Reviewerly/exHarmony huggingface dataset:
IdentifyMix: An efficient two-stage learning approach to combating label noise

Deep neural networks require correct label annotation during supervised learning.It is inevitable, however, that some labels are noisy during the labeling process.A deep neural network retains incorrect labels during training, resulting in a degradation of performance.Therefore, it is essential to identify samples with potentially correct labels.In state-of-the-art methods, small-loss samples are chosen for subsequent training through a sample selection strategy.Howerver, it typically ignores the imbalance in noise ratios between mini-batches when performing sample selection within each minibatch.Further, numerous valuable samples with high losses are discarded, which adversely affects the generalization performance of the model, particularly under conditions of high noise ratios.To this end, this paper proposes IdentifyMix, an effective two-stage learning approach for noisy robust learning that combines an unique sample selection strategy and the semi-supervised learning technique.By observing how the dynamics of network training are changing, AUM (Area Under the Margin) provides a criterion that is applied in this research to identify mislabeled data.Moreover, by combining semi-supervised learning with contrastive learning and data augmentation, it is possible to extract more useful information from mislabeled samples.Experiments on several synthetic and real-world noise benchmarks demonstrate the effectiveness of IdentifyMix compared with state-of-the-art methods.
Federated learning on Riemannian manifolds

Federated learning (FL) has found many important applications in smart-phone-APP based machine learning applications.Although many algorithms have been studied for FL, to the best of our knowledge, algorithms for FL with nonconvex constraints have not been studied.This paper studies FL over Riemannian manifolds, which finds important applications such as federated PCA and federated kPCA.We propose a Riemannian federated SVRG (RFedSVRG) method to solve federated optimization over Riemannian manifolds.We analyze its convergence rate under different scenarios.Numerical experiments are conducted to compare RFedSVRG with the Riemannian counterparts of FedAvg and FedProx.We observed from the numerical experiments that the advantages of RFedSVRG are significant.
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models

Learning vectors that capture the meaning of concepts remains a fundamental challenge. Somewhat surprisingly, perhaps, pre-trained language models have thus far only enabled modest improvements to the quality of such concept embeddings. Current strategies for using language models typically represent a concept by averaging the contextualised representations of its mentions in some corpus. This is potentially sub-optimal for at least two reasons. First, contextualised word vectors have an unusual geometry, which hampers downstream tasks. Second, concept embeddings should capture the semantic properties of concepts, whereas contextualised word vectors are also affected by other factors. To address these issues, we propose two contrastive learning strategies, based on the view that whenever two sentences reveal similar properties, the corresponding contextualised vectors should also be similar. One strategy is fully unsupervised, estimating the properties which are expressed in a sentence from the neighbourhood structure of the contextualised word embeddings. The second strategy instead relies on a distant supervision signal from ConceptNet. Our experimental results show that the resulting vectors substantially outperform existing concept embeddings in predicting the semantic properties of concepts, with the ConceptNet-based strategy achieving the best results. These findings are furthermore confirmed in a clustering task and in the downstream task of ontology completion.
Autonomous Excavator System for Construction Earth Moving

Autonomous Excavator System for Construction Earth Moving Shiyu Jin, Liuwang Kang, Yuan-Chih Peng, Zhenpeng He, Ruitao Song, Lingfeng Qian, and Liangjun Zhang Pages 24-27 (ICRA 2023 Future of Construction Workshop Papers, ISSN 2413-5844) Abstract: Construction automation is vital for increasing efficiency, reducing costs, and enhancing safety in the industry. In this paper, we present our latest advancements in autonomous excavator systems (AES) specifically designed for earth moving operations in construction sites. Our proposed architecture integrates perception, planning, and control by merging multi-modal perception sensors like LiDAR and cameras with state-of-the-art localization and mapping, object detection, terrain traversability mapping, motion planning, and navigation algorithms. To showcase the performance, we successfully conducted a live demonstration for construction earth moving with multiple tasks including trucking loading, unstructured terrain navigation, and trenching. The AES is able to seamlessly complete all three tasks without any human operator intervention, highlighting its advanced level of effectiveness and robustness. To the best of our knowledge, this is the first autonomous excavator system capable of seamlessly performing multiple construction earth moving tasks. Keywords: No keywords DOI: https://doi.org/10.22260/ICRA2023/0009 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
A Heuristic for Entanglement Routing in Quantum Networks

