Reviewerly/exHarmony huggingface dataset:
Diagnosing chicken diseases using fuzzy Tsukamoto web-based expert system

Abstract Chicken is one source of protein favored by the community. One of the benefits we can take on chicken is meat and eggs. But chicken disease becomes one of the problems that are very detrimental to chicken farmers. This was assessed because of the lack of knowledge of farmers in handling conditions that occur in chicken. Accurate information about handling chicken disease among farmers is still difficult to obtain. Therefore, it is necessary to design an expert system application in detecting diseases that are experienced by chickens based on the symptoms shown and how to handle them. The method used is fuzzy Tsukamoto as an inference engine to determine the disease based on its symptoms and use the PHP programming language in making applications and MySQL as its database. Based on the results of the diagnosis in the sample taken showed the results of the fuzzy Tsukamoto diagnosis were 97.97% (very good) with the type of disease: typhoid chicken based on the symptoms seen. The solution given is isolation of chickens that show symptoms and give disinfectant water, spray disinfectant, replace all water and feed, clean the entire cage, and destroy dead chickens.
Using Mathematical Models to Describe the Dynamics of the Spread of Traditional and Cryptocurrency Payment Systems

As new payment systems emerge, it is important to predict their dynamics and separate payments from speculative transactions. Based on the classification of payment systems as ‘one-sided’ and ‘two-sided’ we use mathematical methods to predict their behavior over time. By introducing the fatigue factor and involvement factor, we draw the differential equation that could be used for the analysis of the payment systems’ behavior. The analysis shows that any changes in the initial state of the system fade over time; one-time circumstantial changes (such as sudden regulatory change or promotional campaign) have an only temporary effect. Our equations can also be used to identify the prevailing type of transactions (P2P or C2B) in ‘mixed’ systems. Our model is verified by empirical data from payment card statistics, WebMoney registration rate and can be used to analyze Bitcoin usage as well. Further, research on using the model to explain and predict the competitive effects is also proposed. This is the first attempt at using differential equations for payment system analysis with a model verified by empirical evidence.
Heterogeneous Exascale Computing

Exascale services bring new unique challenges that the current computational, big data and workflow solutions are unable to meet. The chapter includes a detailed description of selected exascale services with known state of the art in extreme date solutions. The integration of requirements and the analysis of the state of the art in the exascale field is centered in on a description of a high-level architectural approach. The next main contribution of the paper is the description of the architecture capable to handle heterogeneous exascale services coming from both academic as well as industrial sphere. Those two models represent a (conceptual, and technological) design of a platform that addresses the requirements of the use cases. The resulting architecture will help us provide computing solutions to exascale challenges within the H2020 project PROCESS.
MODELING OF THE PERCENTAGE OF AIDS SUFFERERS IN EAST JAVA PROVINCE WITH NONPARAMETRIC REGRESSION APPROACH BASED ON SPLINE TRUNCATED ESTIMATOR

Acquired Immune Deficiency Syndrome (AIDS) is a set of symptoms and infection or a syndrome that arise due to damage to the human immune system. AIDS is a health problem that often occurs in developing countries, including in Indonesia. East Java Province was ranked first in the highest number of AIDS sufferers in Indonesia ever reported from 1987-2016 as many as 16,911 people out of a total of 86,780 people. In order to overcome AIDS cases, it is necessary to know the factors that influence it. Data on the percentage of AIDS sufferers and their predictor variables have irregular data patterns or do not match in certain patterns, then the method that can solve these problems is by using the nonparametric regression based on spline truncated estimator. A spline truncated estimator is a segmented polynomial function that has better flexibility because there are knot points indicating changes in data behaviour patterns. The data that used in this study is a secondary data in 2016 obtained from the East Java Provincial Health Office. The results showed that the determination coefficient (R2) based on the best model of 93.84%. This shows that the variables of health facilities, blood donors, health workers, condom users, and residents of 25-29 years are able to explain 93.84% of the percentage of AIDS sufferers in East Java Province in 2016.
Error Detection in the Decentralized Voting Protocol

The decentralized voting protocol based on the Lagrange interpolant construction is analyzed for the potential error detection in the set of shares. We suggest an algorithm for the error locator polynomial construction based on procedure of computation the determinants of the Hankel type generated by the sequence of special symmetric functions of the set of shares.
A Decentralized Application for Fostering Biodiversity: Opportunities and Challenges

