Reviewerly/exHarmony huggingface dataset:
Towards Automatic Machine Learning Pipeline Design

Author(s): Milutinovic, Mitar | Advisor(s): Song, Dawn | Abstract: The rapid increase in the amount of data collected is quickly shifting the bottleneck of making informed decisions from a lack of data to a lack of data scientists to help analyze the collected data. Moreover, the publishing rate of new potential solutions and approaches for data analysis has surpassed what a human data scientist can follow. At the same time, we observe that many tasks a data scientist performs during analysis could be automated. Automatic machine learning (AutoML) research and solutions attempt to automate portions or even the entire data analysis process.We address two challenges in AutoML research: first, how to represent ML programs suitably for metalearning; and second, how to improve evaluations of AutoML systems to be able to compare approaches, not just predictions.To this end, we have designed and implemented a framework for ML programs which provides all the components needed to describe ML programs in a standard way. The framework is extensible and framework’s components are decoupled from each other, e.g., the framework can be used to describe ML programs which use neural networks. We provide reference tooling for execution of programs described in the framework. We have also designed and implemented a service, a metalearning database, that stores information about executed ML programs generated by different AutoML systems.We evaluate our framework by measuring the computational overhead of using the framework as compared to executing ML programs which directly call underlying libraries. We observe that the framework’s ML program execution time is an order of magnitude slower and its memory usage is twice that of ML programs which do not use this framework.We demonstrate our framework’s ability to evaluate AutoML systems by comparing 10 different AutoML systems that use our framework. The results show that the framework can be used both to describe a diverse set of ML programs and to determine unambiguously which AutoML system produced the best ML programs. In many cases, the produced ML programs outperformed ML programs made by human experts.
Paper versus Screen: Impact on Reading Comprehension and Speed

Choosing the appropriate reading format is important for students to achieve effective reading. This quasi-experimental study investigates the effects of paper versus screen on reading comprehension and speed among undergraduate students who are currently enrolled in a public university in Sarawak. The general objective of this study is to investigate the formats for effective reading and comprehension among undergraduates. Instruments that have been used in this study were a set of questionnaires, a set of general interest articles, corresponding comprehension questions, and an online timer. Findings revealed that students tended to have better reading comprehension when reading on the screen. Conversely, students were able to read faster using the paper format. Data also revealed that there is significant difference between paper and screen formats for both reading comprehension and reading speed.
COPING RESOURCES OF FOREIGN STUDENTS IN DIFFICULT LIFE SITUATIONS

The authors note that, at present, a strategy of paramount importance for many universities has been to focus on the internationalization of their activities. In this regard, the problem of adaptation of foreign citizens is actualized. It is noted that foreign students face a number of difficulties that are specific to this group of people and common to this age group. The use of individual and environmental means (resources) by foreign students in a difficult life situation, which is a foreign-language environment, also remains insufficiently studied. The purpose of writing this article is to summarize the theoretical and empirical research on the problem of a person's coping resources in a difficult life situation. In this article, the authors summarize the fundamental and applied aspects of studying the problem of coping resources of foreign students. It is noted that the success of the adaptation of foreign students is due to the ability to cope with difficult life situations. It is revealed that coping resources contribute to the adaptation of an individual based on his or her potential resources and make it possible to effectively realize personal potential aimed at creating an ecologically resource environment. The authors summarize research approaches on the problem of resources, consider conceptual models of resource conservation. The article notes that for foreign students studying in a foreign-language environment is a difficult life situation. The ability of an individual to resist negative influences, to resist stress factors, to overcome emerging obstacles and difficult life situations requires the mobilization of personal potential, which is the basis of self-determination of personality.
Cryptocurrency: A Comprehensive Analysis

Chatterjee, Gaurav Edla, Damodar Reddy Kuppili, VenkatanareshbabuThis research is about the interrelation of cryptocurrencies. Mostly the research of cryptocurrency means research of bitcoin. Only some of the researchers have taken other cryptocurrencies into consideration. This study considers three cryptocurrencies namely bitcoin which has rank 1st in the cryptocurrency list, Ethereum which has attracted a lot of hype because of its additional features and applications and litecoin which comes in the category of altcoin and ranked 6th in the cryptocurrency table. Among the two objectives of this paper, the first objective is to study the degree of correlation between cryptocurrencies for each day. And the second objective is to accurately predict the price of bitcoin, and altcoins like ethereum and litecoin in USD with Deep Neural Network (DNN) model. The price data is sourced from CoinMarketCap and Yahoo Finance and the average of the two has been considered. We have considered 4 years of cryptocurrency data to train our LSTM model. Over this timescale, noise can disturb the signal, so we have opted for daily prices.
Industry Based Adaptive Educational Programs

