Reviewerly/exHarmony huggingface dataset:
Assessing children’s information and knowledge organisation competency in elementary schools of Hong Kong

This article evaluates the information and knowledge organisation competency of third- to fifth-grade primary school students in Hong Kong directly or indirectly. The majority of the students are aged 8–11 years. The types of information and knowledge organisation schemes to be identified or organised include shallow taxonomies (e.g. a list of entities, a list of features of an entity, a list of events) and simple descriptive ontologies (e.g. a sequence of events, reasons of events, relation between entities or events). A total of 86 students participated in the study. Each student was asked to read an English book and a Chinese book, and to answer assessment questions about the content within the books. The questions ask children to identify members of a flat taxonomy and organise simple descriptive ontologies. The children’s overall information and knowledge organisation competency is found to be weak, but children’s information and knowledge organisation capabilities are not equally weak. The children identify features of an entity significantly better than a list of events, and identify reasons significantly better than flat taxonomies and relations. The findings have theoretical and practical implications for book writers, book cover designers, teachers, librarians and designers of information systems for children.
Blockchains in Non-Fungible Tokens.

Non-Fungible Tokens (NFTs) have lately piqued the interest of investors, with some NFTs achieving selling levels previously inconceivable for a non-fungible virtual asset. This raises intriguing questions about "value" and "scarcity" in relation to blockchain technology, viewed through the lens of a digital asset's non-fungibility, This study seeks to attract attention to these issues inasmuch as they may create a future alternative environment for blockchain development and trading.
Turn-of-The-Candle Effect in Bitcoin Returns

This study discovers a statistically and economically significant intraday anomaly on Bitcoin markets. Positive returns of 0.58 bps per minute are disproportionately concentrated at the turn of 15-minute candles (in minutes 0, 15, 30, and 45 of each trading hour). Average returns in other trading minutes are negative. The effect is consistent across exchanges, in quantile and TGARCH-M estimations with heavy tails. A high-frequency strategy exploiting this “turn-of-the-candle” effect can be net-outperforming with initial investment as low as $5,000. The anomaly is detected starting from mid-to-late 2020, and its discovery contributes significantly to the understanding of cryptocurrency adaptive market efficiency.
An Exploratory Study of Entrepreneurship Development in Kogi State

This study aimed to unveil the challenges confronting entrepreneurship development and the impact of government support in Kogi State. The study determined the effect of government support on entrepreneurship skills development in Kogi State and examined the influence of government policies on entrepreneurship skills development in Kogi State. The survey research design was used in this study. A convenient sampling technique was adopted, and 296 participants were taken. Data were gathered and analyzed using descriptive statistics, Multiple Regression and Ordered Probit Regression. The study found that government support had a substantial effect on the development of entrepreneurship skills in Kogi State. Findings also demonstrated that government policies in Kogi State have a limited effect on the development of entrepreneurship skills. The study’s limitations are attached to the small sample size, which may not stand as a perfect representation, as well as the inability of the study to exhaust the factors challenging entrepreneurship development. The results of the study provide insight into cardinal factors affecting entrepreneurship development in Kogi State, the degree of the effectiveness of government support and the government policies that need review. Entrepreneurship skills development is crucial for driving social and economic development. Unveiling the challenges confronting it provides a pipeline for remedies and the needed support from the government. The study is novel as it links with sustainable entrepreneurship programs. The study is intended to widen the knowledge of the audience and establish a theoretical relationship that can be considered by practitioners in the field of entrepreneurship.
What happens next? Combining enhanced multilevel script learning and dual fusion strategies for script event prediction

Script event prediction (SEP), aiming at predicting next event from context event sequences (i.e., scripts), has played an important role in many real-world applications such as government decision-making. While most of the existing research only depend on the top-level event prediction, they ignore the influence of other bottom levels or other relationship modeling manners. In this paper, we focus on the problem of SEP via multilevel script learning where the goal of is to explore a multistage, multiprediction and multilevel information fusion model for SEP. This is challenging in (1) simultaneously modeling of the multilevel event relationship semantic information and (2) effectively designing multilevel information fusion strategies. In this paper, we propose a new script event prediction model based on Enhanced Multilevel script learning and Dual Fusion strategies, named EMDF-Net. Specifically, EMDF-Net designs the multilevel (event/chain/segment level) script learning to model both temporal and casual information as well as the rich structural relevance via neural stacking of self-attention mechanism and graph neural networks. Then it proposes dual fusion strategies to fully integrate different-level information by nonlinear feature composition and weighted score fusion. Finally, a deep supervision strategy is utilized to end-to-end train the whole model and provide a good initialization for information fusion. Experimental results on the popular NYT corpus demonstrate the effectiveness and superiority of EMDF-Net.
Literature Study of The Implementation of Skimming Techniques in Improving Quick Reading Ability in Indonesian Language Students in Class V

