Reviewerly/exHarmony huggingface dataset:
Keywords-oriented Data Augmentation for Chinese

In natural language processing tasks, data is very important, but data collection is not cheap. Large volume data can well serve a series of tasks, especially for deep learning tasks. Data augmentation methods are solutions to data problems, which can work well on rising data quality and quantity, such as generating text without meaning changing and expanding the diversity of data distribution. A user-friendly method of the data augmentation is to sample words in a text then augmenting them. The sampling method is often implemented by a random probability. Although the performance of this solution has been proved over the past few years, random sampling is not the best choice for the data augmentation as it has a chance of randomly introducing some noise into initial data, like stop words. The generated data could interfere with the subsequent tasks and drop the accuracy of the tasks' solutions. Hence, this paper aims to introduce a novel data augmentation method that could avoid involving such noisy data. The strategy is keywords-oriented data augmentation for Chinese (KDA). The KDA proposed in this paper indicates a method of extracting keywords based on category labels, and an augmenting method based on the keywords. In contrast to randomness, the proposed technique firstly selects the key information data, then expands the selected data. The experimental section is compared with another two typical data augmentation techniques on three Chinese data sets for text classification tasks. The result shows that the KDA technique has a better performance in the data augmentation task than the compared two.
Syllabification Model of Indonesian Language Named-Entity Using Syntactic n-Gram

Syllabication or syllabification is an activity to detect syllable boundaries in a word. There are two main ways for automatic syllabification, namely rule-based and data-driven. The rule-based approach is based on the general principle of syllabification, while the data-driven method uses a set of syllabified words to create a syllabification of unknown words. Research on syllabification of words has been done a lot. However, most of these studies only deal with the formal words but still a few studies for named entities. Besides, named entities tend to be more complicated than the regular words. In this research, a syntactic n-Gram is proposed and investigated to syllabify the named entities since it is developed based on the n-gram that has an excellent accuracy and tends to be consistent with various languages. Evaluation on 20 k named-entities based on 4-fold cross-validation show that the proposed model gives a competitive syllable error rate (SER) compare to another similar n-gram-based model.
An Analysis of GitLab's Users and Projects Networks

GitHub has long been perceived as the exclusive provider of hosting for software development in the minds of many programmers. However, it is far from the only available service out there. GitLab is one of the many platforms offering similar capabilities, which has experienced rapid growth in recent years. GitLab currently holds the second-largest collection of repositories among its competitors. Despite its rapid growth, little attention has been paid to this website by academia. This lack of information with regard to users and projects on the platform, as well as the fast increase in the number of GitLab users motivated us to conduct the current study. In this paper, we perform social network analysis on the data we have collected from the public users and repositories of GitLab. We observe that GitLab is similar to other code-hosting services with regard to its network structure. We also find that the most influential users and projects on the website, are associated with the founding team of GitLab. We further analyze the collaboration and membership networks and, among other things, find that both graphs display high values of assortativity with regard to node degree.
R2SN: Refined Semantic Segmentation Network of City Remote Sensing Image

Semantic segmentation is always a key problem in remote sensing image analysis. Especially, it is very useful for city-scale vehicle detection. However, multi-object and imbalanced data classes of remote sensing images bring a huge challenge, which leads that many traditional segmentation approaches were often unsatisfactory. In this paper, we propose a novel Refined Semantic Segmentation Network (R2SN), which apply the classic encoder-to-decoder framework to handle segmentation problem. However, we add the convolution layers in encoder and decoder to make the network can achieve more local information in the training step. The design is more suitable for high-resolution remote sensing image. More specially, the classic Focal loss is introduced in this network, which can guide the model focus on the difficult objects in remote sensing images and effectively handle multi-object segmentation problem. Meanwhile, the classic Hinge loss is also utilized to increase the distinction between classes, which can guarantee the more refined segmentation results. We validate our approach on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation benchmark dataset. The evaluation and comparison results show that our method exceeds the state-of-the-art remote sensing image segmentation methods in terms of mean intersection over union (MIoU), pixel accuracy, and F1-score.
A Method to Create Training Dataset for Dehazing with Cyclegan

