Reviewerly/exHarmony huggingface dataset:
JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition

Zero-shot learning (ZSL) is a paradigm in transfer learning that aims to recognize unknown categories by having a mere description of them. The problem of ZSL has been thoroughly studied in the domain of static object recognition, however, ZSL for dynamic events (ZSER) such as activities and gestures has hardly been investigated. In this context, this paper addresses ZSER by relying on semantic attributes of events to transfer the learned knowledge from seen classes to unseen ones. First, we utilized the Amazon Mechanical Turk platform to create the first attribute-based gesture dataset, referred to as ZSGL, comprising the categories present in MSRC and Italian gesture datasets. Overall, our ZSGL dataset consisted of 26 categories, 65 discriminative attributes, and 16 attribute annotations and 400 examples per category. We used trainable recurrent networks and 3D CNNs to learn the spatio-temporal features. Next, we propose a simple yet effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end</i> approach for ZSER, referred to as Joint Sequential Semantic Encoder (JSSE), to explore temporal patterns, to efficiently represent events in the latent space, and to simultaneously optimize for both the semantic and classification tasks. We evaluate our model on ZSGL and two action datasets (UCF and HMDB), and compared the performance of JSSE against several existing baselines in four experimental conditions: 1. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Within-category</i> , 2. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Across-category</i> , 3. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Closed-set</i> , and 4. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Open-Set</i> . Results show that JSSE considerably outperforms ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p&lt; 0.05$</tex-math></inline-formula> ) other approaches and performs favorably for both the datasets in all experimental conditions.
Diverse Video Captioning by Adaptive Spatio-temporal Attention

To generate proper captions for videos, the inference needs to identify relevant concepts and pay attention to the spatial relationships between them as well as to the temporal development in the clip. Our end-to-end encoder-decoder video captioning framework incorporates two transformer-based architectures, an adapted transformer for a single joint spatio-temporal video analysis as well as a self-attention-based decoder for advanced text generation. Furthermore, we introduce an adaptive frame selection scheme to reduce the number of required incoming frames while maintaining the relevant content when training both transformers. Additionally, we estimate semantic concepts relevant for video captioning by aggregating all ground truth captions of each sample. Our approach achieves state-of-the-art results on the MSVD, as well as on the large-scale MSR-VTT and the VATEX benchmark datasets considering multiple Natural Language Generation (NLG) metrics. Additional evaluations on diversity scores highlight the expressiveness and diversity in the structure of our generated captions.
An AR Tool to Support Warehouse Operations in the Context of Industry 4.0

Augmented Reality (AR) is one of the leading technologies of the Industry 4.0 revolution, offering innovative interfaces to promote the diffusion of digital contents into industrial processes, thanks to flexible and robust solutions and cost-effective devices. In this context, this paper explores the adoption of AR in industrial logistics where several open issues still discourage its effective use in everyday scenarios. After a review of objectives, approaches and technics of AR integration in logistics operations, the paper presents a framework to identify goods in a warehouse, retrieve data relative to the package, display info to the user to drive operations. The approach aims at easing and speeding up the activity of the warehouseman to identify goods, check the relative information and to put each good on the correct shelf. A prototypal application was developed within the Unity platform and integrated with the company ERP system to manage data on the products and retrieve images of the identification labels. A real use case involving a primary company producing agricultural tractors is proposed to test usability of the prototype. Results showed that the developed application allows relevant benefits in terms of process effectiveness, error prevention, aiming at reducing the operator mental workload.
The new Ethiopian Hate Speech and Disinformation Prevention Proclamation: A Sign for Fracture of the Promises of the Reform?

This article studies the new Hate Speech and Disinformation prevention and suppression proclamation No. 1185/2020 that the government of Ethiopia endorsed recently. Contrary to the promises of Reform by Prime Minister Abiy Ahmed in 2018, the Law that has been ratified in 2020. The study employed the method of 'Qualitative Case study' with 'Evidence of Effect' and Realist approaches to the systematic review of literature on Ethiopian Reform and the procedures followed to ratify the draft Bill. It analyzed the draft bill document as a case study reflecting the current state of media freedom in Ethiopia. Supported by some key informant interviews, related literature on media pluralism, press freedom, news reports, and International Organizations such as Human Rights Watch and UNESCO on Hate Speech and Disinformation Proclamation are examined. The result shows that PM Abiy Ahmed has been very ambitious in early actions and decisions in the reform process. However, the internal political crises created polarization in the contents of various media and compelled the draft Law's ratification. The Document of the Proclamation Lacked Clarity in the definition of terms. As a result, it created misunderstandings in the implementation and justifications of the ratified proclamation, as disinformation is only one element of different types of misinformation. It is also unclear why the proclamation repealed the article it assumed to repeal in the Criminal Code of the Federal Democratic Republic of Ethiopia, while there are other related articles. Furthermore, the Law will suppress press freedom and media pluralism which are potential democratic ideals as the country is heading for a democratic political transition. Therefore, to minimize government indiscretion, policymakers must reexamine the substance and drafting of the Law to close the gaps. To cite this article (7th APA style): Eranfeno, G. N. (2022). The new Ethiopian hate speech and disinformation prevention proclamation: A sign for fracture of the promises of the reform? Journal Communication Spectrum: Capturing New Perspectives in Communication 12(2), 112-123. https://doi.org/10.36782/jcs.v12i2.2186
LSTM-ATTENTION TEXT CLASSIFICATION METHOD COMBINED WITH KEY INFORMATION, 1-7.

