Reviewerly/exHarmony huggingface dataset:
The Role of School Administrative Support and Primary School Teachers’ ICT Literacy to Integrate ICT into the Classrooms in Pahang, Malaysia

This study aimed to look at the role of information and communications technology (ICT) literacy of teachers as a mediator between the school administrative support and ICT competence of primary school teachers in Pahang.The researchers developed a mediation model based on relevant literature reviews and subsequently tested the model.This study used a total survey design.The researchers employed stratified cluster sampling to select the pilot study and the actual study samples randomly.A total of 514 teachers were involved in the pilot study and 530 teachers were involved in the actual study.The Cronbach's alpha for instrument was .954while the validity of the instrument used was determined using the SEM-AMOS method.The results based on the pilot data indicated a good model fit with chisquare = 212.406,df = 111, p < .001,CMIN/df = 3.662, GFI = .938,CFI = .941,and RMSEA = .072,whereas results based on the actual study also indicated a good model fit with chi-square = 204.839,df = 58, p < .001,CMIN/df = 3.532, GFI = .942,CFI = .950,and RMSEA = .069.The findings showed that there were direct and indirect effects of school administrative support and teachers' ICT literacy on teachers' ICT competences.There was a statistically significant relationship (p < .05) between school administrative support and teachers' ICT competence and an indirect effect through teachers' ICT literacy as a mediator.Hence, teachers' ICT literacy acts a partial mediator in the model.This study shows that school administrative support and teachers' ICT literacy play an important role in shaping teachers' ICT competence.Furthermore, the study provides further evidence that school principals through teachers' ICT literacy play an important role in integrating ICT into the classroom.School administrators should equip themselves with formal training in technological leadership so that they can better contribute to aiding school teachers to better integrate ICT into their teaching and learning.
Image Super-Resolution Reconstruction Based on a Generative Adversarial Network

In the field of computer vision, super-resolution reconstruction techniques based on deep learning have undergone considerable advancement; however, certain limitations remain, such as insufficient feature extraction and blurred image generation. To address these problems, we propose an image super-resolution reconstruction model based on a generative adversarial network. First, we employ a dual network structure in the generator network to solve the problem of insufficient feature extraction. The dual network structure is divided into an upsample subnetwork and a refinement subnetwork, which upsample and optimize a low-resolution image, respectively. In a scene with large upscaling factors, this structure can reduce the negative effect of noise and enhance the utilization of high-frequency details, thereby generating high-quality reconstruction results. Second, to generate sharper super-resolution images, we use the perceptual loss, which exhibits a fast convergence and excellent visual effect, to guide the generator network training. We apply the ResNeXt-50-$32\times 4\text{d}$ network, which has few parameters and a large depth, to calculate the loss to obtain a reconstructed super-resolution image that is highly realistic. Finally, we introduce the Wasserstein distance into the discriminator network to enhance the discrimination ability and stability of the model. Specifically, this distance is employed to eliminate the activation function in the last layer of the network and avoid the use of the logarithm in calculating the loss function. Extensive experiments on the DIV2K, Set5, Set14, and BSD100 datasets demonstrate the effectiveness of the proposed model.
Improving the Accuracy of Deep Neural Networks Through Developing New Activation Functions

Without activation functions, it would be only possible for the neural network to learn very basic tasks, so the activation function is a key point in the neural network's architecture. The function allows us to learn more complicated tasks and also it impacts the performance to obtain the outcome. So, activation functions represent the continuous and widespread interest of research to identify the most suitable activation function to a specific task. In this paper, we propose four activation functions that bring improvements for different datasets in the Computer Vision task. These functions are a combination of the popular activation functions such as sigmoid, bipolar sigmoid, Rectified Linear Unit (ReLU), and tangent (tanh). By allowing activation functions to be learnable we obtain models more robust. To validate these functions, we tested using more datasets and more architectures with different depths, showing that their properties are significant and useful. Also, we compared them with other powerful activation functions to see how our proposed activation functions impact accuracy.
Cultural Differences in Emoticon Perception: Japanese See the Eyes and Dutch the Mouth of Emoticons

This study investigated cultural differences in the perception of emoticons between Japanese and Dutch participants. We manipulated the eyes and mouth of emoticons independently and asked participants to evaluate the emotion of each emoticon. The results show that Japanese participants tended to focus on the emotion expressed with the eyes while Dutch participants put weight on the shape of the mouth when evaluating emoticons. This tendency is consistent with a previous cross-cultural study comparing people from Japan and the United States (Yuki et al., 2007).
Neural Network-based System for Automatic Passport Stamp Classification

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.
An Intelligent Ubiquitous Learning Environment and Analytics on Images for Contextual Factors Analysis

