Reviewerly/exHarmony huggingface dataset:
Enhancing Multi Exposure Images Using Convolution Neural Network

Due to the poor lighting condition and restricted dynamic vary of digital imaging devices, the recorded photos are usually under-/over-exposed and with low distinction. Most of the previous single image distinction improvement (SICE) strategies modify the tone curve to correct the distinction of an associated input image. Those strategies, however, typically fail in revealing image details due to the restricted data in a very single image. On the opposite hand, the SICE task is often higher accomplished if we will learn additional info from suitably collected coaching information. In this paper, we have a tendency to propose to use the convolutional neural network (CNN) to coach SICE attention. One key issue is the way to construct a coaching information set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this finish, we have a tendency to build a large-scale multi-exposure image knowledge set, that contains 589 in an elaborate way chosen high-resolution multi-exposure sequences with four, 413 images. Thirteen representatives multi-exposure image fusion and stack-based high dynamic vary imaging algorithms are accustomed urge the excellence enhanced footage for each sequence, and subjective experiments are conducted to screen the best quality one because of the reference image of every scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the benefits of our methodology over existing SICE strategies with a major margin.
Tweeting the cosmos: On the bot poetry of<i>The Ephemerides</i>

This article discusses Twitter bots that generate textual outputs for aesthetic effect. It considers in-detail a specific instance, The Ephemerides ( @the_ephemerides ) by Allison Parrish, and presents a critical assessment of how its functional and thematic attributes meditate self-reflexively upon their operational contexts within digital infrastructure. This mode of reading is then presented as having value for analysing generative Twitter bots more widely. The initial balance of this article will concern the broader question of how generative digital writing can be assessed critically, debating key claims made by Simanowski (2011), before presenting a model in which the salient quality of this art form is the distinctly machinic modes of expression it makes available, revealing the software itself to be an active agent in the reading encounter. The remainder of this article will then apply this perspective to a reading of The Ephemerides, considering its meditation on the functional and aesthetic relationship between technical systems that are unobtrusively efficient versus those that are vividly breaking down – with the latter making manifest the contingency that is inherent to the processes of coming to know and act on the world through technology. The goal of this discussion is to consider not only how the generative operations of Twitter bots might be approached fruitfully, but to demonstrate also that such analyses constitute a worthwhile pursuit in the first instance – that even these tightly constrained instances of digital creativity can offer forms expression that reward extended academic criticism.
Alternating Direction Method of Multipliers for Semi-blind Astronomical Image Deconvolution

High resolution astronomical imagery plays a critical role in multiple remote sensing applications. In this work, we introduce a novel post-acquisition computational technique aiming to recover the high-quality versions of blurry and degraded astronomical observations. Additionally, the proposed scheme is able to retrieve significant information regarding the characteristic properties of the blurring kernel, i.e point spread function (PSF). In order to accomplish this goal, we exploit the mathematical frameworks of Sparse Reprentations, and the Alternating Direction Method of Multipliers (ADMM). Experimental results demonstrate the ability of the proposed approach to synthesize high-quality astronomical imagery.
Development of tracking system of moving specific person: Following welfare assist robot production

Recently, the robot industry has seen considerable amount of development in a wide range of robots, including pet robots and nursing care robots. However, robots that track and support specific people under all circumstances have not yet been developed. This paper proposes to create a welfare robot that tracks a specific person and provides him/her with personal care. In this research, we treated tracking systems to recognize and follow the targeted person. And compared with two trackers, KCF was more versatile than particle filter. However, since KCF also weakens the precision with shielding objects, we try to combine the detection equation of the particle filter, and try to take measures such as expanding the detection area when the target person is lost.
Combination of GMM-UBM and DTW for voice command authentication system

In this paper, we present a combination of statistical and template based pattern matching to solve the problem of authentication with very short command words. Same features are used in both methods to reduce the computational weight. The first method uses GMM-UBM (Gaussian Mixture Model with Universal Background Model) which is well known in speaker recognition field, but lacks the ability to model the temporal aspect of speech. The second method provides a remedy for this, with the classical DTW (Dynamic Time Warping) on the cepstrum features. Two scheme of combining the model is explored; firstly with layer design when DTW distance is calculated only if GMM-UBM accepts the speaker, and secondly by weighting the DTW distance using the confidence of GMM-UBM result. With this combination, a 23% and 17% improvement in EER was observed respectively, each with differing characteristics on 3 different error types that is investigated. The experiment was conducted on evaluation set of RSR2015 database part 2, which contains short words meant for command and control task. Performance analysis is done using Detection Error Tradeoff curve (DET) and Equal Error Rate (EER).
Database Independent Analysis of Adverse Events Using Rule-Based Systems

