Reviewerly/exHarmony huggingface dataset:
Dual-Uncertainty Guided Curriculum Learning and Part-Aware Feature Refinement for Domain Adaptive Person Re-Identification

Unsupervised Domain Adaptative person re-identification (UDA ReID) aims to transfer the knowledge of pre-trained model from labeled source domain to unlabeled target domain. Although the current clustering-based methods have achieved promising success, they neglect the tolerance of the model to cope with different-level noise, which may cause the model to memorize some incorrect patterns caused by label noise and overfit on them rapidly in the early stages. In this paper, we introduce a novel Dual Uncertainty guided Curriculum Learning (DUCL) method to tackle the above problems. Specifically, the reliability-based curriculum allocation is proposed to enforce the sample adaptation in an easy-to-hard manner, which is further assisted by a novel dual-uncertainty re-weighting strategy to alleviate the influence of label noise. In addition, we design Part-aware Feature Refinement (PAFR) to enhance the discrimination of model and thereby acquiring more reliable pseudo-labels. Specifically, the part-aware attention maps are exploited in the PAFR to integrate fine-grained semantics into holistic representation. Extensive experiments have validated the superiority of the proposed method.
Distance-Based Weight Transfer for Fine-Tuning From Near-Field to Far-Field Speaker Verification

The scarcity of labeled far-field speech is a constraint for training superior far-field speaker verification systems. In general, fine-tuning the model pre-trained on large-scale near- field speech through a small amount of far-field speech substantially outperforms training from scratch. However, the vanilla fine-tuning suffers from two limitations – catastrophic forgetting and overfitting. In this paper, we propose a weight transfer regularization (WTR) loss to constrain the distance of the weights between the pre-trained model and the fine-tuned model. With the WTR loss, the fine-tuning process takes advantage of the previously acquired discriminative ability from the large-scale near-field speech and avoids catastrophic for- getting. Meanwhile, the analysis based on the PAC-Bayes generalization theory indicates that the WTR loss makes the fine-tuned model have a tighter generalization bound, thus mitigating the overfitting problem. Moreover, three different norm distances for weight transfer are explored, which are L1-norm distance, L2-norm distance, and Max-norm distance. We evaluate the effectiveness of the WTR loss on VoxCeleb (pre-trained) and FFSVC (fine-tuned) datasets. Experimental results show that the distance-based weight transfer fine-tuning strategy significantly outperforms vanilla fine- tuning and other competitive domain adaptation methods.
VLKP:Video Instance Segmentation with Visual-Linguistic Knowledge Prompts

Most video instance segmentation(VIS) models only focused on visual knowledge and ignored intrinsic linguistic knowledge. Based on the observation that incorporating linguistic knowledge can significantly improve the model’s contextual understanding of the video, in this paper, we present a Video Instance Segmentation approach with Visual-Linguistic Knowledge Prompts(VLKP), a novel paradigm for offline video instance Segmentation. Specifically, we propose the visual-linguistic knowledge prompt training strategy, which incorporates linguistic features with visual features to obtain Visual-Linguistic features and processes it instead of traditional visual features. In addition, we design a new temporal shift encoder to convey information between frames and enhance the temporal sensitivity of the model. On two widely adopted VIS benchmarks, i.e., YouTube-VIS-2019 and YouTube-VIS-2021, VLKP with ResNet-50 obtains state-of-the-art results,e.g.,47.7 AP on YouTube-VIS-2019 and 42.0 AP on YouTube-VIS-2021. Code is available at https://github.com/ruixiangC/VLKP.
DialogMI: A Dialogue Model Based on Enhancing Dialogue Mutual Information

Most of the open-domain dialogue models tend to perform insufficiently in generating informative response. The possible reason is that they lack the capability of enhancing the mutual information between generated responses and dialogue history. To address this issue, we present a novel task of the mutual information enhancement and build a dialogue generation framework DialogMI. Besides, we propose two methods to represent the loss function of the novel task and a method to enhance the effect of the mutual information loss. To the best of our knowledge, we are the first to propose a loss that measures mutual information and use it to assist training model. We conduct experiments on Chinese chit-chat dataset and LCCC dataset for response generation. Results on these datasets indicate that Di-alogMI can significantly outperform baselines in generating informative dialogue, leading to better response quality and diversity.
Dual-Feature Enhancement for Weakly Supervised Temporal Action Localization

