Reviewerly/exHarmony huggingface dataset:
Cloud based data management for experiential products: a case study on motion pictures

Abstract Product complexity is on the rise with the advancing technological progresses and improved customer demands. This resulted in the implementation of finest technologies and philosophies in the product development arena. Product Data Management (PDM) is one such promising area contributing to the cost-saving, quality and time aspects of product development. Currently, the applications of PDM are mostly limited to the discrete products. In this work it is attempted to bring the experiential product domain too under the purview of PDM. The case of motion picture is taken for studying the application of PDM to the experiential product domain. The comparability of motion pictures with any generic discrete product is confirmed in this study and the necessity of a system for Motion Picture Data Management (MPDM) is established. Cloud-based Motion Picture Data Management system is proposed as a solution for the requirements in motion picture development. Functionalities, components, and architecture of the system are described in this work.
Human Data Model: An Approach for IoT Applications Development for Elderly Healthcare

Nowadays, the number of devices connected to the Internet is growing at an unstoppable rate. This, added to the amount of information these devices produce, makes new techniques necessary to take advantage of their potential. New devices are continually being introduced in the daily life of people, and they are already producing an unprecedented amount of data related to people’s well-being. However, taking advantage of such information to create innovative solutions heavily relies on manually gathering the needed information from several sources. In this paper, we present a Human Data Model to combine personal data, perform related computations, and proactively schedule computer-human interactions. The model’s main aim is to improve the development of IoT applications by means of reusing the interactions detected in order to meet the sensation or abstractions inferred from the stored information.
The contextual method of Academician Verbitsky: is a revolution in didactics possible?

Introduction . The article provides an overview of the most notable pedagogical innovations (project method, programmed teaching, pedagogy of cooperation), mastered at the scientific level, but not widely used in educational practice. Special attention is paid to the paradigm of contextual education developed by the Academician of the Russian Academy of Education, Head of the Department of Social and Pedagogical Psychology in Sholokhov Moscow State University for the Humanities, A. A. Verbitsky and his scientific school. This paradigm accumulates many effective forms, methods and means, substantiated within the framework of various innovative approaches; however, it finds application only on the experimental sites of individual creative teachers of Russia. The aim of the present publication is to investigate the reasons for the rejection by educational practice to apply pedagogical innovations, conditions and prospects. Methodology and research methods . The author analyses the works of A. A. Verbitsky's scientific school, consistently developing the paradigm of contextual education in monographic and dissertation research, mastering it in the long-term teaching practice of the leading Russian university in the field of intelligent systems in humanities and applied humanities. Results and scientific novelty . The author rejects the widespread opinion about the inhibition of innovations by the conservative pedagogical environment and shows their ideological incompatibility between the centralised management of education and the autocracy of Russian society as a whole. It is proved that the imperative of the perception of pedagogical innovations by education is a change in the social environment. Practical significance . The author's assessments and conclusions create methodological prerequisites for developing a discussion about the mission of pedagogical science and the conditions for its development in modern Russian society. Thus, it could explain why the “didactocentrism”, proclaimed by John Amos Comenius, prevails in Russian education.
Data Mining and Analytics for Construction Project Cost Data Management System

This paper mainly discusses the theory of data mining, discusses the application of decision tree technology in the construction project cost data management system. This paper mainly studies the following contents: firstly, the development of informatization and data mining of construction project cost data management is introduced, and then the management problems of project cost data management are introduced; secondly, the concept and steps of data mining are introduced, and then the common algorithms of data mining are briefly introduced, This paper describes the specific application of data mining in related industries.
COMPARISON BETWEEN LEADING APIS USED IN TRANSLATION APPS

Translator Apps for Mobile devices is a mobile application to translate from English to Kannada (Language spoken in Karnataka) and vice versa. In addition translations can be done between other languages as well. And hence, APIs become an important part of the application. There are a lot of different APIs which can be used to perform translations. This paper presents a comparison between the most frequently used API by checking their accuracy, ease of use and other attributes to highlight which API is best used for Translation purposes.
An Heuristic Algorithm of the Details Distribution by Machines

In this paper, one heuristic algorithm of the details distribution by machines is considered. It will be shown, that there is no polynomial algorithm which can give us an exact solution, but there is a polynomial heuristic algorithm, which can provide us good solution. In the computational experiment it will be shown that this solution differs little or does not differ from the optimal one.
Development of a Book Recommendation Service for Implementation on Android

This article discusses the theme of creating the service that specializes in accounting for reading activities. The purpose of the research is to create methods of classifying and sorting literary works that can be integrated into any literary digital resource to expand its functionality. The created service is an Android application, which provides functions for keeping a reading diary and creating literary recommendations based on works added to the user's personal library. One of the complementary options of the application is the feature of expanding the literature base by scanning ISBN code of books that are not in the Google Books database.
Interconnected Transparent Island Architectures for Low-Latency Hyperscale Datacenters