Contextual factors in which learning occurs are crucial aspects that learning analytics and related disciplines aim to understand for optimizing learning and the environments in which learning occurs. In foreign vocabulary development, taking the notes or memos of learning contexts along with other factors, play an essential role in quick memorization and reflection. However, conventional tools fail to automate the learning contexts generation process as learners still need to take memos or e-notes to describe their vocabulary learning contexts. This paper presents the Image Understanding Project (hereafter IUEcosystem) that could produce smartly-generated learning contexts primarily in a learner’s target languages. The IUEcosystem uses visual content analysis of lifelogging images as the sensor data to produce smartly-generated learning contexts that could be used as an alternative to handwritten memos or electronic notes. The IUEcosystem uses applied artificial intelligence to produce smartly-generated learning contexts. This intelligent learning environment collects a learner’s learning satisfaction and interaction data and, later on, analyzes them to produce time-based notifications for enhancing retention. Furthermore, a new learning design is presented that aims to map a learner’s prior vocabulary knowledge with new learning vocabularies to be learned. This learning design would help learners to review and recall prior knowledge while learning new vocabulary.
Defining a critical data literacy for recommender systems: A media-grounded approach

The digital processing of massive data is becoming a central component of our technological infrastructures.While being able to use these tools efficiently is an issue that cannot be ignored, it appears crucial to provide citizens with the means to control their technical environment.Recommender systems and personalization technologies are currently being blamed for the destabilization of users' informational ecosystems and a growing polarization of opinions.However, a critical review of the current literature on the subject indicates that these recommender systems may also be beneficial to the user in specific circumstances.Building on current critical data literacies approaches, key concepts from the philosophy of technology and a media literacy perspective, this paper suggests a framework defining the competences needed to help users assess these technologies and critically include them in their digital ecosystem.
New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.
Attacking Pairing-Free Attribute-Based Encryption Schemes

Combining several results that have been published in the last years, it is known that it is impossible to design simple and secure attribute-based encryption schemes that work in (classical) settings like the RSA or the pairing-free discrete logarithm ones.The purpose of this paper is to broadcast this message through a wide (maybe non-cryptographic) audience, specially now that attribute-based encryption is considered as a useful tool to secure real systems like the Internet of Things.Today, only attribute-based encryption schemes that employ tools like bilinear pairings or lattices can provide some real (and provable) level of security.As an example of the fact that this message is still unknown for many people, we revisit a (maybe non exhaustive) list of papers proposing such insecure attribute-based encryption schemes: we recall which of these schemes have already been attacked and we describe attacks for the other ones.
Operational Significance of the Quantum Resource Theory of Buscemi Nonlocality

Although entanglement is necessary for observing nonlocality in a Bell experiment, there are entangled states which can never be used to demonstrate nonlocal correlations. In a seminal paper [PRL 108, 200401 (2012)] F. Buscemi extended the standard Bell experiment by allowing Alice and Bob to be asked quantum, instead of classical, questions. This gives rise to a broader notion of nonlocality, one which can be observed for every entangled state. In this work we study a resource theory of this type of nonlocality referred to as Buscemi nonlocality. We propose a geometric quantifier measuring the ability of a given state and local measurements to produce Buscemi nonlocal correlations and establish its operational significance. In particular, we show that any distributed measurement which can demonstrate Buscemi nonlocal correlations provides strictly better performance than any distributed measurement which does not use entanglement in the task of distributed state discrimination. We also show that the maximal amount of Buscemi nonlocality that can be generated using a given state is precisely equal to its entanglement content. Finally, we prove a quantitative relationship between: Buscemi nonlocality, the ability to perform nonclassical teleportation, and entanglement. Using this relationship we propose new discrimination tasks for which nonclassical teleportation and entanglement lead to an advantage over their classical counterparts.
A fast and large bandwidth superconducting variable coupler

Variable microwave-frequency couplers are highly useful components in classical communication systems and likely will play an important role in quantum communication applications. Conventional semiconductor-based microwave couplers have been used with superconducting quantum circuits, enabling, for example, the in situ measurements of multiple devices via a common readout chain. However, the semiconducting elements are lossy and furthermore dissipate energy when switched, making them unsuitable for cryogenic applications requiring rapid, repeated switching. Superconducting Josephson junction-based couplers can be designed for dissipation-free operation with fast switching and are easily integrated with superconducting quantum circuits. These enable on-chip, quantum-coherent routing of microwave photons, providing an appealing alternative to semiconductor switches. Here, we present and characterize a chip-based broadband microwave variable coupler, tunable over 4–8 GHz with over 1.5 GHz instantaneous bandwidth, based on the superconducting quantum interference device with two parallel Josephson junctions. The coupler is dissipation-free and features large on-off ratios in excess of 40 dB, and the coupling can be changed in about 10 ns. The simple design presented here can be readily integrated with superconducting qubit circuits and can be easily generalized to realize a four- or more port device.
To recognize or not to recognize – A database of encrypted images with subjective recognition ground truth

