Reviewerly/exHarmony huggingface dataset:
Imperative Program Synthesis from Answer Set Programs

Our research concerns generating imperative programs from Answer Set Programming Specifications. ASP is highly declarative and is ideal for writing specifications. Further with negation-as-failure it is easy to succinctly represent combinatorial search problems. We are currently working on synthesizing imperative programs from ASP programs by turning the negation into useful computations. This opens up a novel way to synthesize programs from executable specifications.
Persepsi Masyarakat Terhadap Penggunaan Teknologi Dalam Seminar Online Di Masa Covid-19

The presence of corona virus diseases has many impacts on the order of people's lives, many activities that must be done online are no exception with the holding of the seminar. The purpose of this research is to find out people's perceptions towards conducting online or WEB-based seminars. This study uses an evaluative survey metose conducted on June 19, 2020 by distributing questionnaires online. There are as many as 599 people who are willing to give responses with different backgrounds. 307 people work as lecturers, 140 students, 78 teachers and 72 people from the general public. From the survey results it was found that 98.4% said agreeing the WEB-based seminar was very economical and only 1.5% of people disagreed. For ease of sharing information across professions there are 99.7% who say agree and only 0.3% disagree. As many as 99.2% said it could be implemented anytime and anywhere and 0.8% said they did not agree. For sound clarity, there are 98% who say clear and only 2% disagree. And for the ease of implementation there are 99.7% agree that WEB-based seminars are easy to hold and only 0.7% disagree. Thus, the public response to WEB-based seminars is very positive and this is a policy consideration in the future in conducting seminar activities.
UofL Libraries: Lateral Reading: Sources

Practice “lateral reading,” a strategy used by professional fact-checkers to investigate the reliability of online sources.
Your "Labrador" is My "Dog": Fine-Grained, or Not.

Whether what you see in Figure 1 is a labrador or a dog, is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just dog would probably suffice. The real question is therefore -- how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes dog-->gun dog-->retriever-->labrador. To approach this new problem, we first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels, regardless whether they consider themselves experts. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier. This discovery enables us to design a very simple albeit surprisingly effective solution to our new problem, where we (i) leverage level-specific classification heads to disentangle coarse-level features with fine-grained ones, and (ii) allow finer-grained features to participate in coarser-grained label predictions, which in turn helps with better disentanglement. Experiments show that our method achieves superior performance in the new FGVC setting, and performs better than state-of-the-art on traditional single-label FGVC problem as well. Thanks to its simplicity, our method can be easily implemented on top of any existing FGVC frameworks and is parameter-free.
Learning Robust Decision Policies from Observational Data.

We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes. The past policy maybe unknown and in safety-critical applications, such as medical decision support, it is of interest to learn robust policies that reduce the risk of outcomes with high costs. In this paper, we develop a method for learning policies that reduce tails of the cost distribution at a specified level and, moreover, provide a statistically valid bound on the cost of each decision. These properties are valid under finite samples -- even in scenarios with uneven or no overlap between features for different decisions in the observed data -- by building on recent results in conformal prediction. The performance and statistical properties of the proposed method are illustrated using both real and synthetic data.
Traditional VS. Digital Theoretical Approach to Communications

Financial and other ads in the frame of opposition digital/real are analysed here from the standpoints of semiotics. The literature review includes the following topics - theory of digitality; theory of virtuality; some aspects of financial theory (psychology and economy; insurance theory; financial risks’ theory; disaster theory); some aspects of advertising theory (banking and insurances advertising; psychology and advertising - ad appeals; psychoanalysis and advertising;theory of archetypes); semiotics (semiotics and advertising; semantics (semantic field of concept risk; metaphor; litotes); syntactics; pragmatics and theory of drama. The main text of article is structured on the basis of the four semiotic branches – alphabetics, semantics, syntactics and pragmatics. Financial and other ads in the alphabetics’ part are examined from the standpoint of opposition real (traditional)/digital as communicative approach and from the standpoint of concept “continuum”. Some transformations of traditional (analogue) signs into digital ones and the creation of digital ads in the opposition variant/invariant are also studied here. The second part of the article is about digitized ads from the point of view of semantics. Authors concluded that semiotics and linguistics are highly developed sciences with long years of their history. This experience can also be applied in the conditions of digitization.
Extra-budgetary activities of Perm Region libraries: Creativity and efficiency

