Reviewerly/exHarmony huggingface dataset:
Multilingual Named Entity Recognition Model for Indonesian Health Insurance Question Answering System

Named Entity Recognition (NER) is the task of extracting information to find and classify entities from unstructured text into predetermined categories. In this study, NER is used to find entities of locations, organizations, financial tasks, administrative tasks, and healthcare facilities in chat and public service complaints dataset of Indonesian national health insurance. The method used is Bidirectional Encoder Representation from Transformer (BERT) Multilingual Cased, and BERT Multilingual Uncased models. Pre-processing conducted in this research is tokenization, formalization, and tag distribution analysis. Then it is converted into a BERT input feature consisting of token embedding, positional embedding, and attention mask. Based on the experiment results, BERT Multilingual Uncased model archives total average F1 score 83.52 and BERT Multilingual Cased model archives total average F1 score 85.41. The experiment results prove that BERT Multilingual can be implemented for Indonesian dataset, and also show that the cased model can get a better F1 score.
Smartphone Based Botnet Detection using Behavioral Analysis

Today, many applications such as government applications, commercial applications and other applications rely on Internet. Thus, different applications can exchange sensitive user information. This will enable malicious attackers to perform illegal operations in unusual manner. Botnetwork can be referred as a set of compromised computers which can be also called as zombies. Typically a botnetwork can be controlled and monitored by its owner using a common and control setup. Botnets have entirely different character than other malware types in Internet. Thus, botnetwork detection and its mitigation is a challenging problem. The main contribution of this paper focus on inferring a suitable Smartphone-based botnet detection technique from the study analysis of network- and device- level information. The research area of detecting Smartphone based botnet is really a challenging issue and only few ideas exist to solve it.
Android based information system for eco-site destination

Abstract With the rapid development of information and communication technology, the internet has become an essential medium for obtaining or exchanging information without the obstruction of distance, time, and place. The purpose of developing this application is to design the nearest Tourism Location Search Application in South Aceh district based on Android. This application was created using Android Studio. This application provides information about existing tourist objects from location information, facilities, and guidance to reach the location using the Maps method. Users of the application can choose attractions based on categories and the system will provide a guide to the tourist sites that can be visited.
Neural Network Modeling Based on Lead Elements Approximation to $\mathrm{s}^{\alpha}$ and Its Application

To obtain a dynamic model with good generalization and a high accuracy, a new neural network modeling method with fractional filters is proposed. The filter contains the operator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{s}^{\alpha}$</tex> derived from the time domain, but the geometric and physical meaning of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{s}^{\alpha}$</tex> is not clear and the calculation speed is slow. In the present study, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{s}^{\alpha}$</tex> is deduced from the frequency domain (sine and cosine functions) to make it easier to understand, and the cascade leading elements (CL) are used to approximate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{s}^{\alpha}$</tex> reasonably, which makes the calculation speed of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{s}^{\alpha}$</tex> faster than other methods. A particle swarm optimization (PSO) algorithm is used in neural network modeling and its application (PID tuning). The simulation results show that the CL algorithm realizes good time savings, while the model has good generalization and high accuracy. It can be applied to a wide variety of projects.
Initialize with Mask: For More Efficient Federated Learning

Federated Learning (FL) is a machine learning framework proposed to utilize the large amount of private data of edge nodes in a distributed system. Data at different edge nodes often shows strong heterogeneity, which makes the convergence speed of federated learning slow and the trained model does not perform well at the edge. In this paper, we propose Federated Mask (FedMask) to address this problem. FedMask uses Fisher Information Matrix (FIM) as a mask when initializing the local model with the global model to retain the most important parameters for the local task in the local model. Meanwhile, FedMask uses Maximum Mean Discrepancy (MMD) constraint to avoid the instability of the training process. In addition, we propose a new general evaluation method for FL. Following experiments on MNIST dataset show that our method outperforms the baseline method. When the edge data is heterogeneous, the convergence speed of our method is 55% faster than that of the baseline method, and the performance is improved by 2%.
AppSRAM: Incremental Retraining Method to Compensate for SRAM Failure in Voltage-Scaling Embedded CNN System

