Reviewerly/exHarmony huggingface dataset:
Use and Acceptance of E-Books Among Undergraduate Students

The study aimed at establishing the use and acceptance of e-books by book readers in a Namibian university. Applying a mixed methods approach, a survey questionnaire was administered to 35 undergraduate students initially and in a second phase telephonic interviews were conducted with 12 purposively selected surveyed students based on their questionnaire responses to get a deeper understanding of student experience utilising e-books. Findings revealed that the use and acceptance of e-books in Namibia is still in its infancy. Although there is an awareness of e-books among students, most of them prefer print books. One of the reasons for low e-book usage was due to insufficient training on how to access them. It was recommended that faculty officers at this university collaborate with librarians to ensure prescribed e-books are purchased or subscribed to and adequate Information Literacy training is provided to students. Future research that focuses on expanding the sample size to include all faculties, benchmarked against different universities nationally and internationally will give further insight into e-book uptake in the digital age in Africa and globally. Further studies also need to be conducted to establish why about 14% of students don't read books (print or electronic) at all whiles in school.
Detection and Segmentation of Blood Cells Based on Supervised Learning

Cells in our blood can be categorized.Pathologist detects these blood cells and diagnoses different disease.This paper describes the way in which we can detect these cells in body by using the concept of image processing.The manual way of detecting these cells and diagnosing the disease is very time consuming and has more chances of making mistakes.This method is developing to identify the cell when any cell image is given as an input.The methodology used is, image acquisition, image segmentation, trained images, test images, feature extraction of cells, feature vector dataset, similarity of cells.This will detect the different cells.
The Limit of the Empirical Measure of the Product of Two Independent Mallows Permutations

The Mallows measure is a probability measure on $$S_n$$ where the probability of a permutation $$\pi $$ is proportional to $$q^{l(\pi )}$$ with $$q > 0$$ being a parameter and $$l(\pi )$$ the number of inversions in $$\pi $$ . We show the convergence of the random empirical measure of the product of two independent permutations drawn from the Mallows measure, when q is a function of n and $$n(1-q)$$ has limit in $$\mathbb {R}$$ as $$n \rightarrow \infty $$ .
Uncertainty in Conversation: Its Formal Cues Across Modalities and Time

The topic of this chapter is a particular state of mind: uncertainty. One can experience uncertainty in many contexts and situations: in relation to events past, present and future, persons close and far, tasks, choices, measures, decisions desired or to be avoided, and much more. It is very personal, and therefore it has hardly innumerable forms of expression. Due to its personal nature, one may find it difficult to observe it by trying to associate it with just one or another single behavior, especially that it most often develops as a sequence of behavioral events over time. Based on the HuComTech multimodal corpus and using the computing environment Theme we show a complex of those individual events that, together, contribute to the perception of uncertainty. This complex is essentially temporal, in which patterns of behavior are formed from temporally adjacent or non-adjacent events including physically observable ones as well as those which are only interpretations of certain observations. The data are based on dialogues in Hungarian, but their multimodal analysis can be applied to other language environments as well.
Blaze Averter – A Cooking Sustenant for Visually Challenged People

Simple daily activities like cooking, walking etc., are major problems for visually challenged people because of their problem with 'vision'. Though challenged by vision, they would like to lead their life independently. However, tasks like cooking food on their own may be hazardous for them as touching hot utensils or touching a lit stove burner may burn their skin. This project 'BLAZE AVERTER' aims at helping the visually challenged people in cooking their food without burning their body. A proximity temperature sensor would be used to sense the temperature of the surrounding and a flame sensor would be used to detect any flame in the kitchen. A threshold is set which distinguishes high temperature and presence of flame. Any change in set threshold will be identified and transmitted to the speaker to alert the user and protects them from getting burns.
A framework for mining the university student behaviour based on social network

The intersection principle has opened the idea of developing multiple frameworks for mining the meaning any information, including those from the database. The concept of intersection is the foundation to build a social network or a community, so community behaviour can be meant from the available database including information on academics. Meaningful of any data can also be used to explore their data for the related behaviour. This paper explores some of the meanings formulated in the formulation or rules. The concept used to build meaning frameworks and to explore the behaviour of specific social communities, i.e. a framework based on Bayes theorem.
Adversarial task-specific learning

