Reviewerly/exHarmony huggingface dataset:
Protection and Restriction of Grain-Related Intellectual Property Rights in the Context of Food Security Based on Industrial Internet of Things

New technologies such as the internet of things and cloud computing are expected to leverage farming development and introduce more robots and artificial intelligence in farming. Food security is an important strategic issue for national economy and security. Intellectual property is a matter of food security. Its power not only plays a role of promoting food scientific and technological innovation, stimulating private and multinational seed industry investment, saving the cost of research and development, and improving agricultural management capacity but also may damage the biodiversity, increase farmers’ burden, block food technological innovation and diffusion, increase the farmers’ litigation risks, etc. Therefore, from legislation, on the one hand, we should rationally use the current intellectual property system to protect the source of grain-related intellectual property rights, set up the concept of “great protection” of grain-related intellectual property rights, and establish the protection system and operation mechanism with the participation of multiple-related institutions. On the other hand, we should restrict grain-related intellectual property rights according to law to prevent it from controlling the market, restricting competition, hindering innovation, and seeking illegitimate interests. We need to coordinate the relationship between intellectual property rights, food rights and farmers’ rights, and rationally control the genetic utilization restriction technology.
Experimental investigation of equivalent Laplacian and adjacency quantum walks on irregular graphs

The continuous-time quantum walk denotes the evolution of a particle on a given graph governed by Schr\"odinger's equation. For different applications, the corresponding Hamiltonian is proportional to either the Laplacian or the adjacency matrix of the graph. The two quantum walks are equivalent on regular graphs, since each vertex has the same degree. However, for irregular graphs, the evolutions of the two quantum walks are generally different. In this paper, we report an experimental investigation of the two quantum walks on irregular graphs with single photons and interferometric networks. We demonstrate that it is possible to obtain equivalent probability distributions with the two quantum walks on specific irregular graphs, where the particle is initially localized at a vertex or uniformly distributed at multiple vertices. Our results not only deepen the understanding of the equivalence between the two quantum walks, but also extend the application of the continuous-time quantum walk.
Data mining of IoT based sentiments to classify political opinions

In recent years, an exponential increase in the usage of social network services has been observed. These community services are typically used through different applications including personal computers, multiple applications of modern smartphones and wearable technologies. Proper identification and separation of different languages text, topic-based classification of text and classification of active users based on their published comments and posts are major challenges. In this research, our primary focus is to deal with English text collected through different IoT applications to analyse posts/comments to categorise people's opinion in politics. We have developed an IoT framework model for collecting data from social media especially Facebook, preprocessed and clean data to be used for analysis, and separation of data based on different languages. Sentiment analysis techniques are used to detect polarisation of the individual user. The proposed system clustered IoT individuals based on their comments and posts and successfully detected political polarisation. The proposed approach obtained encouraging results with a precision of 66.7%, a recall of 71.4%, and an F-measure of 69.0% in the case of annotated data of 50 users and a precision of 75.0%, a recall of 87.1%, and F-measure of 80.6% in the case of annotated data of 100 users.
Building a virtual Roman city: teaching history through video game design

Abstract In October of 2018, a pedagogical experiment was conducted at York University, Toronto, Canada, in which students were given an assignment. For this assignment they were to conduct research on a variety of Roman public buildings in groups, build digital reconstructions of them using the Unity 3D game engine, and present them to the class in the form of a virtual reality (VR) simulation. In doing so, students were able to create a virtual built environment based on their research, navigate it, and discuss the space with a sense of immersion and scale. Using this experiment as a case study, the goal of this article is twofold: firstly, to assess the pedagogical efficacy of constructionist approaches to teaching students about Roman architecture, specifically using VR and video game design technology. The second goal is to address the technical and pedagogical challenges of using game design software in the classroom and to propose ways in which this assignment can be improved in the future.
Visual Cryptography Using Computation-Free Bit-Plane Reconstruction

