Reviewerly/exHarmony huggingface dataset:
THE USE OF MODELED TALK TOWARD STUDENTS’ MOTIVATION AND VOCABULARY

This study is intended to analyze the use of Modeled Talk towards students’ motivation and vocabulary mastery at the first semester of the seventh grade of SMPN 1 Sakra Barat. This study applied quantitative research method with experimental design. This research was conducted on October to November 2021. The total population of students were 120 students. The study decided two classes as the sample of the research, namely control group and experimental group. In experimental group treated by modeled talk and control group treated by PBL toward students' motivation and vocabulary mastery. Both of the groups had been given pre-test and post-test. The sample was selected by using simple random sampling technique. From this study motivation as dependent variable and also acts as predicator to support students’ vocabulary mastery. The result of data analysis confirmed that the analysis descriptive statistic, there are different in result between post experiment and post control (82.40 &gt; 61.87). it indicates that the use of modeled talk technique effecting the learning target, meanwhile, the ANOVA analysis claims that the value of data analysis underlined that f-value of motivation higher that α = 0.05 (f-value = 49,22 &gt; 0.05). in this point the Ho which is should be rejected is accepted, which means that there is no effect between modeled and motivation. Regarding to the vocabulary score and data analysis, it is known that f-value also &gt; 0.05, which is indicate the acceptance of Ho (Null Hypothesis). In correlational point between motivation and vocabulary, the analysis proves that there is no significant correlation between students' motivation and vocabulary mastery, it could be seen from the value result f-value = 0.10 with p-value (sig.) = 0.752 &gt; 0.05. this result confirms that alternative hypothesis is rejected. Therefore, the use of modeled talk has no significant effect toward students' motivation and vocabulary mastery.
Hubungan Minat Baca Terhadap Kemampuan Membaca Pemahaman Pada Cerita Fiksi Siswa Kelas V SDN Gugus 5 Cakranegara

This study aims to determine the relationship between reading interest (X) and reading comprehension skills in fiction (Y) in class V students of SDN Gugus 5 Cakranegara. This research is a quantitative research with the type of correlational research. Sampling was done by random sampling technique with a total sample of 78 students. The data collection uses a questionnaire for the variable of reading interest and a multiple choice test for the variable of reading comprehension ability in fiction stories. Data analysis used data prerequisite test and hypothesis testing. The data prerequisite test used the normality test of the data and the linearity test of the data, while the hypothesis test used the pearson product moment correlation. The results showed that there was a relationship between reading interest and reading comprehension ability in class V students of SDN Gugus 5 Cakranegara. The value of the correlation coefficient or rcount of 0.335 is greater than rtable of 0.223 (0.335 &gt; 0.223) with a significance level of 5% and a coefficient of determination value of 11.22%. There is a correlation between reading interest and reading comprehension ability in fiction stories, it is hoped that teachers and parents can raise students' awareness of reading and increase students' reading interest so that students' reading comprehension skills increase, so that it can be ascertained that the learning outcomes they have will be better.
Background

Abstract Recent years have seen an explosion in academic, industrial, and popular interest in AI, as exemplified by machine learning and primarily driven by the widely-reported successes of deep- and reinforcement learning (e.g. [314, 315, 351]). Deep learning is essentially predicated on the notion that, with a sufficiently large training set, the statistical correlations captured by training will actually be causal [310]. However, in the absence of convergence theorems to support this, it remains a hypothesis. Indeed, insofar as there is evidence, it increasingly indicates to the contrary, since the application of enormous volumes of computational effort has still failed to deliver models with the generalization capability of an infant. There is accordingly increasing discussion about what further conceptual or practical insights might be required [57]. At the time of writing, the very definition of deep learning is in flux, with one Turing Award laureate defining it as “a way to try to make machines intelligent by allowing computers to learn from examples” and another as “differentiable programming”. We argue in the following that deep learning is highly unlikely to yield intelligence, at the very least while it equates intelligence with “solving a regression problem”.
D-Cloud-Collector: Admissible Forensic Evidence from Mobile Cloud Storage

Abstract Difficulties with accessing device content or even the device itself can seriously hamper smartphone forensics. Mobile cloud storage, which extends on-device capacity, provides an avenue for a forensic collection process that does not require physical access to the device. Rather, it is possible to remotely retrieve credentials from a device of interest through undercover operations, followed by live cloud forensics. While technologically appealing, this approach raises concerns with evidence preservation, ranging from the use of malware-like operations, to linking the collected evidence with the physically absent smartphone, and possible mass surveillance accusations. In this paper, we propose a solution to ease these concerns by employing hardware security modules to provide for controlled live cloud forensics and tamper-evident access logs. A Google Drive-based proof of concept, using the SEcube hardware security module, demonstrates that D-Cloud-Collector is feasible whenever the performance penalty incurred is affordable.
Multimodal Human Facial Emotion Recognition Using ­Densenet-161 and Image Feature Stabilization Algorithm

