Reviewerly/exHarmony huggingface dataset:
Method for constructing estimates of accuracy of measuring equipment based on Bayesian scientific approach

Before putting new unique samples of technical systems into commercial operation, as well as before introducing new technologies into production, as a rule, all kinds of tests are carried out. Small and very small volume of statistical data during testing is a characteristic feature of unique and small-scale products and technical systems. Therefore, the problem of constructing effective statistical estimates with a limited amount of statistical information is an important practical problem. The article proposes the development of the Bayesian approach to the construction of point and interval estimates of the parameters of the known distribution laws. The joint use of a priori and posterior information in the processing of statistical data of a limited volume can significantly increase the reliability of the result. As an example, we consider two most typical distribution laws that arise when testing new unique samples of measuring devices and equipment: normal distribution with an unknown average value and a known dispersion, as well as with an unknown average value and an unknown dispersion. It is shown that for these cases, the parameters of the distribution laws themselves are random variables and obey the normal law and gamma normal law. Recalculation formulas are obtained to refine the parameters of these laws, taking into account a posteriori information. If these formulas are applied several times successively, the process of self-learning of the system or self-tuning of the system occurs. Thus, the proposed scientific approach can find application in the development of intelligent self-learning and self-turning systems.
A Noval Approach of Drops with NTRU in Cloud

Many organisation require large storage landscape due to increase in volume of data and the number of users. The Cloud storage is a crucial component of cloud computing, where cloud storage providers supply a large amount of on-demand data storage so that users can save and retrieve their data anytime anywhere in the cloud. The business enterprise store their data on the cloud for ensuring data reliability and availability. The attack occur on the data nodes and cloud by other users, so we require a high security measures within the cloud. The employed algorithm should take into account the data retrieval time optimization. Thus the enhancing security without compromise in the performance is major issue. The DROPS methodology (Division and Replication of Data in Cloud for Optimal Performance and security) divide the file into chunks and replicate them once and store them in the cloud node. Each node contain only a single fragment of the fragmented file. These fragments are separated at certain distance by means of graph T-coloring, so that the attacker cannot guess the location of the fragments. But the number of fragments increases more and reduces the performance. Since the fragments may contain some valuable information which may be misused by the attacker. To over come, we propose a system using DROPS with NTRU algorithm. So that the performance and security can be enhanced with out any lag. The performance overhead can be decreased without compromise in security.
The UNESCO–IUGS International Geoscience Programme (IGCP) in the service of society since 1972

Abstract Developing an advanced understanding of the Earth's fundamental processes and resources is essential to fulfil the United Nations 2030 Agenda for Sustainable Development. The International Geoscience Programme (IGCP) is the oldest and most successful example of scientific co-operation between a non-governmental organization, the International Union of Geological Sciences (IUGS), and an intergovernmental organization, UNESCO. During almost 50 years, this programme has been the gateway to successful scientific careers in pioneering research for thousands of IGCP project scientists. After almost 50 years of evaluating IGCP projects, the programme itself was evaluated through statistical analysis of the annual progress report during four periods: 1981–82, 1991–92, 2001–02 and 2011–12. Subsequently, these trends were compared to the trends of the last four years (2015–18). This study provided insight into trends and changes in the location of studies, participating countries, involvement of developing countries, gender equality, etc. To fully understand the added value of the IGCP brand identity and its seed funding, project leaders over a period of five years (2008–12) were asked to complete a survey about its work. Overall, both studies confirmed the value of the main aim of the IGCP: enabling and facilitating international collaboration between Earth scientists. Even though IGCP funding has been reduced, the IGCP brand still opens doors to new collaborators, new research and national funding agencies, often impacting the career of the involved researchers very positively.
Systems design: Modeling, analysis, synthesis, and optimization