Visual cryptography (VC) using bit-plane decomposition improves the quality of the reconstructed image. The disadvantage of this scheme is that the decoder needs computation in order to reconstruct the secret image from its bit-planes. To solve this problem, we propose a no-computation bit-plane decomposition visual cryptography (NC-BPDVC). In NC-BPDVC, we convert the grayscale secret image into a multitone image by multilevel halftoning. Then, by exploring the difference between a digital pixel and a printed dot, we design different dot patterns to render a digital pixel. By doing so, we abandon the usual assumption that DPI (dots per inch) equals PPI (pixels per inch) during printing. By adopting the more realistic assumption that DPI can be larger than PPI as is supported by most printers, we use different patterns to render different tone levels. These patterns are carefully designed so that no computation is needed when one needs to reconstruct the multitone image from its bit-planes. Our algorithm is tested on a batch of twenty standard grayscale images. The experimental results confirm the correctness and advantages of the proposed scheme. Compared with the ordinary bit-plane decomposition VC, NC-BPDVC does not need computation. The security of the proposed algorithm is also analyzed and verified.
Improved the sample efficiency of episodic reinforcement learning by forcing state representations

Episodic reinforcement learning (ERL) is a class of algorithms that use episodic memory for improving the performance and the sample efficiency of reinforcement learning (RL). Although it has achieved some success, existing ERL algorithms have to interact with the environment for many rounds to gain satisfying performance. In this paper, we propose the algorithm episodic memory by forcing state representations (EMSR) to improve the performance and sample efficiency of ERL. Specifically, our algorithm uses a transition model to predict the hidden state representations of the agent’s multiple future steps for augmenting reward maximization, which can help the agent learn quickly. In this way, our method can achieve better performance and higher sample efficiency than previous state-of-the-art algorithms. Experimental results demonstrate the superiority of our method.
Role of Bangladesh Bank on Cybersecurity in FinTech

Following the COVID-19 outbreak, the world has seen another rising challenge of accessing financial services. By limiting the transmission of COVID-19, fintech accelerated access to financial services for all aspects of society, allowing individuals to become more flexible and comfortable with fintech. However, cyber-attacks can hinder the progress of this evolution by attacking any type of banking channel, including traditional banking, internet banking, credit cards, ATMs, agent banking, and mobile banking. From the standpoint of central bank risk management, this chapter investigates fintech and the associated field of cybersecurity.
Defending IoT Security Infrastructure with the 6G Network, and Blockchain and Intelligent Learning Models for the Future Research Roadmap

The battle for wireless networks' 6G has started. Many review articles expressed their views and critically evaluated the overall state of the art in order to give readers a clear picture of current advances. Most of the studies, though, are focused on broad views and a big-picture perspective, and do not address the real issues arising from internet of things (IoT) use cases. The authors give a sample of IoT use cases that are indicative of a wide range of IoT implementations, which is a novel approach in the review. The use cases chosen represent the most research-intensive industries that potentially profit from 6G and its enabling technologies. Healthcare, smart grid, transportation, and blockchain learning techniques are among these sectors. They also identified some of the actual hurdles and lessons encountered in putting these use cases into action. The evaluation focuses on the major requirements of the scenarios and how they intersect with the primary drivers for the next generation of wireless networks.
Inside cars: changing automobilities and backseat passengering in experimental film and 360 video

Despite the recent upsurge of interest in passengering, there are no accounts on how backseat passengering links to experimental film and 360 video or how it responds to the broader relationship between automobilities, the organization of car travel and on-screen storytelling. To fill this gap, I follow up on the profiling of the passenger as a distinctive subject and object of infrastructures of mobility to discuss the backseat passenger’s experience, seen as both a socially engaged and embodied practice, in four stylistically distinct works, Larry Gottheim’s Harmonica, Lluis Escartín’s Mohave Cruising, Ken Jacobs’ Berkeley to San Francisco, and ASMR Driving at night: Back seat view. While Harmonica, Mohave Cruising and Berkeley to San Francisco are selective, self-aware experiments, which play out in various forms, ASMR is an authorless 360 drive video that lacks much aesthetic value and continues the long-established culture of scenic road and auto-tourism, turning on-site visitors into virtual tourists. Although each work approaches passengering visualities differently through exhibiting forward, side, parallax and 3 D views, they all articulate a multi-sensorial experience of driving and offer a relatively novel take on the practices of automobility through shifting the perspective of the driver and frontseat passenger to the backseat view.
Low Power Scheduling Approach for Heterogeneous System Based on Heuristic and Greedy Method

