Reviewerly/exHarmony huggingface dataset:
Task graph scheduling in the presence of performance fluctuations ofcomputational resources

Most of the existing work in the area of task graph scheduling considers resources with fixed processing capacity. The algorithms in these works rely on an estimation of the execution times of tasks on different resources. However, in practice, due to fluctuations in performance of cloud resources, these algorithms have challenges in these environments. In this paper, we focus on the problem of fault-tolerant scheduling of task graphs in the presence of performance fluctuations of computational resources. With the aim of reducing the adverse impacts of both soft errors and resource performance degradations, we propose an opportunistic task replication scheme that uses idle durations of resources for replicating tasks. Unlike the previous works, the proposed algorithm does not rely on estimation of task execution times for finding idle resources. We introduce the notion of concurrency graphs and propose a graph theory-based algorithm for finding the number of idle resources during the execution of a set of tasks. The appropriate redundancy for each task is chosen with respect to the number of idle resources and the characteristics of the set of tasks that are being processed concurrently. Simulation experiments show that, in most situations, the proposed algorithm outperforms the previous algorithms in terms of average execution time and cost.
An Improved Image Compression Technique Using EZW and SPHIT Algorithms

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.&#x0D; The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.
Optimized and Dynamic KYC System Based on Blockchain Technology

Systems that use blockchain technology to improve the know-your-customer (KYC) process have only been proposed at a conceptual level and all share certain attributes that make their adoption very difficult. We propose and program a blockchain-based system that reduces and shares out among the financial institutions (FIs) that work with a customer the costs of the KYC process and also makes it possible for FIs to dynamically update information related to customers and disseminates this information among participating FIs. Additionally, our system addresses some of the attributes that hinder the adoption of previously proposed solutions by FIs. The result is a stand-alone solution that reduces the cost of the KYC process without requiring any central instance to store the customer's data, and in which FIs share the initial costs of the KYC process as well as the running costs of keeping the information about customers up to date.
On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The data obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separated into clusters correctly, and the right information from these clusters must be obtained. Having the correct clusters depends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods are very important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of the density-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCAN Gaussian means (FN-DBSCAN-GM), is offered in this study. The main contribution of FN-DBSCAN-GM is to find the parameters automatically and to divide the data into clusters robustly. The effectiveness of FN-DBSCAN-GM has been demonstrated on overlapping datasets (six artificial and two real-life datasets). The performances of these datasets are compared with the percentage of correct classification and validity index. Our experiments showed that this new algorithm was a preferable and robust algorithm.
VLAD Encoding Based on LLC for Image Classification

The Vector of Locally Aggregated Descriptors (VLAD) method, developed from BOW and Fisher Vector, has got great successes in image classification and retrieval. However, the traditional VLAD only assigns local descriptors to the closest visual words in the codebook, which is a hard voting process that leads to a large quantization error. In this paper, we propose an approach to fuse VLAD and locality-constrained linear coding (LLC), compared with the original method, several nearest neighbor centers are considered when assigning local descriptors. We use the reconstruction coefficients of LLC to obtain the weights of several nearest neighbor centers. Due to the excellent representation ability of the reconstruction coefficients for local descriptors, we also combine it with VLAD coding. Experiments were conducted on the 15 Scenes, UIUC Sports Event and Corel 10 datasets to demonstrate that our proposed method has outstanding performance in terms of classification accuracy. Our approach also does not generate much additional computational cost while encoding features.
Using machine learning for intelligent shard sizing on the cloud

Sharding implementations use conservative approximations for determining the number of cloud instances required and the size of the shards to be stored on each of them. Conservative approximations are often inaccurate and result in overloaded deployments, which need reactive refinement. Reactive refinement results in demand for additional resources from an already overloaded system and is counterproductive. This paper proposes an algorithm that eliminates the need for conservative approximations and reduces the need for reactive refinement. A multiple linear regression based machine learning algorithm is used to predict the latency of requests for a given application deployed on a cloud machine. The predicted latency helps to decide accurately and with certainty if the capacity of the cloud machine will satisfy the service level agreement for effective operation of the application. Application of the proposed methods on a popular database schema on the cloud resulted in highly accurate predictions. The results of the deployment and the tests performed to establish the accuracy have been presented in detail and are shown to establish the authenticity of the claims.
An Analysis of Process Type Used in News Item

