Reviewerly/exHarmony huggingface dataset:
Effect of (Mobile Based Learning Program) on Postgraduate Nursing Students’ Satisfaction and Attitudes in Faculty of Nursing Damanhour University

Technology is evolving more rapidly than ever. The younger generations, including nursing students, live daily lives equipped with extremely advanced mobile technology, challenging nursing educators to integrate mobile technology in education to increase the level of involvement. Aim of the study: is to determine the effect of Mobile Based Learning Program on postgraduate nursing students’ satisfaction and attitudes in faculty of nursing Damanhour University, Egypt. Method: A quasi -experimental one group pretest-posttest was adopted. The study was conducted at Faculty of Nursing Damanhour University, Egypt. Purposeful sample, all post graduate nursing students (N = 36), registered at three department Administration, Community health nursing & Nursing Education departments. The study was done during the academic year (2018-2019). Two tools were used: Tool I: Students Satisfaction Scale to identify students’ satisfaction level. Tool II: Mobile Learning Attitude Scale (MLAS) to investigate students’ attitudes of mobile learning in their education. Results: The study reveals that postgraduate nursing students, were satisfied and positive attitude after utilizing MBL. A significant positive intermediate correlation was found between different phases of the study, between pre & post satisfaction test, also between pre & post attitude test, and between post satisfaction and attitude test. Conclusion: Post graduate nursing students exhibit more satisfaction and positive attitude after utilizing MBL. Recommendations: More workshops are needed for both teachers & students in utilizing more mobile application to enhance learner motivation in learning.
Photo Composition with Real-Time Rating

Taking a photo has become a part of our daily life. With the powerfulness and convenience of a smartphone, capturing what we see may have never been easier. However, taking good photos may not be always easy or intuitive for everyone. As numerous studies have shown that photo composition plays a very important role in making a good photo, in this study, we, therefore, propose to develop a photo-taking app to give a real-time suggestion through a scoring mechanism to guide a user to take a good photo. Due to the concern of real-time performance, only eight commonly used composition rules are adopted in our system, and several detailed evaluations have been conducted to prove the effectiveness of our system.
Handling Crimes of Omission by reconciling a criminal core ontology with UFO

Whereas ordinary legal documents are deployed as text documents for human consumption, AI & Law focuses on tackling the challenges surrounding the development of legal ontologies to assign meaning to legal provisions. We have witnessed an explosion of legal domain (and application) ontologies loose ly based on sustainable theories, harming reusability. Fortunately, foundational ontologies foster interoperability between these domain ontologies, providing context-independent concepts that are usable and reusable across multiple domains. In particular, legal ontologies require this “conciliation” with other foundational ontologies, given the inherent heterogeneity of the domain, such as the criminal domain. This paper describes a core legal ontology, built top-down from the UFO foundational ontology, engineered using the positivist legal theory of Hans Kelsen, fostering reusability across a plurality of the criminal field. The UFO proposal makes it possible to evaluate and leverage the conceptual quality of class hierarchies and concept taxonomies through stereotyped modeling primitives and constraints. The latter regulates how the primitives can be combined to support the design of ontologically well-founded conceptual models. This study was conducted to further highlight the depiction of Crimes of Omission, which have not yet been properly explored in the available literature. Although the conceptualization is tied directly to the Brazilian legal domain, it can be applied, with slight adaptations, to other legal systems around the world that follow the Roman-Germanic (or Civil Law) system and Kelsen’s legal theory. Through these ontologies, it is possible to perform lawsuit simulation, such as the identification of a crime and imposition of penalties. At the end of this paper, we detail these possibilities for future work.
Structural transformation of Sub-Saharan Africa: Does the quality of institutions matter in its industrialization process?

