data generator, and a coroutine is a data consumer. However, many developers get these two constructors confused as they both employ the yield keyword; although the purpose of yield in a generator is very different to the purpose of yield within a coroutine. This chapter will discuss both Python language constructs.
Anomaly detection and oversampling approach for classifying imbalanced data using CLUBS technique in IoT healthcare data

Multiple data streams from sensing devices in intelligent settings have improved life quality thanks to the internet of things (IoT). Anomalies and imbalanced data sources are unavoidable due to system complexity and IoT device rollout issues. An imbalanced dataset has more data for one group than another, which may influence the results. IoT data streams are unbalanced, making anomaly detection harder. Data mining and machine learning classification approaches perform poorly on imbalanced datasets in the current setup. To address this, the proposed system suggests an effective anomaly detection method and oversampling approach (ADO) to improve IoT's ability to identify abnormal behaviours in imbalanced data. After clustering of lower and upper boundary standardisation (CLUBS) detects anomaly samples, the ADO technique provides synthetic samples for minority classes. ADO lowers class region overlaps and enhances classification methods. The experimental results using an imbalanced dataset and three classification algorithms, namely K-nearest neighbour (KNN), random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), show that the ADO approach increases classification accuracy (0.64% for KNN, 4.27% for RF and 6.33% for SVM) by removing anomalies and oversampling data.
COMPETITIVE PRINCIPLE OF TEACHING ENGINEERING DESIGN TO STUDENTS

The formation of students’ ability to creative professional activity is carried out in the process of educational activity. Modeling of the competitive environment in the learning process can be provided by involving students in creative contests, competitions, Olympiads. Independent work of motivated students helps to achieve the necessary skills and competencies from the employer’s point of view.
IMPROVING THE SPEAKING SKILLS BY USING PICTURE SERIES

Background of this study is an effort to know which technique is suitable to use in teaching speaking. Teaching a foreign language is considered to be one of the most challenging teaching practices. In Indonesia, students are usually afraid of joining foreign language classes. They may feel unmotivated and discouraged easily. Their mindsets say foreign language is difficult to learn because most of them know nothing from the start. Usually, students know nothing how to express what they want to say. Students who have learned English for several years have not been can communicate used English. So the researcher thought that there should be a solution to cope with that problem. The researcher tried to use describing picture as a method to improve students’ speaking skill especially in descriptive text
The OpenCitations Enhancement Project – final report

&lt;strong&gt;The OpenCitations Enhancement Project Final report for the Alfred P. Sloan Foundation&lt;/strong&gt; &lt;strong&gt;Report period: 1st May 2017 – 30 November 2018.&lt;/strong&gt;
Corrigendum: The february 2018 seismic swarm in São Miguel, Azores

CORRECTION article Front. Earth Sci., 01 September 2023Sec. Solid Earth Geophysics Volume 11 - 2023 | https://doi.org/10.3389/feart.2023.1258767
Review Sentiment Classification and Feature Selection Using Hybridized Support Vector Machine

Recently, researchers have become increasingly curious about using machine learning algorithms to solve complex real-world problems. Machine learning algorithms can be broadly classified as either supervised learning techniques or unsupervised learning techniques. The expansion of social networks has significantly increased the volume of user-generated data, including reviews, comments, and opinions from customers. Processing this much user-generated content is difficult and time consuming, despite the fact that it might be helpful for analysis and decision-making. Therefore, it is essential to develop an intelligent system which automatically mines such vast amounts of content and categorizes them for positive and negative features. Sentiment analysis is useful for automatically monitoring social media, describing the general sentiment or attitude that customers have toward a specific brand or business, and determining whether they are regarded favorably or unfavorably online. By utilizing the Particle Swarm Optimization (PSO) and gray wolf optimization (GWO) strategies, the support vector machine (SVM) was investigated in order to create a novel and efficient prediction system. Support vector machine (SVM) is used in this analysis together with Particle Swarm Optimization (PSO) to classify the data. The experiment's findings demonstrate that PSO improves SVM performance, whereas in the other approach, GWO was utilized to update population positions in the discrete search space, resulting in the best feature subset for SVM-based classification purposes. The experimental data from this work demonstrates that SVM, PSO-SVM, and GWO-SVM have accuracy values of 79.76%, 81.35, and 82.18%, respectively. This shows that the GWO-SVM outperforms the competition.
Save Our Sea : Rancangan Game 2D RPG Berbasis Android Menggunakan Godot Engine