Biodiversity and indigenous knowledge systems present numerous opportunities to advance the state of medicines and health. However, considerable friction points exist for fostering biodiversity value chain. In this paper, we present a case for a blockchain enabled ecocentric decentralized framework for balancing innovation, regulations and sustainability. We enumerate some of the key requirements of the solution we built. We also list our learnings about the challenges in its realization. Such a solution has the potential to address the larger social good by empowering traditional populations to receive benefits automatically.
Syntactic Translation of Message Payloads Between At Least Partially Equivalent Encodings

Recent years have seen a surge of interest in using IoT systems for an increasingly diverse set of applications, with use cases ranging from medicine to mining. Due to the disparate needs of these applications, vendors are adopting a growing number of messaging protocols, encodings and semantics, which result in poor interoperability unless systems are explicitly designed to work together. Key efforts, such as Industry 4.0, put heavy emphasis on being able to compose arbitrary IoT systems to create emergent applications, which makes mitigating this barrier to interoperability a significant objective. In this paper, we present a theoretical method for translating message payloads in transit between endpoints, complementing previous work on protocol translation. The method involves representing and analyzing encoding syntaxes with the aim of identifying the concrete translations that can be performed without risk of syntactic data loss. While the method does not facilitate translation between all possible encodings or semantics, we believe that it could be extended to enable such translation.
Abnormal Pattern Prediction: Detecting Fraudulent Insurance Property Claims with Semi-Supervised Machine-Learning

Abnormal pattern prediction has received a great deal of attention from both academia and industry, with various applications (e.g., fraud, terrorism, intrusion detection, etc.). In practice, many abnormal pattern prediction problems are characterized by the simultaneous presence of skewed data, a large number of unlabeled data and a dynamic and changing pattern. In this paper, we propose a methodology based on semi-supervised techniques and we introduce a new metric – the Cluster-Score – for fraud detection which can deal with these practical challenges. Specifically, the methodology involves transmuting unsupervised models into supervised models using the Cluster-Score metric, which defines an objective boundary between lusters and evaluates the homogeneity of the abnormalities in the cluster construction. The objectives are to increase the number of fraudulent claims detected and to reduce the proportion of claims investigated that are, in fact, non-fraudulent. The results from applying our methodology considerably improved these objectives. The experiments were performed on a real world data-set and are the results of building a fraud detection system.
Understanding exploration and exploitation powers of meta-heuristic stochastic optimization algorithms through statistical analysis

Understanding of exploration and exploitation powers of meta-heuristic stochastic optimization algorithms is very important for algorithm developers. For this reason, we have recently proposed an approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms according to the distribution of the solutions in the search space, which is also presented in this paper. Its main contribution is the support to identify exploration and exploitation powers of the compared algorithms. This is especially important when dealing with multimodal search spaces, which consist of many local optima with similar values, and large-scale continuous optimization problems, where it is hard to understand the reasons for the differences in performances. Experimental results showed that our recently proposed approach gives very promising results.
The Method of Chan Kook Gim’s Old Testament Theology and Its Transition Process

The purpose of this article is to study the theological method with which Chan Kook Gim (1927-2009), an Old Testament professor of Yonsei University, interpreted the Hebrew Bible. To do so, it investigates his Old Testament theology and its transition process in the perspective of Reception Theory. It argues that the transition of his interpretive method for his Old Testament theology reflects his experience of his participation in the democratic movement of South Korea as well as his theological study. It means that his Old Testament theology shows how a biblical theologian in the Third World has struggled to interpret the biblical text under his social context of political and economical injustice.BR Gim’s life as Old Testament theologian can be divided into three periods. The first period extends from his birth to his entrance into Yonsei University as an undergraduate theological student in 1945, when he formed a progressive and practicing Christian faith under the influence of his family, church and school. At that time, he was deeply involved in the biblical literalism and fundamentalism which was prevalent in the Korean church.BR In the second period from 1945 to 1974, he studied the Neo-orthodox theology in Yonsei University and Union Theological Seminary in NY and taught the Old Testament in Yonsei. At that time, he formed a Neo-orthodox Old Testament theology which tried to find out a theological meaning of the biblical text as the Word of God’s revelation, using the methods of the historical criticism and the religion history.BR The third period started with his participation in the democratic movement of South Korea in 1974, when he experienced suffering from the imprisonment and dismissal by the dictatorial government, and tried to interpret the Old Testament in the Third World theological perspective, reflecting the Minjung Theology and the Liberation Theology. Though he did not regard himself as a Minjung Theologian, his writings and social activities show that he was in the same trajectory with them. It means that he pursued the Old Testament Theology in the perspective of the Minjung Theology, participating in the suffering, repression and poverty of Minjung.
Information technologies in teaching chemistry to future specialists in automated engineering

