Reviewerly/exHarmony huggingface dataset:
DropQueries: A Simple Way to Discover Comprehensive Segment Representations

Inspired by the recent progress in object detection ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , DETR), the set prediction mechanism significantly advances the research of semantic segmentation and achieves state-of-the-art performance on popular segmentation benchmarks. The generic pipeline of such a mechanism often firstly takes learnable query features to predict classes and segment masks separately and then blends these class-aware segment masks into the final segmentation mask. One key factor behind the successful training of this pipeline is to apply the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bipartite matching</i> strategy between the set of predictions and ground-truth segments. However, we find that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bipartite matching</i> -based assignment often tends to segment one target class with only a few learnable queries, making many other pre-defined queries useless. In this paper, we propose a simple way, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DropQueries</i> (DQ), to facilitate the set prediction based segmentation architectures. At each iteration of training, our DQ randomly and independently drops each learnable query with a certain probability before <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bipartite matching</i> . In this way, more queries are encouraged to participate in the segmentation process to discover comprehensive segment representations. We conduct extensive experiments using MaskFormer and Mask2Former as two basic yet powerful segmentation architectures. Without bells and whistles, our DQ strategy can bring consistent improvements over strong baselines on popular semantic segmentation benchmarks, including ADE 20K, Cityscapes, COCO Stuff 10K and VSPW.
Enhanced fiber coupled single photon source based on a quantum dot coupled to a gold nanoparticle

In order to obtain a bright single photon source, we utilize the leaky optical cavity provided by a gold nanorod and obtain 62 times Purcell enhancement for the quantum dot on the nanofiber.
Practical Tests for Sub-Rayleigh Source Discrimination with Imperfect Demultiplexers

We show that quantum-optimal tests for discriminating between one and two light sources via spatial-mode demultiplexing become as good as flipping a coin in the presence of arbitrarily small imperfections, and we develop practical alternatives.
Encouraging Submission of FAIR Data at the Journal of Organic Chemistry and Organic Letters

In a welcome move, one of the American chemical society journals has published an encouragement to submit what is called FAIR data to the journal.[1]. A reminder that FAIR data is data that can be Found (F), Accessed (A), Interoperated(I) and Re-used( R). I thought I might try to explore this new tool here. You start at the ACS Research Data Center
An analysis of changes in multicultural education policy by period (Focused on the multicultural education support plan of the Ministry of Education and the Gyeongsangbuk-do Office of education from 2013 to 2022)

교육부와 경상북도 교육청의 대상별 정책 대상 변화과정을 분석한 결과, ‘맞춤형 다문화 교육 실현’, ‘학부모 다문화 교육활동 참여 확대’,‘교원의 다문화 역량 강화’로 그 특징을 요약할 수 있으며, 교육부의 다문화교육 지원계획과 유사한 방향으로 경상북도 다문화교육 지원계획이 추진됨을 확인할 수 있었다. 위에서 언급한 특징을 바탕으로 대상별 관련정책의 문제점을 확인하면 다음과 같다. 다문화 학생과 일반학생을 구분하여 정책을 추진한다는 것이 다문화의 개념을 우리나라를 제외한 문화적 특정 국가의 문화를 축소한다는 의미로 볼 수 있으며, 다문화 학생을 대상으로 하는 정책수에 비해 학부모와 교원을 대상으로 하는 정책수는 교육부와 경상북도 교육청 모두 미비함을 알 수 있었다. 이처럼 교육부와 경상북도 교육청의 다문화 교육정책을 추진함에 있어 다양한 문제점이 내재하고 있음을 알 수 있으며, 이에 대한 개선책으로 우리 지역 특수성을 반영한 정책을 수립하고, 다문화 가정 학부모와 교원의 역량 강화를 위해 다양한 교육정책을 제시할 필요성을 제기하였다.First, as a result of analyzing the policy target change process by target of the Ministry of Education and the Gyeongsangbuk-do Office of Education. its characteristics were summarized as "realization of customized multicultural education," "expansion of parents' multicultural education activities," and "strengthening teachers' capabilities in multicultural education." In addition, it was confirmed that the Ministry of Education's multicultural education support plan and the Gyeongsangbuk-do multicultural education support policy were similar. Based on the above-mentioned characteristics, the problems of related policies for each target are as follows. It has been confirmed that the concept of multiculturalism shrinks the culture of specific cultural countries except Korea when policies are promoted separately between multicultural students and general students. In the case of the Ministry of Education and the Gyeongsangbuk-do Office of Education, the number of policies for parents and teachers was insufficient compared to the number of policies for multicultural students. As such, there are various problems in the promotion of multicultural education policies by the Ministry of Education and the Gyeongsangbuk-do Office of Education, and it is necessary to establish policies that reflect the specificity of our region and present various educational policies to strengthen the capabilities of parents and teachers of multicultural families.
Classification of Microscopic Malaria Parasitized Images Using Deep Learning Feature Fusion