Big data, cloud computing, and artificial intelligence technologies supported by heterogeneous systems are constantly changing our life and cognition of the world. At the same time, its energy consumption affects the operation cost and system reliability, and this attracts the attention of architecture designers and researchers. In order to solve the problem of energy in heterogeneous system environment, inspired by the results of 0-1 programming, a scheduling method of heuristic and greedy energy saving (HGES) approach is proposed to allocate tasks reasonably to achieve the purpose of energy saving. Firstly, all tasks are assigned to each GPU in the system, and then the tasks are divided into high-value tasks and low-value tasks by the calculated average time value and variance value of all tasks. By using the greedy method, the high-value tasks are assigned first, and then the low-value tasks are allocated. In order to verify the effectiveness and rationality of HGES, different tasks with different inputs and different comparison methods are designed and tested. The experimental results on different platforms show that the HGES has better energy saving than that of existing method and can get result faster than that of the 0-1 programming.
Computational Efficiency of Bagging Bootstrap Bandwidth Selection for Density Estimation with Big Data

Bandwidth selection is a crucial problem in kernel density estimation. However, the computational cost of some bandwidth selection methods grows very rapidly as the sample size increases. To address the problem of selecting the bandwidth of the Parzen-Rosenblatt kernel density estimator for samples of very large size, a subagging version of the bootstrap bandwidth selector is proposed and empirically studied. A heuristic rule is also proposed for the selection of the size and number of subsamples used when applying subagging.
From virtual machines to serverless: NWP Postprocessing at Finnish Meteorological Institute

&amp;lt;div&amp;gt;Finnish Meteorological Institute (FMI) provides weather forecasts for a variety of users, including aviation, agriculture, media and the general public. FMI spends significant resources to prepare meteorological data and products to suit our specific needs, dictacted by our geography and culture. Our data-oriented production system forces us to constantly develop novel methods to keep up with the increasing data volume of NWP. Like many other organisations FMI started to move towards cloud computing in the first half of 2010s, a process which is still continuing today. Cloud computing has given us new tools to handle the increasing data volume.&amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;While we initially started with a &amp;amp;#8220;virtual machines in the Cloud&amp;amp;#8221; approach, a more holistic adaption to modern, cloud-native technologies such as S3, Kubernetes and AWS Lambda has enabled us to expand our operations efficiently and flexibly. While we are not a cloud-first organization, our on-premise computing infrastructure has also been upgraded to match the common cloud technologies. This streamlines the operating environment as same software stacks can be used on-prem and in the cloud. Using containers is increasingly important as it makes easier to move code between systems. Containers offer natural isolation from operating system upgrades, which is important when pace of development is high and software depends on specific library versions.&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;&amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt;A big challenge still facing us is the support of software that is not easily translated to cloud workflows. In the meteorological domain this is rather common as software has long lifetime and code refactoring is in many cases not feasible. We must also ensure that our users and developers maintain their skills and knowledge as new technologies come and go.&amp;lt;/div&amp;gt;
Cloud Mask Nowcasting over Germany Using Deep Learning

&amp;lt;p&amp;gt;The transition to renewable energy sources such as solar energy has increased the interest in predicting cloud masks from remote sensing data. Even though deep learning methods have achieved great success on multiple meteorological tasks, only limited research has been conducted on nowcasting cloud masks based on high temporal and spatial resolution satellite data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study investigates forecasting cloud masks over Germany six frames into the future based on satellite images. We compare predictions between three deep learning architectures (ConvLSTM, U-Net, and MetNet) relative to two baseline models (optical flow and persistence). We train and evaluate our models using two years of the ICARE SAFNWC Cloud Mask dataset&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt; , with a temporal resolution of 15 minutes per frame and a spatial resolution of 3&amp;amp;#215;3 km per pixel. In our experiments we use a larger area of 256&amp;amp;#215;256 pixels to predict the target area of size 128&amp;amp;#215;128 pixels, leading to overall better performance compared to using an input size equal to the output size. Besides comparing different network architectures, we also investigate the effect of varying the temporal input size and output size for ConvLSTM. Finally, we examine the effect of adding more features (land/sea mask and elevation map) and changing the loss function.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In summary, we have performed a comprehensive study investigating cloud mask nowcasting using 70,000 spatially and temporally aligned data frames, examining three loss functions, six evaluation metrics, and three deep learning models.&amp;lt;br&amp;gt;During the presentation, we will highlight the main results from the study and present details of the model architectures, datasets, and how space and time affect the performance of the models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;1) Kniffka, Stengel, and Hollmann, &amp;amp;#8220;SEVIRI Cloud Mask Dataset - Edition 1.&amp;amp;#8221;&amp;lt;/p&amp;gt;
7‐5: Synthetic Dataset for Improving UDC Video Restoration Network Performance