Off-budget activities of Perm regional public libraries are discussed; statistical data of their financial revenues are analized. Examples of unique paid services offered by the regional libraries are given.
iVIS: Interpretable Interactive Visualization for User Behavior Clusters

To improve Quality of Experience (QoE) and develop new features, understanding users and making a decision to target specific user groups are important to service providers. Based on the internal interviews, we find that service operators have trouble in identifying user behavior characteristics for numerous services in a short time. To address this challenge, we present iVIS, an interactive visualization system that clusters the user behaviors and visualizes the representative behavior patterns. With iVIS, service providers can interpret the user clusters (e.g., heavy/light users), and drill down to a particular cluster to get details interactively. To evaluate our system, we conduct a case study on the log data from the internal data catalog service which enables researchers to browse and use datasets. We found service operators could rapidly interpret representative user behaviors, and discover new behavior types such as frustrated users (i.e., users who only explored datasets for a while but not use them) or testers (i.e., users who used the service for testing) by refining the clustering results.
Principal Component Analysis Using Structural Similarity Index for Images

Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop. As it has been shown that Mean Squared Error (MSE) is insufficient for this task, other measures have been developed with one of the most effective being Structural Similarity Index (SSIM). Such measures can be used for subspace learning but existing methods in machine learning, such as Principal Component Analysis (PCA), are based on Euclidean distance or MSE and thus cannot properly capture the structural features of images. In this paper, we define an image structure subspace which discriminates different types of image distortions. We propose Image Structural Component Analysis (ISCA) and also kernel ISCA by using SSIM, rather than Euclidean distance, in the formulation of PCA. This paper provides a bridge between image quality assessment and manifold learning opening a broad new area for future research.
Best Note Taking Apps For Android & iPhone

If you enjoy taking notes on your smartphone, computer, or tablet. I will tell you what are the best note taking apps that are currently available for ...
STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS

STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS ABSTRACT Parametric and nonparametric techniques are two broad ...
Morpho-syntactically annotated corpora provided for the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)

This multilingual resource contains corpora for 14 languages, gathered at the occasion of the 1.2 edition of the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). These corpora were meant to serve as additional raw corpora, to help discovering unseen verbal MWEs.The corpora are provided in CONLL-U (https://universaldependencies.org/format.html) format. They contain morphosyntactic annotations (parts of speech, lemmas, morphological features, and syntactic dependencies). Depending on the language, the information comes from treebanks (mostly Universal Dependencies v2.x) or from automatic parsers trained on UD v2.x treebanks (e.g., UDPipe).VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). For the 1.2 shared task edition, the data covers 14 languages, for which VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format. Morphological and syntactic information ­­­­– not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.2 (2020). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.2
Perancangan Sistem Informasi Pariwisata Kabupaten Bulukumba Berbasis Geographic Information System (GIS)

Bulukumba Regency is one of the mainstay areas of the tourism sector in South Sulawesi. Some of the tourism objects that are excellent for tourists in Bulukumba Regency are Bira White Sand Beach, Apparalang Beach and Cliffs, Liukang Island, Ammatoa Traditional Area and other tourist objects. Through the design and manufacture of tourism GIS, it is hoped that it can display a picture of the tourist map of Bulukumba Regency so that it is more attractive and can be enjoyed by the wider community. Presentation of information in web form will make it easier for people to access it.The system development methodology used in this study is the Waterfall model Research and Development method. The design of maps displayed in this system uses the Google Maps API, the software used in building this application is PHP (Pear Hypertext Prepocessor) as a programming language, MySQL, XAMPP, and Sublime text.The geographic information system for tourism in Bulukumba Regency is a system that provides information to the public regarding the location of tourist objects in Bulukumba Regency and its supporting facilities. The information presented on this website includes tourist attraction information, descriptions, coordinate points, pictures and travel advice.
LibGuides: Identifying Sources in Works Cited Lists and Bibliographies: Academic Journal or Magazine Article

Works cited lists are very effective ways to find relevant sources for your research. In order to be able to make use of the information in works cited lists, you must be able to identify the kind of source it is: is it a book, a chapter in a book, a jour
Incidencia de la educación tecnológica de las desafiantes oportunidades de la industria 4.0 y 5.0