We describe basic components of systems design which involve modeling, analysis, synthesis, and optimization. As usual, mathematical models can be obtained in two ways. With the knowledge of physical laws, analytical models are formulated. By performing systems identification, experimental models are determined, often based on regression analyses. System analysis is the way of approach which determines the system performance subject to the assumption that the system structure is known. On the other hand, system synthesis searches for the system structure by using the knowledge about the system performance (a set of observable outputs). Optimization, usually hierarchical and with adaptive controls, involves searching for best solutions to controls and system state changes subject to various constraints. We also define macro- and micro-approaches, functional and structural properties, and briefly characterize systems of communication.
Subspace Clustering and Some Soft Variants

Subspace clustering is an unsupervised machine learning task that, as clustering, decomposes a data set into subgroups that are both distinct and compact, and that, in addition, explicitly takes into account the fact that the data subgroups live in different subspaces of the feature space. This paper provides a brief survey of the main approaches that have been proposed to address this task, distinguishing between the two paradigms used in the literature: the first one builds a local similarity matrix to extract more appropriate data subgroups, whereas the second one explicitly identifies the subspaces, so as to dispose of more complete information about the clusters. It then focuses on soft computing approaches, that in particular exploit the framework of the fuzzy set theory to identify both the data subgroups and their associated subspaces.
New access services in HbbTV based on a deep learning approach for media content analysis

Abstract Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all.
Karyon: A scalable and easy to integrate ontology summarisation framework

In the current Semantic Web Community, as the size and complexity of ontologies increase, ontology summarisation is becoming more important. There are many studies in the literature that use different approaches and metrics. However, many of these studies are not effective in terms of performance or have integration issues with current technologies. In this study, the popular ontology summarisation metrics are examined focusing on their performance in terms of time, and a number of metrics have been selected accordingly. To increase the accuracy of selections made with chosen metrics, we propose a novel metric: ‘name inclusion’. This metric promotes a concept if its name is subsumed by the name of another concept. As the existing summarisation applications have integration issues, we have implemented our summarisation framework to integrate easily with the latest web technologies. Therefore, the algorithm is implemented using Rust language, which performs well and easily integrates with other languages.
Dual Revocation: Attribute and User Revocation Based On CPABE In Cloud Computing

Access Control models are required to secure Cloud Computing from unauthorized access. As an efficient cryptographic tool, Ciphertext Policy Attribute Based Encryption offers a fine-grained access to user assets. But, revocation operation presents a major brake in the implementation of this algorithm. However, existing approaches either cannot deal with this problem or have an important computation and/ or storage overheads. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated MRS-CPABE. It is an efficient solution in case of lazy or complete revocation. We apply information dispersal algorithm and we introduce a match then decrypt process. To assess the perfromance of our solution, we perform a security evaluation with Scyther tool and a formal validation to prove that our proposal can withstand various known attacks. Then, we expose the experiments simulation results.
Fast synthesis of atmospheric image effects

This paper introduces a new method for adding synthetic atmospheric effects, such as haze and fog, to real RGBD images. The given depth maps are used to compute per-pixel transmission and and spatial frequency values, which determine the local contrast and blur, based on physical models of atmospheric absorption and scattering. A fast 2D inhomogeneous diffusion algorithm is developed, which is capable of computing and rendering the effects in real time. The necessary pre-processing methods, including sky identification and matting, are also explained. A GPU implementation is described, and evaluated on a range of RGBD data, including that from outdoor lidar and indoor structured light systems.
Comparison of RGB and HSV Color Spaces for motion Capture and Analysis of Individuals With limb Discrepancy

Motion capture (MoCap) systems are powerful tools in health and rehabilitation fields. While optical MoCaps are very good solutions for gait and movements, they are very expensive. Microsoft Kinect appears as an alternative to them as a low cost markerless RGB-D technology capable of performing kinematic analysis. However, based on machine learning models, Kinect has well-defined body patterns excluding the possibility of being used in people with limb dysmetria. Based on the exposed, this work aims to test a hybrid tool using Kinect v2 to analyze movements of individuals with dysmetria. As the objective is to develop a system capable of being used in clinical environment, two colorspaces - RGB and HSV - are used. Thus, the tests were performed in two environments (clinical and controlled), with an individual with lower limb discrepancy. The subject was submitted to four predefined movements in both environments. Numerical analysis was performed using Matlab to quantify the success rate of capture - where there is no optical occlusion - and limb lengths. The data obtained from the color systems were analyzed in order to verify which one has less interference from the environment and to indicate the viability of using them as a tool for capturing movement of people with different biotypes.
Sentiment Analysis on Twitter to Measure the Perception of Taxation in Colombia

