Reviewerly/exHarmony huggingface dataset:
Challenges for Driver Action Recognition with Face Masks

Advanced Driver Assistance Systems (ADAS) are enabling technologies in Intelligent Transportation Systems. Modern ADAS include algorithms to classify drivers' actions and distractions, aiming at identifying situations in which the driver is inattentive. Such systems typically include components for Driver Action Recognition (DAR) and Visual Distraction Classification (VDC), which prevent risky situations during semi-autonomous driving. DAR and VDC often rely on cameras that track the driver and classify actions based on image recognition algorithms. The COVID-19 pandemic has changed several common social behaviours, including the widespread use of face mask even during driving. In some cases (taxi, bus) face covering policies are compulsory in many legislations. We here show that these behavioural changes challenge state-of-the-art DAR and VDC systems, with the average F1-score in some scenarios dropping by around 30% when exposed to images of drivers wearing masks. Noting a lack of public datasets to update the ML classifiers performing such tasks, we contribute Maskdar, a dataset for Action Recognition of Drivers wearing face Masks. Finally, using Maskdarwe show the importance of including subjects with face masks in datasets for DAR.
Extracting and Identifying Relationships of Key Phrases in Information Flows

This paper proposes a linguistic-statistical method for an automatic extraction and identification relationships of key phrases in information flows in order to further identification narratives as a generalization of a set of such phrases. Means of detecting stable phrases are based on the concepts of machine learning, linguistic analysis and statistical calculations. Next, using modern methods of network analysis, the relationships of key phrases are researched, and their individual clusters that probably correspond to the narratives will be identified. A form of visual display of information flow in terms of key phrases and dates is proposed. This form is a Phraseme Diagram (Ph-Di), the cells of which are filled with numerical values corresponding to the frequencies of key phrases in relation to the dates on which they appear. This approach can be used to address the analysis and visualization of the distribution of narratives for any selected information flows in terms of issues of researcher interest and have a fairly significant time frame.
Usage of the Computational Intelligence to Create Algorithms for Determination the Number of Lightning Strokes to Objects of Critical Infrastructure

Usage of the computational intelligence to create algorithms of the statistical model that describes the electro physical processes at the last stage of the the lightning leader channel movement to the ground, taking into account the possibility of propagation of a counter spark from ground objects has been proposed. This model is based on experimental data on the speed of the lightning leader channel, the electric field strengths necessary for the development of leader channels of negative and positive polarity, etc. The correspondence of this model to the considered processes is confirmed by comparing the results of calculation the probabilities of places destruction by high-voltage discharge of the earth and objects located on it with known experimental data. An example of the model implementation for appreciation of the prognosticated lightning strikes to an object of the critical infrastructure, namely tanks for oil storage, has been presented.
Elements of Synergetics for Modeling and Comprehensive Assessment of Environmental Condition and Sustainable System Development

Determination of the state “researched system - environment” in the presence of manmade provision is solved by the proposal of comprehensive methodological support - elements of synergetics for modeling of the considered objects with their systemological presentation, which is the subj ect of research. The purpose is determined by the formation of methodological support complex of the object study logic in the systematic representation of it “(system - environment) - processes of internal and external nature -system state - environment”. According to the goal, the main tasks are to introduce a systematic approach to determining the model representation of the object, the implementation of such a model on the basis of a number of statistical methods to bind their results with the basics of synergetics to obtain environmental assessment (system - environment) and its development. Mathematical methods of modeling with the inclusion of elements of systemology, statistical methods, synergetics are involved in solving the tasks.
Implementation of Data Visualization in Defense

The use of data visualization in defense is very important. In the application of data visualization can use GIS applications. GIS can help a country in maintaining the integrity of the country. This study aims to show how data visualization using GIS is used in defense. Methodology This study uses a qualitative research methodology. The results of this study are that by knowing visualization data assisted by GIS applications, you can find out the Hazard and Vulnerability of the terrain in the country. With data visualization, it can also provide the supply chain needed in the defense industry as well as in times of war. And everyone who needs this visualization data can better plan the network plan and urban plan that will be used.
Building Morphological Analyzer for Informal Text in Indonesian

Informal text is heavily used by Indonesian in social media. However, NLP tool that can process such text is still very limited. In this work, we built a morphological analyzer for informal text in Indonesian by adding new rules for informal words to an existing Indonesian morphological analyzer named Aksara. Moreover, we also enrich the Aksara lexicon with informal words. The tool can perform tokenization, lemmatization, and part-of-speech (POS) tagging. Aksara uses a rule-based method using a finite-state transducer with a compiler named Foma. To evaluate the tool, we created a gold standard of 102 sentences with 1434 tokens which around 30 % are informal. The test results show that our tool has a tokenization accuracy of 97.21 %, while lemmatization accuracy for case insensitive is 90.37 %, and POS tagging evaluation reached an F1-Score value of 80%.
Transformer-based method for mathematical expression recognition in document images

