Reviewerly/exHarmony huggingface dataset:
An Intention Tree based Model for Social Needs

With the application of more and more new technologies, such as cloud computing, IoT, and big data, the available intelligent service scenarios become complex. For better service identification and recommendation, conversational AI bots need to construct user needs expression models accurately. Most traditional user needs expression models could do better in individual tasks but nearly not support the social requirements. To solve the challenge before, this study expands the Intention Tree Model for Personal Needs(ITM-PN) to the Intention Tree Model for Social Needs based on Negotiation(ITM-SN) to model different users' needs during the process of multi-user negotiation(initial, processing, ending). And finally, this study conducted a case study to prove the ITM-SN is thoroughly and effectively, which could be widely applicable in many fields.
LIVING LAB FOR DIGITAL LITERACY AND ACTIVE AGEING. SYSTEMATIC LITERATURE REVIEW OF SCIENTIFIC PRODUCTION IN THE SOCIAL SCIENCES

The aim of this study is to know the state of the art of Living Labs in the field of Social Sciences.To this end, a Systematic Literature Review was carried out with the aim of answering the following research questions: a) what types of studies are carried out on Living Labs, b) what are the aims and objectives of the Living Lab analysed, c) what is its geographical context, d) the type of users it is aimed at and e) what are the characteristics shared by the different Living Labs analysed in the selected literature.After analysing the literature, the results show a predominance of both qualitative research and studies carried out in the European context, as well as heterogeneity in the objectives of the research analysed and coincidence in the characteristics of the Living Labs analysed.The conclusions point to the need to develop research on Living Labs in the field of Social Sciences and, specifically, from an educational perspective that promotes the technological inclusion of the elderly.
THE EFFECT OF PARTNER READING STRATEGY IN TEACHING READING

This study analyses the students’ responses about the effect of partner reading strategy in teaching reading and observe how the lecturer implement the partner reading strategy in teaching reading. The population of this research was the second grade of English Departmen STKIP Bina Bangsa Getsempena Banda Aceh which consists of 59 students and only 31 students in class A took as the sample of this research. The instruments of this research were observation, questionnaire, and interview. The result of observation in this study shows that many observation sheet factors are followed by the lecturer. Such as, letting students choose their pairs, practice what is to be a good partner. Then the teacher also pays attention during the learning process in class. It is mean the teaching process is quite satisfying because in line with Meisinger et al in Melanie about the procedure of using partner reading strategy. However, for the questionnaire data, many students who responded strongly agreed in the partner reading strategy section, which is about the benefits or good effects of a partner reading strategy that can encourage students' willingness to learn. Based on the result of the study, the teacher should applied partner reading strategy in teaching reading because it can improve the learning process, and make students interested in learning reading.
Assessment and Analysis of Writing Activities of Secondary High School English Textbooks: Vision Series

Writing is one of the four skills of language learning, especially, one of the skills of practical communication in a communicative approach. The purpose of this research is to evaluate and analyze writing activities of the writing sections of secondary high school English textbooks, grades ten, eleven and twelve so called vision 1, 2, 3. The theoretical framework of this research, a descriptive analytical and qualitative method, was based on Kern's (2007) and Harmer's (2004) model and continuum of writing activities. The writing activities of these vision books were divided into three major categories of controlled, guided and free writing and fifteen more sub-categories. The exercises and activities of the writing section of these six volume English textbooks- so called vision 1,2,3 were counted on and the average and percentage of each category was calculated based on Kern's (2002) and Harmer's (2004) model and continuum of writing. The findings of this research indicate that the categories of controlled writing and copying, the first item of Kern's continuum, are the dominant writing tasks in these books. Activities concerning the guided, and especially the free writing activities in these books are rarely seen or they have not been much carefully regarded. In communicative approach, regarding the goods and the existing requirements, teaching of writing is generally concerned with free writing tasks. Therefore, in order to enhance and promote the practical communication skills as an aim of communicative approach, free writing skills are inevitable. The findings of this research point out that English teachers can reinforce and enhance their students' practical communication abilities in their classes by using free style exercises and hence, cover the shortcomings of the books.
Contrastive Response Pairs for Automatic Evaluation of Non-task-oriented Neural Conversational Models