Human Facial Emotion Recognition (FER) is the technology to predict listener’s emotion of static images and videos to uncover data on one's enthusiastic state like happy, sad, frustration, anxiety, surprise, hate and neutral states. It's a part of the affective computing technology, which may be a collaborative area of research on listener’s emotion. The standard steps of Facial Emotion Recognition are i) Face RoI identification ii) Feature Extraction and iii) Emotion Recognition. In this paper, an Image Feature Stabilization Algorithm (IFSA) is proposed to improve the efficiency of facial emotion recognition by implementing Deep Convolutional Neural Network (DCNN) model using the Transfer Learning (TL) technique. The architecture entails employing a FER-compatible pre-trained Densenet-161 based DCNN model and then fine-tuning the model for face emotion data. Initially, the dense layer(s) is/are trained, followed by the fine-tuning of each of the pre-trained DCNN blocks, resulting in an improvement in FER accuracy, particularly for difficult front face views like partial view. Experiments when performed on CK+ dataset by employing a 10-fold cross validation method using various pre-train models like VGG 16, VGG19, ResNet -18, 34, 50, 152, Inception- V3 and DenseNet-161 showed accuracy of 85.9%, 94.6%, 91%, 91%, 91.1%, 95.1%, 97.1% and 98.7% respectively. Thus, Facial Emotion Recognition performed using the finetuned DenseNet-161 architecture demonstrated exceptionally improved accuracy compared to other pretrained models along with the proposed image feature stabilization algorithm. The proposed architecture using Densenet-161 showed improved accuracy of 98.78% and 97.52% in other challenging FER datasets like KDEF and JAFEE.
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge

Cognitively plausible visual dialogue models should keep a mental scoreboard of shared established facts in the dialogue context.We propose a theory-based evaluation method for investigating to what degree models pretrained on the VisDial dataset incrementally build representations that appropriately do scorekeeping.Our conclusion is that the ability to make the distinction between shared and privately known statements along the dialogue is moderately present in the analysed models, but not always incrementally consistent, which may partially be due to the limited need for grounding interactions in the original task.
Research Frontiers in Sequential Pattern Mining

The area of sequential pattern mining is progressing rapidly day by day.The aim of sequential pattern mining is to extract subsequences satisfying a threshold parameter value.Researchers are addressing the challenges and problems occurring in various domains of sequential pattern mining(SPM) by defining new constraints, algorithms to increase the quality of patterns.This paper presents an organized study on the research in the varied domains of sequential pattern mining such as Traditional SPM, Time-interval SPM, High Utility SPM and High Utility Hierarchical SPM.The fields are classified on the basis of support, time or utility as the threshold parameter.A taxonomy of the popular algorithms in these diversified fields of sequential pattern mining is also presented.The paper aims to act as a valuable guide for researchers by suggesting future potentials in sequential pattern mining.
Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs

Causal Queries are usually estimated by means of an estimand, a formula consisting of observational terms that can be computed using passive data. Each query results in a different formula, which makes estimand-based methods extremely ad-hoc. In this work, we propose an estimand-agnostic framework capable of computing any identifiable causal query on an arbitrary Causal Graph (even in the presence of latent confounders) with only one general model. We provide multiple implementations of this general framework that leverage the expressive power of Neural Networks and Normalizing Flows to model complex distributions, and we derive estimation procedures for all kinds of observational, interventional and counterfactual queries, valid for any kind of graph for which the query is identifiable. Finally, we test our techniques in a modelling setting and an estimation benchmark to show how, despite being a query-agnostic framework, it can compete with query-specific models. Our proposal includes an open-source library that allows easy application and extension of our techniques for researchers and practitioners alike.
The Construction of University Education Management Model with Both Rigidity and Softness

In recent years, with the continuous deepening of my country's education reform, education management in colleges and universities has also made corresponding progress and achievements, but new challenges have also emerged.How to flexibly adjust and improve the traditional education management model and fully implement the "hard and soft" education management model is worthy of continuous discussion and practice in major universities.First, this article explains the basic definition and connotation of the combination of rigidity and flexibility, and provides theoretical support for the construction of the university education management model of rigidity and flexibility.Positive meaning; third, provide corresponding development suggestions for colleges and universities to build a rigid and flexible educational management model, so as to promote the better integration of new management concepts into the educational management of colleges and universities.
Sentiment Analysis Decision System for Tracking Climate Change Opinion in Twitter

