Reviewerly/exHarmony huggingface dataset:
Feasibility of Activating Electronic Health Care System in Ibn Rushed Hospital

A descriptive study was conducted on health care providers them use electronic health care system and patients or clients in Ibn Rushed Teaching Hospital in Baghdad-Iraq.To evaluate the feasibility of activating electronic health care system, Data was collected using a developed self-administrated questionnaire; data was collected from March 20 th , 2019 to May 6 th 2019.A purposive sample (non-probability sample) were selected (N=115) the study sample was divided into two parts, the first part is healthcare providers and their number (65), the number of physicians is (32) and the number of nurses is (33).The second part of the sample is patients or clients and their number (50).Data were analyzed through the application of descriptive and inferential statistical approaches by using Statistical Package for Social Science (SPSS) version 19.For health care providers, the findings for revealed that most of the respondents were males with age's range from (31 to 39) years old, most of the respondents were physicians with (8 to 17) years' experience.Most of the responses regarding of feasibility of activating electronic health care system, there are feasibility from activating electronic health care system (83%).As for results patients and clients Most of the respondents were males with age's range from (35 to 55) years old, level education for respondents were Primary and Preparatory education.Most of the responses regarding of feasibility of activating electronic health care system, there are feasibility from activating electronic health care system (90%).The study recommended the government should be well informed about the benefits and latest innovation in e-Health so as to encourage appropriate allocation of budget, so that e-Health policy implementation can be taken more seriously.Highly recommendation the Ministry of Health to impose and generalize the experience of the electronic health care system in all hospitals in the country.Highly recommended that the educational sector inculcate e-Health into the curriculum for the integration of IT solutions into healthcare to flow successfully, this will provide health care givers graduating from learning institutions systemic training and education on the use of the e-Health.Recommended for further studies to adopt both quantitative and qualitative research approaches in similar studies to generate more responses from participants.This would help reveal whether or not the intended benefits of the implemented E-Health to patients are actually realized.
Library: Daemen Digital Commons: Home

Repository for archiving and sharing faculty scholarship, student work, and digital collections.
An Analysis of Context of Culture and Context of Situation in Obama’s Speech Text

Functional systemic linguistics studies the meaning of texts. In Understanding the meaning of the text, it needs the involvment of situation context and cultural context. The aim of this research is to analyze the speech text conveyed Barack Obama in terms of situation context and cultural context. This research used descriptive qualitative approach as research design. Technique of data analysis uses the context situation and cultural context analysis as formulated by Suzanne Eggins (2004) and Halliday and Hasan (1985). The result showed that situation context is determined in term of field, tenor, and mode. The field of the text is about description of the problems faced by American State and how to solve those problems. The tenor of this text describes that relation between Barak Obama and the audience is not equal. The mode of the text shows that text was conveyed orally and rewrited by the jurnalist in the form of written text. Then, the cutural context is determined in term of the word choice in making the meaning of the text. in addition, cultural context is also detemined by the genre and the purpose of text. Thus is analitical text by purposing to give the new hope and the solution solution about crisis happening in America.
Theoretical analysis on imaging technique of local unevennesses occurred on specular surface objects

One of the most important stages in automatic visual inspection for specular surface objects is to generate an image data including features of defects for the inspection. We define the image for inspection as an image data including an intensity change due to surface defects. The type of surface defects on the specular object is classified into local reflectivity losses and local unevennesses. In this paper, we report a study on illuminating, imaging and image processing methods to generate the image for inspection in order to detect the local unevennesses. We considered a situation where a light emitting source displays a sinusoidal intensity pattern and a camera captures the reflection pattern on the specular surface. Numerical computations based on our theoretical analysis shows that the local unevennesses appears as phase gaps of the sinusoidal intensity pattern in the image data captured by the camera. We propose a processing method to convert the phase gaps into the intensity changes in the image for inspection. In addition, we mentioned operational problems to be solved when we apply our methods to an automatic visual inspection system. Furthermore, we discussed a possible method for detecting the local unevennesses with a depth of 1x10-3 mm from the image for inspection.
THE EFFECTIVENESS OF USING SPOTIFY TO TEACH LISTENING COMPREHENSION IN NARRATIVE PODCAST (A PRE-EXPERIMENTAL RESEARCH AT THE NINTH GRADE OF MTs ASSALAFI SUSUKAN IN THE ACADEMIC YEAR 2020/2021)