Since the educational curriculum for English subject in Indonesia is implemented around genres, news item has been one of the genres taught in secondary school in Indonesia. Understanding the authors’ points of view and intention will contribute significant implication for learning foreign language (FL). One of the ways of analyzing one’s experience in written language is by using systemic functional grammar (SFG) theory. Therefore, this article aims at providing SFG analysis of process type used on news item. The news item was selected from Jakarta Post, 2014 in which one article entitled 72% of Indonesian people agree with nuclear power plants was selected and analyzed by categorizing its experiential meaning (Gerot &amp; Wignel, 1995). The analysis portrayed the dominance of verbal process existed in the article and it mainly indicate an effective news item.Since the educational curriculum for English subject in Indonesia is implemented around genres, news item has been one of the genres taught in secondary school in Indonesia. Understanding the authors’ points of view and intention will contribute significant implication for learning foreign language (FL). One of the ways of analyzing one’s experience in written language is by using systemic functional grammar (SFG) theory. Therefore, this article aims at providing SFG analysis of process type used on news item. The news item was selected from Jakarta Post, 2014 in which one article entitled 72% of Indonesian people agree with nuclear power plants was selected and analyzed by categorizing its experiential meaning (Gerot &amp; Wignel, 1995). The analysis portrayed the dominance of verbal process existed in the article and it mainly indicate an effective news item.
A Semi-Supervised Monocular Stereo Matching Method

Supervised monocular depth estimation methods based on learning have shown promising results compared with the traditional methods. However, these methods require a large number of high-quality corresponding ground truth depth data as supervision labels. Due to the limitation of acquisition equipment, it is expensive and impractical to record ground truth depth for different scenes. Compared to supervised methods, the self-supervised monocular depth estimation method without using ground truth depth is a promising research direction, but self-supervised depth estimation from a single image is geometrically ambiguous and suboptimal. In this paper, we propose a novel semi-supervised monocular stereo matching method based on existing approaches to improve the accuracy of depth estimation. This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model. Therefore, we decompose the monocular depth estimation problem into two sub-problems, a right view synthesized process followed by a semi-supervised stereo matching process. In order to improve the accuracy of the synthetic right view, we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint. To reduce the error caused by the reconstructed right view, we propose a semi-supervised stereo matching model that makes use of disparity maps generated by a self-supervised stereo matching model as the supervision cues and joint self-supervised cues to optimize the stereo matching network. In the test, the two networks are able to predict the depth map directly from a single image by pipeline connecting. Both procedures not only obey geometric principles, but also improve estimation accuracy. Test results on the KITTI dataset show that this method is superior to the current mainstream monocular self-supervised depth estimation methods under the same condition.
Frequency and phase relations of entangled photons observed by a two-photon interference experiment

An entangled photon experiment has been performed with a large variation of the temperature of the nonlinear crystal generating the entangled pair by spontaneous downconversion. The photon pairs are separated by a nonpolarizing beamsplitter, and the polarization modes are mixed by half-wave plates. The correlation function of the coincidences is studied as a function of the temperature. In the presence of a narrow interference filter, we observe that the correlation changes between $\ensuremath{-}1$ and $+1$ about seven times within a temperature interval of about $30{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\text{C}$. We show that the common simplified single-mode pair representation of entangled photons is insufficient to describe the results, but that the biphoton description that includes frequency and phase details gives a close to perfect fit with experimental data for two different choices of interference filters. We explain the main ideas of the underlying physics, and we give an interpretation of the two-photon amplitude, which provides an intuitive understanding of the effect of changing the temperature and inserting interference filters.
Factors affecting the quality and efficacy of an ELT-INSET: A case study of Bangladesh