Globalization, the constantly changing market, technology and other factors bring challenges that need to be addressed in a wise and consistent manner. It is necessary to understand that the globalized world not only brings us closer to the latest technological devices that the market has brought out or to the possibility of traveling without restriction to different destinations around the world, it also brings with it the challenges of being in a market in which we compete with the best in the world, in which the education and preparation of other people is far from the quality and discipline that the average Colombian incorporates or obtains during his or her formation. I do not intend to underestimate the potential and the work done by the entire teaching staff or by the efforts that we as students make in order to carry out any project of which we are a part. The purpose of this essay is to make a brief overview of the structure used by developed countries to educate their students, so that based on this we can make a self-analysis and discover for ourselves those competencies, skills and knowledge that we must develop. This will help us understand that if we want to be an active part of a world that is transforming and becoming more and more technological and digitized, we must understand that the market is becoming more and more complex and there is a prevailing need for trained professionals to take charge.
Human Action Recognition Based on Self- Organizing Map in Surveillance Cameras

In recent years, Deep Learning, a Machine Learning algorithm, has been successfully used in many fields, and there have been reports of such a development in the field of behavior recognition. However, in general, high performance hardware is required to execute Deep Learning models in real-time. In this paper, we propose a surveillance camera system for behavior recognition using a self-organizing map (SOM) based on a time-series inference skeleton extracted from image data acquired by a monocular camera using a Machine Learning algorithm on an edge AI processor. The proposed surveillance camera system can be considered as a hybrid twin system. We performed skeleton extraction on an edge AI processor and evaluated experimentally in terms of processing performance and power consumption, and experimented with and evaluated an action recognition method using time-series inference skeleton information. An experimental evaluation of skeleton extraction on an edge AI processor shows the usefulness of the edge AI processor for use in a surveillance camera system. Experimental evaluation of action recognition by SOM showed that it is possible to recognize basic actions. We also found that each region on the U-matrix needs to be clarified in order to improve the percentage of correct answers.
Quantum addition imparts less disorder than mixing and commutes with incoherent channels

Abstract We prove a generalized version of a previously conjectured inequality by Zhang et al (Zhang et al 2018 Phys. Lett. A 382 , 1516–23) for the quantum addition operation defined by Datta et al (Audenaert et al 2016 J. Math. Phy. 57 , 052202) in the context of proving an entropy power inequality for qubits. We also show that the quantum addition operation commutes with an incoherent channel, which may have possible implications for resource theories of coherence in optical settings.
Future of LLL in a digital context

Welcome to the eucen University Lifelong Learning Open Fora!eucen is launching in 2020 a new initiative: A full month of ULLL presentations.We invite you to join us throughout November - participate in an open activity every day!https://ulll-open-fora.eucen.eu/
A Cross-Validation Approach to Knowledge Transfer for SVM Models in the Learning Using Privileged Information Paradigm

The learning using privileged information paradigm has allowed support vector machine models to incorporate privileged information, variables available in the training set but not in the test set, ...
The Pros And Cons Of Curved Smartphone Screens

Smartphones with curved screens have become quite popular in recent years. This is due to the fact that many smartphone manufacturers are offering a ...
Improving Generalization in Reinforcement Learningusing Skill-based Rewards

Reinforcement Learning is a promising approach to develop intelligent agents that can help game developers in testing new content. However, applying it to a game with stochastic transitions like Ca ...
Single Image Super-Resolution Reconstruction Using Nonlocal Low-Rank Prior

In most practical imaging applications, the images with high resolution is desired, but most of imaging images are generally low resolution in practice, which brings many problems. In this paper, we propose an effective image super-resolution reconstruction model using nonlocal low-rank prior. Firstly, this model uses the single image as data input, and the self-similarity inside the single image is used as prior knowledges to improve the matching degree of similar image patches. Then, the reconstruction progress is modeled with maximum a posterior probability framework. Finally, a nonlocal low-rank regularization is adopted to regularize the reconstruction process, which exploits the local and global information of image to improve the reconstruction effect. Experimental results show that the proposed method has achieved better results than the existing methods.
Bundled Crossings Revisited