The recognition of mathematical expressions (MEs) has attracted many researches in recent years. The results of the recognition of MEs allow to develop many useful applications such as mathematical retrieval systems or editing scientific documents. Compared to traditional methods, the encoder-decoder architecture has achieved many advances in the recognition of MEs. The paper presents strategies to improve the recognition accuracy of the recognition of MEs. Firstly, the advanced neural networks that are the DenseNet and the Transformer are employed in the encoder-decoder. Then, the strategy of generation of training dataset of the encoder-decoder model allow to gain better results in the recognition of MEs. The experiments are carried out on the public datasets (Marmot) and the performance comparison with a deep-learning method has shown the significant improvement of the proposed method.
Large-scale AMR Corpus with Re-generated Sentences: Domain Adaptive Pre-training on ACL Anthology Corpus

Abstract Meaning Representation (AMR) is a broad -coverage formalism for capturing the semantics of a given sentence. However, domain adaptation of AMR is limited by the shortage of annotated AMR graphs. In this paper, we explore and build a new large-scale dataset with 2.3 million AMRs in the domain of academic writing. Additionally, we prove that 30% of them are of similar quality as the annotated data in the downstream AMR-to-text task. Our results outperform previous graph-based approaches by over 11 BLEU points. We provide a pipeline that integrates automated generation and evaluation. This can help explore other AMR benchmarks.
Solving Complex Background Problem Using RetinaNet for Sign System for Indonesian Language (SIBI) Gesture-to-Text Translator

SIBI is the standardized sign language system offi-cially used in Indonesia. The application of SIBI is often found to be a hindrance because there are too many gestures that must be memorized. A mobile-based application is needed as gesture-to-text translator. From Rakun et al., Skin Color Segmentation was used as a method to segment hand and facial features using greenscreen background as dataset (3.367% of WER and 80.180% of SAcc). When this application is used, the gesture video is recorded on complex background but performed poorly (135.180% of WER and 0% of SAcc score). The computational time using Skin Color Segmentation is 0.013 s per frame. OpenPose was used to locate hand and facial position. OpenPose can give better performance (6.312% of WER and 69.293% of SAcc score) compared to Skin Color Segmentation but cannot be implemented on mobile application. The computational time using OpenPose is 0.410 s per frame. The focus of this study is to find a model that can locate hand and facial position on complex background and also can be implemented on mobile application. The model we use is RetinaNet. RetinaNet is proven to locate hand and facial position much better (4,100% of WER and 78,990 % of SAcc score) than Skin Color Segmentation and OpenPose. The computational time using RetinaNet is 0.038 s per frame.
Production, Distribution of Oraimono(往来物) related to Joseon in the 17th and 19th centuries and the Perception of Joseon by Japanese People

The purpose of this article is to examine the characteristics of shipbuilding-related oraimono produced, distributed, and consumed in Japan from the 17th to the 19th centuries, focusing on publication date, classification according to content, publication region, and author. Also, the purpose of this study is to examine the temporal change of Japanese commoners’ perception of Joseon by analyzing the contents and illustrations of Oraimono (往来物).BR Oraimono was a textbook that was widely used for the education of children in Japan from the 11th to the 19th centuries. In the Edo period, all kinds of textbooks used in the homes of ordinary people and at Denarai-juku(手習塾), a private educational facility for ordinary people,were called oraimono.BR The knowledge, images, and memories acquired and formed in the educational field during childhood take root more deeply than at any other time and act as the basis for the perception formed in the process of becoming an adult. Commoners in various parts of Japan naturally acquired information and knowledge related to Joseon based on the contents of the Joseon-related Oraimono, which was used as a textbook in each household and in Denaraijuku. And based on this, it is presumed that he had his own view of Joseon.BR In the 17th century, many Oraimono related to Joseon were based on Joseon books containing Confucian ideas. In the 18th century, when peaceful Korea-Japan relations were established, such as dispatching news agencies in the 18th century, the emphasis was placed on preparations for visits by news agencies dispatched from Joseon and publications related to Joseon. Later, as foreign ships approached the coast of Japan in the late 18th century, they became conscious of external pressure, and the theory of territorial expansion in the surrounding area became active as a reaction against this. As a result, interest in Joseon increased again in the middle of the 19th century, and the main focus was on Empress Shin Gong’s(神功皇后), Samhan(三韓) Conquest, the Imjin(壬辰) and Jeongyu(丁酉) Wars, and tributes and offerings from Joseon. After the opening of the country, interest in Western countries grew in the mid-to-late 19th century, and the names, populations, trade products, and products of each country were organized, and some of them dealt with Joseon.BR This change in the contents of Oraimono reflects the situation at the time and the interests of the common people in Japan, and it is thought that it served as the basis for forming the perception of Joseon by the common people in Japan at the time. And I think that this collective learning and memory formation became the basis for the formation of Joseon in Japan that was formed in the modern era.
Managing Edge AI Cameras for Traffic Monitoring

