Reviewerly/exHarmony huggingface dataset:
Análisis de la percepción de calidad del servicio y la satisfacción de los estudiantes de Comunicación Social con respecto al proceso de matrícula académica en la Pontificia Universidad Javeriana

Registering courses and organizing the academic schedule is a process that every student has to do every semester. The social communications faculty of the Pontificia Universidad Javeriana has a special particularity: their curriculum has several options when it comes to choosing the courses students would have to take. This is where the problem begins, in many occasions, there are way more demand on the quotas of the courses than there is supply. Students manifest their dissatisfaction by word of mouth, this is why this work will analyze the service percieved quality and the satisfaction of the students of the Pontificia Universidad Javeriana regarding the process of registering academic courses and managing the class schedule.
Effective Approach to Joint Training of POS Tagging and Dependency Parsing Models

We propose a joint model for POS tagging and dependency parsing. Our model consists of a BiLSTM-CNN-CRF-based POS tagger [26] and a Deep Biaffine Attention-based dependency parser [24]. A combined objective function is used to jointly train both models. Experiment results show very competitive performance on several languages of the Universal Dependencies (UD) v2.2 Treebanks [11].
Analysis and Application of Financial News Text in Chinese Based on Bert Model

The financial news text is distinguished from other corpus by its authoritativeness and characteristics. On the basis of customized text cleanup, fine-tune Bert model, segmentation and Chinese enterprise name database, we propose a new workflow framework in which realizing classification of news emotion, classification of financial negative event and related entity recognition for financial news text in Chinese. In general, Accuracy of the three types of models has compliance with application requirements. Subsequently, the financial news text in Chinese analysis system is designed and implemented on the purpose of full-cycle text analysis. The system consists of financial news collection module, financial news analysis module and financial news standardization and persistence module. Asynchronous design between financial news collection module and financial news analysis module ensures the scalability and sustainable optimization of system.
Business Networks and Small and Medium Enterprise Growth in Rwanda

Networking has been found to be an important source of SME’sSmall and Medium Enterprises (SMEs) growth and development, both in developed and developing countries. This study discusses the importance of networking in the context of Rwanda’s SMEsSmall and Medium Enterprises (SMEs). Literature on firm growthFirm growth and business networksNetworks discusses networksNetworks that the SMEsSmall and Medium Enterprises (SMEs) are involved in, and how these networksNetworks help them grow. This study which is set in the context of a developing country follows a case study approach in studying SMEsSmall and Medium Enterprises (SMEs). Using a cross-case analysis, its findings show that networksNetworks remain important and contribute toward the growth of SMEsSmall and Medium Enterprises (SMEs) because they help them address most of their identified growth challenges. However, networks can address some but not all growth challenges and they can address these challenges differently for different SMEsSmall and Medium Enterprises (SMEs). One case in this study brought together players that would not have been able to meet without the networksNetworks. Hence, the study suggests that complementary solutions need to be found for addressing all growth challenges faced by SMEsSmall and Medium Enterprises (SMEs) holistically.
Study on Online Service Experience of Public Cultural Learning Space: Using the Self-service Seat Selection System of University Library as an Example

The creative thinking of contemporary social context, with the Internet of things big and mobile data services to support Internet + technology, creates our public service model of digital and online development prospects, represented by the library of the public space of cultural learning pattern is facing a profound transformation based on the existing public cultural learning space online service experience processes for research, summarizes the basic characteristics and development trend, and by introducing a SUS analysis and user interviews, precise aiming students user community usage scenarios and specific requirements; Emotional design is integrated into the online service of public cultural learning space, aiming to construct specific paths and guidelines for the interface and emotional connection between students and students in public cultural learning space.
The Training of Specialists in Siberia (Eighteenth Century through the Beginning of the Nineteenth Century)

This article delves into how specialists were trained in Siberia, and it reveals the roles of home education and of specialized educational institutions in seafaring and mining. The authors describe the tutoring system for people who received their education in Europe and Siberia. They also discuss the significance of internships at specialized enterprises in Europe.
Split First and Then Rephrase: Hierarchical Generation for Sentence Simplification

