Reviewerly/exHarmony huggingface dataset:
Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Abstract Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.
Drone-as-a-Service (DaaS) for COVID-19 self-testing kits delivery in smart healthcare setups: A technological perspective

Drones have gained increasing attention in the healthcare industry for mobility and accessibility to remote areas. This perspective-based study proposes a drone-based sample collection system whereby COVID-19 self-testing kits are delivered to and collected from potential patients. This is achieved using the drone as a service (DaaS). A mobile application is also proposed to depict drone navigation and destination location to help ease the process. Through this app, the patient could contact the hospital and give details about their medical condition and the type of emergency. A hypothetical case study for Geelong, Australia, was carried out, and the drone path was optimized using the Artificial Bee Colony (ABC) algorithm. The proposed method aims to reduce person-to-person contact, aid the patient at their home, and deliver any medicine, including first aid kits, to support the patients until further assistance is provided. Artificial intelligence and machine learning-based algorithms coupled with drones will provide state-of-the-art healthcare systems technology.
A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&amp;amp;Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise.
Multi-Collaborative C + + Language Experimental Teaching Reformation Based on PTA

The experimental teaching of C + + language course is very important to consolidate the theoretical knowledge of classroom teaching, cultivate students' practical ability, innovation ability and independent ability to solve problems. However, the current experimental teaching of C + + language course still has the following problems: 1 The teaching methods and teaching resources are limited; 2 Students are passive to accept the knowledge; 3 The interaction between teachers and students are limited in the professional teaching process. So the reformation of C + + language programming teaching is imminent. We analyse the characteristics of C + + programming practice course and puts forward a multi-collaborative C + + language programming reformation based on PTA[1], which is an assistant teaching platform for experiment. The reformation proposed in this paper can integrate the multiple advantages of offline teaching, network platform and entity laboratory, which can also take measures to deal with the experimental teaching itself, build a "feature-oriented" practice problem set, improve students' learning efficiency and optimize the teaching effect of C + + programming practice course.
Beyond the Transformer: A Novel Polynomial Inherent Attention (PIA) Model and Its Great Impact on Neural Machine Translation

This paper describes a novel polynomial inherent attention (PIA) model that outperforms all state-of-the-art transformer models on neural machine translation (NMT) by a wide margin. PIA is based on the simple idea that natural language sentences can be transformed into a special type of binary attention context vectors that accurately capture the semantic context and the relative dependencies between words in a sentence. The transformation is performed using a simple power-of-two polynomial transformation that maintains strict consistent positioning of words in the resulting vectors. It is shown how this transformation reduces the neural machine translation process to a simple neural polynomial regression model that provides excellent solutions to the alignment and positioning problems haunting transformer models. The test BELU scores obtained on the WMT-2014 data set are 75.07 BELU for the EN-FR data set and 66.35 BELU for the EN-DE data set-well above accuracies achieved by state-of-the-art transformer models for the same data sets. The improvements are, respectively, 65.7% and 87.42%.
Digitization of Education in Vietnam in the Crisis of COVID-19 Pandemic

In dealing with the global pandemic, many sectors have extensive crisis management research to lean on but education has been virtually overlooked. With the recent global pandemic, the role of Internet of Things has been greater than ever before as most of economic activities have moved to online platforms. Education sector is one of the most heavily impacted where the immediate effect of lockdowns has been closure of schools, universities, colleges. Lockdowns sprung on everyone and everything, not just mode of studies, but also wellbeing, course effectiveness, student tuition, teacher’s income. Despite the ever-presiding change resistance in academia, digitization of education took place almost instantaneously. The COVID-19 pandemic acted as an accelerator for education digital transformation utilizing a wide variety of digital solutions to facilitate classroom teaching and learning. Through a survey of 913 Vietnamese students, this study seeks to explore the digitization of education in Vietnam and its implications on Vietnamese students. The author expects to explore the students’ readiness for digital transformation and their utilization of IoT in higher education through a quantitative regression analysis. Results show that students’ readiness for digital transformation is positively correlated with all four independent factors and in the following respective order of importance: COVID-19, self-study ability, perceived ease of use, perceived usefulness and attitude.
Performance and efficiency tradeoffs in blockchain overlay networks

