Reviewerly/exHarmony huggingface dataset:
Robotics and Education: A Systematic Review

Digital society is here, and it came to stay. One of our daily lives where information and communication technologies have the most significant impact is education. This systematic review analyzes the presence that technology, robotics specifically, has had on our educational system in recent years. Some keywords have been chosen, and screening has been carried out in the three most extensive databases about didactic and education: Education Resources Information Center, Scopus, and Web of Science chosen for being the most relevant in education sciences. Although among the most remarkable results, it was found that robotics is used as an individual skill developing tool. Its benefits as a teamwork promoting tool are not yet widely applied.
Smart Agricultural Futures Market: Blockchain Technology as a Trust Enabler between Smallholder Farmers and Buyers

Smallholder farmers produce over 70% of the world’s food needs. Yet, the socioeconomic conditions of the smallholder farmers are substandard. One of the primary reasons for this unpropitious situation is that they generate modest income by selling their harvest due to the lack of trusted buyers and organized markets. This research explores how technology can enable the trust to reduce transaction-related risks, empowering unknown parties to transact. Blockchain technology has the potential of mitigating transaction-related risks and promoting trust with a tamper-proof history of transactions and automatic execution of smart contracts. Based on blockchain technology to promote trust, this research has discovered a novel approach for smallholder farmers to conduct exchanges by generating social capital as an individual and using that social capital as collateral for financial exchanges when establishing contracts. This approach empowers farmers to trade smart futures contracts on behalf of the expected harvest at a better rate to receive some cash in advance to be used in the cultivation process to produce a high-quality harvest that attracts better rates. It also enables them to perform aggregated marketing with enhanced market linkages that, in turn, assist in increasing margins made by the farmer.
A Competency Framework for Teaching and Learning Innovation Centers for the 21st Century: Anticipating the Post-COVID-19 Age

During the COVID-19 pandemic, most Higher Education Institutions (HEIs) across the globe moved towards “emergency online education”, experiencing a metamorphosis that advanced their capacities and competencies as never before. Teaching and Learning Centers (TLCs), the internal units that promote sustainable transformations, can play a key role in making this metamorphosis last. Existing models for TLCs have defined the competencies that they could help develop, focusing on teachers’, students’, and managers’ development, but have mislead aspects such as leadership, organizational processes, and infrastructures. This paper evaluates the PROF-XXI framework, which offers a holistic perspective on the competencies that TLCs should develop for supporting deep and sustainable transformations of HEIs. The framework was evaluated with 83 participants from four Latin American institutions and used for analyzing the transformation of their teaching and learning practices during the pandemic lockdown. The result of the analysis shows that the PROF-XXI framework was useful for identifying the teaching and learning competencies addressed by the institutions, their deficiencies, and their strategic changes. Specifically, this study shows that most institutions counted with training plans for teachers before this period, mainly in the competencies of digital technologies and pedagogical quality, but that other initiatives were created to reinforce them, including students’ support actions.
Applying Probabilistic Model Checking to the Behavior Guidance and Abnormality Detection for A-MCI Patients under Wireless Sensor Network

With the development of the Internet of Medical Things (IoMT) , indoor wireless sensor networks (WSNs) have been used to monitor Alzheimer's disease patients daily and guide their behaviors. Alzheimer's disease may seriously impact patients’ memory, and thoughts of “what should I do” can unexpectedly form in their mind. This cognitive impairment can affect patients’ independence and well-being. As a basic infrastructure for future healthcare systems, WSN can collect patient behaviors, such as their positions and states, to support safety and health analyses. Therefore, this paper proposes a probabilistic model checking-based method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception. First, the layout of the home environment is abstracted as a formal grid, and a user activity model (UAM) is proposed in the form of discrete-time Markov chain (DTMC) to describe patients’ activity based on data collected by sensors. Second, because Alzheimer's patients with mild cognitive impairment (A-MCI) often forget their next daily activities, we classify and describe their daily behaviors as verification requirements in the form of probabilistic computational tree logic (PCTL) . Then, the UAM is input into a probabilistic model checking tool and compared against the verification property PCTL to calculate the probability values and assess temporal behaviors. As result, the activity with the largest probability is selected for behavior guidance. Third, we demonstrate the process of detecting abnormalities, including activities with abnormal temporal behaviors and activities with normal temporal behaviors but unexpected probabilities that may be repeated more than twice. The key states are extracted from the UAM to specify the verification properties for abnormality detection. Finally, a case study is presented to demonstrate the usability and feasibility of our proposed method.
Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions

Artificial intelligence has been widely applied to e-commerce and the online business service field. However, few studies have focused on studying the differences in the effects of types of customer service on customer purchase intentions. Based on service encounter theory and superposition theory, we designed two shopping experiments to capture customers’ thoughts and feelings, in order to explore the differences in the effects of three different types of online customer service (AI customer service, manual customer service, and human–machine collaboration customer service) on customer purchase intention, and analyses the superposition effect of human–machine collaboration customer service. The results show that the consumer’s perceived service quality positively influences the customer’s purchase intention, and plays a mediating role in the effect of different types of online customer service on customer purchase intention; the product type plays a moderating role in the relationship between online customer service and customer purchase intention, and human–machine collaboration customer service has a superposition effect. This study helped to deepen the understanding of AI developers and e-commerce platforms regarding the application of AI in online business service, and provides reference suggestions for the formulation of more perfect business service strategies.
Automating Aircraft Scanning for Inspection or 3D Model Creation with a UAV and Optimal Path Planning