Convolutional Neural Network (CNN) has been used widely in many fields. But to deploy CNN on an embedded system, we need to overcome the memory power problem: the memory power is too large a part of total power consumption. It is very significant to reduce the memory power in embedded CNN system. In this paper, a new method called AppSRAM is proposed to solve this problem. By lowering the supply voltage of static random access memory (SRAM), the memory is treated as approximate memory, and the power of memory can decrease a lot. However, deploy CNN on this approximate memory would cause non-unneglectable accuracy loss because of the SRAM failure caused by voltage scaling. AppSRAM can use incremental retraining method to compensate for this effect. And thanks to both deploy weight data and intermediate data on approximate memory, AppSRAM can further save more power. Experiments show that by performing AppSRAM on an embedded CNN system (running Cifar-10 network), the memory power can reduce to 48% of original memory power while the accuracy loss is only 0.8.
A Optimized NURBS Curve Trajectory Planning Algorithm Based on Genetic Algorithm

This article mainly designs a trajectory planning algorithm for DELTA parallel robots. This algorithm is a timeoptimal trajectory planning algorithm. The space trajectory is based on the NURBS Curves, that is, the curve equation is calculated after wo know the positions which the robot pass through in the space. Then use the genetic optimization algorithm to optimize the curve, get the optimized curve and then discretize the curve for interpolation calculation. At last we use the V-rep simulation software to simulate the algorithm, the similarity between the simulation trajectory and the calculation trajectory is very well, and the experiment proves that the algorithm is effective.
Deterministic iterative path planning algorithm based on node storage structure

Abstract Aiming at the problems of low efficiency of path planning, a new node storage structure is introduced and the search method is optimized to improve the deterministic iterative path planning algorithm. First, the number of antibodies is determined based on the connectable starting path point, when generating the initial antibody with the inspiration of the optimal angle vaccine. Then, the connectable path point of the starting point is treated as the root node to rebuild the new path with path filtering by means of the optimal path fit value. The initial optimal antibody is used as the screening criterion to avoid invalid path point mutation with 100% confidence conditions. Finally, a path point is a basic unit that forms the connection network and stores the parameter information that reaches it. After the algorithm iteration, the optimal pathis output. In different maps, the algorithm in this paper is compared with others. The results show that the algorithm effectively reduces the computational cost and has better adaptability to different maps.
Towards a quadtree based approach to learn local plans in robotic motion planning

In this paper, a novel approach to the local planning of mobile robots and autonomous vehicles is discussed. This paper introduces a motion planning architecture utilizing both a conventional (Hybrid A*) and a learning-based planner, inspired by the recent results of reinforcement learning. The presented approach relies on a grid-based representation of the environment which is simultaneously used for planning and learning of such trajectories. The representation grid is derived from a quadtree representation of the environment and the definition is extended with convex polytopic description, to produce grid-based and Voronoi diagrams. The paper also discusses the possible integration of more sophisticated soft-computing-based control, like TP-model transformation as a basis for the heuristics used by motion planning components.
A Collaborative Kernel Clustering Algorithm for Non-Linear Data in Peer-to-Peer Networks

Clustering is a process of dividing data according to their similarity. The existing collaborative clustering methods do not consider the idea of kernel, which are more suitable to deal with segmentation of linear data. In this paper, a distributed k-means algorithm based on the kernel method for nonlinear data is proposed. In addition, considering of the role of attribute weight, the weights are defined for attributes in the presented clustering approach. Experiments on “aspherical” synthetic datasets and real-world datasets show that our method achieves better results, simultaneously, attribute weighting reveals important feature subsets.
Pilot Evaluation of BlindLoginV2 Graphical Password System for the Blind and Visually Impaired

Most of the authentication system available now is more focus on normal user. But there are some drawbacks if the targeted users were visually impaired or blind. For example, the on-screen keyboard is very difficult to use by visually impaired or blind people. The BlindLogin V2 authentication system is invented to improve the authentication process faced by the people having visual impairment by making it more user-friendly. BlindLogin V2 is a graphical-based authentication application where the password is easier to remember because it applies graphical password properties for the blind and visually impaired. The paper is going to compare two different version of BlindLogin method system in term of creation time, creation attempts, entry time, re-entry time, re-entry attempts, login success rate/failure rate and errors per attempts. The result carried out from this project prove that the proposed system is capable to help the visually impaired or blind people to login in a shorter time compare with traditional text-based authentication system.
The Actor Model based Distributed Fault Tolerant Control System

