Reviewerly/exHarmony huggingface dataset:
Software Vulnerability Assessment: Vendor, Scanner, and User Analysis

Given the complexity of existing software stacking on top of each other, many vulnerabilities are just waiting to be discovered. Certain vulnerabilities can lead to severe exploitation and loss. In this paper, we try to understand how safely we, as users, are against potential exploitations due to known vulnerabilities found in commonly used software. First, we assess the capability of current vulnerability scanners to detect known vulnerabilities. In our analysis, we use 6 months of the real vulnerability scanning log. Surprisingly, our analysis shows that the existing vulnerability scanners could take at least 2,999 days to discover 90% of the known vulnerabilities and 50% of the high-severity-level vulnerabilities could take 3,328 days to be discovered. Then, we evaluate the effectiveness of existing software vendors’ patching mechanisms by measuring how long the computing devices were exposed before being patched. Our investigation suggests where we should improve to lower the risk of exploitations.
Generation of Super-Resolution Images from Satellite Images Based on Improved RCAN

Satellite images can be analyzed and used for a variety of purposes. In the future, satellite image analysis will become more important since the number of satellites launches, and the amount of satellite data increase every year. Under these circumstances, there are some problems to be solved. One is the existence of low-resolution satellite images. To analyze the lower resolution of satellite images there are some technical issues such as reduction of noise, misclassification of object recognition. Therefore, high-resolution images are necessary. However, high-resolution satellite images are expensive, and its images may not be available in the past satellite images. Super-resolution which is introduced in image processing is a method to solve these problems. Convolutional neural network (CNN)-based methods are effective, and there is a need for models that can perform super-resolution with higher accuracy. In this paper, we propose a method for super-resolving satellite images, based on the improved the RCAN (residual channel attention network) model with SRM (style-based recalibration module). The proposed method improves the PSNR (peak signal to noise ratio) by 0.0181 dB compared to the conventional RCAN model.
Lightweight Room Layout Estimation using a Single Panoramic Image

Due to limited computational capabilities of embedded systems, the trade-off relationship between algorithm performance and its computational complexity is crucial to apply deep learning models for new camera functions. In this paper, we suggest a lightweight deep representation for room layout estimation using a single panoramic image. Based on HorizonNet [3], which typically requires a lot of computational resources at every step, we propose to replace the feature extraction networks of the residual network (ResNet) [6] and the long short-term memory (LSTM) [8] with a platform-aware neural architecture search model and an gated recurrent unit. In order to use fewer computational re-sources, the proposed architecture utilizes sampling-based optimization to select best hyperparameters. In our quantitative experiments, the lightweight network configured with the presented method uses only about 1/2 fewer parameters than the existing network. We also use real-world panorama images taken with RICOH THETA Z1 to validate its performance. In the qualitative experiments, no significant difference from the original model is observed with the same panorama inputs.
Classification of CTC on Fluorescence Image Based on Improved AlexNet

These days, cancer has been the most primary cause of death in Japan. Cancer often progresses by repeating metastasis, so early detection and early treatment are important. Analysis of Circulating Tumor Cells (CTCs) has come to gather attention as a new biomarker that CTCs can detect primary cancer in human body. However, the number of CTCs in a billion blood cells is only a few, and detecting CTCs is very hard. Accordingly, we propose an automatic detection method of CTCs from fluorescence microscopy images to enable quantitative analysis by computer. This method consists of two parts. The first part, we use some series of filtering to the images and, new dividing method some overlapping nucleus then, from the images cut out the region of interest (ROI). The second part is distinguishing images by using CNN. We applied the proposed method to 5040 images of 6 samples. As a result, we obtained TPR:94.59%, FPR:6.544% by using AlexNet based model.
Earthquake Countermeasures in Scanning Tunneling Microscopy Experiments Using Earthquake Early Warning

