Reviewerly/exHarmony huggingface dataset:
Preference Contrastive Learning for Personalized Recommendation

Recommender systems play a crucial role in providing personalized services but face significant challenges from data sparsity and long-tail bias. Researchers have sought to address these issues using self-supervised contrastive learning. Current contrastive learning primarily relies on self-supervised signals to enhance embedding quality. Despite performance improvement, task-independent contrastive learning contributes limited to the recommendation task. In an effort to adapt contrastive learning to the task, we propose a preference contrastive learning (PCL) model by contrasting preferences of user-items pairs to model users’ interests, instead of the self-supervised user-user/item-item discrimination. The supervised contrastive manner works in a single view of the interaction graph and does not require additional data augmentation and multi-view contrasting anymore. Performance on public datasets shows that the proposed PCL outperforms the state-of-the-art models, demonstrating that preference contrast betters self-supervised contrast for personalized recommendation.
360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation

Deep learning applications on LiDAR data suffer from a strong domain gap when applied to different sensors or tasks. In order for these methods to obtain similar accuracy on different data in comparison to values reported on public benchmarks, a large scale annotated dataset is necessary. However, in practical applications labeled data is costly and time consuming to obtain. Such factors have triggered various research in label-efficient methods, but a large gap remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an effective and streamlined few-shot approach to label-efficient LiDAR segmentation. Our method utilizes an image teacher network to generate semantic predictions for LiDAR data within a single camera view. The teacher is used to pretrain the LiDAR segmentation student network, prior to optional fine-tuning on 360° data. Our method is implemented in a modular manner on the point level and as such is generalizable to different architectures. We improve over the current state-of-the-art results for label-efficient methods and even surpass some traditional fully-supervised segmentation networks.
Exploring Image Classification Robustness and Interpretability with Right for the Right Reasons Data Augmentation

Right for the right reasons (RRR) methods have been proposed to mitigate the issues of shortcut learning in deep learning models. During training, these methods guide the models to learn patterns from signal information while ignoring noisy features. This work investigates the robustness of image classification models to background sensitivity, referring to a model’s capability to accurately classify an image without leveraging the shortcut learning between the image background and the assigned input label. We propose a new approach, the Right for the Right Reasons Data Augmentation (RRDA). This approach augments the image foreground context with the context extracted from different images, thereby stimulating the model to focus on signal features rather than the context. Our experiments demonstrate that RRDA can significantly improve the robustness of image classification models, outperforming other RRR methods, such as GradMask and ActDiff. We also evaluate the impact of architectural choice on robustness, showing that ViT is more robust than ResNet in handling background sensitivity. Finally, we perform an interpretability analysis to understand how models assign importance to signal and context features during the inference process. This involves computing the signal-to-noise ratio as the importance of the signal divided by the importance of the context. Contrary to our expectations, our findings suggest that a high signal-to-noise ratio does not necessarily imply robustness. However, they indicate that applying RRDA can help the models learn to focus on signal features, leading to more interpretable and robust models.
Vision-Language Models Performing Zero-Shot Tasks Exhibit Disparities Between Gender Groups

We explore the extent to which zero-shot vision-language models exhibit gender bias for different vision tasks. Vision models traditionally required task-specific labels for representing concepts, as well as finetuning; zero-shot models like CLIP instead perform tasks with an open-vocabulary, meaning they do not need a fixed set of labels, by using text embeddings to represent concepts. With these capabilities in mind, we ask: Do vision-language models exhibit gender bias when performing zero-shot image classification, object detection and semantic segmentation? We evaluate different vision-language models with multiple datasets across a set of concepts and find (i) all models evaluated show distinct performance differences when identifying concepts based on the gender of the person co-occurring in the image (ii) model calibration (i.e., the relationship between accuracy and confidence) also differs distinctly by gender, even when evaluating on similar representations of concepts and (iii) these observed disparities align with existing gender biases in word embeddings from language models. These findings suggest that, while language greatly expands the capability of vision tasks, it can contribute to propagating social biases in zero-shot settings.
InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection

The recent progress in developing pre-trained models, trained on large-scale datasets, has highlighted the need for robust protocols to effectively adapt them to domain-specific data, especially when there is a limited amount of available data. Data augmentations can play a critical role in enabling data-efficient fine-tuning of pre-trained object detection models. Choosing the right augmentation policy for a given dataset is challenging and relies on knowledge about task-relevant invariances. In this work, we focus on an understudied aspect of this problem – can bounding box annotations be used to design more effective augmentation policies? Through InterAug, we make a critical finding that, we can leverage the annotations to infer the effective context for each object in a scene, as opposed to manipulating the entire scene or only within the pre-specified bounding boxes. Using a rigorous empirical study with multiple benchmarks and architectures, we demonstrate the efficacy of InterAug in improving robustness and handling data scarcity. Finally, InterAug can be used with any off-the-shelf policy, does not require any modification to the architecture, and significantly outperforms existing protocols. Our codes can be found at https://github.com/kowshikthopalli/InterAug.
PENERAPAN METODE DISKUSI KELOMPOK PADA MATA PELAJARAN MATEMATIKA KELAS 4 SDN GADANG 3 KOTA MALANG