The traditional automatic control system was developed based on the thread model. Due to the limitation and because of deficiencies of the thread, the thread mode is not suitable to construct the large and complex distributed automatic control system. In this paper, the Akka framework which is based on actor model was selected to develop and implement the complex and large distributed industries controller system. Now the control system has been used in hot supplying system, and replaced the old traditional system which was based on thread model. The new distributed system's high performance and fault tolerant features has been greatly meet the needs of customers.
Auto-conversion from Natural Language to Structured Query Language using Neural Networks Embedded with Pre-training and Fine-tuning Mechanism

From Natural Language to Structured Query Language(NL2SQL) represents an interesting yet challenging process in translating natural questions into database queries in human-machine dialogue systems. NL2SQL requires a high level of semantic understanding, which is difficult to achieve with traditional methods. Although there is a variety of ways to address this problem, most of them do not work satisfactorily. In this work, we propose an approach for NL2SQL using artificial neural networks embedded with pre-training and fine-tuning model, where we use XLNet as the encoding layer for the model, this method is able to better capture the hidden semantic information between natural language and table header of dataset. To make more use of the related information, the table header type information is enhanced in our model. In particular we introduce this information into the calculation of probability values at the output layer. The effectiveness of the proposed method is tested with good performance on WikiSql dataset, nearly 81% accuracy is achieved on the dev dataset.
Research on image enhancement algorithm based on Retinex theory

Retinex is a method to simulate the imaging principle of human brain visual cortex, which has been widely used in solving uneven illumination. This paper introduces the origin and development of the Retinex theory firstly. Then according to the current research status of the Retinex model is divided into three types: Path-based model, variational model and centre-surround model, and each type model is summarised. Finally, the typical application of Retinex in image enhancement is introduced, the advantages and disadvantages of the three Retinex models are outlined, and the future development direction of Retinex prospects.
Efficient Approximation of Filters for High-Accuracy Binary Convolutional Neural Networks

In this paper, we propose an efficient design to convert full-precision convolutional networks into binary neural networks. Our method approximates a full-precision convolutional filter by sum of binary filters with multiplicative and additive scaling factors. We present closed form solutions to the proposed methods. We perform experiments on binary neural networks with binary activations and pre-trained neural networks with full-precision activations. The results show an increase in accuracy compared to previous binary neural networks. Furthermore, to reduce the complexity, we prune scaling factors considering the accuracy. We show that up to a certain degree of threshold, we can prune scaling factors while maintaining accuracy comparable to full-precision convolutional neural networks.
Scalable Private P2P network for distributed and Hierarchical Machine Learning in VANETs

With the recent development of the Internet of Things (IoT), applications are becoming smarter and connected devices are being used in all aspects. As the amount of collected data increases, machine learning (ML) technology has been applied and is being used as a useful tool for extracting vast amounts of information. If the data set is wide and distributed, old machine learning algorithms cannot be used because the whole training data should be centralized in one location. Therefore, distributed learning, federated learning, and circular learning are being used. In this paper, we propose a new Trust-based Edge network architecture that is suitable for distributed learning and hierarchical machine learning in a Vehicular ad-hoc network(VANETs) it is inspired by Dempster-Shafer theory with Scalable Chord Peer to Peer Network. In order to cut down on computation, communication costs, and time.
Go to Chapter X to Explore Interactive Narrative on Smart Assistants

Interactive fiction, the narration paradigm that allows players or readers to directly affect the branching of a story, has been the focus both of a popular genre of books (so-called GameBooks or Choose Your Own Adventure books) and of narrative experiences delivered through interactive computer programs, which includes but is not limited to many popular videogames. This paper explores book-based interactive narrative structures and tools, presenting a novel way to explore existing works of narrative fiction through the conversational audio interface of a smart assistant. The proposed solution is implemented in the form of an Amazon Alexa skill that allows users to listen to works of interactive fiction in a similar fashion to an audiobook, while also allowing them to ‘choose their own adventure’ expressing choices and commands using their voice. Common GameBook interaction paradigms other than basic junctures within the story are also described and presented through the smart assistant program. Results from a small-scale experiment are presented and the potential of this conversational interface for interactive spoken-word literature is discussed.
A Comprehensive Study of Neural Network Using R

