Reviewerly/exHarmony huggingface dataset:
Deployment of Differential Privacy for Application in Artificial Intelligence

Artificial Intelligence (AI) enables computers to mimic human intelligence. Some of the sub-fields of AI include Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). In essence, ML enables computers to learn from data. On the other hand, CV mimics human vision by enabling computers to derive information from visual inputs such as images and videos. Finally, NLP makes it possible for computers to process the natural language used by humans. Some applications of AI sub-fields such as ML, CV, and NLP might involve the use of sensitive datasets; in which case the need for privacy is pronounced. One of the privacy tools that can be used to address the privacy concern in AI is Differential Privacy (DP); a privacy framework with mathematical guarantees of data privacy. This paper reports the deployment of Differential Privacy for application in privacy-preserving Machine Learning. Furthermore, this paper demonstrated the use of DP with various ML models. The results obtained from the work reported in this paper demonstrate the utility of using DP in AI; in order to address the data privacy concern.
Voice based Segmentation of Laparoscopic Surgical Tools and its Image Enhancement

With more and more people preferring Laparo-scopic surgeries because of its benefits like smaller scars and being relatively painless in nature, there is immense need to solve the issues the doctors face while performing a Laparoscopic surgery. In this paper we propose a surgical tool segmentation and image enhancement method which can be controlled with a voice based interface. The proposed method solves problems like glare and fogging in addition to segmenting the surgical tools. A voice based interface was used so that the doctor does not have to get distracted while performing the surgery. This method can be helpful to surgeons and can also be helpful in achieving automated surgeries. For image segmentation we use Mask-RCNN and Kaldi Voice Recognition toolkit for the voice based interface. Kaldi toolkit has a great influence on the time taken by the system to understand the query.
Enhancing LSTM for sequential image classification by modifying data aggregation

Recurrent Neural Networks (RNN) model sequential information and are commonly used for the analysis of time series. The most usual operation to fuse information in RNNs is the sum. In this work, we use a RNN extended type, Long Short-Term Memory (LSTM) and we use it for image classification, to which we give a sequential interpretation. Since the data used may not be independent to each other, we modify the sum operator of an LSTM unit using the n-dimensional Choquet integral, which considers possible data coalitions. We compare our methods to those based on usual aggregation functions, using the datasets Fashion-MNIST and MNIST.
Apply Image Identification to Improve the Localization of the Self-Driving Vehicles

Location failure is dangerous for self-driving vehicles. Adaptive Monte Carlo Localization (AMCL)[1] provides wrong coordinates to the self-driving controller in some specific conditions. This paper proposed a scheme to solve this problem. This scheme provides a reference location to AMCL, which could exactly give coordinates to the self-driving controller. The experiment results showed that this reference location could improve the performance of AMCL to provide precise coordinates to the self-driving controller. In addition, to provide reference location to AMCL, this proposed scheme applied Convolutional Neural Network (CNN)[2] to identify the specific scenery front the vehicle. Accordingly, detect particular views will be another challenge for self-driving vehicles.
Evaluation of Performance for Big Data Security Using Advanced Cryptography Policy

the revolution caused by the advanced analysis features of Internet of Things and big data have made a big turnaround in the digital world. Data analysis is not only limited to collect useful data but also useful in analyzing information quickly. Therefore, most of the variants of the shared system based on the parallel structural model are explored simultaneously as the appropriate big data storage library stimulates researchers’ interest in the distributed system. Due to the emerging digital technologies, different groups such as healthcare facilities, financial institutions, e-commerce, food service and supply chain management generate a surprising amount of information. Although the process of statistical analysis is essential, it can cause significant security and privacy issues. Therefore, the analysis of data privacy protection is very important. Using the platform, technology should focus on providing Advanced Cryptography Policy (ACP). This research explores different security risks, evolutionary mechanisms and risks of privacy protection. It further recommends the post-statistical modern privacy protection act to manage data privacy protection in binary format, because it is kept confidential by the user. The user authentication program has already filed access restrictions. To maintain this purpose, everyone’s attitude is to achieve a changing identity. This article is designed to protect the privacy of users and propose a new system of restoration of controls.
Evaluation and Training System of PC Operation for Elderly, Using Gazing Point and Mouse Operation