AI cameras are edge devices that can execute lightweight deep learning models with embedded GPU devices. In traffic management applications, traffic flow and traffic incidents can be detected from roadside images by AI cameras, and only the detected high-level information needs to be sent back to the server to avoid network bandwidth consumption and spare server resources. However, due to limited hardware resources at edge devices, the models should be optimized for specific AI cameras before they are deployed. In addition, the environment-related parameters need to be configured properly after model deployment. These tasks call for an AI camera management system. In this research, we design a management and deployment traffic monitoring system which can accomplish model optimization and parameter configuration with ease. Except for the camera hardware installation, other main functions can be called remotely from the management system, including 1) Automatic modeling and code transfer generation; 2) Remote deep learning model deployment; 3) Remote application configuration; 4) Analysis result presentation with a graphical user interface. To validate our proposed system, the embedded GPU devices, including NVIDIA Jetson TX2 and AGX Xavier combined with roadside cameras, are used as the prototype of the AI cameras, and the deployment of intersection flow analysis models and the visualized analysis results are conducted by the proposed system. The experiments validate that the proposed management system achieves all the design goals.
Study of Information Identification Technology Considering of Overlapping and Synonyms Words in Information Distribution

When multiple companies in different industries collaborate to provide ICT services, the systems used by each company differ and the format in which information is expressed differs from system to system. Therefore, information cannot be distributed without manual information conversion. To promote provision of services through the cooperation of multiple companies, the distribution of information among them needs to be automated. In this case, information content needs to be automatically identified to determine the distribution destination. In this paper, we propose a method that can automatically identify information by using a combination of distributed representations and a synonym conversion. The evaluation results show that the method can identify information with an existing type with a 100% accuracy, but needs to be improved regarding identification of whether information is a new type.
Evaluation of the application of Seq2Seq towards the automatic derivation of a set of APIs that satisfy requirements from only ambiguous requirements

Full automation of customer order processing requires technology to accurately understand the content of customer orders and to design appropriate operational processes and the order of execution of APIs. Although previous studies have made progress in examining and introducing automation of routine operations, the task of making requirements from ambiguous customer requests has not been fully automated. This study aims to fully automate the derivation of the API to be used from only ambiguous customer requirements. As a means to achieve this, the application of Seq2Seq, which transforms one series of data into another, was evaluated. In this paper, two models were prepared, one with LSTM applied to the Encoder of Seq2Seq and the other with GRU applied to the Encoder of Seq2Seq. By training each Seq2Seq with the eTOM-API mapping data published by the Tele-Management Forum (TMF), we evaluated whether the constructed Seq2Seq models could derive the appropriate API from any natural language. The goal of this study is to eventually derive a set of appropriate APIs from only ambiguous requests with an accuracy of more than 90%. To achieve the goal, we would like to determine how much data should eventually be available for Seq2Seq. Therefore, in the experimental part, we assessed to what extent the amount of training data affects the accuracy. Before the experiment, it was assumed that an accuracy of around 28.7 % could be achieved by making maximum use of the TMF eTOM-API mapping data. However, the results of the experiment showed no clear improvement in accuracy from data volumes of several hundred to several thousand orders of magnitude. Therefore, it was not possible to estimate the amount of data required to achieve the goal. In the future, the possibility of setting up new features to relate between eTOM-API mapping data and weighting which descriptions are important for API derivation in the eTOM definition text will be investigated.
Using Seq2seq voice conversion with pre-trained representations for audio anonymization: experimental insights