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning.
Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges

Recently, a new Artificial Intelligence (AI) paradigm, known as Federated Learning (FL), has been introduced. It is a decentralized approach to apply Machine Learning (ML) on-device without risking the disclosure and tracing of sensitive and private information. Instead of training the global model on a centralized server (by aggregating the clients’ private data), FL trains a global shared model by only aggregating clients’ locally-computed updates (the clients’ private data remains distributed across the clients’ devices). However, as secure as the FL seems, it by itself does not give the levels of privacy and security required by today’s distributed systems. This paper seeks to provide a holistic view of FL’s security concerns. We outline the most important attacks and vulnerabilities that are highly relevant to FL systems. Then, we present the recent proposed defensive mechanisms. Finally, we highlight the outstanding challenges, and we discuss the possible future research directions.
Local &amp; Federated Learning at the network edge for efficient predictive analytics

The ability to perform computation on devices, such as smartphones, cars, or other nodes present at the Internet of Things leads to constraints regarding bandwidth, storage, and energy, as most of these devices are mobile and operate on batteries. Using their computational power to perform locally machine learning and analytics tasks can enable accurate and real-time predictions at the network edge. A trained machine learning model requires high accuracy towards the prediction outcome, as wrong decisions can lead to negative consequences on the efficient conclusion of applications. Most of the data sensed in these devices are contextual and personal requiring privacy-preserving without their distribution over the network. When working with these privacy-preserving data, not only the protection is important but, also, the model needs the ability to adapt to regular occurring concept drifts and data distribution changes to guarantee a high accuracy of the prediction outcome. We address the importance of personalization and generalization in edge devices to adapt to data distribution updates over continuously evolving environments. The methodology we propose relies on the principles of Federated Learning and Optimal Stopping Theory extended with a personalization component. The privacy-efficient and quality-awareness of personalization and generalization is the overarching aim of this work.
Usability of Memes and Humorous Resources in Virtual Learning Environments

This research consists of a quantitative analysis of the perspective of a group of university professors from different areas of knowledge and from 19 different Latin American countries on the use of humor and memes in virtual learning environments (VLEs) in higher education. The data have been obtained from an own-design survey, and the answers have been analyzed in a descriptive and inferential way with the aim of knowing the opinion of the 401 participants (professors) about the didactic effectiveness of humor and the benefits and employability of memes in virtual classrooms. The analysis differentiates the sample by the professors’ area of knowledge as the main variable, and by gender, age and teaching experience. As results, the participants give a high evaluation of humorous didactic resources, particularly memes, although the evaluation of their usability in the classroom is intermediate. In this sense, it is shown that the area of knowledge has a significant influence on opinions in this regard.
Development of Blockchain Learning Game-Themed Education Program Targeting Elementary Students Based on ASSURE Model

The blockchain education program based on the ASSURE model proposed in this article is of value because it can be applied in blended learning during the COVID-19 pandemic, using learning games to facilitate self-directed learning. We developed the education program in accordance with the six steps of the education design process of the ASSURE model. Firstly, we assessed learners to identify digital literacy issues of South Korean elementary students and jobs desired by them. Secondly, the objective of blockchain education was defined as improving awareness of and attention to blockchain technology by elementary students. Thirdly, gamification applied lessons were used as a teaching method, with educational media and data developed as worksheets and materials that can be used both online and offline. Fourthly, the educational contents and teaching aids were tested to evaluate the developed learning materials. Fifthly, the learning games were designed to offer rewards. Last, we designed the program to teach the principles of consensus mechanisms, private blockchain, and public blockchain. Education experts’ feedback was analyzed using technical statistics and LDA-based topic modeling to assess and modify the program. The education program design approach incorporating gamification elements was effective but needed expansion in coverage to include level-based teaching elements.
Transformer-Based Attention Network for Vehicle Re-Identification

Vehicle re-identification (ReID) focuses on searching for images of the same vehicle across different cameras and can be considered as the most fine-grained ID-level classification task. It is fundamentally challenging due to the significant differences in appearance presented by a vehicle with the same ID (especially from different viewpoints) coupled with the subtle differences between vehicles with different IDs. Spatial attention mechanisms that have been proven to be effective in computer vision tasks also play an important role in vehicle ReID. However, they often require expensive key-point labels or suffer from noisy attention masks when trained without key-point labels. In this work, we propose a transformer-based attention network (TAN) for learning spatial attention information and hence for facilitating learning of discriminative features for vehicle ReID. Specifically, in contrast to previous studies that adopted a transformer network, we designed the attention network as an independent branch that can be flexibly utilized in various tasks. Moreover, we combined the TAN with two other branches: one to extract global features that define the image-level structures, and the other to extract the auxiliary side-attribute features that are invariant to viewpoint, such as color, car type, etc. To validate the proposed approach, experiments were conducted on two vehicle datasets (the VeRi-776 and VehicleID datasets) and a person dataset (Market-1501). The experimental results demonstrated that the proposed TAN is effective in improving the performance of both the vehicle and person ReID tasks, and the proposed method achieves state-of-the-art (SOTA) perfomance.
Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia)

