Reviewerly/exHarmony huggingface dataset:
A Cloud Computing-Based Model for Wildlife Conservation and Health Care Improvement in Endangered Wild Life Animals

The era of the Information Technology revolution has endeavored mankind with enormous technological comforts. Apart from human health care, cloud computing integrated with other technologies can be exploited for the welfare of endangered wildlife animals that are currently on the verge of facing extinction in the near future. International Union for Conservation of Nature (IUCN) has declared a huge number of wildlife species into red list falling into 'critically endangered', 'endangered' and 'vulnerable' species because of pathogenic diseases, poaching, habitat degradation, and climate change. The aim of the chapter is to propose a cloud computing based wildlife health care model to preserve, sustain and protect the global wildlife heritage and wildlife conservation by identifying and providing treatment to diseased animals, monitoring their health conditions periodically and protecting against poaching. The proposed health care model makes use of various wearable sensors implanted on and within the body of an animal. The sensors perform data gathering about different body-related parameters to be transmitted to a cloud system for analysis and reporting a required timely action. When a cloud system senses emergency with respect to the health-related or threat-related incident, the information is immediately signaled to wildlife physician and wildlife ranger respectively to act accordingly.
Fractal Anti-Counterfeit Label Comparison Using Combined Image Features and Clustering

The comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy, which produces unreliable results. To best address these disadvantages, an automatic comparison method for fractal anti-counterfeit labels is proposed. The method can effectively extract the color features, texture features, and shape features of anti-counterfeit labels, and perform cluster analysis and comparison. First, a color volume histogram is used to extract the color and pixel space information features from the fractal anti-counterfeit labels. To compensate for the deficiency of using a single feature, texture and shape features were also extracted based on the median robust extended local binary patterns (MRELBP) and Hu moments. Next, based on feature extraction, k-means clustering is performed to ensure that as many of the same types of labels as possible can be divided into the same clusters and that the comparison can only be performed on one or a few clusters. The experimental results show that the speed and quality of fractal anti-counterfeit label comparison effectively improved after the clustering analysis. Furthermore, based on visual identification and unreliable comparison results, the proposed method is expected to help consumers quickly identify low-quality, counterfeit products.
Automated Theorem Proving via Interacting with Proof Assistants by Dynamic Strategies

Proof assistants offer a formal language to write mathematical definitions, executable algorithms, and theorems together with an environment for interactive development of machine-checked proofs. Developers manually construct definitions and lemmas on proof assistants to prove the theorems. However, the time and labor costs of manually proving theorems in proof assistants remain prohibitively high. Therefore, proving the theorem automatically for the sizeable formal project becomes significant. In this paper, we propose SmartCoq, a proof search system that uses a dynamic strategy to solve the problem of automating the interaction with proof assistants. The design of our dynamic strategy is flexible and straightforward: it can automatically optimize itself based on theorems without manual intervention. SmartCoq uses dynamic strategies to learn from the wrong paths in the past and find a correct path to complete the proof. We demonstrate SmartCoq on the proof obligations from a large practical proof project, the CompCert verified C compiler, and the result shows that it can automatically solve 14.5% of proofs in our test dataset.
Money Authenticity Detector Design Using Artificial Neural Network Method for Blinds

Abstract Heretofore, there are still many people having difficulties to differ authenticity of paper money, especially for blind people. An individual is defined blind when he must use alternative technique to replace his visual function. In this case, a blind person may use his hearing or touching senses to read the demanded information. This research aims to create a device to find out authenticity of paper money with voice output. This device uses TCS3200 sensor and Led UV to detect the authenticity of paper money it gained red, green, and blue as the input process by using artificial neural network. This method would firstly undergo data training so it would have scores to classify the authenticity. The test of paper money used 1DR 1,000.00, IDR 2,000.00, IDR 5,000.00, IDR 10,000.00, IDR 20,000.00, IDR 50,000.00, and IDR 100,000.00 issued in 2000 until 2016 with 4 different test positions. Based on the trials, it gained successful level in detecting the authenticity by using the network as much as 100%.
Sub-pixel displacement measurement based on Taylor expansion theory