Underlying blockchain's scalability and performance is a Peer-to-Peer (P2P) overlay network and protocols for relaying blocks and transactions among participating nodes. In this work, we model and perform a systematic analysis of blockchain communication protocols. We begin by introducing the performance metric of interest for blockchains, the sequence of ordered-completion-times, which captures the progress distributed nodes are making in jointly constructing a consistent blockchain. We then study the characteristics of block relaying protocols. In particular, we show that when nodes cannot perform cut-through relaying, there is no optimal causal block relaying protocol if block relaying times are deterministic. We propose a simple age-based block relaying protocol that is provably near-optimal in terms of minimizing ordered-completion-times when the P2P overlay network is a tree. This analysis is relevant to blockchain relay networks such as Bitcoin-Fibre, Bloxroute etc., where the core part of the overlay is a tree. Finally, using the insights derived for tree overlays, we explore the interplay between transaction and block relaying and prioritization (age, size, FCFS based) on both tree and fully connected overlays via simulation. We observe that policies that relay blocks and incorporate transactions into blocks based on 'age' outperform other natural policies. We also explore a fundamental tradeoff between mining and computational efficiencies and performance (in terms of ordered completion times).
Information space of sensor networks: Lagrangian, energy-momentum tensor, and applications

It is a challenge to investigate the interrelationship between the geometric structure and performance of sensor networks due to the increasingly complex and diverse architecture of them. This paper presents two new formulations for the information space of sensor networks, including Lagrangian and energy–momentum tensor, which are expected to integrate sensor networks target tracking and performance evaluation from a unified perspective. The proposed method presents two geometric objects to represent the dynamic state and manifold structure of the information space of sensor networks. Based on that, the authors conduct the property analysis and target tracking of sensor networks. To the best of our knowledge, it is the first time to investigate and analyze the information energy–momentum tensor of sensor networks and evaluate the performance of sensor networks in the context of target tracking. Simulations and examples confirm the competitive performance of the proposed method.
Combining Semantic and Visual Image Graphs for Efficient Search and Exploration of Large Dynamic Image Collections

Image collections today often consist of millions of images, making it impossible to get an overview of the entire content. In recent years, we have presented several demonstrators for graph-based systems allowing image search and a visual exploration of the collection. Meanwhile, very powerful visual and also joint visual-textual feature vectors have been developed, which are suitable for finding similar images to query images or according to a textual description. A drawback of these image feature vectors is that they have a high number of dimensions, which leads to long search times, especially for large image collections. In this paper, we show how it is possible to significantly reduce the search time even for high-dimensional feature vectors and improve the efficiency of the search system. By combining two different image graphs, on the one hand, an extremely fast approximate nearest neighbor search can be achieved. Experimental results show that the proposed method performs better than state-of-the-art methods. On the other hand, it is possible to visually explore the entire image collection in real time using a standard web browser. Unlike other graph-based search systems, the proposed image graphs can dynamically adapt to the insertion and removal of images from the collection.
Problem of Сonstructing the GMDH Neural Networks with Active Neurons

Characteristics of the existing neural networks of GMDH with active neurons are given and their main advantages and disadvantages are analyzed. Two approaches of increasing the efficiency of inductive construction of complex system models from statistical data based on a new hybrid GMDH neural networks with active neurons using methods of computational intelligence are proposed. The effectiveness of these networks is compared with classical approaches to artificial inductive modelling tasks (noisy linear and nonlinear models).
Readability and Communication in Machine Translation of Arabic Phraseologisms into Spanish

Even though translation from Arabic into Spanish is far from the accuracy level achieved for other language pairs such as English/Spanish, considerable progress has been made in machine translation from Arabic into other languages [1, 2, 4, 45]. From this perspective, this research aims to assess how Arabic proverbs are translated into Spanish by three machine translation tools - Google Translate, Reverso Context, and Tradukka – in order to determine the translation's quality, and measure their accuracy, as well as the tools' ability to recognize said proverbs as independent semantic linguistic units. To this end, we have used twenty Fusha Arabic proverbs: three that have no Spanish equivalent; six that also exist in Spanish; three that bear certain resemblance to existing Spanish proverbs; and eight that possess functional equivalents in Spanish. The study concludes that the machine translation tools tested present a low accuracy level, requiring post-editing by a human translator. They make errors rendering Arabic proverbs that it is possible to translate verbatim as they also exist in Spanish and do not recognize proverbs as independent phraseological units when translating proverbs in context, with few exceptions in the case of Reverso Context.
Multiple Instance Neuroimage Transformer

For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-overlapping 3D blocks from the input volume and perform multi-headed self-attention on a sequence of their linear projections. MINiT, on the other hand, treats each of the non-overlapping 3D blocks of the input MRI as its own instance, splitting it further into non-overlapping 3D patches, on which multi-headed self-attention is computed. As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). The learned attention maps highlight voxels contributing to identifying sex differences in brain morphometry. The code is available at https://github.com/singlaayush/MINIT .
Realization of Quantum Swap Gate and Generation of Entangled Coherent States