Abstract In this paper, we investigate a principle way to learn a common feature space for data of different modalities (e.g. image and text), so that the similarity between different modal items can be directly measured for benefiting cross-modal retrieval task. To effectively keep semantic/distribution consistent for common feature embeddings, we propose a new Adversarial Task-Specific Learning (ATSL) approach to learn distinct embeddings for different retrieval tasks, i.e. images retrieve texts (I2T) or texts retrieve images (T2I). In particular, the proposed ATSL is with the following advantages: (a) semantic attributes are leveraged to encourage the learned common feature embeddings of couples to be semantic consistent; (b) adversarial learning is applied to relieve the inconsistent distribution of common feature embeddings for different modalities; (c) triplet optimization is employed to guarantee that similar items from different modalities are with smaller distances in the learned common space compared with the dissimilar ones; (d) task-specific learning produces better optimized common feature embeddings for different retrieval tasks. Our ATSL is embedded in a deep neural network, which can be learned in an end-to-end manner. We conduct extensive experiments on two popular benchmark datasets, e.g. Flickr30K and MS COCO. We achieve R@1 accuracy of 57.1% and 38.4% for I2T and 56.5% and 38.6% T2I on MS COCO and Flickr30K respectively, which are the new state-of-the-arts.
On PGZ decoding of alternant codes

In this paper, we first review the classical Petterson–Gorenstein–Zierler decoding algorithm for the class of alternant codes, which includes Reed–Solomon, Bose–Chaudhuri–Hocquenghem and classical Goppa codes. Afterwards, we present an improvement of the method to find the number of errors and the error-locator polynomial. Finally, we illustrate the procedure with several examples. In two appendices, we sketch the main features of the computer algebra system designed and developed to support the computations.
Making the Absent Object Present: Towards a holographic museum of modern art (hMoMA)

Fifty-five thousand museums worldwide are custodians of our cultural legacy and over ninety-five percent of their collections are in storage and rarely, if ever, publicly displayed.This early practice based PhD research explores the viability and acceptability of improving public access to these hidden collections by using recently available digital technology.The aim is to enable museum visitors to walk around three-dimensional digital replicas of stored artefacts and moving subjects, as though these replicas are physically present in the real world exhibition space.Curatorial interpretation is envisaged from knowledge experts similarly replicated to appear alongside both digital and physical exhibits in the gallery.Whilst recognising that digital replicas are not the same as the real thing, if stored collections are rarely seen, this is the next best thing, and as technology improves will move even closer to a physical experience.The technology to create these three dimensional digital replicas is a portable volumetric capture rig, one of which has been assembled by the author.This consists of multiple miniature cameras, recording depth as well as image, mounted on portable stands, which are arranged in a circle around the subject.The cameras are connected to a computer and software, which synchronises the information provided by the cameras and creates and records static and moving point-cloud images rendered into life-like three-dimensional replicas either in real-time or in post processing.A person can view these threedimensional digital replicas using Mixed Reality (MR) smart glasses or headsets.These devices allow the user to walk around three-dimensional digital replicas as though physically present in the real-world environment, hence in MR.This is distinct from Augmented Reality (AR) which overlays two-dimensional images on the real world, and Virtual Reality (VR), which displays images in an occluded virtual world.Benefits include: Giving wider public access to collections without commensurate pressures to increase museum exhibition space; 3D replicas of repatriated exhibits can be displayed in donating institutions; The advantages of 3D scanning are no longer limited to static collections as moving contemporary art such as performance can be included (hence the title of this paper, Towards hMoMA).In this paper the author gives an overview of technology developments contributing to this research as well describing his own art practice, which combines digital replicas in mixed reality, binaural audio and physical art installations.Museum.
Les prépositions complexes en français

Abstract In this paper, we develop a new method of identification of complex prepositions, in French. We include well-known semantic and morpho-syntactic tests, and introduce a few others – together, these tests make up a multi-variable grid which we believe can help identify complex prepositions. We run these tests on a list of 75 sequences, most of which are considered to be complex adpositions. We also include sequences which share the same patterns of word formation but do not have prepositional uses, in order to check the validity of our grid. Our results show that the semantic and morpho-syntactic clues used to identify complex adpositions, combined with corpus data exploration, are on the whole quite effective, but to varying degrees depending on the pattern of formation.
Weighted distribution of points on cyclic covers of the projective line over finite fields

For any prescribed finite field Fq and integers m≥2, we study the distribution of the number of Fq-points on (possibly singular) affine curves Cf:ym=f(x), where f is randomly chosen from a fixed collection F(Fq) of polynomials in Fq[x]. These equations are affine models of cyclic m-covers of the projective line. Previously, different authors obtained asymptotic results about distributions of Fq-points on curves associated to certain collections of polynomials f. We obtain analogous results for infinitely many new examples of collections F(Fq) and study how the asymptotic distribution depends on the choice of the collection of polynomials F(Fq).
MHLM Majority Voting Based Hybrid Learning Model for Multi-Document Summarization