This study was to describe the process of skimming techniques for elementary school students to improve reading skills. This study used a literature study approach using the Systematic Literature Review (SLR) method. The journal subject comes from Google Scholar with research limitations of elementary school students that have been analyzed by the researcher. The results of Eve's research show that there is an increase in reading skills in Indonesian language lessons for students. This can be seen from the first and second cycles of active students, educators and student learning outcomes. The first cycle the total value of the student's activeness observation, with a score of 62.5% with a sufficient predicate. Whereas in the second cycle the number of scores obtained on student learning activeness got a score of 87.5% with the predicate good and very good. The results of Muhtar's research stated that after the paired t-test analysis was carried out, it was found that there was an effect of the application of the skimming technique on students' reading ability. Five to determine the level of student ability. After adjusting for the five scale score interval, it is known that the score is 95.25%, therefore this score is included in the Very Good category. This can be proven that the ability of students when reading fast using skimming techniques is included in the excellent category during the speed reading test and in understanding the content of the text reading. From the results of analyzing the research that has been done previously carried out by Hawa, Muhtar, and Fitria that Skimming techniques can help students' ability to read quickly in Indonesian lessons.
Improved estimators for analyzing highly skewed environmental datasets with detection limits

The datasets from environmental studies often contain some measurements that fall below some detection limits. These datasets are often highly skewed making most of the standard models inappropriate to model these data. In order to investigate such data, the mixture models under classical methods have been proposed in the literature. In the case of mixture models, the mixture of gamma and lognormal models has frequently been used. We have proposed the mixture model under the Bayesian approach to analyze the said datasets. Two real skewed datasets with detection limits have been used for the analysis. On comparing with a variety of mixture models, the two-component mixture of Frechet distributions (2CMFD) has been explored to be the most suitable model for modelling highly skewed datasets. In addition, the performance of the proposed Bayes estimators was better than that of those existing in the literature.
A Critical Discourse Analysis Of Martin Luther Kings Socio-Political Speeches

Critical discourse analysis examines the key idea of social power and how it is used to produce social mistakes as a result of power abuse, inequality, and dominance. Martin Luther King's speeches show many of these social ideas. These views and authority are shown either explicitly or indirectly through the speaker's use of language structures. By analyzing the speeches, this paper tries to determine the speaker's target audience comments. This study focuses on a few selected sections that include patterns and terms that hint at these ideologies and power. The aims of this study is: Distinguishing textual and discursive techniques utilized in the speeches under review&#x0D; As a result, this research implies that 1) certain textual approaches are more often used in the selected speeches than others. 2) In the selected speeches, certain discursive strategies are used more frequently than others. When it comes to textual and discursive analysis, certain sociopolitical ideologies are more commonly revealed than others.&#x0D; The most widely used textual analysis methods are transitivity and modality. 1) In the investigated speeches, presupposition is the most common discursive strategy. 2) Certain themes dominate the writing. 3)Textual and discursive analysis often employ racism as an ideology.
AMIDST: A Java toolbox for scalable probabilistic machine learning

The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic graphical models with latent variables. AMIDST provides parallel and distributed implementations of scalable algorithms for doing probabilistic inference and Bayesian parameter learning in the specified models. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continuous variables from a wide range of probability distributions.
Initial seed selection for K-modes clustering – A distance and density based approach

Initial seed artefacts play a vital role in proper categorization of the given data set in partitioning based clustering algorithms. Hence, it is important to identify them. We propose a density with distance based method which ensures identification of seed artefacts from different clusters that leads to more accurate clustering results. Our algorithm improves on the search for initial seed artefacts iteratively until the minimum value of the sum of within sum errors, normalized by their data sizes, is ensured. This is because the initial artefacts are selected from different clusters. Here the choice of seed artefacts guarantees a global optimum clustering solution. We have compared our results with random, Wu, Cao and Khan’s methods of initial seed artefact selection, to show the efficacy of our method.
Capturing the Geometry of Object Categories from Video Supervision

