Reviewerly/exHarmony huggingface dataset:
CMMix: Cross-Modal Mix Augmentation Between Images and Texts for Visual Grounding

Visual grounding (VG) is a representative multi-modal task that has recently gained increasing attention. Nevertheless, existing works still face challenges leading to under-performance due to insufficient training data. To address this, some researchers have attempted to generate new samples by integrating each two (image, text) pairs, inspired by the success of uni-modal CutMix series data augmentation. However, these methods mix images and texts separately and neglect their contextual correspondence. To overcome this limitation, we propose a novel data augmentation method for visual grounding task, called Cross-Modal Mix (CMMix). Our approach employs a fine-grained mix paradigm, where sentence-structure analysis is used to locate the central noun parts in texts, and their corresponding image patches are drafted through noun-specific bounding boxes in VG. In this way, CMMix maintains matching correspondence during mix operation, thereby retaining the coherent relationship between images and texts and resulting in richer and more meaningful mixed samples. Furthermore, we employ a filtering-sample-by-loss strategy to enhance the effectiveness of our method. Through experiments on four VG benchmarks: ReferItGame, RefCOCO, RefCOCO+, and RefCOCOg, the superiority of our method is fully verified.
The Implementation of Artificial Neural Networks for Stock Price Prediction

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.&#x0D; &#x0D; Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling

Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering multiple potentially conflicting objectives simultaneously. A good approach to MO-DFJSS is to combine Genetic Programming (GP) with Non-dominated Sorting Genetic Algorithm II (NSGA-II), namely NSGP-II, to evolve a set of non-dominated scheduling heuristics. However, a limitation of NSGPII is that individuals with different genotypes can exhibit the same behaviour, resulting in a loss of population diversity. Semantic genetic programming (SGP) considers individual semantics during the evolutionary process and can enhance population diversity in various domains. However, its application in the domain of MO-DFJSS remains unexplored. Therefore, it is worthy to incorporate semantic information with NSGPII for MO-DFJSS. This study focuses on semantic diversity and semantic similarity. The results demonstrate that NSGPII considering semantic diversity yields better performance compared with the original NSGPII. Moreover, NSGPII incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents. Further analysis reveals that the improved performance achieved by the proposed methods is attributed to the attainment of a more semantically diverse population through effective control of semantic distances between individuals.
Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System

Users of interactive search dialogue systems specify their preferences with natural language utterances. However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content. In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences. Using unsupervised semantic similarity metrics and the text snippets associated with the search items, the system identifies suitable items for the user’s unconstrained natural language query. In crowd-sourced evaluation, the users chat with our extended restaurant search system. Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using an unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.
Pelatihan Canva for Teachers Membuat Bahan Ajar yang Menarik dan Efektif

Teachers at SD Inpres Bonto-Bontoa currently employ conventional teaching models, while students are more inclined towards technology-based instruction. Therefore, efforts are needed to bridge this gap through the implementation of training sessions. The objective of this community service is to provide training to elementary school teachers on the application of teaching methods using the Canva for Education application, aiming to make the teaching methods applied by teachers more engaging. The community service was carried out directly in the IT Room of SD Inpres Bonto-Bontoa, involving 20 teachers and attended by school supervisors, the principal, several professors, and assisted by students. The implementation of community service at SD Inpres Bonto-Bontoa, facilitated in the form of Canva For Education training, has had a positive impact on enhancing teachers' skills in developing teaching materials, serving as a crucial step in technology adaptation within the education environment.
Novel-Registrable Weights and Region-Level Contrastive Learning for Incremental Few-shot Object Detection

