Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF MULTI-MODAL DISCOURSE ON “COCA-COLA ADVERTISEMENT”

Abstract Investigation into language seems to be never ending – not only in form and meaning but also in function and use in communication. In use, for example, recently, social semiotics as the development of semiotics has witnessed the move from the dominance of monomodality to multimodality. Newspapers and magazines, computers, and mass media in general have articulated multimodal texts (combinations of video, audio, written text, etc.) that aim to reinforce, render more complex, or produce different meanings. This means that the investigation is more than about language, but attends to the full range of communicational forms people use which covers image, gesture, gaze, posture and so on- and the relationships between them. This article presents a multimodal analysis on video advertisement entitled “https://www.google.com/?Funny, Commercial Coca-Cola, Producer, Director, Victor Pukkalsky- YouTube + 2014” accessed on January 13, 2016. This case study describes and interprets the video in order to give the concepts of description and interpretation in the perspective of multimodal semiotics as recommended by Anstey and Bull (2010). By applying the systems of multimodal recommended it was found that five aspects namely 1. Linguistic, 2. Visual, 3. Audio, 4. Gestural and 6 Spatial aspects of the advertisement in multimodal semiotics perspective are covered. Key Words: multimodal, discourse, analysis, advertisement, video, coca-cola
Pricing of additional professional education services provided by private educational institutions

Paid educational services are becoming more and more important as the part of the activities carried out by educational organizations. The financial health of private educational institutions offering additional professional education programs directly depends on the level of prices for the services such institutions provide. As a rule, private educational institutions of additional professional education can be classified as small businesses. In private educational institutions the administrative staff is down to a minimum. Such administrative staff is required to have a significant amount of organizational, legal and economic knowledge, including in the field of pricing of educational services
Andragogical competence of pedagogical universities teachers as a problem of modern higher education

The article substantiates the necessity of applying the andragogical model of education in higher educational institutions. The motives of adults’ applying for universities when they have life and professional experience, as well as the experience of educational activities are considered. Consideration of these features in the organization of the educational process of the university is the basis for the development of teachers’ andragogical competencies.
Multi-Fidelity Learning

The title of one of the seminal papers in the field of multi-fidelity learning and optimization, “Predicting the Output from a Complex Computer Code when Fast Approximations are Available,” gives a good hint at what multi-fidelity optimization and learning is. It is about building an accurate surrogate model for a complex problem based on the partial use of data obtained from calculations that are less accurate than what is possible. The methods need not involve only calculations. They can combine results from experiments performed with different levels of precision. The objective of multi-fidelity optimization is to reduce the need of performing many accurate and presumably costly calculations to scope complex problems by performing fewer such calculations but supplementing them with less costly ones. In materials research, an obvious application is the computation of bandgaps. There is a known hierarchy of choices of pseudo-potentials that range from those lending themselves to calculations quickly and cheaply performed but less accurate in their predictions of bandgaps to those that are more time-consuming and expensive to use but more accurate in their predictions. In multi-fidelity optimization, a Gaussian process is typically called kriging after D. G. Krige, a South African mining engineer who pioneered the method in making mineral distribution predictions based on core samples [1]. Multi-fidelity learning is often called co-kriging.
SISTEM REKOMENDASI PAKET MINUMAN BERDASARKAN PESANAN PELANGGAN DENGAN METODE FREQUENT PATTERN GROWTH

It is proposed that a beverage package is determined based on customer orders by looking for different frequent itemset patterns in the transaction data that has occurred. These data can be analyzed to be more useful. The method used is the association method. With the predicted beverage package the company can increase sales. With so many transaction data stored, the difficulty in managing data requires a method called the association method. Frequent Pattern Growth Algorithm is an alternative algorithm that is quite effective for finding the set of data that most often appears (frequent itemset) in a large data set. The test results from the Frequent Pattern Growth method can determine a number of packages that meet the minimum value of support and confidence with a combination of two itemset
Tools for Understanding Space

