Reviewerly/exHarmony huggingface dataset:
Identification of expectancy, proximity, and compatibility of the Bengali language

Semantic errors are portrayed as in border on upon of the law of sentences. In the Bengali language, we will identify errors depend on three sorts of sentence rules. They are Expectancy , Proximity , Compatibility . We are assessing every single Bengali sentence as indicated by these three sorts of techniques. Cause the accompanying way will function as significance by importance word order and will discover which sentence is sans mistake. Presently, we will place sentences into certain classes with the goal that sentences will be gathered as right or off-base. We picked a Bengali sentence comprising of the completed fundamental structure of the structure subject, object, verb. These sentences are broken into the subject, object, and verb term parts with the end goal of arrangement. From thing and action term order, we classify the subject and action term into a progressively broad class and henceforth we build up the relationship. In light of the order of action terms, what are more, things, we built up a relationship for the subject and action term as well as action term and object of the sentence. In our tenets and terms, we are demonstrating that our framework works for expectancy is 70 percent, Proximity is 80 percent, compatibility is 80 percent. Some are blunders however in most extreme sentences it can discover the mistakes.
A Conditional Random Fields Based Framework for Multiview Sequential Data Modeling

Multiview learning has gained much attention due to the increasing amount of multiview data which are collected from different sources or can be characterized by different types of features. How to properly handle view heterogeneity and balance information between views are the challenges in the domain of multiview learning, especially for sequential data. In this paper, we propose a multiview conditional random field model (multiview CRF) for modeling multiview sequential data, in which the model focuses on different views at a different moment with varying degrees. Especially, additional weight variables are introduced in the CRF through two different mechanisms which play a role of controlling the use of information from different views. Besides, a multiview decomposition network is designed to transform multiview observation at each moment into a unified multiview representation which is further used as an input of weight variable CRF. Variational inference with variance reduction technique is adopted for model training. We conduct experiments on two real-world datasets to compare the proposed methods with other related state-of-the-art methods. Experimental results and careful analysis show that both the weight variables and decomposition network contribute to the outstanding performance over other methods.
Distributed Robust Bayesian Cluster Enumeration Criterion for Unsupervised Learning

Wireless sensor networks have been widely deployed for industrial and consumer applications. The amount of data in such applications is large, and as a results a result, the automatic discovery of the underlying structure in the data (cluster analysis) becomes of prominent interest. A challenging task in cluster analysis is the estimation of the number of clusters. To this end, we propose a robust decentralized diffusion-based cluster enumeration method that enables distributed sensor nodes to estimate the number of clusters in their respective data sets through cooperation with their immediate neighbors. The proposed method is robust to the presence of heavy-tailed noise and outliers, which is useful for sensor networks as outliers can occur due to measurement errors or sensor failure. Through experiments, we show that the proposed method is promising, and achieves the performance of a centralized network using a fusion center.
On self-duality and hulls of cyclic codes over $$\frac{\mathbb {F}_{2^m}[u]}{\langle u^k\rangle }$$ with oddly even length

Let $$\mathbb {F}_{2^m}$$ be a finite field of $$2^m$$ elements and denote $$R=\mathbb {F}_{2^m}[u]/\langle u^k\rangle $$ $$=\mathbb {F}_{2^m}+u\mathbb {F}_{2^m}+\cdots +u^{k-1}\mathbb {F}_{2^m}$$ ( $$u^k=0$$ ), where k is an integer satisfying $$k\ge 2$$ . For any odd positive integer n, an explicit representation for every self-dual cyclic code over R of length 2n and a mass formula to count the number of these codes are given. In particular, a generator matrix is provided for the self-dual 2-quasi-cyclic code of length 4n over $$\mathbb {F}_{2^m}$$ derived by an arbitrary self-dual cyclic code of length 2n over $$\mathbb {F}_{2^m}+u\mathbb {F}_{2^m}$$ and a Gray map from $$\mathbb {F}_{2^m}+u\mathbb {F}_{2^m}$$ onto $$\mathbb {F}_{2^m}^2$$ . Finally, the hull of each cyclic code with length 2n over $$\mathbb {F}_{2^m}+u\mathbb {F}_{2^m}$$ is determined and all distinct self-orthogonal cyclic codes of length 2n over $$\mathbb {F}_{2^m}+u\mathbb {F}_{2^m}$$ are listed.
The Effect of High Performance Computer on Deep Learning: A Face Expression Recognition Case

