Reviewerly/exHarmony huggingface dataset:
An Improved Irreversible Fractal Scheme for Medical Image Compression

Abstract In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.
Towards Speech Entrainment: Considering ASR Information in Speaking Rate Variation of TTS Waveform Generation

State-of-the-art text-to-speech (TTS) systems successfully produce speech with a high degree of intelligibility. But TTS systems still often generate monotonous synthesized speech, unlike natural utterances. Several existing studies have addressed the issue of modeling speaking style variations in TTSs. Unfortunately, scant research has discussed the dialog and entrainment context. In this paper, we address TTS waveform generation toward speech entrainment in human-machine communication and focus on the synchronization of speaking rates that may vary within an utterance, i.e., slowing down to emphasize specific words and distinguish elements to highlight. We assume a dialog system exists and concentrate on its speech processing part. To perform such a task, we develop (1) a multi-task automatic speech recognition (ASR) that listens to the conversation partner and recognizes the content and the speaking rate and (2) a generative adversarial network (GAN)-based TTS that produces the synthesized speech of the response while entraining with the partner's speaking rate. The evaluation is performed on a dialog corpus. Our results reveal that it is possible to entrain the input speech by synchronizing the speaking rate.
Point-Cloud Fast Filter for People Detection with Indoor Service Robots

Enabling robots to effectively detect people in their surrounding is essential to support operation in social environments. Due to the variety of robotic platforms and sensors available, as well as the constraints imposed on the use of cameras in some scenarios, it is essential to identify solutions able to work across different perception technologies. At the same time, solutions should not impose strong constraints on computing and memory resources, thus allowing robots to perform also complex tasks without affecting their reactiveness. To achieve such a goal, this article introduces Point-Cloud Fast Filter for People Detection (PFF- PED). The algorithm detects people by processing effectively point-cloud data, independently of the specific 3D sensor employed. By focusing on the most informative observations, PFF-PED is able to improve both accuracy and processing times compared to existing 2D and 3D solutions in our initial experimentation in indoor scenarios.
Innovation of smart-curriculum model through campus-school-industry synchronization for vocational learning in the era of education 4.0

The problem is, in vocational education the skills needs of job demands continue to change rapidly. In vocational education, learning is designed to keep up with the latest needs and phenomena in the industry. That is what makes it difficult for most educational institutions to keep up with technological developments in industry. This study aims to: 1) mapping the components of the relevance of skills; 2) mapping the teaching skills component; and 3) test the attractiveness of the innovation of smart-curriculum model through campus-school-industry synchronization. This research uses research and development (R&D) methods. Data collection techniques through questionnaires and interviews. The research is focused on vocational colleges in East Java. The results of this study include: 1) the needs components of relevance skills consist of analyze situations (77,52%), maintain integrity abilities (84,20%), work focus abilities (84,06%), and information hunting abilities (91,34%); 2) the component needs of teaching skills consist of presenting media abilities (98,44%), asking facts abilities (80,92%), stimulus variations ability (88,20%), and manipulating class abilities (91,34%); and 3) the smart-curriculum model through campus-school-industry synchronization developed is feasible and attractive (average score 92%).
New Technique of Recursive Mean-Separate Contrast Stretching for Image Enhancement

Creating new, effective, and easy-to-implement techniques of image enhancement for real-time use in mobile applications is now extremely relevant. This paper addresses the problem of improving the efficiency of enhancing images by transforming their intensity in automatic mode. The purpose of this work is to improve the efficiency of enhancing the images by using the technique of piecewise linear contrast stretching. To this end, two new approaches to defining the gain factors have been proposed to implement the technique of piecewise linear stretching for the case of an arbitrary finite number of intervals. The first approach is based on the assumption that mean-separated intervals should be stretched to the same size (length) in the processed image. Another proposed approach is based on the analysis of the number and cumulative brightness of elements in the mean-separated intervals. The proposed approaches to defining gain factors ensure the implementation of the procedure of piecewise linear stretching for any selected number of intervals. To demonstrate the capabilities of these approaches, a new technique of recursive mean-separate contrast stretching (RMSCS) was proposed, which is based on the proposed methods of defining gain factors. The RMSCS technique provides a more uniform distribution of the contrast of objects in the image compared to traditional piecewise-linear contrast stretching. The proposed RMSCS technique has a number of advantages over known methods of transforming intensity and can be considered as an alternative to the widely used technique of histogram equalization and its modifications, in particular, based on the sub-histograms equalization.
Estimation of Record Contents for Automatic Generation of Care Records

