Reviewerly/exHarmony huggingface dataset:
RETRACTED: A novel hybrid optimized and adaptive reconfigurable framework for the implementation of hybrid bio-inspired classifiers for diagnosis

Due to recent advances in IoT (Internet of Things) technologies, availability of reliable data and emergence of machine learning, bio-inspired learning and artificial intelligence, has demonstrated its ability to solve the large complex problems which is not possible before. In particular, machine learning and bio-inspired learning algorithms provides the effective solutions in image processing techniques. However, the implementation of the above-mentioned algorithms in the general CPU requires the intensive usage of bandwidth, area and power which makes the CPU unhealthy of usage and implementation. To overcome this problem, ASIC (application specific integrated circuits), GPU (Graphics Processing Unit) &FPGA (Field Programmable gate arrays) have been employed to improve the performance of the hybrid machine learning (ML) classifiers and deep learning algorithms. FPGA has been recently employed for an effective implementation and to achieve the high performance of the learning algorithms. But integrating the complex learning algorithms in FPGA still remains to be real challenge among the researchers. The paper proposes new reconfigurable architectures for bio- inspired classifiers to diagnosis the medical casualties which can be suitable for the tele health care applications. This paper aim is as follows (i) Design and implementation of Parallel Fusion of FSM and Reconfigurable shared Distributed Arithmetic for Bio-Inspired Classifiers (ii) Development of Accelerator Environment to test the performance of proposed architecture (iii) Performance evaluation of proposed architecture in terms of accuracy of detection in compared with MATLAB simulation iv) Implementation of proposed architectures in different ARtix-7 architectures and determination of power, throughput and area . Moreover, the proposed architecture has been tested with the and compared with the other existing architectures.
FLOOD CONTROL IN SAMPEAN BARU DAM AND IT GATE’S SIMULATIONS

The problem of flooding that often occurs in an area needs serious handling in controlling of the flood. The two main methods for flood control were the structural method and the non-structural method. One of structural method that had been applied both abroad and domestically country was build of the dams. Basically a dam had multipurpose function, one of it had function as a flood control. The design of a dam that had a function for flood control was by controlling the water level in the upstream of the dam, so the dam will not overtopping and the area whereas located at the upstream and at the downstream of the dam will be safe. The Sampean Baru Dam weheras located at Bondowoso Regency East Java Province had function as a flood control and irrigation. This dam was chosen as a research location because this dam has an important role in securing two big cities (Bondowoso and Situbondo) which are densely populated cities. The purpose of this research was to create a flood control scheme by simulating of operation seven gates on the dam. The method used were flood forecasting and reservoir routing. Flood forecasting was done by the synthetic nakayasu hydrograph unit method with the flood period return starting from 2 years up to the Probably Maximum Flood return period. Meanwhile, the reservoir routing method used continuity equation where the analysis based on the function of flood discharge and elevation of water levels in the reservoir. The reservoir routing was carried out by operating seven gates of the dam. The types of gates are tilting gate (only 1 gate and located in the middle, it was called gate number 4) and radial gate in total had 6 gates (gate number 1, 2, 3, 5, 6 and 7). The gate operation considered the stability of the dam. When the floods come, the elevation of the water level in the reservoir will rise. If the water level in the reservoir reaches the permitted level, then the gate starts to open. The results showed the most optimal operation of the gate was the sequence of gate openings starting at gate number 4, then gate number 2 together with gate number 6, continue by gate number 1 together with gate number 7, and if the water elevation was still high then the last opening was gate number 3 together with gate number 5.The results of reservoir routing with flood input data for all return time period showed that the dam was still in a safe condition because the gates could bring the flood discharge peak to the downstream. So the dam does not experience overtopping. So that the dam was safe from overtopping. Keywords: flood control, sampean baru dam, gate operation.
Analysis of TSP Heuristics for Fuel-Constrained UAS Path Planning applied to Polar Research Missions

In this work, various traveling salesman problem (TSP) heuristics are utilized for the path planning of fixed-wing unmanned aircraft performing polar research missions, and these heuristics are then subjected to aircraft fuel-constraints to achieve an efficient route through flight lines while ensuring operational feasibility. These heuristics include a forward-greedy method, a global-greedy method, and ant colony optimization. Path planning between mission flight lines are dynamically constrained by the minimum turning radius of the aircraft through distance-optimal Dubins paths. The performance and computational expense for each heuristic is assessed for Earth Science mission scenarios along the Helheim Glacier, a key area of interest to the scientific community. Realistic fuel constraints are applied for preliminary mission planning for an upcoming UAS research deployment to the area.
Digital Reality as an Environment for the Professional Growth of Youth