Due to the fact that quantum networks can provide higher security in communications, many related issues have been presented and addressed recently. One of the most important issues is to study the routing in quantum networks with multiple source-destination pairs while maximizing the total end-to-end entanglement rate. In previous studies, the shortest paths are typically selected as the entanglement paths for a source-destination pair to increase the total end-to-end entanglement rate. However, this approach is only efficient once the network resources are sufficient. Moreover, source-destination pairs only require a specific entanglement rate rather than the shortest paths. Therefore, in this paper, we suppose that each source-destination pair requires an upper bound of the entanglement rate. Under this assumption, a novel problem is proposed to construct entanglement paths for a set of multiple source-destination pairs such that the total end-to-end entanglement rate is maximized. We then propose a greedy algorithm to solve the problem. Finally, the simulation is conducted to evaluate the performance of the proposed algorithm.
DocGraphLM: Documental Graph Language Model for Information Extraction

Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged-transformer-based models inspired by LLMs, and Graph Neural Networks. In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. To achieve this, we propose 1) a joint encoder architecture to represent documents, and 2) a novel link prediction approach to reconstruct document graphs. DocGraphLM predicts both directions and distances between nodes using a convergent joint loss function that prioritizes neighborhood restoration and downweighs distant node detection. Our experiments on three SotA datasets show consistent improvement on IE and QA tasks with the adoption of graph features. Moreover, we report that adopting the graph features accelerates convergence in the learning process druing training, despite being solely constructed through link prediction.
Escasalus—ML-Based Food Quality Checker

Food deterioration is a serious issue nowadays since eating damaged food can hurt consumers. If an automated procedure is used, it will not only enhance the accuracy of damaged food detection but also lower the amount of physical labor needed. So, we are introducing our intelligent ML-based food quality checking gadget here, which uses the proper sensors and observes the gases released by the food to identify deterioration. Instead of depending just on the value of one sensor, we employ a sensor array that gathers data on the chemical components of food item. This will enhance the accuracy of food quality detection. We create a better ML model by using the data from sensors. The model used here is the sequential model which produces sequences of values. The quality analysis of food involves model training, validation, and model testing phase. Once the model is trained, we would use it to predict the label (edible or not edible) of new data points based on their features. Finally, the users will be able to ensure food quality on an app we build. Our device would be useful for cancer patients undergoing chemoradiotherapy who have difficulty smelling and to ensure the quality of food they consume. It also finds useful in anosmic patients.
Development of a Software Tool for Searching Fake Educational Domain Names

The article discusses the latest research on the problem of fake activity associated with the creation of phishing sites and the sending of phishing emails. The relevance of this problem for educational portals is shown. As a solution to the problem, it is proposed to create and implement a software package for the analysis of fake activity. The article presents the structure of the program and databases of this package, describes the content of screen forms. The results of testing the software package on the dataset of domain names from the RU zone registry are presented. The introduction of the proposed software package for the analysis of fake activity in educational organizations will increase the degree of protection of Internet users from information threats from fake activity.
Data Augmentation Based on Virtual Wrist Devices for Fall Detection

Fall detection based on machine learning algorithms require data samples that have larger risks to gather than other similar topics such as the recognition of Activities of Daily Life (ADL). In this work we aim to detect falls using wrist devices, which have sensors such as accelerometers, gyroscopes and/or magnetometers. In this context, we aim to reduce risks and add a larger number of data samples to an existing fall detection dataset, by using simulated wrist band devices. We resort to two simulation approaches for data augmentation: (i) Virtual characters based on Motion Capture technology aimed at computer games, and (ii) fall simulation by the application of forces on an animated character. We present an evaluation of the data augmentation, which selects the real-life samples as the testing set. On the selected machine learning algorithms, k Nearest Neighbors and Decision Tree got classification improvements by using the augmented data, which shows the promising results of created samples for fall detection.
Developing an Audit and Feedback Dashboard for Family Physicians: User-Centered Design Process