Few-shot object detection (FSOD) methods learn to detect novel objects from a few data, which also requires reusing base class data if detecting base objects is necessary. However, in some real applications, it is difficult to obtain old class data due to privacy or limited storage capacity, causing catastrophic forgetting when learning new classes. Therefore, incremental few-shot object detection (iFSOD) has attracted the attention of researchers in recent years. The iFSOD methods continuously learn novel classes and not forget learned knowledge without storing old class data. In this paper, we propose a novel method using novel-registrable weights and region-level contrastive learning (NWRC) for iFSOD. First, we use novel-registrable weights for RoI classification, which memorizes class-specific weights to alleviate forgetting old knowledge and registers new weights for novel classes. Then we propose region-level contrastive learning in the base training stage by proposal box augmentation, enhancing the generalizability of the feature representations and plasticity of the detector. We verify the effectiveness of our method on two experimental settings of iFSOD on COCO and VOC datasets. The results show that our method has the ability to learn novel classes with a few-shot dataset and not forget old classes.
Why Do Key Decision-Makers Fail to Foresee Extreme ‘Black Swan’ Events? A Case Study of the Pike River Mine Disaster, New Zealand

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.
Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

A recommendation system is a system that provides suggestions or recommendations for a product or service for its users. One of the problems encountered in the recommendation system is sparsity, namely the lack of available data for analysis, resulting in poor performance of the recommendation system because it cannot provide the proper recommendations. On this basis, this study proposes the mean method and the stochastic Hot-Deck Method to calculate missing values to improve the quality of the recommendations. The experimental results show that the hot-deck imputation method gives better results than the mean imputation method with smaller RMSE and MAE values, namely 2,706 and 2,691.
Enhancing Vocabulary in 4th Grade Students through Crossword Puzzles: A Quantitative Experimental Study

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study aimed to assess the impact of crossword puzzle media (TTS) on vocabulary enhancement among 4th grade students at SD Muhammadiyah 2 Sidoarjo. Employing a one-group pretest-posttest design, data was collected through observation, test questions, and documentation. The sample consisted of 28 students from class 4 al-farobi. The findings revealed a substantial increase in students' vocabulary, with pre-test and post-test averages of 60.71 and 82.50, respectively. The paired sample t-test confirmed a significant effect (p &lt; 0.05) of crossword puzzles (TTS) on vocabulary enhancement. This study underscores the efficacy of utilizing crossword puzzles as an effective pedagogical tool for augmenting student vocabulary.&#x0D; Highlight:&#x0D; &#x0D; The study demonstrates the positive impact of crossword puzzle media (TTS) on enhancing vocabulary in 4th grade students.&#x0D; Employing a one-group pretest-posttest design, data was collected through varied methods including observation, test questions, and documentation.&#x0D; Significant improvement in students' vocabulary scores, supported by the paired sample t-test (p &lt; 0.05), emphasizes the effectiveness of crossword puzzles (TTS) as a valuable pedagogical tool.&#x0D; &#x0D; Keyword: Crossword Puzzles, Vocabulary Enhancement, 4th Grade Students, Educational Media, Pedagogical Tool&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Analisis Manajemen Layanan Kepegawaian Berbasis Teknologi Informasi Menggunakan Framework Itil Versi 3 Di STIK Bina Husada

Bina Husada College of Health Sciences (STIK) is a company engaged in the education sector located in South Sumatra Province. The staffing service process for leave, overtime, and updating employee data at STIK Bina Husada still relies on paper based, so the HR (Human Resources) unit has to manually input recap data into SAP (Employee Administration System). Regional HR unit employees will be very vulnerable to making input errors which can be fatal for calculating employee salaries. The application of paper based on some leave, overtime and data update activities also causes the process to run less efficiently and effectively so that the processing time becomes longer. The method used in carrying out the repair process refers to the ITIL V3 domain service design framework. The results of improving the STIK Bina Husda staffing service business process for leave are an acceleration of an average time of 58.11 minutes due to the loss of PIC (Person in Charge) activity in requesting and submitting leave permit forms, as well as equal distribution of utilization to superiors, employees, and the HR area due to reduced responsible role. In the employee overtime process, the average time acceleration is 370.98 minutes due to the loss of the role of person in charge in carrying out manual recording, as well as equal distribution of usage among superiors, employees, and the HR area due to the loss of the role of person in charge. In the process of updating employee data, there was an acceleration of 22.6 minutes due to the loss of activity in providing forms and inputting employee data, and there was a change in the proportion of resource usage due to reduced activity in the HR area.
A Vision-Based Method for Human Activity Recognition Using Local Binary Pattern