Weakly-supervised Temporal Action Localization (WTAL) aims at localizing actions in untrimmed videos with only video-level labels. Most existing methods embrace a "localization by classification" paradigm and adopt a model that pre-trained with recognition task for feature extraction. The gap between recognition and localization tasks leads to inferior performance. Some recent works attempt to utilize feature enhancement to obtain better feature for localization and boost the performance to some extent. However, they are limited to intra-video information exploiting, while ignoring meaningful inter-video information in the dataset. In this paper, we propose a novel Dual-Feature Enhancement (DFE) method for WTAL, which can utilize both intra-and inter-video information. For intra-video, a local feature enhancement module is designed to promote the feature interaction along the temporal dimension within each video. For inter-video information, a global memory module is firstly designed to learn the representations for different categories across different videos. Then, a global feature enhancement module is used to enhance the video features with the help of those global representations in the memory. Besides, to reduce the extra computational cost caused by global enhancement module in the inference stage, a distillation loss is applied to enforce the local branch to learn the information from global branch, so the global enhancement module could be removed during inference. The proposed method achieves state-of-the-art performance on popular benchmarks.
Comparative Study of Algorithms for Sentiment Analysis on IMDB Movie Reviews

Sentiment Analysis is one of the major NLP tasks, which mainly focuses on finding polarity(sentiment) and intention for a given piece of textual data. The data can range from a sentence level to a document level. When we communicate as human to human, it is much more easier to interpret our emotions. But when it comes to machines, it becomes difficult to identify the sentiments. Sentiment Analysis makes use of techniques like Text Analytics and other NLP methods. Movie reviews play a significant role in telling whether it's a success or failure. These reviews will have a great influence on larger set of viewers. So, its important to build a good model which classifies movie reviews accurately. In this paper, we have adopted six machine learning models to classify movie reviews and a comparative study is made among them which helps to decide the best classifier based on several evaluation metrics.
Pelatihan Pembuatan Pop Up Book Sebagai Media Pembelajaran Calistung Bagi Guru UPT SDN 10 Rembon, Lembang Palesan, Kecamatan Rembon

Based on the results of interviews with principals and teachers at UPT SDN 10 Rembon, Lembang Palesan, Rembon Sub-district conducted in August 2022, several problems were obtained in the learning process in the classroom, including: low interest and motivation in learning students in participating in Calistung learning. This causes there are still about 30 students from the total of grades 1-3 who are not fluent in reading, writing, and arithmetic. In addition, another factor is the lack of teacher awareness in the use and use of learning media, teachers lack skills and experience in creating, utilizing, and using learning media, especially in teaching students to read, write, and count. In addition, so far there has been no special socialization for teachers related to the use and use of learning media. The use and use of learning media is very important to achieve learning objectives, and is able to make the learning environment conducive and fun for students. One of the appropriate learning media that can be applied based on problem analysis at partner locations is Pop Up Book-based learning media. The purpose of this training is to support teacher skills in creating, utilizing, and using Pop Up Books as learning media that can be used in Calistung learning. The methods used in this PKM activity include: 1) Preparation; 2) Manufacturing Training; 3) Evaluation and Monitoring. The mandatory output target to be achieved in this training is in the form of articles that will be published in a national journal with ISSN (Comserva journal) in 2023.
A Real Time Tourism Recommender System using KNN and RBM Approach

