Reviewerly/exHarmony huggingface dataset:
Personal Identification Method using Active Acoustic Sensing applied to the Nose pad of Eyeglasses

Smart glasses and VR goggles are becoming more and more popular. In these products, the home screen is displayed without user authentication after the power is turned on. While using the device, the user may be asked to enter passwords when logging in to applications or making payments, and it is common to use the attached touchpad or to use a handheld controller to enter information from the software keyboard on the screen. There are also eyeglass-type devices without displays, however, they generally do not authenticate the user. In this paper, we propose a method to automatically and periodically identify individuals by applying active acoustic sensing technology with a speaker and a microphone mounted on the nose pad. In the proposed method, an acoustic signal is played from a speaker in the nose pads of an eyeglass device, and the acoustic signal that passes through the nose in contact with the device is acquired from a microphone, and the individual is recognized by analyzing the frequency characteristics of the acoustic signal. The proposed method takes advantage of the fact that the nose pad of the eyeglass device always makes natural contact with the nose of the user to be recognized, and that the shape of the user’s nose has its own acoustic characteristics. We implemented a prototype eyeglass-type device equipped with a microphone and speaker, conducted experiments to evaluate the effectiveness of the proposed method that involved 11 participants, and confirmed that the proposed method can recognize individuals with an average equal error rate of 0.09. Experiments were also conducted to evaluate the effectiveness of the proposal through changes in the external and internal states of the nose.
The Status of Implementation of The NYC Reads 365 Program: Basis for a Proposed Program Enhancement

This study investigated the extent of awareness and participation of parents and students in the implementation of the NYC Reads 365 Program, as assessed by the parents, students, and the school librarian. The researcher used the descriptive-correlation research design to secure evidence concerning an existing situation. Specifically, the extent of awareness and participation of parents and students in the NYC Reads 365 Program, and to test the association between the respondents’ extent of awareness and extent of participation. The results of the study revealed that both the student and parent-respondents have “moderate awareness “and “moderate participation” in the implementation of the NYC Reads 365 Program. The researcher discovered that there was a significant relationship between the student respondents’ extent of awareness and extent of participation in the conduct of the NYC Reads. The R-value indicated that the higher the students’ extent of awareness, the higher the extent of their participation. Lower awareness also indicated lower participation. The researcher also discovered a significant relationship between the parent respondents’ extent of awareness and extent of participation in the conduct of the NYC Reads. The R-value indicated that the higher the parents’ extent of awareness, the higher the extent of their participation, and lower awareness also indicated lower participation. Based on the findings of the study, it can be concluded that the status of implementation of the NYC Reads 365 Program that the activities intended to be participated in by the students and parents who are the primary beneficiaries of the program are not fully implemented, the stakeholders of the said program have not fully succeeded in the dissemination and awareness campaigns of the program.
Approaching error in foreign language teaching (Italian) applying the communicative method

Sketching out a list of the most famous linguists who devoted themselves to study errors and tried to treat them between the '60s up to 2018 this paper deals with the questions that arise when a teacher of Italian as Foreign Language starts considering a few Russian students' frequent errors in learning the language.Should errors be corrected?If so, which ones? When?How? and also Who should correct them?Then the study briefly focuses on a couple of differences between Italian and Russian morphosyntax.As a result, they make students who belong to 3 different levels of the Common European Framework to commit errors in written/oral exercises.Lastly it is explained the way students' errors can be approached through communicative teaching.Our mother tongue or the influence of another language, no matter how well we know it, along with our motivation, self-esteem, emotionalism and other psychological elements affect errors.What is more, we can say that even the teachers' clarity of explanation, their empathy, right timing in error correction, tone of voice, as well as knowledge sharing among peers, a non-judgmental and cooperative environment account for the student's learning and error approach.
On the Effectiveness of Virtual IMU Data for Eating Detection with Wrist Sensors