Traditional vision measurement algorithms have many shortcomings, such as complex process, multi-parameter adjustment and the integer pixel accuracy. To solve these problems, a sub-pixel displacement measurement algorithm based on Taylor expansion is proposed. The displacement vector between adjacent frames is modeled in the first step, and then shift equation of the template image is constructed based on assumptions of constant brightness and spatial consistency. After performing a first-order Taylor series approximation, the displacement vector is obtained by least squares estimation. Finally, through iteratively updating the position of template image, displacement is rapidly extracted. In order to verify the accuracy and effectiveness of the Taylor expansion algorithm, the accuracy test of the grating ruler platform in the presence of the target and the vibration measurement experiment on vibration motor in the absence of the target are designed. Experimental results show that, compared with template matching algorithm based on normalized cross correlation, for the experiments mentioned in this paper, the proposed algorithm can control the calculation time for one frame in less than 1 ms, and achieve higher sub-pixel computation accuracy. Compared with the actual set frequency of 10.78 Hz, the measured frequency of the motor is very consistent with a relative error of 1.1%. It also verifies that the Taylor expansion algorithm proposed in this paper is a reliable and effective non-contact measurement method with important practical application value for vibration measurement.
Sistem Manajemen Cafe dan Resto Berbasis Web dengan Framework Codeigniter dan MySQL

The cafe and restaurant management system is built to know the restaurant-restaurant or cafe-cafe business process by utilizing or using a smartrestaurant system. The method used in this study is the waterfall method. Research shows that utilizing a restaurant's smart application can provide convenience in transacting some of which are useful for small to mid-sized restaurant / cafe management which can be used to record waiter menus and cashiers for sales transactions. Ease in recording financial statements, income statement and accounts receivable. Time-effective and efficient transactions in the use of paper for paper (papperless); Recording of cash out and payroll employees
AICO, Artificial Intelligent COach

Choosing effective strategies before playing against an opponent team is a laborious task and one of the main challenges that American football coaches have to cope with.For this reason, we have developed an artificial intelligent American football coach (AICO), a novel system which helps coaches to decide the best defensive strategies to be used against an opponent.Similar to coaches who prepare a winning game plan based on their vast experience and previously obtained opponents' statistics, AICO uses power of machine learning and video analysis.Tracking every player of the last recorded matches of the opponent team, AICO learns the strategies used by them and then calculates how successfully their own defensive strategies will perform against them.We have used 7350 videos in our experiments obtaining that AICO can recognize the opponent's strategies with about 93% accuracy and provides the successful rate of each strategy to be used against them with 94% accuracy.
IMPRESSION AS A PROBLEM IN MILITARY PSYCHOLOGY

Psychological problem of an impression seems to be one of the most important problems in military psychology, its correct understanding will mean successful reference-points both in military operations and in coping with consequences of participation in hostilities, particularly PTSD. We consider concept of «impression» as an artifact which has absorbed enormous amount of nuances of values and senses. It took its place within subtle process of human cognition of one’s own internal world. Research analysis must provide answers about psychological essence of impression, its origin, further fate, disappearance and connections with other elements of psyche. We cannot agree with concept of an impression being referred as an image. In any case, even if we can speak about an image (focus on spontaneity and integrity of an impression is definitely suitable here), then it couldn’t be neither visual nor auditory; even if the impression is an image, then it is an amodal image of the world (A. N. Leontiev). Impressions occur to be related to perception, but at the same time are not reduced to the latter. An impression has very close and obvious relations to emotions and feelings. Experience indicates about pronounced individual character of impressions. It is reflected in the fact that not every complex irritant can facilitate emergence of one’s impression. On the other hand, different people in the same impression-causing situation can get very different impressions. Impressions remain as memory traces and personal experience accumulators.
Graphical Oracles to Assess Computer-Aided Diagnosis Systems: A Case Study in Mammogram Masses and Calcifications Detection