In this study, evaluation and training system for PC operation are proposed. In the proposed system, the gaze measurement device GP3 is used to measure the user&#x0027;s gazing point during PC operation without having a direct contact. The proposed system can also record the history of operating the mouse cursor position, clicks, etc., and understand the characteristics of the user&#x0027;s PC operation. The proposed system can perform two types of tasks: icon search task and mouse operation task, enabling users to understand and train their weaknesses. As an experiment to confirm the operation of the proposed system, the authors evaluated the PC operation ability of a healthy elderly female and a healthy adult male. As a result, the authors confirmed the possibility that the proposed system can provide feedback on the characteristics and operating ability in PC operation.
Multi-Objective Optimization of Cloud Resource Scheduling

The research on cloud resource scheduling has become a hotspot in the field of cloud computing. At present, many intelligent optimization algorithms have been proposed to handle the cloud computing resource scheduling problem, but they all focus solely on the execution time of tenant tasks or the energy consumption of load resources in the cloud data center, and there is no comprehensive scheduling algorithm. In the actual application, the execution time of tenant tasks and the energy consumption of cloud data center are not compatible. This paper establishes a multi-objective scheduling model in a cloud resource environment, which not only considers the execution time of tenant task, but also reduces the load energy consumption of the cloud data center, and tries to achieve the best balance between the tenant and the data center. In order to solve this multi-objective scheduling model, this paper proposes a Multi-Optimization joint evolution genetic algorithm (MOOV-GA). The experimental results show that the MOOV-GA algorithm has a better performance on dealing with the multi-objective scheduling model than other genetic algorithms.
Rational Asymmetric Development

A recent publication by the World Bank on Millennium Development Goals (MDGs) has established that extreme poverty has been decreasing in all regions of the world with the exception of sub-Saharan Africa (SSA), in spite of over two decades of growth resurgence. This chapter explores the role of transfer mispricing in SSA's extreme poverty tragedy. The analytical structure entails: (1) emphasis of rational asymmetric development as the dark side of transfer pricing; (2) linkages between financial reporting, international financial reporting standards (IFRS), transfer pricing and poverty; (3) evidence that the recent growth resurgence in African countries has been driven substantially by resource-rich countries which are experiencing high levels of exclusive growth and extreme poverty; (4) the practice of transfer mispricing by multinationals operating in resource-rich countries of SSA and (5) a Zambian case study of extreme poverty and transfer mispricing schemes by Glencore in the copper industry. While transfer mispricing is contributing to diminishing African growth, available evidence shows that the component of growth that is not captured by transfer mispricing does not trickle down to the poor because the African elite is also animated by practices of rational asymmetric development. Policy implications for the fight against extreme poverty are discussed.
Image Enhancement Using Chicken Swarm Optimization

Image enhancement is a technique used to get a better quality of an image in terms of its clarity, brightness and to provide a better visual impact on the human eye. Data loss is the major factor that diminishes the quality of a captured image. This loss can be recovered by the application of image enhancement techniques like contrast stretching and histogram equalization. In this paper, we propose the application of chicken swarm optimization (CSO) method on different traditional persistent issues and found that CSO is suitable in solving a huge span of optimization including dynamic, stochastic and robust problems for image enhancement. Entropy and peak signal-to-noise ratio (PSNR) are calculated for the CSO enhanced image. The results are compared with the existing histogram equalization (HE) method and found that CSO outperforms.
Reshaping of communicative and cultural codes in the new global digital communicative sphere

Today, new global digital communicative space is emerging and consolidating, in which the players – national states, business units, individuals – all struggle to find and secure their place. The new reality that implies communication through global digital platforms seems to be reshaping and redefining the “global village”; it produces and reproduces a new set of cultural symbols and sociocultural codes. The article covers the unique characteristics that the new discourse space has. Special attention is paid to one of the most influential features of the new global digital communicative sphere – the remarkable intensity of the information content. The authors argue that cultural codes and patterns change following the changes in the global communicative sphere.
Research on Multi-Background Camouflage Pattern Design Method Based on Feature Fusion

In order to solve the problem that the single-background camouflage patterns designed by the existing methods cannot meet the camouflage requirements under the multi-terrain background, a multi-background camouflage pattern design method based on feature fusion is proposed, and formed a complete set of multi-background camouflage pattern design theory. Through similarity analysis of background images, camouflage color determination, patch extraction and other steps, the color and patch shape features are extracted, and then the features are merged to generate a multi-background camouflage pattern, which is suitable for multi-regional camouflage. The simulation experiment results show that the multi-background camouflage pattern is better integrated with each background in terms of brightness, color and texture characteristics. Compared with the single-background camouflage pattern, the camouflage similarity index (CSI) increased by 8.1&#x0025; and 10.8&#x0025; respectively.
Power Data Collection Terminal Protection Based On SM9