ABSTRACTRahma, Ghaniyya Wafda. 2020. The Effectiveness of Using Spotify to Teach Listening Comprehension in Narrative Podcast (A Pre-Experimental Research at The Ninth Grade of MTs Assalafi Susukan in The Academic Year 2020/2021). A graduating paper. English Education Department. Teacher Training and Education Faculty. State Institute for Islamic Studies of Salatiga. Counselor: Nur Muthmainnah, M.PdThe objectives of the study were; (1) to find out whether Spotify is effective to be applied in teaching listening comprehension or not, (2) to find out the difference of students’ listening comprehension before and after taught using Spotify in Narrative Podcast. Pre-experimental design was used in this research. The subjects were 25 Ninth Grade Students of MTs Assalafi Susukan. The researcher used pre-test before treatment and post-test for treatment results as the instruments for data collection. Furthermore, the data was analyzed and interpreted through quantitative procedure used SPSS 22.0 version. The result indicates that the mean score of the post-test is higher than the mean score of the pre-test, which the mean of post-test is 93.60, and the pre-test is 49.10. It means that there is difference of students’ listening comprehension in Narrative Podcast before and after taught by using Spotify Application. Furthermore, based on the statistical analysis using T-test with SPSS 22.0 version for windows, the result of T-test showed that the significant value (sig-2 tailed) is 0,038, and it is lower than 0.05 (0.038 < 0.05). It indicates that there is a significant difference score in listening comprehension mastery before and after being taught by using Spotify Application in Students of MTs Assalafi Susukan. In short, based on the results, Spotify Application is effectively used in teaching listening comprehension in Students of MTs Assalafi Susukan.Keywords: Spotify, Podcast, Narrative, Listening Comprehension.
Network Traffic Classification Based on Deep Learning

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important.How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security.With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods.In this paper, we provide a comprehensive review of network traffic classification based on deep learning.Firstly, we introduce the research background and progress of network traffic classification.Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks.In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning.Finally, the future research directions of network traffic classification based on deep learning are prospected.
Definite and Indefinite Articles With Abstract Nouns in L2

Past studies have examined the problems that learners have with English definite, indefinite, and zero articles, particularly for learners whose L1 lacks an article system, such as Korean. Many of the empirical and theoretical studies of article acquisition have examined particular error types and types of nouns that are more problematic. In doing so, they have typically focused on more concrete nouns to the exclusion of abstract nouns. Abstract nouns tend to be used without articles, i.e., as bare nouns, a fact that learners may be aware of. However, such nouns can sometimes be used with articles for a more specific nuance. This study examines the difficulties that abstract nouns might pose for learners, as standard article rules may be unhelpful, especially for less familiar nouns. For this study, a linguistic survey with more abstract nouns was administered. The results showed that they had difficulties with more abstract noun patterns (as measured by semantic indices), and their difficulties were also affected by lexical frequency and complex noun phrases. Article patterns for more abstract nouns can be difficult to learn and teach, as their nuances can vary according to context. Some suggestions are sketched out for teaching such noun patterns.
FORENSIC EVIDENCE AND CASE ATTRITION IN CRIMINAL JUSTICE SYSTEM

It is well established that the forensic evidence presence in the Criminal Justice System brings complex questions regarding its collection, impact on legal outcomes, and interpretation by forensic specialists and judges. Primary aim of this paper is to discover how cases with forensic evidence proceed through the criminal process. Illustrating forensic evidence presence and the attrition process, we were able to highlight potential differences between subgroups in this interdisciplinary community (law enforcement, forensic specialists, and court members). Deficient funding and research opportunities in correlation with a lack of engagement with new technologies, absence of sufficient databases are main issues while trying to establish contribution of forensic evidence in legal outcomes. By bringing scientists, the judge members, police and forensic science practitioners together, we are creating bright-spots of activity in an effort to properly address the issues as a community.
GLIMPSE OF CYBER CRIMES IN THE INDIAN PENAL CODE AND INFORMATION TECHNOLOGY ACT - AN UNEASY CO-EXISTENCE

This paper would be discussing about what actually are the cybercrimes, its various categories and how it is impacted on the society with the application.The Information technology act is responsible for dealing the aspect of cyber crimes and also under I.P.C . are offences concerning computers, information technology, internet and computer game. Many of the cyber-crimes penalised by the IPC and therefore the IT Act have an equivalent ingredients and even nomenclature.
PERANAN TEORI MULTIPLE INTELEGENCE DALAM PROSES PEMBELAJARAN TEMATIK PADA KURIKULUM 2013 DI SEKOLAH DASAR