The next change in technological mode in the second half of the 20th century led to the destruction of rigid social structures, the desire for subjectivity, and erosion of the existing compromises and interactions. The popularization of the narrative of the “digital society” or “Industry 4.0” is not yet accompanied by the emergence of a universally recognized paradigm that reflects the emerging reality. In conditions of intensive technological and socio-economic changes in the surrounding reality, uncertainty and risk-taking of the future, the responsibility of a self-determined young person in the professional sphere for independently made decisions increases. The key issue is the preparation of a professional who is adequate to the trends of digital development. The priority components of the institutional environment of the forming professional are organizational structures, employers and the educational system. The process of transforming these institutions into digital reality with unforeseen results creates conditions for the uncertainty of the future. The need to implement various professional launch strategies increases the risks of the digital generation. At the same time, researchers highlight the typical characteristics of the digital generation, which make it difficult to form specialist competencies and enter professional activities.
Introducing the Global Educational Learning Observatory (GELO) and the Global Readiness Explorer (GREx): A framework and dashboard to investigate tech competence and culture

The Organization for Economic Co-operation and Development calls for immediate paradigmatic shifts in how educational institutions and the broader society address employment skills. A wide variety of local and global sources, including trade magazines, government sites, and pan-governmental sites, echo this urgent call, recommending increased emphasis on skills development in complex problem solving, critical thinking, creativity, and collaboration. The overarching aim of the Global Educational Learning Observatory (GELO) project is to develop a framework for creating an international network of institutions utilizing data-driven and science-informed evidence of best practices in online and mobile learning, while endeavoring to reach out to a large variety of individuals in formal and informal educational institutions globally. To achieve this, the project aims to (i) assemble a nucleus of formal educational institutions; (ii) construct the necessary tools to extend research on formal learning models; and (iii) reach into workplaces and more public spaces to integrate with informal learning settings. The primary source of data derives from a customizable dashboard, the Global Readiness Explorer (GREx), and a variety of quantitative tools that may be implemented within it. In addition, local initiatives and the development of overarching policies will require the use of qualitative and mixed methodologies for the collection and analysis of textual, video. and other types of artifacts to supplement the quantitative data. In GELO, partner institutions can use the broad range of data types to make decisions about course and professional learning offerings in response to the needs identified through analysis of the data. The results of this project will allow the project team to use digital technology tools to more fully explore informal learning settings around the world as a means of transforming our common understandings of traditional, institutional, and more contemporary, lifelong learning trends.
Extraction of Archetype from Near Duplicates in Software Documentation

Software documentation contains a large amount of duplicate text, which is often comprised of near duplicates - repetitions of the same text with slight differences. They emerge due to numerous copy-pastes that have been slightly modified. Uncontrolled near duplicates complicate documentation support to a significant degree. There are some research papers on detection and management of duplicates in software documentation, but only the Duplicate Finder approach addresses the problem of near duplicates. Nevertheless, Duplicate Finder's search algorithms do not provide extraction of archetype (common text) for detected groups of near duplicates (a set of near duplicates belong to one group if they have a lot of commonalities). Archetype of group can be used in visualization of the common text and differences of duplicates for manual analysis, as well as for reuse of documentation. In this paper, we present an algorithm for archetype extraction and results of experiments on documentation of several well-known open source Java projects JUnit, Mockito, SLF4J.
Peano's Counterexample to Harmony

Harmony and conservative extension are two criteria proposed to discern between acceptable and unacceptable rules. Despite some interesting works in this field, the exact relation between them is still not clear. In this article, some standard counterexamples to the equivalence between them are summarized, and a recent formulation of the notion of stability is used to express a more refined conjecture about their relation. Then Prawitz's proposal of a counterexample based on the truth predicate to this refined conjecture is shown to rest on dubious assumptions. As a consequence, two new counterexamples are proposed: one uses the extension of logic with a small amount of arithmetic, while the other uses the extension of a small fragment of arithmetic with a problematic operator defined by Peano. It is argued that both these new counterexamples work fine to reject the conjecture and that the last one works also as a rejection of harmony as a complete criterion of acceptability of rules.
Fuzzy Data Analysis for the Optimal Choice of Polymer Systems