An infectious disease that causes a chronic and potentially life-threatening infection caused by microorganisms of the Plasmodium class, is malaria, or malarial disease. It is critical to detect the presence of Malaria parasites as early as possible to ensure that antimalarial treatment is adequate to cure the particular type of Plasmodium. This is to reduce death rates and to focus on various infections in the event of an adverse outcome. The purpose of this study was to develop an artificial intelligence approach capable of separating parasitized erythrocytes from normal basophilic erythrocytes as well as platelets overlying the red blood cells to overcome the high cost of Ma-laria diagnostic equipment. The tone and texture characteristics of erythrocyte images were extracted using histo-gram thresholds and watershed methods, and then fused with Squeeze Net and ShuffleNet algorithms. The measures included planning, preparing, approving, and testing Deep Convolution Neural Network Segmentation without preparation using a graphic processor unit. A total of 96 percent accuracy and specificity was obtained for the position of malaria in red blood cells based on the results of all of the tests. It has been demonstrated that deep learning can be effective in the field of clinical pathology. This provides new directions for development as well as increasing awareness of researchers in this field.
Problems and countermeasures in the university campus sports culture communication in the background of we-media

The 21st century has witnessed the rapid development of we-media with the diversification of derivative media types, rapid dissemination of media information, and massive and miscellaneous information content. The people are involved in the era of we-media, and anyone is no exception. The rapid development of we-media technology also provides carrier support for the dissemination of culture. Taking college campus sports culture communication as an example, the strength of we-media culture communication should not be underestimated. Based on recognizing the advantages of campus sports culture communication of we-media, we should pay attention to the dissemination of vulgar information and the fermentation of bad public opinion. It is also necessary to take decisive measures to avoid the risk of public opinion and guide the standardized dissemination of campus sports culture of we-media. This paper mainly discussed the problems of university campus sports culture communication in the environment of we-media, with a primary focus on the typical problems in cultural communication and scientific countermeasures, so that the university campus sports culture can be efficient and spread with high quality in a good we-media environment.
Universal Post-Training (PT) Backdoor Defenses

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Federated Learning in Healthcare with Unsupervised and Semi-Supervised Methods

The federated paradigm has made possible the development of techniques capable of solving advanced problems in the healthcare field through the protection of data privacy. However, most existing research is centered around supervised methods and real world data tends to be unevenly distributed and scarcely labelled. This paper aims to provide an overview of existing unsupervised and semi-supervised methods implemented in a federated healthcare setting in order to identify state of the art methods and detect current challenges and future lines of research.
How Tasty Is This Dish? Studying User-Recipe Interactions with a Rating Prediction Algorithm and Graph Neural Networks

Food computing has gained significant attention in recent years due to its direct relation to our health, habits, and cultural traditions. Food-related data have been extensively studied, and graph-based solutions have emerged to combine user-recipe data for various purposes, such as recipe recommendation and food-data alignment tasks. In this study, we propose a graph-based approach to predict the rating a specific user would give a recipe, harnessing the structured form of user-recipe interaction data. The approach incorporates two additional features into the user-recipe interactions graph: 1) user-recipe review embeddings generated by a sentence-based transformer model and 2) a selection of healthy recipe features inferred from nutritional content and international nutrition standards. Results obtained from experiments on a publicly available dataset demonstrate that the proposed method achieves competitive performance compared to recent advancements.
Bot Detection in Twitter: An Overview

Bot detection in social media, particularly on Twitter, has become a crucial issue in recent years due to the increasing use of bots for malicious uses such as the spreading of false information in order to manipulate public opinion. In this paper, we review the most widely available tools for bot detection and the categorization models that exist in the literature. This paper put focus on providing a concise and informative overview of state-of-the-art bot detection on Twitter. This overview can be useful for developing more effective detection methods. Overall, our paper provides valuable insights into the current state of bot detection in social media, suggesting new challenges and possible future trends and research.
On the Limits of Lossy Compression for Human Activity Recognition in Sensor Networks

