Reviewerly/exHarmony huggingface dataset:
A Computational Approach to Identifying Cultural Keywords across Languages

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful lin- guistic analyses (Wierzbicka, 1990; Peeters, 2020b). Because keywords are expected to have different statistical properties than related words in other languages, we argue that quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally-specific words that range from “obvious” examples such as Amsterdam in Dutch to non-obvious yet independently proposed examples such as душа (soul) in Russian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally-specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.
APPLICATION OF ELEMENTS OF PROBLEM-BASED LEARNING AS A MEANS OF INCREASEING THE EFFICIENCY OF THE BIOLOGY LESSON IN A MODERN SCHOOL

Modern extremely difficult working conditions encourage teachers to make changes in the theory of pedagogy and the practice of the modern educational process. The concept of the New Ukrainian School envisages an orientation towards learning that will develop critical and productive thinking of schoolchildren, their creative approach to solving emerging problems. In the conditions of modern education, it becomes an objective necessity for the teacher to master and use innovative technologies in his lessons during training. Innovative learning is based on a certain number of technologies, among which there is also a place for problem-based learning. The article substantiates the expediency and investigates the possibility of using elements of problem-based learning in biology lessons as a means of increasing the effectiveness of the lesson in a modern school. 28 students of the 10th grade of secondary comprehensive school in Dnipro took part in the study, which was conducted in three stages. In order to find out the influence of elements of problem-based learning on increasing the effectiveness of the lesson, we used: problem situations, problem questions and tasks that were aimed at increasing the level of mental development of schoolchildren, their ability to independently search for new knowledge and creativity. The level of cognitive activity of students in biology lessons was assessed using the B. Pashnev questionnaire. The analysis of the results shows that at the beginning of the study, both girls and boys mainly had an average level of cognitive activity in class (72%). 18% of students had a low level of cognitive activity, and 10% of students had a high level. After the active use of elements of problem-based learning in lessons, a high level of cognitive activity became characteristic of 14% of students, i.e., the number of children with this level increased by 4%; the average level increased by 5% compared to the previous survey; the low level decreased by 9%. Thus, the elements of problem-based learning that were used to increase the effectiveness of the lesson are justified. It is important to use active learning methods that correspond to the learning content. It is in this way that it is possible to develop in students the ability to independently apply the acquired knowledge in non-standard situations. Key words: problem-based learning, motivational activity, problem situation, problem task, problem questions.
CATATAN TENTANG PENGAJARAN LAFAL BAHASA INDONESIA TERHADAP PENUTUR JATI BAHASA JEPANG

Accurate pronunciation in learning Indonesian is one of the keys for a fluent communication and also a help in developing language skill. However, many native Japanese speakers do not pay much attention to their pronunciation. Generally, Japanese native speakers have difficulty in differentiation between /r/ and /l/, differentiation between /u/ and /ə/, pronunciation of ng /ŋ/ in syllable-initial, differentiation or pronunciation of consonants in syllable-final, and so on, in learning Indonesian. Furihata (2015) proposed his practice of teaching pronunciation for consonants in syllable-final with using the concept of mora phonemes in Japanese. This paper will discuss: (1) phonological features in Japanese, especially on mora phonemes, with focusing difference between Indonesian and Japanese, and (2) tendency of Indonesian pronunciation produced by Japanese native speakers.
Rethinking Feature Aggregation Method in Single Image Super-Resolution

Abstract With the tremendous development of lightweight convolution neural networks, they have been widely employed in the research of lightweight single image super-resolution. However, these methods focus on the lightweight design of feature extraction blocks and neglect the lightweight aggregation methods that aggregate features from the feature extraction blocks. This paper analyzes the memory consumption of generic aggregation methods and proposes an adaptive local aggregation method. It adopts a set of trainable parameters and channels attention to implement a selective aggregation approach, further enhancing the aggregated network's generalization ability. To further lighten the model and improve the overall performance, we propose a novel re-parameterized branching block structure with spatial and channel diversity. While re-parameterizable structures have been employed in several types of research, they ignore that too many branches add additional training time overhead and are not friendly to devices with strong parallel computing power like GPU. Therefore, we redesign the branch structure by retaining part of the branches from the prior work with a significant performance gain and adding a lightweight branch structure, successfully balancing the expressivity and training time-cost of the block. Comprehensive experiments demonstrate the necessity and superiority of our method over state-of-the-art single image super-resolution methods.
Sparticle production at colliders

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
eXplainable AI for Quantum Machine Learning

Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measurements on quantum circuits introduce probabilistic errors which impact the convergence of these methods. On the other hand, the phase space of a quantum circuit expands exponentially with the number of qubits, complicating efforts to execute xAI methods in polynomial time. In this paper we will discuss the performance of established xAI methods, such as Baseline SHAP and Integrated Gradients. Using the internal mechanics of PQCs we study ways to speed up their computation.
A Survey of Cyber Crime Awareness Among Netizens of Higher Education Institutions: A Case Study of Zetech University

