Reviewerly/exHarmony huggingface dataset:
Big Data Security with Access Control Model and Honeypot in Cloud Computing

Big data typically coins with large volume of data and various enterprises are involving day to day in cloud environment.Nowadays, Cloud facility adoption has enlarged.With the acceptance of cloud facility, numerous of the enterprises are expending to store and process Large Data in cloud.Safety methods provided by the facility providers might not be sufficient to safe the data in the cloud.Enterprise as well as users are suffering with proper security aspect to store, retrieve and process big data in cloud environment.An access control model with honeypot is presented in this paper.Access control model deals with various parameters of authentication, log etc. Various link and areas are included as honeypot to catch hackers or unauthorized users.
PENGEMBANGAN MEDIA PEMBELAJARAN LUDO PADA MATA PELAJARAN EKONOMI KELAS XI SMA PURNAMA TRIMURJO

The purpose of this study is to produce avalid medium used asean economic learning medium. This research isa development (research and development) with 4D development model (sugiyono 2015:37) the development stage consists of define, design, development and disesminstion. Ludo learning media is assessed feasbility by 2 experts consisting of two lectures, a material expert consisting of one subject economics teacher, and 15 students ofclass XI SMA Purnama Trimurjo were tested in small groups.the results of the overal datawere 84% validation of ludo media exprets, 74% initialmaterial expert validation, 79% final material validation, 91% of learners responses during small group trials. I can be concluded ludo learning media worthy of use asa a medium of economic learning and can facilitate learners in learning activites. Keywords: Development, Learning of Media, Ludo
Improving Video Delivery with Fourier Analysis of Traffic in Multi-Access Edge Computing

This paper presents a new video delivery scheme in mobile networks using Multi-Access Edge Computing (MEC). Our goal is to improve the quality of video streaming experienced by the mobile video consumer. Our approach is based upon Dynamic Adaptive Streaming over HTTP. We present a novel algorithm, which uses information obtained from the Radio Network Information Service of MEC to provide the mobile user with a video quality matching the current radio link quality and channel capacity. We evaluate our approach using a real experiment performed on a Long Term Evolution (LTE) femto cell test-bed. Our algorithm displays enhanced adaptation of video rates in comparison to other state of the art solutions.
Evaluation and Mapping Spatial Distribution of Copper Pollution in Soil and Water of Vineyard Garden

Evaluation and Mapping Spatial Distribution of Copper Pollution in Soil and Water of Vineyard Garden
Saving Face: Shared Experience and Dialogue on Social Touch, in Playful Smart Public Space

Can shared experience and dialogue on social touch be orchestrated in playful smart public spaces? In smart city public spaces, in which physical and virtual realities are currently merging, new forms of social connections, interfaces and experiences are being explored. Within art practice, such new connections include new forms of affective social communication with additional social and sensorial connections to enable and enhance empathic, intimate experience in playful smart public space. This chapter explores a novel design for shared intimate experience of playful social touch in three orchestrations of ‘Saving Face’, in different cultural and geographical environments of smart city (semi-) public spaces, in Beijing, Utrecht, Dessau-Berlin. These orchestrations are purposefully designed to create a radically unfamiliar sensory synthesis to disrupt the perception of ‘who sees and who is being seen, who touches and who is being touched’. Participants playfully ‘touch themselves and feel being touched, to connect with others on a screen’. All three orchestrations show that shared experience and dialogue on social touch can be mediated by playful smart cities technologies in public spaces, but rely on design of mediated, intimate and exposed forms of ‘self-touch for social touch’, ambivalent relations, exposure of dialogue and hosting.
Codebraid: Live Code in Pandoc Markdown

Codebraid executes code blocks and inline code in Pandoc Markdown documents as part of the document build process.Code can be executed with a built-in system or Jupyter kernels.Either way, a single document can involve multiple programming languages, as well as multiple independent sessions or processes per language.Because Codebraid only uses standard Pandoc Markdown syntax, Pandoc handles all Markdown parsing and format conversions.In the final output document produced by Pandoc, a code chunk can be replaced by a display of any combination of its original Markdown source, its code, the stdout or stderr resulting from execution, or rich output in the case of Jupyter kernels.There is also support for programmatically copying code or output to other parts of a document.
Deep mutual learning for visual tracking