In this letter, we propose a novel hyperscale datacenter network architecture named Interconnected Transparent Island (ITI). The ITI architecture efficiently interconnects several transparent islands with each other. Each island consists of full-mesh transparent rings connecting all pods through single hop all-optical links. The proposed architecture offers high scalability, fewer overall hops and lower power consumption with the ability to connect over one Million servers. We have compared our proposed architecture with Facebook F16 fabric aggregator topology. Results from theoretical analysis and simulations show that ITI is a viable topology for large-scale datacenter networks.
Electronics and Internet of Things Integration

Internet of things a new concept of the new emerging world of technology, a huge server with massive database interconnectivity between systems and the autonomous nature of machines around us makes an entire system of IOT. But what is the actual meaning of IOT and how is it implemented in our daily lives? According to a research an average person is surrounded by at least 5-10 computers in his daily work schedule however those are not conventional computers but miniature IC's, Numerous Motherboards and so on and so forth. Have we ever thought what ease of work does all these systems, working in sync with us as humans, has to offer? A LOT! What would be The Magnitude of productivity if all these systems could work autonomously without/with very less Human Effort Connected to each other? This is exactly what the INTERNET OF THINGS has to offer. In IOT, Machines around us are connected to each other with the internet which provide a whole lot of provision for the control (the user) to manipulate and use the device according to its own will. IOT is a concept of interconnectivity and technically can be termed as “Cloud Computing” which uses large sets of data around the web for connecting numerous systems with each other and Produce a sustainable working of thesystem.
Securely Distributing Files in Cloud Environment by Dispensing Asymmetric Key Management System applying Hashing

An emerging widely used technology cloud computing which a paddle of computing resources is available for the users. Through the internet-based the resources could be supplied to cloud consumers at their request but it is not directly active management by the user. This application-based software infrastructure can store data on remote serves, which can be accessed through the internet and a user who wants to access data stored in the cloud have to use an internet browser or cloud computing software. Data protection has become one of the significant issues in cloud computing when users must rely on their cloud providers for security purposes. In this article, a system that can embarrass the disclosure of the key for distributing a file that will assure security dispensing asymmetric key and sharing it among the cloud environment and user perform the integrity check themselves rather than using third-party services by using compression or hash function where the hash is created using a hash function and it was not mentioned in the previous paper. After the user receives the data every hash is compared with other hash values to check the differences of the data. The time-consumption of encryption and decryption of the data is calculated and compared with the previous paper and the experiment shows that our calculation took around 80% less time.
Intrusive Rocks of Iran

Intrusive bodies of Iran play a significant role in geology and the knowledge of Iran's geological phenomena, as well as they have strong relations with mining areas. In other words, most of the epigenetic and orthomagmatic metal deposits are dependent on intrusive bodies especially copper and iron; so, they have been studied separately in this chapter.In this chapter, 254 intrusive bodies so far recognized in Iran have been studied and evaluated. All these intrusive bodies are arranged in a table for the ease of judgment and comparison. The following information is given in this table for each intrusive body: Geographical location and 1.25000 and 1.100000 geological map in which the intrusive bodies are located. Characteristics of petrography and magmatic series, geochemistry, absolute age and geological age of the massif, their tectonic environments and the results of many studies are summarized and categorized in a Table. All intrusive masses in this table have been categorized based on their age according to the structural zone in which they occur. Finally, the distribution map of intrusive masses is given.
Monocular gray code 3D shape measurement based on improved Siamese Network

Abstract As 3D information is widely used in more and more industries, simplification of 3D measurement operations and higher accuracy of measurement results are important development directions in the future. Compared with the binocular measurement system, simple equipment and convenient operation are the advantages of the monocular measurement; the disadvantage is that the measurement accuracy is low and it is greatly affected by the environment. This paper proposes a method that combines deep learning with traditional monocular Gray code 3D shape measurement technology. The space transformation network is used to improve the Siamese Network, and the single-view photos are taken through spatial transformation to obtain dual-view photos, and then matched through the Siamese Network. The purpose is to use monocular measurement equipment and methods to obtain the effect of binocular measurement. The deep learning network in this article has been pre-trained with a self-made data set before use. Compared with the original method, this method has higher accuracy in plane measurement, and has a significant improvement in depth measurement, successfully reducing the depth measurement error to less than 0.1mm.
Mongolian Spelling Error Correction Using Word NGram Method

In this paper, we have primarily studied a Mongolian Cyrillic spelling error detection and correction using word n-gram method. Mongolian is an agglutinative language where one word can form into many words. The first step to use n-grams is to find the language specific word n-grams by using a corpus. But the corpus cannot be a big enough to contain all the possible word n-grams. Therefore, we used the word n-gram method with the back-off smoothing technique and the minimum edit distance (MED) algorithm to detect spelling errors and suggest correct word alternatives in sentences typed in Mongolian Cyrillic. We have designed and implemented a system which detects and corrects a misspelled word in Mongolian text. The results were compared with the other spell check software (http://spellcheck.gov.mn) and found to be much better.
Hybrid method of conventional neural network training