Structural transformation seems to have bypassed the secondary sector in Africa. Unfor-tunately, the national experiences of “factory less” development are very scarce and idio-syncratic to serve as a model (Cadot et al., 2016). This study contributes to a better un-derstanding of the structural transformation of Sub-Saharan African countries by evaluat-ing the impact of institutions on their industrial performances. Data used to estimate the different dynamic panel models specified within the period 1997-2016 are from the Worldwide Development Indicators and the World Governance Indicators. The results of this study shed light on the role of institutions as key determinants of industrial perfor-mances of African countries. The strong relationship found between the two variables is robust to various specifications and institutional measures. The following institutional measures seem to have the highest impacts on industrialization: Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality and Rule of Law. Improving the quality of African institutions will therefore allow the continent to perform better than its current 1.6% share of the world wide’s manufacturing value add-ed.
Improving the Personalized Recommendation in the Cold-start Scenarios

Recommender systems generate items that should be interesting for the customers. However, recommenders usually fail in the cold-start scenario - when a new item or a new customer appears. In our work, we study the cold-start problem for a new customer. For a cold-start customer we find the most similar customers and use a “their” pre-trained collaborative filtering model to recommend. We compare several recommendation approaches and similarity metrics to analyze the accuracy and computational performance.
Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.
Permutable Cut-and-Choose Oblivious Transfer and Its Application

Oblivious transfer (OT) is a significant primitive with wide use in secure two-party computation, private set intersection private and other cryptographic schemes. In the past ten years, different variants of OT primitive like cut-and-choose OT (CCOT) and outsourced OT (OOT), have been proposed so as to satisfy various emerging models. In this paper, we firstly propose and formalize a new primitive called permutable cut-and-choose OT (PCCOT) which generalizes the original CCOT functionality. Furthermore, we construct an efficient PCCOT protocol in the presence of malicious adversaries using the Decisional Diffie-Hellman (DDH) hard assumption. It is worth mentioning that we apply the PCCOT primitive to the efficient construction of secure wildcard pattern matching (WPM) protocol. The WPM functionality allows a party to determine the locations of its pattern with wildcard characters occurs in a long text of another party while revealing nothing to either party in addition to the length of their own inputs. Our proposed secure WPM protocol via PCCOT is secure against semi-honest adversary with 2 rounds and has identical communication cost as the the state-of-the-art scheme.
Ekybot: Framework Proposal for Chatbot in Financial Enterprises

The high proportion of customers of financial enterprises such as banking or insurance have forced to think in new forms to attend the customers. Chatbots are modern and useful tools with which customers are attended using natural language. In this work, we present a financial chatbot called Ekybot, which uses different channels as Skype and Facebook. Ekybot assist customers and enhanced services of the financial enterprises. The most interesting finding of this work is the framework that was developed on a variety of cognitive services.
A short note on decomposition and composition of knowledge

Decomposition and subsequent composition of objects help us to better understand and handle objects, especially in the case when the object in question is too big or complex. This general statement is fully valid with reference to human knowledge. When passing knowledge on to students or colleagues (whether orally or in writing), there is no other possibility than to do it piece by piece. This paper contributes to a better understanding of the situations when knowledge can be decomposed. Using an abstract mathematical apparatus, we reveal what a suitable decomposition of knowledge is and suggest axioms under which the knowledge can be reconstructed from its pieces.
An Ultra-short-term Forecasting Model for High-resolution Solar Irradiance Based on SOM and Deep Learning Algorithm

Solar irradiance forecasting is prerequisite for step-wise photovoltaic (PV) power predictions. In ultra-short-term time scale, the strong and diversified volatility of high-resolution irradiance data may severely restrict the accuracy of common forecasting models, which can be tackled through classified modeling and deep learning algorithm. In this paper, an ultra-short-term forecasting model for high-resolution solar irradiance data is proposed. The self-organizing mapping (SOM) is applied to realize data clustering and label prediction through the topology structure of irradiance sequence, then the deep learning algorithm is applied to establish the classified forecasting models corresponding to predicted labels. The method proposed in this paper contains 3 steps: firstly, the irradiance sequence with a time resolution of 5 minutes is divided into irradiance segments by per hour, and then are normalized. Secondly, the irradiance segments are classified based on SOM and the label of the data to be predicted is determined according to the topological structure of SOM. Thirdly, the data with the same label transformation is found to establish classified forecasting model based on the deep learning algorithm. A case study adopting the solar irradiance data from Sioux Falls, South Dakota, shows the proposed method presents better accuracy than direct prediction at different forecast time scales, and the deep learning algorithm has better forecasting performance.
Asymmetrical Conversations