For solving problems of optimizing chemical-technological systems the method of data analysis in the conditions of fuzzy, incomplete, heterogeneous source information is proposed. The results of the study develop the mathematical support of information systems for supporting the scientific and production activities of a chemist-technologist in terms of modeling linguistic resources and improving the reliability of decision support methods. Operators that allow formalizing chemical information in terms of fuzzy sets are proposed. The methodology for using these operators is illustrated by the example of solving the problem of choosing a polymer according to the requirements formulated by the consumer. A computational experiment was carried out and the results were interpreted and discussed.
An Improved A* Path Planning Algorithm for Indoor Intelligent Robot

In this paper, we introduce an improved A* path planning algorithm for indoor intelligent robot. Aiming at the problem of intelligent robot car path planning in complex indoor environment with obstacles such as wall. Firstly, the indoor environment is divided into grids and we can get the connected topology. The connectivity between grids is characterized by adjacency matrices. Then we study the influence of different heuristic functions on the efficiency of path planning algorithm. Based on the traditional A* algorithm, the direction factor is introduced. Moreover we also consider the impact of distance and direction on search efficiency. Finally, the algorithm is simulated by Matlab. The experimental results show that compared with the traditional A* algorithm, the proposed algorithm has a significant improvement in path search efficiency.
Middleware interoperability performance using interoperable reinforcement learning technique for enterprise business applications

Business level service can be implemented by middleware in enterprise systems to improve interoperability and also provides the transparent implementation of client and server process. To improve the performance of Business to Business (B2B) and Business to Consumer (B2C) in terms of enterprise-wid e Service Oriented Architecture (SOA), we need to have middleware interoperability especially with representative engineering namely Common Object Request Broker Architecture (CORBA) proposed by Object Management Group, Object Request Broker (ORB) software named ORBeline is available as CORBA complaint. Distinctive models for client-server communications have already been developed and executed in particular Handle Driven ORB (H-ORB), Forwarding ORB (F-ORB), Process Planner ORB (P-ORB), and the Adaptive ORB (A-ORB). This paper focuses how to improve the performance of the interoperability in Process Planner ORB (P-ORB) with respect to client-server interaction in N-tier architecture along with multithreading environment.
Construction of Multilinked Multirotor Aerial Robot with 1 DoF Thrust Vectorable Link Module and Fully Actuated Control with Aerial Transformation

We present a novel fully actuated transformable multilinked aerial robot which consists of the link modules with 1 DoF thrust vectoring mechanism. Commonly used UAV is underactuated due to its simplicity and high flight duration, but can not control the position and orientation independently. To overcome this problem, fully actuated multirotor aerial robots have been developed. In this paper we propose a new design of link module which has a tilted rotor and 1 DoF thrust vectoring joint which enables to avoid singularity forms and keep the flight stable during transformation. Then we propose a transformation planning method utilizing the 1 DoF thrust vectoring angle with consideration of guaranteed minimum force/torque. Finally we perform an aerial transformation experiment with a real platform to demonstrate the feasibility of our proposed methods.
Approximating Predictive Probabilities of Gibbs-Type Priors

Gibbs-type random probability measures, or Gibbs-type priors, are arguably the most "natural" generalization of the celebrated Dirichlet prior. Among them the two parameter Poisson-Dirichlet prior certainly stands out for the mathematical tractability and interpretability of its predictive probabilities, which made it the natural candidate in several applications. Given a sample of size $n$, in this paper we show that the predictive probabilities of any Gibbs-type prior admit a large $n$ approximation, with an error term vanishing as $o(1/n)$, which maintains the same desirable features as the predictive probabilities of the two parameter Poisson-Dirichlet prior.
A Classification Methodology Based on Subspace Graphs Learning

In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during the training phase using an ensemble of spanning trees. It takes the best classifier, partitioning the area near a pattern into γ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ-2</sup> sub-spaces and combining all possible spanning trees that can be created starting from γ nodes. The proposed method leverages on a supervised classification methodology and the concept of minimum distance. We evaluate our approach on well-known benchmark datasets and results obtained demonstrate that it achieves comparable and, in many cases, state-of-the-art results. Moreover, it obtains good performance even with unbalanced datasets.
Recommendation System with Association Rule Mining and Mobile Augmented Reality