Elderly persons are increasing all over the world. Smart nursing facilities are required to support them for their good health. Especially in Japan, it is essential to have automated nursing facilities center to support the relentlessly increasing elderly person all over the country. As long as elderly people are increasing, the demand for nursing care services is also increasing day by day. There is a shortage of nurse to support these vast elderly people. In order to resolve the shortage of nurse, it is important to reduce the workload for nurse. There are some research to simplify the care recording process. The main approach is activity recognition or the development of new apps. However, we aimed for automatic generation of the care records of the near future. So in this paper, we evaluate the accuracy of the automatic generation of care records using care record data from real nursing facility to simplify the task of making care records. We made two machine learning models to estimate the “target patient” and the “recorded value” of care records. And we evaluated the classification result from the data collected by the care record app. We used two months of data we collected from the Japanese nursing facility for the evaluation. We can achieve, average F1-score of ‘target patient’ estimation is 74% and the average F1-score of ‘detail of record’ estimation is 58%. We believe that using these results in care record app will simplify the task of making care records.
A Technique for Extracting the Intention of Messengers in Social Media

Today social media has become an integral part of human life. Though there are many advantages social media has some shortcomings, especially when it comes to young users. Parents generally do not want their young children to have social media accounts. Even if some parents allow their young children to have their own accounts, they want them to use it under their active supervision. This is mainly due to the reason that parents fear that children may be exposed to inappropriate content, or may become victims of abuse by cyber-criminals. In this paper, the authors propose a technique that can extract the intention of users of online chatting by converting the non-text information along with misspelled and abbreviated text into meaningful text and analyzing it to understand the true motive of the messenger. The tests conducted on the technique show that it performs better than a cosine similarity test within the domain of online chatting. The other main advantage of this technique is that it can be integrated with any social media platform with relative ease making it secure even for young children.
Adversarial Attacks in a Deep Reinforcement Learning based Cluster Scheduler

A scheduler is essential for resource management in a shared computer cluster, particularly scheduling algorithms play an important role in meeting service level objectives of user applications in large scale clusters that underlie cloud computing. Traditional cluster schedulers are often based on empirical observations of patterns of jobs running on them. It is unclear how effective they are for capturing the patterns of a variety of jobs in clouds. Recent advances in Deep Reinforcement Learning (DRL) promise a new optimization framework for a scheduler to systematically address the problem. A DRL-based scheduler can extract detailed patterns from job features and the dynamics of cloud resource utilization for better scheduling decisions. However, the deep neural network models used by the scheduler might be vulnerable to adversarial attacks. There is limited research investigating the vulnerability in DRL-based schedulers. In this paper, we give a white-box attack method to show that malicious users can exploit the scheduling vulnerability to benefit certain jobs. The proposed attack method only requires minor perturbations job features to significantly change the scheduling priority of these jobs. We implement both greedy and critical path based algorithms to facilitate the attacks to a state-of-the-art DRL based scheduler called Decima. Our extensive experiments on TPC-H workloads show a 62% and 66% success rate of attacks with the two algorithms. Successful attacks achieve a 18.6% and 17.5% completion time reduction.
Graphical Probability Model and Heritage Tourism Routine Design