Haze usually blurs the characteristics of images shotted in adverse weather conditions. It brings many challenges for computer vision such as object detection. Due to the lack of effective training dataset of remote sensing image, the dehazing usually utilize the physical model rather than the deep learning approach based on a large number of training dataset. An effective method to create training dataset may provide some new ideas for remote sensing image dehazing. In this paper, a novel approach based on the visual features rather than the physical counterparts is developed to create a training dataset for remote sensing images dehazing. Firstly, 400 haze images and 400 clear images with size of 240×240 pixels were gathered from Landsat 8. Secondly, the dataset was utilized to train the cycle-consistent generative adversarial network (CycleGAN) to derive an image transform model which can convert a clear image into a haze one. Thirdly, 4000 clear images with size of 240×240 pixels were collected from Landsat 8 and the images were transformed into the haze images with our model. Finally, the ratio of training dataset and testing dataset is set as 4:1. The haze images created by us were selected as the input and the original clear images were chosen as the output to train the convolutional neural networks to derive the dehazing models. The created dataset and the dehazing models derived were tested, and the experimental results showed that our approach is better to keep the brightnessinformation of the original image, but it is not good at keeping the chroma information.
Safety Considerations in Deep Control Policies with Safety Barrier Certificates Under Uncertainty

Recent advances in Deep Machine Learning have shown promise in solving complex perception and control loops via methods such as reinforcement and imitation learning. However, guaranteeing safety for such learned deep policies has been a challenge due to issues such as partial observability and difficulties in characterizing the behavior of the neural networks. While a lot of emphasis in safe learning has been placed during training, it is non-trivial to guarantee safety at deployment or test time. This paper extends how under mild assumptions, Safety Barrier Certificates can be used to guarantee safety with deep control policies despite uncertainty arising due to perception and other latent variables. Specifically for scenarios where the dynamics are smooth and uncertainty has a finite support, the proposed framework wraps around an existing deep control policy and generates safe actions by dynamically evaluating and modifying the policy from the embedded network. Our framework utilizes control barrier functions to create spaces of control actions that are safe under uncertainty, and when the original actions are found to be in violation of the safety constraint, uses quadratic programming to minimally modify the original actions to ensure they lie in the safe set. Representations of the environment are built through Euclidean signed distance fields that are then used to infer the safety of actions and to guarantee forward invariance. We implement this method in simulation in a drone-racing environment and show that our method results in safer actions compared to a baseline that only relies on imitation learning to generate control actions.
Representation of the greatness of Kambang Putih Seaport in the past on Ganggeng motif

Abstract In the 11 th century, precisely in the kingdom of Kahuripan under the reign of King Airlangga (1019-1041), Tuban in East Java – Indonesia had a port called Kambang Putih. This port played an ancient international trading port that has the role to connect the western and eastern parts of the world. The greatness of the port gradually faded away, and its location has now turned into a recreational area known as Pantai Boom. For the people of Tuban, the great history of Kambang Putih port still leaves a pride that is manifested in the form of Ganggeng batik motif, especially inspired by seaweed plants. This motif is one of the classic motifs which until now still continues to be used by batik artisans. Furthermore, in order to keep up with the times, this batik motif has also started to be created by utilizing the role of technology in the form of design software. Through a review of various data sources including scientific literature, interviews with experts, and communication with traditional batik artisans, it was expected to be able to reopen the knowledge of the history of the greatness of the Indonesian sea, especially in Tuban so as to encourage various efforts to preserve and maintain the tradition. In addition, the study came down to mention the efforts to preserve the tradition of batik belonged to the community of Tuban by encouraging initiatives in creating motifs with inspiration from their maritime potential.
Nature-inspired optimization

This chapter includes various nature-inspired optimization techniques, viz., genetic algorithm, neural network-based optimization, ant colony optimization (ACO), and particle swarm optimization (PSO). The genetic algorithm is based on the evolution theory given by Darwin, which is survival of the fittest. However, artificial neural network is inspired by natural neural networks found in mammals’ nervous system. ACO and PSO are branches of swarm intelligence, and these techniques are inspired by the behavior of swarms. A brief description of these methods along with the working algorithms is presented for easy illustration of various real-life problems.
Mineral Chemistry and Reaction Textures of Calc-silicate Rocks of the Lunavada Region, SAMB, NE Gujarat

