Reviewerly/exHarmony huggingface dataset:
Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&amp;E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.
Serial and parallel implementation of Needleman-Wunsch algorithm

Needleman-Wunsch dynamic programming algorithm measures the similarity of the pairwise sequence and finds the optimal pair given the number of sequences. The task becomes nontrivial as the number of sequences to compare or the length of sequences increases. This research aims to parallelize the computation involved in the algorithm to speed up the performance using CUDA. However, there is a data dependency issue due to the property of a dynamic programming algorithm. As a solution, this research introduces the heterogeneous anti-diagonal approach, which benefits from the interaction between the serial implementation on CPU and the parallel implementation on GPU. We then measure and compare the computation time between the proposed approach and a straightforward serial approach that uses CPU only. Measurements of computation times are performed under the same experimental setup and using various pairwise sequences at different lengths. The experiment showed that the proposed approach outperforms the serial method in terms of computation time by approximately three times. Moreover, the computation time of the proposed heterogeneous anti-diagonal approach increases gradually despite the big increments in sequence length, whereas the computation time of the serial approach grows rapidly.
Mengenal Aksara Bali: Balinese Script game education based on mobile application

Balinese Scriptwriting, as one of Balinese cultural richness, is going to extinct because of its decreasing use. Governor of Bali, I Wayan Koster, expressed concern about the increasingly diminishing interest of the community, especially among the younger generation using it. This research focused on the development of a Balinese Script game education based on a mobile application called Mengenal Aksara Bali. In this research area, Mengenal Aksara Bali offered a solution as an application that is not only for playing,but also provided features for learning Balinese Script glyphs (Aksara Wresastraand numbers). Mengenal Aksara Bali was implemented by using Construct2(a powerful game creator designed especially for 2D games). Through Black Box testing, its functions in the application wasrunning as expected
A modified symmetric local binary pattern for image features extraction

The process of identifying images and patterns is one of the most important processes of digital image processing, which is used in many applications such as fingerprint recognition, face recognition and pattern recognition. Due to the large size of the image, the process of identifying the image requires a great time, which in turn leads us to extract some characteristics of the magnitude of the volume, which can be used as an identifier to retrieve the image or recognize it and thus we have devoted a lot of time to identify the image. In this research paper, a modified symmetric local binary pattern (MSLBP) method was proposed to extract texture features. The proposed algorithm was implemented on many digital fingerprint’s images and the local structure features of these images were obtained. Several image recognition experiments are conducted on these features and compared with other algorithms. The results of the proposed algorithm showed that the digital image was represented in a very small size and furthermore the speed and accuracy of image recognition based on the proposed method was increased significantly. Unlike the methods based on LBP, the proposed method gives the same features of the image even if the image was rotated with any angle.
Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
Topological Properties of Epidemic Aftershock Processes

Earthquakes in seismological catalogs and acoustic emission events in lab experiments can be statistically described as a linear Hawkes point process, where the spatio-temporal rate of events is a linear superposition of background intensity and the aftershock clusters triggered by preceding activity. Traditionally, statistical seismology has interpreted this model as the outcome of an epidemic branching process, where one-to-one causal links can be established between mainshocks and aftershocks. Declustering techniques have been used to infer the underlying triggering trees and relate their topological properties with epidemic branching models. Here, we review how the standard Epidemic Type Aftershock Sequence (ETAS) model extends from the Galton-Watson (GW) branching processes and bridges two extreme cases: Poisson sampling and scale-free power-law trees. We report the most essential topological properties expected in GW epidemic trees: the branching probability, the distribution of tree size, the expected family size, and the relation between average leaf-depth and tree size. We find that such topological properties depend exclusively on two sampling parameters of the standard ETAS model: the average branching ratio $N_b$ and the exponent ratio $α/b$ determining the branching probability distribution. From these results, one can use the memory-less GW as a null-model for empirical triggering processes and assess the validity of the ETAS model to reproduce the statistics of natural and artificial catalogs.
Profiled glucose forecasting using genetic programming and clustering

