Reviewerly/exHarmony huggingface dataset:
Basic Activity Recognition from Wearable Sensors Using a Lightweight Deep Neural Network

The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor’s readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.
Latency-, Sync-, and Bandwidth-Agnostic Tightly-Timed Telematic and Crowdsourced Musicking Made Possible Using L2Ork Tweeter

The following paper presents L2Ork Tweeter, a new control-data-driven free and open source crowdsourced telematic musicking platform and a new interface for musical expression that deterministically addresses three of the greatest challenges associated with the telematic music medium, that of latency, sync, and bandwidth. Motivated by the COVID-19 pandemic, Tweeterâs introduction in April 2020 has ensured uninterrupted operation of Virginia Techâs Linux Laptop Orchestra (L2Ork), resulting in 6 international performances over the past 18 months. In addition to enabling tightly-timed sync between clients, it also uniquely supports all stages of NIME-centric telematic musicking, from collaborative instrument design and instruction, to improvisation, composition, rehearsal, and performance, including audience participation. Tweeter is also envisioned as a prototype for the crowdsourced approach to telematic musicking. Below, the paper delves deeper into motivation, constraints, design and implementation, and the observed impact as an applied instance of a proposed paradigm-shift in telematic musicking and its newfound identity fueled by the live crowdsourced telematic music genre.
Massive-Scale Data Mining to Enhance Digital Library with Applications in College Education

The context of digital library has changed from insufficient information to information overload, and its corresponding service mode should also change from "people looking for information" to "information looking for people." Using a grounded theory approach, this paper extracts 78 initial concepts, 24 basic categories, and 6 main categories by coding and analyzing the raw data obtained from the interviews. On this basis, the relationship path and action mechanism between categories are discovered; based on which a theoretical model of the influence mechanism of digital library intelligent information recommendation service satisfaction is constructed. The research results have shown that, under the moderating effect of user preference, the quality of data mining system, recommendation information quality, recommendation service quality, and recommendation form together have an impact on the satisfaction of digital library intelligent information recommendation service. The results of our work can provide useful reference for the optimization and healthy development of digital library services. Meanwhile, it has some theoretical and practical contributions. How to quickly obtain the information people required from a large amount of information is particularly important. Personalized construction is an inevitable service trend for the development of digital libraries in the new era. We in this paper study the current situation of digital libraries and the development of personalized services in digital libraries. We focus on data mining-related technologies, the development of digital library data exploration technologies, and the provision of Internet application services. The problems in this area were summarized, and the countermeasures were put forward based on this. It can be concluded that the concept of a digital library is not just a collection of data with information management tools, it is an environment that brings together collections, services, and people to support the entire data flow. It converts information into domain knowledge, from creation to dissemination. It guides the process from use to save. The trial registration number is ChiCTR2200055403.
Towards a framework for designing technology with country: A perspective from Australia

Lifestyles, principles and methods adopted by First Nations peoples have attracted increased interest from non-Indigenous researchers and professionals.Following greater awareness about the destructive effects of colonization on sustainable precolonial ways of living that thrived for millennia, a growing movement towards understanding of Indigenous ways of relating to Country has led to questions about culturally and environmentally appropriate approaches to design digital systems, technologies, services, and products.In this paper, we investigate recently emerging frameworks for design with Country identified from the literature, compiling a list of 40 precepts and 15 principles to inform our interaction design process.Furthermore, we propose a process timeline, mapping to it the identified principles and a set of methods.
ELT students’ metacognitive writing strategies

Academic Writing is one of the writing courses that are required for students majoring in an English education program, where students can have firsthand experience in making use of the language that they have learned in order to compose an academic text. The difficulty of making these academic texts using a second or foreign language requires effective use of metacognitive writing strategies. However, students’ use of these strategies did not all produce the expected positive result. This study aimed to find out the kind of metacognitive writing strategies that Academic Writing students use and how these strategies affected their writing. The participants of this study were nineteen Academic Writing students at a university in Central Java. The data were collected qualitatively by using mixed questionnaires and semi-structured interviews to get in-depth information from the participants. The findings found that the participants made use of all the strategies at different rates. Each participant produced different writing outcomes due to the differences in metacognitive awareness and personal preferences in writing. The findings in this study also reflected the positive outcome from making use of metacognitive writing strategies on overcoming the participants’ shortcomings in writing. Other findings, such as participants’ perceptions on metacognitive writing strategies and how they preferred to learn metacognitive writing strategies based on their experiences would also be found in this study.
Autonomous Modulation Categorization Algorithm for Amplify and Forward Relaying Diversity Systems