Human activity recognition is crucial for tactile internet, virtual reality, and digital-twin applications. Previous works have analyzed machine learning for this purpose but often need to pay more attention to typical challenges when deploying these machine learning models in production. First, data must be transmitted to the network node on which the machine-learning model is running. However, scaling human activity recognition by the number of devices and users puts additional constraints on the available transmission channel. While transmitting less data saves bandwidth, removing redundant information from the data can also benefit machine learning. This paper addresses the problem of transmitting wearable sensor data for human activity recognition. To this end, we analyze the extent to which wearable sensor data can be compressed via sparse coding without sacrificing loss in recognition performance. We empirically illustrate, on various datasets, that only a fraction of sensor information is relevant for human activity recognition.
The Promise of Query Answering Systems in Sexuality Studies: Current State, Challenges and Limitations

Sexuality is a field of study that attempts to comprehend human behaviour, improve sexual health and understand culture and gender, among others. Recent advances and developments in artificial intelligence, specifically in query answering and natural language processing, can help to study the social relationship between population and sexuality. They are powerful tools to cope with crucial problems in the field, such as subjectivity, social desirability and social opinion biases. In this work, we review the state-of-the-art of AI-based methods in sexuality-related studies. Focusing on the psychological perspective, we analyse the role of query answering in this area of research. We discuss the necessary foundations, challenges, and limitations a query answering system must cover in this specialised and complex field.
Observational Preorders for Alternating Transition Systems

We define two notions of observational preorders on Alternating transition systems. The first is based on the notion of being able to enforce a property. The second is based on the idea of viewing strategies as a generalised notion of context. We show that alternating simulation as defined by Alur et al. [3] is a sound proof technique for the enforcing preorder and a complete proof technique for the “contextual” preorder. We conclude by comparing alternating simulation with the classic notion of simulation on labelled transition systems.
Evaluasi Terhadap Implementasi Manajemen Perpustakaan Sebagai Sarana Pembelajaran di SMA Muhammadiyah 1 Gresik

In Permendikbud No. 3 of 2020 concerning Standards for Learning facilities and infrastructure are the minimum criteria regarding facilities and infrastructure in accordance with the needs of the content and learning process in the framework of fulfilling graduate learning outcomes. In general, this study aims to evaluate the implementation of learning-based library management organized by SMA Muhammadiyah 1 Gresik. Based on its purpose, this research was designed as a qualitative research. In this case the population in this study are students, teachers, foundation owners, and school principals. The research subjects were a portion of the population at SMA Muhammadiyah 1 Gresik. The results obtained in this study are expected to provide an explanation of how learning-based library management is implemented in terms of learning process standards. The results showed that the maintenance of learning-based library facilities at SMA Muhammadiyah 1 Gresik was carried out in the context of efficiency and effectiveness. Continuous (regular, routine) maintenance is carried out in this school library. Library maintenance involves teachers, librarians, and student
Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model

Anaphora resolution is a traditional task in the natural language processing community, defined as a cohesion phenomenon where one entity points back to a previous entity. Event discourse deixis (EDD) is a kind of more complex anaphora in which the anaphors refer to event descriptions such as sentences or clauses. Event discourse deixis resolution (EDDR) is able to help machines understand the richer linguistic and semantic information in the discourse. However, compared to anaphora resolution, EDDR has received relatively less research attention. In this work, we investigate the EDDR task by designing the corresponding dataset and model. First, we manually construct a high-quality Chinese corpus for EDDR, including 4,417 documents and 5,929 event chains that consist of event antecedents and anaphors. Second, we propose a deep neural network model for EDDR, which formulates the task into two subtasks, namely event anaphor recognition and event antecedent recognition. Our model is trained under the two subtasks jointly so that the EDDR task can be performed end to end. Besides our final model, we also build seven pipeline and joint models as baselines to build comprehensive benchmarks for follow-up research. Experimental results on our EDDR dataset show that our model outperforms all the baselines and achieves about 53%, 44%, 53%, and 63% F1s using standard anaphora resolution metrics such as CoNLL, MUC, B3, and Ceafe. The performances show that EDDR is a challenging task and worth researching in the future. Our dataset and model will be released to facilitate follow-up research.
Design and implementation of secured file delivery protocol using enhanced elliptic curve cryptography for class I and class II transactions