Governments and enterprises need to make decisions and generate reports following the defined regulations and other legislative & normative documents. This is why the decision makers of the public & private agencies analyze the data that are stored mostly in (relational) databases from different service-oriented applications. Separate services and domains generate a different type of databases, which create significant challenges to generate simultaneous reasoning from different databases since these databases may consist of different attributes. In this paper, we present a database independent Rule-based reporting systems (RuleRS) architecture, which integrates multiple databases, in particular, multiple (relational) databases, with a rule reasoner and a rule engine to support producing decisions and generating reports simultaneously according to defined legal norms. We argue that the resulting RuleRS provide an efficient and flexible solution to the problem at hand for reasoning using defeasible inference. Experimental evaluations of RuleRS performance are also conducted for simultaneous reasoning from multiple relational databases.
Chemical Assessment Framework and Ontology

Virtual chemical laboratory teaching helps develop and acquire skills. To build such laboratories, several research ontologies have proposed to describe chemical elements as well as their interaction. As part of the learning process, integrating such ontologies would require adding evaluation items. This article proposes an ontology that will help the learner to self-evaluate on laboratory material, safety notices and the stages through which chemistry practical work is carried out. In the literature, many methodologies address the ontology development issue. To achieve our ontology, we mainly adopt a six-step methodology: Determine the scope of the ontology; consider reusing existing ontologies; list significant terms; define classes and class hierarchy; define properties and facets of classes; and create instances. This paper presents the construction of our ontology, which can be used in many scopes of application. We plan to develop a web and mobile assessment platform to help learners to assess themselves and prepare the report of an experiment.
Privacy and Social Movements

Privacy is an often controversial, misunderstood, and underestimated concept, including on purpose by actors like governments or the Silicon Valley giants of Google, Apple, Facebook, and Amazon (GAFA). Unfortunately, due to today’s technologies, the potential for intrusions into personal privacy have increased exponentially. Even though there are different laws that protect privacy (i.e. GDPR) what is considered to be private by individuals and what is legally protected as private can differ. Different apps have different privacy policies, which leads to the protection of usually limited types of information, but not others. This situation is dangerous for social movements. Hackers and academics have been developing tools like Tor and Signal to encrypt our messages, and protect the right to privacy and anonymity. However, so much more needs to be done, both from a technical perspective, and through advocacy. Individuals, NGOs, activists: Everyone is a potential target of surveillance. In this paper we discuss the impact of privacy violations on social movements and the tools for encrypted messaging currently used by social movements. We show that the misunderstandings around privacy by users and the causes that prevent the developing of better tools for social movements.
The Use Of Crossword Puzzle In Teaching Vocabulary At The Fifth Grade Of SD N Jelupang 2

The study discussed the teaching of vocabulary through crossword puzzle among grade V students of SD N Jelupang 2 in the academic year of 2018/2019. This study used qualitative method because the writer was the main instrument (an observer) and the data gotten was explained the real data of the relationships, activities, situations or materials. The writer also took data from classroom observation, classroom activity, teaching learning process, interviewed to the teacher, students task and vocabulary test. The result of the study concluded that there were improvements of students’ vocabulary and enthusiasm in learning vocabulary. It could be seen from the result of classroom observation that showed better performance, and the result of student was very good. The results of vocabulary test also shown that 56 % obtain excellent, 26 % obtain good , 9 % obtain satisfactory, 6 % obtain poor, and 3 % obtain failure. It indicates that by applying crossword puzzle, the students’ vocabulary has improved. Key words : Teaching, Teaching Vocabulary, Crossword Puzzle.
LibGuides: Ithaka S+R Faculty Survey: Home

This guide presents data gathered from the 2018 Ithaka S+R Local Faculty Survey fielded by the A-State Library
Remote Sensing Image Deblurring Algorithm Based on WGAN