The universe is big. But it is also very fine-grained, and so there is important detail at both extremely tiny and unimaginably huge scales of size and distance. To fit this into the human mind, we need some tools. One of the most important of such tools is the ability to relax: abandon the idea that it can all make sense at once, and in detail. Instead, we learn how to split it up into pieces that are manageable to the human mind, and we then learn how to relate those piece to each other. In the sections that follow, I describe several methods that are useful for making sense of both the bigness and the tininess, while still keeping track of how it all fits together.
Learning with Small Databases

In contrast to the combinatorial approaches using high throughput calculations that generate large data sets on ideal systems at T = 0 and P = 0 discussed in Chapter 3 and elsewhere in this book, real materials problems typically involve several components, are often solid solutions, and contain defects at T ≠ 0 and P ≠ 0. For crystals however, electronic structure calculations, say, based on density functional theory (DFT), often return reliable structural information and useful estimates of such physical quantities as dielectric and elastic constants and, in general, these calculations cannot return information on important functionalities such as whether the material is a candidate for memory applications, neuromorphic computing, etc. Most functionalities are established and quantified by experiment. As experiments can be quite time consuming and expensive, often data on only relatively few well-characterized samples (between ten and in the hundreds) are available. Hence, it is important to consider approaches that we can tailor and use for small data sets. The relevant question then becomes how we can effectively learn from existing, limited data to guide the next experiments in order to minimize the number of materials synthesized and measurements needed to be carried out to find a material with better targeted properties than what we may have in the training data [1]. Because of the emphasis on design, efficiency, and costs, it should come as little surprise that industry, as well as application areas such as drug design and cancer genomics, are very much at the forefront in developing iterative feedback methods, that is, adaptive learning methods, to reduce the number of experiments or calculations needed [2].
Perception in Autonomous Driving

In autonomous driving, the goal of perception is to sense the surrounding dynamic environment, to build reliable and detailed representation based on sensory data. In order for autonomous driving vehicles to be safe and intelligent, perception modules must be able to detect any obstacle, to recognize road surface, lane dividers, traffic signs and lights, to track moving objects in 3D, etc. Since all subsequent driving decisions, planning, and control depend on a correct perception output, its importance cannot be overstated. In this chapter, major functionalities of perception are covered, along with public datasets, problem definitions, and typical algorithms. Algorithms based on neural networks are discussed in the next chapter.
Decision, Planning, and Control

In this chapter, we continue with our general discussion of the planning and control modules by expanding on the concepts of behavior decision, motion planning, and feedback control. Decision, planning, and control are the modules that compute how the autonomous vehicle should maneuver. These modules constitute the traditional narrow concept of planning and control. While they all solve the same problem of how the autonomous vehicle should behave, they operate at different levels of the problem abstraction.
Spatial Encoding

In Chapter 1, we discussed the fundamentals of NMR. In this chapter, we discuss how to obtain data for the formation of images with spatial encoding performed using magnetic field gradients.
Smart Contracts: When Functions May Give Rise to Legally Enforceable Obligations

The purpose of this paper is to outline the nature of smart contracts and explore whether more commonly known smart contract functions can give rise to legal relations. It concludes that the key to resolving contractual questions involving smart contracts is to look at the substance of the smart contract in light of surrounding communications, constituent information and the parties’ conduct. Wherever possible, the notion of ‘functional equivalence’ should be upheld when applying traditional contract law doctrines.
Principles of Data Visualization

Information visualization aims at visually representing different types of data (e.g., geographic, numerical, text, network) in order to enable and reinforce cognition. Information visualization offers intuitive ways for information perception and manipulation that essentially amplify the overall cognitive performance of information processing, especially for non-expert users. Visual analytics combines information visualization with data exploration capabilities. It enables users to explore and analyze unknown (in terms of semantics and structure) sets of information, discover hidden correlations and causalities, and make sense of data in ways that are not always possible with traditional quantitative data analysis and mining techniques. This is of great importance, especially given the massive volumes of digital information concerning nearly every aspect of human activity that are currently being produced and collected. The so-called Big Data era refers to this tremendous volume of information collected by digital means and analyzed to produce new knowledge in a plethora of scientific domains.
Learning with Large Databases

