Reviewerly/exHarmony huggingface dataset:
Dependency Parser for Bengali-English Code-Mixed Data enhanced with a Synthetic Treebank

The development of code-mixing (CM) NLP systems has significantly gained importance in recent times due to an upsurge in the usage of CM data by multilingual speakers.However, this proves to be a challenging task due to the complexities created by the presence of multiple languages together.The complexities get further compounded by the inconsistencies present in the raw data on social media and other platforms.In this paper, we present a neural stack based dependency parser for CM data of Bengali and English by utilizing pre-existing resources for closely related Hindi and English CM treebank as well as monolingual treebanks for Bengali, Hindi and English.To address the issue of scarcity of annotated resources for Bengali-English CM pair, we present a rule based system to computationally generate a synthetic code-mixing treebank for Bengali and English (Syn-BE) which is used to further improve the accuracy of our dependency parser.For evaluation purpose, we present a dataset of 500 Bengali-English tweets annotated under Universal Dependencies scheme.
SingleCross-clustering: an algorithm for finding elongated clusters with automatic estimation of outliers and number of clusters

Many clustering methods perform well with spherical clusters but poorly with elongated clusters. The Single-linkage method is suitable for finding such type of long clusters, but it can be sensitive to outliers and noise in the data, causing the so-called chain-effect. This work proposes a modification of Cross-Clustering algorithm, the SingleCross-Clustering (SCC), a partial clustering algorithm that estimates the number of clusters, recognizes outliers and that is useful for the identification of elongated clusters. SCC has been validated by comparing it with a number of existing clustering methods, showing on both simulated and real datasets that SCC is a reliable solution for the identification of the correct number of clusters and of the number of clusters memberships. The algorithm has been implemented in the R package CrossClustering, which can be downloaded for free from the CRAN contributed package repository.
Low complexity high efficiency coding of light fields using ensemble classifiers

Light field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.
Using elastic net restricted kernel canonical correlation analysis for cross-language information retrieval

Kernel methods, which are a non-linear variant of linear methods, are used to increase the flexibility and allow to examine non-linear relationships by linear methods. The conventional solution of the restricted kernel canonical correlation analysis problem has a major drawback, it solves the problem in a reasonable time frame only for problems with few variables. We successfully overcame this limitation by implementing the method with the alternating least-squares algorithm. This allowed us to apply the method to cross-language information retrieval problem on a big dataset. We compared the results to other established methods and they were encouraging.
A Scalable Algorithm for Multi-class Support Vector Machine on Geo-Distributed Datasets

Training machine learning models on large scale data to efficiently discover valuable information while maintaining the security and privacy of data remains an important research issue. Often many real-life applications such as healthcare systems or financial organizations distribute data over many data centers and these data centers may have a different privacy policy. The joint decision over this dataset while not sharing the local information of the data centers is a must and becomes a challenging problem. The State-of-the-art method mainly relies on cryptographic technique to ensure the privacy for data communication between the data centers. This technique alone is not suitable for the large-scale geo-distributed datasets as this is designed for small-scale systems. In this paper, we propose a novel approach to achieve privacy-preserving SVM algorithm where the training set is distributed and each partition can contain large-scale data. We utilize the traditional SVM model to train the dataset of local data centers and with a few parameters sent by those training models, a centralized machine calculates the final result. We show that the proposed model is secure in an adverse environment and use simulations to demonstrate its correctness and computation speed compared to other parallel SVM training models.
Surface curvature measurement of microlenses using a white-light interference microscope and fast geometric fit algorithm

We present a technique for measuring the microlens radius of curvature (ROC). The technique is based on the principle of three-dimensional (3-D) surface topography using a white light interference microscope. By measuring 3-D point clouds of the surface combined with fitting the measured data to an ideal sphere, the radius of the surface is determined. We take advantage of the fast speed of the maximum intensity method and the anti-interference capability of the fitting method of fringe analysis techniques in white-light interferometry to measure the 3-D topography of the microlens array surface. To measure each microlens’s ROC, we use the fast geometric fit algorithm for sphere where the input data are the spherical surface point cloud. We have built a white light interference microscope and experimentally measured the geometric parameter and the surface curvature of some microlens of a commercial microlens array. Our measurement results of the ROC, the surface height, and the diameter of microlens showed a good agreement with the results obtained using the Alpha-Step D-500 stylus profiler and the values reported by the manufacturer, indicating the applicability of this technique.
MACM: How to Reduce the Multi-Round SCA to the Single-Round Attack on the Feistel-SP Networks