Text summarization from multiple documents is an active research area in the current scenario as the data in the World Wide Web (WWW) is found in abundance. The text summarization process is time-consuming and hectic for the users to retrieve the relevant contents from this mass collection of the data. Numerous techniques have been proposed to provide the relevant information to the users in the form of the summary. Accordingly, this article presents the majority voting based hybrid learning model (MHLM) for multi-document summarization. First, the multiple documents are subjected to pre-processing, and the features, such as title-based, sentence length, numerical data and TF-IDF features are extracted for all the individual sentences of the document. Then, the feature set is sent to the proposed MHLM classifier, which includes the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network (NN) classifiers for evaluating the significance of the sentences present in the document. These classifiers provide the significance scores based on four features extracted from the sentences in the document. Then, the majority voting model decides the significant texts based on the significance scores and develops the summary for the user and thereby, reduces the redundancy, increasing the quality of the summary similar to the original document. The experiment performed with the DUC 2002 data set is used to analyze the effectiveness of the proposed MHLM that attains the precision and recall at a rate of 0.94, f-measure at a rate of 0.93, and ROUGE-1 at a rate of 0.6324.
Iterative Hypergraph Computation Based on Hyperedge-Connected Graphs

A hypergraph allows a hyperedge to connect arbitrary number of vertices, which can be used to capture the complex and high-order relationships. By analyzing the iterative processing on bipartite graphs, a method of converting the original hypergraph into a hyperedge-connected graph and corresponding iterative processing method are proposed. Then, the iterative processing solution based on hyperedge-connected graphs is combined with Push-based and Pull-based message acquisition mechanisms. On top of the distributed graph processing system HybridGraph, a hypergraph iterative processing framework HyraphD is implemented. Finally, extensive experiments are conducted on several real-world datasets and hypergraph learning algorithms. Experimental results confirm the efficiency and the scalability of HyraphD.
Pipe Manufacturers’ Role in Design/Build Pipeline Projects

There are four main parties in a design/build pipeline project: the owner and his representative; the general contractor; the contractor’s design engineer (the engineer of record); and the pipe manufacturer. The selection of the pipe manufacturer by the design/build team is critical, since besides providing the pipe design calculations if required, layout drawings, and manufacturing the pipeline, the design/build team can depend on the manufacturer’s expertise to provide recommendations and alternatives to resolve unforeseen field related issues. The additional contribution by the pipe manufacturer can be limited or significant depending on the pre-engineering level of the project and the experience of the project team. Three examples of design/build pipeline projects requiring different levels of technical support by the pipe manufacturer, will be presented: the Tuyaux Bonna/American Pipe and Construction Design/Build team for the rehabilitation of 19 miles of 78-inch PCCP with a steel liner; Archer Western/Winzler & Kelly for the construction 9.5 miles of potable and reclaimed water steel pipelines ranging in diameter from 24-inch up to 48 inches; and Kiewit/Parson’s for the construction of 6000 ft. of 120-inch diameter high pressure steel-tunnel-liner penstock requiring specialized steel with very high toughness properties. Lessons learned from the three projects will also be presented.
영어 다독 활동의 효과: 메타분석

The Journal of Studies in Language 35.1, 069-083. The effects of extensive reading have been extensively examined by many researchers in approximately the two last decades. The current study aims to investigate the overall effectiveness of extensive English reading. A meta-analysis is conducted to answer research questions and to identify future research directions. Two types of empirical studies are included: those including group contrasts based on a comparison of a control group and experimental groups, and pre-post contrasts that only include experimental groups in the analysis. This study includes 31 papers from research databases on extensive reading researches in a classroom setting and synthesizes 48 cognitive effects and 20 affective effects from these papers under investigation in a manner of quantifying their means and standard deviations to derive generalizations. Findings show a medium effect size (ｇ=.579) for pre-post contrasts and a larger one (ｇ=.713)for group contrasts. In sum, the available research to date suggests that extensive reading improves students'' reading proficiency and should be a part of language learning curricula. (Chungnam National University)
RI-SSGE