From a point of view that is both complementary and supplementary, this chapter revisits some of the uses and opportunities for materials design and discovery just discussed in Section 2.2.3. The previous subsection’s focus was mainly about macroscopic-scale feature generation and selection and illustrated the benefit that attention on both topics produces with examples where data was gathered or generated for specific classes for specific applications. In this chapter, we are more concerned with the predictive potential of machine learning when applied to very large, community accessible databases. We review the available large databases, discuss some machine learning work using these data aimed at defining general rules for the likelihood of substituting in known compounds one element for another and illustrate the potential difference in predictive potential of using ground state vs. observed data of material structure and properties. We also explain decision tree-based versions of the random forests and gradient tree boosting machine learning methods. We note that these methods return a measure of the relative importance of the features and the dependency of one feature with the others.
Deep Learning in Autonomous Driving Perception

In the previous chapter, we discussed perception issues in autonomous driving. In recent years, the concept of deep neural networks, also known as deep learning, has greatly changed the field of computer vision, making significant progress in solving various problems such as image classification, object detection, semantic segmentation, etc. Most state-of-the-art algorithms now apply one type of neural network that is based on convolution operation, and the field is rapidly progressing. In this chapter, we will cover selected deep-learning-based algorithms for perception in autonomous driving.
Prediction and Routing

In this chapter, we will describe the Prediction and Routing modules with an emphasis on how they are integrated in the autonomous vehicle planning and control framework. The Prediction module is responsible for predicting the future behavior of surrounding objects identified by the Perception module. It produces predicted trajectories that are fed into the downstream planning and control modules. The Routing module we describe here is a lane level routing based on lane segmentation of the HD-maps. Routing simply informs the autonomous vehicle how to reach its destination by following a sequence of lanes on the HD-maps. Its output is also sent to the downstream planning and control modules.
Reinforcement Learning-Based Planning and Control

While optimization-based approaches still enjoy mainstream appeal in solving motion planning and control problems, learning-based approaches have become increasingly popular with recent developments in artificial intelligence. Even though current state-of-the-art learning-based approaches to planning and control have their limitations, we feel they will become extremely important in the future and that, as technical trends, they should not be overlooked. More particularly, reinforcement learning has been widely used in solving problems that take place in the form of rounds or time steps with stepwise guiding information such as rewards. Therefore, it has been experimented with as a methodology to solve different levels of autonomous driving planning and control problems. We thus conclude that reinforcement learning-based planning and control will gradually become a viable solution to autonomous driving planning and control problems or at least become a necessary complement to current optimization approaches.
Semi-supervised cross-modal retrieval with graph-based semantic alignment network

Semi-supervised cross-modal retrieval is an eclectic paradigm which learns common representations via exploiting underlying semantic information from both labeled and unlabeled data. Most existing methods ignore the rich semantic information of text data and are unable to fully utilize the text data in common representation learning. Moreover, they only considered the correlation of the data with the same semantic label, but ignored the correlation between the data with different semantic label. In this paper, we propose a novel semi-supervised cross-modal retrieval method, called Graph-based Semantic Alignment Network (GSAN), which learns common representation by aligning the features of different modalities with semantic embeddings of text data. Firstly, we design a Deep Supervised Semantic Encoding (DSSE) module to train the semantic projector and label predictor which can exploit the semantic embeddings and the predicted labels from unlabeled data of text modality. Then, GAN-based Bidirectional Fusion (GBF) module is designed to learn the mapping networks of two modalities (image and text). In order to make the mapping networks generate semantically discriminative and modality-invariant representations, we utilize the underlying semantic information exploited by DSSE to construct Graph-based Triplet Constraint (GTC) which can enforce feature embeddings from the semantically-matched (image and text) pairs to be more similar and push those mismatched ones away. By the benefit of fully using of semantic information, our approach can only use fewer label data and achieves the performance of state-of-the-art methods. In addition, since we only utilize the mapping networks trained in GBF module to generate common representations in referring stage, our approach is efficient and time saving in real world application. Extensive experiments on four widely-used datasets show the effectiveness of GSAN.
Identifying and addressing data asymmetries so as to enable (better) science