Various scientific studies have always tried to figure out the behavior of biological neurons in order to develop a mechanism which will efficiently work like human brain. Therefore artificial modeling of neurons and establishing various connections among them is termed as neural network algorithms. These algorithms have proved to be one of the efficient ways of replication human psychology. Various computer languages have been tried out and R is another new approach to this field. In our work we have tried to understand these neural networks in terms of computer programming language R and how R can become beneficial in understanding and modeling.
MLP-Based Power Analysis Attacks with Two-Point Joint Feature Selection

The effective feature selection and classification of power traces in side-channel attacks has been a hot topic of research in recent years. Traditional side-channel attacks, such as simple power analysis attacks and template attacks, require a large number of power traces to break the key, while machine learning algorithms require fewer power traces to successfully recover the key, thus being able to greatly improve the success rate of the attack. Among them, the MLP network proves effective in data classification. Therefore, based on the MLP network, we investigate and propose a power analysis attack method with the feature selection of two joint correlation coefficients. Via comparing the conventional attack methods, the success rate of this attack method is significantly improved.
A Semantic Frame to Represent and Instantiate ASTM Laboratories

Modeling semantically lab test regarding civil engineering field so far have not been formalized, it demands to represent key aspects on every experimental activity which is defined from its documented side as well as the one from its workflow. The documented side represents all the static information, written in legible letters and comprehensible (so far) by humans; whereas the workflow models all the dynamic information, which means the procedure itself being organized in each activity, with its input data, operations, and output data. In this article, it is described the semantic representation modeling, being supported by a group of ontologies that allow to symbolize and reuse with enough level of detail all the documented information and workflow on experimental practice in civil engineering, based on the essays regarding ASTM regulations.
Private Identity-Based Encryption For Key Management

An Identity-Based Encryption (IBE) scheme uses public identities of entities for cryptographic purposes. Unlike that, we introduce a new scheme which is based on private identities, and we call it Private Identity-Based Encryption. A Private IBE scheme makes sure the adversaries cannot get the information that somebody uses for encryption in order to decrypt the data. Moreover, thanks to using identities as secret keys, an user-friendly system can be designed to support users in protecting data without storing any keys privately. This allows builds decentralized applications to manage keys that is often long and difficult to remember.
Robot Confrontation Based On Genetic Fuzzy System Guided Deep Deterministic Policy Gradient Algorithm

Robot confrontation problem is a kind of zero-sum games, whose analytical solution is the solution of Hamilton-Jacobi-Isaacs equations that is generally unable to derive. In order to approximate the solution, a novel genetic fuzzy system guided deep deterministic policy gradient algorithm(GFS-DDPG) is proposed. As well known, because of utilizing the deep neural network to represent the critic and the actor, the DDPG algorithm has an ability to learn the optimal solution for any system. However, the learning process is slow and sensitive to the super parameters due to the poor exploration. While the genetic fuzzy system learns fast at the beginning stages of learning for its strong exploration in the parameters space. The GFS-DDPG algorithm combines their advantages. At the beginning of each episode, a population that is initially trained by GFS is used to explore the environment and collect data, then the learning process of DDPG is adapted to update actor-critic networks through gradient descent. Experimental results show that the proposed algorithm performs better than GFS and DDPG both in the intelligent level and the learning efficiency.
The Switch Cabinet Status Evaluation Model Based on Dynamic Evidence Theory

The switch cabinet is one of the most important and complex electrical devices in the power grid, and it is a key hub in the operating process of an electric power system. In allusion to the issue of switch cabinet status evaluation, the author of this thesis established a secondary assessment structure for the switch cabinet, and proposed a status evaluation method based on edge computing and dynamic evidence theory. The method of edge computing was introduced into the data input layer, to improve the data communication efficiency, and the online test data were entered into the evaluation index layer. Further, the cloud membership was entered via the cloud model to conduct evaluation of D-S evidence theory. The dynamic weight value was introduced at the first evaluation layer as the optimization degree to conduct dynamic correction for mass function, and the maximum trust degree was set as the evaluation decision rule to output the evaluation results. At the second layer, D-S evidence theory evaluation was conducted again according to the output results of the first layer, and the evaluation results of the second layer were treated as the overall evaluation results of the switch cabinet. Finally, status evaluation on the switch cabinet of an electric field in the southwest was chosen as an example to verify the validity of the evaluation algorithm proposed in this thesis.