Aiming at the problem that the text information could not be fully understood in traditional text classification, this paper proposed a recurrent neural network classification model based on the attention mechanism combined with key information and gave a sentence points extraction method.The semantics of the text was first expressed by the sentence points, which could fully express the semantic information and enrich its feature, so as to solve the problem of the feature sparsity in the text classification.Then, the bidirectional long short-term memory network with the attention mechanism was selected as the classifier for learning.Set the word vector and sentence vector as the input of the network, respectively, the outcomes could be spliced to obtain the final category.Experimental results show that this method could improve the accuracy of text classification.
Smart City Development in Hong Kong: An Ethical Analysis

Like many cities in advanced economies, Hong Kong has embraced the "smart city" agenda. In this article, we engage with a number of ethical issues surrounding smart city development. We assess the ethical implications of four different smart city initiatives in Hong Kong – the use of a facial recognition system, the Smart Lamppost Pilot Scheme, the Free-flow Tolling System, and Electronic Health Record.Sharing System – from the perspective of relational egalitarianism. Our analysis suggests that there are various moral risks – that a particular smart city initiative may fall short of some relevant normative requirements – depending on the levels of their voluntariness and transparency. Finally, we consider a number of strategies for mitigating these moral risks and maintaining socially egalitarian relationships among citizens of a smart city.
Implementation of Culturally Oriented Projects as an Actual Educational Activity at the University

The modern labor market is interested in specialists who, in the process of studying at a university, have formed not only highly professional, but also the so-called interdisciplinary (interdisciplinary) competencies and skills. One of the relevant competencies for most professions is the so-called general cultural competencies. In the article, on the example of the implementation of two culturally oriented projects "Cool Epics" and "International Scientific Conference", the implementation of the educational process was demonstrated using project activities that form general cultural competencies among university students.
An investigation of design in virtual reality across the variation of training degree and visual realism

“Research showed that immersive technologies can significantly improve the design process. However, it is important to consider the ease of implementation of solutions (e.g. price, simplicity). Therefore, the objective of this study was to analyze the uses of two types of virtual environments that are relatively simple to implement: a basic model of a room and its 3D scan. Participants made sketches using a virtual reality application, provided by the instructors, in each of the two different VR environments. The sketches are proposals to a furniture co-creation task. Results indicate a better co-creation process during the second session than during the first, which reveals that training is an important criterion in this case. Furthermore, co-creation is felt to be better in the case of the modeled place compared to the 3D scan. This result could be due to the presence of useless virtual objects that can cause a distraction to the participants. These results are discussed from an applicative standpoint.
Visualizing the Passage of Time with Video Temporal Pyramids

What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some things are too slow to experience in real-time, such as glacial retreat or the gradual shift from summer to fall. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing. Also, the length of the timelapse video is directly tied to its temporal resolution, which necessitates tradeoffs between those two facets. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.
STEM Tools, Education Guidelines and Curricula Training Considering Society and Businesses Demands in the Transition to Post COVID

It is practically beyond cliché that children are our future: if we teach them well, they will lead the way.Therefore, the mission of teach is transversal and is not only a responsibility of schools but also a responsibility of all parties, such as families, organizations, stakeholders, companies and communities.The collaboration between them benefits the education and removes the gap between the theory teached in schools and the applicability for the requirements of businesses.Bearing that in mind, a group of concerned entities from universities and enterprises developed the project PAFSE Partnerships for science education [1].With the focus "Teaching public health science in pandemic times", it contributes to the topic "Open schooling and collaboration in science education" under the programme "Science with and for Society (SWAFS)".The present paper focus on an educational scenario developed in the PAFSE project: "Droplets & the physics of viruses transmission", presenting specifications, considerations and guidelines to implement it.The PAFSE project is innovative in that it makes use of project-based learning, problem-based learning and digital learning packages to engage a wide and diverse range of actors.Addressing public health challenges and indirectly backing the achievement of the sustainable development goals (SDGs), partners are developing other educational scenarios through an open schooling framework and using social media for dissemination.Engaging more than 3000 students and their families in the project activities, the outcomes will be disseminated to more than 1000 schools.It will also reach the society through activities involving communities, social organizations, parents' associations and others.The PAFSE project students have 12-15 years old and are in the 3rd Cycle.The concerns and guidelines given in the present paper comes in a moment of wondering the evolution of the pandemic and the eventual shifting on measures to deal with it.
An improved naive Bayesian classification method based on symmetric uncertainty