We propose an unsupervised method to learn the 3D geometry of object categories by looking around them. Differently from traditional approaches, this method does not require CAD models or manual supervision. Instead, using only video sequences showing object instances from a moving viewpoint, the method learns a deep neural network that can predict several aspects of the 3D geometry of such objects from single images. The network has three components. The first is a Siamese viewpoint factorization network that robustly aligns the input videos and learns to predict the absolute viewpoint of the object from a single image. The second is a depth estimation network that performs monocular depth prediction. The third is a shape completion network that predicts the full 3D shape of the object from the output of the monocular depth prediction module. While the three modules solve very different task, we show that they all benefit significantly from allowing networks to perform probabilistic predictions. This results in a self-assessment mechanism which is crucial for obtaining high quality predictions. Our network achieves state-of-the-art results on viewpoint prediction, depth estimation, and 3D point cloud estimation on public benchmarks.
Variational Noise-Contrastive Estimation

Unnormalised latent variable models are a broad and flexible class of statistical models. However, learning their parameters from data is intractable, and few estimation techniques are currently available for such models. To increase the number of techniques in our arsenal, we propose variational noise-contrastive estimation (VNCE), building on NCE which is a method that only applies to unnormalised models. The core idea is to use a variational lower bound to the NCE objective function, which can be optimised in the same fashion as the evidence lower bound (ELBO) in standard variational inference (VI). We prove that VNCE can be used for both parameter estimation of unnormalised models and posterior inference of latent variables. The developed theory shows that VNCE has the same level of generality as standard VI, meaning that advances made there can be directly imported to the unnormalised setting. We validate VNCE on toy models and apply it to a realistic problem of estimating an undirected graphical model from incomplete data.
A hybrid multiresolution wavelet transform based encryption scheme

In this paper, a hybrid multi-resolution wavelet transform based optical cryptosystem is proposed. The hybrid multi-resolution wavelet is formed by using conventional Walsh transform and fractional Fourier transform, here the fractional orders of the fractional Fourier transform extends the key-space of the proposed cryptosystem to avoid any brute-force attack. The designed cryptosystem possesses non-linear characteristics. To validate and verify the proposed cryptosystem computerized simulations have been carried out on the gray scale images. Results demonstrate that the proposed cryptosystem offers higher protection against occlusion and noise attacks.
Business Decision Support System based on Sentiment Analysis

Since organizational decisions are vital to organizational development, customers' views and feedback are equally important to inform good decisions.Given this relevance, this paper seeks to automate a sentiment analysis system -SentDesk-that can aid tracking sentiments in customers' reviews and feedback.The study was contextualised in some business organisations in Ghana.Three business organizational marketers were made to annotate emotions and as well tag sentiments to each instance in the corpora.Kappa and Krippendoff coefficients were computed to obtain the annotation agreement in the corpora.The SentDesk system was evaluated in the environment alongside comparing the output to that of the average sentiments tagged by the marketers.Also, the SentDesk system was evaluated in the environment by the selected marketers after they had tested the platform.By finding the average kappa value from the corpora (CFR + ISEAR), the average kappa coefficient was found to be 0.40 (40%).The results of evaluating the SentDesk system with humans shows that the system performed as better as humans.The study also revealed that, while annotating emotions and sentiments in the datasets, counsellor's own emotions influences their perception of emotions.
VICA, a visual counseling agent for emotional distress

We present VICA, a Visual Counseling Agent designed to create an engaging multimedia face-to-face interaction. VICA is a human-friendly agent equipped with high-performance voice conversation designed to help psychologically stressed users, to offload their emotional burden. Such users specifically include non-computer-savvy elderly persons or clients. Our agent builds replies exploiting interlocutor’s utterances expressing such as wishes, obstacles, emotions, etc. Statements asking for confirmation, details, emotional summary, or relations among such expressions are added to the utterances. We claim that VICA is suitable for positive counseling scenarios where multimedia specifically high-performance voice communication is instrumental for even the old or digital divided users to continue dialogue towards their self-awareness. To prove this claim, VICA’s effect is evaluated with respect to a previous text-based counseling agent CRECA and ELIZA including its successors. An experiment involving 14 subjects shows VICA effects as follows: (i) the dialogue continuation (CPS: Conversation-turns Per Session) of VICA for the older half (age > 40) substantially improved 53% to CRECA and 71% to ELIZA. (ii) VICA’s capability to foster peace of mind and other positive feelings was assessed with a very high score of 5 or 6 mostly, out of 7 stages of the Likert scale, again by the older. Compared on average, such capability of VICA for the older is 5.14 while CRECA (all subjects are young students, age < 25) is 4.50, ELIZA is 3.50, and the best of ELIZA’s successors for the older (> 25) is 4.41.
Ethics and inscription in social robot design. A visual ethnography