Vision-based human activity recognition (HAR) plays a significant role in various practical demands. Recently, different solutions have been proposed and their performance is constantly improved. In this paper, we used a histogram of oriented optical flow (HOOF) that was improved by an extended and vigorous local binary pattern (LBP). Moreover, the effect of the local binary pattern with the HOOF method is also examined in different videos. The performance of the presented method is assessed by the KTH dataset, which contains six categories of human activities. The results showed that the validity of detection doesn’t change in waving, jogging, and running activities; but for other activities including hand clapping, walking, and boxing, the accuracy of detection became greater than 97 percent, which indicates a positive effect of using the features of local patterns.
A Two-stage Sparse Optimization Algorithm for Gated Recurrent Units Estimators

Industrial processes are always with sophisticated characteristics in particular highly nonlinear, multivariate coupling, and dynamic time lag. To overcome these dilemmas, a gated recurrent unit (GRU) neural network based on ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> - regularization is proposed to design effective prediction algorithms for the processes. Firstly, a primordial GRU model is trained from the given data. Secondly, a two-stage regularization algorithm is integrated into the fundamental model to simplify the input gate and the hidden layers of the GRU, respectively, which simultaneously achieves input variable selection and structural optimization. Finally, the performance of the proposed double ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> -regularized GRU is verified through a practical industrial dataset and compared with other machine learning algorithms. Experimental results demonstrate that the proposed algorithm can extract relevant features and simplify the model structure, therefore improving the prediction accuracy of the model.
Efficient Data Query for Large-scale Manufacturing Product Lifecycle based on Blockchain

The data query for large-scale manufacturing product lifecycle provides the data basis for finding out the processes and causes of the quality problems. At present, lifecycle data query methods are inefficient due to the complex search process. Therefore, this paper designs an efficient data query method for large-scale manufacturing product lifecycle. The new method uses blockchain technology, improves the original transaction structure, and sets K-V(Key-Value) database tables. Firstly, add pre-index and post-index to the transaction data structure. Secondly, store the correspondence between the pre/post-index and the hash value of transactions through K-V database tables. Thirdly, a new efficient lifecycle data query algorithm is designed using the pre&post-index. Finally, a comparative experiment is carried out to validate the efficiency of the efficient lifecycle data query method. The results show that the new method is more efficient than current methods.
A Study of Exergame System Using Hand Gestures for Tenosynovitis Prevention

As more and more people have been addicted to cell phones, long-term use of fingers may cause tenosynovitis. Then, it becomes very important to prevent it by providing proper exercise opportunities to such people. To play video games using hand gestures, called exergame, will be a good candidate. In this paper, we study a simple exergame system on a PC under this motivation. We design three sets of hand gestures to control the key operations to play the adopted five games, and implement the recognition program in Python using Mediapipe. They have different levels of di culty for a player to perform. As the preliminary evaluation of the proposal, we asked three students in Okayama University, Japan, to play them by following the prepared manual, and to give comments and opinions on usability and effectiveness of the system.
Combination of unsupervised discretization methods for credit risk

Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost. This study introduces an innovative approach by combining traditional discretization techniques with clustering-based discretization, specifically k means and Gaussian mixture models. The study proposes two combinations: Discrete Competitive Combination (DCC) and Discrete Exhaustive Combination (DEC). Discrete Competitive Combination selects features based on the discretization method that performs better on each feature, whereas Discrete Exhaustive Combination includes every discretization method to complement the information not captured by each technique. The proposed combinations were tested on 11 different credit risk datasets by fitting a logistic regression model using the weight of evidence transformation over the training partition and contrasted over the validation partition. The experimental findings showed that both combinations similarly outperform individual methods for the logistic regression without compromising the computational efficiency. More importantly, the proposed method is a feasible and competitive alternative to conventional methods without reducing explainability.
Assessing the Challenges &amp; Opportunities of Digital Marketing in Ethiopia: The case of Somali Regional State