The purpose of this study was to find out how the learning strategies and media were used to teach grade 4 mathematics at Gadang 3 Elementary School, Malang City. This research uses a descriptive qualitative research type. Data collection techniques in this study were by means of interviews, observation, and documentation. The subjects of this study were grade 4 teachers. The data analysis techniques used were data collection, data reduction, data display, and veryfying. The results of this study indicate that the existence of learning strategies in the form of study groups and joint discussions can improve student learning outcomes due to the heterogeneity of each group which encourages participants to learn competitively but still fun. Students are asked to help each other between friends so that other students do not feel left behind by the others. In addition to learning strategies, the learning media used are also the result of student designs, this makes students able to easily use these learning media. Learning media is also made of materials that are easily found around. Prior to implementing the study group strategy, grade 4 teachers had implemented several strategies. However, the class teacher considers this strategy to be the most effective in learning, especially in mathematics.
DEVELOPMENT OF A WEB APPLICATION FOR AUTOMATING THE FORMATION OF PROJECT TEAMS

The task of distributing students to project works and forming project teams is considered. The structure of the database and the project was developed, taking into account different levels of access to information in the program. Tests have also been created to check the functionality and performance of the program. A study of the distribution of user load using the developed tests was conducted. The developed application is built on a client-server architecture, with interaction implemented using REST API and the HTTP protocol. The server part is responsible for processing and verifying data, interacting with the database. The client part provides a user-friendly and understandable interface, receiving and transmitting data to the server. The programming language TypeScript and the Node.js environment, as well as the JetBrains WebStorm IDE, are used to solve the set task. A web application has been implemented, which makes it possible to assign students to project work and form project teams, as well as monitor the implementation of projects. Each user must be authenticated to use the system's functionality, which can be done using a login and password. There are three types of users in the system: administrator, mentor, and student. The project administrator can create and manage projects, independently form or set team distribution criteria, add a mentor to the project. The project is broken down into weeks throughout the semester, allowing all system users to see the progress of the project. The project mentor can mark the project status, issue tasks to students and make notes on each student's work, create meetings, and grade students at the end of the semester. Students can register for the project, view project details and materials, see tasks issued by the mentor, and mark progress on their completion. Most actions in the system should come in the form of email notifications to project participants. The possibility of e-mail reminders about the need to complete tasks for students and issuing tasks for the mentor has also been implemented.
Implementation of K-means Clustering Algorithm to Analyze the Familial Sentiments Towards COVID-19 Vaccination For Elementary School Students in Kalawat District

Due to the Ministry of Health's policy, the Indonesian government mandates the public to receive the COVID-19 vaccination as a form of immunity against the coronavirus. This vaccination is not only for adults but also for children of a certain age. Regarding the provision of vaccination for elementary school students aged between 6 to 11 years, the families' responses to this predicament can cause significant barriers to those students being fully vaccinated. Thus, this research developed a web-based application that incorporated the K-means clustering method to group the sentiments of the families into three clusters, namely positive, neutral, and negative. The results showed that the application can identify and cluster the different familial responses from 279 respondents in Kalawat District toward the administration of COVID-19 vaccination to their underage children. The most dominant familial sentiment is positive followed by neutral and negative sentiments with the number of respondents as many as 120 respondents (43%), 113 respondents (41%), and 46 respondents (16%) respectively. This research can help the Health Office in North Minahasa Regency to evaluate public sentiments about vaccination for elementary school students as well as look for better ways to encourage vaccine trust and confidence in this district.
Sistem Informasi Reporting Sales And Marketing Berbasis Web (Studi Kasus Amaroossa Hotels Indonesia)

Marketing Sales Department is one of the most important departments in Amaroossa Hotel Indonesia. The purpose of this research is to build a data processing information system of reports from sales and marketing where the source of data from sales and marketing reports be it daily, weekly, monthly reports, and so forth. The application development method uses the waterfall method which starts from the requirements analysis, system and software design, system implementation, and system testing. Whereas for system design using Data Flow Diagrams (DFD) and system development methods are built using Visual Studio Code for coding purposes using the PHP programming language by applying laravel framework. The results of this study produce a website-based system of management of report data from sales and marketing, where the system has several features such as file upload, file view, and file download. Testing system testing uses blackbox testing where testing that has been done on the system produces the conclusion that the system is running well.
User-Selected Sets of Algorithmic Implementation of Fuzzy Processing Subsystem for Embedded Intelligent Control Systems

Embedded intelligent control systems (EICS) have been used in a wide range of tasks involving the adaptive control of technical objects and processes. For the hardware implementation of EICS, developers actively use function-oriented microcontrollers, that aimed to be the effective implementation of control algorithms, including adaptive and intelligent control. Embedded control systems based on the concept of fuzzy set theory have recently been developed for specific individual control operations, allowing more efficient implementation of complex object control algorithms. This paper approaches the study of the different implementation of control algorithms in embedded microcontrollers based on fuzzy sets theory to result in reduced hardware area and complexity, high operating speed, and adaptability to various applicable domains. It shows that the performance of fuzzy computing specialized in embedded systems has significantly increased and makes possible a huge scope for further improvement of the implementation of fuzzy control algorithms at the hardware level.
Processability Analysis of the Acquisition of English L2 Syntax by Japanese and Chinese Speakers