This paper proposes a method to obtain accurate forecastings of the subcutaneous glucose values from diabetic patients. Statistical techniques are applied to identify everyday situations of glucose behaviors and discover glucose profiles. This knowledge is used to create predictive models with genetic programming. The time series of glucose values, measured using continuous glucose monitoring systems, are divided into 4-hour, non-overlapping slots and clustered using a technique based on decision trees called chi-square automatic interaction detection. The glucose profiles are classified using the decision variables in order to customize the models for different profiles. Genetic programming models created with glucose values from the original dataset are compared to those of models created with classified glucose values. Significant differences and associations are observed between the glucose profiles. In general, using profiled glucose models improves the accuracy of the predictions with respect to those of models created with the original dataset.
State Channel as a Service Based on a Distributed and Decentralized Web

Currently, developers and researchers are dedicated to finding better ways to achieve greater scalability of various blockchain platforms. Focused on the Ethereum blockchain platform, the state channels are currently the only maturely researched and implemented a solution for achieving scalability. However, there are still several problems, such as transparency of state channel networks, transaction traceability, and the incapability that the off-chain state is transferred back on the blockchain network in an ad-hoc manner. To solve the aforementioned problems, we propose a novel state channel solution in the form of a State Channel as a Service, which, although off-chain, still incorporates a secure distributed and decentralized network. This solves the challenge of transparency and traceability while giving users the confidence that only the valid last off-chain state is transferred back on the blockchain network. With the proposed solution, there is no need for users to monitor state channel activities for possible malicious actions. Implementation was performed in the form of a payment channel system, presenting a potential use case for the SCaaS. Furthermore, we performed a security and performance analysis, which shows that the solution is secure and by a factor of 12 more efficient than the classical on-chain payment systems. To evaluate and prove the contributions of the proposed solution, quantitative, and qualitative comparisons with selected related works (i.e., Raiden and Celer) were also performed.
A survey of biometric approaches of authentication

Information security refers to a means of preventing unauthorized users from access to information.Risk management usually adopts in information security to provide solutions to security challenges by minimizing risks.Risk can be minimized by administrative control and defense mechanisms.Access control can also enforce the user right access and thereby minimizing risks.Authentication is one of the techniques used in access control systems to protect unauthorized access.Many approaches for authentication have been proposed to address security challenges.These approaches have a conflict with the system usability and therefore required the modification of tradition password techniques for better solutions.Information Authentication is the common method used by security experts to verify the users' identities before getting access right into the system.Access controls are enforced for all users, irrespective of categories they belong.Traditional authentication methods [1] were enough to protect the unauthorized access right as many security breaches were reported.Therefore, advanced security methods that are based on human features required.
Research on inverse simulation of physical training process based on wireless sensor network

In order to improve the control ability of the human body in the process of physical training, it is necessary to carry out the inverse simulation analysis of the physical training process and establish the process control model of the physical training. The complex problem of high-dimensional spatial motion planning involved in physical training is decomposed into a series of sub-problems in low-dimensional space, and the inertial attitude parameter fusion is carried out according to the position and pose state of the human body in the end of the workspace during the process of physical training. The design of sensor node and base station in the system can realize real-time collection of motion parameters of motion collectors. The multi-dimensional control of physical training process is carried out by fuzzy constraint and inverse integral control, and the attitude parameters of human body are adjusted by means of mechanical analysis model and inertial parameter analysis method. The simulation results show that the inversion simulation control has better convergence, higher control quality, and better inverse simulation performance in the process of physical training, which can effectively guide physical training and improve the effect of physical training.
Grammatical Evolution-based Analog Circuit Synthesis

Computer aided circuit design is becoming one of the mainstream methods for helping circuit designers. Multiple new methods have been developed in this field including Evolutionary Electronics. A lot of work has been done in this field but there is still a room for improvement since some of the solutions lack the flexibility (diversity of components, limited topology etc.) in circuit design or lack complex fitness functions that would enable the synthesis of more complex circuits. The research presented in this article aims to improve this by introducing Grammatical Evolution-based approach for circuit synthesis. Grammatical Evolution offers great flexibility since it is rule based – adding a new element is as simple as writing one additional line of initialization code. In addition, the use of a complex multi-criteria function allows us to create circuits that can be as complex as required thus further increasing the flexibility of the approach. To achieve this, we use a combination of Python and SPICE to create a series of netlists, evaluate them in the PyOpus environment, and select the best possible circuit for the task. We demonstrate the efficiency of our approach in three different case studies where we automatically generate oscillators and high/low-pass filters of second and third order.
Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria.
Design and Analysis for Fall Detection System Simplification

This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system that can be easily implemented and adopted. The methodology is based on the configuration of specific types of sensors, machine-learning methods and procedures. The protocol is divided into four phases: (1) database creation (2) data analysis (3) system simplification and (4) evaluation. Using this methodology, we created a multimodal database for fall detection and human activity recognition, namely UP-Fall Detection. It comprises data samples from 17 subjects that perform 5 types of falls and 6 different simple activities, during 3 trials. All information was gathered using 5 wearable sensors (tri-axis accelerometer, gyroscope and light intensity), 1 electroencephalograph helmet, 6 infrared sensors as ambient sensors, and 2 cameras in lateral and front viewpoints. The proposed novel methodology adds some important stages to perform a deep analysis of the following design issues in order to simplify a fall detection system: a) select which sensors or combination of sensors are to be used in a simple fall detection system, b) determine the best placement of the sources of information, and c) select the most suitable machine learning classification method for fall and human activity detection and recognition. Even though some multimodal approaches reported in literature only focus on one or two of the above-mentioned issues, our methodology allows simultaneously solving these three design problems related to a human fall and activity detection and recognition system.
Augmented reality application centered on the child as a resource in a virtual learning environment

Given its characteristics, augmented reality (AR) is presented as an alternative resource in a virtual learning environment (VLE) by providing virtual elements in the real world, which can facilitate learning. This article aims to describe the development of a mobile application with guided AR under the fundamentals of User Centered Design (UCD) in order to be integrated into a VLE. The development strategy consisted of using the UCD and in its phases making use of tools such as: surveys for the collection of information, Quality Function Deployment to identify and prioritize requirements, AR technology for prototyping and finally usability tests to identify design problems. As a result, it was possible to demonstrate the viability of this procedure in the development of interfaces of this type, considering that the resources that contextualize the needs and tastes of its users can provide better facilities and motivation within their learning. The case study studied is that of English for primary school children, which allowed verifying that this detailed framework can be replicated to other areas of interest and allow teachers to integrate AR applications in face-to-face or online learning environments.
Metric Factorization with Item Cooccurrence for Recommendation

In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.
Emerging Technologies and Developments in Cloud Computing: A Systematic Review

For many organizations, cloud computing is an adoptable technology with its dynamic nature of flexibility and usage of virtualized resources as services over the Internet.Because of this, it mostly impacts the IT industry and the academia.It is an exceptional alternative for both IT industry and academia to operate their information systems efficiently without spending more capital on computers and network devices, particularly under budget shortage.In this paper, we reveal advance review focusing on cloud computing concept including definition, history, characteristics, service models, deployment model, benefits and challenges.Furthermore, cloud computing architecture, cloud computing technologies and production, cloud computing applications in Indian context and motivation towards cloud in recent time are also discussed.This paper can be very helpful for new researcher of this field and who is interested in knowing what it is going on recently in cloud computing.
The Correlation Analysis between Korean Middle School Students’ Emotional Level and Friendship in Science Learning

This study aimed to find out the correlation between emotional levels and friendship in science learning. The participants in this study were 30 third-grade students at a girls’ middle school in a Korean metropolitan city. For nine months, their science classes were conducted by one teacher in charge of science class and five students were organized into a group. The science learning emotional test tool consisted of 35 questions on the rating scale of 5 phases and the peer nomination method was adopted for the friendship survey in science learning. The test was conducted during the last hour of science class. In the results of analyzing students’ science learning emotion levels, students showed high average scores in three emotional elements of positive science learning (joy, satisfaction, and interest), showing overall positive feelings about science learning. After analyzing friendship among the students, the connection between students was analyzed to be close to 1 and none of the students were isolated. Two out of 30 students were given ninth appointment from peers and formed a central axis in the classroom network. On the other hand, two students in the class were not chosen by other students and only pointed out helpful colleagues in science learning. After analyzing the correlation between the emotional level and friendship in science learning based on these two studies, it was analyzed that there was a strong static correlation between two variables. Therefore, since the bond between emotion and friendship in science learning is closely related, there is a need for science learning to take into account the interrelationship between two variables.