Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.
Which Turn do Neural Models Exploit the Most to Solve GuessWhat? Diving into the Dialogue History Encoding in Transformers and LSTMs

We focus on visually grounded dialogue history encoding. We show that GuessWhat? can be used as a “diagnostic” dataset to understand whether State-of-the-Art encoders manage to capture salient information in the dialogue history. We compare models across several dimensions: the architecture (Recurrent Neural Networks vs. Transform-ers), the input modalities (only language vs. language and vision), and the model background knowledge (trained from scratch vs. pre-trained and then ﬁne-tuned on the downstream task). We show that pre-trained Transformers are able to identify the most salient information independently of the order in which the dialogue history is processed whereas LSTM based models do not.
Personalizing Item Recommendation via Price Understanding

Personalization has gained a lot of traction in the e-commerce domain since there is ample evidence for short-term and long-term benefits of understanding user preferences and ensuring user satisfaction. However, effectively personalizing recommendations is a challenging task, especially at scale. Price is often a key consideration for purchases, and user behavior varies widely depending on demographic and psychological factors. While difficult to model, this is an important signal to consider for user-item recommendation. In this paper, we focus on personalizing and improving the relevance of item recommendations for e-commerce users by leveraging price as an essential input. More concretely, we segregate items into price bands indicating how expensive they are, infer user affinity to price bands based on historical behavior and use features derived from this knowledge to re-rank items in a real-world recommendation scenario. We experiment with various statistical and machine learning methods to determine item price bands, user price affinities and item price similarities, and demonstrate impact on the recommendation quality for millions of users and items.
Combining Universal Adversarial Perturbations

Universal adversarial perturbations (UAPs) are small perturbations imposed on images that are able to fool a single convolutional neural network image classiﬁer. They have been shown to generalise well to other neural networks. Here, we report on our reproduction eﬀort of the results given in a work by Moosavi-Dezfooli et al. on UAPs and study two methods to construct UAPs for several neural networks. While the results are not strong enough to make general conclusions, they suggest that UAPs indeed proﬁt from being constructed on several neural networks. Also, we show that a linear interpolation between two UAPs does not produce a viable UAP on both networks.
Obtaining the Minimal Terminologically Saturated Document Set with Controlled Snowball Sampling

Collecting the scientiﬁc papers to write the Related Work section, keeping up-to-date expertise in the topic of interest, or studying new scientiﬁc direction is the ill-deﬁned information need that does not allow certainty about the completeness of search results. The controlled snowball method suggested by authors in the previous papers was extended with the objective criterion of the result completeness that allows stopping the search. The criterion is based on the assumption that the complete document set contains all terms describing the topic of interest. So, appending new document to the complete collection does not extend the list of terms. In the experiments, we compare our method of gathering the scientiﬁc papers describing the topic ”Ontologies (computer science)” with other three common approaches: search by automatical detected topic in ”Microsoft Academic” database, a keyword search in Google Scholar database, and query ACM digital library with author keywords. For each of the collected sets, the automatic term extraction was performed, and the size of the minimal saturated ordered document set is found. It was shown that terminological saturation is observed for the sets collected with controlled snowball method and with topic search in ”Microsoft Academic” database. Moreover, the proposed controlled snowball provides the 10% smaller document set.
Application of Space-Time Patterns in Tutoring

The inevitable increase in the diversity of information flows, their processing and visualization methods exacerbates interdisciplinary problems. They lead to cognitive bias, which limits the potential of IT and intelligent learning systems. Therefore, the aim of the work is to expand interdisciplinary relations through the use of complementary space-time signatures and patterns of functioning of self-organizing objects. The object of research is the system “student – cognitive environment – teach-er”, the functioning of which is determined by the interdisciplinary ergonomic laws of mutual adaptation and transformation. They are a generalization of the extreme principles of physics, as well as the principles of cybernetics, synerget-ics, computer science, etc. The cognitive visualization of the spatio-temporal structure of fractal signals of various nature in the form of signatures and patterns is carried out. The basis of visualization – interdisciplinary – approach, methods, concepts and criteria. This allows us to identify hidden patterns of functioning of self-organized dynamic systems. The use of signatures and patterns of fractal signals of self-organized objects will contribute to the development of critical thinking and intuition in learning and also promising for machine
Less is MORE: a MultimOdal system for tag REfinement

