Reviewerly/exHarmony huggingface dataset:
The Early Detection System of Risk Factors on Pregnancy, LaboUR, and Puerperium Women (A Case Study on Midwife Private Practice) in Central Lampung District in 2018

Maternal mortality is a challenge for the public health field across the world, and developing countries account for 99% of maternal deaths globally. It is estimated that 15% to 20% of all pregnant women will experience a high-risk state and obstetric complication. The evidence-based comprehensive guideline provided by the midwife is expected to detect early risk factors for pregnancy, labour, and postpartum before complication occurs. The early detection system through risk factor assessment using the scoring system requires too much time to make being referred to. Risk management cannot be used to be rule for midwives, and pregnant women and their families have not been well enough informed about the mother’s risk requirement. The purpose of this research is to build an early detection system in the midwife private practice of the Central Lampung regency to detect early pregnancy, labour, and post-childbirth to perform risk assessment, to provide risk management as needed, and to design a database of pregnant mothers to be able to produce a report of maternal service. This research uses a prototyping approach as the basis for system development. The early detection system enables midwives to assess the health of pregnant, maternal, and post-childbirth women; perform case management; provide information needed by pregnant women and families; and facilitate midwives in recording and delivering a quality report.
Privacy Preserving Profile Matching in Mobile Social Networks: A Comprehensive Survey

The advancements in wireless communication technologies and smart mobile devices enabled the proliferation of Mobile Social Networks (MSN). MSN is more flexible and popular than Online Social Network (OSN). It provides a platform for intelligent device users to search on the Internet and connect to people in close proximity to obtain the required information. Connecting with other people in close proximity is the prominent feature of MSN. In social network connections are built based on common interest and location traces. To connect with more people who have similar interest profile matching is one of the essential steps. A new connection is built by comparing the profile of two strange users. During this process, private information of a user may get leaked. A major issue greatly raised and potentially vulnerable in MSN is user privacy preservation. During profile matching the ill-intended user may receive the private information of another user and misuse it. The issue of privacy in profile matching is focused by many researchers. This paper reviews the work done in the domain of privacy and security issues of profile matching and provides a comprehensive analysis on it.
Idempotent Factorizations in the Cryptography Classroom

The RSA cryptosystem [14] requires two primes (p, q). Its security is based on the time required to factor n = pq without knowing p or q, since no polynomial-time factoring algorithms are known. Cu...
Short-Term Video Stabilization Using Ground Plane Segmentation for Low Altitude UAV Object Tracking (Conference Presentation)

Fast, efficient and robust algorithms are needed for real-time visual tracking that could also run smoothly on the airborne embedded systems. Flux tensor can be used to provide motion-based cues in visual tracking. In order to use any object motion detection on a raw image sequence captured by a moving platform, the motion caused by the camera movement must be stabilized first. Using feature points to estimate the homography matrix between the frames is a simple registration method that can be used for the stabilization. In order to have a good homography estimation, most of the feature points should lay on the same plane in the images. However, when the scene has complex structures it becomes very challenging to estimate a good homography. In this work, we propose a robust video stabilization algorithm which allows the flux motion detection to efficiently identify moving objects. Our experiments show satisfactory results when other methods shown to fail on the same type of raw videos.
Metadata learning of non-visual features: co-occurrence overlap function for rectangular regions and ground truth data

To incorporate object locations in a multi-target detection model, we assume that a close duplicate cannot be learned by the model efficiently. So, we use a region-based approach which uses more object location compared to the ground truth locations to localize the targets. The proposed model is able to learn a similarity metric with respect to the ground truth locations which is robust (low false positives) enough for varying images conditions, small aerial target sizes and using few training samples. We report preliminary results on how transfer learning of meta-data a effects small aerial target localization accuracies. Quality ranking from Intersection-over-Union (IOU) in region segmentation models on the aerial ground truth data using pre-trained models from ImageNet, AlexNet, and CIFAR-10 and initialization with three aerial datasets such as the satellite imagery XView2.
RANCANG BANGUN SISTEM INFORMASI PARIWISATA SULAWESI SELATAN BERBASIS ANDROID DENGAN MENGGUNAKAN METODE PROTOTYPING