Remote sensing images are blurred due to large and wide imaging, long shooting distance, fast scanning speed, interference from external light, etc. At the same time, because of that remote sensing images have the characteristics of diverse and dense shooting objects, deblurring remote sensing images is a major problem in remote sensing research. Therefore, we propose a remote sensing image deblurring algorithm, which based on WGAN. The algorithm is different from the traditional method in estimating the blur kernel of image. What’s more our method does not require an explicit estimation of the blur kernel, and it implements an end-to-end image deblurring process. We use a WGAN-based deblurring model. First, the training images are processed in pairs. Then, in order to increase the generalization ability, a image of 256 * 256 that is a sub-region cropped at the random position in the original image is chosen as the input image. Finally, to achieve a better deblurring effect, a content loss function and a perceptual loss function are added to the loss function to achieve the specific implementation. The remote sensing image deblurring model trained by the proposed method has achieved better results on the remote sensing image dataset. The experimental results show that the proposed algorithm have better performance than the traditional method in filtering out the blur of remote sensing images, which could optimize the overall visual effect subjectively and improve the peak signal-to-noise ratio of the image objectively.
Chapter 8 The Entrepreneurial Criminal: How Trust Coordinates Illicit Drug Cryptomarkets

This chapter investigates how new technologies of encryption and cryptocurrencies enable entrepreneurial opportunities outside legality in the dark net. Since ventures on illicit dark net markets lack access to the legal system and to law enforcement agencies, they must rely on mechanisms for settling disputes with business partners without the involvement of mediating agencies. To this end, the presence of trust is decisive in coordinating cryptomarket activities. Hence, entrepreneurs on dark net markets utilize technology to gain trust, establishing new ways of drug dealing, with disruptive potential for classic illicit drug markets. Against this background, this chapter shows how technological change affects the identity of entrepreneurs on the dark net. Special emphasis is given to the entrepreneurs’ self-concept, their consumer service, knowledge and capabilities and how, in a holistic view, this development innovates the traditional way of dealing illicit drugs.
Peeking under the Hood: How the Measurement Setup Influences the Video Streaming Behavior

Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.
Possibilities for applied joint speech processing and computer vision systems

This report addresses the current state of the Computer Vision and the Speech Processing fields. It explains some of the most used algorithms in these fields and provides an overview of the current use of these technologies in the market. The objective of this report is to find new possible industrial and commercial applications that use both Computer Vision and Speech Processing.
Online collaborative filtering via fusing the rating consistencies

Collaborative filtering (CF) is one of the key techniques used in online recommender systems, most traditional CF algorithms, however, are designed in the offline settings, hence are not suitable for the systems that need to be adjusted whenever new data arrive. Addressing to the problem, we propose Lefuse, an online collaborative filtering model. Our model is based on a set of identically and independently distributed assumptions on the user–item ratings, with the assumptions, we derive a counterpart set of consistency conditions, and formulate the prediction task as a constrained optimization problem. Specifically, under the Gaussian distribution setting, we derive the closed-form solution to the problem, which leads to a time and space efficient online CF algorithm. In the evaluations, we conduct comprehensive experiments with the proposed model and three other state-of-the-art online CF algorithms on four large scale real rating data sets, all results show the superiority of our algorithm.
Leadership in the Era of High-Hume

This paper tackles the transformation processes taking place in the contemporary society which are related not only to the society complex informatization, but also to a new humanitarian revolution which might have a decisive impact both on a human, as well as on the further development of the whole society. High-Hume represents high humanitarian technologies which appeared due to development of high technologies (high-tech), and form into an independent socio-cultural phenomenon. High-Hume technologies can be defined as the research, analytical, information, organization actions, in which a pattern of their performance and assessment can be traced, but the constituents of this humanitarian pattern do not have clear definitions and are not subject to mathematical assessment. This paper argues that a human who becomes the main resource and a driving factor of the society development occupies the most important place in the information society. At the same time, a human is also the main factor of risk in the contemporary society. In order to mitigate possible negative consequences of the society informatization, a concept of advanced education (High-Ed) is suggested which implements timely preparation of a person for the future and influences mainly on his/her moral values and personal traits. The paper demonstrates that the quantum leadership, basing on three types of thinking: rational, associative, and creative, becomes the most essential and the leading one in the contemporary society.
Heterogeneous Information Network Hashing for Fast Nearest Neighbor Search