With the proliferation of image-based social media, an extremely large amount of multimodal data is being produced. Very often image contents are published together with a set of user defined metadata such as tags and textual descriptions. Despite being very useful to enhance traditional image retrieval, user defined tags on social media have been proven to be noneffective to index images because they are influenced by personal experiences of the owners as well as their will of promoting the published contents. To be analyzed and indexed, multimodal data require algorithms able to jointly deal with textual and visual data. This research presents a multimodal approach to the problem of tag refinement, which consists in separating the relevant descriptors (tags) of images from noisy ones. The proposed method exploits both Natural Language Processing (NLP) and Computer Vision (CV) techniques based on deep learning to find a match between the textual information and visual content of social media posts. Textual semantic features are represented with (multilingual) word embeddings, while visual ones are obtained with image classification. The proposed system is evaluated on a manually annotated Italian dataset extracted from Instagram achieving 68% of weighted F1-score.
Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting

Renewable energy has become popular compared with traditional energy like coal. The relative demand for renewable energy compared to traditional energy is an important index to determine the energy supply structure. Forecasting the relative demand index has become quite essential. Data mining methods like decision trees are quite effective in such time series forecasting, but theory behind them is rarely discussed in research. In this paper, some theories are explored about decision trees including the behavior of bias, variance, and squared prediction error using trees and the prediction interval analysis. After that, real UK grid data are used in interval forecasting application. In the renewable energy ratio forecasting application, the ratio of renewable energy supply over that of traditional energy can be dynamically forecasted with an interval coverage accuracy higher than 80% and a small width around 22, which is similar to its standard deviation.
Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model

Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world’s different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission.
Immediate Data-Driven Positive Feedback Increases Engagement on Programming Homework for Novices

Learning theories and psychological research show that positive feedback during practice can increase learners’ motivation, and correlates with their learning. In our prior work, we built a system that provides immediate positive feed-back using expert-authored features, and found a promising impact on students’ performance and engagement with the system. However, scaling this expert-feedback system to new programming tasks requires extensive human eﬀort. In this paper, we present a system that provides automated, data-driven, immediate positive feedback (DD-IPF) to students while programming. This system uses a data-driven feature detector that automatically detects feature completion in the current student’s code based on features learned from historical student data. To explore the impact of DD-IPF on students’ programming behavior, we performed a quasi-experimental study across two semesters in a block-based programming class. Our results showed that students with DD-IPF were more engaged, as measured by time spent on the programming task, and also showed marginal improvement in their grades, compared to students in a prior semester solving the same task without feedback. This suggests that positive feedback based on data-driven feature detection can provide beneﬁts in student engagement and performance. We conclude with design recommendations for data-driven programming feedback systems.
Interactive Augmented Reality Technologies for Model Visualization in the School Textbook

AR-technologies can be widely used in the educational process using ICT. Especially valuable is the use of AR-based applications for the visualization of illustrations of models of objects and processes in school books. The use of electronic educational resources using AR-technologies can improve the quality of students' knowledge, as well as provide teachers with a wide range of new opportunities. Using AR technology allows you to design and create effective learning resources. This develops students' creative thinking and increases their motivation to learn. In this paper, we consider a model of a learning system using augmented reality technologies for visualizing illustrations in school textbooks. A survey of secondary school teachers showed the possibility, interest and effectiveness of using e-learning tools based on AR technology. Pupils can use this electronic resource both in class at school and at home during independent study. Students of Kherson State University, interviewed in the STEM education system, showed a willingness to work with augmented reality technologies. To assess the prospects of using AR-based applications in the educational process, an expert method was used. Experts evaluated the prospects of using AR technology to visualize 2D and 3D models of educational objects. For evaluation, a five-point Likert system was chosen. The developed model can be used as a means to create the basis for future research, development and dissemination in the system of educational institutions. The proposed model of the learning system was tested in the classrooms of students in the learning process at STEM.
Motor skill learning decreases motor variability and increases planning horizon

We investigated motor skill learning using a path tracking task, where human subjects had to track various curved paths at a constant speed while maintaining the cursor within the path width. Subjects’ accuracy increased with practice, even when tracking novel untrained paths. Using a “searchlight” paradigm, where only a short segment of the path ahead of the cursor was shown, we found that subjects with a higher tracking skill differed from the novice subjects in two respects. First, they had lower motor variability, in agreement with previous findings. Second, they took a longer section of the future path into account when performing the task, i.e. had a longer planning horizon. We estimate that between one third and one half of the performance increase was due to the increase in planning horizon. An optimal control model with a fixed horizon (receding horizon control) that increases with tracking skill quantitatively captured the subjects’ movement behaviour. These findings demonstrate that human subjects not only increase their motor acuity but also their planning horizon when acquiring a motor skill. New and Noteworthy We show that when learning a motor skill humans are using information about the environment from an increasingly longer amount of the movement path ahead to improve performance. Crucial features of the behavioural performance can be captured by modelling the behavioural data with a receding horizon optimal control model.
Applying a Newly Learned Second Language Dimension to the Unknown: The Influence of Second Language Mandarin Tones on the Naïve Perception of Thai Tones