Abstract In this paper, by turning to examples of visual inscriptions adopted within HRI research, and more specifically social robotic interaction, I wish to explore through visual ethnography some of the challenges faced in designing ethical robots. Firstly, visual ethnography allows for an alternative categorisation of the inscriptions of HRI based on visual characteristics. Secondly, visual inscriptions show multiple and paradoxical meanings when appearing juxtaposed revealing challenges of diminished and asymmetric consideration of human orientated concerns in favour of technical and experimental certainty. Thirdly, by taking a human orientated perspective of experimental arrangements, the understanding of ethics becomes a way of framing and of looking at inscriptions. The paper calls for a better understanding of the role of inscription practices in HRI generally in order to find new approaches useful to bolster a more robust inclusion of ethics within the field.
Predicting Air Quality from Low-Cost Sensor Measurements

Urban air pollution poses a significant global health risk, but due to the high expense of measuring air quality, the amount of available data on pollutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actual air quality conditions, making them an apt target for machine learning techniques. In this paper we use a dataset of measurements from a low cost air-quality instrument—the ODIN-SD—to examine which techniques are most appropriate, and the limitations of such an approach. From theoretical and experimental considerations, we conclude that a robust linear regression over measurements of air quality metrics, as well as relative humidity and temperature measurements produces the model with greatest accuracy. We also discuss issues of concept drift which occur in this context, and quantify how much training data is required to strike the right balance between predictive accuracy and efficient data collection.
The effect of VR and traditional videos on learner retention and decision making

Abstract&#x0D; &#x0D; Virtual reality (VR) as computer-mediated 3-D environments including real-life situations presented via different tools in which users can interact effectively with visuals. This study is significant as it primarily aimed to investigate effects of VR on retention by comparing it with traditional 2-D videos. The main aim of this study is to compare the effect of VR and 2-D videos on learner retention and decision making. About 18 students from the Department of English language teaching in an age range of 18–20 (nine males and nine females) voluntarily participated in this study and took course credits. As all participants were tested under the same two conditions in different time periods; repeated measures were chosen for statistical analysis. It can be inferred that both technologies have similar effects on short-term retention and VR does not provide any extra retention performance. However, VR was found to have a significant effect on long-term retention.&#x0D; &#x0D; Keywords: Virtual reality, eye tracking, retention, decision making.&#x0D; &#x0D;
A Cognitive Semantic Analysis of Meaning Interrelationship

Expanding the English vocabulary for Iraqi second language (L2) learners is a challenging task.In the present work, Langacker's cognitive linguistic theory of domains ( 1987) is adopted to provide a new sight into vocabulary learning, and to expand the vocabulary repertoire of Iraqi students.This paper aims at testing the validity of expanding the English vocabulary of L2 Iraqi learners throughout the theory of domains.It also tries to find how can domains theory increase the package of vocabulary for L2 learners.Accordingly, an experimental study is to be conducted on forty-six college students of second-year level from, University of Baghdad, Iraq.The data of the pre-test and post-test were analyzed using the Statistical Package for Social Sciences (SPSS) l editor.Results have shown the following: First, the progress of more than (0.05≤) has been detected as far as students' understanding of the semantic domains of the lexical concepts.Second, the main source of difficulty regarding vocabulary acquisition has been treated throughout Langacker's theory.Third, the domains theory has proven its effectiveness in accurately comprehending the semantic domains of the English lexical concepts.
THE EFFECT OF USING INQUIRY BASED LEARNING STRATEGY ON STUDENTS SPEAKING ABILITY ( A Case Study at SMAN 7 Bengkulu Selatan)

This research is aimed at finding out to what extent IBL strategy can affect students speaking ability and to find out the significant of IBL strategy on students speaking ability. The populations of this study were all students in second grade at SMAN 7 Bengkulu. The samples of this study were 30 students on one class of second grade at SMAN 7 Bengkulu Selatan. This study used quasi-experimental study with one group design in order to test hypotheses. The study was conducted by pretest, the treatment in three meetings by using IBL strategy and posttest to the students. The instruments of this study were pretest and posttest. The result of this research showed that there were increasing scores of students speaking ability from 40,27 to 51,33, the increasing scores covered in; vocabulary aspect (13,54), grammar aspect (10,67), fluency aspect (7,33), and pronunciation aspect (12,67). The conclusions of the study were: firstly, IBL strategy affected students speaking ability with average increasing score of 11,05 covering the four aspects (vocabulary, gramar, fluency and pronunciation) of speaking. Secondly, IBL strategy affected sufficiently significant ( 0,05) on students speaking ability. This current study contributes to give the teachers an additional strategy on the effective strategy for teaching of speaking.