Reviewerly/exHarmony huggingface dataset:
Toward a Controllable Disentanglement Network

This paper addresses two crucial problems of learning disentangled image representations, namely controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality. To encourage disentanglement, we devise a distance covariance based decorrelation regularization. Further, for the reconstruction step, our model leverages a soft target representation combined with the latent image code. By exploring the real-valued space of the soft target representation, we are able to synthesize novel images with the designated properties. To improve the perceptual quality of images generated by autoencoder (AE)-based models, we extend the encoder-decoder architecture with the generative adversarial network (GAN) by collapsing the AE decoder and the GAN generator into one. We also design a classification based protocol to quantitatively evaluate the disentanglement strength of our model. Experimental results showcase the benefits of the proposed model.
Advantages of 3D Technology in Stereometry Training

This paper presents a new software for stereometry training.Its name is StereoMV (Stereo Math Vision).It is results from a dissertation work on the topic "Stereoscopic Training System".It introduces virtual reality systems that are both immersive and non-immersive.The difference between them is in the equipment and the stereo effect they offer.A comparative analysis is performed between the virtual environments that are part of the software.3D visualization is of four types: traditional, 3D active, 3D passive, and through immersive systems such as CAVE (Cave Automatic Virtual Environment) and HMD (Head Mounted Display).There are also four visualization modes in Java3D.Mixed Immediate Mode is used to implement stereoscopic projection using 3D active technology.Passive technology uses anaglyph glasses with a red and blue filter.Traditional visualization can be implemented from any computer.The system's most important function is exporting the objects in a file with the extension .obj.All this is thanks to a Java 3D library and a new boundary method involved in generating geometric objects, which sets the program apart from existing software solutions for training in the discipline of stereometry.The software can be used for stereo visualization of random 3D models from various scientific fields.Unlike planimetry in stereometry, a three-dimensional drawing would be much more difficult to understand, and that's why 3D technology comes to the rescue.This way, the student's interest will be strengthened, from where their spatial thinking will be further developed.
Improving Performance of Cloud Storage Systems Using Support-Based Replication Algorithm

Data replication is a mechanism for creating a copy of the same file block on many sites. It is used in cloud storage systems to improve the performance of data availability. The current replication technique helps users fix the number of replicas required. The problem with the existing approaches is that the user cannot determine which file should be duplicated and how many copies are necessary, causing the cloud storage system's performance to suffer. The problem mentioned above has an impact on the performance of cloud storage systems. Thus, our proposed replication method determines the replication factor based on the support values of the file blocks to determine the precise number of duplicates to be replicated. We have also proposed an efficient technique to place the replicas based on the local support values to increase the performance of the cloud storage system. Our results indicate that our proposed replication algorithm performs better than the algorithm used in the Hadoop distributed file system.
AdaFocal: Calibration-aware Adaptive Focal Loss

Much recent work has been devoted to the problem of ensuring that a neural network's confidence scores match the true probability of being correct, i.e. the calibration problem. Of note, it was found that training with focal loss leads to better calibration than cross-entropy while achieving similar level of accuracy \cite{mukhoti2020}. This success stems from focal loss regularizing the entropy of the model's prediction (controlled by the parameter $\gamma$), thereby reining in the model's overconfidence. Further improvement is expected if $\gamma$ is selected independently for each training sample (Sample-Dependent Focal Loss (FLSD-53) \cite{mukhoti2020}). However, FLSD-53 is based on heuristics and does not generalize well. In this paper, we propose a calibration-aware adaptive focal loss called AdaFocal that utilizes the calibration properties of focal (and inverse-focal) loss and adaptively modifies $\gamma_t$ for different groups of samples based on $\gamma_{t-1}$ from the previous step and the knowledge of model's under/over-confidence on the validation set. We evaluate AdaFocal on various image recognition and one NLP task, covering a wide variety of network architectures, to confirm the improvement in calibration while achieving similar levels of accuracy. Additionally, we show that models trained with AdaFocal achieve a significant boost in out-of-distribution detection.
Blockchain Technological Transformation Management Ecosystem: Your First Venture Companion

