Reviewerly/exHarmony huggingface dataset:
A harmonized multi-lingual terminology for ICT devices and services

Users who are unfamiliar with the terminology, technical meaning, or intended functionality of modern mobile ICT (Information and Communications Technology) devices and services may be reluctant to use those devices and services and miss out on their potential benefits. A limited understanding of those devices and services on the users’ part also prevents them from exploiting the true potential of ICT and hinders the uptake and use of services, including those of societal relevance. ETSI Guide EG 203 499 presents an alternative, offering a solution focusing on improving the overall user experience and accessibility through by providing recommendations for a harmonized terminology, covering more than 800 basic, commonly-used ICT features in the 27 European languages, that are the official languages of the European Union (EU) and EFTA, the European Free Trade Area. Technical Committee Human Factors (TC HF) of the European Telecommunication Standards Institute (ETSI) has initiated this ongoing work, to develop a freely-available ETSI document (ETSI Guide EG 203 499, current version of October 2022, a further update to be published in the first half of 2024). The method used for developing the terminologies and examples of the terminologies published in the latest ETSI Guide version are presented. ETSI Guide 203 499 will not only facilitate the use of ICT devices and services for users but may also serve as a reference for harmonized (on a voluntary basis) user interface elements and user manuals to be provided by the industrial ICT actors concerned (e.g. manufacturers).
Multi-Source Information Fusion network for building occupancy estimation

The human dimension information is crucial for efficient building energy saving, comfort conditions, health and productivity, and security management. Existing vision-based building indoor occupancy measurement approaches have achieved remarkable progress, but struggle to achieve high and robust accuracy because of the complex indoor environments. Vision-based methods face many challenges, including background objects and diverse scales, which bring practical problems to indoor applications.In this paper, to address these issues, we propose a Multi-Source Information Fusion network in video head detection for estimating building occupancy. Our method utilizes cameras to capture surveillance videos and analyzes them through a deep neural network. We use the multi-source feature to effectively guide the single-frame detector to propose robust head boxes. We apply a multi-source fusion network to extract features. Besides, we extend head detection datasets with multi-source information, including optical flow maps, Depth maps, frame difference maps, etc. Our method achieves superior performance through ablation studies compared to existing methods on practical building surveillance videos. Experiments validate its potential for building energy saving and comfort improvement with a high occupancy estimation accuracy.
A new conversational interaction concept for document creation and editing on mobile devices for visually impaired users

This paper describes the ongoing development of a conversational interaction concept that allows visually impaired users to easily create and edit text documents on mobile devices using mainly voice input. In order to verify the concept, a prototype app was developed and tested for both iOS and Android systems, based on the natural-language understanding (NLU) platform Google Dialogflow. The app and interaction concept were repeatedly tested by users with and without visual impairments. Based on their feedback, the concept was continuously refined, adapted and improved on both mobile platforms. In an iterative user-centred design approach, the following research questions were investigated: Can a visually impaired user rely mainly on speech commands to efficiently create and edit a document on mobile devices? User testing found that an interaction concept based on conversational speech commands was easy and intuitive for visually impaired users. However, it was also found that relying on speech commands alone created its own obstacles, and that a combination of gestures and voice interaction would be more robust. Future research and more extensive useability tests should be carried out among visually impaired users in order to optimize the interaction concept.
Quadratically Transformed Luminance Chrominance Spaces

Several color spaces defined in the literature are obtained from the RGB space through non-linear transforms, potentially limiting their applicability in a regularization context. Therefore, in this paper, we introduce three new color spaces, QTLC- $$r_*^2$$ , QTLC- $$g_*^2$$ , and QTLC- $$r_* g_*$$ , and the corresponding quadratic mappings from the RGB space. To test their uniformity, we use them to consider the image defading problem through a novel two-step technique: first, we define a fast heuristic to restore the natural color saturation; then, we propose a regularization technique to deblur the image. We experimentally observe that presumably thanks to its higher uniformity, the QTLC- $$r_* g_*$$ space produces natural-looking images in the reconstruction and should be preferred to the QTLC- $$r_*^2$$ and QTLC- $$g_*^2$$ spaces.
TECD: A Transformer Encoder Convolutional Decoder for High-Dimensional Biomedical Data