Under‐display camera (UDC) is a new form factor that implements a full‐screen display by locating the camera under the display panel. UDC suffer from image degradation and flare due to the panel in front of the camera. In particular, flare cannot be restored by conventional image processing due to the saturated pixels. Therefore, deep learning network that can restore these degradations is proposed. The network is trained by synthetic dataset generated by accurate optical simulation. However, the deep learning networks might not be suitable for video calls that require real‐time image processing due to their large amount of computation. The guided filter methodology is adapted to the network to reduce the amount of computation. Total amount of calculation is reduce by 90% and achieved 30 FPS at the FHD (1920×1080×3) resolution. In this paper, the method to generate realistic synthetic dataset that can solve the problems occurred during video restoration.
Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method

Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitioned according to its localized response to saturated regions. By eliminating the contribution from those saturated regions to the partition function calculations, we show that the estimation of the multifractal statistics can be correctly calculated even with image saturation levels up to 20% (where 20% is the number of saturated pixels over the total number of pixels in the image).
P‐117: AI Analysis of HOP Circuit Failure and Improvement

AMOLED's HOP(hybrid of Oxide semiconductor and Polysilicon TFT) technology was born to meet the market's demand for low power consumption. Also, the IGZO oxide process was introduced with new driving technology to support the low driving frequency operation. However, this new GC circuit of oxide TFT driving caused new and randomly appearing horizontal defects which do not exist in LTPS circuits. To solve this issue, we studied to find the design parameters using pipelined deep learning models composed of CGAN(Conditional Generative Adversarial Network), VGM(Variational Gaussian Mixture), and XAI(eXplainable Artificial intelligence) technologies to figure out what causes these defects. We collected HOP panel design information to achieve the given goal and confirmed the degree of defect per HOP model. Since the number of HOP models is limited, XAI analysis might also be negligible. We solve this issue by generating data using CGAN and VGM in sequence. After, ALE(Accumulated Local Effects), a popular XAI technique, is applied to find affected design parameters and change‐related circuit designs to help verify the proposed algorithm's performance. As a result of this design change, we identified that the related defect rate decreases from 15000ppm to less than 500 ppm, in which XAI plays a significant role in solving a newly introduced defect issue. That was believed for a long time a challenging problem to analyze using the legacy SPICE tool in the OLED circuit field. Our result also suggests that even predicting the defect rate of future HOP models becomes feasible with our proposed deep learning architecture.
12‐4: Tomography May Enable a Wavelength‐thin Camera

The author proposes that objects might be imaged from any small region on a flat panel by a wavelength‐thick array of 2‐dimensional cameras. The 1‐dimensional images formed by each camera will be combined without iteration into a 2‐dimensional image using Fourier Transforms in the manner of tomography.
Developing an Artificial Intelligence based system to Detect Fraud and Corruption in Government

Both the costs and investments in Government departments have increased in recent years. Technology developments and the increase of lifespan were crucial for the expansion of this sector in an economic and social point of view. Besides the improvements in Government management, corruption and abuse within are increasing. Private and governmental institutions are making efforts to reduce it by detecting and preventing eventual cases of corruption and fraud. However, the results are insufficient and new approaches to face the problem are becoming more important. Nowadays, Data Mining techniques are considered as a good strategy to detect fraud and corruption in different areas such as credit card, bank accounts and telecommunications. In this Paper we explore a new approach using genetic algorithms to improve the number of detections. The two key aspects of this approach are first the organization of the healthcare data according to three different points of view, claims, patients, and providers. The second key aspect is related with the application of the machine learning algorithm to the three areas referred and the correlation of the results obtained from the three applications. The simulations tested show an unquestionable high success of detection of the fraudulent situations, around 90% of the fraudulent situations are correctly detect, using nine different fraud scenarios. As well as a detail analyses of the both the fraudulent claims characteristics and the performance of the algorithm in different situations. The results also show an evident reduction of the false alarm situations with the correlations of results of the three genetic algorithms. The average accuracy of the developed algorithm is 90%, and it's possible to observe in certain situations a total detection of all the fraudulent situations.