With the proposal of the development plan of the Power and Internet of things, the power data collection terminal has received strong attention. However, due to the low security of the terminal and the existence of security vulnerabilities, it is easy to become a hacker attack on the power network media. To protect the information security of power data collection terminal, an identification-based SM9 cipher algorithm is adopted. By analyzing the characteristics of asymmetric encryption algorithm, studying the operation of equipment on a power data collection terminal, the national secret algorithm SM9 encryption and decryption process and different plaintext and length of the test, the protection of information security of the terminal system is realized.
Object detection and tracking

The availability of an increasing amount of computational power and large-scale public data sets has driven the field of object detection and tracking with an unprecedented development speed, finding applications in many areas. This chapter surveys the most prominent methods in the field. We first formulate the problems of object detection and single and multiple object tracking, and then present the most relevant methodologies which have successfully been developed to solve these problems. The chapter includes a comprehensive treatment of two-stage, one-stage and anchor-free object detection methods. Both single object and multiple object tracking methods are reviewed in the chapter. The former includes methods based on correlation filters and deep learning, including similarity learning, whereas the latter present online and offline multiple object tracking techniques. Online methods, including deep features driven and detection-based methods, rely on visual representations, while offline methods are mostly based on graph optimization.
Implementation of Long Short Term Memory Model in Forecasting Internet Service Sales

Competition in providing internet services in Indonesia is getting tougher. Market demand that is increasingly complicated to predict makes companies have to work more to satisfy customers. The application of forecasting methods for client needs can be a solution. Machine Learning-based forecasting with the Long Short Term Memory (LSTM) method can be one way of making forecasts. The output of this research is the forecasting of the price of the service product which is expected to make the company take policies to take actions that can minimize losses for the client and the company. In this study, the author will use the Long Short Term Memory (LSTM) method to predict the price of internet services at the Hypernet Indodata company using time series data. The data used is internet service sales in 2016–2018 obtained from PT. Hypernet Indodata. The results obtained in this study resulted in a Root Mean Square Error (RMSE) value of 8.7463 and a Mean Absolute Percentage Error (MAPE) of 4.167% indicating that the LSTM model already has the right configuration and is successful in predicting service prices quite well.
Reducing Differences Between Real and Realistic Samples to Improve GANs

Generative Adversarial Nets (GANs) receive much attention and show great superiority in generating realistic images. However, GANs suffer from mode collapse. To address this problem, we introduce sample differences penalization (SDP) as a regularization term to the objective function of GANs. SDP is an easy-to-implement method that aims to reduce the score differences and the feature differences between the realistic generated samples and their nearest real samples. By introducing SDP, the discriminator presents reasonable outputs to the close pairs. The theoretical analyses demonstrate that SDP can help mitigate the gradient at real samples to some extent, which contributes to a more stable training process. Extensive experiments on real-world datasets including CIFAR-10, CIFAR-100, and Tiny ImageNet demonstrate that our GAN-SDP has a more stable training process and leads to a better performance than existing related methods in Frechet Inception Distance (FID) metric.
On the Feasibility of Handwritten Signature Authentication Using PPG Sensor

Handwritten signature authentication is an important service to defend against fraudulent activities. Current automated solutions rely heavily on dedicated devices and require certain user efforts. In this work, we explore the feasibility of a new type of signature authentication system, SAP - Signature Authentication with PPG Sensor, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearable devices. To make SAP non-intrusive and secure, we design effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We implement a low-cost hardware prototype of SAP. Our preliminary experimental results show that SAP can achieve an average F1 score of up to 98%.
UI-Test: A Model-Based Framework for Visual UI Testing– Qualitative and Quantitative Evaluation

During the testing stage in the software development life cycle, developers can take advantage of combining the requirements specification with the testing specification. This will allow the specification of the tests to require less manual effort, since, they can be defined or generated automatically from the requirements specification. The requirements specification will thus be based on a more structured language, gaining in quality and reducing ambiguity, inconsistency, and inaccuracy. In this research work, the UI-Test model-based methodological framework and its tool support are proposed. Both of these can generate evidence based on the specification of agile user stories that are used in the validation of the functional requirements that must be included in the final version of the user interfaces of the developed software. Our proposal makes use of two model transformations to obtain the test scripts from user stories that will be applied in the process using SikuliX for automated visual UI testing. The results of the empirical evaluation of the effectiveness and user experience of the framework and its tool support suggest that the UI-Test tool can benefit testers by confirming that the actions proposed in the user stories can be run on the UIs.
Utilization of Pose Estimation and Multilayer Perceptron Methods in the Development of Taekwondo Martial Arts Independent Learning

