Reviewerly/exHarmony huggingface dataset:
Cantidad adecuada de emojis y caracteres para generar eWOM en Facebook

Currently Retail Food brands understand the importance of social networks in meeting business objectives.Therefore, seek the participation of your clients in these portals through actions such as reactions, comments and shared content (metrics that represent eWOM).Although there are studies that vary the effects of the characteristics of the publications on the performance of eWOM, there are no investigations that identify the appropriate amount of emojis and characters in brand publications to generate greater interaction.Through a negative binomial regression model, the present study establishes the effects of emojis and the extension of the message on user participation on Facebook.The findings reveal that an increase in the number of emojis and characters has positive effects on eWOM actions.However, excessive use of these tools can lead to their decrease.
Towards building a Urdu Language Corpus using Common Crawl

Urdu is the most popular language in Pakistan which is spoken by millions of people across the globe. While English is considered the dominant web content language, characteristics of Urdu language web content are still unknown. In this paper, we study the World-Wide-Web (WWW) by focusing on the content present in the Perso-Arabic script. Leveraging from the Common Crawl Corpus, which is the largest publicly available web content of 2.87 billion documents for the period of December 2016, we examine different aspects of Urdu web content. We use the Compact Language Detector (CLD2) for language detection. We find that the global WWW population has a share of 0.04% for Urdu web content with respect to document frequency. 70.9% of the top-level Urdu domains consist of . com, . org, and . info. Besides, urdulughat is the most dominating second-level domain. 40% of the domains are hosted in the United States while only 0.33% are hosted within Pakistan. Moreover, 25.68% web-pages have Urdu as primary language and only 11.78% of web-pages are exclusively in Urdu. Our Urdu corpus consists of 1.25 billion total and 18.14 million unique tokens. Furthermore, the corpus follows the Zipf’s law distribution. This Urdu Corpus can be used for text summarization, text classification, and cross-lingual information retrieval.
Keeping critical assets safe when teleworking is the new norm

The Covid-19 pandemic has upended almost every aspect of our lives – and work is no exception. With social distancing protocols in effect across the world, tele-commuting (or remote work), has suddenly become the norm for many, rather than the exception. And the flexibility offered by remote work is a major benefit. There are many advantages to remote working. Yet our new professional reality also comes with fresh challenges. Perhaps none of these is more important than the need to safeguard critical corporate assets within an information security landscape that has been profoundly altered, almost overnight, says Gus Evangelakos of XM Cyber.
BIDAL-HCMUS@LSC2020: An Interactive Multimodal Lifelog Retrieval with Query-to-Sample Attention-based Search Engine

In this paper, we introduce an interactive multimodal lifelog retrieval system with the search engine built by utilizing the attention mechanism. The algorithm upon which the system relies is constructed by applying two observations: (1) most of the images belonged to one event probably contain cues (e.g., objects) that relate to the content of queries. These cues contribute to the representative of the event, and (2) instances of one event can be associated with the content and context of such an event. Hence, when we can determine the seed (by leveraging the first observation), we can find all relevant instances (by utilizing the second observation). We also take a benefit of querying by samples (e.g., images) by converting text query to images using the attention-based mechanism. Thus, we can enrich and add more semantic meaning into the simple text query of users towards having more accurate results, as well as discovering hidden results that cannot reach by using only text queries. The system is designed for both novice and expert users with several filters to help users express their queries from general to particular descriptions and to polish their results.
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks

We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show that a careful analysis of the straight-through estimator for threshold gradients allows for a natural range-precision trade-off leading to better optima. Our quantizers are constrained to use power-of-2 scale-factors and per-tensor scaling of weights and activations to make it amenable for hardware implementations. We present analytical support for the general robustness of our methods and empirically validate them on various CNNs for ImageNet classification. We are able to achieve near-floating-point accuracy on traditionally difficult networks such as MobileNets with less than 5 epochs of quantized (8-bit) retraining. Finally, we present Graffitist, a framework that enables automatic quantization of TensorFlow graphs for TQT (available at this https URL ).
Abstractive Summarization for Low Resource Data Using Domain Transfer and Data Synthesis.