Rainfall modeling is one of the most critical factors in agricultural monitoring and statistics, transportation schedules, and urban flood prevention. Weather anomaly during the dry season in urban coastal areas of tropical countries such as Jakarta, Indonesia has become a challenging issue that causes unexpected changes in rain patterns. In this paper, we propose the Bayesian Network (BN) approach to model the probabilistic nature of rain patterns in urban areas and causal relationships among its predictor variables. Rain occurrences are predicted using temperature, relative humidity, mean-sea level (MSL) pressure, cloud cover, and precipitation variables. Data are obtained from the remote sensing sources of the National Oceanic and Atmospheric Administration (NOAA) satellite in Jakarta 2020-2021. We compare both of the score-based, i.e., Hill Climbing (HC), and hybrid structure learning algorithms of Bayesian Network including the techniques of Max-Min Hill Climbing (MMHC), General 2-Phase Restricted Maximization (RSMAX2), and Hybrid-Hybrid Parents &amp; Children (H2PC). Further, we also compare the performance of score-based model (Hill Climbing) under five different popular scorings: Bayesian Information Criterion (BIC), K2, Log-Likelihood, Bayesian Dirichlet Equivalent (BDE), and Akaike Information Criterion (AIC) methods. The main contributions of this study are as follows: (1) insights that the hybrid structure learning algorithms of Bayesian Network models are either superior in performance or at least comparable to its score-based counterparts (2) our proposed best performed Bayesian Network model that is able to predict the rain occurrences in Jakarta with a promising overall accuracy of more than 81 percent.
FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations

Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequency-level artifacts in generated images. We find that ignoring the frequency-level artifacts can improve the detector's generalization across various GAN models, but it can reduce the model's performance for the trained GAN models. Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models. Our framework generates the frequency-level perturbation maps to make the generated images indistinguishable from the real images. By updating the deepfake detector along with the training of the perturbation generator, our model is trained to detect the frequency-level artifacts at the initial iterations and consider the image-level irregularities at the last iterations. For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories. Numerous experiments validate the state-of-the-art performance of our deepfake detector.
Limits on sequential sharing of nonlocal advantage of quantum coherence

Sequential sharing of nonlocal correlation is inherently related to its application. We address the question as to how many observers can share the nonlocal advantage of quantum coherence (NAQC) in a $(d\times d)$-dimensional state, where $d$ is a prime or a power of a prime. We first analyze the trade-off between disturbance and information gain of the general $d$-dimensional unsharp measurements. Then in a scenario where multiple Alices perform unsharp measurements on one party of the state sequentially and independently and a single Bob measures coherence of the conditional states on the other party, we show that at most one Alice can demonstrate NAQC with Bob. This limit holds even when considering the weak measurements with optimal pointer states. These results may shed light on the interplay between nonlocal correlations and quantum measurements on high-dimensional systems and the hierarchy of different quantum correlations.
Towards Speaker Age Estimation With Label Distribution Learning

Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise age identification remains a challenge due to label ambiguity, i.e., utterances from adjacent age of the same person are often indistinguishable. To address this, we utilize the ambiguous information among the age labels, convert each age label into a discrete label distribution and leverage the label distribution learning (LDL) method to fit the data. For each audio data sample, our method produces a age distribution of its speaker, and on top of the distribution we also perform two other tasks: age prediction and age uncertainty minimization. Therefore, our method naturally combines the age classification and regression approaches, which enhances the robustness of our method. We conduct experiments on the public NIST SRE08-10 dataset and a real-world dataset, which exhibit that our method outperforms baseline methods by a relatively large margin, yielding a 10% reduction in terms of mean absolute error (MAE) on a real-world dataset.
MPCCL: Multiview predictive coding with contrastive learning for person re-identification

In this paper, we investigate a new representation learning approach, termed as Multiview Predictive Coding with Contrastive Learning (MPCCL), for person re-identification (re-ID). Different from the conventional re-ID approaches that focus on learning representations from semantic label, our approach learns the identification of invariant information via representation reconstruction, which explores more fine-grained semantic information in representation space. Specifically, given a chosen identity, the learned representation of its single view can be reconstructed by those of other views. Therefore, kernel density estimation (KDE) is firstly introduced for the adaptive reconstruction of the representation. Then, contrastive learning is adopted to increase the distance between the representations of the same views with different identities. Finally, representation reconstruction and contrastive learning jointly supervise the representation learning process, thus obtaining fine-grained semantic information and appearance-free representations. Extensive experiments on several re-ID datasets demonstrate that the proposed approach yields state-of-the-art results.