According to Mitchell Green, speech act theory traditionally idealizes away from crucial aspects of conversational contexts, including those in which the speaker’s social position affects the possibility of her performing certain speech acts. In recent times, asymmetries in communicative situations have become a lively object of study for linguists, philosophers of language and moral philosophers: several scholars view hate speech itself in terms of speech acts, namely acts of subordination (acts establishing or reinforcing unfair hierarchies). The aim of this paper is to address one of the main objections to accounts of hate speech in terms of illocutionary speech acts, that is the Authority Problem . While the social role of the speaker is the focus of several approaches (Langton 2018a, 2018b; Maitra 2012; Kukla 2014; Green 2014, 2017a, 2017b), the social role of the audience has too often been neglected. The author will show that not only must the speaker have a certain kind of standing or social position in order to perform speech acts of subordination, but also the audience must typically have a certain kind of standing or social position in order to either license or object to the speaker’s authority, and her acts of subordination.
Algebraic Operations on Spatiotemporal Data Based on RDF

In the context of the Semantic Web, the Resource Description Framework (RDF), a language proposed by W3C, has been used for conceptual description, data modeling, and data querying. The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of spatiotemporal RDF algebra has just started and still needs further attention. This paper aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. To accomplish our study, we defined a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations were investigated. We defined five types of graph algebras, and, in particular, the filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, we put forward a spatiotemporal RDF syntax specification to help users browse, query, and reason with spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the spatiotemporal RDF semantics and provide a number of advanced functions for building data queries.
Towards A Robust Morphological Analyzer for Kunwinjku

Kunwinjku is an indigenous Australian language spoken in northern Australia which exhibits agglutinative and polysynthetic properties. Members of the community have expressed interest in co-developing language applications that promote their values and priorities. Modeling the morphology of the Kunwinjku language is an important step towards accomplishing the community’s goals. Finite State Transducers have long been the go-to method for modeling morphologically rich languages, and in this paper we discuss some of the distinct modeling challenges present in the morphosyntax of verbs in Kunwinjku. We show that a fairly straightforward implementation using standard features of the foma toolkit can account for much of the verb structure. Continuing challenges include robustness in the face of variation and unseen vocabulary, as well as how to handle complex reduplicative processes. Our future work will build off the baseline and challenges presented here.
Improving Message Embedding by using some Attributes of Color Image

In this paper, we are using enhancing feedback control on a new continuous 4D autonomous hyper chaotic system proposed by Sadiq A. Mehdi and A. Hayder, Qasim [Analysis of a New Hyperchaotic System with six cross-product nonlinearities terms, 2017], this system has three critical points employs ten terms include six quadratic cross-product nonlinearity terms, We notice that when we apply any linear control method that relies on a single unit control added to the system, the system behavior in this case cannot control it, so we applied enhancing linear feedback control at origin and we noticed that a necessary condition for suppression is getting positive feedback coefficient.Theoretical analysis and numerical simulation check the validity of the results obtained.
Preparatory Component for Adoption E-Voting

General election is a general activity conducted by democratic countries. The process of conventional general election is highly risky, among others resulted in controversy and requires a relatively great amount of cost. There are many countries that try to shift using e-voting technology in general elections. Large democratic countries that were successful in applying e-voting were India and Brazil, and some that were unsuccessful including England and the Netherlands. This study proposes a comprehensive research, assessment and development scheme for the readiness of the implementation of e-voting so that in the future there will be no obstacles and will end successfully.
Visual Terrain Classification Methods for Mobile Robots Using Hybrid Coding Architecture

Visual terrain classification can provide crucial and important information for motion control and autonomous navigation for mobile robots in complex terrain environment, becoming an important but challenging task. This paper uses a novel hybrid coding architecture, Deep Filter Banks (DFB), combining stacked denoising sparse autoencoder (SDSAE) and Fisher Vector (FV) for visual terrain classification. Then, we propose a terrain dataset, termed "Terrain8", which is the first publicly available benchmark for visual terrain classification. This dataset contains 2400 terrain images, covering 8 terrain classes with 300 images in each class. Our method achieves superior performance on the Terrain8 dataset. Moreover, we design the framework to deal with terrain videos and carry out the field experiments in arc-legged mobile robot. The field experimental results also indicate the effectiveness of our proposed methods.
Removing random-valued impulse noise with reliable weight