Responses generated by neural conversational models (NCMs) for non-task-oriented systems are difficult to evaluate. We propose contrastive response pairs (CRPs) for automatically evaluating responses from non-task-oriented NCMs. We conducted an error analysis on responses generated by an encoder-decoder recurrent neural network (RNN) type NCM and created three types of CRPs corresponding to the three most frequent errors found in the analysis. Three NCMs of different response quality were objectively evaluated with the CRPs and compared to a subjective assessment. The correctness obtained by the three types of CRPs were consistent with the results of the subjective assessment.
VIRTUAL & AUGMENTED REALITY TECHNOLOGY HELP LEARNING OF BIOLOGY IN SECONDARY SCHOOLS

Some Science, Technology, Engineering, and Mathematics (STEM) subjects in secondary schools or high schools, such as biology, physics, or mathematics are considered tough to students. It is due to the lack of tools to visualise details or effects in the learning process, with traditional learning materials of textbooks, blackboards, and static models. The classroom education is evolving with the technology advancements. Development works in three-dimensional (3D) graphics rendering, virtual and augmented reality (VAR) have been reported to provide interactive visualisations for classroom education. Teaching of the subject of biology is one of such examples in the classroom education which could benefit to the emerging technologies with different types of supportive tools. This project is developed to apply virtual and augmented reality technology-enhanced learning (VARTEL) in biology education of secondary schools. Pilot study has been performed with the collaborating secondary schools. The VARTEL technologies developed in the research project can be rolled out to more mainstream schools in Singapore and other countries.
RMCORR: Stata module to compute a correlation for data with repeated measures

rmcorr computes a correlation coefficient for data in which subjects have repeated measures (i.e. multiple observations), as proposed by Bland and Altman (1995). Additionally, confidence intervals are calculated by using Fisher's z transform (Gleason 1996). When the figure option is specified, a graph is generated that includes a scatterplot of each subject's observations and a linear fit through those points. This graph assists the user in visualizing the variation amongst subjects.
Eye-Tracking Multi-Modal Inference

Author(s): Beller, Ari; Xu, Yingchen; Gerstenberg, Tobias | Abstract: When you see a glass fall off the table, you can predict it will break without seeing it hit the ground. Similarly, if you hear the glass shatter, you can infer what happened without seeing anything at all. Our knowledge of the causal mechanisms structuring our world allows us to make impressively accurate inferences based on incomplete information spread across multiple sensory modalities. In this work, we study the cognitive processing that supports this remarkable behavior. We utilize the Plinko domain, an intuitive physics setup where marbles are dropped into a box from one of three holes, colliding with obstacles as they fall to the ground. Participants judge where they think the ball fell from based on visual and auditory evidence. We track participants' eye-gaze to gain deeper insight into their mental processes, and develop models that characterize the computational processes underlying participant behavior.
Application of Machine Learning for Emotion Classification

In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creat- ing a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training pro- cedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back- propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regu- larization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre- trained architectures have been released under an open-source license in our public repository.
Generative Adversarial Learning Neural Network Based Secure Data Transmission in Cognitive Radios

In this paper, a communication model in cognitive radios is developed uses the machine learning to learn the dynamics of jamming attack in a cognitive radios. It is designed further to make their transmission decision which automatically adapts with the transmission dynamics in order to mitigate the launched jamming attacks. The Generative Adversarial Learning Neural Network (GALNN or GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompasses minimax game theory.The elimination of the jamming attack is carried out with the assistance of the defence strategies and with increased detection rate in GAN.The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics in reducing the misdetection rate and false alarm rate. The results show that the GDNN obtains increased rate of successful transmission by taking optimal actions to acts as a defense mechanism to mislead the jammer, where jammer makes high misclassification errors on transmission dynamics.
Evaluation of Alternative Advanced Convolutional Neural Networks for Train Driver Action Recognition