Since the master key length becomes longer and longer in ciphers, an adversary often needs to preform the multi-round side channel analysis (SCA) in order to recover the master key by enough round keys. Traditional multi-round SCA is launched by adaptive manner in practice, which means that the input of each round is calculated in an on-the-fly way based on all round keys of anterior rounds. However, compared to the classical single-round SCA, the multi-round SCA in adaptive manner is severely limited in several practical scenarios, because all round keys of anterior rounds must be properly recovered before the attack against the next round. In this paper, we focus on the Feistel-SP networks, break the interdependency between the alternating measurement and analysis phases, propose a Multi-round non-Adaptive Chosen Message (MACM) approach, which can reduce the multi-round SCA to the single-round attack. In MACM, the set of plaintexts applied to multiple rounds is calculated in an off-line way. We also prove that the revealed round keys by MACM are adequate to recover the master key. Furthermore, we carefully analyze the advantages of MACM regarding to robustness and compatibility. In order to further manifest the validity of MACM, we perform extensive experiments on three typical Feistel-SP ciphers, Camellia, CLEFIA and SM4, the master keys are recovered as expected, and the number of traces in MACM is at least 25% less than that in the adaptive manner.
Holistic knowledge management: adherence analysis of the Castillo and Cazarini model

The holistic paradigm has been identified as the preferred method for the development of KM frameworks. Under this premise, this research aims to analyse the adherence of the Castillo and Cazarini framework to holistic KM and to discern if its structure considers all the dimensions related to this form of management. Qualitative Content Analysis was employed to examine the framework and quantification was used to expose the percentage of this structure’s adherence to holistic KM. As a result, the Castillo and Cazarini framework partially adheres to holistic KM. This result allows inferring that there is an opportunity for the development of new frameworks that consider all the dimensions presented in this research. Additionally, the boundaries between KM and other forms of management require a greater and more in-depth theoretical analysis to discern what constitutes knowledge management more explicitly.
A Synonym Extraction Method Based on Intimacy

This paper proposes a new concept—intimacy for synonyms extraction. Firstly, we find the first n similar word labeled A1...An of the word A, and then, find the first n similar word groups of A1,A2...An respectively, and calculate the intimacy based on whether or not A appears in these word groups and the position A appears to obtain the final intimacy score. Finally, sort the final intimacy score and obtain the first n synonyms of A. In this paper, we test on Wikipedia dataset, and compare with word similarity computing tasks. Experiments show that the synonyms found by this method are higher than other methods both in subjective and objective evaluation.
Designing Lectora Based Interactive CD Learning Media in Basic Programming Subjects (Case Study of Class X SMKN 2 Padang)

Abstract This research and development aims to produce products in the form of Interactive Media CDs for students of class X majoring in software engineering at Padang State Vocational High School 2 based on the lectora inspire application. The research method used is the method of research and development, using the ADDIE development model. The development steps are: (1) Analysis, (2) Design, (3) Development, (4) Implement, (5) Evaluate. Product development validation includes. The results of the assessment by experts in terms of the aspect of content eligibility: 71.00%, language component: 71.25%, presentation component: 71.00%, graphical component: 71.25%. Overall, the validator test evaluation of the Basic Programming Interactive CD Media is 71.13%, so that the level of validity can be interpreted Fairly Valid to be used. The results of the practicality test assessment in terms of the user state: 80.11%, the effectiveness of learning time: 79.60%, benefits: 80.90%. Overall, the practicality assessment of the Basic Programming Interactive CD Media is 80.20%, so that the practical level can be interpreted as practically used. The results of the effectiveness test were reviewed from the aspect of learning pleasure: 79.08%, the presence of interesting teaching materials in learning: 71.50%. Overall, the assessment of the effectiveness.
A Provably Secure Certificateless Proxy Signature Scheme Against Malicious-But-Passive KGC Attacks