Split-and-rephrase is a strategy known to be used when humans need to break down a complex sentence into a meaning preserving sequence of shorter ones. Recent work proposed to model split-and-rephrase as a supervised sequence generation problem. However, different from other types of sequence generations, the task of split-and-rephrase inevitably introduces overlaps across splits to compensate for the missing context caused by separating a sentence. Serving as the baseline of this task, the vanilla SEQ2SEQ model usually suffers from inappropriate duplication because of the lack of a mechanism to plan how the source sentence should be split into shorter units. This work demonstrates that the problem of inappropriate duplication can be tackled by explicitly modeling the hierarchy within split-and-rephrase: Our model first introduces a separator network capable of selecting semantic components from the source sentence to form a representation for each split. Then, a decoder generates each split on the basis of its representation. Analyses demonstrate that with the aid of the separator, a model can effectively learn attention to avoid duplication and detect clues for splitting a sentence. Experimental results on the WikiSplit corpus show that our model outperforms the non-hierarchical SEQ2SEQ model by 1.4 points in terms of duplication rate and by 0.3 points in terms of coverage rate.
Security of Image File with Tiny Encryption Algorithm And Modified Significant Bit Pseudo Random Number Generator

Abstract One of the negative impacts of the development of the era of digital technology on communication media today is the damage and loss of information data. Access to communication media is a means of delivering information that is vulnerable to manipulation. Therefore, cryptography is used to keep the information confidential. Cryptography is an art in encoding messages into other forms so that the message cannot be read by unauthorized parties. Tiny Encryption Algorithm (TEA) algorithm is a symmetric cryptographic algorithm in which the key is divided into four subsections and uses 128 key bits and operates 64 bits of data blocks and then divided into two blocks into 32 bits during the encryption process. In order to make the encryption stronger, it is combined with steganography where the steganography method used is Least Significant Bit (LSB) which has the function of inserting data into the last bit of other data. Linear Congruential Generator (LCG) is a random number generator in the LSB method where its function is as a flow of inputting data that has been encrypted randomly into another data as a cover so that the combination of these methods becomes Modified Least Significant Bit Pseudo Random Number Generator. The combination of cryptography and steganography is able to create a security in sending a confidential message to be safer.
Genetic Approach based Image Retrieval by Using Histogram and Textual Features

Digital content increases in the world where image plays an important role. So getting relevant set of content as per user requirement becomes a major issue. Hence the researcher works on this problem and proposed number of approaches. This paper proposed a genetic algorithm based retrieval of images. Here visual feature histogram was fetched during training with attach annotation from the image. Both this feature value was passed in the genetic algorithm having twice crossover and population updating in one iteration. The resultant of the genetic algorithm is cluster center images, so the dataset is arrange in cluster structure. Experiment was done on real images and results shows that the proposed model was better by fetching relevant images as per user requirements.
Implementation of Discovery Based Learning and Talking Chips to Improve Attitudes Appreciating the Others Opinion

Abstract This study aims to determine results of implementation the discovery based learning models and talking chips to improving the attitude of respecting the others opinion on geography learning. To achieve these objectives, this study design was used by the Classroom Action Research (CAR). This research activity is a series of Lecturer Assignment Programs in Schools (LAPS). This research was conducted on geography learning in class XI IPS 5 of SMAN 1 Selong, East Lombok. The research carried out included six meetings on basic competencies in understanding regional conditions and Indonesia’s strategic position as the world’s maritime axis. From the six times the meeting was divided into three cycles. The method of data collection is done by observing the students learning activities. Data analysis in this study was done by descriptive analysis of percentages. The results showed an increase in the average percentage of attitudes respecting the opinions of others in each cycle. In the first cycle the average percentage showed a score of 69.5%, while in the second and third cycles it increased to 76% and 84%. So it can be concluded that the implementation of the discovery based learning model and talking chips succeeded in increasing the attitude of respecting the others opinion for students in learning geography.
Labeling

Living in the generation of data explosion, how would socialized gender classification reflect to our gender norms? How might it provoke human introspection on both gender boundary and gender bias? In this paper, we present Labeling, a project that has combined a critical design mindset with reflective design that described both generated collection of gender labeling and novel application of algorithmic bias. By creating an immersive context as a reflective medium as our present stage, this project aims to explore introspective contemplation on gender bias with participants, and questioning the existential meaning of gender boundary.
An Analysis of Multi-organ Segmentation Performance of CNNs on Abdominal Organs with an Emphasis on Kidney