Students as learning subjects in the learning process have different abilities and potentials from one another. In essence, every individual has 8 intelligences including linguistic, logical-mathematical, visual-spatial, kinesthetic, interpersonal, intrapersonal, musical, and naturalist known as Multiple Intelligence. Integrated thematic learning activities by applying the Multiple Intelligence theory lead to a variety of student intelligence and adapted to the needs of learning materials. With the integration of thematic learning and the theory of Multiple Intelligence, it is hoped that it can assist teachers in exploring and developing student intelligence and can spur students' dominant intelligence optimally and strive to maintain other intelligences at the minimum standards set by the institution or school.
Realization of Automatic Control System Based on Artificial Intelligence

Artificial intelligence is also called machine intelligence. It is a comprehensive discipline developed by the penetration of multiple disciplines. Based on artificial intelligence, automatic control science has also gradually emerged and developed, and will become an inevitable trend in the development of industrial control technology. However, NCS itself has problems such as network induced delay, disordered data packet timing, and packet loss. This paper studies two artificial intelligence control algorithms, namely Smith predictive fuzzy PID controller and generalized predictive control based on dynamic BP network error correction, and applies it to network automation control system to reduce the impact of delay on system control performance: Finally An automatic control system for ink color presetting of color newspapers is designed, and fuzzy PID control algorithm is applied to the system to realize the control of the ink output. Aiming at the non-stationarity of the signal to be cancelled, based on the research of traditional nonlinear filtering, we design an artificial neural network as an adaptive processor, relying on its powerful functions similar to the human brain to solve the inherent defects of the conventional filtering mode. The introduction of neural networks has greatly improved the ability of the system to solve complex and highly nonlinear problems. The model designed in this paper has laid a good foundation for solving nonlinear system problems, and the algorithm has a moderate degree of complexity, which has high application value. Experimental research shows that the method is verified by MATILAB experimental simulation to improve the stability of the system and make the system have good dynamic performance and robustness.
Generating Easy-to-understand Referring Expressions for Target Identifications

This paper addresses the generation of referring expressions that not only refer to objects correctly but also let humans find them quickly. As a target becomes relatively less salient, identifying referred objects itself becomes more difficult. However, the existing studies regarded all sentences that refer to objects correctly as equally good, ignoring whether they are easily understood by humans. If the target is not salient, humans utilize relationships with the salient contexts around it to help listeners to comprehend it better. To derive this information from human annotations, our model is designed to extract information from the target and from the environment. Moreover, we regard that sentences that are easily understood are those that are comprehended correctly and quickly by humans. We optimized this by using the time required to locate the referred objects by humans and their accuracies. To evaluate our system, we created a new referring expression dataset whose images were acquired from Grand Theft Auto V (GTA V), limiting targets to persons. Experimental results show the effectiveness of our approach. Our code and dataset are available at https://github.com/mikittt/easy-to-understand-REG.
Research On The Application Of Network Intrusion Feature Extraction In Power Network

In order to overcome the problems of low accuracy and high time cost of traditional methods of power grid intrusion feature extraction, a method of power grid intrusion feature extraction based on ...
IMPACT OF KNOWLEDGE SHARING AND TEAM WORK ON TEAM PERFORMANCE WITH THE MODERATING ROLE OF SUPERVISOR SUPPORT

This study focused on the proposed hypothetical model to explore the association between the knowledge sharing, teamwork and team performance of construction project based organizations professionals in Rawalpindi and Islamabad cities, while observing the supervisor support in moderating role. The study was conducted by cross- sectional survey among employees of selected organizations of Rawalpindi and Islamabad cities, respectively. Sample consists of 224 employees and data collection was done by the distribution of structured questionnaires through convenience sampling technique. In statistical analysis to draw conclusions the analytical tools such as reliability, correlation, and regression analysis were used.It was obvious from the results that there was a positive association between knowledge sharing-team performance and team work-team performance relationships. Similarly it was also evident that supervisor support positively moderates between knowledge sharing-team performance and teamwork-team performance relationships. It is recommended that the organizations should emphasize to enhance the perception of supervisor support among employees due to which employees will share knowledge and will work as team and team performance will boost up. In team environment supervisor support helps in resolution of employee problems and strengthens the team members. In this way team members outperform and generate good result for the team as well for the whole organization.
Best Quality SEO Services in India: Persistentinfotech