Mobile augmented reality is a popular technology field that allows simultaneous interaction between the real world and the virtual world with the help of mobile devices. The notion of object recognition and classification, which plays an important role in mobile augmented reality applications, is the process of identifying an object on the image and assigning it to a certain class label. In this study, a novel three-dimensional and real time recommendation system has been developed with association rule mining and mobile augmented reality. The proposed system consists of three stages: (1) Recognition and classification of the object on the image (2) Determination of the related rules of the object with other objects in the dataset by using the Apriori algorithm (3) Adding recommendations that generated from the rules to the image as virtual objects with mobile augmented reality. Although the developed system in this study is a general recommendation system, it was applied in the field of market basket analysis as an experimental study.
Optimization of Rain Utilization Through Community Empowerment as A Step For Drought Mitigation in The Belu Regency

Drought is a condition where an area coverage lacks rainfall from normal conditions which occurs prolonged to reach one season, resulting in the inability to meet water needs. Drought itself is a recurring incident, but in its handling it is always deemed insufficient. The impact from the danger of drought can be as widespread as conflicts between communities who have their own needs for clean water. Belu Regency, located in East Nusa Tenggara Province, is one of the region that is prone to drought with the category Extreme Drought. Drought that occurs in this region happened naturally, namely meteorological drought associated with below normal rainfall levels in one season, where rain occurs within a span of 3-4 months while drought 8-9 months; hydrological drought and agricultural dryness. On the other hand, the need for clean water is increasing with increasing population from time to time, so the availability of water is still far from meeting the water needs. This can be seen from the value of the water balance, East Nusa Tenggara region has a deficit of 85.12 m3/sec. Drought is also related and influenced by the condition of watersheds, such as the reduction of land for the absorption, storage and parking of rainwater. This is also one of the factors causing flooding during the rainy season, so the priority is how floods can be accommodated during the rainy season so the water can be utilized in the dry season. According to this problems, so we need a community that is able to be a driving force in community empowerment, where with this community is expected to increase people knowledge of drought, lifestyle, the importance of community care in protecting the environment. Community empowerment through the community named Gerakan Mandiri Peduli Lingkungan with a few of programs that is early education of drought, rain and water sources; environmental care; utilization of rainwater harvesting systems; land conservation; and others, as a step in optimizing the use of rain, so the citizens can actively participates in facing drought.
Learning Timed Automata from Interaction Traces

The design of load-critical human-machine systems presumes thorough modelling and analysis of interaction profiles the systems are meant to withstand at peak loads. The need for mathematical modelling of interactions is often ignored due to significant modelling effort and lack of relevant tools. We propose an algorithm for automatic learning a subclass of Uppaal timed automata models from system and its environment interaction logs. The learning method relies on synchronous communication assumption that is characteristic to communication protocols of networked HMS distributed components. The method is demonstrated on IEEE1394 protocol learning example. Beside enhancing automatic test generation, the learned model allows verifying test feasibility and test optimization already in early phases of test design.
Using Algorithms for the Study of Intelligent Interfaces for Solving Pattern Recognition Tasks

Today, there are many algorithms for tracking pattern recognition on a digital image. These are the cascading classifiers of Viola and Jones, the generalized Hough transform, the Kapoor-Wynn method, and others. However, algorithms for tracking and pattern recognition with high reliability and stability indicators require significant time-consuming execution of the algorithm and the recognition of new patterns. In this article, two algorithms will be investigated using the intelligent interface as an example: an image recognition algorithm that recognizes a physical object in the camera's vision and an operator's hand gesture tracking algorithm.
Coronary Ascending Aorta Segmentation in CTA Images Using SegNet Method

Coronary extraction is a crucial step in the assessment of cardiovascular diseases during clinical diagnosis and surgical planning. Coronary artery structures in CTA (Computed Tomography Angiography) images are affected by other nearby tissues due to scattered distribution. Therefore, in this paper, we present a deep learning method for the Coronary ascending aorta segmentation on CTA images. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. In the process of convolution operation, in order to reduce the computation, a new decomposition convolution method of reducing number of parameters is introduced. The adoption of this approach is not only greatly reduces the compute largely, but also has little effect on the performance loss during convolution calculation. This approach greatly improves the degree of automated segmentation with less manual intervention.
A Conditional VAE-Based Conversation Model

The recent sequence-to-sequence with attention (S2SA) model achieves high generation quality in modeling open-domain conversations. However, it often generates generic and uninformative responses. By incorporating abstract features drawn from a latent variable into the attention block, we propose a Conditional Variational Auto-encoder based neural conversation model that directly models a conversation as a one-to-many problem. We apply the proposed model on two datasets and compare with recent neural conversation models on automatic evaluation metrics. Experimental results demonstrate that the proposed model can generate more diverse, informative and interesting responses.