As a society, we need to become more sophisticated in assessing and addressing data asymmetries-and their resulting political and economic power inequalities-particularly in the realm of open science, research, and development. This article seeks to start filling the analytical gap regarding data asymmetries globally, with a specific focus on the asymmetrical availability of privately-held data for open science, and a look at current efforts to address these data asymmetries. It provides a taxonomy of asymmetries, as well as both their societal and institutional impacts. Moreover, this contribution outlines a set of solutions that could provide a toolbox for open science practitioners and data demand-side actors that stand to benefit from increased access to data. The concept of data liquidity (and portability) is explored at length in connection with efforts to generate an ecosystem of responsible data exchanges. We also examine how data holders and demand-side actors are experimenting with new and emerging operational models and governance frameworks for purpose-driven, cross-sector data collaboratives that connect previously siloed datasets. Key solutions discussed include professionalizing and re-imagining data steward roles and functions (i.e., individuals or groups who are tasked with managing data and their ethical and responsible reuse within organizations). We present these solutions through case studies on notable efforts to address science data asymmetries. We examine these cases using a repurposable analytical framework that could inform future research. We conclude with recommended actions that could support the creation of an evidence base on work to address data asymmetries and unlock the public value of greater science data liquidity and responsible reuse.
Improving word embeddings in Portuguese: increasing accuracy while reducing the size of the corpus

The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas.
Multi‐view subspace clustering with incomplete graph information

Abstract The core of multi‐view clustering is how to exploit the shared and specific information of multi‐view data properly. The data missing and incompleteness bring great challenges to multi‐view clustering. In this paper, we propose an innovative multi‐view subspace clustering method with incomplete graph information, so‐called incomplete multiple graphs clustering. Specifically, we creatively separate one shared and multiple specific graphs from multiple raw graph data, and exploit the mask fusion strategy and block diagonal regulariser to obtain the inherent category information. To handle the incomplete multiple graph data, we utilise multiple indicator matrices to mark the missing elements existed in each raw graph. In addition, the weight of each raw graph is adaptively learnt according to the graph importance. The alternative direction optimization algorithm is employed to solve our proposed methods. Finally, we also analyse the algorithm convergence and the computation complexity in detail. The clustering results on six real‐world datasets show that our method obviously outperforms a serious of classic incomplete multi‐view clustering methods.
Blood‐contaminated endoscopic image restoration based on residual VQ‐VAE with cascaded structure

This research studies an image reconstruction technology that can help doctors observe endoscopic images obscured by blood in transurethral procedures. A new network structure is proposed, which applies encoder–decoder deep neural network to achieve the purpose of denoising and uses residual learning and cascade learning methods to relieve the problem of insufficient clarity and lack of detail in reconstruction image when restoring endoscopic images suffered from blood occlusion. The residual mechanism is used to estimate noise and compensate the images, instead of reconstructing overall image by an encoder and decoder deep network. It can potentially make the details of the image to be preserved and improve the resolution of the reconstructed image. This denoising model is embedded into a cascade network framework to further improve the quality of the restored image through the idea of repeated learning multiple times. On a customized endoscopic image sample set with real blood noise, for verifying the denoising ability, we designed two approaches of data augmentation to generate noised images obscured by different types and degree blood noises. In experiments, the new model has been compared to other approaches and the certain improved denoising effects can be observed.
Methods for improving the implementation of advanced encryption standard hardware accelerator on field programmable gate array‐A survey

Abstract With the majority of users migrating to a cloud computing platform, the responsibility for data security has increased considerably. As data with various levels of sensitivity moves out of the confines of the system firewall, there is no control over where it resides; it depends on the cloud server being used. This makes the use of secured cryptographic algorithms necessary to improve data security and maintain the confidentiality of data. The encryption algorithm used most extensively today is the Advanced Encryption Standard (AES). Hardware acceleration with Field Programmable Gate Array can improve the performance of AES. This study examines various approaches for improving data encryption methods that can be utilized as hardware accelerators to improve the fundamental AES cipher. Some research publications suggest ways to boost throughput while others advise ways to save area and power. Many subsequent developments have also used other cryptographic methods such as RSA and ECC to improve key management security. This study examines the outcomes of multiple ways for metrics including throughput, area, power, and data security, and explains some of the best implementation solutions for each metric individually.