Modulation categorization, which is a significant duty performed by smart receivers, is critical for applications in both the military and civilian sectors. This research topic has been intensively investigated for single-hop wireless communications. However, there are a few studies that concentrate on multiple hop communications systems. In this paper, we design a novel autonomous modulation categorization technique for amplify-and-forward relaying systems. Analytical formulations of correlation functions used as the foundation of the proposed method are developed. By exploiting the spatial redundancy, we theoretically demonstrate that a set of modulation forms produces peaks for particular correlation functions whereas the other set does not. We design a multiple-level hypothesis assessment for judgment based on this property. The suggested approach has the benefit of not requiring channel coefficients or noise power information. Computer simulations are conducted to test the proposed method’s categorization performance. The findings demonstrate that the suggested method produces appropriate results under various operating situations. The suggested approach reaches an accuracy of about one hundred percent when the SNR is 10 dB or higher. On the other hand, the traditional algorithms fail to provide acceptable performance even at high SNR.
Quantitative evaluation of aspects of embodiment in new digital musical instruments

This paper discusses a quantitative method to evaluate whether an expert player is able to execute skilled actions on an unfamiliar interface while keeping the focus of their performance on the musical outcome rather than on the technology itself.In our study, twelve professional electric guitar players used an augmented plectrum to replicate prerecorded timbre variations in a set of musical excerpts.The task was undertaken in two experimental conditions: a reference condition, and a subtle gradual change in the sensitivity of the augmented plectrum which is designed to affect the guitarist's performance without making them consciously aware of its effect.We propose that players' subconscious response to the disruption of changing the sensitivity, as well as their overall ability to replicate the stimuli, may indicate the strength of the relationship they developed with the new interface.The case study presented in this paper highlights the strengths and limitations of this method.
Instructional Design and Educational Satisfaction for Virtual Environment Simulation in Undergraduate Nursing Education: The Mediating Effect of Learning Immersion

Abstract Background Schools were closed down after the onset of COVID-19, with non-face-to-face practices or distance education in nursing education replacing video learning or simulation classes in nursing education clinical practicum. This led to an increase in interest in virtual environment simulation education. While technology-based teaching methods might feel new and intriguing to learners, it is necessary to evaluate learner satisfaction with such an educational method beyond its novelty value. Therefore, this study aimed to examine the mediating effect of learning immersion on the relationships between instructional design and educational satisfaction for virtual environment simulation. Methods This study used a descriptive cross-sectional research design. The study sample included students in the third or fourth year of the nursing curriculum in South Korea. The participants were 164 nursing students who had an experience with virtual environment simulation practices during the past year before September 2021. Data were collected using an online questionnaire. The questionnaire addressed the characteristics of nursing students, instructional design, learning immersion and educational satisfaction. The collected data were analyzed using path analysis. Results The indirect effect of the path between instructional design and educational satisfaction mediated through learning immersion in virtual environment simulation was found to be significant. Furthermore, the direct path was also statistically significant. Conclusion Educational content based on virtual environment simulation should be implemented based on instructional design. It is necessary to recognize the importance of instructional design that can promote learning immersion in virtual environment simulation, as well as to prepare consistent standards for the instructional design.
Air Quality Forecasting in DKI Jakarta Using Artificial Neural Network

The increase in the use of motorized vehicles increases air pollution conditions, especially in big cities such as the capital city of Indonesia, Jakarta. The pollution that pollutes this city contains various kinds of chemical particles that are harmful to living things when they enter the body. several efforts to reduce this pollution have been carried out, one of which is by identifying the pollutants contained in the air. This study uses data obtained from monitoring stations to predict the content of pollutants in the air at some time in the future. the method used is data mining forecasting with a neural network model. by using rapid miner obtained several graphic descriptions of pollutant conditions in Jakarta that go up and down. pollutant levels of SO2, CO, PM10 and NO2 all increased in the November-December period and at the same time period, ozone was at its lowest point. Results from Prediction air quality using Artificial Neural Network with 5 parameters, shown on this pollutant PM10 had an RMSE of 9,477; SO2 had an RMSE 5,474; CO had an RMSE 8,392; O3 had an RMSE 18,250; NO2 had an RMSE 5,171. Can be concluded that the RMSE value of O3 is higher than the others and the lowest value of NO2.
What do Researchers Need when Implementing Novel Interaction Techniques?