According to the harsh condition of the conditional independence of feature attributes in the Naive Bayesian algorithm, an improved Naive Bayesian classification method based on feature correlation weighting is proposed. The degree of correlation between feature attributes, class attributes and feature attributes is judged by symmetric uncertainty, irrelevant feature attributes and redundant attributes are eliminated, and then the correlation between feature attributes and class variables is high and the correlation degree between feature attributes is low. The basic principle of the weight assignment of feature attributes, and finally experimental verification on the public data set. According to the experimental results, the algorithm can effectively reduce the influence of the correlation between the features and effectively improve the classification accuracy.
A Framework for Big Data Sovereignty: The European Industrial Data Space (EIDS)

Abstract The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.
Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.
Formation of TPACK and Acmeological Competency of Future Teachers in Foreign Language Education

The aim of this study is to determine the effects of acmeological-based teaching activities in foreign language lessons on learners' foreign language self-efficacy, foreign language learning motivation and Technological Pedagogical Content Knowledge (TPACK) skills. The research was carried out in the Faculty of Foreign Languages, Department of the Theory and Practice of Foreign Languages Training of a university in Kazakhstan in the 2021-2022 academic year. The study, which was conducted with 35 students in the experimental and 35 control groups for 6 weeks, was designed according to the experimental model with the control group, which is one of the quantitative research methods. Foreign language learning self-efficacy scale, foreign language learning motivation scale and TPACK skill test were used as data collection tools in the study. According to the findings of the study, it was determined that there was a significant difference in favor of the experimental group in the post-test scores (foreign language learning self-efficacy, foreign language learning motivation and TPACK skill test scores) of the experimental group, in which activities based on the acmeological approach, and the control group, in which traditional teaching was applied.
Exploiting Structures in Weight Matrices for Efficient Real-Time Drone Control with Neural Networks

We consider the task of using a neural network for controlling a quadrotor drone to perform flight maneuvers. For that, the network must be evaluated with high frequency on the microcontroller of the drone. In order to maintain the evaluation frequency for larger networks, we search for structures in the weight matrices of the trained network. By exploiting structures in the weight matrices, the propagation of information through the network can be made more efficient. In this paper, we focus on four structure classes, namely low rank matrices, matrices of low displacement rank, sequentially semiseparable matrices and products of sparse matrices. We approximate the trained weight matrices with matrices from each structure class and analyze the flying capabilities of the approximated neural network controller. Our results show that there is structure in the weight matrices, which can be exploited to speed up the inference, while still being able to perform the flight maneuvers in the real world. The best results were obtained with products of sparse matrices, which could even outperform non-approximated networks with the same number of parameters in some cases.
On-Tube Attribute Visualization for Multivariate Trajectory Data

Stylized tubes are an established visualization primitive for line data as encountered in many scientific fields, ranging from characteristic lines in flow fields, fiber tracks reconstructed from diffusion tensor imaging, to trajectories of moving objects as they arise from cyber-physical systems in many engineering disciplines. Typical challenges include large data set sizes demanding for efficient rendering techniques as well as a large number of attributes that cannot be mapped simultaneously to the basic visual attributes provided by a tube-based visualization. In this work, we tackle both challenges with a new on-tube visualization approach. We improve recent work on high-quality GPU ray casting of Hermite spline tubes supporting ambient occlusion and extend it by a new layered procedural texturing technique. In the proposed framework, a large number of data set attributes can be mapped simultaneously to a variety of glyphs and plots that are embedded in texture space and organized in layers. Efficient rendering with minimal data transfer is achieved by generating the glyphs procedurally and drawing them in a deferred shading pass. We integrated these techniques in a prototype visualization tool that facilitates flexible mapping of data set attributes to visual tube and glyph attributes. We studied our approach on a variety of example data from different fields and found it to provide a highly adaptable and extensible toolbox to quickly craft tailor-made tube-based trajectory visualizations.
Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Human motion tracking is a fundamental building block for various applications including computer animation, human-computer interaction, healthcare, and so on. To reduce the burden of wearing multiple sensors, human motion prediction from sparse sensor inputs has become a hot topic in human motion tracking. However, such predictions are non-trivial as (i) the widely adopted data-driven approaches can easily collapse to average poses, and (ii) the predicted motions contain unnatural jitters. In this work, we address the aforementioned issues by proposing a novel framework which can accurately predict the human joint moving angles from the signals of only four flexible sensors, thereby achieving the tracking of human joints in multi-degrees of freedom. Specifically, we mitigate the collapse to average poses by implementing the model with a Bi-LSTM neural network that makes full use of short-time sequence information; we reduce jitters by adding a median pooling layer to the network, which smooths consecutive motions. Although being bio-compatible and ideal for improving the wearing experience, the flexible sensors are prone to aging which increases prediction errors. Observing that the aging of flexible sensors usually results in drifts of their resistance ranges, we further propose a novel dynamic calibration technique to rescale sensor ranges, which further improves the prediction accuracy. Experimental results show that our method achieves a low and stable tracking error of 4.51 degrees across different motion types with only four sensors.