The cross fusion of quantum mechanics and information science forms quantum information science. Quantum logic gates and quantum entanglement are very important building blocks in quantum information processing. In this paper, we propose one-step schemes for realizing quantum swap gates and generating two-mode entangled coherent states via circuit QED. In our scheme, due to the adiabatic elimination of the excited state of the qutrit under the condition of large detuning, the decoherence of the spontaneous emission of the qutrit can be ignored. The fidelity of the quantum swap gate remains at a very high level. In addition, we also explore the nonclassical properties of two-mode entangled coherent states prepared in our scheme by addressing the second-order correlation function and intermodal squeezing. In particular, two classes of entangled coherent states demonstrate distinct entanglement and nonclassical behavior.
Dynamic Clustering and Power Control for Two-Tier Over-the-Air Federated Learning

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge. However, direct computing based on the information from the devices may not be resource efficient due to the relatively large communication cost between the devices and parameter server (PS). This paper studies the joint communication and learning design for the over-theair computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.
Safe Reinforcement Learning-based Indoor 3D Location Privacy Protection

Users share their current locations to the location-based service (LBS) server to request LBS in high-rise indoor buildings. However, this also raises the risk of users' location privacy leakage. An untrusted LBS server can infer users' personal private information and then sends spam or fraudulent messages to the user. Existing works mainly focus on protecting two-dimensional (2D) location privacy, ignoring the height dimension of the user's location data, i.e., three-dimensional (3D) geolocation. Besides, employing a high-risk location privacy protection policy may cause severe privacy leakage. In this paper, we study the 3D location privacy protection mechanism by randomly perturbing the user's location data based on 3D geo-indistinguishability. To better balance the privacy protection and the quality of service (QoS), we propose a scheme to adaptively adjust the perturbation policy based on reinforcement learning (RL) with safe exploration. The core idea of safe exploration is to continue evaluating the risk level of each state-action pair to rule out the high-risk state-action pair. Due to the large number of state-action pairs in real-life situations, we further propose a safe deep RL-based 3D location privacy protection scheme (SDRLP), using convolutional neural networks (CNNs) to better capture the system state and estimate the risk level. Simulation results demonstrate that the SDRLP-based scheme increases privacy and reduces QoS loss compared with the SRLP-based scheme.
Secure and privacy-preserving sharing of personal health records with multi-party pre-authorization verification

Wireless communications play an important role in ensuring ease of access to shared electronic health records (EHR) across health service providers which is essential and significant for prompt patients’ care, especially in cases of emergency medical conditions. With the need to support anytime, anywhere access to, potentially bandwidth hungry, medical records, electronic healthcare applications will continue to benefit from advanced wireless network technologies such as 5G and beyond. With sharing, it is crucial to provide patients with security and privacy guarantees, and allow them to certain control of access to their data. Existing solutions mostly assume that patients are available to authorize requests to access their EHR, which is impractical as the patient may be unconscious. This paper proposes a secure and privacy protecting protocol whereby the patient can pre-delegate the authorization for the access of his/her EHR. Our patient(user)-centric proposal combines Self-Sovereign Identity (SSI) concepts and model with Secure Multi-party Computation (SMPC) and Threshold Cryptography (TC) to enable secure identity and authorization verification. A block cipher encryption sharing approach is adopted for the threshold SMPC which extends the AES-GCM symmetric encryption model into a full-fledged cryptographic platform. Two mechanisms are implemented for the block cipher encryption, namely XOR and Cascade, and experiments are conducted to compare them. We conclude that the XOR mechanism can scale for larger thresholds, while Cascade performed better for a lower threshold (≤ 3). This paper also performs a threat analysis of the protocol and approach, and validates its correctness and complexity. We conclude that the approach can achieve the security and privacy protection of the patient’s personal EHR, as well as the autonomy of the patient to control the authorization for the access and sharing.
A Probabilistic Model Checking Approach to Self-adapting Machine Learning Systems

Machine Learning (ML) is increasingly used in domains such as cyber-physical systems and enterprise systems. These systems typically operate in non-static environments, prone to unpredictable changes that can adversely impact the accuracy of the ML models, which are usually in the critical path of the systems. Mispredictions of ML components can thus affect other components in the system, and ultimately impact overall system utility in non-trivial ways. From this perspective, self-adaptation techniques appear as a natural solution to reason about how to react to environment changes via adaptation tactics that can potentially improve the quality of ML models (e.g., model retrain), and ultimately maximize system utility. However, adapting ML components is non-trivial, since adaptation tactics have costs and it may not be clear in a given context whether the benefits of ML adaptation outweigh its costs. In this paper, we present a formal probabilistic framework, based on model checking, that incorporates the essential governing factors for reasoning at an architectural level about adapting ML classifiers in a system context. The proposed framework can be used in a self-adaptive system to create adaptation strategies that maximize rewards of a multi-dimensional utility space. Resorting to a running example from the enterprise systems domain, we show how the proposed framework can be employed to determine the gains achievable via ML adaptation and to find the boundary that renders adaptation worthwhile.