Manual hand tailored biomedical image segmentation of different organs is a time consuming and laborious task. However, for the last decade or so, many deep learning convolutional neural network (CNN) models have emerged claiming to have close to human level results on biomedical image segmentation of different type organs while automating the task. Multi-organ segmentation is the process of segmenting multiple organs of the same patient. This offers a convenient solution to automation by providing segmentation of multiple organs at a time. Since 2015, we seen massive improvements of deep CNNs. This has led to better multi-organ segmentation architectures and has influenced the study of multi-organ segmentation. In this paper, we analyze the performance of different multi-organ segmentation studies. Our main focus was kidney, spleen and pancreas. We emphasized on kidney with the believe that we will see an increase in kidney segmentation tasks and challenges. We found that multi-organ segmentation architectures have been improving over time and are performing quite well. However, we also found that there is substantial performance variance across the different studies even after using the same architecture and datasets on those studies.
The Crime of Identity Theft as an Aftermath of the Development of Modern Technologies – Legal and Criminological Aspects of the Phenomenon in Poland

Abstract The problem of identity theft is current all over the world. Nowadays we can even observe a tendency for an increasing occurrence of this kind of behaviour. The rapid development of new communication tools and the Internet has made it easier to commit a crime of identity theft than ever before. Despite this seemingly obvious trend in the world, in Poland since 2011, when this phenomenon was criminalized, convictions for identity theft are still one of the rarest situations. Identity theft as a proportion of general identified offences in 2017, according to Police statistics, was only .30%. The paper will present selected legal aspects of identity theft from the Polish Criminal Code, as well as criminological analysis of the phenomenon in Poland. The results of research on identity theft conducted in the District Court in Białystok on court files that ended in a final court judgment covering the years 2011–2016 will also be presented. In the course of the research process, seven cases were found and examined. The analysis was enabled by using a questionnaire, which included forty-nine questions grouped into categories, i.e. characteristics of the perpetrator of the crime, characteristics of the victim, description of the committed act, as well as the course of the pre-trial and judicial proceedings. In the paper, an attempt will be made to find an answer to the question of why the dark number of identity theft crimes in Poland is still great, and this will help characterize identity theft in the area of Białystok.
A Fast Attitude Estimation Method Using Homography Matrix

In this paper, a new algorithm is proposed to calculate the attitude estimation from two images. It is based on a new single homography matrix decomposition, which is faster than the traditional method. It provides a detailed analysis of the error propagation of the method. The method uses a set of image recovery paths, and the plane parameters can be automatically calculated by reference image before navigation. The main contribution is to extract all the plane parameters by a simple and reliable method, and to use these information by rapid single strain decomposition in navigation. The experimental results show that the method has good performance.
Automation Framework for testing Dynamic Configurable tool

For the success of any software system thorough testing and verification is very important. The acceptance of the software by the users and the term for usage depends on how well it works. For this purpose testing is an essential step. However, testing to find defects or bugs is time- consuming, expensive and has a high possibility of human error. In Automated testing, software tools are used to run detailed, repetitive, and data-intensive tests automatically. In this work we focus on identifying best tool for testing a Dynamic and Configurable web app. We provide a thorough literature survey of the various testing tools available for testing MEAN (Mongo Express Angular Node) applications. As a part of this work an Automation Test Suite is developed to test Dynamic and Configurable MEAN application.
Stochastic wavelets from minimizers of an uncertainty principle: An example

This paper proposes a method through which a family of wavelets can be obtained. This is done by choosing each member based on a random variable. The method is preferred in situations where a single mother wavelet proves inadequate and an evolving sequence of mother wavelets is needed but a priori the next member in the sequence is uncertain. The adopted approach is distinct from the way spatiotemporal wavelets are used or even the way stochastic processes have been studied using spatiotemporal wavelets.
Cultural Heritage Content Management System by Deep Learning

This research aims to develop a cultural heritage information management system with deep neural network. The cultural heritage information in case of Thai's architecture was used. The main contribution of this research was to develop an algorithm for retrieving information from image for telling story inside that image. The interesting information inside the image will be retrieved to present to the interested people who is interested in its contents. The development consists of telling stories from image. The appearance of the shape inside image can be used to distinct characteristics of image for example the era, architecture and style of image. The architecture was created including the story of the archaeological site through the learning of machine learning and image processing. The experimental results for a cultural heritage information management system with deep neural network was analyze by using the classification results of the proposed algorithms to classify era and architecture of the tested image. To test the performance of the purposed algorithms, images from the well-known historical area in Thailand were used which are image dataset in Phra Nakhon Si Ayutta province, Sukhothai province and Bangkok. The confusion matrix of the proposed algorithms gives the accuracy 80.67%, 79.35%and 82.47% in Ayutthaya era, Sukhothai era and Rattanakosin era. Results show that the proposed technique can efficiently find the correct descriptions compared to using the traditional method.
Is the Onion Router a Secure Network?