Interaction frameworks are the tools of choice for researchers and UI designers when prototyping new and original interaction techniques. But with little knowledge about actual needs, these frameworks provide incomplete support that restricts, slows down or even prevents the exploration of new ideas. In this context, researchers resort to hacking methods, creating code that lacks robustness beyond experiments, combining libraries of different levels and paradigms, and eventually limiting the dissemination and reproducibility of their work. To better understand this problem, we interviewed 9 HCI researchers and conducted an online survey. From the results we give an overview of the criteria for choosing frameworks, the problems often met with them, and the "tricks" used as solutions. Then we propose three design principles to better support prototyping for research in UI frameworks: (i) duplicate singular elements (e.g. mouse, caret) to foster opportunities for extensions, (ii) accumulate rather than replace to keep a history of changes, and (iii) defer the execution of predefined behaviors to enable their monitoring and replacement.
GEOCHEMICAL ASSESSMENT OF THE POTENTIALLY TOXIC ELEMENTS BACKGROUND CONTENTS IN SOILS IN THE AREA OF THE SARALA GOLD ORE CLUSTER (THE REPUBLIC OF KHAKASSIA, RUSSIA)

We assessed the contents of Fe, Ni, Cu, Zn, As, Cr, Pb, Hg in soils of the residential settlements and surrounding areas exposed to anthropogenic load due to mining activities. The study object is located in the Kuznetsk Alatau (part of the Sarala gold ore cluster – SGOC), the village of Priiskovy and its tailings dump, the village of Ordzhonikidzevsky, the Republic of Khakassia). The background contents in the SGOC soils are the following: Fe 3.08–5.06 %, Ni 24.74–45.13 g/t, Cu 22.33–38.81 g/t, Zn 86.72–200 g/t, As 5.45–11.25 g/t, Cr 35.60–73.25 g/t, Pb 14.12–26.17 g/t, Hg 0.01–0.14 g/t. It was found that the background area has increased the natural contents of the studied elements. The soils sampled from the territory of the tailing dump are distinguished by the elevated element contents relative to the background values and especially by a sharp jump of the As and Zn contents (abnormal contents). The contents of all the elements examined in the soils of the territories in the vicinity of the tailing dump also exceed the background values of the SGOC with different proportions of cases and have the increased As, Cr, and Pb contents relative to the maximum permissible concentration of soils. Clustering of the data shows the presence of two clusters, which indicate the availability of the background and anomalous values associated with the technogenic impact of the mining industry in the study area of the Sarala gold ore cluster.
Shitty stories: Experimenting with probiotic participation through design

The Shit! project brings focus to the human-gut microbiome, to consider how we might move towards more harmonious coexistence with the uncountable microscopic entities that inhabit our gut.The work recognises humans as multi-species assemblages, and the Western scientific models that form how we conceptualise, measure and engage with ourselves as embodied species, insufficient to account for the multiplicity of relational scales at play.We present a workshop undertaken with the Danish Colitis and Crohn's Patient Association that converges food, faeces and performativity.We position this work as an exploration of what we provisionally term probiotic participation through design.Framed as a collective inquiry, the workshop examines the potential of multispecies narratives among people suffering from chronic gastrointestinal disorders.We argue that one avenue towards better human-gut microbiome co-existence could be threaded through participatory, material and embodied design engagements-with faeces-caught up in and entangled with participants' other concerns.
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks

The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. More specifically, we collected sensor data from 10 participants while consuming pizza. Using this information, and other comparable data available for similar events such as smoking and medication-taking, and dissimilar activities of jogging, we developed a LSTM-ANN architecture that has demonstrated 90% success in identifying individual bites compared to a puff, medication-taking or jogging activities.
Creative AI Futures: Theory and practice