Frequent earthquakes in Japan can be problematic for vibration-sensitive measurements. For example, in scanning tunneling microscope (STM) experiments, seismic vibrations cause noises in the data and sometimes lead to a tip crash against the sample surface. The risk of tip crash rises during spectroscopic imaging measurements using STM, where the feedback control is disabled for most of the long measurement time. Here, we have developed a system to prevent tip crashes by retracting the tip in response to the earthquake early warning of the Japan Meteorological Agency. The technique utilizes a built-in function of a commercial STM controller, and an earthquake monitoring service offered by the National Research Institute for Earth Science and Disaster Prevention. We present data to demonstrate how the system works at an earthquake. Our work provides hints for conducting vibration-sensitive measurements in earthquake-prone countries.
English Language Attitude of the Local Tourism Workers at Munggu Tourism Village Badung Regency Bali

The research aimed at studying and analyzing English language attitude of the local tourism workers at Munggu Tourism Village of Badung Regency Bali. The primary research data are directly collected from the research informan using the non-participatin observation, structured interview processes, and also from the research questionnaire completion. The informan were selected using non-probability sampling technique which are: purposive, snowball, and abundant sampling techniques with the total members of 30 samples are analysed. The research finding showed that 0,88% of the informan showed positive attitude toward English, from the genders point of view, the total numbers of 0,76% and 0,25% showed positive attitude toward English. Based on the tourism jobs the total members of informan showed positive attitude toward English
A study on the Construction of a Big Data-based Urban Information and Public Transportation Accessibility Analysis Platforms: Focused on Gwangju Metropolitan City

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the “Big Data” environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.
$\textsf{LightMAC}$: Fork it and make it faster

In FSE'16, Lyukx et al. have proposed LightMAC that achieves a query length independent security of O(q 2 /2 n ) when instantiated with two independently keyed block ciphers.This high security comes at the cost of reducing the data injection rate to (n -s) bits per primitive invocation and restricting the query length up to (n -s)2 s bits, for a fixed counter size s.Recently in ASIACRYPT'21, Chattopadhyay et al. have shown that LightMAC achieves the same security even when it is instantiated with a single keyed block cipher.However, it limits the length of a message to (n -s) min{2 n/4 , 2 s } bits for a fixed counter size s.In this paper, we propose LightFORK, a forkcipher variant of LightMAC that achieves an improved query length independent security bound of the order of O(q 2 /2 n+s ), maintaining full n bit message injection per primitive call, where n and s denote the block size and tweak size of the forkcipher respectively.The maximum message length is also increased to n2 n/6+s/2 bits.Our security proof is based on a new technique called resetting with delayed sampling, which is an extension of the reset-sampling technique of Chattopadhyay et al.
Educational humanoid robot using 3D printer and Arduino

Among the types of robots, it is not easy to make a robot that walks on two legs. For this, it is necessary to be able to create software that can control precise servomotor settings and various operations. This process is very difficult for beginners and not easy to make because there is no suitable Arduino shield shape for the robot. Therefore, in this study, a method for manufacturing Arduino and plug-in type shield was proposed. In addition, the process of developing a PC control program that can simplify motion control, manufacturing a robot, and setting servo motor values to easily control motion was introduced. It is expected that this will be of great help to novice developers who are interested in making robots.
Infrared Pedestrian Dataset Training using Swin Transformer model

Recently, studies to replace CNN-based models with Transformer models are being actively conducted. In this paper, the Swin Transformer model, which has recently been attracting attention for its excellent performance, was trained using infrared images and its performance was examined. A Swin Transformer designed for RGB images was tuned for infrared image training to train a separate infrared pedestrian dataset and a simple experiment was performed to further improve the infrared image training performance. In addition, in order to properly tune the Swin Transformer Backbone model to the infrared image data and to improve the training performance, we trained separately configured RGB and infrared datasets and analyzed the results. As a result, it was concluded that the Swin Transformer would be suitable for infrared data training if it was slightly tuned for infrared data and lightweight to avoid overfitting. Based on this experiment, the author plans to create a model suitable for infrared datasets in the future and apply it to various practical applications.
Implementation of the Racing Game with the Virtual Reality and Cable Suspended Parallel Robot (CSPR)