An effective way to reduce clutter in a graph drawing that has (many) crossings is to group edges that travel in parallel into \emph{bundles}. Each edge can participate in many such bundles. Any crossing in this bundled graph occurs between two bundles, i.e., as a \emph{bundled crossing}. We consider the problem of bundled crossing minimization: A graph is given and the goal is to find a bundled drawing with at most $k$ bundled crossings. We show that the problem is NP-hard when we require a simple drawing. Our main result is an FPT algorithm (in $k$) when we require a simple circular layout. These results make use of the connection between bundled crossings and graph genus.
On Tree-Based Methods for Similarity Learning

In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods. Building automatically such measures is the specific purpose of metric/similarity learning. In [21], similarity learning is formulated as a pairwise bipartite ranking problem: ideally, the larger the probability that two observations in the feature space belong to the same class (or share the same label), the higher the similarity measure between them. From this perspective, the $$\mathrm{ROC}$$ curve is an appropriate performance criterion and it is the goal of this article to extend recursive tree-based $$\mathrm{ROC}$$ optimization techniques in order to propose efficient similarity learning algorithms. The validity of such iterative partitioning procedures in the pairwise setting is established by means of results pertaining to the theory of U-processes and from a practical angle, it is discussed at length how to implement them by means of splitting rules specifically tailored to the similarity learning task. Beyond these theoretical/methodological contributions, numerical experiments are displayed and provide strong empirical evidence of the performance of the algorithmic approaches we propose.
Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog

Task Oriented Parsing (TOP) attempts to map utterances to compositional requests, including multiple intents and their slots. Previous work focus on a tree-based hierarchical meaning representation, and applying constituency parsing techniques to address TOP. In this paper, we propose a new format of meaning representation that is more compact and amenable to sequence-to-sequence (seq-to-seq) models. A simple copy-augmented seq-to-seq parser is built and evaluated over a public TOP dataset, resulting in 3.44% improvement over prior best seq-to-seq parser (exact match accuracy), which is also comparable to constituency parsers’ performance.
Penerapan Genre Based Approach (GBA) dalam Pembelajaran Bahasa Inggris SMP

As a language skill, English needs to be learned through clear steps through concept building, modelling, and practising processes. It is necessary, then, to find out an appropriate learning model capable of providing those steps. This paper aimed to reflect the teaching and learning process on the theme of Comparing Things and Animals by applying the Genre Based Approach with the Teaching-Learning Cycles (TLC) Model. This research was conducted at Class VIIIF SMP 14 Mataram, Lombok involving 28 students with 2 observers (a School Principal and an English Teacher). Data were collected using various techniques, such as observation and reflection, assignments and tests, focus group discussions (FGD), and documentation. The data were then analyzed descriptively. From the data analysis and learning reflection, it was concluded that (1) the application of GBA with the TLC Model provided students with gradual experiences of concept building, modelling, and practising both in groups and individually; (2) the application of GBA was also able to accommodate 4C elements in 21st Century Learning Skills; (3) there was an increase in the level of mastery learning from 32.14% with an average of 63.28 on the pre-test to 85.19% with an average of 79.67 on the post-test; (4) both observers provide a positive assessment of the learning process by applying GBA with TLC Model.
En undersökning om end-to-end kryptering av SMS med hjälp av PKCS #1

In today’s society, especially after everything that was reported by Edward Snowden when he, during 2013, showed how USA’s NSA worked with global surveillance, there is a great need to keep communi ...
LSC-Tomball: Library Research Guides: Downloading eBooks: ProQuest Ebook Central

This guide will help you find, search and download eBooks from the library's online academic collection of eBooks.
Comparative Study of Prediction of 28 Days Strength Using Fuzzy Logic and Model Tree

Concrete is a construction material which is used in n construction processes as a major stress resistance due to its strength characteristics. Designing a concrete mix is a tough task which includes right proportion of ingredients to be mixed in particular order and in particular environment with the objective to produce concrete mix with specified strength, durability, workability, and as economical as possible. Structural engineering field is full of nonlinear problems. This paper speaks about one of the basic nonlinear problems which is a strength prediction. We have tried to convert the nonlinear problem of strength into a linear problem by using model tree analysis and predicted the strength of various mix proportions using fuzzy logic. The objective of this research work is to study fuzzy logic tool and model tree regression analysis processes for prediction of concrete compressive strength, respectively, and its result comparison. Results of this study states that model tree regression analysis works more efficiently than a fuzzy logic.