This paper analyses creative activity enabled by ML and recognised under the banner of 'AI art' or 'creative AI'.The theoretical discussion is anchored in the critical reflection on the activities in which the authors have been involved as part of the Creative AI Lab, which is a collaboration between the R&D Platform at Serpentine Galleries and King's College London's Department of Digital Humanities.The paper proposes a 5C model ('Creative -Critical -Constructive -Collaborative -Computational'), which brings together technical research and conceptual inquiry into AI art, while shifting focus from artefacts to their wider contexts, processes and infrastructures.It also outlines directions for future research.AI.Artistic collaboration.Back-end
Adaptable mobile vision systems through multi-exit neural networks

Semantic segmentation constitutes the backbone of many mobile vision systems, spanning from robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within the limited resource envelope of embedded/mobile devices, optimising for efficient execution becomes important. To this end, we propose a framework for converting state-of-the-art segmentation models to MESS networks: specially trained CNNs that employ parametrised early exits along their depth. Upon deployment, the predictions of these exits can be exploited either in a dynamic (input-adaptive) way, to save computation during inference on easier samples; or in a static (device-adaptive) setting, to accommodate deployment under varying device capabilities without the need of retraining. Designing and training such networks naively can hurt performance. Thus, we propose a two-staged training process that pushes semantically important features early in the network. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the deployment scenario and application-specific requirements. Optimising for speed, MESS networks deliver latency gains of up to 2.65× over state-of-the-art methods with no accuracy degradation. Accordingly, optimising for accuracy, we achieve an improvement of up to 5.33 pp, under the same computational budget.
Identification of Non-Vaccinated People Using Face Recognition Based on CNN

Preventive medical care relies on vaccinations to provide significant health benefits. Vaccination is an important and effective preventive health measure. There is no better way to reduce the risk of pandemic spread of SARS-CoV-2/COVID-19 than vaccination. As a preventive measure, the government has begun vaccinating Indians against Corona infection. It is therefore important, in addition to developing and supplying vaccines, that enough people are willing to obtain vaccines. However, of the populations worldwide, there are concerning proportions that are reluctant to get vaccinated. In order to end the pandemic, it is highly essential to deal with another omnipresent issue: outright rejection of vaccinations. To achieve population immunity first we have to find the non-vaccinated population should be detected and to this end, this project proposed an Aadhaar-based facial recognition system is used to find non-vaccinated citizen and alert them using Artificial Intelligence. Deep learning which is in the form of Convolutional Neural Networks (CNNs) are used to carry out the face recognition process and it is also proven to be an efficient method to carry out face recognition due to its high fidelity. A CNN is a Deep Neural Network (DNN), which is designed to perform challenging tasks like image processing, which is crucial for facial recognition. The CNN structure is composed of numerous layers of neurons that connect the neurons: an input layer, an output layer, and layers between these two layers. In the midst of the epidemic coronavirus outbreak (COVID-19), a person's current inoculation status will be updated based on face recognition to safeguard him/her from COVID-19 and it may also serve as proof of vaccination for other purposes. Facial recognition technology (FRT) along with the Aadhaar helps to authenticate people before entering into any types of service. This project provides COVID-19 immunization status, which is determined by observing at their face, and certify that they have been vaccinated.
Slapbox: Redesign of a Digital Musical Instrument Towards Reliable Long-Term Practice

Digital musical instruments (DMIs) built to be used in performance settings need to go beyond the prototypical stage of design to become robust, reliable, and responsive devices for extensive usage.This paper presents the Tapbox and the Slapbox, two generations of a standalone DMI built for percussion practice.After summarizing the requirements for performance DMIs from previous surveys, we introduce the Tapbox and comment on its strong and weak points.We then focus on the design process of the Slapbox, an improved version that captures a broader range of percussive gestures.Design tasks are reflected upon, including enclosure design, sensor evaluations, gesture extraction algorithms, and sound synthesis methods and mappings.Practical exploration of the Slapbox by two professional percussionists is performed and their insights summarized, providing directions for future work.