In this paper, the facial expression recognition(FER) application is achieved by integrating Machine Learning and High Performance Computing (HPC). The convolutional neural network(CNN) will be trained utilizing the XSEDE high-performance computing resources. To observe the performance of HPC, CNN model has been trained with different values of parameters and their training times are provided. Compared to an ordinary computer, HPC with CPU and Google Colaboratory with GPU, this research explores the highest work efficiency of HPC with GPU with respect to operating time.
Predicting Manufacturing Feasibility Using Context Analysis

In the present scenario, the technology revisions are making the market vulnerable to predict. This gives birth to the requirement of a new model which can stop loss of a production business by predicting the market using news analysis, associative rule mining, precise predicting techniques and context analysis. This paper presents a novel idea of dealing with manufacturers’ problem of product dump due to the rapid change in technology and the changing demand of customers. Every new product launched with new features or with existing features but less price gives a tough competition to already existing products in the market. By the time the manufacturer comes to know that the demand has been decreased, the manufacturer is already in the loss and he has to dump already manufactured pieces due to rapid down sale. In this paper, a model is proposed with an algorithm to quickly identify the required number of pieces in a time frame.
Promoting Independence with a Schedule Management Assistant that Anticipates Disruptions

We motivate and overview a system for schedule management assistance that we are developing specifically to help adolescents with disabilities who are transitioning to independent adulthood. We summarize how we have overcome a number of engineering challenges in creating a prototype system. The expert feedback on our prototype suggests how and why the tool is expected to be useful, and has directed our focus toward handling schedule disruptions. In the latter part of this paper, we provide deeper technical material on new metrics and strategies for giving scheduling advice that is resilient to disruptions while also giving the user more freedom.
Hot Topics: <i>Fires of Life</i> Review

Previous articleNext article No AccessBook ReviewHot Topics: Fires of Life Review Fires of Life: Endothermy in Birds and Mammals. By Barry Gordon Lovegrove. New Haven, CT: Yale University Press, 2019.Stewart C. NicolStewart C. NicolUniversity of Tasmania, Hobart, Tasmania, Australia Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Physiological and Biochemical Zoology Volume 93, Number 2March/April 2020 Sponsored by Division of Comparative Physiology and Biochemistry, Society for Integrative and Comparative Biology Article DOIhttps://doi.org/10.1086/707497 Views: 89Total views on this site Citations: 1Citations are reported from Crossref HistoryPublished online January 16, 2020 For permission to reuse, please contact [email protected]PDF download Crossref reports the following articles citing this article:亚楠 张 Time Series Forecasting Based on Echo State Network Optimized by Improved Particle Swarm Algorithm, Computer Science and Application 11, no.0808 (Jan 2021): 2070–2079.https://doi.org/10.12677/CSA.2021.118212
2 satisfiability logical rule by using ant colony optimization in Hopfield Neural Network

This finding presents the limitation of traditional Hopfield Neural Network (HNN) in doing 2 Satisfiability problem (2SAT). More precisely, both traditional exhaustive search method (ES) and ant colony optimization (ACO) were proposed in doing 2SAT problem. Both learning method will reduce logical inconsistencies of 2SAT in HNN. Since both learning method will eventually complete the learning phase, the efficiency of both method is difficult to describe. In this study, both learning method will undergo restricted learning environment during learning phase of HNN. The robustness of ACO and ES in doing 2SAT will be evaluated based on root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The results obtained from the computer simulation demonstrates the effectiveness of ACO in doing 2SAT in HNN.
Development of Easy Attachable Biological Information Measurement Device for Various Head Mounted Displays

It is important to measure the user's biological information when experiencing virtual reality (VR) content. By measuring such biological information during a VR stimulation, the body's response to the stimulation can be verified. In addition, it is possible to change the stimulation interactively by estimating the feeling from the measured biological information. However, the user load required to mount the sensor for biological information sensing under the existing VR content experience is significant, and the noise due to body movement is also a problem. In this paper, a biometric device that can be mounted on a head mounted display (HMD) was developed. Because an HMD is attached strongly to the face, it is thought to be robust to body movement and thus the mounting load of the sensor can be ignored. The developed device can simply be mounted on an HMD. A pulse waveform can be acquired from the optical pulse wave sensor arranged on the nose side of the HMD, and the respiration waveform can be acquired from a thermopile arranged in the nostril area of the HMD. We condacted the experiment to verified that a pulse wave and the respiration can be measured with sufficient accuracy for a calculation of the tension and excitement of the user. As a result of the experiment, it was confirmed that the pulse wave can be measured with an error of less than 1% in nine out of 14 users and that the respiration can be measured with an error of 0.6% if user does not move. The respiration was measured with high accuracy regardless of the type of HMD used.
On Updating Probabilistic Graphical Models in Bayesian Optimisation Algorithm