&lt;p&gt;This research study introduces an ID-based identity authentication protocol that utilizes the enhanced elliptic curve digital signature algorithm, a cryptographic method developed on elliptic curve cryptography. The protocol enhances the Consultative Committee for Space Data Systems (CCSDS) File Delivery Protocol (CFDP), a pioneering protocol explicitly defined for distant space communications. This study employs both dependable and uncertain modes of the CFDP protocol. To make more secure data transactions, two key security risks are effectively mitigated in this research as a result of applying the proposed enhanced elliptic curve cryptography algorithm (ECC) over the ternary galois field. First, it thwarts the impersonation of a harmful entity during a passive attack. Second, it prevents masquerade attacks, further reinforcing the security of space data transmission. This ID-based authentication protocol, therefore, offers a significant advancement in protecting far-space communications, optimizing the integrity of data exchanged across vast distances.&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
Learning Environment in the Optics of Critical Constructivism

Introduction:This article is an attempt to show the constructivist learning environment as a model, as a concept that assumes that the student is an active, not a passive participant in the process of gaining in-depth, flexible and systemic knowledge rather than fragmentary, rigid or mechanically reproduced. The text focuses on the contemporary needs of students based on radical and social constructivism. The main dimensions of critical constructivism and elements of a constructivist learning environment are outlined based on Peter Taylor and Barry Fraser’s concept. Research Aim:The aim is to show critical constructivism as a set of various theories, concepts creating conditions for independent construction of knowledge and creating a friendly-learning environment. Evidence-based Facts:The concept is derived from the teaching imperative (John Dewey, Jean Piaget, Lev Vygotsky and Howard Gardner). It assumes that learning is an active process of constructing knowledge based on the experience of the learner and such construction is usually completed subjectively and metacognitively). The concept of constructivism is quite often used by both theoreticians (academic lecturers and researchers) as well as practitioners (school teachers). Summary: In the article, we analyze the role played by a constructivist learning environment that radically questions the vision of a student as a passive recipient and gives him the status of an active subject contributing to the construction of a learning-friendly environment. Education should be seen as supporting the student in the process of creating meanings that allow to construct your own understanding of the world.
Utilization of Digital Image and Convolution Neural Network Algorithm in Customer Satisfaction Survey with Facial Expressions

The human face provides us with a lot of information about a person, and arguably the two most important pieces of information in a face are a person's identity and their emotional state. Judgments of identity and emotion facilitate social interactions. Services are a crucial part of the activities of all organizations, especially those in the service sector. Good services support customer satisfaction and ultimately impact the progress of the organization. The Convolutional Neural Network algorithm has become the most widely used neural architecture in various tasks, including image classification, audio pattern recognition, machine translation of text, and speech recognition. The data groups (angry, fearful, happy, neutral, sad, and surprised) tested with a threshold value of 30 epochs achieved a loss (error) accuracy of 1.5146 on the test data. The accuracy on the test data is 0.61. The proposed Convolutional Neural Network algorithm and digital image utilization achieved high accuracy performance to assist in evaluating a service-related field.
Knowledge Dynamics and Expert Knowledge Translation: A Case Study

Knowledge dynamics means variation of knowledge in time and space. Over time, knowledge levels can increase through accumulation, or decrease through loss. Also, knowledge may change its quality through transformation from one form into another. In space, knowledge can move from one place to another one, generating a flow (i.e., knowledge flow). The present paper aims at analyzing the knowledge dynamics associated to the process of expert knowledge translation, present in almost all life domains. Knowledge translation is based on communication and transformation of knowledge. Among many models used to explain knowledge translation, we present in this paper the expert knowledge translation that is used whenever an expert is sharing his expertise with one or several individuals interested in that knowledge. We analyze the whole process of expert knowledge translation from a conceptual perspective and then we present a case study describing expert knowledge translation. Knowledge translation is necessary because the source and the receiver of the messages have different semantic domains and levels of understanding based on their education, background and their analytical skills. The main barrier in knowledge translation is the absorptive capacity of the receiver. If the absorptive capacity is rather small, the translation efficiency is very small. This situation can be improved if the translation process is broken into several smaller knowledge translation processes like in a cascade. The paper presents the model of such a cascade for expert knowledge translation showing how to construct it. As an application, the paper presents a case study focused on a company manufacturing gas turbines (GTM). The company is a global supplier of gas turbines and we show how engineering expert knowledge is translated in a cascade from design to manufacturing, automation, testing, installing and commissioning turbines to end users.