Reviewerly/exHarmony huggingface dataset:
IBM Watson Studio: A Platform to Transform Data to Intelligence

Data Science (Tukey in Exploratory data analysis: Past, present, and future. Princeton University Press, 1993; Naur in Communications of ACM 9:485, 1966; Hayashi et al. in Data Science, Classification and Related Methods, Proceedings of Fifth Conference of the International Federation of Classification Societies (IFCS—96). Kobe, Japan, 1998) is fast emerging as the inter-disciplinary field that converges in specialist professionals, domain expertise, data modelling expertise, statistical expertise and computer science. Data scientists transform raw data to intelligence through a systematic process of data understanding and model building based on their understanding of the data. However, to become effective in achieving their objective of transforming raw data to intelligence, they are challenged with a disjointed collection of tools, processes and unsatisfactory data acquisition and curation techniques.
Image Inpainting Technique Based on Smart Terminal: A Case Study in CPS Ancient Image Data

Cyber-physical system (CPS) can intelligently feel the interactive information between data and terminal. The combination of CPS data and intelligent terminal can make terminal devices communicate with each other, coordinate operation, and share information in a complex environment. At present, the demand for smart terminal technology in university libraries and national cultural museums is also increasing. However, the data for smart terminal display and development of shared services face the problem that the original data is difficult to collect and the incomplete images are difficult to repair. In order to display the ancient books in a smart terminal, we have established a model for image inpainting through deep learning. In this paper, we set up a smart terminal display solution to display the digital protection effect of incomplete character images. Then, we propose a model based on Conditional Generative Adversarial Network (CGAN) to solve the problem of repairing incomplete character images. We put this model into the computing module and deployed it to the cloud computing platform of the smart terminal. Moreover, we construct a handwritten Yi character image data set in the cloud. To verify the CGAN calculation model, we design and construct two quality detection tests. According to the experimental results, the image inpainting quality of the CGAN is better than the traditional image restoration technology. Based on the experimental analysis and evaluation criteria, we find that the CGAN calculation model is of great significance for ancient book inpainting.
A Multi-objective Clustering Ensemble Algorithm with Automatic k-Determination

Evolutionary algorithm has been widely employed for data clustering, resulting various evolutionary clustering methods. For a given clustering task, these methods generally deliver different clustering solutions and there is no clear indication which one is the best. Clustering ensemble, which tries to encompass all information contained in different clustering solutions given by different methods, is considered to be an effective approach of improving the robust of solution. However, existing clustering ensemble method generally requires a cluster number as parameter to be specified in advance. Further, they could have the difficulty to preserve the diversity of candidate clustering solutions, which plays an important role in the success of these methods. Considering these two aspects, we propose a multi-objective clustering ensemble algorithm with automatic k-determination. In the proposed method, a modified version of Dual-Similarity Clustering Ensemble (MDSCE), which does not require the cluster number in advance, is developed and employed as crossover operator to generate new clustering partitions during optimizing process. Further, a k-means based process employing the cluster number from MDSCE is employed to generate diverse and high-quality individuals. For evaluation purpose, we have tested our algorithm on a series of real datasets. The results show that the proposed algorithm is able to deliver high-quality clustering solutions and outperforms related clustering algorithms.
Improving Online Handwriting Text/Non-text Classification Accuracy Under Condition of Stroke Context Absence

In this paper, an approach to text/non-text stroke classification for on-line handwriting recognition is proposed. This approach allows to improve classification accuracy when the stroke's context is absent. Having a label for the input stroke, we set this label for a feature vector which corresponds to each timestamp of the given input stroke. A trained neural network classifies each timestamp for the given input sequence. Finally, a decoder assigns a class label to the whole stroke. This approach was tested on the online handwritten dataset IAMonDO in on-line mode and has shown 98.5% accuracy. This approach could be used for other time-series classification tasks when the context information is not available.
Covariate Shift Adaptation for Structured Regression With Frank–Wolfe Algorithms