In this work, we propose a novel deep learning method to improve the accuracy and the speed of particle filter based object trackers. The main contributions include two aspects. First, to enhance the discrimination of feature representations, we propose two identical CNNs, each of which shares information from the other by introducing the Kullback-Leibler (KL) loss. Second, in order to boost the speed of trackers, we introduce the region of interest (ROI) align, and optimize the structure-identical networks with knowledge distillation and deep mutual learning. It makes the forward propagation only calculate once instead of hundreds of iterations. Experimental results on the OTB2015 and VOT2015 benchmarks demonstrate that our method performs better than several state-of-the-art algorithms.
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning

Various NLP problems -such as the prediction of sentence similarity, entailment, and discourse relations -are all instances of the same general task: the modeling of semantic relations between a pair of textual elements.A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g.concatenation or sum) of those vectors as features for the prediction.At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples).In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models.We show that such compositions are not expressive enough for several tasks (e.g.natural language inference).We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions.The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.
Three Classes of Optimal Ternary Cyclic Codes and the Weight Distributions of Their Duals

Cyclic codes as a subclass of linear codes have wide applications in communication systems, consumer electronics and data storage systems, due to their efficient encoding and decoding algorithms. We construct three classes of optimal ternary cyclic codes, which meet some certain bound. The weight distributions of their duals are also completely determined. The results show that their duals have few nonzero weights.
Erasure decoding of five times extended Reed-Solomon codes

Abstract Recently a new family of error control codes was proposed which are equivalent to five times extended Reed-Solomon codes. In this paper an erasure decoding algorithm for these codes is proposed.
Derandomized balanced allocation

In this paper, we study the maximum loads of explicit hash families in the d-choice schemes when allocating sequentially n balls into n bins. We consider the Uniform-Greedy scheme [ABKU99], which provides d independent bins for each ball and places the ball into the bin with the least load, and its non-uniform variant --- the Always-Go-Left scheme introduced by Vocking [Voc03]. We construct a hash family with O(log n log log n) random bits based on the previous work of Celis et al. [CRSW13] and show the following results.1. With high probability, this hash family has a maximum load of [MATH HERE] in the Uniform-Greedy scheme.2. With high probability, it has a maximum load of [MATH HERE] in the Always-Go-Left scheme for a constant ϕd > 1.61.The maximum loads of our hash family match the maximum loads of a perfectly random hash function [ABKU99, Voc03] in the Uniform-Greedy and Always-Go-Left scheme separately, up to the low order term of constants. Previously, the best known hash families matching the same maximum loads of a perfectly random hash function in d-choice schemes were O(log n)-wise independent functions [Voc03], which needs Θ(log2n) random bits.
Bulk-bin-packing based migration management of reserved virtual machine requests for green cloud computing

The dynamic consolidation of Virtual Machines (VMs) into a minimum number of Physical Machines (PMs) is a key energy-efficient practice in a cloud data centre, to reduce the running PMs and save electricity costs.We proposed a migration based VM consolidation approach for reserved requests.Real Dataset EC2 was used in the simulation experiments.The proposed BBPMM has demonstrated the elastic capability of adjusting the running PMs and it reduced 38% of running PMs in a reservation transition period.
Runtime Verification in Erlang by Using Contracts

During its lifetime, a program regularly undergoes changes that seek to improve its functionality or efficiency. However, such modifications may also introduce new errors. In this work, we use the design-by-contract approach to allow programmers to formally state, in the code, some of the knowledge and assumptions originally made when the code was first written. Such contracts can then be checked at runtime, to ensure that modifications made to a program did not violate those assumptions. Applying these principles we have designed a runtime verification system for the Erlang language, permitting to specify as annotations the contracts needed for both sequential and concurrent code. As a second contribution we extend the commonly used Erlang gen_server behaviour (a design pattern) permitting to specify declaratively when a server is ready to service a client request. The ideas presented in this paper have been implemented in a tool named EDBC. Its source code is available at github.com as an open-source and free project.
Implanting Rational Knowledge into Distributed Representation at Morpheme Level