SEO or Search engine optimization is a technique that is applied by us for increasing the number of visitors to a particular website. The Best
An Approach for Textual Based Clustering Using Word Embedding

Numerous endeavors have been made to improve the retrieval procedure in Textual Case-Based Reasoning (TCBR) utilizing clustering and feature selection strategies. SOPHisticated Information Analysis (SOPHIA) approach is one of the most successful efforts which is characterized by its ability to work without the domain of knowledge or language dependency. SOPHIA is based on the conditional probability, which facilitates an advanced Knowledge Discovery (KD) framework for case-based retrieval. SOPHIA attracts clusters by themes which contain only one word in each. However, using one word is not sufficient to construct cluster attractors because the exclusion of the other words associated with that word in the same context could not give a full picture of the theme. The main contribution of this chapter is to introduce an enhanced clustering approach called GloSOPHIA (GloVe SOPHIA) that extends SOPHIA by integrating word embedding technique to enhance KD in TCBR. A new algorithm is proposed to feed SOPHIA with similar terms vector space gained from Global Vector (GloVe) embedding technique. The proposed approach is evaluated on two different language corpora and the results are compared with SOPHIA, K-means, and Self- Organizing Map (SOM) in several evaluation criteria. The results indicate that GloSOPHIA outperforms the other clustering methods in most of the evaluation criteria.
Can Bitcoin be the Future of Digital Payments?

This study aimed to investigate the conditions in which Bitcoin has developed as a leading cryptocurrency and, according to Nakamoto (2008), could become an instrument for everyday payments around the world. In comparison to other digital payment solutions, Bitcoin is based on a peer-to-peer electronic cash system using “the blockchain”. This innovative technology allows for decentralised storage and movement of currency in a fully anonymous way, introducing advantageous methods for encrypted security and faster transactions (Hagiu &amp; Beach, 2014). Scepticism regards Bitcoin’s foundation, energy consumption and price volatility, however, did not take long to arise (Holthaus, 2017). Ten years from its white paper release, Bitcoin is further supported by the same drivers which could sustain its growth as the future of digital payments (Russo, 2018). In order to investigate the key drivers and feasibility of acceptance, a London based survey was used to understand the desirability of Bitcoin as a day-to-day tool for digital payments. Additionally, this research analysed Bitcoin’s stakeholders and forecast drivers of sustainability for its application to become the future of the payment industry. A space which relies on policies that involve multiple layers of society, governments, regulators and tech-firms, all on a global scale. The findings confirmed how the increasing lack of trust of political and financial institutions, coupled with the increasing cases of data-breaches by tech-firms, encouraged over 70% of respondents to consider more decentralised and anonymous methods for their day-to-day actions; like payments. Policy makers need to cope with societies increasingly separating politically but gathering together digitally (LBS, 2017). For Bitcoin to truly establish itself as a global digital payment solution, key stakeholder acceptance must converge alongside the introduction of more robust regulation.
Interactive Anomaly Detection Based on Clustering and Online Mirror Descent

In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this paper, we propose a method (called OMD-Clustering) that exploits this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to investigate them. The OMD-Clustering method iteratively suggests the top-1 anomalous instance to a human expert and receives feedback. Before suggesting the next anomaly, the method re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimilar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space. An experimental evaluation on several real-world datasets is conducted. The results show that OMD-Clustering achieves significant improvement in both detection precision and expert effort compared to state-of-the-art interactive anomaly detection methods.
Cost Sensitive Optimization of Deepfake Detector

Since the invention of cinema, the manipulated videos have existed. But generating manipulated videos that can fool the viewer has been a time-consuming endeavor. With the dramatic improvements in the deep generative modeling, generating believable looking fake videos has become a reality. In the present work, we concentrate on the so-called deepfake videos, where the source face is swapped with the targets. We argue that deepfake detection task should be viewed as a screening task, where the user, such as the video streaming platform, will screen a large number of videos daily. It is clear then that only a small fraction of the uploaded videos are deepfakes, so the detection performance needs to be measured in a cost-sensitive way. Preferably, the model parameters also need to be estimated in the same way. This is precisely what we propose here.