The paper proposes a hybrid method for training convolutional neural networks. The method consists of combining second and first-order methods for different elements of the architecture of a convolutional neural network. The hybrid convolution neural network training method allows to achieve significantly better convergence compared to Adam; however, it requires fewer computational operations to implement. Using the proposed method, it is possible to train networks on which learning paralysis occurs when using first-order methods. Moreover, the proposed method could adjust its computational complexity to the hardware on which the computation is performed; at the same time, the hybrid method allows using the mini-packet learning approach. The analysis of the ratio of computations between convolutional neural networks and fully connected artificial neural networks is presented. The mathematical apparatus of error optimization of artificial neural networks is considered, including the method of backpropagation of the error, the Levenberg-Marquardt algorithm. The main limitations of these methods that arise when training a convolutional neural network are analyzed. The analysis of the stability of the proposed method when the initialization parameters are changed. The results of the applicability of the method in various problems are presented.
An Outlier Detection Method Based On Symmetry and Curvature Threshold

Detecting outlier is a challenging task. At present, all methods calculate the outlier degree based on euclidean distance, but with the increase of size, the distance characteristics become weaker, the detection accuracy is low, and the K value is sensitive. In this paper, we present an outlier detection method based on the symmetry and curvature threshold idea named SYMMETRY. On the basis of KNN (K-NearestNeighbor), with the help of LNN (Locally Nearest Neighbor), symmetry is introduced, the k-value calculation formula is proposed, and the threshold is obtained automatically by curvature method. The main advantages of our method are as follows: based on symmetry, the outlier characteristics are more obvious, but the cluster points are not affected. Compared with distance-based methods, the defects and parameter sensitivity of distance characteristics are weakened. The algorithm does not require parameter input with strong stability and high precision. For the same amount of data, the larger the dimension, the faster the program runs. In the experimental part, the SYMMETRY algorithm will be compared with DB, LOF, RBDA, LDOF, ODIN, RDOS, INFLO, these seven classical KNN-based algorithms, using ROC curve evaluation algorithm, and finally prove that our algorithm performs well.
Novel Artificial Human Optimization Field Algorithms - The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.
Cost-enabled QoS aware task scheduling in the cloud management system

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.
A Simple yet Effective Unsupervised Adversarial Example Generation Framework for Vulnerability Assessment on Deep Learning

Generating adversarial examples to discover the vulnerability of deep learning model is critical yet challenging. Although adversarial sample generation methods have attracted the attention of lots of recent research, they heavily depend on supervised label information. As a result, they could only work on a specific classification task and fail to evaluate the vulnerability of unsupervised deep learning models. In this paper, we propose a simple yet effective unsupervised adversarial example generation framework for vulnerability assessment of deep learning. This framework achieves an effective unsupervised adversarial example generation by jointly maximizing the distribution divergence of the deep learning model-generated representations of regular examples and adversarial examples and minimizing the dissimilarity between regular examples and adversarial examples in the original space. In this way, the adversarial examples generated by this framework can mislead downstream tasks while camouflaging themselves. We further implement this framework to an adversarial image generation methods to demonstrate the performance of the proposed framework. Extensive experiments on four benchmarks demonstrate that the proposed method can find the vulnerability of the LeNet5 (a widely used deep learning model) much more effective even compared to state-of-the-art supervised competitors.
Stratification to Improve Systematic Sampling for Big Data Mining Using Approximate Clustering

Big data mining is the modern research of predicting knowledge for decision-making systems, and clustering is the data mining technique that predicts the classes of data without any prior knowledge. Big data mining supports high-volume, high-variety, and high-velocity data sets that reason classical data mining algorithms face speed-up, scale-up, memory utilization, computing efficiency, and effectiveness related problems. Data volume is the primary attribute of big data mining that is responsible for the variety and velocity related challenges. Sampling is a data reduction technique that handles data volume-related challenges under the single and multiple machine execution environments of big data mining such as classification and clustering. A good sampling process increases the speed, scalability, flexibility, accuracy, quality, efficiency, and utilized the memory resources for any data mining techniques without the influence of their characteristics. This paper proposed two sampling approaches for big data mining using the K-Means clustering algorithm known as SYK-Means (Systematic Sampling-based K-Means) and SSYK-Means (Stratified Systematic Sampling-based K-Means). The experimental results of the SYK-Means and SSYK-Means compared with classical K-Means and achieved better effectiveness and efficiency through R squares, Davies Bouldin, Calinski Harabasz, Silhouette Coefficient, CPU time, and convergence indices.
Unsupervised Semantic Association Learning with Latent Label Inference

In this paper, we unify a diverse set of learning tasks in NLP, semantic retrieval and related areas, under a common umbrella, which we call unsupervised semantic association learning (USAL). Examples of this generic task include word sense disambiguation, answer selection and question retrieval. We then present a novel modeling framework to tackle such tasks. The framework introduces, under the deep learning paradigm, a latent label indexing the true target in the candidate target set. An EM algorithm is then developed for learning the deep model and inferring the latent variables, principled under variational techniques and noise contrastive estimation. We apply the model and algorithm to several semantic retrieval benchmark tasks and the superior performance of the proposed approach is demonstrated via empirical studies.