&#x0D; &#x0D; &#x0D; &#x0D; Given the integral role of English teachers in promoting quality English education, English language teaching in-service teacher education and training (ELT-INSET) plays a crucial role in developing quality and professional competence of English teachers. It is in this regard, when the inevitability of ELT-INSET is growing worldwide, our article identifies quality parameters of an ELT-INSET conducted in Bangladesh. This qualitative case study was conducted as part of a doctoral project employing semi-structured interviews with one ELT-INSET programme coordinator, three teacher trainers and 12 trainees (English teachers) of an ELT-INSET. The cross-case analysis of the interview data identified six factors adversely affecting the quality and efficacy of the ELT-INSET. The factors included the absence of needs analysis culture, an ineffective trainee selection mechanism and proper monitoring system, the quality issue of teacher trainers, contents and materials, the lack of required logistics support and finally, the bureaucratic power-coercive ELT-INSET management. The article, by shedding light on those findings, finally informs the policymakers with some implications with which to develop their policy and to enhance and ensure the quality and efficacy of ELT-INSET programmes. The implications might also be applicable irrespective of INSET for other subjects and polities with the same context beyond Bangladesh.&#x0D; &#x0D; &#x0D; &#x0D;
Special issue on “Uncertainty in Cloud Computing: Concepts, Challenges and Current Solutions”

In this paper, we discuss the relationship between different types of reduction and set definability. We recall the definition of a decision reduct, a γ-decision reduct, a decision bireduct and a γ-decision bireduct in a Pawlak approximation space and the notion of set definability both in a Pawlak and a covering approximation space. We extend the notion of discernibility between objects in a Pawlak approximation space to a covering approximation space. Moreover, we introduce the definition of a decision reduct, a γ-decision reduct, a decision bireduct and a γ-decision bireduct in a covering approximation space. In addition, we study interrelationships between the four types of reduction, the correspondence with positive regions and the relationship to set definability in Pawlak and covering approximation spaces.
Vector of Locally Aggregated Embeddings for Text Representation

Hadi Amiri, Mitra Mohtarami. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Neural net worth

Yoshua Bengio, Geoffrey Hinton, and Yann LeCun this month will receive the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
From legal to technical concept: Towards an automated classification of

Advances in the automated detection of offensive Internet postings make this mechanism very attractive to social media companies, who are increasingly under pressure to monitor and action activity on their sites.However, these advances also have important implications as a threat to the fundamental right of free expression.In this article, we analyze which Twitter posts could actually be deemed offenses under German criminal law.German law follows the deductive method of the Roman law tradition based on abstract rules as opposed to the inductive reasoning in Anglo-American common law systems.This allows us to show how legal conclusions can be reached and implemented without relying on existing court decisions.We present a data annotation schema, consisting of a series of binary decisions, for determining whether a specific post would constitute a criminal offense.This schema serves as a step towards an inexpensive creation of a sufficient amount of data for automated classification.We find that the majority of posts deemed morally offensive actually do not constitute a criminal offense and still contribute to public discourse.Furthermore, laymen can provide sufficiently reliable data to an expert reference but are, for instance, more lenient in the interpretation of what constitutes a disparaging statement.
Mobile Multimedia Evaluation: Development of Stop Drugs Tutorial

This research is intended to evaluate the development of mobile multimedia based stop drug tutorial. This tutorial as a campaign to stop drugs, that should encourage people’s ability to integrate all information on mobile application. The tutorial focuses on how people understand about drugs and what to do if someone is drugs addicted. Stopping drug tutorial is created using ADDIE instructional development method, and during the developing stage, it is done using Multimedia Development Life Cycle according to Luther. Subjects of the research are experts in education, visual communication and information technology in the preliminary testing. Evaluation was conducted using the analytic descriptive method on mobile multimedia evaluation method, and interpreted in a narrative way based on the research findings. The evaluation of mobile multimedia application uses combination of multimedia evaluation that is used by educationalist with ISO 9126 that was used by software developer. The research findings show that mobile multimedia stop drugs application can be used with minor revision.
Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and k-mer Hashing