The successful training of human activity recognition (HAR) systems typically heavily depends on the availability of sufficient amounts of labeled sensor data. Unfortunately, obtaining large-scale labeled datasets is usually expensive and often limited by practical and / or privacy reasons. Recently, IMUTube was introduced to tackle this data scarcity problem through generating weakly-labeled, virtual IMU data from unconstrained video repositories, such as YouTube. IMUTube was demonstrated to be very effective at classifying locomotion or gym exercises that involve large movements of body parts. Yet, many important daily activities, such as eating, do not exhibit such substantial body (part) movements but are rather based on more subtle, fine-grained motions. This work explores the utility of IMUTube for such subtle motion activities with specific, exemplary application to eating detection. We found that–surprisingly–IMUTube is also very effective for this challenging HAR domain. Our experiment demonstrates that eating recognition systems benefit from virtual IMU data extracted from video datasets with significant improvements of recognition accuracy (increases of 8.4% and 5.9% F1-score absolute for both curated and in-the-wild video datasets, respectively relative to 71.5% F1-score of the baseline), which is encouraging for the broader use of systems like IMUTube.
Improving Recommendation Diversity by Maximizing Semantic Volume

The amount of information transmitted via electronic media is increasing, and recommender systems are being introduced. However, it has been pointed out that there are problems such as filter bubbles and echo chambers, which provide users with biased information due to excessive recommendations. In previous studies, we have discussed changes in user behavior based on the diversity of articles. In this study, we propose a recommender system that introduces a mechanism to improve the linguistic diversity of articles, and show through experiments that the system is able to recommend a variety of articles. We also have clarified the condition that merely displaying a variety of articles is not sufficient to improve the diversity of articles clicked by users, and that it is necessary to recommend articles that capture the interests of users.
Investigating Domain-agnostic Performance in Activity Recognition using Accelerometer Data

Human activity recognition (HAR) models suffer significant performance degradation when faced with data heterogeneity (device, users, environments) at test time. Current approaches to this problem using domain adaptation or transfer learning attempt to improve performance in one specific target domain, often using data from said domain. Requiring access to data from the target domain is limiting and cannot be generally assumed. In addition, there is often no single target domain, but rather multiple ones arising from different sources of data heterogeneity. One way to achieve good performance in this setting would be to gather data from all potential domains the model may encounter at deployment - this is generally infeasible.
Let’s Embed Your Knowledge into AI by Trial and Error Instead of Annotation

The large annotation cost has always remained a challenge for machine learning applications in ubiquitous computing. Programmatic weak supervision (PWS) is a promising solution, but an efficient method and system for domain experts to build it interactively from scratch has not yet been developed. This paper shows the necessity of not only increasing PWS sources but also modifying or removing them and the method to achieve this. Also, we developed a prototype system to build artificial intelligence through trial and error with PWS. The user study with 15 users verified the necessity and effects of modifying and removing PWS sources through this method.
IoT-Enabled Environment Illuminance Optimization for Augmented Reality

In order to provide high quality augmented reality (AR) experiences, environmental conditions such as light level must be conducive to a high level of virtual content stability, accurate eye tracking and good virtual content visibility. This is challenging due to the dynamic nature of environmental conditions and AR application requirements. In this poster we present the first automatic environment optimization system for AR which adapts to both environment lighting and texture. First we conduct experiments that demonstrate the effect of light level on virtual object stability and eye tracking. We then detail our edge computing system architecture which uses IoT devices to sense and adjust environment conditions, and lays the foundation for future environment-aware AR applications.
Location Representations for Accelerating the Training of Next POI Recommender System

With the widespread use of mobile devices, many next Point-Of-Interest (POI) recommender systems train users’ data on their devices to provide personalized recommendation services, which requires the algorithms to have an efficient convergence rate. Besides, POI location, an important feature in improving the POI recommendation performance, has been represented in various ways, such as encoding locations with distinctive IDs (e.g., different areas with different IDs). However, the positional relationship of a POI of interest and other POIs is ignored, which is crucial to predicting the next POI. In this study, we propose describing a POI’s location representation as the positional relationship with other POIs. Based on the idea that users prefer to visit closer regions, our approach transfers the distance interval of POIs to the similarity of the location representations, thus the possibility of a user visiting a POI can be revealed from the representation similarities. The proposed location representations that contain relative location information of all POIs can be applied to initialize POI recommendation models to accelerate model convergence. Experiments on our approach show that the proposed method on a sequential recommendation model improves [email protected] by 4%, and the convergence rate (average regret before [email protected]) increases 4% with a 10% [email protected] improvement.
The Second Workshop on Multiple Input Modalities and Sensations for VR/AR Interactions (MIMSVAI)