In recent years, machine learning and deep learning methods have been increasingly explored in a variety of application domains, including healthcare. Despite the rapid advances in this field, several challenges still need to be addressed to properly model complex biomedical datasets, such as genomic datasets or physiological signals from wearable sensors, that exhibit a very high dimensionality, i.e., a high number of variables or features which can be mutually related. As evidenced by the literature, the induction of reliable predictive models becomes intrinsically harder as the data dimensionality increases. To give a contribution to this field, this paper explores a new deep learning approach that leverages the emerging paradigm of Transformers, which can capture long-range dependencies among the input features and combines them with a Convolutional Neural Network, which is suited for capturing local patterns and dependencies. The resulting architecture has shown very promising results on six biomedical datasets with high dimensionality (several thousands of features), paving the way for further research in this area.
An Analysis of Research Trends in Korea Music Education by Text Mining

The purpose of this study is to analyze the characteristics of topics by period and identify research trends by using text mining techniques in papers published in ‘Korea Journal of Research in Music Education (KJRME)’, a representative academic journal for music education in Korea. Using text network analysis, abstracts of a total of 621 published papers were analyzed using the Python program. conclusions are asfollowing. First, the tendency of domestic music education is student-centered. Second, according to the frequency analysis by period in KJRME, there were many basic and general studies at the beginning of the study. Third, as a result of topic modeling of KJRME, domestic music education has been studied with various themes.
Path Following for UAV using Nonlinear Model Predictive Control

Abstract For the fixed-wing UAV control system, the lateral path-following system is an important part, which ensures that the UAV flies stably in accordance with the predetermined route within a certain height. For the lateral path following of UAV based on nonlinear model predictive control, the model of the lateral kinematics of the fixed-wing UAV is established first, and then the path-following problem of UAV at a constant height is considered. The objective function with constraints based on the NMPC is established, and finally the nonlinear optimization algorithm is used to minimize the designed objective function. The optimal control quantity in the rolling interval is obtained. Matlab is used to simulate the designed controller and compare it with the commonly used L1 guidance law, and the results of the simulation demonstrate that the NMPC has superior control leverage.
Crypto rules diverge across developing Asia

Significance Most Asian states recognise the benefits of cryptocurrency adoption, such as low transaction costs for remittances and the introduction of blockchain technology into public services. However, the various risks related to cryptos have resulted in significant variation in the pattern of regulation across the region. Impacts Cryptos are proving a cost-effective financial instrument for some countries dependent on remittance income. The precise scale of illegal activity in cryptos is hard to ascertain but money laundering and cyber scams are common. Retail investors still lack reliable and tested redress mechanisms in cases of crypto-based fraud or collapse of token values.
Analysis of Digital Television User Acceptance After Analog Switch-Off (ASO) Implementation in Jabodetabek

The government officially stopped broadcasting analog television or what is known as Analog Switch Off (ASO) on November 2, 2022, in Jabodetabek. It impacts analog TV users in Jabodetabek, who can no longer receive television broadcasts on their analog televisions. This research aimed to determine user acceptance of digital television broadcasts after implementing ASO in the Jabodetabek area. In the present study, the experience of digital TV users is evaluated using the UTAUT (Unified Theory of Acceptance and Use of Technology) 2 model and the Structural Equation Model (SEM). The data was obtained from a questionnaire distributed through digital media for the Jabodetabek area. According to data processing, the two factors that significantly influence the user experience of digital television broadcasts are the Analog Switch Off (ASO) policy factor and the habit factor. The hypothesized model suitability test fits the resulting data and can affect the Behavioral Intention 41.4.
Where to draw the line? Precision and recall

The Papermill Alarm draws a line between papers which have the characteristics of papermill-products and those that don’t. Given data like that shown below, where red dots represent papermill-products and green dots represent normal papers, where should we draw that line?Some dummy data to illustrate the point. Interesting aside: we actually use dummy data like this all the time as we develop processes at Clear Skies. This prevents the sensitive data that we work with being used or seen...
An Analysis of English Classroom Interaction Pattern at Eleventh Grade of SMKN 4 Kerinci Based On El Hanafi Theory

