Reviewerly/exHarmony huggingface dataset:
PubSub-ML: A Model Streaming Alternative to Federated Learning

Federated learning is a decentralized learning framework where participating sites are engaged in a tight collaboration, forcing them into symmetric sharing and the agreement in terms of data samples, feature spaces, model types and architectures, privacy settings, and training processes. We propose PubSub-ML, Publish-Subscribe for Machine Learning, as a solution in a loose collaboration setting where each site maintains local autonomy on these decisions. In PubSub-ML, each site is either a publisher or a subscriber or both. The publishers publish differentially private machine learning models and the subscribers subscribe to published models in order to construct customized models for local use, essentially benefiting from other sites' data by distilling knowledge from publishers' models while respecting data privacy. The term “model streaming” comes from the extension of PubSub-ML to decentralized data streams with concept drift. Our extensive empirical evaluation shows that PubSub-ML outperforms federated learning methods by a significant margin.
A method for simultaneously calibrating projectors and correcting nonlinearities

Abstract In FPP, projector nonlinearity is a significant factor affecting calibration accuracy and the reconstruction effect. Existing techniques frequently separate system calibration from nonlinearity correction, and the majority of these correction techniques necessitate the acquisition of additional images, making the process laborious. In this study, we provide a technique for solving both the nonlinear error function and the projector parameters at the same time. The parameters of the nonlinear error function are first estimated using a theoretical calculation of the phase error due to nonlinearity, based on the distribution relationship between the reprojection error and the phase of the characteristic points. The resulting nonlinear error function is then used to correct for the wrapped phase. It was shown that the projector reprojection error was decreased by 14.7 percent (RMS) and the object reconstruction quality was enhanced after two iterations.
Design of dynamic gripping control system for robotic arm based on the NJ motion controller

Abstract Due to the increasing improvement of people’s material living conditions, the requirements and needs for mechanical products are rising, thus indirectly promoting the development of mechanical devices. The study aims to provide an in-depth discussion on the power capture of NJ-type robots, the working principle, and arithmetic methods of NJ-type controllers, and to conduct simulation tests of NJ-type robots. The experiments showed that 99.775% of the test data were distributed between 30 ms and 50 ms, and the actual rotational speed was only about 250 ms. The missed and wrong capture rates were 3.58% and 0%, respectively, which indicated that the actual rotational speed was almost the same as the specified one. The results show that the manipulator designed with the NJ-based motion controller achieves a high level of accuracy and stability, and can achieve accurate grasping, which provides convenient conditions for people to perform self-service table tennis entertainment in their daily lives.
Eero Tarasti, existential semiotics, music, and mind. On the existential and cognitive notions of situation

In recent decades, there has been a shift both in semiotics and in cognitive science to novel, perhaps more flexible currents of research. This applies to semiotic and cognitive musicology as well, and raises interest in the conceptual correlates in the historical shifts, particularly in how the shifts have opened avenue for addressing both the social and cultural, and the subject’s embodied mind in the study of signification within a systematic framework. Emblematic of the paradigmatic shifts, situation emerges as a key notion. From the current, pragmatist perspective, a comparison of Eero Tarasti’s existential-semiotic and Mauri Kaipainen’s cognitive notions of situation reveals striking similarities. For one, situations become meaningful only in their contexts and through their use, as they are dynamically established in the processes of being in the world. Besides the evident methodological and topical differences, fundamental differences are to be found in the attention paid to the epistemic conditions of subject’s being in the world, and the acknowledged complexity of the situations, illustrated by the recursive agent/patient-levels by Tarasti. For the study of musical signification, the pragmatist approach may help reconcile the differencies, contributing to the necessary groundwork for a theory that could incorporate complex, iterative levels of narration spanning between signs as acts, stylistic constraints of musical discourse, social contexts, and epistemes of culture, while also taking into account the view of mind as embodied, embedded, enactive, and extended cognition.
Self-Paced Grammar Learning Guide and the Grammatical Competence of Grade 10 Students