Abstract: Tourism has become a significant contributor to both the industry and national economy. Over the years, the desire to travel and explore new places has led to a substantial increase in the number of tourists. This boom in tourism has resulted in many businesses, both global and local, as well as governments investing heavily in the industry. However, while governments invest in maintaining and promoting tourist attractions, there is often no emphasis on improving the overall experience of tourists.To address this issue, a real-time recommender system can be implemented. This system will provide tourists with recommendations based on their preferences, rather than just providing them with information. As the environment for recommender systems has become increasingly complex and dynamic, with diverse information available in real-time, it is necessary to develop an effective touring recommender system based on real-time characteristics. This system will provide tourists with real-time recommendations, thus enhancing their experience.To achieve this, a recommendation framework that provides comprehensive information for tourists is needed. This framework will guide tourists from the initial stage of exploring which country to visit, to providing a complete comprehensive guide for them once they arrive at their vacation destination. The goal of this solution is to give tourists the best possible experience, eliminating the need to depend on the help of others.Several features will be implemented in this application, such as comprehensive information on places to stay, dine, and visit, using the current location and budget, popularity, and other characteristics. The system will also create a timetable for tourists based on their duration of stay and provide real-time assistance. Overall, the system will provide a personalized and optimized touring experience for the tourist.With the implementation of this recommender system, tourists will be able to make more informed decisions about where to go, what to do, and where to stay. By using real-time data and personalized recommendations, tourists can enjoy a more engaging and satisfying vacation experience. This system will also benefit the tourism industry as a whole, by increasing customer satisfaction and promoting repeat visits
PENGARUH KEHADIRAN DOSEN DAN PROSES PEMBELAJARAN TERHADAP PRESTASI AKADEMIK MAHASISWA PADA STMIK PROFESIONAL MAKASSAR

One of the success of a university is to produce output that has good academic achievement, this can be achieved if students have good academic achievement during the learning process in college in each semester. This study aims to see the effect of lecturer attendance, learning process and student attendance on student academic achievement. This research was conducted at the College of Informatics and Computer Management (STMIK) Professional Makassar. Questionnaires will be distributed to 221 students of STMIK Professional Makasssar in odd Semester of TA.2022/ 2023, taken by random stratified sampling, the questionnaire is designed in the form of Likert scale with four options, then analyzed by multiple regression method, with significant level / α = 5 %. Analysis using SPSS 17 software. The results showed that simultaneously, academic achievement can be influenced by lecturer attendance, learning process and student attendance. The amount of influence of the 3 variables simultaneously on academic achievement is 24.5% (Value R2 = 0.245), the rest of 75.5% influenced by other factors outside the regression model. The amount of correlation between independent variables of lecturer attendance, learning process and student attendance toward student achievement is 0,495. The most influential factor on student achievement is student attendance factor because the regression coefficient of student attendance variable is bigger than the other two variables (attendance of lecturer and learning process) that is equal to 0,017.
Anticipation in Pianist-Accompanist’s Concert Practice in the Context of Emotional Intelligence

The purpose of the article is to investigate the functioning of anticipation in correlation with emotional intelligence in a pianist-accompanist’s work in the concert phase. Emotional intelligence is the ability to work with emotions, including the ability to anticipate them. Anticipation is the property of the psyche to foresee the next events in time, including emotions. This is the point of intersection that the concept of the article is built on. Research methodology. To achieve the declared purpose, we use the following methods. Systemic method allows us to explore the principles of functioning of anticipation as an attribute of the emotional intelligence system. Psychological method is aimed to consider the congenital and acquired patterns of the human brain through the prism of psychology. A method of theoretical modeling helps to construct speculative models of potential real situations. The scientific novelty of the research consists in revealing the direct co-dependence of anticipation and emotional intelligence and projecting their functioning on the pianist-accompanist’s creative work. There is always an element of unpredictability in the process of concert performance, which ensures the openness of the field of emotional reactions. For a pianist-accompanist, the difficulty lies not only in working out one's own emotions, but also in preparing a response to a partner's concert emotion. To minimise losses on stage, one will need the ability to work with the tools of emotional intelligence, among which anticipation plays a significant role. Conclusions. The importance of understanding the acts of anticipation for a successful use of the resources of emotional intelligence is substantiated. Expanding the range of resources of one’s own body by consciously transferring innate predispositions into the processes controlled by consciousness raises the professional activity of a pianist-accompanist to a higher level of creative mastery.&#x0D; Key words: anticipation, expectation, emotional intelligence, pianist-accompanist.
Phishing Websites Spotting with Help of using Machine Learning Tools

