Reviewerly/exHarmony huggingface dataset:
Teacher Education Institutions in the Philippines towards Education 4.0

Education must reflect the world it is training students for. Education 4.0 ensures that teaching-learning experiences will take advantage of the limitless opportunities created by advanced technology. This study determines the readiness of teacher education institutions in the Philippines for Education 4.0 in the areas of faculty, teaching and learning, infrastructure, and research as perceived by the administrators and faculty members of teacher education institutions in the Philippines. This study made use of survey questionnaire indicating the four parameters mentioned, and data were analysed using quantitative method. The findings of this study suggest that administrators and faculty members perceived that they are ready in terms of their skills in selecting and integrating digital resources for teaching and learning as they are also given capacity buildings through seminars and conferences related to technological literacy. However, they are neither skillful in using the learning management system and other online class modality; nor in the utilization of augmented reality, robotics, and digital enablers like 3D printing. This finding may be attributed to the unavailability and inaccessibility of digital infrastructure and virtual laboratories in most teacher education institutions. The respondents also disagreed that the teacher education institutions are ready in terms of research programs and initiatives for Education 4.0. Thus, there is a need for teacher education institutions to rethink infrastructure planning, redesign research initiatives, and strengthen teacher-training capabilities to be Education 4.0 ready. https://doi.org/10.26803/ijlter.19.8.8
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach

A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the sensitive attributes is essential, while, in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of the individuals’ sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints while guaranteeing the privacy of sensitive attributes. The paper analyses the tension between accuracy, privacy, and fairness and the experimental evaluation illustrates the benefits of the proposed model on several prediction tasks.
Experimental Certification of Nonclassicality via Phase-Space Inequalities

In spite of its fundamental importance in quantum science and technology, the experimental certification of nonclassicality is still a challenging task, especially in realistic scenarios where losses and noise imbue the system. Here, we present the first experimental implementation of the recently introduced phase-space inequalities for nonclassicality certification, which conceptually unite phase-space representations with correlation conditions. We demonstrate the practicality and sensitivity of this approach by studying nonclassicality of a family of noisy and lossy quantum states of light. To this end, we experimentally generate single-photon-added thermal states with various thermal mean photon numbers and detect them at different loss levels. Based on the reconstructed Wigner and Husimi $Q$ functions, the inequality conditions detect nonclassicality despite the fact that the involved distributions are nonnegative, which includes cases of high losses (93%) and cases where other established methods do not reveal nonclassicality. We show the advantages of the implemented approach and discuss possible extensions that assure a wide applicability for quantum science and technologies.
ACNN: a Full Resolution DCNN for Medical Image Segmentation

Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, a new and full resolution DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Instance Normalization (IN) is proposed. Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions (IoUs) than U-Net and Deeplabv3+, but with reduced trainable parameters.
Qubit-efficient entanglement spectroscopy using qubit resets

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">qubit-efficient</mml:mtext></mml:mrow></mml:math> quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-th power of the density operator of a quantum system, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi><mml:mi>r</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>ρ</mml:mi><mml:mi>n</mml:mi></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math>, (related to the Rényi entropy of order <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>, are variants of previous algorithms with width proportional to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>, an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">effective circuit depth</mml:mtext></mml:mrow></mml:math> as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi><mml:mi>r</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>ρ</mml:mi><mml:mi>n</mml:mi></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> for larger n than possible with previous algorithms.
Online Teaching During Covid-19: How to Maintain Students Motivated in an EFL Class

This study aims to know the e-learning modality that stimulates students' learning process according to twenty-two English teachers in Santander, Colombia, and the correlation between learners' motivation and the challenges and opportunities in an online EFL class. To carry out this study, a mixed research approach was applied. To gather such information, a questionnaire was sent to the participants via email. Once data was scrutinized, it was found that this group of English teachers view synchronous encounters and access to information regardless of the geographical position as elements that can positively impact the students' motivation levels. Furthermore, this sample stated their role is significant, especially when mediating online lessons.
FIND: Human-in-the-Loop Debugging Deep Text Classifiers

Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets.These classifiers are thus likely to have undesirable properties.For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting.In this paper, we propose FINDa framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features.Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar traintest distributions).
Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator

In recent years, machine vision algorithms have played an influential role as core technologies in several practical applications, such as surveillance, autonomous driving, and object recognition/localization. However, as almost all such algorithms are applicable to clear weather conditions, their performance is severely affected by any atmospheric turbidity. Several image visibility restoration algorithms have been proposed to address this issue, and they have proven to be a highly efficient solution. This paper proposes a novel method to recover clear images from degraded ones. To this end, the proposed algorithm uses a supervised machine learning-based technique to estimate the pixel-wise extinction coefficients of the transmission medium and a novel compensation scheme to rectify the post-dehazing false enlargement of white objects. Also, a corresponding hardware accelerator implemented on a Field Programmable Gate Array chip is in order for facilitating real-time processing, a critical requirement of practical camera-based systems. Experimental results on both synthetic and real image datasets verified the proposed method's superiority over existing benchmark approaches. Furthermore, the hardware synthesis results revealed that the accelerator exhibits a processing rate of nearly 271.67 Mpixel/s, enabling it to process 4K videos at 30.7 frames per second in real time.
Adapting Competitiveness and Gamification to a Digital Platform for Foreign Language Learning