The study aimed to determine the grammatical competence of Grade 10 students using a self-paced grammar learning guide. The one-group pretest-posttest research design was utilized to determine the performance of the respondents. Also, this study used an intact sampling technique which is a type of non-probability sampling used to produce results that can be generalized only by making very strong assumptions about the sample(s). An intact group is an already-formed group. In this study, the respondents included thirty (30) Grade 10 learners enrolled in Alaminos Integrated National High School, Alaminos Laguna for the school year 2020-2021 who underwent self-paced study using the designed grammar learning guide for eight (8) consecutive school days. The study pointed out the following hypothesis: There is no significant difference between the pretest and post-test scores of students in using the self-paced grammar learning guide. Thus, the study yielded the findings that there is a significant difference in the pretest and posttest scores, and mean gain scores of the students using a self-paced grammar learning guide. Since the study revealed that there is a significant difference in the pretest and posttest of respondents in using the self-paced grammar learning guide, the school administration may encourage teachers to develop a learning guide that will be suited to the needs of the students and use this other than textbooks to develop and improve students’ academic competence. Keywords: Self-paced, Grammar, Learning Guide, Grammatical Competence
Fusing Attribute Type Features for Attribute Value Extraction from Product via Question Answering

Extracting attribute values from product titles is a crucial e-commerce task. Previous attribute extraction method was insufficient because the exist dataset lacked attribute type information. To overcome these obstacles and promote product attribute research, we first improve the AliExpress dataset with appending entity type information to better facilitate research on product attribute value extraction. Then, we propose an Attribute Type Attention via Question Answering (ATAQA) approach to fully utilize the attribute type information for attribute value extraction, which captures semantic interaction of attribute and attribute type with a sentence and get the vectors containing attributes, attribute types and sentence features. We combine the attribute type vector and the attribute vector to get the processed vector, and then we use the self-attention layer connect the attribute vector and the processed vector, finally export a sentence-attribute-comprehension representation to the CRF for final tagging. The proposed approach outperforms previous best methods by a significant margin, as shown by the experimental results. Our Data is available at https://github.com/wandugu/AE-improved.
Self-organized UAV Cluster Trajectory Planning Method Based on Ant Colony Algorithm

In the development of modern society, because of the strong application performance of uav, it can replace man-machine to perform more dangerous and complex tasks, so it has been widely used in economic construction and modern combat. According to the analysis of uav application in recent years, it is found that mission planning and autonomous flight are the main subjects of practical exploration, and the quality of flight path is the key factor to judge whether the mission can be carried out smoothly. Therefore, an adaptive polymorphic ant colony algorithm is proposed based on the detailed description of ant colony algorithm and the current research results of self-organizing UAV cluster path planning. The final results show that the application of ANT colony algorithm in the path planning of self-organizing UAV cluster can not only optimize the path, but also improve the flight accuracy.
A Workshop / Optimist's Guide to Finding Optimal Infrastructure for a Course in Full-Stack Development

Teaching full-stack web development requires the instructor not only keep up with rapidly changing technology but also grapple with thorny questions of hosting. Many of the solutions for professional developers, unsurprisingly, do not work well in an academic environment, and their use entails much compromise and jury-rigging. In this workshop, participants will discover how to make the setting up of that essential infrastructure much easier as they work through small examples of full-stack work (no prior experience necessary). We look at tooling that provides environments similar to what one would find in industry (compatible with all major languages and platforms), but not at the expense of what academics need: support for creating and grading assignments, collaboration, privacy, FERPA compliance, and the like.
Human Activity Recognition in Videos Using Deep Learning

Human Activity Recognition (HAR) is a challenging classification task. In the past, it traditionally involved the identification of the movement and activities of a person based on sensor inputs, apply signal processing to receive features and fit the features into a machine learning model. In recent times, deep learning methods have shown good results in automatic Human Activity Recognition. In this paper, we propose a pre-trained CNN (Inception-v3) and LSTM based methodology for Human Activity Recognition. The proposed methodology is evaluated on the publicly available UCF-101 dataset. The results show that the proposed methodology outperforms recent state-of-art methods in terms of accuracy (79.21%) and top-5 accuracy (92.92%) on the HAR task.
Human-Robot Conversational Interaction (HRCI)