As knowledge graph becomes popular in recent years, more and more attention has been paid to Knowledge Base Question-Answer (KBQA) systems. For KBQA systems, Question Understanding, as the first stage, aims to convert factual question into the interpretable form to machine just like Λ-DCS. And some latest works used query subgraph to change the Question Understanding task into the Question to Subgraph(Question2Subgraph) task with which the subgraph can be simply and directly mapped to Λ-DCS. In this paper, we focus on factual question to subgraph task (Qƒ, G) and prove that more complex questions can be easily solved based on it. Then, we propose a novel framework with Rule Inference and Sentence Schema Graph Embedding (RI-SSGE) to solve (Qƒ, G) task. Inspired by isomeride structures in Chemistry, we concentrate RI-SSGE on structure detection of questions to avoid the problem of poor generalization in other models, which are based on templates on various specific domain knowledge graphs. To address the problem of error propagation, RI-SSGE creatively combines the traditional rule inference method and the graph representation method together, and thus guarantees the performance of the whole framework. Having observed that human can exploit the hidden relations by joining the question and the knowledge graph structure together, we raise a novel Sentence-Schema-Graph (SSG) in the last network representation learning stage of RI-SSGE, which is designed to imitate human's way of thinking. We experimented on Geoquery-880 and AceQG[11] datasets which has 133,143 (Factual Question, Subgraph) pairs on an open academic knowledge graph and results demonstrate the advantages of RI-SSGE over other baselines.
An Obsession with Definitions

Many people seem to think that cryptography is all about creating and analyzing cryptographic schemes. This view ignores the centrality of definitions in shaping the character of the field. More than schemes or their analysis, it is definitions that most occupy my thoughts. In this paper, written to accompany an invited talk at Latincrypt 2017, I try to explain my own fascination with definitions. I outline a few of the definitions I’ve recently worked on—garbling schemes, online AE, and onion encryption—and provide some general advice and comments about the definitional enterprise.
The combinatorics of hidden diversity

In this paper, we study the following "balls in buckets" problem. Suppose there is a sequence B1,B2,…,Bn of buckets having integer sizes s1,s2,…,sn, respectively. For a given target fraction α, 0<α<1, our goal is to sequentially place balls in buckets until at least ⌈αn⌉ buckets are full, so as to minimize the number of balls used, which we shall denote by OPTα(I) for a given instance I. If we knew the size of each bucket, we could obtain an optimal assignment, simply by filling the buckets in order of increasing size until the desired number had been filled. Here we consider the case where, although we know n and α, we do not know the specific bucket sizes si, and when we place a ball in bucket Bj, we only learn whether or not the bucket Bj is now full. We study what can be done under four variants of incomplete information: We know nothing at all about the bucket sizes; we know the maximum bucket size; we know the sizes s1≤s2≤⋯≤sm that occur in the instance; and we know the profile of the sizes: the size list as above, and, for each size, si, the number ki of buckets that have that size, The game above showcases the rich variety of interesting combinatorial and algorithmic questions that this setup gives rise to, and in addition has applications in an area of cryptography known as secure multi-party computation, where taking over ("corrupting") a party by an adversary has a cost, and where a hidden diversity—corresponding to lack of information on the amount of computational resources the adversary should invest to corrupt a participant—translates into robustness and efficiency benefits.
Scalable Change Detection Using Place-Specific Compressive Change Classifiers

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. In this paper, we present an efficient approach of change-classifier-learning, more specifically, in the proposed approach, a collection of place-specific change classifiers is employed. Our approach requires the memorization of only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Furthermore, through the proposed approach the most recent map can be incorporated into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. The proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.
Evaluation of Different PV Power Forecasting Systems on Grid-Connected PV Plants in the Mediterranean Area

The increasing spread of renewable generation requires a deep transformation in electrical generation, transmission and distribution. The negative impact of renewables on electric grid, due to their non-programmability, can be mitigated with reliable power forecasting systems. This study compares different probabilistic forecasting systems of photovoltaic production for the next two days with high temporal resolution (15 minutes or 30 minutes). They are tested on three Mediterranean plants. This paper points out how different performances can be achieved according to model configuration, predictors used as input, climatic characteristics and size of plant. A new approach, based on an optimal combination of various models, is also presented. Finally, the study highlights the importance of an accurate characterization of meteorological conditions in order to achieve an improvement in renewable power forecasting.
Automated Rediscovery of the Maxwell Equations

There is sufficient information in the far-field of a radiating dipole antenna to rediscover the Maxwell Equations and the wave equations of light, including the speed of light c . We created TheoSea, a Julia program that does this in a few seconds, and the key insight is that the compactness of theories drives the search. The program is a computational embodiment of the scientific method: observation, consideration of candidate theories, and validation.