Previously, researchers paid no attention to the creation of unambiguous morpheme embeddings independent from the corpus, while such information plays an important role in expressing the exact meanings of words for parataxis languages like Chinese. In this paper, after constructing the Chinese lexical and semantic ontology based on word-formation, we propose a novel approach to implanting the structured rational knowledge into distributed representation at morpheme level, naturally avoiding heavy disambiguation in the corpus. We design a template to create the instances as pseudo-sentences merely from the pieces of knowledge of morphemes built in the lexicon. To exploit hierarchical information and tackle the data sparseness problem, the instance proliferation technique is applied based on similarity to expand the collection of pseudo-sentences. The distributed representation for morphemes can then be trained on these pseudo-sentences using word2vec. For evaluation, we validate the paradigmatic and syntagmatic relations of morpheme embeddings, and apply the obtained embeddings to word similarity measurement, achieving significant improvements over the classical models by more than 5 Spearman scores or 8 percentage points, which shows very promising prospects for adoption of the new source of knowledge.
Sketches for arithmetic universes

A theory of sketches for arithmetic universes (AUs) is developed, as a base-independent surrogate for suitable geometric theories. A restricted notion of sketch, called here \emph{context}, is defined with the property that every non-strict model is uniquely isomorphic to a strict model. This allows us to reconcile the syntactic, dealt with strictly using universal algebra, with the semantic, in which non-strict models must be considered. For any context T, a concrete construction is given of the AU AU&lt;T&gt; freely generated by it. A 2-category Con of contexts is defined, with a full and faithful 2-functor to the 2-category of AUs and strict AU-functors, given by T |-&gt; AU&lt;T&gt;. It has finite pie limits, and also all pullbacks of a certain class of ``extension'' maps. Every object, morphism or 2-cell of Con is a finite structure.
DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.
Point cloud saliency detection via local sparse coding

The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates adescriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle.Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review.
Piecewise constant signal and image denoising using a selective averaging method with multiple neighbors

Piecewise constant signals and images are an important kind of data. Typical examples include bar code signals, logos, cartoons, QR codes (Quick Response codes), and text images, which are widely used in both general commercial and automotive industry use. One previous work called a general selective averaging method (GSAM) was introduced to remove noise from them. It chooses homogeneous neighbors from the two closest pixels (one pixel at each side) to update the current pixel. One limitation is that it suffered from appearing sparse noisy pixels in the denoised result when the noise level is high. In this paper, we try to solve this problem by proposing a selective averaging method with multiple neighbors. To update the intensity value at each pixel, the proposed algorithm averages more homogeneous neighbors selected from a large domain, which is based on the property of the local geometry of signals and images. This greatly reduces sparse noisy pixels left in the final result by GSAM. Similarly, our method adopts the Neumann boundary condition at edges, and thus preserves edges well. In 1D case, some theoretical results are given to guarantee the convergence of our algorithm. In 2D case, except eliminating additive Gaussian noise, this algorithm can be used for restoring noisy images corrupted by speckle noise. Intensive experiments on both gray and color image denoising demonstrate that the proposed method is quite effective for piecewise constant image denoising and achieves superior performance visually and quantitatively.
DETECTION AND PREVENTION OF SOCIAL MEDIA CYBERCRIME AMONG STUDENTS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 3773-3779ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0977Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
COGNITIVE INFORMATION SYSTEMS - ARTIFICIAL INTELLIGENCE &amp; MANAGEMENT DECISIONS

We investigate the interlink between the management decisions and Artificial and Computational Intelligence (ACI) in the context of Cognitive Information Systems (CIS).The analysis is focused on the current trends of application of ACI within the realm of Information Systems (IS) for supporting management decision.One of the research issues is whether there is a difference between CIS and IS based on Artificial Intelligence (AI).Is there any chance to distinguish them from each other?If yes, we should locate the subtle differences and similarities between them.There are various definitions for the recent and the future AI, there are approaches to circumscribe the concept of CIS.The research issue is how the Cognitive Architecture, Science, IS and AI can be reconciled for management information systems (MIS) to support decision making.
Using Grounded Word Representations to Study Theories of Lexical Concepts