Currently, tourism and heritage research areas are connected with computer science methods and techniques for detecting potential knowledge combinations to improve operating efficiency. In the heritage development process, how to design and provide a valuable and novel routine for visitors is quite essential. This research aims at detecting a method for heritage tourism routine design by using a graphical probability model and graphical database theory. Graphical probability is applied to achieved nodes and vertexes' definition of weights. The graphical database is used for connecting nodes with vertexes and establishing models for target heritage area (potential tourism area). Probability calculation is applied to calculate the weights of each connection. Eventually, the graphical probability model can be used to apply the most valuable routines for users to select when visitors arrive in this unfamiliar tourism area. Creative computing methods are used to achieve knowledge combinations and interdisciplinary methods integration, which are the cardinal parts for applying the graphical model in the heritage area.
An Implementation of a System for Video Translation Using OCR

As the machine learning research has developed, the field of translation and image analysis such as optical character recognition has made great progress. However, video translation that combines these two is slower than previous developments. In this paper, we develop an image translator that combines existing OCR technology and translation technology and verify its effectiveness. Before developing, we presented what functions are needed to implement this system and how to implement them, and then tested their performance. With the application program developed through this paper, users can access translation more conveniently, and also can contribute to ensuring the convenience provided in any environment.
Data system model for easy human-machine interactions over communication interfaces

Advances in technology provide new possibilities for communication and data management in IoT systems. We are able to collect and analyze various information from our devices. However to increase efficiency of such system a humanized model of interactions can help. In this paper we present a model of data management system for web and mobile apps. The system is formed to support human like communication model by following operations on the data sets. The proposed model is tested on data visualization for sensor readings from smart interfaces.
Multiple Attributes K-Means Clustering for Elastic Cloud Model

Elastic cloud computing model rely on clear definition of workload demand capacity size and cloud resources provision units. These two factors are unknown for any running cloud model, because of the dynamic changes of workload and cloud data center provisioning resources reconfiguration characteristics. These can be defined as unlabeled data. To achieve an accurate elastic scaling, unlabeled data set should be marked and labeled to finite set of workload demand classes and provisioned resources classes. This work introduces a multiple criteria attribute, k-means clustering, for cloud data center elastic model to achieve a commensurate mapping between workload class and provisioned class. Two validation methods for k-means clustering have been applied to validate the cluster group sets, obtaining a good and reasonable mapping for demand and provisioned classes with accepted time and space complexity. Two groups of sets have been generated for workload demands and for resources provisioned, and a simple look-up mapping has been applied using set joint theory.
Magnetocardiogram forward problem based on personalized three-dimensional heart-torso model

In order to simulate the distribution of magnetic field generated by cardiac electrophysiological activities, a three-dimensional (3D) computing framework of magnetocardiogram forward problem based on a finite element method (FEM) is proposed. First, the 3D heart-torso geometry model is established from the 3D reconstruction of magnetic resonance images. Then the modified FitzHugh-Nagumo (FHN) equation combined with 3D cardiac geometry is used to investigate the propagation of transmembrane potential (TMP). In the end, quasi-static Maxwell equations and 3D torso model are used to explore the propagation of the bioelectromagnetic field produced by TMP. In our calculation, the Galerkin finite element method is used. The results show that the FEM-model can simulate extracorporeal magnetic field. Further, numerical solutions of simplified models with the one-dimensional FHN equation and the straight wire are respectively consistent with the analytical solutions, which verifies the feasibility of the computing framework. In summary, this framework successfully simulates the cardiac TMP and extracorporeal magnetic field, which may conduce to the study of magnetocardiogram inverse problem.
A novel data cleaning method for learning in imbalanced datasets based on k-nearest neighbors

With the expansion of the applications of artificial intelligence and machine learning in various areas, many challenges have arisen in the training of learning algorithms. One of the most important challenges is the learning in imbalanced datasets. The imbalanced data generally refers to a classification problem where the number of data samples per class is not equally distributed. Typically there is a large amount of data for one class (referred to as the majority class) and much fewer data for the other class (referred to as the minority class). In such datasets, learning algorithms are biased toward learning the majority class to reach better accuracy, which leads to the lack of learning in minority class data. This article first introduces a method called neighbor competition scoring (NCS) for assigning scores to samples. Each data point is assigned a lower score if it is farther away from its class samples or closer to the samples of other classes. Then, with the help of these scores, neighbor competition undersampling (NCU) method is presented for undersampling the majority class samples that are less important than other samples. The proposed method has been compared with some popular data-based methods in 10 datasets and has performed better according to the experiments.
Affordable Mobile Virtual Reality Earthquake Simulation