Abstract The article reveals the problem of using information technologies in the process of teaching chemistry to future specialists in automated engineering. The modern level of development of automated engineering requires highly educated specialists, creative people capable of free thinking. This sets a task for higher education to develop methods for the development of such competitive personality. In recent decades this task has been successfully solved through the development and implementation of various information technologies in the educational process.
Python Scrapers for Scraping Cryptomarkets on Tor

Cryptomarkets are commercial websites on the web that operate via darknet, a portion of the Internet that limits the ability to trace users’ identity. Cryptomarkets have facilitated illicit product trading and transformed the methods used for illicit product transactions. The survellience and understanding of cryptomarkets is critical for law enforcement and public health. In this paper, we design and implement Python scrapers for scraping cryptomarkets. The design of the scraper system is described with details and the source code of the scrapers is shared with the public.
Work-in-Progress: Enhancing Collaboration Using Augmented Reality Design Reviews for Product Validation on the Example of Additive Manufacturing

The “DigiKAM” project aims to create a digital collaboration network that enables the efficient exploitation of Additive Manufacturing (AM). Via a scalable platform solution, spatially separated AM customers and experts are interconnected throughout the entire development process, i.e. from potential identification to development and production. The digital collaboration will be supported by Augmented Reality (AR) Design Reviews in which the realistic visualization and shared viewing of components at the later operation site enables a faster and more meaningful validation. However, there is no integrated software solution that meets all requirements. In addition, AR know-how is necessary to prepare and execute AR Design Reviews, which the collaboration partners usually do not have. Therefore, the goal of this project is a set of tools and methods that enables AR Design Reviews to be conducted without AR know-how. Application scenarios for AR Design Reviews are identified and analyzed in order to derive rules for the requirements definition and the selection of AR hardware and software. Furthermore automated tools are developed to provide support for the complex parameterization of the conversion process from CAD to AR-capable data. In addition, a software will be developed and prototypically implemented to execute collaborative AR Design Reviews.
Enabling Effective Transparency

Much of the current research in transparency and explainability is highly technical and focuses on how to derive explanations from models and algorithms. Less thought is being given to how users actually want to receive transparency and explanations from intelligent systems. My work tackles transparency and explainability from a user-centric perspective. I examine why transparency is desirable by showing that users may be susceptible to deception from intelligent systems. I demonstrate when users want transparency. Finally, my work begins to uncover how users want transparency conveyed. This body of work intends to create a path for designing transparency that puts user needs first rather than creating transparency as a convenient afterthought of model selection.
Could blockchain save journalism? An explorative study of blockchain’s potential to make journalism a more sustainable business

Ever since the Bitcoin phenomenon gained momentum in recent years, much has been written about blockchain applications and prospects in the FinTech industry, business and healthcare. Yet, very litt ...
Automatic USCT Image Processing Segmentation for Osteoporosis Detection

Ultrasound Computed Tomography (USCT) can be used to cortical bone imaging but is limited by the strong variations in acoustic impedance between the medium and its environment. The aim of this work is to test an automatic image processing recognition to enhance the detection of the boundaries. Image processing algorithms are used for automatic detection of edges and defects. In first step, the application of pre-processing algorithm is done. In second step, we have applied k-Means and Ostu algorithm. As a result, we improve the edge detection to bring up the calculation bones structures length. Hence, osteopathologies detection was achieved and results outperform related works by Signal-to-Noise Ratio improvement and saving time execution.
Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection

Downsampling input images is a simple trick to speed up visual object-detection algorithms, especially on robotic vision and applied mobile vision systems. However, this trick comes with a significant decline in accuracy. In this paper, dual-resolution dual-path Convolutional Neural Networks (CNNs), named DualNets, are proposed to bump up the accuracy of those detection applications. In contrast to previous methods that simply downsample the input images, DualNets explicitly take dual inputs in different resolutions and extract complementary visual features from these using dual CNN paths. The two paths in a DualNet are a backbone path and an auxiliary path that accepts larger inputs and then rapidly downsamples them to relatively small feature maps. With the help of the carefully designed auxiliary CNN paths in DualNets, auxiliary features are extracted from the larger input with controllable computation. Auxiliary features are then fused with the backbone features using a proposed progressive residual fusion strategy to enrich feature representation.This architecture, as the feature extractor, is further integrated with the Single Shot Detector (SSD) to accomplish latency-sensitive visual object-detection tasks. We evaluate the resulting detection pipeline on Pascal VOC and MS COCO benchmarks. Results show that the proposed DualNets can raise the accuracy of those CNN detection applications that are sensitive to computation payloads.
Presence, Immersion and Usability of Mobile Augmented Reality