Abstract&#x0D; Tourism is one of the sectors that has the opportunity to become the largest contributor to foreign exchange in Indonesia. Indonesia's tourism growth was recorded at 7.2 percent per year, higher than the average world tourism growth of 4.7 percent. The availability of information that is easy and fast to access can make people know about tourism in the Province of South Sulawesi so that it is expected to have an impact on increasing tourist arrivals. In this study, a tourism information system for mobile Sulawesi Province has been developed. The information system was developed by utilizing the prototyping model. The result of this research is a mobile-based tourism information system that is able to present tourism information such as tourist destinations, culinary tours, events, and photos of tourism objects. Tourism information system can be run on Android devices. The developed system can present tourism information which includes 110 destinations, 39 events, 45 culinaries, and photos of tourist attractions from 12 regions. The result of system testing was the features of the system can function properly and successfully show tourism information.
Development of a Facial Feature Based Image Steganography Technology

A new image steganography method is proposed for data hiding. This method uses least significant bit (LSB) insertion to hide a message in one of the facial features of a given image. The proposed technique chooses an image of a face from a dataset of 8-bit color images of head poses and performs facial recognition on the image to extract the Cartesian coordinates of the eyes, mouth, and nose. A facial feature is chosen at random and each bit of the binary representation of the message is hidden at the least significant bit in the pixels of the chosen facial feature.
Development of a Hand Gesture Based Control Interface Using Deep Learning

This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces. Hand detection is achieved using fast detection and tracking algorithms, and classification by a light convolutional neural network. The experimental results show a real-time response with an accuracy of 95.09%, and making use of low power consumption. These results demonstrate that the proposed system could be applied in a large range of applications such as virtual reality, robotics, autonomous driving systems, human-machine interfaces, augmented reality among others.
Convenience Environment in Library and Archives Service: Promotion Activities Evaluation in Pekanbaru

Abstract This study aimed at explaining the implementation of promotional activities done by Library and Archives Service (BPA) of Pekanbaru City in increasing the number of visitors. This qualitative study was conducted to investigate the implementation of promotional activities, the obstacles often faced when a promotional event was implemented, and the attempts to overcome the challenges. Based on the result of the study, it was found out that BPA has conducted some library promotional activities since 2013 namely brochure distribution; online promotion in the website; handing out BPA stickers, merchandises, and tokens of appreciation; and designing Pekanbaru ( digital library in hand ). Some other promotional activities were a mobile library visit to elementary schools and crowded public places such as Car Free Day and the Great Mosque of An-Nur. The competition events ever conducted were elementary school level story-telling contest, kindergarten level coloring contest, and writing game via websites for elementary school, junior high school, and senior high school students throughout Pekanbaru City. The library also participated in the exhibition conducted by the Library and Archives Service of the Riau Province. Based on the interview result with authority in the service and promotion department, it was stated that the Pekanbaru society has started to get familiar to the existence of the Library and Archives Service (BPA) of Pekanbaru City. As the evidence, the number of visitors was increasing every year. On the other hand, the other obstacles faced by the institution were the limited human resources and the small supporting fund, and the policies which were slightly unsupportive to the service and promotion section.
Life Cycle Cost Prediction of Substation Based on Advanced PSO and Least Squares Support Vector Machine

Abstract The rapid prediction of the full life cycle cost of substation has guiding significance for the construction of substation. In this paper, a substation full life cycle cost prediction model based on advanced particle swarm optimization (advanced PSO, APSO) least squares support vector machine is established. The relevant characteristic index of the substation life cycle is used as the input of the model, and the output is the substation full life cycle cost. The simulation results are compared with the prediction results of APSO optimized LS-SVM, traditional LS-SVM, BP neural network four prediction models and related performance indicators. The simulation results show that the APSO optimized LS-SVM model has better prediction accuracy, and can predict and evaluate the life cycle cost quickly and accurately during substation design and construction, and improve the economics of substation construction.
Pengujian Black Box pada Aplikasi Sistem Informasi Akademik Menggunakan Teknik Equivalence Partitioning