Earthquake is one of the natural disasters that often occurs in Indonesia. Recorded data from 1779 to 2010, earthquakes that occurred in Indonesia were more than 48,000 with strength greater than four on the Richter Scale. Therefore, earthquake simulations are carried out in offices such as schools, offices, and so on. This research focuses on using virtual reality as an earthquake simulation drill to give a better experience to the user. This research also tries to reach as many people as possible so that it's built on Android and doesn't need any additional tools, just VR glasses, with the help of Google VR prefab. The results show that the earthquake simulation drill through virtual reality is more familiar and save time than the traditional earthquake simulation drill used by the comprehensive institution.
The origins of severe software defects method

Identifying the causes which may potentially generate high financial damage was the goal of our research. To reach this goal, we conducted a case study on a system in the field of education. We studied the software defects of this system over several months and classified them based on two classification concepts: the EVOLIS and their severity. These defects prevent any essential operation or activity to be conducted through the concerned system or other systems connected to it. In fact, the occurrence of these failures causes a double financial cost to organizations: one in fixing them and the other one because of the unavailability of the system or systems. We targeted three types of software defects as sources of these failures. We conducted this study by classifying 665 software defects of a school management system and we found that the top two trigger groups are the technology and the IS architecture groups. This result led us to propose a method to identify the origins of severe software defects.
A study on the minimum display time of characters at the gazing point during reading

This paper aims to clarify the lower limit of the gaze time required during reading by investigating at a display time of 98 ms or less using the visual information processing analyzer developed in the previous research. As a result, it was found that normal reading, as if there were no window or visual field restriction, could not be performed with a display time of 42 ms or less. But that normal reading could be performed when sentences were displayed in a window of 7 characters at 56 ms.
Machine learning techniques on agro meteorological parameters to predict the crop yield in support of building decision support system to farmer’s community

Abstract Over the years, farmer grievances have been rapidly increasing due the barrage of issues that they face on a regular basis. This includes not getting good harvests, shortage of resources, etc. Decision making is an important aspect of farming, as it can improve results tremendously, hence helping farmers. With the rise in technology, and great research in the realm of Data Science, there is an increasing demand for technical solutions for farmers. To solve this, we built a Decision Support System that predicts a crop to be grown based on agrometeorological factors such as rainfall, temperature, and humidity. The system was built using various machine learning algorithms. We compared multiple models, change algorithmic parameters, and decide the best model to be used. This system aims to aid the decision-making process of a farmer, and hence help them gain maximum yield with the knowledge and resources available.
Application of Photoelectric Conversion to Dentary Bone Conduction Device Using Analog Signals

It is attempt that uses dental bones to receive audio signals are rare in the past. Therefore, the main contribution of this article is to understand and analyze the audio signal through the teeth. Our homemade laser pointer has a wavelength of 630-650 nm and a maximum output of 5 milliwatts. It will light up when the smartphone's music starts playing at a music frequency that matches the light frequency. The frequency signal of the light received by the solar panel is converted into an electrical analog signal, and then output to the DC motor. The motor shaft will not rotate under a small current, but will only slightly vibrate according to the analog frequency of the current. By biting the vibration axis with teeth, a person can transmit audio to the ossicles (ie malleus, incus and bone) through the dental bones. This device can enable people with congenital or acquired hearing impairment to access external audio.
Security Games with Insider Threats

Many cyber-security defense strategies rely on the information asymmetry between the defender and the attacker. Examples of information asymmetry include passwords and network configuration parameters. Using private information, defenders can drastically increase the computation complexity of the attacker and render his/her attacks inefficient. Availability of insider information can alter the equilibrium and favor the attacker. This paper discusses some of the attributes of private information and describes a three-player game with a partially collaborating insider to illustrate its impact.