This research is a development of a 2D role-playing game or RPG game using the Godot Engine. The game's rules have been designed to feature a character, a fish, which will be played by the player, embarking on a mission to combat an evil corporation that is polluting the ocean by disposing of marine waste. The gameplay includes platforming challenges, environmental puzzles, and battles. The technical aspects of the game have been explained, detailing the methods and techniques used in its development. This includes implementing player movement, physics, sprite animations, and interactions with various objects, including algorithms for waste collection and encounters with enemies.&#x0D; The main objectives of the game are to overcome platforming obstacles, solve environmental puzzles, and defeat NPC enemy characters. The game's progress is measured by collecting various types of waste as an indicator of environmental cleanup. The successful implementation of the game has allowed it to be played on a simulation scale.
Silenced voices: unravelling India’s dissent crisis through historical and contemporary analysis of free speech and suppression

In India, the world’s largest democracy, the right to express dissenting opinions has come under increasing pressure due to governmental reactions to violent and non-violent activities and opposition. As well as direct suppression, a concerning trend has arisen whereby individuals and groups may choose to self-censor rather than risk the consequences of speaking out. These outcomes are related in this paper to the treatment of social media, where official policies and laws regulate and censor content on social media on the dubious basis that it is ‘offensive’ or ‘objectionable’. These restraints often lack clear guidelines and are applied arbitrarily, leading to a chilling effect on free speech and the expression of dissenting views. Furthermore, India lacks a comprehensive legal framework to address hate speech, resulting in a patchwork of provisions within the Indian Penal Code that inadequately define and circumscribe hate speech. This article contends that the ‘freedom to criticise’ should be legally better protected to ensure that diverse opinions can be safely held and expressed in order to maintain and reflect the pluralistic nature of Indian democracy.
深層学習と画像処理を用いた寄生蜂の形質抽出

Parasitoid wasp is used for pest control due to by using enemy relationship. However, it is essential to identify them because parasitic host is different for each parasitoid wasp if they use parasitoid wasps for pest control. Currently, traits extraction, measurement, calculate and create specimen used for identification. However, there process is burden for parasitoid wasp researcher. In addition, they are shortage of parasitoid wasp researcher while the burden is heavy. Therefore, it assists parasitoid wasp researcher extracting node that is vein linked used in identification by using deep learning and image processing in this research.
A refractive three-dimensional reconstruction method for fringe projection with a planar medium

Abstract In special measurement situations, a vision measurement system needs to measure objects through a viewing observation window made of a planar transparent medium. The refraction at the interface between a medium and air leads to large measurement errors in the pin-hole imaging model. Most existing solutions require complex processes of calculating refracted light equations and/or matching stereo images. Here, we propose a refractive three-dimensional (3D) reconstruction method for fringe projection with a planar medium. We derive a coordinate conversion relationship from a pseudo-object point caused by light refraction to a real-object point based on flat refractive geometry. Then, we integrate the relationship into regular fringe projection framework for unbiased 3D reconstruction. Two experiments, including 3D shape measurements of a step and a regular sphere are performed. The results verify the effectiveness and accuracy of the proposed refractive 3D reconstruction method.
A Study on Guessing plaintexts by Neural Network against Data Encryption Standard (DES)

One of our research purpose is to evaluate a symmetric key encryption scheme by using a machine learning. As the first step, this paper shows results of a known plaintext attack by using the neural network against Data Encryption Standard (DES) as the symmetric key encryption scheme. A plaintext guessing attack from ciphertexts against 16-round DES did not yield good results. On the other hand, we report interesting results in their attacks against 1- and 2-round DES.