The aim of this work is to summarize the experience in the development and implementation of industry-oriented educational programs in the field of digital engineering in the educational process.The article presents an example of the interaction of the Higher Engineering School of NRNU MEPhI and the Engineering Division of ROSATOM State Corporation in terms of training for industrial-oriented master programs. The authors present the data on the needs of ROSATOM State Corporation for young personnel in the field of digital engineering, discuss the advantages of the introduction of industrial-oriented master programs in the educational process and the main problems encountered in their development. The article makes the case for collaboration between representatives of the university and the enterprise on the creation and filling of training modules with theoretical and practical material, and gives the main recommendations for solving the emerging difficulties.
Application of Remote Sensing in Mapping Hydrothermal Alteration Zones and Geological Structures as Areas of Economic Mineralization in Mwitika-Makongo Area, SE Kenya

Remote sensing investigations combined with Geographical investigation systems (GIS) have become more important for the study of geological mineralization and structural geology like lineaments.Automatic extraction of lineaments from satellite imagery help in giving an overview of the tectonic events in the study area.The main objective of the study was to map hydrothermally altered rocks and geological structures that may be associated with mineral deposits in Mwitika-Makongo area.The study involves the use of Landsat-8/OLI image.The satellite images were classified using ENVI 5.3 and ArcGIS 10.5.PCI geomatics 2016 was used for extraction of lineaments while Rose diagram was generated using Rockworks 16.Different remote sensing techniques, such as colour composite, band ratio and principal component analysis were applied to identify geological units and features related to economic mineralization.Colour composite band combination (5, 6, 7) showed hydrothermally altered geological units as blue.Band ratio combination (4/2, 6/7, 6/5) showed the areas that were hydrothermally altered as blue.Lineament mapping was done using PCA, with the first three PCs having the highest percentage of Eigenvalues.A lineament density map showed higher values in Makongo hill followed by Kalima Kathei hill.The structural trend from the rose diagram is in the NE direction.The results of image analysis and lineament extraction show that the economic minerals like iron ore are located in areas of hydrothermal alterations where intrusive rocks like Pyroxenite were found.
Sexual violence in digital society

As digital modes of communication and participation become further enmeshed into everyday lives, so, too, do these technologies feature in shifting enactments of sexual violence. Over many decades, research has examined various aspects of sexual victimization, as well as its extent, gendered nature, and impacts. However, scholarly examination of the ways in which digital technologies contribute to both the cultures and practices of sexual violence has been slow to develop in comparison to the rapid pace of the technologies themselves. Moreover, although the cybercrime scholarship has examined the role of technology in creating new opportunities and tools for criminally motivated offenders in general, less attention has been paid to the development of conceptual frameworks for understanding the role of technologies in gender-based violences.In this chapter, we move beyond a conventional analysis focused on technology as a mere facilitator of sexual offending, towards a more conceptual framing of the intersection of human, social, and technical factors that contribute to technology facilitated sexual violence (TFSV).
Unsupervised Learning of Image Data Using Generative Adversarial Network

Over the past few years, with the introduction of deep learning techniques such as convolution neural network (CNN), supervised learning with CNN had achieved a huge success in the computer vision area such as classifying digital images. However, supervised learning has a major drawback, in which it requires a large dataset for them to perform more effectively. As the data used in training grew bigger, the cost of labeling data for training becomes more expensive and impractical. In order to resolve this issue, unsupervised learning is encouraged to be used as it can draw inferences from datasets consisting of unlabeled input data. Generative adversarial network (GAN) is one of the unsupervised learning technique that has the ability to create natural-looking images, converting text description into images, recover resolution of images and last but not least, its power of representation learning from unlabeled data. Thus, this study attempts to evaluate the effectiveness of GAN algorithm in performing the supervised task and unsupervised task such as classification and clustering. Based on the results obtained, the GAN algorithm can learn the internal representation of data without labels and can act as good features extractor. Future works include applying GAN framework in other domains such as video, natural language processing and text to image synthesis.
DEVELOPMENT OF FUTURE ENGINEERS' PROFESSIONAL COMMUNICATIVE COMPETENCE AT ESP CLASSES

sont présentés.
Sentiment Categorization through Natural Language Processing : A Survey

Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment analysis, which is also known as opinion mining, studies people’s sentiments towards certain entities. Sentiment Analysis isn’t an unfamiliar term anymore. Today, smart phones, high speed Internet and various forums and social networks, have made it very common for people to give voice to their opinions. Therefore, a lot of textual data is available in various forms where people express their opinions. Analysing this data to know the underlying sentiment behind it has also become quite popular these days. Various techniques and applications have been created in the past and even today to perform sentiment analysis. This paper contributes towards understanding some of the modern techniques and in knowing which technique to use under what circumstances. It also studies feature extraction which is an important aspect of sentiment analysis. Feature extraction allows us to identify the features in the given text and analyse the sentiment for each feature.
Solving Tangram Puzzles Using Raster-Based Mathematical Morphology

The Tangram is a dissection puzzle composed of polygonal pieces which can be combined to form different patterns. Solving the Tangram is a two-dimensional irregular shape packing problem known to be NP-hard. Tangram patterns may be composed of multiple connected components, and assembling them may require the reflection transformation and unconstrained rotations of the pieces. In this work, we propose a novel approach for the automatic solution of the Tangram based on a raster representation of the puzzle. In order to adapt the geometrical techniques that are applied to the prevention of piece overlapping and the reduction of space between pieces, we use morphological operators and representations commonly used in the discrete domain such as the dilation operator, the distance transform and the morphological skeletonization. We investigate the effects of the raster representation in the puzzle assembly process and verify the effectiveness of the proposed method in solving different Tangram puzzles.
Underwater Image Enhancement Based on Fusion Technique via Color Correction and Illumination Adjustment

Underwater image is affected by absorption and scattering when light propagates through water; resulting in poor visibility, reduced contrast, color attenuation. So before going into any restoration processing activity of an image, it is mandatory to enhance the image first. In this paper, a fusion based technique is introduced, where the method is to use wavelet decomposition. The proposed method has two inputs enhanced from the original underwater image. One is color corrected image and other is illumination adjusted image. Illumination adjustment is done by adaptive histogram equalization, gamma correction and filtering in the proposed system. Histogram equalization and adaptive histogram equalization techniques are used for color correction on the adjusted image. These modified images are then fused based on their maximum coefficient values and the resulted output shows promising result improving the haze of the underwater image. This is a very simple method and the resulted image is clearer than the hazy image.
Does blame shifting work?

Contract systems, especially of the higher-order flavor, go hand in hand with blame. The pragmatic purpose of blame is to narrow down the code that a programmer needs to examine to locate the bug when the contract system discovers a contract violation. Or so the literature on higher-order contracts claims. In reality, however, there is neither empirical nor theoretical evidence that connects blame with the location of bugs. The reputation of blame as a tool for weeding out bugs rests on anecdotes about how programmers use contracts to shift blame and their attention from one part of a program to another until they discover the source of the problem. This paper aims to fill the apparent gap and shed light to the relation between blame and bugs. To that end, we introduce an empirical methodology for investigating whether, for a given contract system, it is possible to translate blame information to the location of bugs in a systematic manner. Our methodology is inspired by how programmers attempt to increase the precision of the contracts of a blamed component in order to shift blame to another component, which becomes the next candidate for containing the bug. In particular, we construct a framework that enables us to ask for a contract system whether (i) the process of blame shifting causes blame to eventually settle to the component that contains the bug; and (ii) every shift moves blame ``closer'' to the faulty component. Our methodology offers a rigorous means for evaluating the pragmatics of contract systems, and we employ it to analyze Racket's contract system. Along the way, we uncover subtle points about the pragmatic meaning of contracts and blame in Racket: (i) the expressiveness of Racket's off-the-shelf contract language is not sufficient to narrow down the blamed portion of the code to the faulty component in all cases; and (ii) contracts that trigger state changes (even unexpectedly, perhaps in the runtime system's data structures or caches) interfere with program evaluation in subtle ways and thus blame shifting can lead programmers on a detour when searching for a bug. These points highlight how evaluations such as ours suggest fixes to language design.
Learning-Based Confidence Estimation for Multi-modal Classifier Fusion