This paper concerns structured regression problems wherein the issue of covariate shift is addressed, which aims at reducing the discrepancy in training and test data distributions, using computationally efficient and sparse optimization principles. In particular, the projection-free Frank-Wolfe optimization algorithms are used to learn the importance weights and re-weight the training data in the context of covariate shift. To determine the unbiased estimates of the weights, Kullback-Leibler importance estimation procedure is used but its computational cost can be high since it is based on projected gradient optimization. Instead of using the standard Frank-Wolfe algorithm, we adapt its variants and propose away-steps Frank-Wolfe and pairwise Frank-Wolfe covariate shift algorithms to correct the covariate shift. The results highlight the improved computational efficiency and sparsity achieved while learning the importance weights on synthetic as well as benchmark datasets. Furthermore, importance weighted Sharma-Mittal twin Gaussian process structured regression framework is proposed to incorporate the learned weights from covariate shift algorithms, and its equations are derived for importance weighted derivatives and uncertainties. The performance of proposed algorithms is evaluated on two applications of structured regression, namely, human pose estimation and music mood estimation, where the benefit of handling covariate shift is demonstrated with improved performance relative to the state-of-the-art techniques.
Analysing Body Motions Using Motion Capture Data

Analysing manual work is an important task in industrial companies with high labour costs and labour intensive work processes. Industrial Engineer s can use the results of these analyses to identify potentials and improve productivity to maintain and improve competitiveness. To obtain goal-oriented results the processes need to be analysed in detail. One method that yields detailed information is the MTM-1-method. However, it requires a lot of effort and special knowledge. This presents a big hurdle, especially for companies with small production quantity. One option to reduce the effort and the required knowledge is using motion capture technology. This technology is capable to record human motions and to translate them into data that can be processed digitally. Known representatives are 3D cameras and motion capture suits. They track positions and postures of the human body, thus allowing conclusions about human movements. This paper presents an approach to detect body motions in accordance to MTM-1 using motion capture data from 3D cameras. The detected motions are then analysed with respect to productivity to show improvement potential.
RTCOP

In this paper, we propose a context-oriented programming (COP) framework for embedded software based on C++. The framework aims to enable us to apply COP to embedded systems. The next generation of software systems such as the internet of things or Industry 4.0 requires the ability to dynamically adapt to various contexts such as the changes in their surrounding environment. COP is an approach that is suitable for such software development. COP treats context explicitly and provides mechanisms to adapt dynamically to the changes in the context at runtime. The currently existing COP languages are extensions of Java, Smalltalk, etc. However, these programming languages are not suitable for embedded systems. Even now, numerous embedded software packages are developed with C or C++; thus, it is important to extend C++ for COP. Our COP framework named RTCOP exhibits mechanisms of method dispatching and layer activation for implementation of COP. Furthermore, to achieve adequate performance for embedded systems, the framework demonstrates the following features. (1) It is configurable for reducing memory, (2) the speed of method dispatching and layer activation is within a practical range. In this paper, we describe our proposal of the COP framework.
Network video quality assessment method using fuzzy decision tree

At present the service of network video is increasing rapidly. The video quality of experience is usually defined as user experience. However, the objective quality assessment for network video is unsure but is an urgent need. To improve the objective method, this study proposes an assessment method using a fuzzy decision tree. This work has two parts: (i) It considers different impair factors. Next, the decision tree is built by the set of measured factors from the different network environments. This tree can define the quality of network video, but cannot give the concrete objective scores. (ii) In order to give the accuracy objective scores, all the impair factors of leaf nodes are divided into two groups and inputted to fuzzy logic systems. The outputs are used as the next inputs and inputted into another fuzzy logic system to give the objective score. At last, the performance of the proposed method is tested on network videos with different network environments. At the same time, other methods are compared with it. The experimental results show that this method can get more accurate objective scores. This means the proposed method can improve the objective assessment model.
Phonetic acquisition in cortical dynamics, a computational approach

Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units-such as phonemes-are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of-for example-backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream.
Item Delivery Simulation Using Dijkstra Algorithm for Solving Traveling Salesman Problem

Abstract Companies that contribute to travel have many problems in the process of item delivery. Distances and priorities are considered for a process of item delivery based on the highest priority. A delivery target that can be done one day evidently exceed the expected limit and that is the impact. This is an example of the waste time and operational costs that should be at the same time that two or more addresses can be sent. Traveling Salesman Problem (TSP) was define a classical problem to finding the shortest route that salesman can be passed when visiting several places without visit again in the same place more than once. In this study, TSP requires all calculations of possible routes to be obtained. Then choose one of the shortest routes by prioritizing the things considered, namely distance and priority. Delivery is done quickly through the shortest route according to priority using the Dijkstra algorithm. Simulation shows that the Dijkstra algorithm must be approved by use clustering data for Dijkstra’s priorities and sub-routes to solve TSP problems. Simulation shows that the Dijkstra algorithm must be modified using Dijkstra’s priority clustering and sub-routing to solve TSP problems. The resulting route has an influence between two graphs. Complete graph has a distance efficiency of 47.8% and execution time of 48.1% compared to non-complete graphs.
Ideation framework of block chain adoption in Malaysia higher education

Abstract Cryptocurrency in blockchain technology shapes secure transactions in theideology of distributed networking systems. The current trend in this global era pertaining to follow blockchain in varioius fields and it plays vital role in financial accounting, educational sectors and research areas. In educational sectors the most challenging part is difficult in record keeping by using a single database system and its maintenance. However, several researches explored the maturity of solving financial problems using blockchain has achieved the expected outcome. Hence this paper mainly focused on the ideation framework of blockchain technology adoption in higher education to increase the efficiency of distributed system for providing betterreliability and security. Here study has been conducted on various universities based on blockchain technology adoption in education also few popular ranking universities considered under the literature review. In future the proposed framework will be useful for all higher learning educational institutions in Malaysia.
Layout-Aware Embedding for Quantum Annealing Processors

Due to the physical limit in connectivity between qubits in Quantum Annealing Processors (QAPs), when sampling from a problem formulated as an Ising graph model, it is necessary to embed the problem onto the physical lattice of qubits. A valid mapping of the problem nodes into qubits often requires qubit chains to ensure connectivity. We introduce the concept of layout-awareness for embedding; wherein information about the layout of the input and target graphs is used to guide the allocation of qubits to each problem node. We then evaluate the consequent impact on the sampling distribution obtained from D-Wave’s QAP, and provide a set of tools to assist developers in targeting QAP architectures using layout-awareness. We quantify the results from a layout-agnostic and a layout-aware embedding algorithm on (a) the success rate and time at finding valid embeddings, (b) the metrics of the resulting chains and interactions, and (c) the energy profile of the annealing samples. The latter results are obtained by running experiments on a D-Wave Quantum Annealer, and are directly related to the ability of the device to solve complex problems. Our technique effectively reduces the search space, which improves the time and success rate of the embedding algorithm and/or finds mappings that result in lower energy samples from the QAP. Together, these contributions are an important step towards an understanding of how near-future Computer-Aided Design (CAD) tools can work in concert with quantum computing technologies to solve previously intractable problems.
Benchmarking Graph Database Backends—What Works Well with Wikidata?

Knowledge bases often utilize graphs as logical model. RDF-based knowledge bases (KB) are prime examples, as RDF (Resource Description Framework) does use graph as logical model. Graph databases are an emerging breed of NoSQL-type databases, offering graph as the logical model. Although there are specialized databases, the so-called triple stores, for storing RDF data, graph databases can also be promising candidates for storing knowledge. In this paper, we benchmark different graph database implementations loaded with Wikidata, a real-life, large-scale knowledge base. Graph databases come in all shapes and sizes, offer different APIs and graph models. Hence we used a measurement system, that can abstract away the API differences. For the modeling aspect, we made measurements with different graph encodings previously suggested in the literature, in order to observe the impact of the encoding aspect on the overall performance.&#x0D;
Intergenerational Family Storytelling and Modeling with Large-Scale Data Sets