Due to globalization growth, learning a second language is a necessity to develop in an increasingly demanding multicultural environment. However, at present we still find that some traditional methodologies are still being applied for teaching, which represents a problem for current students, also called digital natives, because these methodologies should adapt to this digital age of technology and knowledge. To solve this problem, a digital resource for foreign language learning was developed taking into account competitiveness and gamification as bases to motivate students and engage them into the course. The objective of this tool is to improve the processing of theoretical information obtained making use of a virtual environment which has competitive activities and gamification elements such as: obtaining medals for completing tasks, user's progress bar and ranking according to the scores obtained, so that students are motivated and improve their learning. This research seeks to analyze the effects of applying competitiveness and gamification in a virtual environment aimed at foreign language learning. It was found that 81.03% of students are more inclined to use gamified digital tools and also 82.76% of students feel more motivated to learn a second language using this methodology.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles

Model interpretability has become an important problem in machine learning (ML) due to the increased effect algorithmic decisions have on humans. Counterfactual explanations can help users understand not only why ML models make certain decisions, but also how these decisions can be changed. We frame the problem of finding counterfactual explanations as an optimization task and extend previous work that could only be applied to differentiable models. In order to accommodate non-differentiable models such as tree ensembles, we use probabilistic model approximations in the optimization framework. We introduce an approximation technique that is effective for finding counterfactual explanations for predictions of the original model and show that our counterfactual examples are significantly closer to the original instances than those produced by other methods specifically designed for tree ensembles.
An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.
GrabAR: Occlusion-aware Grabbing Virtual Objects in AR

Existing augmented reality (AR) applications often ignore the occlusion between real hands and virtual objects when incorporating virtual objects in user's views. The challenges come from the lack of accurate depth and mismatch between real and virtual depth. This paper presents GrabAR1, a new approach that directly predicts the real-and-virtual occlusion and bypasses the depth acquisition and inference. Our goal is to enhance AR applications with interactions between hand (real) and grabbable objects (virtual). With paired images of hand and object as inputs, we formulate a compact deep neural network that learns to generate the occlusion mask. To train the network, we compile a large dataset, including synthetic data and real data. We then embed the trained network in a prototyping AR system to support real-time grabbing of virtual objects. Further, we demonstrate the performance of our method on various virtual objects, compare our method with others through two user studies, and showcase a rich variety of interaction scenarios, in which we can use bare hand to grab virtual objects and directly manipulate them.
Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

This article proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.
Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Recently, unmanned aerial vehicles (UAV) are widely used in many fields due to the low cost and high flexibility.One of the most popular applications of UAV is vehicle detection in aerial images which plays an important role in traffic surveillance and urban planning.Although, many deep learning based detectors have achieved state-of-the-art (SOTA) performance in natural images, the significant variation in object scales caused by the altitude change of the UAV platform brings great challenges to these detectors for precise localization of vehicles in aerial images.To improve the detection performance for vehicles with different scales, we propose a novel detection algorithm which consists of three stages.In the first stage, to reduce the distortion of vehicles during image resizing and keep more information of aerial images, we utilize an image cropping strategy to divide the image into two patches.In the second stage, we combine the original image and two patches into a batch and detect vehicles with a Convolutional Neural Network (CNN).For feature representation in our detector, we propose Scale-specific Prediction to strengthen the multi-scale features of vehicles with context information.In the final stage, to fuse detections and suppress false alarms, we propose an Outlier-Aware Non-Maximum Suppression.Extensive experiments are conducted to demonstrate the superiority of the proposed algorithm by comparison with other SOTA solutions.
Lies in the Air: Characterizing Fake-base-station Spam Ecosystem in China

Fake base station (FBS) has been exploited by criminals to attack mobile users by spamming fraudulent messages for over a decade. Despite that prior work has proposed several techniques to mitigate this issue, FBS spam is still a long-standing challenging issue in some countries, such as China, and causes billions of dollars of financial loss every year. Therefore, understanding and exploring the thematic strategies in the FBS spam ecosystem at a large scale would improve the defense mechanisms.
A Provably Secure Two-Factor Authentication Scheme for USB Storage Devices

Universal Serial Bus (USB) is widely used, for example to facilitate hot-swapping and plug-and-play. However, USB ports can be exploited by an adversary to extract private or personal data from the connected devices. Hence, a number of organizations and workplaces have prohibited their employees from using USB devices, and there have been efforts to design secure USB storage device schemes to more effectively resist different known security attacks. However, designing such schemes is challenging. For example, in this article we revisit the Wei et al.'s scheme, and demonstrate that it is vulnerable to attacks such as password guessing and user impersonation. We also explain that the scheme does not verify the correctness of user's input in the login phase, which is another design flaw. Then, we present an improved scheme and prove it secure in the random oracle model.
Blockchain-Based Data Sharing for Decentralized Tourism Destinations Recommendation System

One thing that tourists need to plan their tourism activities is a recommendation system. The tourism destinations recommendation system in this study has three primary nodes, namely user, server, and sensor. Each node requires the ability to share data to produce recommendations that the user expects through their mobile devices. In this paper, we propose the data-sharing system scheme uses a blockchain-based decentralized network that each node can be connected directly to each other, to support the exchange of data between them. The block architecture used in the blockchain network has three main parts, namely block information, hashes, and data. Each type of node has a different structure and direction of data communication. Where the user node sends destination assessment data to the server node, then the server node sends data from the machine learning process to the user node. The sensor sends dynamic data about popularity, traffic, and weather to the user node as consideration for finalizing the generating recommendations process. In the process of sending data, each node in the blockchain network goes through several functions, including hashing, block validation, chaining block, and broadcast. We conduct web-based experiments and analysis of the data-sharing system to illustrate the system works. The experimental results show that the system handles data circulation with an average time of mine is 84.5 ms in sending multi-criteria assessment data from the user and 119.1 ms in sending data of machine learning result from the server.