With the advance of VR/AR technology, more and more VR/AR applications are emerging and have been popular among new users. Interacting with virtual reality and augmented reality technologies will require the development of alternative input modalities as well as coherent integration of multiple realistic sensations to increase the level of perceived realism. These developments will create a more immersive VR/AR experience. However, a lack of robust and intuitive interaction interfaces and realistic sensations hinders users’ experience for achieving a fascinating acceptance in various application areas of VR/AR interactions. This workshop discusses the challenges and applications of designing a higher coherence between different input modalities and sensations to offer more engaging VR/AR experiences, which can create opportunities for the researchers from both UbiComp and VR/AR fields to jointly discuss and brainstorm alternative input modalities and sensations for VR/AR interactions.
Excerpt from “Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena”

By adding sensing capabilities to ear-worn devices, earables have emerged as a new platform. The ears are located closely to a number of important anatomical structures (e.g., brain, blood vessels). Also, the ear canal deforms upon facial movements and the ears can be comfortably touched by the hands. In a recent paper, we conducted a systematic literature review of 271 earable papers. We synthesized an open-ended taxonomy of 47 phenomena that can be sensed in, on, or around the ear. We identified 13 fundamental phenomena from which all other phenomena can be derived, and discuss sensing principles to detect them. The phenomena were reviewed in-depth in four main areas: (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential earables have to offer as a ubiquitous, general-purpose platform.
Smart Teaching Materials with Real-Time Augmented Reality Support for Introductory Physics Education

In this demonstration, we present a system design that helps to reduce the split attention effect in multimedia learning by providing an interactive environment with augmented reality support for elementary physics education. The system consists of three main components: smart boxes, smart cables, and visualization app. Each smart box contains an input to plug different electrical components (bulb, battery, and switches) and two sockets to interconnect the boxes with each other using smart cables. These boxes are equipped with various sensor modalities that provide information related to connected cable identifications and the physical status of the boxes. This information is shared through a Bluetooth Low Energy interface with the connected visualization device. Visualization devices range between handheld tablets with augmented reality capabilities and headwear smart glasses. These devices are used to run the supportive app. The app is responsible to track the smart boxes using markers and provide a 3D augmented visualization of information coming from them. This system targets introductory physics education, in addition holds the potential to provide assistance for more advanced electrical circuits in secondary or higher physics education.
Excerpt of Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables

Auritus is an extendable and open-source optimization toolkit designed to enhance and replicate earable applications. Auritus serves two primary functions. Firstly, Auritus handles data collection, pre-processing, and labeling tasks for creating customized earable datasets using graphical tools. The system includes an open-source dataset with 2.43 million inertial samples related to head and full-body movements, consisting of 34 head poses and 9 activities from 45 volunteers. Secondly, Auritus provides a tightly-integrated hardware-in-the-loop (HIL) optimizer and TinyML interface to develop lightweight and real-time machine-learning (ML) models for activity detection and filters for head-pose tracking. Auritus recognizes activities with 91% leave 1-out test accuracy (98% test accuracy) using real-time models as small as 6-13 kB. Our models are 98-740 × smaller and 3-6% more accurate over the state-of-the-art. We also estimate head pose with absolute errors as low as 5 degrees using 20kB filters, achieving up to 1.6 × precision improvement over existing techniques. Auritus is available at https://github.com/nesl/auritus.
Filtering Relevant Comments in Social Media Using Deep Learning

This paper presents an online tool to filter automatically comments on YouTube based on five models trained on several datasets with a deep learning algorithm. Those datasets were collected from the Kaggle Competition site. We used the language model BERT to realize the comments classification. As BERT is a pre-trained network for masked language modeling or next-sentence prediction, it has to be fine-tuned for document classification. We spent several days fine-tuning our models and testing several techniques to build the last layer of a new model classifier based on BERT. We present in this paper our experiments and results. We obtained a good accuracy on the test set for each model. We used BERT-Tiny (the smallest BERT model) in order to deploy the model on our server and make faster the prediction time for classifying new comments. Our online tool connected in real-time to the YouTube API is ready to serve any queries from users. We describe the web user interface and feedback from more than 50 users during the first six months of the online launch.
Emergent and Predictable Memorization in Large Language Models

Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal identifiable information (PII). The prevalence of such undesirable memorization can pose issues for model trainers, and may even require discarding an otherwise functional model. We therefore seek to predict which sequences will be memorized before a large model's full train-time by extrapolating the memorization behavior of lower-compute trial runs. We measure memorization of the Pythia model suite and plot scaling laws for forecasting memorization, allowing us to provide equi-compute recommendations to maximize the reliability (recall) of such predictions. We additionally provide further novel discoveries on the distribution of memorization scores across models and data. We release all code and data necessary to reproduce the results in this paper at https://github.com/EleutherAI/pythia
Development of an Electronic Platform for Vocational Skills Training Based on Collective Data Sharing Technology