The blockchain technology has been parenting Bitcoins and many other cryptocurrencies that have strived to be traded on the global markets.It has been only within the last 2 decades that eventually the concept of blockchain technology was flourished through evolution of IoT, or Internet of Things, and the first well defined cryptocurrency, namely Bitcoin, as a main biproduct of it was evolved to what we know as blockchain technology.Unlike what most people recognize cryptocurrency, as a main concept of its parenting blockchain technology, the cryptos are not just the main or everything about blockchain.Blockchain technology, is rather much more powerfully designed to interconnect and facilitate trade among many businesses in commerce, including insurance, banking, healthcare industry, etc., through an amazing cryptocurrency, a non-fiat currency and medium of exchange.Bitcoin's extension into the 21 st century's monetary system was -and will continue to be -registered as a historical turning point of the medium of exchange, store of value, and unit of account, among other desirable properties of money.We dare to have concluded that the blockchain technology is another revolution inside the capitalistic ecosystem for enforcing its powerful potential in the conversion of centralized businesses into somewhat more decentralized, where competition and efficiency would be more practically fostered than ever before.There will be more reliable and verifiable information, permanently stored on electronic ledgers, connected to enormous other nodes merged with those already accessible through the blockchain platform, operated by entrepreneurs, such as "Your First Venture Companion," just as a tentative example.
DETECTION OF CYBER ATTACKS USING ARTIFICIAL INTELLIGENCE

Cyber-physical systems(cps) have made significant progress in many dynamic applications due to the integration between physical processes, computational resources, and communication capabilities.However, cyber-attacks are a major threat to these systems.Unlike faults that occurs by accidents cyber-physical systems, cyber-attacks occur intelligently and stealthy.Some of these attacks which are called deception attacks, inject false data from sensors or controllers, and also by compromising with some cyber components, corrupt data, or enter misinformation into the system.If the system is unaware of the existence of these attacks, it won't be able to detect them, and performance may be disrupted or disabled altogether.Therefore, it is necessary to adapt algorithms to identify these types of attacks in these systems.It should be noted that the data generated in these systems is produced in very large number, with so much variety, and high speed, so it is important to use machine learning algorithms to facilitate the analysis and evaluation of data and to identify hidden patterns.In this research, the CPS is modeled as a network of agents that move in union with each other, and one agent is considered as a leader, and the other agents are commanded by the leader.The proposed method in this study is to use the structure of deep neural networks for the detection phase, which should inform the system of the existence of the attack in the initial moments of the attack.The use of resilient control algorithms in the network to isolate the misbehave agent in the leader-follower mechanism has been investigated.In the presented control method, after the attack detection phase with the use of a deep neural network, the control system uses the reputation algorithm to isolate the misbehave agent.Experimental analysis shows us that deep learning algorithms can detect attacks with higher performance that usual methods and can make cyber security simpler, more proactive, less expensive and far more effective.
Research on the Relationship between Motion Performance and User Experience of Golf Virtual Simulation Putting Simulator

This paper designs and develops a virtual golf simulation putting simulator based on the existing computer technology and conducts in-depth research and analysis on the relationship between its motion performance and user experience. The network architecture of the distributed virtual golf simulation system and the scene data management model are established, based on which the server-side system design and the client-side network communication module design of the distributed virtual golf simulation system are carried out. In the requirement analysis, the functional requirements such as building VR scenes, data communication and recognition models, and the non-functional requirements such as system security and ease of use are analyzed; in the outline design, the hardware equipment and logical architecture of the automatic user experience optimization system are described; in the detailed design, the functional modules of the system are designed in detail, including VR induction experience, physiological signal dataset user experience identification, data communication, optimization strategy, and so on, and important class diagrams and flowcharts are given. The intervention effects of positive thinking training on sports performance and improving athletes’ attention and receptivity have been verified and recognized by coaches and athletes. The putting simulator in the experimental class had higher hole-in-hole parameters than the control class, a highly significant difference; the putting simulator in the experimental class had higher hole-in-hole parameters than the control class, with a highly significant difference. These 3D models may contain more detailed information. In a virtual scene, the more detailed information a model contains, the more polygons the model needs, so that the computer needs to draw many polygons per frame, which has a great impact on the real-time performance of scene drawing. The parameters of the 5-yard chip-and-shoot in the experimental class were higher than those in the control class, and there was a very significant difference between the parameters of the 15-yard chip-and-shoot in the experimental class and those in the control class. The experimental results show that the model optimization processing method and rendering acceleration technology proposed in this paper can largely improve the rendering efficiency of 3D virtual scenes.
An Exploration of Post-Editing Effectiveness in Text Summarization

Automatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? In similar text generation tasks (e.g., machine translation), human-AI collaboration in the form of "post-editing" AI-generated text reduces human workload and improves the quality of AI output. Therefore, we explored whether post-editing offers advantages in text summarization. Specifically, we conducted an experiment with 72 participants, comparing post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text. This study sheds valuable insights on when post-editing is useful for text summarization: it helped in some cases (e.g., when participants lacked domain knowledge) but not in others (e.g., when provided summaries include inaccurate information). Participants' different editing strategies and needs for assistance offer implications for future human-AI summarization systems.
A New Evaluation Method: Evaluation Data and Metrics for Chinese Grammatical Error Correction

Abstract As a fundamental task in natural language processing (NLP), Chinese Grammatical Error Correction (CGEC) [1–3] has gradually received widespread attention and become a research hotspot. However, one obvious deficiency of the existing CGEC evaluation systems is that the evaluation values of the same error correction models are signif- icantly influenced by the Chinese word segmentation (CWS) results or different language models. However, it is expected that these met- rics should be independent of the CWS results and language models for a fair evaluation. To this end, we propose three novel eval- uation metrics for CGEC in two dimensions: reference-based and reference-less. What’s more, according to these three evaluation met- rics, we build a new evaluation metric that can comprehensively evaluate the CGEC model from multiple dimensions. We deeply eval- uate and analyze the reasonableness and validity of the proposed metrics, and we expect them to become a new standard for CGEC.
Task Transfer and Domain Adaptation for Zero-Shot Question Answering

Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be available. To address this, we use supervised pretraining on source-domain data to reduce sample complexity on domain-specific downstream tasks. We evaluate zero-shot performance on domain-specific reading comprehension tasks by combining task transfer with domain adaptation to fine-tune a pretrained model with no labelled data from the target task. Our approach outperforms Domain-Adaptive Pretraining on downstream domain-specific reading comprehension tasks in 3 out of 4 domains.
EXPERIMENTAL METHODOLOGY FOR EVALUATION OF THE PRELIMINARY TRAINING OF MOUNTAIN GUIDES

ABSTRACT Introduction. As the industry experts like to say: “The mountain guide is the guide at the ‘’Museum of Nature’’. But what distinguishes these people who “do not stay anywhere longer than their enthusiasm’’? The training of mountain guides is carried out in the Professional Education Center part of the National Agency for Vocational Education and Training. The training begins with an entrance exam, which assesses the preparation of candidates, and subsequently, the results are used to rank for the upcoming training. The purpose of our research is the development of an experimental methodology for evaluating the preliminary training of candidates for mountain guides. Methods. The research was held in 2022 at the Professional Education Center “Mountains and people”. Our methods included several criteria: moving on a mountain route with a topographic map crossing terrain, a theoretical test, and an intervthese criteria formed an overall score with a maximum score of 20 points. We applied our method to 29 participants. Results: Our participants showed an average time of 4 h 38 min for moving on a mountain route with a topographic map and crossing terrain. These times corresponded with an average of 8.8 points in our evaluation method. The average score for the theoretical test was 3.3 points, and the average score for the interview was 3.8 points. The average overall score was 14.8 points, which we define as very informative for the selection of candidates for mountain guides. Conclusion. We recommend this assessment methodology to National Agency for Vocational Education and Training. It is likely to be implemented in the examination requirements of all Professional Education Centers in the country.
INTERDISCIPLINARY TRAINING METHODOLOGY IMPACT ON MOTOR QUALITIES OF SCHOOL BOYS