Protecting the integrity and confidentiality of information in sophisticated network systems is becoming increasingly vital and difficult. The computer may have been used in order to commit the crime and in other cases the computer may have been the target of the crime. The purpose of this research was to determine the level of awareness of cyber-attacks among students and staff of higher education institutions and to propose mechanisms to overcome cybercrimes attacks. A mixed technique approach was utilized in the study. The study was conducted at Zetech University with a purposively randomly selected population of 260 staff and students, and a sample of 150 was obtained. The descriptive statistics was used in the quantitative design to show the distribution of scores using a few indices. According to the findings, 63.4 percent of the participants had no cyber security awareness training, whereas 36.6 percent had. It is impossible to exaggerate the value of workplace cyber security awareness. The majority of participants (91.1%) were aware of the many sorts of cybercrimes, whereas 8% were unsure. 58.9% of respondents strongly concurred that they had never trusted websites that asked for their bank card information. Focusing on cyber security awareness reduces cybercrimes by more than 50%, according to 65.2 percent of participants. 72.3 percent firmly agreed that using encryption techniques to safeguard sensitive information was a good idea. The study's findings demonstrate that a rise in cybercrime is causing physical harm to individuals, with the majority of respondents saying that hackers had stolen their data and harmed them. Based on our research, we advise higher education institutions to invest in cutting-edge research facilities, place more emphasis on internal and external cyber security research and development, and emphasize that top management allocates sufficient financial resources to IT infrastructure and cyber security awareness trainings. Higher education institutions should regularly host conferences and training sessions for all staff and students on cyber security. The team in charge of overseeing the networks and IT infrastructure must make sure that email filtering and intrusion detection systems have been put in place to detect harmful assaults on university networks and systems.
An In-Depth Discussion on the Education Management of College Students from the Perspective of New Media

In-depth discussion on the education management of college students from the perspective of new media is the main idea of the designed study. At present, the research and practice of school knowledge management mainly involves two aspects: teacher's personal knowledge management and also school-level knowledge management, and there are few researches on college students' self-knowledge management. Multi-channel information carriers such as the Internet, coal, books and magazines make their ideological concerns show a clear wide-area feature, covering various issues of history and reality, domestic and international, but it is precisely this kind of informatization. The extreme richness, rapid change and dispersion of various information in society also breed the superficial development of the ideological and cultural needs of contemporary college students. This paper gives then ovel suggestions.
STATE SUPPORT FOR THE IMPLEMENTATION OF REMOTE TECHNOLOGIES IN THE EDUCATIONAL PROCESS MANAGEMENT OF THE EDUCATIONAL PROCESS: THE EXPERIENCE OF CHINA

The article examines the experience of introducing distance technologies in preschool education in China. In order to prevent the COVID-19 epidemic, preschool educational organizations in many countries of the world have switched to online learning, that is why the Ministry of Education of the People’s Republic of China was forced to provide support to teachers and families of preschool children. The experience of state support studied in the article can be used in the preparation of regional or municipal programs, as well as state projects to support preschool educational institutions in the Russian Federation at the stage of distributing the budget of a particular region or municipality.
The Development an Image Processing Algorithm to Detect Perforated and Non-Perforated Objects

In this study, an algorithm was developed that can detect and present number of perforated and nonperforated objects on an image directly or on an image which was captured momentarily by a camera.Firstly, the algorithm was developed using C programming language on Matlab environment and its performance was tested on the artificially produced binary images.At the same time, a developed version of the algorithm was created on Visual Studio Code environment using Python programming language that is used frequently in many software.After successful trials on the prepared artificial images, a real-time object detection algorithm was achieved with inputs of algorithm provided with images taken momentarily by camera.To use the developed algorithm at different applications, Raspberry-Pi based experimental setup was prepared from single board computer platforms and the developed algorithm was run on.In conclusion, it was seen that the number and the exact positions of perforated and non-perforated objects on the images can be detected successfully.
Neural Fields for Scalable Scene Reconstruction

Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.
Facial Limb Detection for the Protection of Face in Occupational Safety

A little carelessness may endanger life safety or loss of life may occur in heavy industry, factories and sectors where occupational safety is very important. In order to prevent or minimize them, it is necessary to take advantage of informatics facilities. Today, the fact that artificial intelligence and the internet of things have come to a very advanced position can provide life safety by developing autonomous systems and building modules that will give warnings, warning the employee working in these sectors in a wrong position immediately. In this study, a YOLO-based system was developed and a proposal was made that aims to protect human facial parts. Tests show that results can be obtained quickly and to prevent harm to life.
Syntax-informed Question Answering with Heterogeneous Graph Transformer

Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models are expensive to train. We propose to evaluate whether such pre-trained models can benefit from the addition of explicit linguistics information without requiring retraining from scratch. We present a linguistics-informed question answering approach that extends and fine-tunes a pre-trained transformer-based neural language model with symbolic knowledge encoded with a heterogeneous graph transformer. We illustrate the approach by the addition of syntactic information in the form of dependency and constituency graphic structures connecting tokens and virtual vertices. A comparative empirical performance evaluation with BERT as its baseline and with Stanford Question Answering Dataset demonstrates the competitiveness of the proposed approach. We argue, in conclusion and in the light of further results of preliminary experiments, that the approach is extensible to further linguistics information including semantics and pragmatics.
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation

We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using PnP. Our framework allows the system to take previous uncertainties into consideration when predicting the current frame, resulting in predictions that are more accurate and stable than single frame methods. Extensive experiments show that our method outperforms existing approaches on the challenging Objectron benchmark of annotated object videos. We also demonstrate the usability of our work in an augmented reality setting.
Cycle-GAN for eye-tracking

This manuscript presents a not typical implementation of the cycle generative adversarial networks (Cycle-GAN) method for eye-tracking tasks.
Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning

Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task. However, we discover that existing syntax-based models suffer from two issues: noisy information aggregation and loss of distant correlations. In this paper, we propose a novel model termed Neural Subgraph Explorer, which (1) reduces the noisy information via pruning target-irrelevant nodes on the syntax graph; (2) introduces beneficial first-order connections between the target and its related words into the obtained graph. Specifically, we design a multi-hop actions score estimator to evaluate the value of each word regarding the specific target. The discrete action sequence is sampled through Gumble-Softmax and then used for both of the syntax graph and the self-attention graph. To introduce the first-order connections between the target and its relevant words, the two pruned graphs are merged. Finally, graph convolution is conducted on the obtained unified graph to update the hidden states. And this process is stacked with multiple layers. To our knowledge, this is the first attempt of target-oriented syntax graph pruning in this task. Experimental results demonstrate the superiority of our model, which achieves new state-of-the-art performance.
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding

Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of labeled data is unavailable or expensive to obtain. Existing works studying weak supervision for NLU either mostly focus on a specific task or simulate weak supervision signals from ground-truth labels. It is thus hard to compare different approaches and evaluate the benefit of weak supervision without access to a unified and systematic benchmark with diverse tasks and real-world weak labeling rules. In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU. WALNUT consists of NLU tasks with different types, including document-level and token-level prediction tasks. WALNUT is the first semi-weakly supervised learning benchmark for NLU, where each task contains weak labels generated by multiple real-world weak sources, together with a small set of clean labels. We conduct baseline evaluations on WALNUT to systematically evaluate the effectiveness of various weak supervision methods and model architectures. Our results demonstrate the benefit of weak supervision for low-resource NLU tasks and highlight interesting patterns across tasks. We expect WALNUT to stimulate further research on methodologies to leverage weak supervision more effectively. The benchmark and code for baselines are available at \url{aka.ms/walnut_benchmark}.
Discourse-Aware Soft Prompting for Text Generation

Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don't generalize across all generation tasks. We show that soft-prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text. We investigate two design choices: First, we apply \textit{hierarchical blocking} on the prefix parameters to simulate a higher-level discourse structure of human written text. Second, we apply \textit{attention sparsity} on the prefix parameters at different layers of the network and learn sparse transformations on the softmax-function. We show that structured design of prefix parameters yields more coherent, faithful and relevant generations than the baseline prefix-tuning on all generation tasks.
Do knowledge management systems motivate and satisfy the academic staff in higher education Institutions?

Higher education knowledge management has three goals: improving task quality and efficiency, training human resources at all operational levels, and expanding an organization’s field knowledge base to enhance the organization’s knowledge or intellectual investment. This research aimed to elucidate the effect of knowledge management on teaching motivation and academic staff satisfaction in Vietnamese universities. The purpose of this study is to establish a clear relationship between knowledge management and teaching motivation and academic staff satisfaction; three dimensions of knowledge management systems, namely knowledge acquisition, knowledge dissemination, and knowledge utilization, all of which contribute to increased teaching motivation and academic staff satisfaction when knowledge management systems are implemented. The quantitative research technique was utilized to gather data in this study by surveying 381 professors using a questionnaire. SPSS 22.0 and SmartPLS software were used to process data obtained via survey professors engaged in teaching. Managerial implications for knowledge management systems have been suggested to increase academic staff satisfaction and teaching motivation.
Workshop on Creating Game Using Scratch as an Activity to Support the Vision of Indonesia Gold 2045 at SMK Negeri 5 Pekanbaru

In order to welcome creative industries that synergize with technology, the Indonesian government has begun to prepare a vision for the golden Indonesia 2045. One of its pillars is the development of Indonesian human resources along with the mastery of science and technology. To realize this vision, superior human resources possessing creativity, critical thinking, communication, and collaboration skills are needed. The right method in character building is the development of potential skills from early age with the help of the world of education. One of the formal educational institutions building students’ characters to be skillful and competitive workforce, and developing entrepreneurial skills from an early age is vocational high school (SMK). Therefore, introduction of programming using Scratch to students of SMK Negeri 5 Pekanbaru is a learning method that is expected to develop the desired HR characters. Moreover, simple game creation training using Scratch can provide an overview of the use of simple coding as an introduction to how technology works. The activities were carried out in 4 method models, namely planning, action, observation, and reflection. The success rate of this activity is measured based on the results of score comparison between pre-test and post-test. The post-test results showed an increase of 96.67% compared to the participants’ initial understanding.