Abstract In certificateless proxy signature (CLPS), the key generation center is responsible for initializing the system parameters and can obtain the opportunity to adaptively set some trapdoors in them when wanting to launch some attacks. Until now, how to withstand the malicious-but-passive key generation center (MKGC) attacks in CLPS is still an interesting problem. In this paper, we focus on the challenging issue and introduce a CLPS scheme provably secure in the standard model. To the best of our knowledge, we are the first to demonstrate its security under MKGC attacks by adopting the technology of embedding the classic difficulty problems into the target entity public key rather than the system parameters during the security proof process.
Solution Attractor of Local Search System: A Method to Reduce Computational Complexity of the Traveling Salesman Problem

The traveling salesman problem (TSP) is presumably difficult to solve exactly using local search algorithms. It can be exactly solved by only one algorithm—the enumerative search algorithm. However, the scanning of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This chapter attempts to answer these questions. A local search algorithm is a discrete dynamical system, in which a search trajectory searches a part of the solution space and stops at a locally optimal point. A solution attractor of a local search system for the TSP is defined as a subset of the solution space that contains all locally optimal tours. The solution attractor concept gives us great insight into the computational complexity of the TSP. If we know where the solution attractor is located in the solution space, we simply completely search the solution attractor, rather than the entire solution space, to find the globally optimal tour. This chapter describes the solution attractor of local search system for the TSP and then presents a novel search system—the attractor-based search system—that can solve the TSP much efficiently with global optimality guarantee.
Traffic marking recognition based on generating antagonistic neural network

This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.
Proofs of Physical Reliability for Cloud Storage Systems

Cloud service providers (CSPs) promise to reliably store repositories outsourced by clients. Unfortunately, once files have left the client's control, he has no means to verify their redundant storage. In this article, we develop Proof of Physical Reliability (PoPR) auditing mechanisms that prove that a CSP stores an outsourced repository across multiple physical storage nodes. A PoPR complements the existing proof-of-retrievability (PoR) and proof-of-data possession (PDP) methods that are concerned with file retrievability, but without any verification of the fault-tolerance to physical storage nodes failures. A PoPR goes beyond retrievability by verifying that a file is redundantly stored across multiple physical storage nodes according to a pre-agreed layout and can, therefore, survive node failures. The verification mechanism relies on a combination of storage integrity and timing tests on the simultaneous retrieval of a collection of file symbols from multiple storage nodes. Compared to the state-of-the-art, our approach accommodates CSPs with heterogeneous storage devices (hard disks, SSDs, etc.) and does not assume constant data processing nor network delays. Instead, it can operate under any delayvariance, because it relies only on (loose) delay bounds. We analytically prove the security of our construction and experimentally validate its success in heterogeneous storage settings.
GalStamps: Analyzing Real and Simulated Galaxy Observations

One way astronomers and astrophysicists study galaxy formation and evolution is by analyzing and comparing real galaxy observations, captured by telescopes, and simulated galaxy observations, generated from theoretical models. They approach this through a combination of statistical and visual analysis, conducted either independently or sequentially. During the first year of an ongoing design study with astronomers and astrophysicists, we explored approaches to integrating statistical and visual analysis to enhance understanding of these data. Contributions from this stage of the study include a data and task abstraction for statistically and visually analyzing real and simulated galaxy observations, as well as an initial design, implemented in a prototype called GalStamps, and evaluated through two case studies with domain experts.
Framework for Analysis of Multi-party Collaboration

In recent years, platforms have become important for allowing ecosystems to emerge that allow users to collaborate and create unprecedented forms of innovation. For the platform provider, the ecosystem represents a massive business opportunity if the platform succeeds to make the collaborations among the users value-creating and to facilitate trust. While the requirements flow for evolving existing ecosystems is understood, it is unclear how to analyse an ecosystem that is to be. In this paper, we draw on recent work on collaboration modelling in requirements engineering and propose an integrated framework for the analysis of multi-party collaboration that is to be supported by a platform. Drawing on a real-world case, we describe how the framework is applied and the results that have been obtained with it. The results indicate that the framework was useful to understand the ecosystem context for a planned platform in the domain of artificial intelligence, allowed identification of platform requirements and offered a basis to plan validation.
The methods for Contour Analysis of Images