Twitter is a social microblogging tool in which users can publish textual content up to 280 characters. Since its birth in 2006, Twitter has become one of the most widely used platforms for sharing current content, such as news or events in real-time, which also allows you to briefly and concisely comment on virtually any topic. This research presents the analysis of public opinion feelings about taxes on digital platforms to support the decision making of the National Taxes and Customs Office (DIAN), and the Ministry of Finance, for which an extraction and compilation of tweet data is developed, a preprocessing of information in order to obtain a corpus in Spanish that can be classified into positive, negative and neutral categories; then learning algorithms are tested, which allow training models to subsequently classify new collected tweets.
Challenges in re-Platforming Mixed Language PL/I and COBOL IS to an Open Systems Platform

Re-platforming - the transfer of a legacy system to a new target platform - is often seen as a first cost-reduction step to enable a long term reengineering effort of a legacy system. However, recreating a functionally equivalent ecosystem on a different platform entails significant technical and organisational challenges. This extended abstract describes challenges, their solutions as well as an important lesson learned during a re-platforming project of 40 year old PL/I and COBOL applications.
Multi-objective Optimal Path Planning for Mobile Robots using State Space Cell Mapping

Cell mapping is a promising approach in multiple objective optimization problems. By discretizing the open-loop dynamical system in both the state space and time, it is possible to reduce the mathematical complexity of the problem while introducing constraints in practical mobile robots. Here, we propose to perform the discretization of the controlled system (closed-loop). This can be done introducing an additional constraint in the model that can be interpreted as a zero-order hold (ZOH) on the control. Because of the structure of the optimal problem, it is necessary to release a degree of freedom. This is achieved by allowing nonuniform discretization of time. We study the problem of a particle on a bidimensional configuration space with obstacles, optimizing time, distance and control energy. We also present some numerical results in a case of study with a three wheeled omnidirectional mobile robot in a constrained C-space.
Modeling and optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle

We propose a data-driven framework to enable the modeling and optimization of human-machine interaction processes, e.g., systems aimed at assisting humans in decision-making or learning, work-load allocation, and interactive advertising. This is a challenging problem for several reasons. First, humans' behavior is hard to model or infer, as it may reflect biases, long term memory, and sensitivity to sequencing, i.e., transience and exponential complexity in the length of the interaction. Second, due to the interactive nature of such processes, the machine policy used to engage with human may bias possible data-driven inferences. Finally, in choosing machine policies that optimize interaction rewards, one must, on the one hand, avoid being overly sensitive to error/variability in the estimated human model, and on the other, being overly deterministic/predictable which may result in poor human `engagement' in the interaction. To meet these challenges, we propose a robust approach, based on the maximum entropy principle, which iteratively estimates human behavior and optimizes the machine policy, namely Alternating Entropy-Reward Ascent (AREA) algorithm. We characterize AREA, in terms of its space and time complexity and convergence. We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making.
Smart Contract Based Academic Paper Review System

The centralized and opaque nature of the existing peer review systems and academic management practices of journals have led to various transparency and accountability issues. In this work, we propose a novel smart- contract-based review system that stores data using blockchain and web applications. The proposed method can realize transparent operation of the academic paper review process and improve the quality of research through comparisons of opinions, research communication, and reviewer compensation. In addition, the data generated during dissertation review processes can be accessed transparently, while ensuring security of the generated data.
Privacy-Aware Distributed Graph-Based Semi-Supervised Learning