Computer-Aided Diagnosis (CAD) systems provide a second opinion to health professionals about the possible existence of an anomaly. Evaluation of CAD systems is a challenge and most of the traditional metrics requires the constant participation of experts. This paper presents an approach for evaluating CAD systems using concepts of Content-Based Image Retrieval and graphical oracles. After implementing feature descriptors and selecting three similarity functions, two metrics are proposed to measure the efficiency of CAD systems. A case study was conducted considering three simulated CAD systems to detect masses and calcifications in mammographic images. The results indicated that the our approach is as robust as traditional metrics with respect to performance evaluation. However, our approach is more flexible than traditional metrics because evaluators can choose the more adequate features to assess a particular CAD system.
Deep Learning Strategies in Media Teaching System Based on ADABoost Algorithm

Artificial intelligence is applied in various industries, adding new development impetus to the industry. Under the background of artificial intelligence, to promote the deep learning of English teaching, we should make layered learning plan by using the high efficiency of the teaching end of artificial intelligence; create targeted learning methods by using the individualization of artificial intelligence teaching; promote collaborative learning by using the teaching resources of artificial intelligence; and promote the students’ autonomous deep learning by using the generativeness of thinking class.
Fuzzy dynamic models of situation analysis, decision support, and control in complex systems

Abstract In this work we consider the theoretical foundations of building fuzzy models of system dynamics, analysis of situations, their development, and control in complex weakly-structured systems based on knowledge modeling and expert preferences. The proposed fuzzy models can serve as mathematical tool for analyzing the behavior of a system in various situations, as well as to serve as the basis of specialized computer simulation systems of decision support in unstructured fuzzy situations. As is known, decision making problems under uncertainty can be divided into two groups: decision making in static situations and decision making in dynamic situations. For decision making in static situations, the methods and models of decision support based on the theory of choice are developed. For decision-making in dynamic situations, the dynamic models, including those based on expert knowledge, are used. To build a dynamic model of a situation, the expert knowledge about physical, economic, or social processes occurring in a system, which are represented as a cognitive map, is employed. The models of analysis of static situations are focused on evaluation and ordering of alternatives, and models of analysis of dynamic situations are focused on generating of strategies (alternatives) to achieve the targets of control, that is, the desired target state of the situation. Thus, one of the aims of this paper is to develop mathematical methods for constructing integrated decision support models based on fuzzy models of hierarchical estimation, control, and fuzzy cognitive modeling.
Named Entity Recognition of South China Sea Conflicts

Abstract Online news articles not only provide us with useful and reliable information and reports, it also eases information extraction and gathering for research purposes especially in Natural Language Processing (NLP) and machine learning (ML). The topics regarding the South China Sea have been popular lately due to the rise of conflicts between several countries claim on the islands in the sea. Gathering data through Internet and online sources proves to be easy, but to process a huge amount of data and to identify only useful information is no longer possible. Because of that, relevant information and the classification of news articles in relation to the conflicts need to be done. In this paper, a model is proposed to use NER that search for and classifies important information regarding to the conflicts. In order to do that, a combination of POS and NER are needed to extract meaningful information from the news. This study also aims to classify conflict related news by using Conditional Random Field (CRF) algorithm as classification method by training and testing the data.
Constructing Connectome Atlas by Graph Laplacian Learning

The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a “common” brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.
Building a Scalable Infrastructure

This paper documents our experience in building a scalable cyberinfrastructure to grow computational research and enhance collaboration across the research enterprise. It describes a modernized research computing system built on the principles of Software Defined Infrastructure (SDI) and DevOps. This approach helps develop, test and deploy enhancements to our High Performance Computing (HPC) platform. By separating development from production, the approach offers opportunities to train developers new to distributed systems and HPC platforms, safely gaining advanced skills in demand but not common in today’s job market. Providing a deployment pipeline empowers teams with varying skill levels to effectively contribute enhancements to HPC platforms extending the capacity of operations professionals. The paper highlights our experience in setting up a web frontend for HPC resources in the form of Open OnDemand. The Open OnDemand web interface new users transition from their dedicated workstations to the HPC ecosystem.
HYBRID TRANSFORM BASED ROBUST WATERMARKING SCHEME FOR VIDEO OBJECTS