Open large-scale data sets (LSDS; [4]) and visualization tools have opened a new design space for youth and family collaborative learning. Using a corpus of video-recorded interviews, we examine how youth and parents together explore their personal family migration histories---their geobiographies---and broader socioeconomic, historical trends using dynamic data visualization tools. We introduce the Family Alignment of data Models and Stories (FAMS) process to describe how parent-child interactions with LSDS, through cycles of aligning family narratives to data, produce family stories about migration. Applying the FAMS model to four family cases revealed that grounding family narratives in jointly constructed interpretations of data sets encourages generative co-constructions of those stories, which contrasts with family storytelling settings in which narrators' interpretations compete to become the basis for the accepted version of events [23]. We discuss how the FAMS model can facilitate deeper understandings of intergenerational engagement with LSDS.
Translation and Adoption: Exploring Vocabulary Work in Expert-Layperson Encounters

An advisory service encounter brings together a domain expert with a layperson in a complex life situation. Because of the different backgrounds and expertise levels, the interlocutors and meanings is an essential part of advisory services and, generally, of expert-layperson collaboration. Establishing and maintaining a common lexicon is a specific and, at the same type, frequent type of collaborative work. Nevertheless, it remains unclear what efforts this collaborative work involves and what role collaborative IT applications play in this regard. A collaborative application can well support the maintenance of a common lexicon by providing a way to externalize terms or definitions. Or it can generate additional work by providing further terms and definitions to be incorporated in the common lexicon. That puzzle gets reflected in specific design dilemmas: should the system use expert or conventional terms, what is the source of the adequate terminology, to what extent should the system adapt to the individual lexical choices, etc. This manuscript explores the work involved in establishing and maintaining a common lexicon in advisory services between an expert and a layperson. In particular, it demonstrates how external material, a dedicated collaborative application developed for supporting advisory services, impacts the maintenance of a common lexicon. First, the manuscript depicts practices involved in translation and adoption of terminology from the system into the conversation. Second, it characterizes the system's impact on interlocutors' vocabulary. Overall, the study contributes to the discourse on expert-layperson collaboration by characterizing an important type of work, the vocabulary work, and by depicting the role of collaborative applications for this type of work.
Embedding Complementary Deep Networks for Image Classification

In this paper, a deep embedding algorithm is developed to achieve higher accuracy rates on large-scale image classification. By adapting the importance of the object classes to their error rates, our deep embedding algorithm can train multiple complementary deep networks sequentially, where each of them focuses on achieving higher accuracy rates for different subsets of object classes in an easy-to-hard way. By integrating such complementary deep networks to generate an ensemble network, our deep embedding algorithm can improve the accuracy rates for the hard object classes (which initially have higher error rates) at certain degrees while effectively preserving high accuracy rates for the easy object classes. Our deep embedding algorithm has achieved higher overall accuracy rates on large scale image classification.
Watermarking Algorithm for Encrypted Medical Image Based on DCT-DFRFT

Medical image is a special type of information that must be strictly protected. However, when these images are stored and transmitted on the internet, they may be vulnerable to malicious attack with poor security. Therefore, this paper studies the digital watermarking algorithm of encrypted medical image based on DCT-DFRFT (Discrete Cosine transform–Discrete Fractional Fourier transform). First, DCT (Discrete Cosine Transform) and tent map are used to encrypt the original medical image and the watermarking. Then, DCT-DFRFT (Discrete Cosine transform–Discrete Fractional Fourier transform) is applied to extract the feature vectors of the encrypted medical image, and zero-watermarking technology is used to generate the secret key to complete the embedding and extraction. The robustness is verified by calculating the normalized correlation coefficient (NC value) between the embedded and extracted watermarking. The experimental results show that this algorithm is robust to both conventional and geometric attacks.