The purpose of this study is to assist MTS Al-Hidayah in school academic management which is focused on school info, lesson schedules, school activities, facilitating data search, and producing accurate reports. In the academic information system at the school there are still many shortcomings when validating the data to be entered, so that it can cause the data stored in the database to be incompatible with the expected data. So it is proposed testing using the Black Box method based on Equivalence Partitioning as a whole regarding the use, benefits, and results obtained from the useof the software. Equivalence Partitioning Technique that discusses testing in the aspect of validation enter seen from a valid class. The results of this study can be concluded that the results of testing the accuracy of the school information system software at MTS Al-Hidayah will provide the best solution for the school for the future.The purpose of this study is to assist MTS Al-Hidayah in school academic management which is focused on school info, lesson schedules, school activities, facilitating data search, and producing accurate reports. In the academic information system at the school there are still many shortcomings when validating the data to be entered, so that it can cause the data stored in the database to be incompatible with the expected data. So it is proposed testing using the Black Box method based on Equivalence Partitioning as a whole regarding the use, benefits, and results obtained from the useof the software. Equivalence Partitioning Technique that discusses testing in the aspect of validation enter seen from a valid class. The results of this study can be concluded that the results of testing the accuracy of the school information system software at MTS Al-Hidayah will provide the best solution for the school for the future.
Designing Computing academic programs with disciplinary and competency frameworks

We characterize qualitative skill gaps that impact the employability of computing graduates. We consider the shortage of computing talent in Latin America and particularly in Costa Rica. We establish the need for collaboration between industry and universities, which must go through developing a common language. Mutual understanding enables defining educational and career profiles and improving job descriptions for computing staff. We present a curriculum development process based on educational and engineering principles. We summarize representative experiences of curricular design of higher education computing programs with disciplinary frameworks and competencies validated by the IT industry based in Costa Rica. We prospect on future curricular design exercises of higher education computing programs.
Remittances, Financial Inclusion and Income Inequality in Africa

This paper extends the existing literature on financial inclusion by analyzing the role of financial inclusion (FI) within remittances on income inequality (Gini, Atkinson and Palma-Ratio). It examines whether FI amplifies the reduction in income inequality in a panel of 47 countries over the period 2004-2014. The empirical evidence is based on Generalised Method Moments. We used Five financial inclusion indicators (ATMs for 100,000 adults; banking branches for 100,000 adults; credits: deposits and insurance), remittances and three income inequality variables (Gini index; Atkinson and Palma ratio) as part of this study. The results show that migrant remittances and FI reduce income inequality. The results further indicate that FI implifies the impact of the migrant remittances on income inequality, revealing a complementarity between remittances and FI to reduce income inequality. The complementary action of financial inclusion on migrants' remittances offers wider access to financial services which also leads to an increase in remittances and therefore reduces inequalities.
Why Class-D Audio Amplifiers Work Well: A Theoretical Explanation

Most current high-quality electronic audio systems use class-D audio amplifiers (D-amps, for short), in which a signal is represented by a sequence of pulses of fixed height, pulses whose duration at any given moment of time linearly depends on the amplitude of the input signal at this moment of time. In this paper, we explain the efficiency of this signal representation by showing that this representation is the least vulnerable to additive noise (that affect measuring the signal itself) and to measurement errors corresponding to measuring time.
Cybercrime, ransomware, and the informatics nurse

Lee Kim is the director of privacy and security at the Health Information and Management Systems Society in Arlington, Va. This article originally appeared as: Kim L. Cybercrime, ransomware, and the role of the informatics nurse. Nursing. 2020;50(3):63-65. The author has disclosed no financial relationships related to this article.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective

Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks. We condition the networks at a pixel-level (segmentation mask) and at a global-level information (acquisition environment or lesion type). Such conditioning provides immediate access to the image-label pairs while controlling global class specific appearance of the synthesized images. To stimulate synthesis of the features relevant for the segmentation task, an additional passive player in a form of segmentor is introduced into the adversarial game. We validate the approach on two medical datasets: BraTS, ISIC. By controlling the class distribution through injection of synthetic images into the training set we achieve control over the accuracy levels of the datasets' classes.
SECURE DATA STORAGE IN THE CLOUD USING USER VALIDATION METHOD