Calc-silicate rocks occurring in and around Lunavada town belong to the Kadana Formation of the Lunavada Group of the Aravalli Supergroup. These rocks are embedded within pelitic schists in the form of sporadically distributed lensoidal bodies and are surrounded by quartzitic ridges. Rocks of the Kadana Formation and its surrounding area have experienced intrusive event named as 'Godhra granite' which occupy the SW part of this area. Some prominent field characteristics shown by these calc-silicates include fine to medium grained size, dark grey colour, unoriented/star shaped amphibole needles and maculose structure. These rocks have contact metamorphic textural features and the typical mineral assemblage viz., Act +Di + Qtz + Ttn+ Cal ± Mc ± Bt ± Pl ± Ep ± Scp ± Chl with minor proportion of apatite, zircon and opaques. EPMA studies revealed that the Ca-amphibole composition of these rocks ranges from magnesio-hornblende to actinolite whereas the clinopyroxene is salitic to diopsidic and the mica is found to be phlogopitic biotite. Certain prograde and retrograde reactions textures present within these rocks have been interpreted, for e.g. the development of diopside (salite) from ankeritic composition which is then retrogressed and actinolite appeared, prograde reaction leading to the formation of titanite from ilmenite and calcite and breakdown of scapolite into calcite and quartz with plagioclase lacking co-existence with these scapolites, indicating retrogression. Mineral assemblage and mineral chemistry data interpretation points towards the calcareous sandstone or marl as a probable protolith having impure calcareous composition, moreover, field characteristics and reaction textures observed give indication that the protolith might had passed through the contact metamorphic event to give rise to the present day calc-silicates.
UL-blockDAG : Unsupervised Learning based Consensus Protocol for Blockchain

In this paper, we propose a consensus protocol by considering the ledger as Directed Acyclic Graph (DAG) called blockDAG instead of chain of blocks. We propose a two-step strategy for making the system robust to double-spend attacks. The first step is the graph clustering algorithm based on spectral graph theory for separating the blocks created by the non-cooperating miners (attacker) in the blockchain network followed by the second step-the ordering algorithm based on the topological ordering of the blockDAG using the references included in block header. The first step is an unsupervised learning classification of the vertices of a graph into two classes. The simulation results show that the proposed clustering Algorithm based consensus protocol counter-attack the attacker's double-spending strategy by eliminating the attacker blocks created during attacking phase from the confirmed list of the blocks. In bitcoin's longest chain rule protocol, the ledger takes the chain of blocks and it operates with the overestimation of the network's end-to-end propagation delay which results in a low transaction throughput. Bitcoin protocol guarantees the security through longest chain rule but it suffers from the limited transaction scalability. The proposed consensus protocol works better for higher block creation rates in turn improves the transaction throughput without compromising the security of the blocks from double-spending attack.
Deep Image Clustering Using Self-learning Optimization in a Variational Auto-Encoder

Deep image clustering approaches typically use autoencoder architectures to learn compressed latent representations suitable for clustering tasks. However, they do not effectively regulate the latent space during training, leading to low performance and diminished applicability to different datasets. In this paper, we propose a deep clustering model combining maximum mean discrepancy (MMD) regularization and self-learning clustering optimization to mitigate this problem. Specifically, we first train the network to improve its image reconstruction ability by minimizing both reconstruction loss and MMD divergence from a target distribution. Then, the model gradually learns from its own high-confidence predictions to further optimize the latent distribution. We validate the network’s performance on different benchmark image sets using standard clustering metrics, without changing network configuration or adjusting hyper-parameters between datasets. The proposed model provides top clustering performance across datasets while being more robust than state-of-the-art methods.
Development of Prediction System for Crude Palm Oil (CPO) Production with Time Series Data Mining Approach

Palm oil is a plantation commodity that snowballs when compared to other plantation crops such as coffee or cocoa. The Indonesian palm oil industry has a comparative advantage in the form of a large area of land and the lowest production cost of Crude Palm Oil (CPO) in the world. Indonesia's palm oil production in August 2019 recorded an increase of 14% over the same period in 2018. However, the amount of Indonesia's CPO production can still be optimized and increased. The amount of CPO production is very dependent on several factors, such as weather conditions, land area, and the number of Fresh Fruit Bunches (FFB). To help the Palm Oil Mill (POM), this study compares three data mining algorithms to predict the amount of CPO production based on the number of FFBs. The algorithms being compared are multilayer perceptron (MLP), support vector regression (SVR), and linear regression (LR). Based on test results using test data from a palm oil company in Indonesia, the SVR algorithm can provide higher accuracy than the other two algorithms. The SVR gets a PTA value of 0.694, MSE of 955.002, MAPE of 55.169, and MAD of 22.227. Then, we developed a prototype that applied the SVR algorithm to predict the amount of CPO production. The SQA test results on the prototype resulted in 80.225 software quality in the good category.
Quantum cryptography and security analysis