Global warming or climate change is one of the most trend topics of the decade in the world, according to scientists the earth is getting warm more every year, hence people are more and more complaining about this phenomenon, some of them believe that climate change is happening, and we should worry and act about it. Despite that, the Intergovernmental Panel on Climate Change (IPCC) confirms that global warming is real and causes climate change, the majority of people are still in doubt about that, this group is generally called deniers or skeptics who think that it is not real and not caused by human. This group of people is costing a lot for countries, by affecting others who think that we should take corrective actions toward global warming. Thus, it is required to create models that identify the impact of people’s thoughts to help governments for achieving better control of their citizens. In this work we present a new model for analyzing public opinion on social media platforms especially Twitter about climate change subject, we adopted the Sentiment Analysis technique, which is a field of natural language processing, we provided an effective model based on Convolutional Neural Network (CNN) for detecting people's reviews on climate change in social media platforms. Our model may assist decision-makers in producing appropriate strategies to ameliorate the impacts of the climate change phenomenon.
Promotion and Realisation of FAIR and Open Science through CRIS- and Data Management-teams

Innovation is more than a buzz word. It is also a knowledge area, that offers systematic differentiation in the methods and approaches. For universities to innovate in – and beyond – the CRIS and meet the ability of creating architectural innovation in their services lies a way of thinking. This builds on top of the definition of architectural innovation coming from Henderson & Clark (1990) and later explored further: “In short, authentic purpose seems likely to increase the odds that an organization will “see” the need for architectural innovation as it emerges, that it will develop a shared sense of strategy and an organization aligned with that strategy, that its employees will work harder and be significantly more engaged and productive than those in conventional firms, and that it will have an advantage in developing the trust that is essential to effective action under ambiguity”.
The Sketching Club as a Didactic Strategy

The purpose of this paper is to rethink the methodology of teaching freehand analytical drawing and the approach in which to incorporate technology and new graphic tools, exploring attractive forms of learning that can be combined with the traditional “workshop” model that remains effective, although current health circumstances have put it in crisis. The aim is to contribute with our experience of freehand drawing through a renewed “sketching club” model adapted to the learning objectives. The group that learns to draw is enriched by the spontaneity of those who join simply for the pleasure of drawing and because of the interdisciplinary character that this incorporation brings. The sketching club model, with more than 200 years of existence, lost its prestige due to its elitist character, among other reasons. For some years we have been exploring the possibility of incorporating this figure, in a complementary way into formal learning, because it meets the appropriate conditions to enrich the experience of learning drawing in the current context. The mission is to test its effectiveness with groups of sketchers, who draw as a hobby, transversal in nature and spontaneously incorporating the new digital tools. An experience based on the conviction that skill in the practice of annotation and conceptual sketching and the capacities involved in it, intervene in the development of creative processes from the idea to its formalisation.
RS Invariant Image Classification and Retrieval with Pretrained Deep Learning Models

CBIR deals with seeking of related images from large dataset, like Internet is a demanding task.Since last two decades scientists are working in this area in various angles.Deep learning provided state-of-the art result for image categorization and recovery.But pre-trained deep learning models are not strong enough to rotation and scale variations.A technique is proposed in this work to improve the precision and recall of image retrieval.This method concentrates on the extraction of high-level features with rotation and scaling invariant from ResNet18 CNN (Convolutional Neural Network) model.These features used for segregation of images using VGG19 deep learning model.Finally, after classification if the class of given query image is correct, we will get the 100% results for both precision and recall as the ideal requirement of image retrieval technique.Our experimental results shows that not only our proposed technique outstrip current techniques for rotated and scaled query images but also it has preferable results for retrieval time requirements.The performance investigation exhibit that the presented method upgrades the average precision value from 76.50% for combined features DCD (Dominant Color Descriptor), wavelet and curvelet to 99.1% and average recall value from 14.21% to 19.82% for rotated and scaled images utilizing Corel dataset.Also, the average retrieval time required is 1.39 sec, which is lower than existing modern techniques.
Directive Speech Acts on Commercial Advertising Discourse of Electronic Media and Its Implementation as Indonesian Language Teaching Material in Junior High Schools