ABSTRACT The interdisciplinary approach encourages students to put into practice the new knowledge and skills they have acquired in the course of their education in different school subjects. It helps them develop abilities to find content and functional connections between processes and phenomena studied in different school disciplines. The present study aimed to establish the effect of the interdisciplinary methodology applied in the subject of Physical Education and Sport on the development of motor skills of boys in the second grade at primary school. The object of the study was the level of improvement of the motor skills of school boys in the second grade. The experimental group included 8 boys from the Primary School “Otez Paisii” in the town of Knezha, Bulgaria, where the new interdisciplinary methodology was implemented. The control group using the traditional education methods included 14 boys from the 120 Primary School “G.S. Rakovski” in Sofia, Bulgaria. The experimental methodology was applied for a period of two school years. The students were tested twice in this period; the first was in September 2020, and the second was in May 2022. The interdisciplinary methodology was specially developed to involve several school subjects – Physical Education and Sport, Bulgarian Language and Literature, Mathematics, English Language, Man and Society, and Man and Nature. The compulsory course program in Physical Education and Sport contains adapted educational content from the other school subjects, grounded on meaningful connections with them. The following tests were used to evaluate the students’ motor skills: 30-meter run, standing long jump (with a two-footed take-off), throwing a solid ball - 1 kg, 200-meter shuttle run, Т-test, sit-ups, depth of bending, and frequency of knocking. After processing the acquired test results, it was established that the methodology used for interdisciplinary education in Physical Education and Sport was very efficient in developing the motor skills of ten-year-old boys.
The Effectiveness of Using Self-Video Recording towards Students’ Speaking Performance at MTsN 2 Solok

This research aims to expose the effectiveness of using Self-Video Recording toward students’ speaking performance. There are several problems that had been found in this research. Firstly, students’ speaking scores were not satisfied yet. Secondly, students felt difficult to acquire foreign language speaking skills due to their insecurity and lack of confidence. This was descriptive-quantitative research. The population of this research was all the students in the 2nd grade of MTsN 2 Solok. The researcher applied the technique of purposive sampling to determine the sample which was class VIII-A and VIII-D included 56 students. Documentation was used as the method of data collection. Results indicated that self-video recording is very effective for teaching speaking English. The rating quality was very effective with a mean score of 83.69 and a proportion of students between 81-100%. Based on the result of the study, the self-video recording is very effective in teaching speaking in the 2nd grade of MTsN 2 Solok.
Effect of Foreign Direct Investment on Economic Growth in Nigeria: Does Exchange Rate Matter?

This study investigates the effect of foreign direct investment on economic growth in Nigeria for the period from 1986 to 2020; a particular attention is also given on the role of exchange rate in the relationship between foreign direct investment and economic growth using annual time series data sourced from the database of World Development Indicator (WDI) of the World Bank and Central Bank of Nigeria (CBN) 2021 Statistical Bulletin. Autoregressive Distributed Lag (ARDL) model was employed for the analysis. The study found that FDI has positive and significant effect on economic growth. Exchange rate also has a positive and significant effect on the economic growth. Findings also show that the regression is significant at 5% level of significance as F-statistic is less than 0.05. This entails that the growth effect of FDI is enhanced in the presence of a stable exchange rate. Based on the findings, the study suggests an improvement in the institutional quality so as to attract the further inflow of foreign direct investment in Nigeria. The study also suggests that government should make exchange rate stable so that more foreign investment can be attracted for desired economic growth and development in the country.
Mediating space: Framing strategy and sense of presence across the real-virtual environments

This study discusses the idea of mediated space as a space formed through the combination of actor and entities in real and virtual environment. This mix of real and virtual environments is made possible through the involvement of the screen as the technological device that mediate the connection between them. Question then arises regarding the process that happen during this mediation, specifically regarding how to frame the virtual environment on the screen to establish an engagement with actor in real environment. Using the case of video game titled Persona 5, this study investigates this question through the concept of framing strategy and sense of presence. The discussion addresses two main points: (1) Utilisation of different framing strategies—based on the idea of immersion, mapping, and miniaturisation—provide different possibilities of action and interaction for the actor; (2) each framing strategy contribute to different dimension of presence that can complement each other within the formation process of mediated space.
Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator

While standard Estonian is not a low-resourced language, the different dialects of the language are under-resourced from the point of view of NLP, given that there are no vast hand normalized resources available for training a machine learning model to normalize dialectal Estonian to standard Estonian.In this paper, we crawl a small corpus of parallel dialectal Estonianstandard Estonian sentences.In addition, we take a savvy approach of generating more synthetic training data for the normalization task by using an existing dialect generator model built for Finnish to "dialectalize" standard Estonian sentences from the Universal Dependencies tree banks.Our BERT based normalization model achieves a word error rate that is 26.49 points lower when using both the synthetic data and Estonian data in comparison to training the model with only the available Estonian data.Our results suggest that synthetic data generated by a model trained on a more resourced related language can indeed boost the results for a less resourced language.