The recording and processing of vocal data is raising increasing privacy concerns among the end-users as well as service provides. These privacy issues pose severe limitations in scenarios involving smart-cities where public spaces can be monitored by audio-visual sensors to improve the quality of the services offered to citizens. One option to address these problems is to move the processing on the edge devices close to the events, so that potentially identifiable information does not travel over the internet. However, this is often not possible due to hardware limitations on edge devices. An intriguing alternative is the development of voice anonymization techniques that remove individual speaker characteristics, while maintaining the linguistic and acoustic information in data. In this paper we explore a SOTA sequence-to-sequence voice conversion approach, based originally on x-vectors and ASR bottleneck features, and investigate the use of different pre-trained speech and speaker representations to decouple the two acoustic information. In addition, we analyse different strategies for the selection of the target voice representation. Results on public datasets in terms of equal error and word error rates show that good privacy preservation with limited impact on the quality of the voice-converted speech is achieved with respect to the original method.
Application of E-Learning in Theoretical Part of the Subject Informatics

The aim of this paper was to examine effectiveness of application of digital interactive content in the subject of Informatics. The study involved 62 fifth-grade primary school students. For the purposes of the research, initial tests were used, which examined students' initial knowledge to check the uniformity of groups. Students were divided into control and experimental groups in two cycles. Control group used classical model of teaching, while experimental group used digital content (e-learning). After teaching, final tests were conducted to determine a slightly better results of experimental group. Also, students' impression of adoption of teaching content was examined, as well as students' preferences for a certain teaching model. Statistically significant differences were found between control and experimental groups in one cycle for both examined variables. Thus, students find classical model of teaching more effective, while they prefer learning using digital content. Finally, in open-ended questions, students listed what they consider to be advantages and disadvantages of e-learning. The research provides encouraging results that can serve as a stimulus for new research in the theoretical part of the subject Informatics on a larger sample.
Cooling Power Consumption Dependency Simulation for Real-World Data Center Data

Data centers (DC) are considered one of the top electricity consumers and there is an increasing interest in finding more sustainable approaches to their power supply needs. Purchase of renewable energy is the straightforward approach but involves large investments and long-term commitments with a need for DC power consumption planning. In this paper, we propose a data-driven DC cooling power consumption prediction method that uses IT equipment power consumption and outdoor air temperature data as input features. The contributions of this work are the design of a differenced data-based modeling method with baseline value calculation and performance testing based on real-world data from air-based and liquid-based cooling DCs. In the experiments, we compare our proposed method with a raw data-based approach and identified the proposed method as the more stable performing one. The results show that the proposed method performs better when training data is limited and can handle sudden value drifts in the consumed cooling power caused by changes in cooling devices' operation settings with baseline adaptation.
The study on usage of table functions instead of basic operators inside encryption algorithm

In this paper we study the possibilities and advantages of replacing basic operators such as XOR into more complex table functions inside encryption algorithm. We define the E# and RTGE# operators instead of XOR in simplified form and test these functions as a replacement inside of symmetric model based on Feistel network. The DES algorithm has been chosen for the test. The results show the increase in the complexity while retaining all of security properties for encrypted messages.
A High-performance Deployment Framework for Pipelined CNN Accelerators with Flexible DSE Strategy

The pipelined DCNN(Deep Convolutional Neural Networks) accelerator can effectively take advantage of the inter-layer parallelism, so it is widely used, e.g., video stream processing. But the large amount of intermediate results generated in the pipelined accelerator imposes a considerable burden on the on-chip storage resources on FPGAs. To ease the overburden storage demand, a storage-optimized design space exploration (DSE) method is proposed at the cost of a slight drop of computing resource utilization ratio. The experimental results show that the DSE strategy can achieve 98.49% and 98.00% CE (Computation Engines) utilization ratio on VGG16 and ResNet101, respectively. In addition, the resource optimization strategy can save 27.84% of BRAM resources on VGG 16, while the CE utilization ratio dropped by only 3.04%. An automated deployment framework that is adaptable to different networks with high computing resource utilization ratio is also proposed in this paper, which can achieve workload balancing automatically by optimizing the computing resource allocation of each layer.
Real-Time Recursive Routing in Payment Channel Network: A Bidding-based Design

Payment Channel Network (PCN) is proposed as a promising layer-two solution to tackle the scalability problem of current blockchain systems, which allows the two transacting parties to perform off-chain transactions through their established payment channel. For the transacting parties who are not directly connected, PCN allows them to route the transaction through some intermediate nodes with sufficient balance. Designing an efficient routing protocol is one of the most important and challenging problems in improving the performance of PCN. To tackle this challenge, we propose Real-Time Recursive Routing (RTRR), an efficient routing algorithm that can achieve a short routing time with strong privacy protection and high flexibility in the dynamic scenario. In addition, we investigate the bidding process in RTRR and derive the equilibrium strategy, which implies that the proposed protocol prefers to route the transaction through the nodes with a higher success rate, contributing to a better performance. Both the theoretical analyses and the empirical experiment results demonstrate the high efficiency of RTRR.