Cloud Computing is a modern design to isolate the Daily Computing Problems. Cloud Computing is a virtual pool of assets and it additionally provides the assets to customers through web interface. Cloud Computing is a web improvement and it is used as a piece of PC advancement. Cloud Computing is another model that originates from matrix registering, utility figuring, disseminated processing, and different innovations. Cloud Computing has more focal points, for example, huge scope calculation, information stockpiling, virtualization, high unwavering quality, and low cost. The security issue is significant and it forestalls the quick advancement of Cloud Computing. Advancement in Cloud processing is getting tremendous craze and its information security has been pulling an increasingly more consideration. Although numerous techniques are accessible for giving security and protection, but not with an emphasis on the dynamic idea of the information. Right now, present a strategy that guarantees the privacy of information put away in the cloud utilizing homo morphic encryption. A strategy that incorporates byte-level auto morphism to give information honesty and classification. This paper provides some experimental comparisons for some key issues like encoded capacity, honesty check, get to control, etc. Also presents a few ideas and qualities for interpret the issues. We have also proposed encryption and decryption strategies while putting away the information in the cloud.
Hierarchical Ensemble Reduction and Learning for Resource-constrained Computing

Generic tree ensembles (such as Random Forest, RF) rely on a substantial amount of individual models to attain desirable performance. The cost of maintaining a large ensemble could become prohibitive in applications where computing resources are stringent. In this work, a hierarchical ensemble reduction and learning framework is proposed. Experiments show our method consistently outperforms RF in terms of both accuracy and retained ensemble size. In other words, ensemble reduction is achieved with enhancement in accuracy rather than degradation. The method can be executed efficiently, up to &gt;590× time reduction than a recent ensemble reduction work. We also developed Boolean logic encoding techniques to directly tackle multiclass problems. Moreover, our framework bridges the gap between software-based ensemble methods and hardware computing in the IoT era. We developed a novel conversion paradigm that supports the automatic deployment of &gt;500 trees on a chip. Our proposed method reduces power consumption and overall area utilization by &gt;21.5% and &gt;62%, respectively, comparing with RF. The hierarchical approach provides rich opportunities to balance between the computation (training and response time), the hardware resource (memory and energy), and accuracy.
Identifying People Using Body Sway in Case of Self-occlusion

We propose a method of identifying people in case of self-occlusion by using body sway measured at the head using a top-view camera. To accurately represent the identities of people as reflected in body sway, it is important to acquire accurate appearances in images. However, such images frequently contain defects, especially self-occlusion, that degrade the performance of one of the prevalent methods for identifying people because it uses whole-body regions to identify people. To solve the problem of self-occlusion in this context, our method computes silhouette images of regions at the head by applying a segmentation technique. To reflect people’s identities using body sway, we spatially divide the head region into local blocks and temporally measure movements in them. The results of experiments show that the proposed method can improve the performance of the prevalent method of identification from 17.3% to 57.9%.
Visualizations techniques for forensic training applications

The Swedish National Forensic Centre, NFC, has been developing methods for 3D modeling of crime scenes since and methods for Virtual Reality (VR) visualizations since 2016. Documentation in 3D opens up new possibilities for visualization, documentation and forensic analysis and VR as well as Augmented Reality (AR) may within the near future become common practice in several forensic training situations. NFC has developed a proof of concept system for VR Crime scene reconstruction which has been tested by over two hundred individuals, both from law enforcement and from other fields, and the most common comment is that it is incredibly realistic reconstruction and that it is easy to understand how this can be of value within a crime scene investigation. The main limitations have been seen to lie within the 3D-modelling itself, creating close to perfect and realistic models takes a lot of time and effort, time that usually is not reasonable to add to an criminal investigation. However, for training purposes the payback of increased efficiency might be high enough to motivate the cost. For example being able to train in situations that are usually hard to recreate due to for example risk of injury or public safety reasons or being able to quickly switch between completely different environments without having to travel or make preparations. There is also a fundamental difference with learning from experience rather than from theory, and this is a main motivator behind trying to create an as immersive and realistic training experience as possible.
Artificial intelligence implementation for multi-domain sensor suite optimization

In the era of big data, it appears imperative that future battle management systems be able to identify, decipher, and prioritize actionable information. This calls for fusing information and synchronizing operations across multiple domains and multiple sensor modalities. Fusing data from a diverse range of sensors across multiple domains is critical for improved situational awareness to enhance warfighters’ effectiveness. The basis for analyzing multiple field radar system data in real time remains a challenging yet promising threshold for military operational intelligence. Multiple domain sensor systems used to gather field intelligence requires gathering different types of information processing at required speeds that fall short of human reaction time and cognition. To press the advancement of field intelligence, the analysis, fusion and optimization of multi-domain systems, sensor data analysis is explored using probabilistic machine learning and supplemented heuristic signal processing to provide a basis for multi-system data integration, analysis and sensor suite selection.