Abstract This study investigates whether L2 Mandarin learners can generalize experience with Mandarin tones to unfamiliar tones (i.e., Thai). Three language groups – L1 English/ L2 Mandarin learners (n=18), L1 Mandarin speakers (n=30), L1 monolingual English speakers (n=23) – were tested on the perception of unfamiliar Thai tones on ABX tasks. L2 Mandarin learners and L1 Mandarin speakers perceived Thai tones more accurately than L1 English non-learners. Mandarin learners L1 speakers showed priming on Mandarin tones on a lexical decision task with repetition priming, suggesting L2 tones had been encoded within lexical representations of L2 Mandarin words. However, results must be interpreted cautiously, with an absence of expected priming and presence of unexpected priming. In sum, learners can transfer L2 tone experience to unfamiliar tones, expanding the Feature Hypothesis (McAllister, Flege, & Piske, 2002) to include L2 influence as well. In addition, results indicate a potential disconnect between perception and encoding.
Predicting Heart Diseases from Large Scale IoT Data Using a Map-Reduce Paradigm

Abstract Over the last few years, the huge amount of data represented a major obstacle to data analysis. Big data implies that the volume of data undergoes a faster progress than computational speeds, thereby demanding a larger data storage capacity. The Internet of Things (IoT) is a main source of data that is closely related to big data, as the former extends to a variety of fields such as healthcare, entertainment, and disaster control. Despite the different advantages associated with the composition of Big Data analytics and IoT, there are a number of complex difficulties and issues involved that need to be resolved and managed to ensure an accurate data analysis. Some of these solutions include the utilization of map-reduce techniques, processing, and large data scale, particularly for the relatively less time that this method requires to process large data from the Internet of Things. Machine learning algorithms of this kind are often implemented in the healthcare sector. Medical facilities need to be advanced so that more appropriate decisions can be made in terms of patient diagnosis and treatment options. In this work, two datasets have been used: the first set, used in the prediction of heart diseases, obtained an accuracy rate of 84.5 for RF and 83 for J48, whereas the second dataset is related to weather stations (automated sensors) and obtained accuracy rates of 88.5 and 86.5 for RF and J48, respectively.
User-centred design of humanoid robots’ communication

Abstract Interaction between humans and robots will benefit if people have at least a rough mental model of what a robot knows about the world and what it plans to do. But how do we design human-robot interactions to facilitate this? Previous research has shown that one can change people’s mental models of robots by manipulating the robots’ physical appearance. However, this has mostly not been done in a user-centred way, i.e. without a focus on what users need and want. Starting from theories of how humans form and adapt mental models of others, we investigated how the participatory design method, PICTIVE, can be used to generate design ideas about how a humanoid robot could communicate. Five participants went through three phases based on eight scenarios from the state-of-the-art tasks in the RoboCup@Home social robotics competition. The results indicate that participatory design can be a suitable method to generate design concepts for robots’ communication in human-robot interaction.
The protein molecule : conformation, stability and folding

The functions of proteins are closely related to their conformations - the aspect on which this book primarily concentrates on. It further treats the stability and folding of proteins and discribes the molecular basis of protein functions. The book is useful not only for protein engineers whowant to improve protein functions and stability but also for biochemistswho want to understand the protein functions on the basis of the physicochemical properties of the protein molecule.
In-Season Strength Training in Elite Junior Swimmers: The Role of the Low-Volume, High-Velocity Training on Swimming Performance

Abstract The aim of this study was to analyze the effect of long-term combined strength training (ST) and plyometrics on strength, power and swimming performances in elite junior swimmers during a competitive season. Ten elite junior swimmers (5 women and 5 men) completed the study (age: 16.6 ± 0.7 years; mass: 62.2 ± 5.4 kg; stature: 1.70 ± 0.07 m). The participants trained twice a week during 20 weeks. The ST program consisted of upper- and lower limbs exercises with low loads and low volume, lifting the load at maximal intended velocity. The effect of the training protocol was assessed using the 1RM in the full squat (SQ) and bench press (BP), jump height (CMJ), the maximal number of repetitions completed in the pull-up (PU) exercise and time during 50-m freestyle. Training program resulted in significant improvements in CMJ (12.1%, ES: 0.57), maximal dynamic strength in the SQ (16.4%, ES: 0.46) and BP (12.1%, ES: 0.34) exercises, the maximum number of repetitions completed during the PU test (90.7%, ES: 0.57) and swimming performance (-3.9%, ES: 0.45). There were no significant differences between both genders. The relative changes in swimming performance showed significant relationship with the relative changes in 1RM of SQ for pooled data (r=-0.66, p<0.05) and the relative changes in the PU exercise in female swimmers (r=-0.99, p<0.05). Therefore, coaches and strength and conditioning professionals should consider including in-season dry-land ST programs within the training routine in order to obtain further improvements in swimming performance.