Heterogeneous information networks (HINs) are widely used to model real-world information systems due to their strong capability of capturing complex and diverse relations between multiple entities in real situations. For most of the analytical tasks in HINs (e.g., link prediction and node recommendation), network embedding techniques are prevalently used to project the nodes into real-valued feature vectors, based on which we can calculate the proximity between node pairs with nearest neighbor search (NNS) algorithms. However, the extensive usage of real-valued vector representation in existing network embedding methods imposes overwhelming computational and storage challenges, especially when the scale of the network is large. To tackle this issue, in this paper, we conduct an initial investigation of learning binary hash codes for nodes in HINs to obtain the remarkable acceleration of the NNS algorithms. Specifically, we propose a novel heterogeneous information network hashing algorithm based on collective matrix factorization. Through fully characterizing various types of relations among nodes and designing a principled optimization procedure, we successfully project the nodes in HIN into a unified Hamming space, with which the computational and storage burden of NNS can be significantly alleviated. The experimental results demonstrate that the proposed algorithm can indeed lead to faster NNS and requires lower memory usage than several state-of-the-art network embedding methods while showing comparable performance in typical learning tasks on HINs, including link prediction and cross-type node similarity search.
Automatic Pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence

Nowadays, image segmentation performs a large-scale contribution in the medical images. Magnetic resonance (MR) image is a good substantial strategy during the fetal and neonatal periods that make earlier diagnosis of individual tumor analysis. It is used as topmost image investigation model for pattern discovery of brain tumor. The aim of our study is to discriminate the unique images related to every one of the patterns of the brain tumor on neonatal MRI. Our proposed system consists of four steps. The first step is to explore the acquisition of the neonatal MRI sequence. The second step performs segmentation of the locality of abnormality. The third step is the segregation of tumor from edema and its enhanced sequences. The fourth step is the estimation of the drawn area of tumor and calculation of the mean. Our proposed method successfully detects the tumor area in the given neonatal MRI images.
The role of computer modeling in training specialists of the food direction

Methods and tools, as well as platforms on which information technologies are implemented in the educational process, are now quite diverse. Their choice is closely related to the solving problems. Information technologies, realized in the form of virtual laboratory works, are indispensable tools for modeling situations in the absence of the possibility of their real reconstruction. Their use in the educational process develops creative thinking, raises the motivation to study the subject and forms the research culture of students. They are ideal for providing research work in universities. The use of virtual works solves not only the financial problems of the educational institution for the acquisition of the instrument base and reagents, but also ensures the safety of experiments with potentially dangerous objects. All the developments are oriented at the individual work of students and contain methodological materials that enable them to be used in a real educational process. Virtual laboratory works are used in most educational institutions in Russia, and their versions in other languages are the leading electronic resources abroad.
Knowledge Base Error Detection with Relation Sensitive Embedding

Recently, knowledge bases (KBs) have become more and more essential and helpful data source for various applications and researches. Although modern KBs have included thousands of millions of facts, they still suffer from incompleteness compared with the total amount of facts in real world. Furthermore, a lot of inaccurate and outdated facts may be contained in the KBs. Although there have been many studies dealing with incompleteness of the KBs, very few of works have taken into account detecting the errors in the KBs. Broadly speaking, there are three main challenges in detecting errors in the KBs. (1) Symbolic and logical form of the knowledge representations cannot detect the inconsistencies very well on large scale KBs. (2) It is hard to capture the correlations between relations. (3) There is no golden standard to learn or observe the patterns of inaccurate facts. In this work, we propose a Relation Sensitive Embedding Approach (RSEA) to detect the inconsistencies from KBs. We first design two correlation functions to measure the relatedness between two relations. Then, a dynamic cluster algorithm is presented to aggregate highly correlated relations into the same clusters. Finally, we encode discrete knowledge facts with effects of correlated relations into continuous vector space, which can effectively detect errors in KBs. We perform extensive experiments on two benchmark datasets, and the results show that our approach achieves high performance in detecting incorrect knowledge facts in these KBs.