Researchers analyzed directive speech acts in electronic media commercial advertisements.This research was conducted to obtain in-depth data analysis of directive speech acts in electronic media commercial advertisements, investigate the form and purpose of commercial advertisement of electronic media.The researchers used a qualitative descriptive method.The data for this study were obtained from 118 commercial advertisements contained in advertisements for beauty, clothing, starter packs, food, applications, banking, jewelry, housing, apartments, and vehicles published in electronic media.In this study, data collection techniques applied notetaking techniques and observation techniques, as well as interview techniques.The data analysis technique of this research applied pragmatics.The results of this study indicate that commercial advertisements contained directive speech acts, including: recommending 35%, inviting 26%, commanding 23%, demanding 4%, forcing 2%, and suggesting 10%.The types of directive speech act in this study were categorized as follows, namely: recommending used offering and directing, inviting used persuading and seducing, requesting used encouraging and ordering, demanding used challenging and charging, forcing used urging and requiring, and suggesting used calling and recommending.Another finding from this study is that the speech act of commercial advertising discourse of electronic media could be implemented into examples of Indonesian language advertising text teaching materials in high school (SMP) grade 2 in Basic Competence (3.3, 4.3) and (3.4,4.4) in the preparation of advertising texts of handouts.The directive speech acts in commercial advertisements in electronic media are relevant and can be used as teaching materials in schools to improve students' knowledge, especially the pragmatics aspect.
Moral Dilemmas between Reality and Virtual in the Digital Age

With the continuous innovation of artificial intelligence, virtual reality, 5G, and the progress of computer hardware equipment and network infrastructure, there are more and more moral dilemmas between virtual and the reality perceived by human beings.Taking the classical cave metaphor as an ideological resource, the information dissemination space created by big data can be regarded as a new data cave.The data cave shaped by technological rationality brings some new problems.It breaks the information dominance in the traditional era through technological empowerment, but it is accompanied by the risk of over-concentration of new data power; It not only enlightens the free personality of the audience but also makes the individual fall into a new data control network.Therefore, living in the data cave, it is very necessary to reflect on the over digitization trend of self and society.This paper attempts to sort out the relevance and differences between the virtual world and the real society in the increasingly complex technological ecology, and also try to find the basis and logical starting point to understand the moral dilemmas between them.
Development of the Siberian Ingrian Finnish Speech Corpus

In this paper we present the speech corpus for the Siberian Ingrian Finnish language.The speech corpus includes: audio data, annotations, software tools for data processing, two databases and a web application.We have published part of the audio data and annotations.The software tool for parsing annotation files and feeding a relational database is developed and published under a free license.A web application is developed and available.At this moment, about 300 words and 200 phrases can be displayed using this web application.
Protocols for symmetric secret key establishment: Modern approach

Introduction/purpose: The problem of efficient distribution of cryptographic keys in communication systems has existed since its first days and is especially emphasized by the emergence of mass communication systems. Defining and implementing efficient protocols for symmetric cryptographic keys establishment in such circumstances is of great importance in raising information security in cyberspace. Methods: Using the methods of Information Theory and Secure Multiparty Computation, protocols for direct establishment of cryptographic keys between communication parties have been defined. Results: The paper defines two new approaches to the problem of establishing cryptographic keys. The novelty in the protocol defined in the security model based on information theory is based on the source of common randomness, which in this case is the EEG signal of each subject participating in the communication system. Experimental results show that the amount of information leaking to the attacker is close to zero. A novelty in the second case, which provides security with keys at the level of computer security by applying Secure Multiparty Computation, is in the new application field, namely generation and distribution of symmetric cryptographic keys. It is characteristic of both approaches that within the framework of formal theories, it is possible to draw conclusions about their security characteristics in a formal way. Conclusions: The paper describes two new approaches for establishing cryptographic keys in symmetric cryptographic systems with experimental results. The significance of the proposed solutions lies in the fact that they enable the establishment of secure communication between communication parties from end to end, avoiding the influence of a trusted third party. In that way, the achieved communication level security significantly increases in relation to classical cryptographic systems.
Explaining Social Behaviors Via Cognitive Map

Human thinking and behaviors vary under many circumstances such as social interactions and stress.A major psychological view that forms the foundation of this phenomenon was schema, which refers to a hierarchical representation of explicit knowledge and suggests that this allows us to reason and make inferences to a novel situation.In addition, script, which is involved in explaining multiple procedural behaviors, may play an important role in determining how we behave given by the prior knowledge.Recent studies in neuroscience may explain these proposed psychological theories.Especially cognitive map, an idea proposed by Tolman, which was initially used to explain spatial cognition but later found to be explained the encoding of more abstract variables, may explain human thinking and behaviors.Moreover, the geometric properties of the activity of the neuronal population, via neural manifold modeling, provides more insights to the mapping of cognitive variables.