Processability Theory (PT; Pienemann, 1998) argues that there is a universal hierarchy in the acquisition of second language (L2) grammar. As for L2 syntax, PT proposes the Lexical Mapping Hypothesis (LMH; Pienemann, et al. 2005) in which learners are predicted to progress from ‘default mapping’, where the argument hierarchy maps onto the grammatical function hierarchy in a default way, to ‘non-default mapping’, where the strictly hierarchical mapping is disrupted under semantic and/or pragmatic pressure (Pienemann, et al., 2015). More recently, an additional intermediate stage between default and non-default mapping stages has been proposed by Bettoni and Di Biase (2015) accounting for locative, ditransitive, and other constructions containing ‘default mapping plus additional argument.’ Although this intermediate stage has been tested to some extent (e.g., Di Biase, et al., 2015; Kawaguchi, 2015; Liu, et al., 2023; Yamaguchi &amp; Kawaguchi, 2022), more detailed analyses of how L2 learners proceed from the default mapping stage to the non-default mapping stage are needed. The participants in the present study are 139 English L2 learners, that is 88 learners of English as a foreign language (EFL) in Japan and 21 Japanese and 30 Chinese speakers in English as a second language (ESL) environments in Australia. Their speech production in English is examined based on the updated LMH (Bettoni &amp; Di Biase, 2015). Results show that, in terms of additional arguments in the intermediate stage, the oblique (OBL) construction emerges before the double object (OBJ) construction. This finding contributes to the further development of PT hypotheses for L2 syntactic development.
Blockchain and IoT Integration for Smart Transportation in Cargo

Ensuring the safe transportation of hazardous materials is crucial for safeguarding both the environment and human well-being. The integration of IoT and blockchain technology in smart transportation systems has the potential to transform the cargo industry by providing efficient, secure, and reliable tracking and management of cargo throughout the supply chain. By utilizing IoT sensors to track cargo in real-time and storing the data on a decentralized blockchain platform, intermediaries can be eliminated, reducing costs, and increasing transparency. The use of smart contracts can automate many processes, reducing manual intervention and improving the speed and accuracy of transactions. This paper suggests a blockchain and IoT-based smart transportation model for cargo to monitor the sensor data in a safe and secure way.
Controlling Media Player with Hands: A Transformer Approach and a Quality of Experience Assessment

In this paper, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared to two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. It results that: i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the gesture natural interaction.
Approach to Retrieve Content-based Image from a Clustered Database Based on a Dominant Colour

Over the past few years, there has been a significant increase in both the availability and significance of image retrieval systems. One of the most important aspects of the photographs during the search process is their colour. Since there are many widely used techniques for color-based comparison and retrieval. In this research, we present a Dominant Colour extraction scheme that allows us to extract the image's colour features. We utilise quantization to represent the extracted prominent colours within a bound after extracting the dominant colours. We cluster the database and index the images based on quantized value. When an image which is set as question is sent as input into the system, it will only identify cluster in the image and search the image rather than the entire database utilised similarity metric in.
Language Constrain in Academic Writing in The Form of Thesis For Students of Letters Faculty of Universitas Muslim Indonesia Makassar

This research aims to describe the students’ dominant influence in writing their thesis and the factors they faced in writing their thesis. The method of the study was library research, which meant that the researcher only collected data from their thesis, which was under the researcher’s supervision, and students tested in the thesis examination. The population of this research was all language aspects in the language constraints in academic writing. Because the language aspects are too large, the researcher limited the sample using a purposive sample technique, such as diction, effective sentences (coherence and parallelism), conjunctions, punctuation, and paragraphs (open, main, and closed). Then, the techniques for collecting data were observation, noting, and documentation. Those data were taken from 21 students writing their thesis and tested in the thesis examination. The result of this research was the dominant students' influence in writing their thesis was to compose effective sentences, chose exact words (diction), used conjunction, and made/built open and closed paragraphs. The factors that made it them were students' less practiced writing, less reading, and only depending on their lecture
Efficient Compressor Testing on Railways with A Mobile Application

The aim of the project is to develop an android application for the railways to maintain the daily process of taking readings of the air compressor and details of the air compressors. The number of smartphone users and mobile applications are growing rapidly. So, it is convenient and easy to develop the android application for taking the readings of the air compressors and the details of the air compressors in the mobile phones. This project is used to record its day-to-day readings in an efficient manner. This application fetches the details and readings of the air compressors and send a mail to the higher officials. This project helps to maintain all activities of the air compressors and also records its day-to-day readings. This project helps to reduce the manual work. This proposed app has been developed with an intention to make the user-friendly thus reducing the manual work. The system has been developed with advanced features. The objective of our project is to establish mobile application to maintain all the details of the air compressors.