The paper aims to describe the implementation of racing game named “Arena of Speed” based a Virtual Reality (VR) utilizing a Cable Suspended Parallel Robot (CSPR), a VR headset, and a hand controller. The game “Arena of Speed” is utilized as a special kind of graphical user interface, displaying a computer-generated immersive which can be accessed utilizing hand controller. CSPR can freely manipulate objects in a large workspace with up to 6 degrees of freedom using 8 cables, which activates the proprioceptive sensation of and provides the user with a realistic and exhilarating racing experience. The method of synchronizing the movement of the virtual car with the CSPR is proposed, which includes analyzing and sending data between devices as well as calculating and operating the robot based on game data. The biggest hurdle in replicating the racing experience in real time is that the entire system must work well together and meet the specified deadline. The results of the experiments indicate that the system can provide players playing “Arena of Speed” with the realistic sensation of racing on a racetrack; however, the sys-tem occasionally misses its deadline with an acceptably low chance. Besides the entertainment purpose, it may also be ideal for individuals who want to experience driving or racing but do not have the opportunity or condition to do so in real life.
Central bank in the age of digital finance1

This chapter discusses the impact of the digitization of finance on central banking. Digitization allows to improve the efficiency of many processes carried out by central banks. However, the essence of central banking remains unchanged. Central banks still perform the functions of a bank of issue, a bank of banks and a state bank. Focusing on the function of the bank of issue, the chapter reflects on the impact of digitization of finance on the role of cash in the economy. Despite competition from electronic payments, cash remains widely used and in many economies continues to be the primary payment instrument. At the same time, it is difficult to expect that the increase in the popularity of cryptocurrencies or stable coins will have a significant impact on the demand for currencies issued by central banks. The concept of a central bank digital currency (CBDC) and the current state of central banks’ work on CBDCs were also outlined. The introduction of CBDC would have significant implications not only for the issuing function of the central bank but also for the functions of the bank of banks. It is fairly common to believe that the introduction of CBDC could bring significant benefits. This is especially true in those jurisdictions where the use of CBDC to conduct payments could replace the cashless payment systems that are often dysfunctional. However, eliminating inefficiencies in payments is also possible by improving the existing payment systems. At the same time, the introduction of CBDCs may pose significant risks to financial stability and the nature of banking systems in market economies. So far only a few central banks have introduced this form of money. Therefore, the implications of the introduction of CBDC for the central banking and monetary systems are still to be determined, especially as the use of CBDC remains so far limited.
The digital finance in central banking

We are facing an economic transformation which by the dissemination of digital technologies squeezes cash payments by non-cash ones. In this chapter, we analyze the progress of this process and try to find answers to the questions to follow. Should we conclude that paper money will cease to exist in the nearest future? What are the economic results of changing forms of payments? Does the digital financial transition work positively for the effectiveness and dynamics of economic systems? So long as there is enough evidence to declare that cash will be used worldwide in the years to come, Central banks do not intend to withdraw paper money. Nevertheless, there is no doubt that cash will only supplement prevailing digital operations. This development was officially announced by monetary authorities in many countries and is really happening. Departing cash generally influences economic growth in a positive way but this impact is not big. The expansion of electronic money is usually caused by the desire to make payments and economic operations more comfortable and rapidly quicker. One can say that there is some extra national income generated by new financial technologies but what seems to be important is what Robert Solow said in 1987: You can see the computer age everywhere but in the productivity statistics. After more than three decades all evidence we get makes this judgment still apt. We shall put our attention to the following subjects: electronic money, digital technologies and the essence of money, the relative positions of cash and cashless money, symptoms of the weakening of cash money and what it really means, cash supply and GDP, the statistics of all kinds of money, cash preference in chosen countries, the expansion of electronic money, stability and change of the position of money and financial policy in the economy of today. These questions come down to one: do we have in the XXI century the same money we had in the twentieth century?
Empowering Students with Visual Impairments to Prepare for Earthquake (A Study of Rajkiya Visually Impaired Inter School of Lucknow City)