In this paper, we present a patch based weighted means filter for removing an impulse noise by adapting the fundamental idea of the non-local means filter to the random-valued impulse noise. Our approach is to give a weight to a pixel in order to evaluate the probability that the pixel is contaminated by the impulse noise, which we call Reliable Weight of the pixel. With the help of the Reliable Weights we introduce the similarity function to measure the similarity among patches of the image contaminated by a random impulse noise. It turns out that the similarity function has significant anti impulse noise interference ability. We then incorporate the Reliable Weights and the similarity function into a filter designed to remove the random impulse noise. Under suitable conditions, we establish two convergence theorems to demonstrate that our method is feasible. Simulation results confirm that our filter is competitive compared to recently proposed methods.
Determining the Chaotic Dynamics of Hydrological Data in Flood-Prone Area

Flood-prone areas are associated with hydrological time series data such as rainfall, water level and river flow.The possibility to predict flood is to relate all the three data involved.However, in order to develop a multivariable prediction model based on chaos approach, each datum needs to identify chaotic dynamics.As such, the Sungai Galas, Dabong in Kelantan, Malaysia which is a flood disaster area has been selected for the analysis.Rainfall, water level and river flow data in this area were collected to be analysed using the Cao method to identify the presence of chaotic dynamics.The hydrological data is uncertain, which is difficult to predict because the data involved is located in the area of flood disaster.The analysis showed the presence of chaotic dynamics on rainfall, water level and river flow data in the Sungai Galas which involved uncertain data located in flood affected areas by using Cao method.Therefore, a multivariable flood prediction model can be implemented using a chaos approach.
Investigating Feature Spaces for Isolated Word Recognition

The study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and three feature spaces were investigated for the frequency domain, namely: Linear Prediction Coefficient (LPC) spectrum, Hartley spectrum, and cochleagram. Due to the fact that deep learning requires an adequate training set size of the corpus and its content may significantly influence the outcome, thus for the data augmentation purpose, the created dataset was extended with mixes of the speech signal with noise with various SNRs (Signal-to-Noise Ratio). In order to evaluate the applicability of the implemented feature spaces for isolated word recognition task, three experiments were conducted, i.e., 10-, 70-, and 111-word cases were analyzed.
Crime Level Prediction using Stacked Maps with Deep Convolutional Autoencoder

Chicago is denoted one of the most dangerous cities in the United States and its crime data are free available on the web portal of the town. This information has been often used to analyze and predict crime events, using statistical and machine learning space time models. Given the recent widespread success of deep neural networks, we believe that these tools could be used for producing high quality predictions of the criminality. In particular, in this work we propose a deep convolutional autoencoder neural network adapted to multiple inputs of a temporary nature for the crime prediction on a particular day. First, a preliminary analysis of data, based on the Principal Component analisis (PCA) and correlations between variables are provided. Then, the proposed prediction model is applied to forecast the nexday crime events in the town of Chicago. Finally, multiple metrics are reported to evaluate the quality of the proposed deep neural network model. By comparing the results for different scenarios (tasks), the best model has been obtained using two days events as inputs for predicting the third day. In this case the coefficient of determination reached the 97% in the validation set. Despite the use of temporal data, the deep convolutional model has shown a great predictive capacity, being this particular case trough six convolutions. As a future work, we are going to consider different real datasets, with a greater number of historical events. On the methodological part, we are going to incorporate temporary models such as the Long short-term memory (LSTM).
Path Planning for Autonomous Mobile Robot Navigation with Evolutionary Particle Swarm Optimization

In this article, we proposed a new path planner called EPSO path planner. It based on evolutionary particle swarm optimization to generating the collision-free path for a mobile robot. The proposed algorithm finds the optimal path by performing random sampling on grid based environment. The efficiency of the proposed algorithm is demonstrated by simulation. The shortest path obtained was 11.45.