This paper proposes a privacy-aware framework for distributed semi-supervised learning. In particular, we consider a semi-supervised learning problem where the training data is distributed among multiple data-owners, who wish to protect the privacy of their individual datasets from the other parties during training. We propose a novel framework for protecting the privacy of individual datasets while achieving good accuracy. Then, we characterize the privacy of our framework, by defining a metric that quantifies the number of candidate data points that are consistent with information shared by data-owners. The number of candidates (and thus the privacy) decreases as more information is shared between data-owners, leading to a privacy-utility (accuracy) trade-off. Our experiments show a significant increase in classification accuracy compared to local training, i.e., using the individual datasets only, while the complexity of our approach is significantly lower than that of other benchmarks, such as secure multi-party computing or homomorphic encryption.
Encoder–decoder with double spatial pyramid for semantic segmentation

Semantic segmentation, as a dense pixelwise classification task, is of great significance to scene understanding. Many approaches based on convolutional neural network still suffer from two kinds of challenges: (1) insufficient semantic information results in semantic obfuscation between similar categories, (2) loss of spatial information leads to inaccurate location of inconspicuous objects. To tackle these challenges, we design a network with an encoder–decoder architecture based on two proposed modules: global pyramid attention module (GPAM) and pyramid decoder module (PDM). Specifically, GPAM exploits an attention mechanism as global prior knowledge to adaptively capture discriminative features for enhancing semantic representation, and PDM employs small convolutions connected in parallel to predict adjacent position relationships for refining spatial information. A series of ablation experiments are conducted to demonstrate the effectiveness of our designs, and our network achieves a mean intersection over union score of 83.4% on PASCAL VOC 2012 dataset and 78.5% on Cityscapes dataset.
Accelerating Solution of Generalized Linear Models by Solving Normal Equation Using GPGPU on a Large Real-World Tall-Skinny Data Set

The amount of data available has grown rapidly in recently years, not least in the context of Industrialization 4.0 and the advent of Cyber Physical Systems (CPS) and IoT devices. Thus, Machine Learning and Big Data analysis are being taken seriously as promising solutions to cope with the challenges of exponentially growing data. A widely known and widely used approach to quantify the relationship between a dependent variable and multiple numerical predictors is the Generalized Linear Model (GLM). In this paper, we introduce an approach to accelerate the GLM's fitting algorithm. Our approach is involved in two steps. First, reimplement GLM fitting algorithm which applies Normal Equation [1] method when solving the Linear Least Squares Equation. Then, we port the implemented GLM fitting to be executable with GPGPU. When Normal Equation method is applied to tall-skinny data, which is typically found in log data of CPS, solving the post Linear Least Squares equation becomes trivial, and the computational burden is transferred from solving that equation to matrix multiplication which can be parallelized in a straightforward manner. In an experiment employing actual user log access data, the Normal Equation method was executed 1.9 times faster when being executed in CPU, combined with 16.8-fold acceleration by GPGPU, leading to being 31.3 times faster overall.
An Intuitive Interface for Digital Synthesizer by Pseudo-intention Learning

Digital musical instruments are essential technologies in modern musical composition and performance. However, the interface of the synthesizer is not intuitive enough and require extra knowledge because of the parameters. To address this problem, we propose pseudo-intention learning: a novel data collection method for supervised learning in musical instrument development. Pseudo-intention learning collects a data set of the paired target tone and input performed by the user. We developed a conversion framework that reflects the composer's intention by combining standard convolutional neural network and pseudo-intention learning. As a proof of concept, we constructed an interface that can freely manipulate the sound source of a digital snare drum and demonstrated its effectiveness with a pilot study. We confirmed that the tone parameters generated by our system reflected the user's intention. We also discuss applying this method to richer musical expression.
MLP on FPGA: Optimal Coding of Data and Activation Function

An efficient coding leading to the implementation on FPGA of an optimal architecture of a multilayer Perceptron is proposed. The implementation of this architecture meets the requirements of embark, on the one hand by minimizing the consumed power, the execution time, the logical resources used, etc ..) and on the other hand by ensuring the portability of the computer application (the ability of the latter to run on different platforms and environments). Massive parallelization, efficient coding of the sigmoid and pipeline processing are used. The neuronal approach developed and implemented on FPGA was evaluated in the context of the application on objective criteria according to the two aspects of software and hardware. The results obtained are in line with expectations and objectives.