The assessment of very low quality visual data is known to be difficult. In particular, the ability of humans to recognize encrypted visual data is currently impossible to determine computationally. The human vision research community has widely studied some particular topics, such as image quality assessment or the determination of a visibility threshold, while others are still barely researched, specifically visual content recognition. To this day, there does not exist a reliable recognition index that can be employed for such tasks. In order to enable the study of human image content recognition, and in an attempt to propose a corresponding recognizability index, we build a dataset of selectively encrypted images together with subjective ground-truth about their human intelligibility. The methods of acquisition, setup, protocol, outlier detection, are described and we suggest how to calculate a recognition score as well as a recognition threshold. The performance of traditional visual quality indices to predict human visual content recognition is assessed on these data and found to be inapt to estimate recognition of visual content. Contrasting, structure based recognition indices as proposed for this task are shown to represent a promising starting point for further research. To facilitate the creation of a recognition index and to foster further research into human visual content recognition and its relation to the human visual system we will make the database publicly available.
Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity

Detecting Internet malicious activities has been and continues to be a critical issue that needs to be addressed effectively.This is essential to protect our personal information, computing resources, and financial capitals from unsolicited actions, such as, credential information theft, downloading and installing malware, extortion, etc.The introduction of the social media such as Twitter has given malicious users a new and a promising platform to perform their activities, ranging from a simple spam message to taking a full control over the victim's machine.Twitter revealed that its algorithms for detecting spam are not very effective; most of the trending hashtags include unrelated spam and advertising tweets which indicates that there is a problem with the currently used spam detection framework.This paper proposes a new approach for detecting spam in Twitter microblogging using Machine Learning (ML) techniques and domain popularity services.The proposed approach comprises two main stages: 1) Tweets are collected periodically and filtered by selecting the ones that appear more frequently than a decided threshold in the specified period (i.e. common tweets).Then, an inspection is conducted on the common tweets by checking the associated URL domain with Alexa's top one million globally viewed websites.If a tweet is common on Twitter but does not appear on the top one million globally viewed websites, it is flagged as a potential spam.2) The second stage kicks in by running ML algorithms on the flagged tweets to extract features that help detect the cluster of spam and prevent it in real-time.The performance of the proposed approach has been evaluated using three most popular classification models (random forest, J48, and Naïve Bayes).For all classifiers, results showed the effectiveness of the proposed method in terms of different performance metrics (e.g.precision, sensitivity, F1score, accuracy) and using different test scenarios.
Joining semantic and augmented reality to design smart homes for assistance

Introduction Smart homes for assistance help compensate cognitive deficits, thus favoring aging in place. However, to be effective, the assistance must be adapted to the abilities, deficits, and habits of the person. Beside the elder, caregivers are the ones who know the person’s needs best. This article presents a Do-it-Yourself approach for helping caregivers designing a smart home for assistance. Methods A co-construction process between a caregiver and a virtual adviser was designed. The knowledge of the virtual adviser about smart homes, activities of daily living and assistance is organized in an ontology. The caregiver interacts with the virtual adviser in augmented reality to describe the home and the resident’s habits inside it. The process is illustrated with an ordinary activity: ‘Drink water’. Results The proposed process highlights two main steps: describing the environment and determining the resident’s habits and the assistance required to improve activity performance. Visual guidance and feedback are provided to ease the process. Conclusion Designing a co-construction process with a virtual adviser allows interactive knowledge sharing with the caregivers who are experts of the person’s needs. Future work should focus on evaluating the prototype presented and providing deeper advice such as highlighting incomplete or incorrect scenarios, or navigation aid.
Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning. Our code is available on GitHub.
JOINT WEATHER PREDICTING BY HMM AND ANN

In India, climate change tremendously due to the flow of wind, temperature, precipitation, etc. based on parameters.Our idea is to combine the two city or states data to calculate dependence on the neighboring city/states.Also, to analysis past history data of climate 21 years.To predicate the future five years.To show, that the it will predict the future five years of data.By mutual interaction of the most predication models Hybrid Markova model and artificial neural network.ANN and HMM will both predicate the flow two cities and states due to which the predication analysis of climate India will be accurate.This paper defines the data analysis on predication based model and due to which the flow of model will be accurate.This paper shows a strategy to conjecture and settles on a choice of weather condition.In the majority of the urban communities around the globe, individuals attempt to settle on relaxation exercises on their extra time however climate condition would not be appropriate for them.Since the climate conditions in India are flighty, a methodology must be created to gauge climate effectively.By gauging climate definitely, we can anticipate and beat numerous dangers that could prompt extraordinary misfortune to a country.Thus, so as to do this, the Hidden Markov Model and Artificial Neural Network has been deciding for shared cooperation of two states to foresee the climate condition and as yield third states are been predicated.
EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph

Research on notable accomplishments and important events in the life of people of public interest usually requires close reading of long encyclopedic or biographical sources, which is a tedious and time-consuming task. Whereas semantic reference sources, such as the EventKG knowledge graph, provide structured representations of relevant facts, they often include hundreds of events and temporal relations for particular entities. In this paper, we present EventKG+BT – a timeline generation system that creates concise and interactive spatio-temporal representations of biographies from a knowledge graph using distant supervision.