In this paper, data dimensionality reduction and feature extraction are carried out based on the discrete set data, and the set data sharing algorithm and simulation examples are proposed on the decentralized cloud sharing platform. Maslow's Hierarchy of Needs Theory, Constructivist Learning Theory, etc. School-enterprise cooperation to build a "school-in-a-plant", building a multi-functional on-campus training base integrating "integrated teaching, productive practice, product research and development, skills training and appraisal, and technical services"; to improve the overall level of professional development and service capabilities.
Intelligent System Optimization of course Network Teaching Based on Distributed Cloud IaaS System

This paper aims to design and implement a general IaaS cloud platform resource pool network management system to meet the management requirements of network resources in private cloud construction, using Nginx as the web server, uWSGI application server and Tornado application server to process users The non-real-time data and real-time data request of . The article analyzes the problems faced by local colleges and universities in the information big data environment by interpreting the connotation of future education and combining the experience of online course construction reform. Learning course-driven" is the foothold. It intelligently analyzes students' mastery of knowledge and teachers' teaching evaluation, and gives teachers important feedback information, so that teachers can adjust teaching strategies in a timely manner, so that students can master knowledge at a deeper level.
The EU Commission’s proposal to codify visual disclaimers—a great leap for EU design law

Journal Article The EU Commission’s proposal to codify visual disclaimers—a great leap for EU design law Get access Henning Hartwig Henning Hartwig Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 18, Issue 6, June 2023, Pages 432–445, https://doi.org/10.1093/jiplp/jpad033 Published: 25 April 2023 Article history Received: 01 March 2023 Editorial decision: 14 March 2023 Accepted: 16 March 2023 Corrected and typeset: 25 April 2023 Published: 25 April 2023
Aspect-Based Sentiment Analysis of Hotels in Bali on Tripadvisor Using BERT Algorithm

The covid pandemic that began in 2020 has caused enormous losses worldwide, including in Indonesia. Human-to-human contact is the source of the transmission of the covid virus, so the government urges people to maintain cleanliness when interacting. Bali is a popular destination for foreign and domestic visitors in Indonesia. Hospitality businesses in Bali unquestionably face a high risk of covid transmission; consequently, changes in hotel business processes are unavoidable; the implementation of new business processes can have a negative effect on business performance. In order to maintain Bali's reputation as the most popular tourist destination in Indonesia, the government must evaluate the performance of several hotel services that have implemented new business processes. The Aspect-Based Sentiment Analysis (ABSA) methodology can be utilized for performance evaluation. One of the finest algorithms for analyzing text, Bidirectional Encoder Representations from Transformer (BERT), is required for sentiment analysis. The data consists of textual customer evaluations of hotels in Bali that have implemented a new protocol or Standard Operational Procedure (SOP), retrieved from the Tripadvisor website. In the form of a number of evaluations of various aspects of the hotel, the research results can assist the government in analyzing the performance of hotels in Bali based on predetermined criteria.
The Necessity Upgrading the Standard AttributeInformation DB of Han-characters

This paper discussed the data information, the most basic information collection for the use of Han-characters in Korea, the necessity of upgrading, and finally, the method of upgrading.&#x0D; Han-characters are a collection of information that has a formal meaning. Han- are expressed in numerous visual signs for reasons that cannot deviate from the properties of the characters in the table. Currently, there are more than 80,000 characters developed by Unicode. These 80,000 characters not only have different shapes, but also have various information such as pitch and meaning information accordingly, as well as bookkeeping, stroke count, and components. We call this attribute information. According to this attribute information, Han-characters can be arranged, listed, searched, and used. In some cases, metadata may be included in the attribute information. Metadata is a source, its own type, child relationship, consonant relationship, and type relationship.&#x0D; Accurate Han-character attribute information is needed to organize and use Han-characters because the Han- character attribute information becomes the standard for searching and using characters. Nevertheless, it is often difficult to implement a standardized search system because there are still many errors in the dictionary, dictionary, and attribute information applied to the Unicode character search machine in Korea.&#x0D; This paper aims to clarify the necessity of upgrading by reviewing the problem of “music” and transfer problems included in the attribute information DB of Han-characters.