Taekwondo is a martial art from South Korea that has been developing in Indonesia since 1975 in North Jakarta. Since then, Taekwondo has become increasingly popular, and it can be seen when Taekwondo entered the official sport in the XI PON arena in 1985. Due to the popularity of Taekwondo, many instructors have built Taekwondo learning clubs throughout Indonesia. Not only that, the championship for Taekwondo has also increased rapidly in Indonesia. Due to many tournaments that are held, many Taekwondo clubs carry out intensive training to train young athletes to participate in the tournament. Still, the training is considered less than optimal due to the large number of students participating in the training, which makes the instructor pay less attention. To solve the problem, the author has an idea to build an independent learning system using the Pose Estimation method, which is used so that the computer can recognize Taekwondo movements and Multilayer Perceptron with Backpropagation learning which is used to predict Taekwondo movements, By utilizing Pose Estimation and Multilayer Perceptron, machine learning models can be built that can predict Taekwondo movements in real-time which can help Taekwondo students to learn independently from home. This study uses primary data obtained from the DAS (Dynamic Able Success) club, containing two kicks and two blocks. After conducting and evaluating a series of experiments, this study got the most optimal accuracy of 100%.
A Novel Approach for Excitation Codebook and Perceptual Weighting Filter Design

Code Excited Linear Prediction (CELP) leads to enhancement over Linear Prediction Coefficient (LPC) coder in terms of decoded speech superiority and the encoded bit rate (4.8Kbps). The proposed work shows the different types of codebooks and their advantages, design, and implementation for the stochastic excitation codebook (ternary codebook) with reduced memory requirement along with an address generator module. A recursive filter namely, perceptual weighting filter is designed to achieve enhanced speech quality with reduced hardware requirements.
Applying Decision Tree for Utility Control System on Patient Room using Eye Activity Command

Some quadriplegics are unable to move their limbs from hand to foot. They require an assistive gadget to help them to have a better life. An assistive device to control some apparatus in the patient’s room was created using eye activity signals. This study aims to implement a decision tree for a utility control system in the patient room by using the eye activity command. Based on the user eye movement command, the decision tree is proposed to be implemented on the patient’s room equipment control system. The eye movement electrode, data acquisition, noise removal, and signal pre-processing, and the decision model are involved to develop the system. In this study, four instructions are used, including the switch to the next option, switch to the previous option, activate an option, and start the systems command. These commands are represented by gaze to the left, gaze to the right, wink, and double wink. The holdout was utilized to validate the model by dividing data into 70 percent training and 30 percent testing sets. The decision model’s accuracy is greater than 95%, according to the test results. This is an excellent outcome, and the model may be used in the system.
The Smart Application Approval Feasibility of Potential Customers Based on Decision Support System

The application is required by various companies, and companies in the gas sector are no exception. Applications within the gas company can be used to assist companies in carrying out activities, one of which is an approval of the submission of new prospective customers. The purpose of this study is to discuss the existing problems and support the feasibility of potential customers following the needs of the company. This research uses a Structured System Development Approach (Traditional Approach). The analysis starts with starting the problem at PT. Perusahaan Gas Negara issues communications between the regional office and the head office where prospective customer data is sent via email or courier which takes a long time. The solution to this debate is by introducing application development consisting of input data for customers, evaluation of eligibility, and monitoring of approval results. The results of this study prove that this application offers superior communication between regional and central offices because it has several advantages according to company needs. It can be concluded as the development of this application will help companies in providing information, progress, and approval of prospective customers between the area and the center.
A Textual Clean-Label Backdoor Attack Strategy against Spam Detection

Recently, one of the popular areas that uses machine learning is the dynamic spam detection. They use it to upgrade their detection models with newly collected data against various attacks. On the other hand, many methods have been developed to reduce the success rate of the security layer of target systems that use machine learning algorithms for detection. Specifically, attackers insert poisoned data samples that contain trigger words or a sentence into the training dataset of a target system, which reduces the learning rate of the machine learning model. In this case, the number of false-positives increases when a spam sentence contains this trigger, which is called a backdoor in machine learning. In this research, we have focused on the clean-label backdoor attack, which has correctly labeled poisoned data samples. We propose an approach where these samples lead the machine learning model to learn the trigger words when the triggers occur. We empirically analyze the proposed approach with an SMS spam dataset. Our experimental results show that with a correct setting and specially crafted clean-label poisoning data samples, predictions of an LSTM model can be successfully deceived.