This paper attempts to answer the question of how government agencies use The Onion Router, or Tor, in conducting investigations. The essay observes that Tor is free open-source software that can be used by anyone who desires to communicate with others in a secure manner. In surveying the literature, it is found that Tor can be employed for both legal and illegal purposes. As the major financial contributor, Tor is used by government to secure its communications. Because Tor by bad actors, and because many of the Tor relays are operated by government agencies, Tor has been successfully employed in catching unsuspecting criminals. The conclusion of this exposition is that if one wants to communicate securely with someone else, and not be visible to government agencies, Tor should not be the vehicle of choice. There are alternative modes of communication that can thwart the risk of government surveillance.
Using Cognitive Services within CPS/SCADA Systems Federations - Concepts, Research Areas and Challenges

Artificial intelligence has entered all the underpinnings of modern science to such an extent that any field with a pretense of progress is bound to make room for this chapter. It is a cornerstone of modern applied science. This article will circumscribe a few fundamental aspects which characterize the AI universe, highlighting the important features thereof. The field of applications for CPS/SCADA has been significantly boosted by the integration of AI within its most basic structures. An important feature of the development and integration of AI in the modern world of software is brought about by the providers of AI services which allow for their integration, with ease, in the SCADA application as well as in many other applications. In the last section of the article, we discuss aspects related to the most important suppliers of this type of service, those belonging to the present-day.
A Machine Learning Platform in Healthcare with Actor Model Approach

Today Information Technology is strongly invaded by disciplines known as Big Data and Machine Learning which pervasively play a key role in all sectors involved in the current digital revolution. Cloud Computing now allows excellent computational resources. The requirement to obtain near real-time results emerges. The model with actors plays its primary role. This paper describes the state of the art of Big Data architectures for near real-time processing, and then describes the model with actors. A possible solution is to offer the programmer a level of abstraction, independent of the domain being dealt with, which makes it possible not to deal with the technical aspects of scalability, competition and interaction with the various Big Data frameworks adopted, making use of the actor-based programming paradigm. As a test of the platform, a case study was implemented in the healthcare domain. The study of an analysis model is presented to assess the effectiveness of a wearable sensor in identifying elderly patients with Parkinson’s disease (PD), “use case” Tele Parkinson with no history of falls that will undergo falls in the following 12 months.
Discovering Mutated Motifs in DNA Sequences: A Comparative Analysis

Locating motifs in DNA sequences is a traditional conjunctional and challenging problem in the discipline of bioinformatics. Motif refers to the biologically functional short, recurring common sequence pattern in DNA strands involved in important processes taking place at the genetic level in an organism. Motifs are the small set of immunity genes present in the DNA sequences as a binding site and turn on whenever the organism gets infected. Motif discovery is integral to problems such as antibody biomarker identification and transcription factor binding sites (TBFS) in the field of genetics and holds greater importance to enable advances in understanding human genetics, social intelligence, biology and health. This problem has been broadly studied over the years, but the complexity and optimality of most of the existing algorithms are still very high. Recently, this field of bioinformatics has grown significantly, and many algorithms have been proposed to solve this problem. However, high complexity is the most challenging aspect of this problem which still grabs the attention of many researchers. This paper analyzes the several algorithms mainly on the basis of their computing models and optimality of the solution and presents the critical reviews of the methods adopted for finding the planted motif. These algorithms can also be used for string matching, data mining and pattern detection, etc.
Efficient Image Watercolorization Based on Smart Phones

Image watercolorization is a hot research topic in the field of non-photorealistic rendering. There are many methods proposed to simulate the various features of watercolor painting including edge darkening, pigment dispersion, turbulence, and paper texture. However, almost all of them are implemented for desktop systems and cannot be directly transplanted to mobile devices. From the view of resultant effects and computing efficiency, an image watercolorization framework is proposed for smart phones, which employs several existing image processing algorithms to simulate the visual features of watercolor painting. Finally, the method is optimized in parallel to improve the efficiency. The experiments show the presented method has good performance.
Constructions of Lightweight MDS Diffusion Layers from Hankel Matrices

Maximal distance separable (MDS) matrices are used as optimal diffusion layers in many block ciphers and hash functions. Recently, the designers paid more attention to the lightweight MDS matrices because it can reduce the hardware resource. In this paper, we give a new method to construct the lightweight MDS matrices. We provide some theoretical results and two kinds of 4 × 4 lightweight Hankel MDS matrices. We also prove that the 2s × 2s involution Hankel MDS matrix does not exist in finite field. Furthermore, we searched the 4 × 4 Hankel MDS matrices over GL(4, F2) and GL(8, F2) that have the better s-XOR counts until now.