Training abstractive summarization models typically requires large amounts of data, which can be a limitation for many domains. In this paper we explore using domain transfer and data synthesis to improve the performance of recent abstractive summarization methods when applied to small corpora of student reflections. First, we explored whether tuning state of the art model trained on newspaper data could boost performance on student reflection data. Evaluations demonstrated that summaries produced by the tuned model achieved higher ROUGE scores compared to model trained on just student reflection data or just newspaper data. The tuned model also achieved higher scores compared to extractive summarization baselines, and additionally was judged to produce more coherent and readable summaries in human evaluations. Second, we explored whether synthesizing summaries of student data could additionally boost performance. We proposed a template-based model to synthesize new data, which when incorporated into training further increased ROUGE scores. Finally, we showed that combining data synthesis with domain transfer achieved higher ROUGE scores compared to only using one of the two approaches.
The Intentional Fast-Forward Narrative Planner

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.
CONTENT OF TEACHING THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN A SCHOOL INFORMATICS COURSE

The article considers the feasibility of integrating artificial intelligence technologies into school education and identifies a problem in identifying didactic elements in the field of artificial intelligence, which must be mastered in a school informatics course. The purpose of the article is to propose variant of the content of teaching the elements of artificial intelligence for the general education of schoolchildren as part of the curricular and extracurricular activities in informatics. An analysis of the psychological, pedagogical and scientific-methodical literature in the field of artificial intelligence made it possible to identify the appropriateness of teaching schoolchildren the elements of artificial intelligence in the framework of a comprehensive informatics course, as the theoretical foundations of modern information technologies. Summarizing and systematizing the learning experience of schoolchildren in the field of artificial intelligence made it possible to form variant of the content of teaching the elements of artificial intelligence, which can be implemented in a compulsory informatics course for 9th grade, as well as in elective classes. The results of the study are the theoretical basis for the further development of the components of the methodological system of teaching the elements of artificial intelligence in a school informatics course. The research materials may be useful to specialists in the field of teaching informatics and to informatics teachers.
CATALYTIC CRACKING USING CATALYSTS BASED ON HETERO POLYACIDS

The paper discusses the issues of catalytic cracking processes and presents the experimental results of the authors with the use of the catalysts based on the natural Shankanai zeolite (Kazakhstan) and heteropoly acids (HPA).SEM analysis of the synthesized catalyst structure indicates the presence of its main elements prevailing on the surface of the zeolite.Also, the intense spectra of the components of the HPA (tungsten and phosphorus) are revealed.Possibly, it is because of the high degrees of dispersion and distribution of PW12-HPA particles over the clinoptilolite surface.In this research an analysis of the final reaction product was carried out by NMR spectroscopy.NMR analysis allowed us to calculate the fraction of each component of the mixture.It was shown that the proton spectrum of the sample contains signals of paraffin compounds, which are the main components of the mixture.Aromatic components are absent.Chemical shifts of up to 1 ppm are characteristic mainly of protons of the methyl groups of saturated hydrocarbons and long alkyl substituents in aromatic systems.In this part of the spectrum, it is also assumed that some CH and CH2 groups of naphthenic fragments are resonated.
Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.
Wireless Physical-Layer Identification Assisted 5G Network Security

The specific emitter identification for physical-layer identification security utilize the exclusive features of the emitter to distinguish permissible emitters for 5G network security. In this paper, we develop an emitter identification based on Contour Stella Image. This paper collects output signal of eight transmitters to validate our method. Contour Stella Image is used to convert signal into a picture and the problem of signal identification turns into picture classification. After getting the Contour Stella Image of the collected dataset. Convolutional Neural Network(CNN) is used to discriminate these picture and identify the transmitters farther. In this paper,we used Alex-Net of CNN. We also use traditional method to distinguish image. The result shows that the method of identifying individuals by transforming signals into pictures is effective.
A Multi-Objective Optimized Service Level Agreement Approach Applied on a Cloud Computing Ecosystem

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients' needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.
Quantum metrology via chaos in a driven Bose-Josephson system

Entanglement preparation and signal accumulation are essential for quantum parameter estimations, which pose significant challenges to both theories and experiments. Here, we propose how to utilize chaotic dynamics in a periodically driven Bose-Josephson system for achieving high-precision measurements beyond the standard quantum limit (SQL). Starting from an initial nonentangled state, the chaotic dynamics generates many-body quantum entanglement and simultaneously encodes the parameter to be estimated. By using suitable chaotic dynamics, the ultimate measurement precision of the estimated parameter can beat the SQL. The sub-SQL measurement precision scaling can also be reached via specific observables, such as collective spin measurement, which can be realized with state-of-art techniques. Our study not only provides insights for understanding quantum chaos and quantum-classical correspondence, but also of promising applications in entanglement-enhanced quantum metrology.
Ensuring the Consistency between User Requirements and Task Models: A Behavior-Based Automated Approach

Evaluating and ensuring the consistency between user requirements and modeling artifacts is a long-time issue for model-based software design. Conflicts in requirements specifications can lead to many design errors and have a decisive impact on the quality of systems under development. This article presents an approach based on Behavior-Driven Development (BDD) to provide automated assessment for task models, which are intended to model the flow of user and system tasks in an interactive system. The approach has been evaluated by exploiting user requirements described by a group of experts in the domain of business trips. Such requirements gave rise to a set of BDD stories that have been used to automatically assess scenarios extracted from task models that were reengineered from an existing web system for booking business trips. The results have shown our approach, by performing a static analysis of the source files, was able to identify different types of inconsistencies between the user requirements and the set of task models analyzed.
Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature

Color image segmentation on cytoplasm Pap smear single cell image identified as normal condition is an interesting subject to study. It is caused by the image limitation and morphological transformation complexity of the cell structural part. Feature analysis on cytoplasm area is an important thing in the process of biomedical image analysis because of the noise and complex background and the bad cytoplasm contrast as well. Thus, an analysis on the feature area on cytoplasm automatically is an urge thing to do to identify Pap smear normal cell image based on feature analysis on cytoplasm area in single cell image identified as normal condition. The purpose of this research is to analyze how far the process color image segmentation on cytoplasm by using normal single cell image is able to produce features of texture and form analysis. To analyze the form of cytoplasm, this research used RGB color to HSV color conversion method which produces metric and eccentricity value. It is then continued to the process of threshold image and counting the wide area by changing threshold into binary image. On the other side, to analyze the texture, this research applied an analysis using gray-level co-occurrence matrix (GLCM) using K-means method to produce contrast, correlation, energy, and homogeneity parameters. The result of the research is the segmentation outcome to Pap smear normal single cell image sample to get metric, eccentricity, contrast, correlation, and energy features.
Feature Selection Techniques for Disease Diagnosis System: A Survey

Reducing dimensionality as a preprocessing phase towards artificial intelligence effectively eliminates unnecessary and repetitive information, raises the efficiency of learning accuracy, and improving output understandability. Nonetheless, the rapid surge in data dimension presents a real challenge to several current collections of features and extraction methods as regards efficiency and effectiveness. Many researchers build and use plenty of feature selection algorithms. But an emerging field of machine learning is still to be based on data mining and method of analysis for information processing. Due to the recent increase in data variation and speed, many feature selection algorithms face serious efficiency and performance problems. Different kinds of feature selection algorithms are available in research such as algorithms based on filters, algorithms based on wrappers, and algorithms based on hybrids. Additionally, a literature study analyzes some of the existing popular feature selection algorithms and also addresses the strong points and difficulties of those algorithms.
Inferring attributes with picture metadata embeddings

Users in online social networks are vulnerable to attribute inference attacks due to some published data. Thus, the picture owner's gender has a strong influence on individuals' emotional reactions to the photo. In this work, we present a graph-embedding approach for gender inference attacks based on pictures meta-data such as (i) alt-texts generated by Facebook to describe the content of images, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we apply a semi-supervised technique, node2vec, for learning a mapping of pictures meta-data to a low-dimensional vector space. Next, we study in this vector space the gender closeness of users who published similar photos and/or received similar reactions. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. Experimental results show that privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable.
Study of Instructional Illustrations on ICTs

Technology has the potential to improve the lives of underprivileged communities from developing regions of the world, especially those with low-literacy skills. Human Computer Interaction (HCI) researchers have conducted studies to understand an effective way to communicate with this group using Information and Communication Technologies (ICTs). Beyond facilitating communication, ICTs have transformed the way low-literate users send money, learn skills and seek references, etc. As instructions (guidelines, rules, laws or warnings) play an important part while using these services on ICTs, finding effective ways to deliver those instructions has become crucial for HCI practitioners, as low-literate users face difficulties in using ICTs with only textual interfaces [1].
NFDI4Culture - Consortium for research data on material and immaterial cultural heritage

Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural identity of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists in Germany. NFDI4Culture aims to fill this gap and create a user-centered, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies. The research landscape addressed by the consortium is characterized by strong institutional differentiation. Research units in the consortium's community of interest comprise university institutes, art colleges, academies, galleries, libraries, archives and museums. This diverse landscape is also characterized by an abundance of research objects, methodologies and a great potential for data-driven research. In a unique effort carried out by the applicant and co-applicants of this proposal and ten academic societies, this community is interconnected for the first time through a federated approach that is ideally suited to the needs of the participating researchers. To promote collaboration within the NFDI, to share knowledge and technology and to provide extensive support for its users have been the guiding principles of the consortium from the beginning and will be at the heart of all workflows and decision-making processes. Thanks to these principles, NFDI4Culture has gathered strong support ranging from individual researchers to high-level cultural heritage organizations such as the UNESCO, the International Council of Museums, the Open Knowledge Foundation and Wikimedia. On this basis, NFDI4Culture will take innovative measures that promote a cultural change towards a more reflective and sustainable handling of research data and at the same time boost qualification and professionalization in data-driven research in the domain of cultural heritage. This will create a long-lasting impact on science, cultural economy and society as a whole.