This paper reflects an analysis of contour analysis methods. Their efficiency and computational costs are analysed. The Laplace operator is highlighted. This method is faster and has a lower computational cost. At the same time, the results are not much worse than from other methods.
Optimize Elasticity in Cloud Computing using Container Based Virtualization

Cloud computing emphasis on using and underlying infrastructure in a much efficient way. That’s why it is gaining immense importance in today’s industry. Like every other field, cloud computing also has some key feature for estimating the standard of working of every cloud provider. Elasticity is one of these key features. The term elasticity in cloud computing is directly related to response time (a server takes towards user request during resource providing and de-providing. With increase in demand and a huge shift of industry towards cloud, the problem of handling user requests also arisen. For a long time, the concept of virtualization held industry with all its merits and demerits to handle multiple requests over cloud. Biggest disadvantage of virtualization shown heavy load on underlying kernel or server but from past some decades an alternative technology emerges and get popular in a short time due to great efficiency known as containerization. In this paper we will discuss about elasticity in cloud, working of containers to see how it can help to improve elasticity in cloud for this will using some tools for analyzing two technologies i.e. virtualization and containerization. We will observe whether containers show less response time than virtual machine. If yes that’s mean elasticity can be improved in cloud on larger scale which may improve cloud efficiency to a large extent and will make cloud more eye catching.
Spatio-temporal Multi-level Fusion for Human Action Recognition

Two-stream convolutional networks have achieved great success for action recognition tasks. In this paper, we propose a spatiotemporal network that integrates the spatial and temporal features at multi-level to model the correlation between spatial and temporal information. Based on TSN model [16] where videos are divided into segments, our model integrates spatio-temporal information at either local or global levels. At local levels, temporal information is transferred to spatial stream in each segment. Considering at a global level, we integrate features of entire action extracted from two streams to obtain the final action representation. Moreover, in order to take into consideration the chronological sequence of the segments, we propose strategies for segment aggregation by using Conv3D and LSTM (Long-short term memory). In the training process, we also applied and evaluated several strategies such as auxiliary classifier, cross modality initialization to improve the convergence rate. Experimentation on the standard dataset UCF-101 (split-1) demonstrates the effectiveness of proposed network. Our model achieved an accuracy of 87.1% for spatial network, higher than TSN (85.5%) thanks to segment aggregation strategy with LSTM seq-to-seq. In proposed two-stream network, the strategy of multi-level fusion allows to get a better model in comparing with network using only global fusion with an improvement of 1.4% in accuracy and 1.2% in F1-score. Our two-stream network obtained also very promising results with an accuracy of 92.57%.
Content-Context Model of Teaching Mathematics in Economic University

Introduction: A competent specialist for the digital economy must have applied mathematical methods and computer technology, as well as professional skills The paper proposes an integrative model of learning mathematics at the Computer Workshop for students-economists in the Bachelor. The authors for the fi rst time use two technologies within one model: contextual and content, and expand opportunities of content training, applying it in an unconventional form. Materials and methods: The main research method is pedagogical modeling based on the following methodological approaches: competence, integrative, activity, context, content and the concept of the zone of immediate development. Results: The proposed model corresponds to the strategic goal of the Financial University under the Government of the Russian Federation on the early profiling of education. A contextual approach is used to strengthen the applied orientation of basic training of students: the content of the academic discipline is fi lled with a professional context (subject-technological and social). The second component of the model is the content approach. A joint study of interrelated disciplines allows us to demonstrate general patterns and most effectively distribute the classroom load, and the learning process becomes more motivated, informed and active. The model is implemented at a Computer Workshop with the aim of mathematical preparation of students, the formation of professional skills in the use of computer technology in Economics and Finance. Mathematics is used as the content of the model, the applied orientation of learning is enhanced by the economic context, and computer technologies act as means of implementation. Student survey shows the eff ectiveness of this model. Discussion and Conclusions: The relevance of owning mathematical methods and computer technology for specialists of the broadest profi le allows us to generalize the constructed model, fi lling it with various professional contexts.