Alignment-free classification of sequences has enabled high-throughput processing of sequencing data in many bioinformatics pipelines. Much work has been done to speed-up the indexing of k-mers through hash-table and other data structures. These efforts have led to very fast indexes, but because they are k-mer based, they often lack sensitivity due to sequencing errors or polymorphisms. Spaced seeds are a special type of pattern that accounts for errors or mutations. They allow to improve the sensitivity and they are now routinely used instead of k-mers in many applications. The major drawback of spaced seeds is that they cannot be efficiently hashed and thus their usage increases substantially the computational time. In this paper we address the problem of efficient spaced seed hashing. We propose an iterative algorithm that combines multiple spaced seed hashes by exploiting the similarity of adjacent hash values in order to efficiently compute the next hash. We report a series of experiments on HTS reads hashing, with several spaced seeds. Our algorithm can compute the hashing values of spaced seeds with a speedup of 6.2x, outperforming previous methods. Software and Datasets are available at ISSH
Acquisition method of users’ browsing behavior preference based on the fusion of social network link and theme model

For the acquisition of user behavior preference in social network, usually a data mining will be conducted on the nearest neighborhood users or latestprojects based on the user’s historical behavior data, so as to find similar behaviorrelationship for quantitative analysis; it can also focus on the awareness on the user-related context information based on cognitive psychology, so as to find its internal links forthe potential mining. However, these methods ignore the intrinsic link between the browsing behavior and the preferred topics in the user link connection, resulting in the limited precision and accuracy of the preference acquisition. Inspired by the theory of complex network link prediction and the topic model, anacquisition method for users’ browsing behavior preference was proposed in this paper. In the multi-dimensional network link environment, by measuring the importance of the node via network centrality and search the social network link via setting the similarity threshold, the real-time multi-link information and the big data about users’ browsing under each link were acquired, then the data were filteredand cleaned by using adjustable parameters. On this basis, according to the least squares criterion the data underwent a fusion and were used to construct a data node distribution model for user browsing behavior, then the frequent feature items of user browsing behavior preference were extracted. Based on the extracted feature terms, the variational Bayes approximation reasoning method was used to construct the preference topic model. Finally, the hierarchical VSM model representation method was used to establish the preference acquisition model of user browsing behavior, and the model was updated in real time by user feedback processing mechanism. The experimental results on the real data set showed that the link search method and the preference topic model provided by this paper are accurate and efficient. Compared with the classical cooperative filtering method and the context-awareness method, the precision, accuracy and effectiveness of the preference acquisition model provided this paper are significantly improved, and its adaptability has been significantly strengthened.
Predicting Video Frames Using Feature Based Locally Guided Objectives

This paper presents feature reconstruction based approach using Generative Adversarial Networks (GAN) to solve the problem of predicting future frames from natural video scenes. Recent GAN based methods often generate blurry outcomes and fail miserably in case of long-range prediction. Our proposed method incorporates an intermediate feature generating GAN to minimize the disparity between the ground truth and predicted outputs. For this, we propose two novel objective functions: (a) Locally Guided Gram Loss (LGGL) and (b) Multi-Scale Correlation Loss (MSCL) to further enhance the quality of the predicted frames. LGGL aides the feature generating GAN to maximize the similarity between the intermediate features of the ground-truth and the network output by constructing Gram matrices from locally extracted patches over several levels of the generator. MSCL incorporates a correlation based objective to effectively model the temporal relationships between the predicted and ground-truth frames at the frame generating stage. Our proposed model is end-to-end trainable and exhibits superior performance compared to the state-of-the-art on four real-world benchmark video datasets.