We propose a novel confidence estimation method for predictions from a multi-class classifier. Unlike existing methods, we learn a confidence-estimator on the basis of a held-out set from the training data. The predicted confidence values by the proposed system are used to improve the accuracy of multi-modal emotion and sentiment classification. The scores of different classes from the individual modalities are superposed on the basis of confidence values. Experimental results demonstrate that the accuracy of the proposed confidence based fusion method is significantly superior to that of the classifier trained on any modality separately, and achieves superior performance compared to other fusion methods.
Acceleration of the Development for Motion Planning Algorithms Using V-REP

Motion planning for automation equipment has been used in many areas, including assembly lines in the automated factory, indoor cleaning, and automatic driving. Motion planning algorithms can be developed and validated via V-REP (Virtual Robot Experimentation Platform), a freely available and powerful simulation experiment platform, before being applied to actual devices. This paper describes the implementation details about the applications of V-REP functionalities to accelerate the development of motion planning algorithms, ranging from control mechanisms to environment modeling to calculation modules. In addition, a framework based on the remote API client control mode for motion planning is presented to suit for different algorithms. Finally, two scenarios are constructed to demonstrate the convenience and flexibility of the platform. All of our work provide a documented guideline for users who attempt to take advantage of the platform for developing motion planning algorithms.
Learning to Write Multi-Stylized Chinese Characters by Generative Adversarial Networks

With the development of Generative Adversarial Networks (GAN), more and more researches have been conducted in the field of Chinese fonts transformation and researchers are able to generate high-quality images of Chinese characters. These font transformation models can transform a source font to a target font using GAN. However, current methods have limitations that 1) generated images are oftentimes blurry and 2) models can only learn and produce one target font at a time. To address these problems, we have developed a brand-new model to perform Chinese font transformation. First, font information is attached to images to tell the generator the fonts that we want to transform. Then, the generator extracts and learns feature mappings through convolutional networks and generates photo-realistic images using transposed convolutional networks. The ground truth images are then used as supervisory information to ensure that characters and fonts generated are consistent with themselves. This model only needs to be trained once, but it is able to transform one font to multiple fonts and produce new fonts. Extensive experiments on seven Chinese font datasets show the superiority of the proposed method over several other methods in Chinese font transformation.
Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts

Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.
Potential Maximal Cliques Parameterized by Edge Clique Cover.

Many graph problems can be formulated as a task of finding an optimal triangulation of a given graph with respect to some notion of optimality. In this paper we give algorithms to such problems parameterized by the size of a minimum edge clique cover ($cc$) of the graph. The parameter $cc$ is both natural and well-motivated in many problems on this setting. For example, in the perfect phylogeny problem $cc$ is at most the number of taxa, in fractional hypertreewidth $cc$ is at most the number of hyperedges, and in treewidth of Bayesian networks $cc$ is at most the number of non-root nodes of the Bayesian network. Our results are based on the framework of potential maximal cliques. We show that the number of minimal separators of graphs is at most $2^{cc}$ and the number of potential maximal cliques is at most $3^{cc}$. Furthermore, these objects can be listed in times $O^*(2^{cc})$ and $O^*(3^{cc})$, respectively, even when no edge clique cover is given as input; the $O^*(\cdot)$ notation omits factors polynomial in the input size. Using these enumeration algorithms we obtain $O^*(3^{cc})$ time algorithms for problems in the potential maximal clique framework, including for example treewidth, minimum fill-in, and feedback vertex set. We also obtain an $O^*(3^m)$ time algorithm for fractional hypertreewidth, where $m$ is the number of hyperedges. In the case when an edge clique cover of size $cc'$ is given as an input we further improve the time complexity to $O^*(2^{cc'})$ for treewidth, minimum fill-in, and chordal sandwich. This implies an $O^*(2^n)$ time algorithm for perfect phylogeny, where $n$ is the number of taxa. We also give polynomial space algorithms with time complexities $O^*(9^{cc'})$ and $O^*(9.001^{cc})$ for problems in this framework.