Action accuracy of train drivers is closely related to the train safety. However, at present, the video of cabs of train drivers is difficult to be monitored in real time, along with poor alarm accuracy, long response time and difficulties in analysis of video data. Therefore, action recognition in video of the train driver is studied in this paper. First, based on the clips of collected video, the emergence of key actions in the whole video can be distinguished, and image enhancement and segmentation technology can be used to extract the images of key actions. Second, three improved convolutional neural networks (C3D, R(2+1)D and R3D) are compared to realize accurate recognition of actions. Third, a comparative experiment based on pre-training models and non-pre-training models is designed to analyze the application effects of different algorithms in this problem. The results show that the method proposed can improve the accuracy of action recognition, which effectively reduce the accidents of train driving.
Computational-Neuroscientific Correspondence of Oscillating-TN SOM Neural Networks

Author(s): Revithis, Spyridon | Abstract: Oscillating-TN (Topological Neighborhood) Self-Organising-Map (SOM) artificial neural networks can facilitate the study of neurodevelopmental cognitive phenomena. Their cognitive modelling significance rests primarily on the premise of biological realism. Despite the difference in neuronal activity description between spike-train brain signaling and the rate-based computer SOM models, there is a valid analogy in cortical columnar activation synchrony. A cortical macrocolumn can be modeled as a computer-trained SOM with emerging or structural minicolumns represented by SOM-TN groups of neurons. Neural excitation and lateral inhibition result in structural cortical changes modeled by SOM Hebbian TN-activation. Oscillating-TN SOMs can model brain plasticity and regulate sensory desensitization. Neural synchrony can be modeled at various levels: macroscopically, there is an analogy between an oscillating local field potential and a SOM oscillating-TN width computational session. There are also arguments to support the hypothesis that SOM stability or entrenchment during computational map formation associates with neural oscillatory sensory prediction.
De la enseñanza semipresencial a la enseñanza online en tiempos de Covid19.

This article presents the description and analysis of a university teaching experience designed and developed under the blended teaching modality, but with the closure of the physical spaces of the universities during the Covid-19 pandemic, it was redesigned and offered entirely at a distance . The pedagogical principles that inspire it are based on the student's self-organization and autonomy, the offer of virtual environments with flexible learning itineraries, and the projects vs. methodology. subject learning. The article offers an analysis based on a questionnaire with closed and open questions of the opinions of the students in relation to what the transit and the experience of taking this blended subject represented in a totally online format. The results indicate a high level of satisfaction, highlighting as positive aspects the flexible itineraries offered, the proposal of tasks and projects for self-employment, the online tutoring received, as well as the acquisition of the digital skills necessary for their professional future.
Public Auditing for Secure and Efficient Cloud Data Storage: A Comprehensive Survey

Nowadays storage of data is big problem because the huge generation of multimedia data likes images, audio, video etc. whose size is very large. For storing of these data size of conventional storage is not sufficient so we need remote storage such as cloud which is resilient infrastructure, reliable and high quality performance for the cloud users. In the cloud there is no direct physical control over the records because the cloud uses its resource pool for storing. Consequently data reliability fortification and auditing is not a modest task. The user prerequisites to depend on a Third Party Auditor (TPA) who is working as a public auditor for authenticating the data integrity and privacy. This paper presents the various auditing techniques of cloud computing for improving security and then future research challenges which need to be adopt by researchers to make system obvious.
Ταξινόμηση και μεταφορά τεχνοτροπίας (στυλ) κινουμένων σχεδίων με χρήση βαθέων νευρωνικών δικτύων

Image recognition and classification are the two most common use cases of CNNs in the field of Machine Learning. Going a step further, detecting and classifying different styles, as well as transferring characteristics of a style, from one image to another is an interesting application of CNNs.The goal is to create a model which, given enough images of different styles (whether that would be different art styles, animation styles etc.), recognize, differentiate and accurately predict the style of an image. For the second part, using parts of a model, one can recognize and transfer the characteristics of a particular style to an image of a different style.In this diploma thesis, we endeavor to differentiate between Western and Eastern animation styles (more commonly referred to as “cartoon” and “anime” respectively). This is split into two parts. The first part is style recognition and classification, where we try to classify the category of an image and the second part is style transfer, where we try to transfer the characteristics of each style to an image of the different style.To achieve this, we create and evaluate a few CNN models, with different architectures and differing levels of complexity in their designs, and compare their performance and results. In particular, for the style recognition part, we create 3 different models, a VGG16 model, a VGG19 model and a custom CNN model with 4 convolution layers, which we train, evaluate and compare their performance and results. As for the style transfer part, we create a VGG19 model and using specific layers, which contain the information we need, we transfer the style characteristics from one image on to another.
Analysis of Internet User Information Protection Strategy Based on Blockchain Technology