Augmented reality (AR) is becoming truly mobile, as it was intended to be, through smartphone and embedded computer based platforms. For these computing platforms, there are three typical display systems used: (1) hand-held video see-through (smartphone) LCD as is, (2) video see-through (smartphone) LCD inserted into and isolated with the cardboard case and magnifying lenses, and (3) optical see-through (glass-like) displays. Recently, an alternative form has appeared in the market in which the magnifying lenses are simply clipped onto the smartphone. The four displays differ in few ways: e.g. wearability and convenience, image quality, size of the imagery and field of view, isolation from the outside world, the real world representation (video or actual), which all potentially can affect the levels of their usability, presence and immersion. In this paper, we examine and compare the levels of usability, presence and immersion as provided by these four different display configurations of Mobile AR. We also control another related factor, namely the amount of ambient light, which might have similar effects according to the different display types, carrying out the experiment under three different conditions: indoor (office-level lighting), outdoor (medium sun light), or outdoor (bright sun light). Our experiment first showed that the current level of technology for the optical see-through glass type displays still fall short to provide the minimum usability, display quality and presence/immersion for practical usage. The other three displays showed generally similar levels of usability and presence/immersion, which indicates that the isolation from the real world, is not important in AR unlike in virtual reality (VR). It is also thought that in the case of AR, the usability is the most important factor for users for their choice of the display type, which also affect the perceived level of immersion and presence.
AR Cooking: Comparing Display Methods for the Instructions of Cookwares on AR Goggles

We explore effective display methods for instructions on complex tools on augmented reality (AR) goggles. We focused on three types of cookwares as complex tools in everyday life: avocado cutter, orange opener, and can opener. We implemented three AR applications for displaying the usage of three cookwares: images with text, videos, and three-dimensional (3D) animation. In this study, we investigated how each of these AR applications affects the understanding of complicated products and their applications. Using Microsoft HoloLens, we conducted user studies with the three aforementioned display methods. We measured the task completion time and investigated the psychological element. Based on the task completion time and whether the task could be completed, we found that the most efficient display method for the instruction of cookwares on AR goggles is 3D animation. We investigated how participants felt about cooking using AR goggles based on participant’s comments. We believe that our contribution can be useful for the design of AR-based application.
Space-efficient computation of the LCP array from the Burrows-Wheeler transform

We show that the Longest Common Prefix Array of a text collection of total size n on alphabet [1,sigma] can be computed from the Burrows-Wheeler transformed collection in O(n log sigma) time using o(n log sigma) bits of working space on top of the input and output. Our result improves (on small alphabets) and generalizes (to string collections) the previous solution from Beller et al., which required O(n) bits of extra working space. We also show how to merge the BWTs of two collections of total size n within the same time and space bounds. The procedure at the core of our algorithms can be used to enumerate suffix tree intervals in succinct space from the BWT, which is of independent interest. An engineered implementation of our first algorithm on DNA alphabet induces the LCP of a large (16 GiB) collection of short (100 bases) reads at a rate of 2.92 megabases per second using in total 1.5 Bytes per base in RAM. Our second algorithm merges the BWTs of two short-reads collections of 8 GiB each at a rate of 1.7 megabases per second and uses 0.625 Bytes per base in RAM. An extension of this algorithm that computes also the LCP array of the merged collection processes the data at a rate of 1.48 megabases per second and uses 1.625 Bytes per base in RAM.
An Adaptive Random Walk Algorithm for Selecting Tips in the Tangle

Number of devices using Internet is increasing every day and this fact makes the need to improve the Internet of things protocol more than ever. Data protection and privacy is one of the key challenges in the Internet of Things technology. Dangers involved in the centralized technology of Blockchain system have led to the idea of using Tangle, which is a decentralized system. The main purpose of this new technology is to improve the problems and limitations of Blockchain such as high cost and time to confirm a transaction. In this new architecture, every node is involved in maintaining network security. This way, if a transaction is created, then it should select and confirm two unconfirmed transactions issued before. A walking algorithm is needed for this selection. Walking algorithms presented in literature thus far are either weighted or unweighted. An unweighted random walk algorithm can approve transactions nearly proportional to the time of their arrivals while a weighted algorithm can better defend against lazy and malicious transactions. In this paper, a new random walk algorithm is presented that has the benefits of both algorithms at the same time. The idea is to adapt the weight value to the current situation of transactions. Numerical results have shown the superiority of the proposed algorithm in comparison to the existing algorithms in providing a balance between timeliness of approving transactions and protecting against malicious activities.