The fields of cognitive science and philosophy have proposed many different theories for how humans represent “concepts”. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories–Classical Theory and Prototype Theory–in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations.
Medical Treatment Migration Prediction in Healthcare via Attention-Based Bidirectional GRU

With the rapid expansion of the number of floating populations in China, a large number of people are gradually migrating to different hospitals to seek medical treatment. How to accurately predict the future medical treatment behaviors of patients has become an important research issue in healthcare. In this paper, an Attention-based Bidirectional Gated Recurrent Unit (AB-GRU) medical treatment migration prediction model is proposed to predict which hospital patients will go to in the future. The model groups patients who are prone to medical treatment migration, and achieves disease prediction and medical treatment migration prediction for each group. In terms of disease prediction, considering the predictive performance problem of a single prediction algorithm, a standard deviation weight recombination method is used to achieve disease prediction. When disease prediction has been completed, considering the impact of medical visit on the future medical behavior, on the basis of bidirectional gated recurrent unit (GRU) framework, we introduce an attention mechanism to determine the strength of hidden state at different moments, which can improve the predictive performance of the model. The experiment demonstrates that the predictive model proposed in this paper is more accurate than the traditional predictive models.
Symmetries of weight enumerators and applications to Reed-Muller codes

Gleason's 1970 theorem on weight enumerators of self-dual codes has played a crucial role for research in coding theory during the last four decades. Plenty of generalizations have been proved but, to our knowledge, they are all based on the symmetries given by MacWilliams' identities. This paper is intended to be a first step towards a more general investigation of symmetries of weight enumerators. We list the possible groups of symmetries, dealing both with the finite and infinite case, we develop a new algorithm to compute the group of symmetries of a given weight enumerator and apply these methods to the family of Reed-Muller codes, giving, in the binary case, an analogue of Gleason's theorem for all parameters.
A High Quartet Distance Construction

Given two binary trees on $N$ labeled leaves, the quartet distance between the trees is the number of disagreeing quartets. By permuting the leaves at random, the expected quartets distance between the two trees is $\frac{2}{3}\binom{N}{4}$. However, no strongly explicit construction reaching this bound asymptotically was known. We consider complete, balanced binary trees on $N=2^n$ leaves, labeled by $n$ long bit sequences. Ordering the leaves in one tree by the prefix order, and in the other tree by the suffix order, we show that the resulting quartet distance is $\left(\frac{2}{3} + o(1)\right)\binom{N}{4}$, and it always exceeds the $\frac{2}{3}\binom{N}{4}$ bound.
Summary of recent progress in searches foradditional Higgs bosons

There has been a tremendous increase of sensitivity in searches for charged Higgs bosons and additional neutral Higgs bosons since the last CHARGED workshop in 2016. We will review recent experimental and theory developments as presented at CHARGED2018, and conclude with future prospects for the field.
A quantum key distribution protocol based on LDPC error correcting codes

Quantum encryption guarantees the absolute security of key distribution from the physical mechanism. However, in the process of quantum key distribution (QKD), quantum channel noise will reduce the transmission efficiency. Quantum low-density parity check (quantum LDPC) code has become a hot research topic of quantum error correcting code due to its great flexibility in the choice of code length and code rate and its dependence on sparse graphs. In order to improve the transmission reliability, a QKD protocol based on BB84 and quantum LDPC code is designed. First, we construct a quantum LDCP error correcting code. Then we utilize the error correcting code and BB84 to construct our QKD protocol. Finally, we analyze the influence of the quantum LDPC code on the proposed protocol. The results show that the key transmission efficiency of the our protocol is improved, and the validity of the QKD protocol based on BB84 and quantum LDPC codes in noisy quantum channels is verified.