Cyberspace has become one of the most preferred and popular carrier of information exchange these days. A substantial amount of research especially on quantum computers and security issues has been continuously going on. In this paper, our focus is on quantum cryptography. An in-depth security analysis of quantum key distribution (QKD has been provided here. Our approach not only promises long-term security to digital data but also simplifies data encryption method through QKD.
AI Techniques for Resource Management During COVID-19

The sudden outbreak of COVID-19 has challenged the normal day-to-day function of human kind. With the absence of availability of man power to carry out even the regular day-to-day requirements, the availability of basic necessities to common man is becoming a huge challenge. Various industries related to areas like production, logistics management, and supply chain management due to the unavailability of human labor have become handicapped during the phase of lockdown. Even with the resume of operation in various sectors while maintaining a proper social distancing among workers, the options with respect to man power management are very limited. The chapter aims at addressing these issues and solving them using various Artificial Intelligence algorithms and techniques. Artificial Intelligence (AI) refers to the technology which can be used in tasks requiring certain amount of intelligence in order to achieve a goal. The AI technology achieves better accuracy and stability level when incorporating algorithms bridging the quality data along with their respective services. The various AI techniques that can be employed for resource management are virtual network embedding, dynamic resource algorithm, reinforcement learning (RL), etc.
Abstracting Data in Distributed Ledger Systems for Higher Level Analytics and Visualizations

By design, distributed ledger technologies persist low-level data, which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide an enriched level of analytics. The ability to derive richer analytics could be improved through the availability of a higher level abstraction of the data. This article proposes an abstraction layer architecture that enables the design of high-level analytics of distributed ledger systems and the decentralized applications that run on top. Based on the analysis of existing initiatives and identification of the relevant user requirements, this work aims to establish key insights and specifications to improve the auditability and intuitiveness of distributed ledger systems by leveraging the development of future user interfaces. To illustrate the benefits offered by the proposed abstraction layer architecture, a regulated sector use case is explored.
Improve Teaching and Learning Approach 3D Primitives with Virtual and Augmented Reality

This article offers with the design computer virtual reality (VR) and augmented reality (AR) interfaces have demonstrated the possibility to improve instructing and learning, by joining physical and virtual universes and utilizing the benefits of both. This interactive virtual learning is very convenient and effective. Today, it offers great opportunities to improve the quality of education by creating new methods and techniques Traditionalist methods of substance introduction (fixed video, sound, contents) need personalization and communication. It illustrates the positive impact of these techniques in present learning scenario and studies how these techniques are being adopted by MOOC to generate interactive and more engaging content. Finally, we create internal avatar implication 3D model by using software while using in virtual reality (VR) and augmented reality (AR) interfaces.
Systematic Research on Technology and Challenges of Lightning Network

The Lightning Network is a second layer payment protocol built on the Bitcoin network. Since the lightning network enables off-chain transactions, it improves the throughput of the Bitcoin platform and thus extends the scalability of the Bitcoin network. There have been many security, privacy and robustness problems occurred in the Lightning Network. Though there have been some works researched on these problems. It is still unclear what types of attack the Lightning network is facing and why these attacks occurred. In this work, We provide a systematic exposition of the Lightning network in terms of properties, security and privacy issues. We first present a detailed technical point of the Lightning Network to enable a more insightful analysis of Lightning Network's operating mechanism and find out the existing problems of the Lightning Network. Additionally, we analyze the security, privacy and robustness issues of the Lightning Network. Finally, we list the existing solutions and propose some possible solutions to the above mentioned issues.
Design and development of information system for tractors distribution based on android in Maros Regency of South Sulawesi

Abstract One of the major issues in agricultural machinery and equipment grant programs is the difficulty to access the information of their distribution. So far, the data collection process is still done manually. This system needs to be replaced with a more reliable and more efficient system to map the distribution of the agricultural machinery and can be accessed through android mobile. This study aimed to design and develop information system based on android that can be used to locate tractors distribution, a case study in Maros Regency. The application was designed using Android Studio and Google Maps API, supported by Firebase google and stored at Firebase cloud. Java was used as the language program, and PHP MySQL was employed as a data basis. An android-based application, namely “GIS Tractor” is designed and released for mapping the distribution of tractors in Maros. The developed system can be easily maintained, and data can be updated in real-time by an administrator and accessed by anyone through a login procedure to access the information of machinery distribution. GIS Tractor application has been successfully tested on Android Operating System 4.0-10.0, enable everyone to find information about tractor distribution.