This study assesses the challenges and opportunities of digital marketing in Somali regional state of Ethiopia. We selected the sample purposively in this study. The collected data were analyzed by using descriptive statistics, regression model was also used to analyse the data. Quantitative data were used. The researcher collected the data with the help of questionnaire. The collected data shows that growing online markets, growing human capacity, growing telecommunication sectors and favourable government policies were digital marketing opportunities, the study also found that risk of privacy and security, e-payment accessibility, limited usage of computer technology, high cost of internet, frequent power disruption and lack of awareness are the challenging factors of digital marketing in Somali region. The findings also show the existence of positive attitude toward digital marketing in the study area. The study also witnesses that the current condition of digital marketing activities in Somali region is poor. The study shows that security and privacy, infrastructure, e-payment accessibility, and awareness have direct impact on digital marketing performance in Somali regional state. The study recommends improvement of government infrastructures, developing of e-payment systems and creation of awareness toward digital marketing
Future Cybercrimes in the Metaverse

In an emerging era of virtual reality, cybercrimes become a significant challenge. The metaverse is a digital realm where people can socialize, work, play, invest, learn, entertain, and much more. The nature of the metaverse raises the concerns of cybercrimes. In this chapter, a deep understanding of potential threats is discussed. Several threats will be highlighted, such as virtual identity theft and impersonation, virtual real estate frauds, malware and ransomware, virtual espionage, advanced phishing, and social engineering. To forecast these threats, insights from cybercrimes are adapted and extrapolated to fit the unique environment of the metaverse. This will guide law enforcement, technical people, policymakers, regulators, and researchers.
“They do it in order to clown around”: the uses of sexual knowledge and secrecy among children from elementary schools in Catalonia.

Abstract was submitted for the "Secrecy and Knowledge: Social and Cultural Responses to Secrecy" seminar at Newcastle University in 2022 but was ultimately rejected. The main argument was that Sexuality, as a type of public secrecy, is used by children to play with concealed knowledge. The seminar aimed to explore this particular use of sexuality among elementary school children in Catalonia.
Classification of Breast Cancer Using Radiological Society of North America Data by EfficientNet

Breast cancer is a common cancer that affects women all over the world. Therefore, detection at an early stage is crucial for reducing the mortality rate linked to this disease. Mammography is the primary screening method for breast cancer. However, it has drawbacks, including high rates of false-positive and negative results, inter-observer variability, and limited sensitivity with dense breast tissue. To solve such problems, breast cancer was analyzed and classified using mammography images and deep learning models from the Radiological Society of North America (RSNA) database. This database contains processed and raw images from the RSNA that consist of annotated malignancies and clinical data. Using deep learning models based on convolutional neural network (CNN) models such as visual geometry group (VGG), Googlenet, EfficientNet, and Residual Networks, mammograms were classified into cancer or non-cancer categories. In this study, a novel architecture was proposed by combining CNNs and attention mechanisms, which extracted and highlighted the relevant features. A dataset of 8000 patients with 47,000 photographs was used to train and assess the model via 5-fold cross-validation. The results outperformed prior methods using the same database and reached an average accuracy rate of 95%. The results showed that mammography with deep learning methods considerably improved breast cancer detection and diagnosis.
THE ROLE OF AI IN DIGITAL ERA

This review paper looks with an overview of role of AI in education during the digital era. During the time of COVID- 19 the usage of AI has increased rapidly. The paper helps us to highlights the benefits of AI in education sector like learning, teaching and ethical concerns associated with it.AI is a crucial factor in educational development and some threats are associated with it like safety, security and most important privacy. As a consequence, AI technologies positively and negatively affect the education sector.