Conversation is one of the primary methods of interaction between humans and robots. It provides a natural way of communication with the robot, thereby reducing the obstacles that can be faced through other interfaces (e.g., text or touch) that may cause difficulties to certain populations, such as the elderly or those with disabilities, promoting inclusivity in Human-Robot Interaction (HRI). Work in HRI has contributed significantly to the design, understanding and evaluation of human-robot conversational interactions. Concurrently, the Conversational User Interfaces (CUI) community has developed with similar aims, though with a wider focus on conversational interactions across a range of devices and platforms. This workshop aims to bring together the CUI and HRI communities through a one-day workshop to outline key shared opportunities and challenges in developing conversational interactions with robots, resulting in collaborative publications targeted at the CUI 2023 provocations track.
Manufacturing holographic optical elements for automotive industry needs

Holographic Optical Elements (HOE) are well known optical devices which can be used as for example light focusing and/or directing the light to desired areas. Until now, there have been no manufacturing facilities capable to manufacture substantial quantities of volume holographic optical elements with in-application stable properties devoted to being used as taillights or head-up displays in a relatively harsh automotive environment. We describe in this article the working principles of an industrial manufacturing process of holographic optical elements targeting automotive industry needs.
GREG HEFFLEY’S ROLE CONFUSION IN THE DIARY OF A WIMPY KID (2007): A PSYCHOSOCIAL ANALYSIS

This research is conducted to analyze psychological condition of Greg Heffley, the protagonist in the Diary of a Wimpy Kid (2007) in his early teenage year. Theory used in this research is Erik Erikson’s theory of psychosocial development. Method of this research is qualitative descriptive that uses novel as the primary data. Results of the research shows that Greg Heffley as the protagonist experiences a role confusion due to the transition from childhood to teenage. It is worsened by his unsupportive best friend who still acts like a child when interacting with him. It is found also that Greg Heffley at this stage begins to be aware of his sexual and occupational identity. It is concluded that Greg Heffley is not smooth in transitioning from childhood to teenage with all the situation that he has.
An Empirical Study on the English Teachers’ Informatization Ability in Yunnan Ethnic Minority Area

This paper studies the teachers’ informatization ability by sampling 1508 English teachers from secondary schools in the eight ethnic minority prefectures of Yunnan Province. The questionnaire data collected were analyzed with SPSS 22.0. The questions addressed were 1) The current situation of English teachers’ information-based teaching ability; 2) The relationship between English teachers’ information-based teaching ability and teaching level as well as English proficiency. Thus, this study came to the conclusion, namely, 1) The information-based teaching of English teachers in the minority areas of Yunnan Province is in a moderate level. 2) There is a significant positive but weak correlation between informatization teaching ability and teaching level and English proficiency.
Research on Data Mining Technology of Online Intelligent Education Under Collaborative Learning

The educational data mining technology is delayed and has limitations in the process of operation. It cannot effectively deal with the characteristics of behavioral sequence, which leads to the problem of low accuracy and recall rate. This paper proposes a data mining technology for online intelligent education under collaborative learning. Construct the learner portrait under collaborative learning from the four dimensions of curriculum learning characteristics, task completion characteristics, interpersonal interaction characteristics and learning performance, and extract the characteristics of educational data. The decision tree method is used to cluster the students’ behavior characteristics, and the online intelligent education data mining algorithm is designed to analyze the correlation between students’ behavior habits and comprehensive quality. The experimental results show that compared with the data mining technology based on model driven and homomorphic encryption privacy protection, the accuracy and recall of the data mining technology in this paper are improved, which can analyze educational data more sensitively and provide reference for decision-making.
Towards Improving the Anti-attack Capability of the RangeNet++

With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experimental results show that the proposed approaches can effectively improve the RangeNet + +’s defense ability against adversarial attacks, and meanwhile enhance the RangeNet++ model’s robustness.
Content-Aware Hierarchical Representation Selection for Cross-View Geo-Localization

Cross-view geo-localization (CVGL) aims to retrieve the images that contain the same geographic target content and are from different views. However, the target content usually scatters over the whole image, and they are indiscernible from the background. Thus, it is difficult to learn feature representation that focuses on these contents, rendering CVGL a challenging and unsolved task. In this work, we design a Content-Aware Hierarchical Representation Selection (CA-HRS) module, which can be seamlessly integrated into current deep networks to facilitate CVGL. This module can help focus more on the target content while ignoring the background region, thus as to learn more discriminative feature representation. Specifically, this module learns hierarchical important factors to each location of the feature maps according to their importance and enhances the feature representation based on the learned factors. We conduct experiments on several large-scale datasets (i.e., University-1652, CVUSA and CVACT), and the experiment results show the proposed module can obtain obvious performance improvement over current competing algorithms. Codes are available at https://github.com/Allen-lz/CA-HRS .
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval

Metric learning aims to build a distance metric typically by learning an effective embedding function that maps similar objects into nearby points in its embedding space. Despite recent advances in deep metric learning, it remains challenging for the learned metric to generalize to unseen classes with a substantial domain gap. To tackle the issue, we explore a new problem of few-shot metric learning that aims to adapt the embedding function to the target domain with only a few annotated data. We introduce three few-shot metric learning baselines and propose the Channel-Rectifier Meta-Learning (CRML), which effectively adapts the metric space online by adjusting channels of intermediate layers. Experimental analyses on miniImageNet, CUB-200-2011, MPII, as well as a new dataset, miniDeepFashion, demonstrate that our method consistently improves the learned metric by adapting it to target classes and achieves a greater gain in image retrieval when the domain gap from the source classes is larger.
Research Progress on the Impact of Artificial Intelligence on China's Economy

The information age is dawning, as science and technology advance at a rapid rate, ushering us into a new epoch.The Internet's leadership of new media has not only enhanced people's quality of life, but also broadened the pathways for people to acquire, spread, exchange, and process information, thus making our lives more convenient and productive.The swift advancement of science and technology has made artificial intelligence an indispensable element of our current society.It not only affects our daily life, but also profoundly affects our creativity and social development.The steam engine revolution has been followed by artificial intelligence, a scientific revolution that will surely spur future progress.Consequently, the utilization of this potent instrument to foster China's economic expansion is of immense and far-reaching importance to be examined.Beginning with the definition of artificial intelligence, this paper delves into the fundamental ideas of artificial intelligence and its associated theoretical foundation.Subsequently, a thorough examination of the primary applications of artificial intelligence and its essential part is conducted.Thirdly, by reviewing the three industrial revolutions in history, it summarizes that each industrial revolution will bring about different degrees of industrial structure adjustment, which will lead to changes in labor demand and eventually lead to changes in employment.It is proposed that, in light of the current state of China's economy, we should make full utilization of artificial intelligence technology to foster the sustained, healthy, and rapid growth of the country.
TARA CAR: Tourist attraction reimagined application for Cordillera using augmented reality

In response to the significant impact of tourism recovery in this time of healthcare pandemic, this study aims to develop a mobile application that enhances the tourist experience while exploring the exquisite attractions of the Cordillera Administrative Region (CAR) using augmented reality (AR). Anchored with the design thinking approach, the researchers used the feature-driven development methodology in developing the system. Usability testing was conducted using the USE tool to measure the extent of usability and acceptability of the application among the tourists of CAR identified through random sampling. Features include immersive experience through AR and GPS on accessing information in terms of destination's location, transportation, itineraries, accommodations, and to any point of interest of the tourist. Another significant feature of TARA CAR is a tourist management tool that records the number of tourists in a particular tourist destination to minimize the influx of tourists in the area. As to the extent of usability, the system is accepted with an overall rating of 6.04. The respondents agreed that the application has achieved effectively its objectives and the features provided in the system catered to the needs of the users. Features that provided satisfaction for respondents in the system includes convenience, handy and user-friendly. The application is a perfect tool for every tourist that enhances their experience and increases joy while exploring the world. It adds new value and opens new opportunities for both tourists and the tourism industry. Indeed, this application is the travelers’ new ally.
Modifications of genetic, biogeography and particle swarm algorithms for solving multiextremal optimization problems

The article offers the developed modifications of multiagent nature-inspired algorithms for finding the absolute extremum of multimodal objective functions. The proposed algorithms are based on hybridization with the quasi-Newton method for local descent at each iteration. Variants of genetic, biogeography, and particle swarm methods proposed by the authors are used for global search. We have performed the numerical comparison of implemented algorithms employing a representative set of multiextremal functions. The achieved research results confirm the efficiency of the proposed algorithms and the possibility of constructing computational schemes, which can be the foundation for effective methods for studying global search problems.