Rights of school children with special needs to safe learning environment are primarily a fundamental right. School safety with reference to earthquake for disabled students in India is lacking either in its efficacy, infrastructure, implementation or other causes. For students with special needs schools physical environment becomes more hostile and difficult to deal with during and after earthquake. Present paper attempts to investigate the school safety with respect to earthquake on basis of four components preparedness, response, recovery, mitigation among the blind students. The study concludes that awareness among visually impaired students regarding disaster risk reduction is moderately low leading to moderate chances of being vulnerable during Earthquake.
Where art and cinema converge: An interview with Massimiliano Siccardi

In the course of a one-hour interview that took place in July 2021, Italian multimedia artist Massimiliano Siccardi, the creative mind behind the 2021 Immersive Van Gogh exhibit, discusses the ideas behind the exhibit as well as his vision for the future of immersive art. The former dancer and choreographer explores the possibilities for immersive techniques to revolutionize the concept of spectatorship as a collaboration between artist and viewer. The driving force behind Siccardi’s work is a commitment to the notion that art needs to be experienced, though the nature of that experience remains highly subjective. His work at Carrières de Lumières, and in 2021 with Lighthouse Immersive, exploit the merger of sound and image to create innovative immersive experiences that are intended to inspire the viewer’s appreciation and heighten interest in the works of art at the heart of the exhibit.
Particle Swarm optimization Based Reliable Control Algorithm for Fuzzy Systems

This paper deals with the fault-tolerant control problem for T-S fuzzy systems based on the particle swarm optimization (PSO) method. To cope the stability and fault-tolerants simultaneously, a novel fault-tolerant control algorithm is developed. The required conditions of the addressed system are developed with the aid of Lyapunov stability theory. Precisely, the particle swarm optimization algorithm is implemented to optimize the fuzzy controller. Finally, simulation results are provided to demonstrate the effectiveness of the obtained results.
Short text classification method with dual channel hypergraph convolution networks

In some fields such as e-commerce and social media platforms and sentiment analysis, efficient short text classification is crucial to enable users to locate pertinent information effectively. Along with the increasing number of short texts, classifying short texts with brief contents and sparse features has become a major research topic in recent years. Towards this end, a short text classification method based on a dual channel hypergraph convolutional network is proposed to flexibly capture the complex higher-order relationships among short texts and words. Specifically, our method firstly models the pre-processed short text data into short text hypergraph and short text association graph; secondly, two different short text feature representations are learned via a dual channel hypergraph convolutional network and fused by an attention network to enhance the short text embedding; at last, a classification model is adopted to perform short text classification. Extensive experimental results indicate that the method has superior short text classification effect and stability compared with the existing model, which has better performance among comparable short text classification models.
Mask-based Text Scoring for Product Title Summarization

In e-commerce, long product titles with rich information help attract users, but they are usually truncated for display on small-screen mobile devices, which results in neglection of important information and in turn low click-through rate. This paper presents a novel product title summarization method via the use of a mask-based text information scoring network. Via quantified evaluation of expressiveness, the most telling points are identified from the original title for a concise version which best retains its content. Our experiments show that, even without external information, our proposed method MPTS outperforms established benchmark models by 1.48% (ROUGE-1), 5.11% (ROUGE-2) and 1.37% (ROUGE-L) respectively.
Quantum-memory-assisted entropic uncertainty and quantum correlation in structured reservoir

Abstract In this work, we investigate the dynamics of quantum-memory-assisted entropic uncertainty relation (EUR) and quantum correlation quantified by entropic discord and geometric discord within the Markovian reservoir and non-Markovian reservoir. Particularly, when the initial state is a pure sate or mixed state the EUR gradually increases during the evolution at first, but subsequently tends to stable in the long-time limit. On other hand, for the separable state the EUR almost keeps constant. In contrast, quantum discord decay asymptotically to zero for all types of initial states. As an application, we prove that both two quantum discords witnessed by the EUR are determined by the version of discord chosen, which regardless of the reservoir.
Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution

Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions.