Reviewerly/exHarmony huggingface dataset:
ATrie Group Join: A Parallel Star Group Join and Aggregation for In-Memory Column-Stores

This article presents a new holistic and efficient approach to big data analysis. We introduce a new parallel algorithm, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATrie Group Join (ATGJ)</i> , that integrates join, grouping and aggregation operations to accelerate big data analytical workloads in in-memory column-stores. ATGJ performs a single scan of the fact columns and uses a mixture of data and task parallelism for the optimal use of computing resources. It uses a novel technique to perform group-by and aggregation realising the grouping attributes as a tree shaped deterministic finite automation known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aggregate Trie</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATrie</i> . ATrie facilitates grouping of attributes and processing of data in tight loops that significantly improves the performance on modern hardware. Unlike other competing algorithms, use of ATrie avoids the creation of multiple data structures with the increasing number of dimension tables and grouping attributes. Also, we demonstrate that ATGJ performs efficiently even when the ATrie becomes bushy. We evaluated the algorithm using Star Schema Benchmark (SSBM) to show that it is significantly faster and scales better than other algorithms for the number of concurrent threads, the number of group-by attributes, the data set size and the query complexity.
SecureMCMR: Computation Outsourcing for MapReduce Applications

In the last decade, cloud infrastructures such as Google Cloud and Amazon AWS have grown vastly in scale and utilization. Therefore, research into the security and confidentiality of sensitive data passed through these infrastructures is of great importance. We present SecureMCMR, a system that utilizes two public clouds for privacy preserving computation outsourcing for MapReduce applications. We also present analysis of 87 MapReduce applications and the operations they use. Our results on three MapReduce applications show overhead of 160%, 254%, and 380% over plaintext execution.
Good Cues to Learn From Scratch a Confidence Measure for Passive Depth Sensors

As reported in the stereo literature, confidence estimation represents a powerful cue to detect outliers as well as to improve depth accuracy. Purposely, we proposed a strategy enabling us to achieve state-of-the-art results by learning a confidence measure in the disparity domain only with a CNN. Since this method does not require the cost volume, it is very appealing because potentially suited for any depth-sensing technologies, including, for instance, those based on deep networks. By following this intuition, in this paper, we deeply investigate the performance of confidence estimation methods, known in the literature and new ones proposed in this paper, neglecting the use of the cost volume. Specifically, we estimate from scratch confidence measures feeding deep networks with raw depth estimates and optionally images and assess their performance deploying three datasets and three stereo algorithms. We also investigate, for the first time, their performance with disparity maps inferred by deep stereo end-to-end architectures. Moreover, we move beyond the stereo matching context, estimating confidence from depth maps generated by a monocular network. Our extensive experiments with different architectures highlight that inferring confidence prediction from the raw reference disparity only, as proposed in our previous work, is not only the most versatile solution but also the most effective one in most cases.
Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images

Breast cancer is one of the main causes of cancer death worldwide, and early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious. The relevance and potential of automatic classification algorithms using Hematoxylin-Eosin stained histopathological images have already been demonstrated, but the reported results are still sub-optimal for clinical use. Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. Based on the predominant cancer type, the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. The convolutional neural networks (CNN) is proposed to retrieve information at different scales, including both nuclei and overall tissue organization. This chapter utilizes several deep neural network architectures and gradient boosted trees classifier to classify the histology images among four classes. Hence, this approach has outperformed existing approaches in terms of accuracy and implementation complexity.
Proactive Approach of Effective Placement of VM in Cloud Computing

Virtual machine is one of the major areas of infrastructure as a service in cloud computing. The VM provision for service provider and user should be inexpensive. In last decades, number of scientists proposed various schemes. There is less opportunity for the service provider to use from the resource pooling. Cloud computing is providing hosting service over Internet, and to service provider, number of request of resource can be served using Internet. Recently, resource management is required to maintain autoscaling of resource and improving the efficiency of resource in cloud computing. There are various approaches available for workload predication that is based on single model approach. It is very critical to find the result on the basis of traditional model. Different methods and techniques were analyzed by us in order to identify the virtual machine allocation. We have defined a new dynamic resource allocation and policy-based improvement of the effective management of the resources. Our proposed implementation shows a better performance and improves the VM allocation with accuracy and less time consuming.
LATIN IN INTERNET MEMES: NEW COMMUNICATIVE FUNCTION

The article is devoted to the research of Internet memes as universally precedential cultural phenomena, to determining Latin special role in the construction and functioning of virtual memes. Latin Internet memes are considered as emotionally colored units of cultural information the first source of which are different elements of the knowledge of Latin that are copied and disseminated among Internet users during communication as a text (inscription, reference) or as a text and an icon part (drawing, photo, table). On the base of new virtual units of communication the functions of memes in Internet communication were determined, in particular: game, representative, communicative, phatic, integrative, affective. Local Latin Internet memes served as the material for the research placed on English sites “Classic Latin Memes”, “Dank Latin Memes”, as well as on Russian site “Latine masculino”. Dead Latin within Internet memes renews its communicative function and gains more popularity in the Internet space thanks to its perception as “prestigious” language of communication. By style the majority of Latin Internet memes are ironically marked and motivating, though demotivating memes are also possible. The goal of motivating memes is “to revive”, modernize archaic Latin and make it more accessible and attractive in order to encourage the focus audience to study it. Internet communication via memes’ mediation represents complex mental processes, when the user has not only to perceive a background picture and decode (translate) Latin text, but also to comprehend cultural connotations and associative links which result in comic effect during memes’ perception. The research of Latin Internet memes in pragmatic, communicative and cognitive aspects is of positive perspective both from synchronic and diachronic points of view, since it presents new opportunities for the study of modern evolutionary processes in the history of ancient language, reveals a mechanism of decoding cultural information, contributes to the development of Internet linguistics.
Multi-task learning model for aspect term extraction and aspect polarity classification based on dual-labels

Aspect-based sentiment analysis (ABSA) is a hot and significant task of natural language processing, which is composed of two subtasks, the aspect term extraction (ATE) and aspect polarity classification (APC). Previous researches generally studied two subtasks independently and designed neural network models for ATE and APC respectively. However, it integrates various manual features into the model, which will consume plenty of computing resources and labor. Moreover, the quality of the ATE results will affect the performance of APC. This paper proposes a multi-task learning model based on dual auxiliary labels for ATE and APC. In this paper, general IOB labels, and sentimental IOB labels are equipped to efficiently solve both ATE and APC tasks without manual features adopted. Experiments are conducted on two general ABSA benchmark datasets of SemEval-2014. The experimental results reveal that the proposed model is of great performance and efficient for both ATE and APC tasks compared to the main baseline models.
A Content-Based Deep Hybrid Approach with Segmented Max-Pooling

Convolutional matrix factorization (ConvMF), which integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF), has been recently proposed to utilize the contextual information and achieve higher rating prediction accuracy of model-based collaborative filtering (CF) recommender systems. While ConvMF uses max-pooling, which may lose the feature’s location and frequency information. In order to solve this problem, a novel approach with segmented max-pooling (ConvMF-S) has been proposed in this paper. ConvMF-S can extract multiple features and keep their location and frequency information. Experiments show that the rating prediction accuracy has been improved.
Parallel and Regular Algorithm of Elliptic Curve Scalar Multiplication over Binary Fields

Accelerating scalar multiplication has always been a significant topic when people talk about the elliptic curve cryptosystem. Many approaches have been come up with to achieve this aim. An interesting perspective is that computers nowadays usually have multicore processors which could be used to do cryptographic computations in parallel style. Inspired by this idea, we present a new parallel and efficient algorithm to speed up scalar multiplication. First, we introduce a new regular halve-and-add method which is very efficient by utilizing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>λ</mml:mi></mml:math> projective coordinate. Then, we compare many different algorithms calculating double-and-add and halve-and-add. Finally, we combine the best double-and-add and halve-and-add methods to get a new faster parallel algorithm which costs around <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mn>12.0</mml:mn><mml:mo>%</mml:mo></mml:math> less than the previous best. Furthermore, our algorithm is regular without any dummy operations, so it naturally provides protection against simple side-channel attacks.
An efficient parallel direction-based clustering algorithm

Clustering, which explores the visualization and distribution of data, has recently been studied widely. Although the existing clustering algorithms can well detect arbitrary shape clusters, most of them face the limitation that they cluster points on the basis of two physical metrics, distance and density, but ignore the orientation relationship of data distribution. Beside, they have a difficulty of selecting suitable parameters, which are important inputs of the clustering algorithms. In this paper, we firstly introduce a new physical metric, namely direction. Then, based on this new metric, we propose an adaptive direction-based clustering algorithm, namely ADC, which can automatically calculate appropriate parameters. Finally, we develop a parallel ADC algorithm based on multi-processors to improve the performance of the ADC algorithm. Compared with other clustering algorithms, experimental results demonstrate that the proposed algorithms are more general and can get much better clustering results. In addition, the parallel ADC algorithm has the best scalability over large data sets.
Natural deduction and semantic models of justification logic in the proof assistant Coq

Abstract The purpose of this paper is to present a formalization of the language, semantics and axiomatization of justification logic in Coq. We present proofs in a natural deduction style derived from the axiomatic approach of justification logic. Additionally, we present possible world semantics in Coq based on Fitting models to formalize the semantic satisfaction of formulas. As an important result, with this implementation, it is possible to give a proof of soundness for $\mathsf{L}\mathsf{P}$ with respect to Fitting models.
Multi-Generational Database Inference Controllers

Inference is the process of forming conclusions from premises. The process becomes a problem if the users draw unauthorized conclusions from the legitimate responses that they receive. The Inference Problem that arises in databases has been studied for close to forty years starting with statistical databases to database inference controllers based on deductive database techniques to the privacy problem which can be considered to be a variation of the inference problem. The Database Inference Controllers are systems that prevent a user from drawing unauthorized conclusions. In this paper we divide the Database Inference Controllers that have been developed to date into two generations and then discuss a design for the third generation of Database Inference Controllers.
Research of Product Data Measurement Mode Based on Neural Network

With the evolution of traditional brick and mortar retail stores to online shopping, consumers are posting reviews directly on product pages in real time. We ultilize the RBF network to predict the sales number. We construct a GM (2,1) model to predict the sales of hair dryers, microwave ovens, and pacifiers in the next five years. The state transfer probability between positive, neural and negative reviews is built. The GINI correlation coefficient method is applied to determine whether the reviewer s attitude has a strong relationship with the star rating. The innovation of this article is that we build a comprehensive model with neural network, Markov and GINI correlation coefficient. Besides, Pearson correlation coefficient is used to test the GINI correlation coefficient which make the result more complete.
Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers

Traditionally, histopathological evaluations of tissue sections are performed for the diagnosis and grading of cancers using subjective appraisal of the tissue and cell phenotypes. In the last decade, a combination of unprecedented advances in imaging and computing technologies and novel machine learning-based algorithms has driven the field of histopathology into a new dimension. Machine learning and deep learning methodologies applied on digitalized hematoxylin and eosin stained tissue sections have out-performed conventional methods to accurately identify cancer cells or classify tumors into prognostic groups. Nevertheless, we believe that a precise, standardized measurement of nuclear morphology and chromatin texture based on a DNA stain can further improve the diagnosis of cancers and identify patients with high-risk of recurrence, alone or in combination with other clinical, pathological or molecular information. Changes in the morphology of cell nuclei and tissue architecture are intrinsic characteristics and hallmarks of cancer. Nuclei from cancerous samples exhibit different morphological and chromatin texture than nuclei from normal cells, thus, reflecting the structural and molecular effects of genetic and epigenetic alterations driving cancer processes. By image analysis, the chromatin texture can be measured in high-resolution breaking down the components of the nuclear changes into multiple quantifiable units that can be studied independently and in combination using advanced machine learning methods. This allows the investigator to examine associations of such changes with cancer progression and clinical outcomes. There is now increasing interest in developing new algorithms and platforms to decipher spatial relationship between cell subpopulations in whole tissue sections.
A post-quantum key exchange protocol from the intersection of quadric surfaces

In this paper, we present a new key exchange protocol in which Alice and Bob have secret keys given by quadric surfaces embedded in a large ambient space by means of the Veronese embedding and public keys given by hyperplanes containing the embedded quadrics. Both of them reconstruct the isomorphism class of the intersection which is a curve of genus 1, and is uniquely determined by the j-invariant. An eavesdropper, to find this j-invariant, has to solve problems which are conjecturally quantum-resistant.
Nitrogen Fertilizer Prediction of Maize Plant with TCS3200 Sensor Based on Digital Image Processing

Abstract Fertilization for maize plants so far does not use the appropriate calculation of crop needs, thus it causes continuous soil damage. Another technique to predict nitrogen content in maize plants is using a leaf color chart. But it has some disadvantages, such as depending on sunlight for color plant reading, and inconsistent accuracy interpretation of leaf color by the eye. Based on problems, the research was aim to design a nitrogen content prediction system based on digital images with an RGB color index. RGB digital image and nitrogen content are used as a database system to predict nitrogen need for maize plants. Arduino UNO is used for data processor and the TCS3200 color sensor is used as a substitute for a camera on image processing. The performance test uses 40 leaves of maize plants with the age of 15, 30 and 40 days. The results show that the device can detect nitrogen content in maize plants based on the RGB database with a 90% success rate. For R value 447,4-601,6, G value 384,7-510, and B value 519,95-625,16, we obtain that nitrogen need for plant about 175 kg/ha. For R value 325,9-377,5, G value 285,6-311,5, and B value 426,4-456,1, we obtain that nitrogen need for plant about 150 kg/ha. And for R value 290,37, G value 239,12, and B value 385,62, the nitrogen need for plant about 125 kg/ha. The greener the color of maize leaf, the lower the nitrogen fertilizer need, conversely the lower the green color on the leaf, the higher the level of nitrogen fertilizer needs.
Defining basic archetypes of an intelligent agent’s behaviour in an open-ended interactive environment

Despite the emergence of advanced technologies, the behavioural complexities remain underexplored, especially from the viewpoint of their potential in interactive installations. This study aims to explore the future interactivity of an agent’s behaviour by envisioning speculative future human operations using a human-controlled interactive installation called LumiLand. This empirical study reveals that an agent can craft a user experience by (1) controlling the plot, (2) cocreating content with a user in a social manner, and (3) promptly adjusting undefined behaviours and situations. Based on Janlert and Stolterman’s interaction model, we present an interaction flow with four primary classifications: Leading, Responding, Poking, and Linking. We believe that a human-centered approach for understanding agent interactivity could help create entertaining human–computer interactions (HCI) by improving the agent design and exploring the challenges that will be faced during such interactions.
Ontological Structure-Based Retrieval System for Tamil

Ontological structure of Tamil (OST) is an outcome of an extensive research activity that went on in the field of lexical semantics of Tamil for the last three decades. Rajendran’s (Semantic structure of Tamil vocabulary. Report of the UGC sponsored postdoctoral work (in manuscript). Deccan College Post-Doctoral Research Institute, Pune, 1983) post-doctoral research work went through several stages before culminating into OST. It depicts the travel from Tamil thesaurus to Tamil WordNet and into OST. OST is a lexical resource which amalgamates all sorts of information available in a dictionary, thesaurus and WordNet. The Dravidian WordNets (in which Tamil WordNet is one of the four components) built under the Indo-WordNet project depended on an ontology developed by Western conceptualization of the world found in English. This has not taken into consideration the Indian conceptualization of the world depicted in the nikhandu tradition. There are many lexical gaps between English WordNet and Tamil WordNet. Moreover, building a WordNet based on Hindi WordNet which in turn is built on English WordNet will take many years to complete and it would miss the conceptualization depicted in Indian tradition. Apart from this, the extension approach of building Tamil WordNet using Hindi WordNet cannot fulfil Dravidian conceptualization. A merger approach of building separate WordNets and collapsing them into one would have been a preferable approach. The present OST tried to overcome the lacunae found in Tamil WordNet. OST is based on the Indian and Dravidian conceptualization and the process of building one is comparatively very simple. We have the plan to mend it into a generic one so that all the Dravidian languages can be easily accommodated into it.
Application of PBL in ERP Textbook Design and Technical University Students Learning Effectiveness Study

In the existing Enterprise Resource Planning (ERP) teaching models for technical University students, the information system based courses are currently dominated in most of the teaching programs and teaching materials, although there are detailed and complete system procedures provided by the system providers, but the learning assessment results is still unsatisfactory. The companies are not satisfied with the ERP practical skills and basic concepts of the graduating students, the skill gap between the schools and workplaces on ERP are still exist. Therefore, the theme of this study is to apply problem-based learning (PBL) as the design spirit of situational case, ERP teaching plan and the compilation of textbooks, to evaluate the effect of PBL methods on ERP courses teaching, and then design suitable teaching materials and curriculum plans for the technical University students. The problem design strategies and examples of PBLERP course and textbook are described, and the students’ learning effectiveness are evaluated with a pre-and-post test of problem-solving ability inventory (PSAI), where the results show positive ascension in all aspects, especially in the motivation and confidence to solve problems.
Assessing Unintended Memorization in Neural Discriminative Sequence Models

Despite their success in a multitude of tasks, neural models trained on natural language have been shown to memorize the intricacies of their training data, posing a potential privacy threat. In this work, we propose a metric to quantify unintended memorization in neural discriminative sequence models. The proposed metric, named d-exposure (discriminative exposure), utilizes language ambiguity and classification confidence to elicit the model’s propensity to memorization. Through experimental work on a named entity recognition task, we show the validity of d-exposure to measure memorization. In addition, we show that d-exposure is not a measure of overfitting as it does not increase when the model overfits.