With the birth of blockchain technology, revolutionary changes have taken place in information storage and control. Blockchain technology not only reduces the risk of personal information leakage, but also provides a new reliable and practical technical support for Internet governance at this stage. Because of the connectivity of the network, users are easily accessed when using computers, and their Internet environment is destroyed, such as malicious attacks and virus infections. Because blockchain technology can't be tampered with, it uses asymmetric encryption technology, and it can trace the infringement, and has a high degree of trust endorsement, which brings a new way to protect the personal information of netizens. In this paper, aiming at personal information security in computer Internet under the background of digital economy, we will analyze the role of blockchain technology in personal information protection, and put forward strategies to improve the security performance of Internet, so as to improve personal information security.
The Effect of Mobile Marketing on Customer Satisfaction the Perspective of Jordanian Commercial Banks

This study aimed to present a new model that combines two important variables in the science of digital marketing and is the reason for the success and continuity of any facility. This study combined mobile marketing as an independent variable and customer satisfaction as an dependent variable, the study community was customers of Jordanian commercial banks In the capital Amman, data were collected by original questionnaires, the study sample was 400 respondents and the results were that there was a statistically significant relationship between mobile marketing as an independent variable on the customers satisfaction as an dependent of Jordanian commercial banks on the other hand.The study also showed that tow is a statistically significant relationship between the mobile marketing and the customer's satisfaction in Jordanian commercial banks.
Voice Assistant Personalities for Health Information Seeking.

As voice assistants become increasingly popular, attention has been drawn to their personalities and their impact on trust and likeability. While it has been found that assistant personalities that matched their users in personality were more trusted and liked in an automotive context, other assistive situations have yet remained unexplored. This study investigates how the personality of a voice assistant impacts user's perceptions of trust and attractiveness in the context of seeking general health information. A voice assistant prototype, consisting of two different assistant personalities, was designed and tested via a Wizard-of-Oz method. We found that personality did not have an impact on trust and system attractiveness in this context, and that instead of affective cues, users paid more attention to cognitive cues in assessing the voice assistant. Our work holds important lessons for designing voice assistants.
KAJIAN STRUKTUR CHECK DAM 3 BATANG HULU KURANJI KOTA PADANG STUDY OF CHECK DAM STRUCTURE 3 BATANG HULU KURANJI PADANG CITY

Batang Kuranji is a river located in Kota Padang. The high rainfall and human factors that cause changes in characteristics, especially in the upstream area make the water from the flow of kuranji stems in the rainy season often overflows, and cause flash floods, therefore built Check Dam at the head of the river batang kuranji to prevent the shallowing of the riverbed. Thisresearch aims toreview the structure of the Check Dam 3 building on Batang Kuranji in the city of Padang. This study refers to SNI 2851:2015 with rainfall data for 15 years used from 2005 to 2019, with batu busuk observation station and rice fields obtained from PSDA. Luas DAS is obtained from ArcGIS Applications. Dari hydroligi analysis obtained rainfall plan (R100th) 153,152 m3/dt with Gumbel method, Discharge flood plan for the 100 year anniversary period used Haspers method obtained (Q100th) 165.19 m3/dt. The type of Check Dam that is planned is the type of pelimpah (head work) with a height of Check Dam 8.5 m. Tilt of the body at the upstream 0.6, the distance between the main dam and sub dam 25.2 m, the thickness of the apron floor 1.6 m, with an estimated volume of sediment flow that can be accommodated by 14797.6 m3. The stability of the Check Dam construction was obtained at a value of 3.43 &gt;1.5 and a sliding of 1.53 &gt; 1.5 with a safety coefficient of 1.5, so that the construction of the Check Dam was stable.
Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.]

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.