Abstract: Phishing assaults cost internet users billions of dollars every year and are a constantly growing hazard in the cyberspace. It is illegal to gather sensitive information from consumers through a number of social engineering techniques. Email, instant messaging, pop-up messages, web pages, and other forms of communication can all be used to identify phishing tactics. This work offers a model that can determine whether a URL link is genuine or fraudulent. The data set used for the classification was sourced from the University of New Brunswick dataset bank, which has a collection of benign, spam, phishing, malware, and defacement URLs, as well as from an open source service called "Phish Tank," which contains phishing URLs in multiple formats such as CSV, JSON, etc. Phishing URLs are identified using a combination of deep neural network methods and more than six machine learning models. The goal of this study is to create a web application software that can identify phishing URLs from a database of more than 5,000 URLs that have been randomly selected, divided into 80,000 training samples and 20,000 testing samples, and then divided again into equal portions of phishing and legitimate URLs. To distinguish between legal and phishing URLs, the URL dataset is trained and tested using feature selections like address bar-based features, domain-based features, HTML &amp; JavaScript-based features. Finally, the study provided a model for classifying URLs as phishing or legitimate. This would be extremely useful in assisting individuals and businesses in identifying phishing attacks by authenticating any link provided to them to prove its validity.
Penerapan Metode Kerja Kelompok dalam Meningkatkan Hasil Belajar Bahasa Indonesia Siswa Kelas VIII.F SMP Negeri 12 Mataram

This study aims to find out the results of implementing group study in improving the learning outcomes of Indonesian language students in Class VIII.F at SMP Negeri 12 Mataram in the academic year 2022/2023. This research is a classroom action research. The subjects in this study were 33 students of class VIII.F SMP Negeri 12 Mataram with 19 male students and 14 female students. Data collection techniques used are observation, documentation, and tests. Observations are used to collect data on learning interest. The test is used to measure learning achievement in Indonesian subjects. Data analysis used is descriptive qualitative and descriptive quantitative. The increase in students' cognitive learning outcomes before and after the implementation of group learning at SMP Negeri 12 Mataram can be seen from the percentage gain of each indicator, namely: The average value of the initial data students obtained 73.98% increased to 80.15 in cycle I and obtained 88.06 in cycle II. While students who completed the data of 23 people then obtained 27 in cycle I and experienced an increase to 33 people in cycle II with each classical completeness obtained initial data of 69.64% to 81.82% in cycle I and increased to 100% in cycle II, this shows that all the indicators expected by the researcher on the reading ability of class I students were successfully achieved. Lesson planning carried out by the teacher increased from 75.00% in cycle I and 85.00% in cycle II. So the lesson planning carried out by the teacher is very good. While the implementation of learning carried out by the teacher also experienced an increase from 79.17% in cycle I to 87.50% in cycle II. So the implementation of learning carried out by the teacher has also increased. Student learning activities that the average percentage in the first cycle is 80.00%. But in cycle II, student learning activities increased to 90.00%. So student learning activities in learning Indonesian are good.
Implementasi Metode Partitioning Around Medoids (PAM) Clustering pada Data Penduduk Miskin di Sumatera Barat

The problem of poverty is a complex problem so that it becomes a priority in development in a country. Various efforts have been made such as social assistance programs, but the assistance provided is not always evenly distributed and not on target. Therefore, it is necessary to determine priority handling for the community by grouping data on the poor, especially in the Province of West Sumatra, Indonesia. In this study, an analysis was carried out using all districts/cities in West Sumatra and poverty indicators, namely the percentage of poor people, poverty depth index, poverty severity index, literacy rate, average length of schooling, expected length of schooling, and open unemployment rate. Grouping was carried out using the Partitioning Around Medoids (PAM) method, or better known as the K-Medoids method. The research results obtained 2 poverty level clusters, namely cluster 1 is a high level with 11 districts/cities members, and cluster 2 is a low level with 8 districts/cities members.
Addressing the selection bias in voice assistance: training voice assistance model in python with equal data selection

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.
Learning Task-Aligned Mask Query for Instance Segmentation

Recently, query-based instance segmentation methods have achieved comparable performance to previous state-of-the-art methods. However, the query lacks the learning of the consistency between classification and segmentation tasks, which may lead to misalignment between classification score and mask quality (i.e., mask IoU) and can not result in a reliable ranking for predictions. In this work, we propose a novel instance segmentation method, termed AlignMask, which effectively learns task-aligned mask queries for instance end-toend. Specifically, we propose Aligned Query Learning (AQL) to learn task-aligned features for pixel embedding and transformer decoder, which helps segmentation quality estimation of the mask query. We also use Aligned Label Assignment to explicitly align the optimization goals for classification score and mask quality of the query. Extensive experiments on MSCOCO show that our proposed AlignMask achieves competitive performance with state-of-the-art models.