With the rapid advancement in technology in the recent years, the usage of Internet has immensely increased leading to voluminous generation and transfer of multimedia data. With the increase in data dissemination, there exists a need for prevention of potential issues like copyright violation, data authenticity and ownership identity theft. This paper provides a method to guard the copyright information with the insertion of a watermark image in the video frames that helps to protect the ownership identity of the original video. In the suggested technique, an image is embedded into LLLL band obtained by second level wavelet disintegration as a watermark as well as into the HH band acquired by applying single level discrete wavelet transform. Finally, the robustness of the image watermark has been evaluated after implementing several attacks like cropping, rotation and noise addition and effectively taken out the watermark from the affected frames of the video.
Synergized Bootstrapping: The Whole is Faster than the Sum of Its Parts

Re-sampling methods are popular for assessing uncertainty, for testing hypotheses, or for cross-validation because of their simplicity. They all rely on a similar scheme: generating replicated datasets by sampling data points from an original dataset, fitting a model or conducting a statistical test on each of these, and aggregating the results. However, when fitting the model or conducting the statistical test becomes time-consuming, re-sampling methods become impractical because of the many replications. Many methods have been proposed to alleviate the computational burden, but they generally do not incorporate two key features of re-sampled datasets. One, re-sampled datasets all stem from the same origin and therefore have similar characteristics. Two, there is a large class of cost functions for which the cost of a parameter set given data can be computed by summing its costs across the individual data points. As a consequence, once the costs of the individual data points are known, the parameter set’s cost can be obtained for any of the cost functions related to one of the replicated datasets. The synergized bootstrap method put forward in this paper exploits these two features to accelerate the optimization procedures for re-sampling methods. It is applied to the non-parametric bootstrapping of the parameters of a univariate mixture model, of which the min-log-likelihood function can be shown to have multiple local minima, using the differential evolution heuristic as global optimizer. It is demonstrated that the synergized method can lead to incredible accelerations (up to 100-500 times faster) while being more accurate than the standard DE method.
IoT-Based Activity Recognition for Process Assistance in Human-Robot Disaster Response

Mobile robots like drones or ground vehicles can be a valuable addition to emergency response teams, because they reduce the risk and the burden for human team members. However, the need to manage and coordinate human-robot team operations during ongoing missions adds an additional dimension to an already complex and stressful situation. BPM approaches can help to visualize and document the disaster response processes underlying a mission. In this paper, we show how data from a ground robot's reconnaissance run can be used to provide process assistance to the officers. By automatically recognizing executed activities and structuring them as an ad-hoc process instance, we are able to document the executed process and provide real-time information about the mission status. The resulting mission progress process model can be used for additional services, such as officer training or mission documentation. Our approach is implemented as a prototype and demonstrated using data from an ongoing research project on rescue robotics.
An Enhanced Segmentation Technique for Smokers RBC Rouleax Coin Stacking

Abstract Smoking is one of the major factors of having coin stacking formation of Red Blood Cell or commonly known as RBC that can cause blood clots and can lead to stroke. Smokers tends have thicker, high count, and overlapping of RBC compared to non-smokers. Blood cell detection plays significant part in biomedical field. There are two methods in detection of RBC, manual inspection by medical experts and automated machines. The manual inspection process is detection of blood cells under a microscope that is more prone to human error and time consuming, while automated hardware solutions like automated haematology machines are available, due to high cost it is not widely available to poor and developing countries who have a high statistic of smokers. Smokers have a high tendency of overlapping RBC or commonly known as rouleaux coin stacking cell formation. This study presents an enhanced segmentation technique that can detect the high degree of overlapping RBCs of smokers using digital image processing that can be helpful in the medical field.
Computational Cognitive-Semantic Based Semantic Learning, Representation and Growth: A Perspective

In this era of data-analytics, the unstructured text remains the main data format. The vector space model is commonly used in representing and modeling text semantics; however, it has some limitations. The main alternative for the vector space model is the graph model from graph theory. Then, the question is: On what basis should text semantics be modeled using graph modeling? Using semantic-graphs, cognitive-semantics tries to answer this question, as it models underlying mechanisms of our human cognition modules in learning, representing and expanding semantics. The fact that textual data is produced in the form of human natural language by human cognition skills means that a reverse-engineering methodology could be promising to extract back semantics from text. In this paper, we present a systematic perspective of the main computational graph-based cognitive-semantic models of human memory, that have been used for the semantic processing of unstructured text. The applications, strengths, and limitations of each model are described. Finally, open problems, future work and conclusions are presented.