The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA) that uses a Bayesian network as probabilistic graphical model (PGM). During the evolutionary process, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem resulting in a very time-consuming process. However, we show in this paper that significant changes in PGM structure do not occur so frequently, and can be particularly sparse at the end of evolution. A statistical study of BOA is thus presented to characterise a pattern of PGM adjustments that can be used as a guide to reduce the frequency of PGM updates. This is accomplished by proposing a new BOA-based optimisation approach (FBOA) whose PGM is not updated at each iteration. This new approach avoids the computational burden usually found in the standard BOA. Inspired by fitness landscape analysis concepts, we perform an investigation in the search space of an NK-landscape optimisation problem and compare the performances of both algorithms by using the correlation between the landscape ruggedness of the problem and the expected runtime of the algorithms. The experiments show that FBOA presents competitive results with significant saving of computational time.
Using K-Means Clustering and Data Visualization for Monetizing logistics Data

Logistics companies possess collect large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. They can extract useful market knowledge by using data mining technologies such as visualization and clustering. The detailed results of such big data analytics methods can also be monetized under certain circumstances. We studied the data on the transactions of a logistics company in the Middle East. K-Means clustering of their data proved to generate deeper insight into several clusters of customers having different profiles. The results propose a best fit model for the clustering. Since the clustering and visualization results are relevant, reliable and anonymous they fit the monetization criteria as well. Improved data driven marketing applications are possible for the customers.
Knowledge Graph Embeddings over Hundreds of Linked Datasets

There is an increasing trend of using Linked Datasets for creating embeddings from URI sequences, since such embeddings can be exploited for several tasks, i.e., for machine learning problems, tasks related to content-based similarity, and others. Existing techniques exploit either a single or a few datasets (or RDF graphs) for creating URI sequences for one or more entities. However, there are not available approaches, where data from multiple datasets are combined, for enriching the URI sequences for a given entity. For this reason, we introduce a prototype, called LODVec, that exploits LODsyndesis knowledge graph, which is the largest knowledge graph including all inferred equivalence relationships. LODVec exploits this graph for creating URI sequences for millions of entities by combining data from 400 datasets, whereas it offers several configurable options for creating such URI sequences that are based on metadata (e.g., provenance). Moreover, it uses as input the produced URI sequences for creating URI embeddings through word2vec model. We evaluate the gain of exploiting several datasets (instead of a single or few ones) and the impact of cross-dataset reasoning for machine-learning based tasks (i.e., classification and regression), and we compare the effectiveness of several configurations and machine learning models.
Dense Image Captioning Based on Precise Feature Extraction

Image captioning is a challenging problem in computer vision, which has numerous practical applications. Recently, the method of dense image captioning has emerged, which realizes the full understanding of the image by localizing and describing multiple salient regions covering the image. Despite there are state-of-the-art approaches encouraging progress, the ability to position and to describe the target area correspondingly is not enough as we expect. To alleviate this challenge, a precise feature extraction method (PFE) is proposed in this paper to further enhance the effect of dense image captioning. Our model is evaluated on the Visual Genome dataset. It demonstrated that our method is better than other state-of-the-art methods.
Implementation of ECU Specific Requirements in ODX Generation Using Variation Management

Abstract As the need for diagnostic applications in the advanced technological vehicles is increasing, the need for standardized format for diagnostic data is also growing. ODX (Open Diagnostic data eXchange) file is one of the standardized XML‐based formats based on ISO 22901 used for exchanging diagnostic data in modern world vehicles. Different types of diagnostic identifiers like DIDs, RIDs and DTCs are defined in a structured way in ODX files. These diagnostic identifiers will be used throughout the life cycle of vehicle development like software development, testing, production, after‐sales and service. The new features like Autonomous and Electrification demands to have common and distinctive diagnostics requirements for different vehicle platforms. This poses architectural challenges in managing common features and specific features with in a superset architecture (eg: Defining a single DID differently for different ECUs). One of the ways to tackle this challenge is through variation management concept. Variation management enables embedding information about different flavors of controllers into the same architecture. This helps downstream software teams to optimize their software to have their specific requirements. This paper focuses on variation management solution to accommodate ECU specific diagnostic requirements in ODX generation.
Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks

The increased availability of High-Performance Computing resources can enable data scientists to deploy and evaluate data-driven approaches, notably in the field of deep learning, at a rapid pace. As deep neural networks become more complex and are ingesting increasingly larger datasets, it becomes unpractical to perform the training phase on single machine instances due to memory constraints, and extremely long training time. Rather than scaling up, scaling out the computing resources is a productive approach to improve performance. The paradigm of data parallelism allows us to split the training dataset into manageable chunks that can be processed in parallel. In this work, we evaluate the scaling performance of training a 3D generative adversarial network (GAN) on an IBM POWER8 cluster, equipped with 12 NVIDIA P100 GPUs. The full training duration of the GAN, including evaluation, is reduced from 20 h and 16 min on a single GPU, to 2 h and 14 min on all 12 GPUs. We achieve a scaling efficiency of 98.9% when scaling from 1 to 12 GPUs, taking only the training process into consideration.
A Decentralized Consensus Secure and Authentication Framework for Blockchain-Based Healthcare Application

The rapid growth of mobile services and Internet of Things (IoT) has caused severe demands of a management system for Mobile Edge Computing (MEC) where User Equipments (UEs) benefit from high computational power, capacity and communication as well as the offered services by MEC. However, a comprehensive management is required to orchestrate the services and resource in MEC to fairly distribute to UEs with the aim of ensuring the Quality of Experience (QoE). In this paper, we propose a decentralization concensus secure and authentication framework for MEC management/orchestration system with crucial security and authentication components by which it ensures the delivery of users’ quality of experience.
Development of an Ad-hoc Smart Room Based on User-oriented Context-aware System Architecture

Context is defined as information that represents the environment surrounding us. A system to provide services to users using context is called a context-aware system (CAS), and it has been widely studied. However, many conventional CASs are not flexible. As conventional CASs cannot be freely customized by end-users, they lack adaptability to environments, making them difficult to use in various environments. To solve this problem, we propose a user-oriented context-aware system architecture (U-CASA) for building ad-hoc smart rooms. This architecture allows end-users to install various IoT sensor devices to the target environment and to make it a smart room. The end-user can also freely customize the system settings and can link various contexts to a variety of services. In order to evaluate the proposed architecture, we built two ad-hoc smart rooms based on U-CASA. As a result of the demonstration experiment with behavior scenarios, we confirmed that two smart rooms can perform properly and can provide services despite their different environments.
Some properties of continued fraction expansions related to certain sequence of integers and its applications in elliptic curve cryptography

In this paper, we consider the sequence of integers ri, si, ti ∈ ℤ of regular continued fraction (RCF) expansions generated from the extended Euclidean algorithm. These sequences always satisfy ri = sia + tib where ri is a remainder whereas si and ti arising from the extended Euclidean algorithm are equal, up to sign, to the convergent of the continued fraction expansion of a/b. We discuss the behavior of these sequences and provide their full proof in detail with their simple calculation of sample. Throughout this work, we deal with the concept of Euclidean algorithm and extended Euclidean algorithm together with continued fraction algorithm. These algorithms are involved in the improvement of computational efficiency of the elliptic curve cryptography (ECC). Henceforth from that, we tend to associate these sequences in ECC. Last but not least, we found that the value of integers (ri, si, ti) satisfy various properties in RCF which then used to solve the shortest vector problem in representing point multiplications in ECC, namely the Gallant, Lambert & Vanstone (GLV) integer decomposition method and the integer sub decomposition (ISD) method.
A fire safety control system of educational institutions

Abstract The article describes the software system that allows to evaluate the state of fire safety of educational objects. The estimation is based on the analysis of the integral information on all factors influencing a condition of fire safety taking into account specificity of object. The value of fire risk is used as an evaluation indicator. It is calculated in accordance with the methodology approved by the Ministry of Emergency Situations. To facilitate the decision-making process, the system generates a set of recommendations for the decision-maker aimed at improving the level of fire safety of the object of education.
Visualizing Program Quality - A Topological Taxonomy of Features

In this paper we design a hierarchical, interactive visualization to simplify the assessment of BSc program quality. The idea is based on extracting features from direct assessment data at various levels in the QA taxonomy and linking these features to enable the user to navigate the visualization and obtain insights at different levels of detail. The data was gathered over a period of 5 academic semesters and processed in Oracle before being modelled in Microsoft's Power-BI Desktop which is gaining prominence as a platform for business intelligence.
Vulnerability analysis on the image‐based authentication: Through the WM_INPUT message

Summary We verified the mouse data exposure using the WM_INPUT message handler that obtains the mouse‐inputted data to derive the vulnerability of the image‐based authentication. Consequently, the mouse data were exposed on most of the banking and payment sites of South Korea. Experiment results show that the mouse data are exposed in image‐based authentication in six Internet banking services. Therefore, we proved that the safety of the authentication information is vulnerable even when image‐based authentication is applied. In the future, we will propose an image‐based authentication method to solve the vulnerability found from this paper and derive a set of criteria for the vulnerability analysis of image‐based authentication for the security assessment.