Background Audit and feedback (A&amp;F), the summary and provision of clinical performance data, is a common quality improvement strategy. Successful design and implementation of A&amp;F—or any quality improvement strategy—should incorporate evidence-informed best practices as well as context-specific end user input. Objective We used A&amp;F theory and user-centered design to inform the development of a web-based primary care A&amp;F dashboard. We describe the design process and how it influenced the design of the dashboard. Methods Our design process included 3 phases: prototype development based on A&amp;F theory and input from clinical improvement leaders; workshop with family physician quality improvement leaders to develop personas (ie, fictional users that represent an archetype character representative of our key users) and application of those personas to design decisions; and user-centered interviews with family physicians to learn about the physician’s reactions to the revised dashboard. Results The team applied A&amp;F best practices to the dashboard prototype. Personas were used to identify target groups with challenges and behaviors as a tool for informed design decision-making. Our workshop produced 3 user personas, Dr Skeptic, Frazzled Physician, and Eager Implementer, representing common users based on the team’s experience of A&amp;F. Interviews were conducted to further validate findings from the persona workshop and found that (1) physicians were interested in how they compare with peers; however, if performance was above average, they were not motivated to improve even if gaps compared to other standards in their care remained; (2) burnout levels were high as physicians are trying to catch up on missed care during the pandemic and are therefore less motivated to act on the data; and (3) additional desired features included integration within the electronic medical record, and more up-to-date and accurate data. Conclusions We found that carefully incorporating data from user interviews helped operationalize generic best practices for A&amp;F to achieve an acceptable dashboard that could meet the needs and goals of physicians. We demonstrate such a design process in this paper. A&amp;F dashboards should address physicians’ data skepticism, present data in a way that spurs action, and support physicians to have the time and capacity to engage in quality improvement work; the steps we followed may help those responsible for quality improvement strategy implementation achieve these aims.
PENGEMBANGAN E- MODULE INTERAKTIF BERBASIS WEBSITE GOOGLE SITE DENGAN METODE TEAM BASED PROJECT PADA MATERI PERPINDAHAN KALOR DI KELAS V SD NEGERI 101768 TEMBUNG

Abstract: Research on developing interactive E-Module products based on the Google Site Website with the Team Based Project Method, researchers used the Research and Development Model 4D (define, design, develop, and disseminate) method, the research instruments were observation, interviews, tests, and validation questionnaires. The results of the research are the e-module, Google Site Website and Information Board products. As for the 3 aspects of the assessment, the content of the material obtained 88.42% (Valid), the display of design and technology of the e-module obtained 81.05% (Valid), the Practicality of the e-module obtained 93.88% (Valid). Data from the pre-test in class V-A showed that 23 students had not completed and post-test results showed that 22 students had completed and 5 students had not completed with a percentage of 80.55% (effective).Keywords: Science learning outcomes, E-Module, Team Based Project, Elementary School StudentsAbstrak: Penelitian mengembangkan produk E- Module Interaktif Berbasis Website Google Site Dengan Metode Team Based Project, Peneliti menggunakan metode Research and Development Model 4D (define, design, develop, dan dessiminate ), instrumen penelitiannya observasi, wawancara, test, dan angket validasi. Hasil penelitian tersusunnya e- module interaktif berbasis Website Google Site dan produk Papan Informasi. Adapun 3 aspek penilaian meliputi, Isi materi e-module diperoleh 88,42% (Valid), Tampilan desain dan teknologi e-module diperoleh 81,05% (Valid), Praktikalitas e- module diperoleh 93,88% (Praktis). Data dari pre-test di kelas V-A terdapat 23 siswa belum tuntas dan 4 siswa tuntas. Sedangkan data hasil post-test terdapat 22 siswa tuntas dan 5 siswa tidak tuntas dengan persentase 80,55% (efektif).Kata Kunci: Hasil Belajar IPA, Modul Elektronik, Proyek Berbasis Kelompok, Siswa Sekolah Dasar
Ensemble Approach to Classify Spam SMS from Bengali Text

The Short Message Service (SMS) is a popular communication tool, but it has some security weaknesses, such as the influx of spam messages from cyber criminals. While several studies have been conducted on filtering and categorizing spam messages in various languages, including English, limited research has been done on detecting spam in Bengali (endonym Bangla) text. This study aims to fill this gap by classifying Bengali SMS messages as either spam or ham (legitimate messages). To accomplish this, the study used machine learning algorithms, including support vector machine (SVM) with a linear kernel and decision tree (DT), logistic regression (LR), and random forest (RF) with various parameters, as baseline models. Ensemble approaches, such as bagging, boosting, and stacking, were then used to enhance the performance of the models. The results show that the ensemble approach successfully identified spam messages in Bengali text, with XGBoost producing the most favorable outcome. The contribution of this study lies in its focus on Bengali text and the demonstration of the ensemble method’s performance on a small dataset. The tool developed in this study can provide a secure and efficient SMS service to customers by reducing the burden of spam messages and improving the overall user experience. Additionally, the tool can be marketed as a value-added service for customers who are concerned about the security of their personal and financial information. Overall, this study highlights the importance of machine learning algorithms, specifically ensemble methods, in detecting spam messages in Bengali text and provides a valuable contribution to the field of SMS security.
Collaborative Platforms and Matchmaking Algorithms for Research and Education, Establishment, and Optimization of Consortia

Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia.
Stackelberg Game-Based Offloading Strategy for Digital Twin in Internet of Vehicles

The combination of digital twin (DT) and Internet of Vehicles (IoV) has gained significant attention from both academia and industry in recent times. DT can establish a high fidelity virtual representation of IoV based on the real-time sensor data, and feedback the decision policy, therefore generating possible improvements. Especially, as the advance of Mobile Edge Computing (MEC) technique, it has the potential to facilitate digital twin¡¯s computationally intensive tasks. However, how to schedule the MEC computing resource is the key to efficient operation of the whole system. Therefore, this study aims to investigate pricing considerations and resource management that exist between the vehicle and MEC server in order to mitigate this issue. Specifically, we model the interaction between the MEC server and vehicles as a Stackelberg game, where the leader (i.e., the MEC service provider) sets prices, and then the vehicles act as followers. By leveraging information about social interactions from other vehicles, utility functions are formulated by the vehicles. Additionally, the study analyzes the existence and uniqueness of the Stackelberg equilibrium, and proposes a dynamic iterative algorithm to find the appropriate Nash equilibrium for the proposed Stackelberg game. Experimental results demonstrate that the proposed scheme effectively formulates suitable prices and meets computational requirements.
Sequential device-independent certification of indefinite causal order

Indefinite causal order has found numerous applications in quantum computation, quantum communication, and quantum metrology. Before its usage, the quality of the indefinite causal order needs to be first certified, and the certification should ideally be device-independent (DI) to avoid the impact of device imperfections. In this work, we initiate the study of the sequential DI certification of an indefinite causal order. This can be useful in experimental platforms where the generation of an indefinite causal order is difficult. We show that an arbitrary number of sequential DI certifications of an indefinite causal order can be achieved with a quantum switch and also analyze practical requirements for experimental implementations of the certifications. Our work opens the possibility of reusing the resource of an indefinite causal order multiple times in device-independent quantum information processing.
Problems of formation and development of skills of future lawyers in the field of law-making

The authors have analyzed the problems of formation and development of law students’ creativity skills in the sphere of law making and also the results of open questionnaire which allowed to reveal not only students’ knowledge about the nature of law making but also their desire and ability to create laws in the future have been presented. The article deals with the options of law-making activity, a comparative analysis of different views of this process. Educational tools, which develop creativity and can diversify the educational process in higher education institution, are shown. It is argued that it is the use of nonstandard forms and methods of teaching law students contributes to the goals of law-making and professional and personal improvement of the future professional. It is concluded that the development of creativity in lawmaking is an urgent problem of modern legal education that can be solved with the help of special legal and pedagogical tools to make the learning process more creative and professionally oriented.
RDS: Related Document Search for Economics Data Using Ontologies and Hybrid Semantics

The World Wide Web is filled with documents. Extracting documents specifically on a particular domain of choice or on a particular topic is not an easy task because of the presence of so many documents. So, as a result there is a need for document recommendation specifically for specialized domains like economics, finance, healthcare, etc. There are many search engines which do not have indexing capabilities to index all the entities under a specific domain. So, as a result, it is very difficult to retrieve documents which are highly technical or sociotechnical like finance as a domain or economics as a domain or healthcare related domain. In this case, we are modelling an algorithm for document recommendation for economics data. It is a model which is ontology-driven semantic infused pertaining to economics domain. An overall precision of 96.29% with the lowest FDR of 0.04 has been achieved by the proposed RDS framework which is the best-in-class model when compared with the baseline models.
Review of Character Recognition Techniques for MODI Script

Objectives: The main objective of this study is to review and compare the various methods used for Modi script recognition. Methods: The author has chosen various methods from 2010 to 2022 that are used to process MODI Script. The distinct methods employed for feature extraction and classification are compared for various datasets. A discussion on the significance of the selection of correct feature extraction and classification techniques and the comments on the methods suited to specific applications is provided. Findings: Currently, there are very few MODI translators. In contrast, millions of historical documents written in MODI remain unexplored. Novelty: The Convolutional Neural Networks (CNNs) has been used successfully for recognizing MODI Script characters. In the present study the author finds that, compared to all techniques, CNN provides maximum accuracy of 99.78%. Hence, CNN is the best character recognition technique for the MODI script. Keywords: Distance Classifier; Feature Extraction; and Classification; Handwritten Characters; MODI Lipi; Character Recognition