This research aimed at investigating the classroom interaction patterns on English classes at Eleventh grade of SMKN 4 Kerinci. This research is a descriptive research. The subjects of the research were four English teachers who taught grade XI, they are Teknik Bisnis Sepeda Motor class, Bisnis Daring Dan Pemasaran class, Otomatisasi Tata Kelola Perkantoran class, and Multimedia class. To see the pattern of interaction, theory from El Hanafi was used. The classification of classroom interaction are: (1) teacher-student interaction with one-way traffic interaction, (2) teacher-student interaction with two-way traffic interaction, (3) teacher-student interaction with multi-way traffic interaction, and (4) student-student interaction pattern. Each teacher was recorded one time of the teaching and learning process. There were two instrumentations used: audio recorder and the field note . Based on data analysis, it was found that the most dominant interaction pattern from classroom discourse was the teacher-student interaction with two-way traffic interaction (64%), and then it is followed by teacher-student interaction with one-way traffic interaction (34%), the multi-way interaction pattern 2%, and none of the student-student interaction pattern found in the interactions of the foor classes of English lesson. This indicates that the teachers mostly spend their teaching and learning process by asking questions to the students with limited interaction in a topic of talk. Students’ responses were seen not so important to involve them to the materials and the process of teaching and learning in the classroom. Therefore, the interaction pattern that mostly appeared was teacher-student interaction with two-way traffic interaction. So it is important for the teacher to realize the importance of the classroom interaction characteristic and to develop her teaching skill and method in delivering teaching materials in the classroom. Keywords: classroom interaction pattern, El Hanafi, English class.
Face Gesture Recognition for Amputees Like Eye Winking for Controlling Mouse Events

People need artificial means of mobility like a virtual keyboard for a variety of reasons.the number of persons who, as a result of a medical condition, must move around with the aid of some object.Additionally, by putting in place a controlling system, it gives them the ability to move independently, which is highly beneficial.Face gesture controls are a fantastic idea for the future of natural input and, more significantly, for the handicapped.The user's face is being photographed by the camera.The system begins by identifying a user's face.The mouse pointer is then moved in accordance with the user's facial expressions.The user's facial expression controls the cursor and Right eye blink controls the mouse's right click, while the left eye blink controls the mouse's left click.
Block codes on pomset metric

Given a regular multiset [Formula: see text] on [Formula: see text], a partial order [Formula: see text] on [Formula: see text], and a label map [Formula: see text] defined by [Formula: see text] with [Formula: see text], we define a pomset block metric [Formula: see text] on the direct sum [Formula: see text] of [Formula: see text] based on the pomset [Formula: see text]. The pomset block metric extends the classical pomset metric introduced by Sudha and Selvaraj and generalizes the poset block metric introduced by Alves et al. over [Formula: see text]. The space [Formula: see text] is called the pomset block space and we determine the complete weight distribution of it. Further, [Formula: see text]-perfect pomset block codes for ideals with partial and full counts are described. Then, for block codes with chain pomset, the packing radius and Singleton bound are established. The relation between maximum distance separable (MDS) codes and [Formula: see text]-perfect codes for any ideal [Formula: see text] is investigated. Moreover, the duality theorem for an MDS pomset block code is established when all the blocks have the same size.
Addressing heterophily in node classification with graph echo state networks

Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node’s neighbourhood. While effective on graphs that exhibit a high ratio of intra-class edges, this approach poses challenges in the opposite case, i.e. heterophily, where nodes belonging to the same class are usually further apart. In graphs with a high degree of heterophily, the smoothed representations based on close neighbours computed by convolutional models are no longer effective. So far, architectural variations in message-passing models to reduce excessive smoothing or rewiring the input graph to improve longer-range message passing have been proposed. In this paper, we address the challenges of heterophilic graphs with Graph Echo State Network (GESN) for node classification. GESN is a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to most fully trained deep models that implement ad hoc variations in the architectural bias or perform rewiring as a preprocessing step on the input graph, with an improvement in terms of efficiency/accuracy trade-off. Furthermore, our analysis shows that GESN is able to effectively encode the structural relationships of a graph node, by showing a correlation between iterations of the recursive embedding function and the distribution of shortest paths in a graph.
About the Creation of a Russian-Language Ontology for Digital Humanities

It is proposed to develop a Russian-language ontology for digital humanities as part of the infrastructure of this discipline, which receives organizational registration both in the world and in Russia. The main foreign sources for such an ontology are described. It also describes the Russian projects of recent years in the field of conceptual composition of linguistics and philology in general, which are advisable to use. The formation of the conceptual model of ontology is briefly described. As a technological basis, the Ontolex-Lemon project model is proposed on the platform of a Semantic Network and linguistic linked open data. The necessity of collaboration of various specialists and organizations is emphasized.
Data Heterogeneity Differential Privacy: From Theory to Algorithm

Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP). In this paper, we first give sharper excess risk bounds of DP stochastic gradient descent (SGD) method. Considering most of the previous methods are under convex conditions, we use Polyak-Łojasiewicz condition to relax it in this paper. Then, after observing that different training data instances affect the machine learning model to different extent, we consider the heterogeneity of training data and attempt to improve the performance of DP-SGD from a new perspective. Specifically, by introducing the influence function (IF), we quantitatively measure the contributions of various training data on the final machine learning model. If the contribution made by a single data instance is so little that attackers cannot infer anything from the model, we do not add noise when training with it. Based on this observation, we design a ‘Performance Improving’ DP-SGD algorithm: PIDP-SGD. Theoretical and experimental results show that our proposed PIDP-SGD improves the performance significantly.
Speeding up Simulations for Radiotherapy Research by Means of Machine Learning

Radiotherapy is one of the most widely used treatments for cancer by irradiating the tumor volume. However, one of its disadvantages is that healthy tissue is also affected, producing various side effects. For this reason, preliminary studies are required beforehand to determine the dose to be administered in each case, to avoid possible damage and to make sure that the dose received by the tumor is the correct one. These studies are carried out both using simulations and with routine machinery procedures using a mannequin that simulates the area to be treated. In this work a way of speeding up the previous study process is tackled, starting from simulated data whose optimized obtaining will be the objective of this work. The PENELOPE Monte Carlo simulation software is used to recreate the process and obtain the necessary previous data. Subsequently, regression models are applied to obtain the values of interest and accelerate the procedure, reducing, in addition, the energy consumption and storage required while obtaining very accurate approximations.
The Papermill Alarm: Patterns in PubMed

This post is about The Papermill Alarm: an API for detecting potential papermill-products.There’s a field of study called ‘stylometry’ where we look at the statistical properties of someone’s writing and use that to model their ‘style’. People write in idiosyncratic ways. So, for example, I often start sentences with ‘so, for example’ when I could more succinctly say ‘for example’. The stylistic properties of written work can therefore, potentially, give away the identity of the author....
Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network

Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI attacks can be formalized as an optimization problem that seeks private data in a certain space. Recent MI attacks leverage a generative adversarial network (GAN) as an image prior to narrow the search space, and can successfully reconstruct even the high-dimensional data (e.g., face images). However, these generative MI attacks do not fully exploit the potential capabilities of the target model, still leading to a vague and coupled search space, i.e., different classes of images are coupled in the search space. Besides, the widely used cross-entropy loss in these attacks suffers from gradient vanishing. To address these problems, we propose Pseudo Label-Guided MI (PLG-MI) attack via conditional GAN (cGAN). At first, a top-n selection strategy is proposed to provide pseudo-labels for public data, and use pseudo-labels to guide the training of the cGAN. In this way, the search space is decoupled for different classes of images. Then a max-margin loss is introduced to improve the search process on the subspace of a target class. Extensive experiments demonstrate that our PLG-MI attack significantly improves the attack success rate and visual quality for various datasets and models, notably, 2 ∼ 3× better than state-of-the-art attacks under large distributional shifts. Our code is available at: https://github.com/LetheSec/PLG-MI-Attack.
User Satisfaction Analysis of E-Samsat SUMUT Application Using End User Computing Satisfaction (EUCS) Approach

Technological developments have continued to develop until now and the community has even made it a necessity, one of which is the motorized vehicle tax payment processing service, where payments can be made via e-banking or a predetermined Bank ATM. Because of this, users believe that there is little to no difference between traditional Samsat and E-Samsat, the user returns to the Samsat Office to obtain validation of vehicle registration certificates (STNK). User satisfaction is formulating user satisfaction and comparing expectations and reality as follows: if expectations reality then very satisfied, if expectations = reality then satisfied and If expectations reality then not satisfied. The purpose of this study is to find out what factors influence user satisfaction in the North Sumatra E-Samsat application and recommendations to application developers so that user satisfaction does not decrease. This study uses quantitative methodology. Quantitative is a research method in which data collection techniques are carried out through surveys and questionnaire instruments and data analysis is carried out statistically. This study will use the End User Computing Satisfaction method, the EUCS method was chosen because it has 5 variables of content, accuracy, format, ease of use, and timeliness to measure factors that influence user satisfaction from information systems. The results of the discussion from this study using the EUCS method each variable get a score of 4 (satisfied) out of 5 total scores. But it's not perfect yet, so some improvements are needed to further increase user satisfaction.