Reviewerly/exHarmony huggingface dataset:
Virtual Reality in Initial Teacher Education (VRITE): a reverse mentoring model of professional learning for learning leaders

This small-scale design-based study describes a cyclical model of professional learning between three stakeholders in initial teacher education (ITE) namely: university-based educators (UEs), student teachers (STs) and co-operating teachers (CTs). This model promotes the development of digital learning leaders through an innovative mentoring process. This process started with university-based educators (UEs) mentoring their student teachers (STs) in the pedagogical use of Virtual Reality (VR) and the creation of re-usable learning objects (RLOs). STs were supported and encouraged to cascade this learning to their placement schools as digital learning leaders connecting the innovative practice from the university directly to their classroom practice. Through bi-directional reverse-mentoring the STs and CTs supported each other technically (with the VR) and pedagogical (through the links to the curriculum) to create additional subject-specific RLOs which the STs were able to demonstrate to the UEs on their return to university. Thus, providing the final link in the cycle of learning leaders across the triad of partners in ITE.
An Optimized M-Term Karatsuba-Like Binary Polynomial Multiplier for Finite Field Arithmetic

Finite field multiplication is a fundamental and frequently used operation in various cryptographic circuits and systems. Because of its high complexity, this operation generally determines the overall complexity and cost of these systems. Therefore, finite field multipliers and their hardware implementation have received considerable attention from researchers. This article proposes a methodology to design an efficient Galois field multiplier. First, space and time complexities for theoretical and field-programmable gate array (FPGA) implementations of M-term Karatsuba-like finite field multipliers were obtained. In addition, an algorithm was developed to obtain an efficient design based on a composite M-term Karatsuba-like multiplier. Furthermore, the proposed multipliers were verified and implemented on various FPGA devices, and implementation results were presented. Reported device utilization and latency indicated that the proposed multiplier is roughly 26% faster and 15% more efficient in the area–delay product compared to the standard Karatsuba multiplier. Moreover, comparison with state of the art also indicated that the proposed design is leading in terms of effectiveness and speed.
Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs

In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.
A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework

This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.
Cross-modal Adversarial Reprogramming

With the abundance of large-scale deep learning models, it has become possible to repurpose pre-trained networks for new tasks. Recent works on adversarial reprogramming have shown that it is possible to repurpose neural networks for alternate tasks without modifying the network architecture or parameters. However these works only consider original and target tasks within the same data domain. In this work, we broaden the scope of adversarial reprogramming beyond the data modality of the original task. We analyze the feasibility of adversarially repurposing image classification neural networks for Natural Language Processing (NLP) and other sequence classification tasks. We design an efficient adversarial program that maps a sequence of discrete tokens into an image which can be classified to the desired class by an image classification model. We demonstrate that by using highly efficient adversarial programs, we can reprogram image classifiers to achieve competitive performance on a variety of text and sequence classification benchmarks without retraining the network.
Fully Complex Deep Learning Classifiers for Signal Modulation Recognition in Non-Cooperative Environment

Deep learning (DL) classifiers have significantly outperformed traditional likelihood-based or feature-based classifiers for signal modulation recognition in non-cooperative environments.However, despite these recent improvements, the conventional DL classifiers still have an unintended problem in handling the received signal in which the in-phase and quadrature components are separated.Even though the two components seem to be individually uncorrelated to each other, they are definitely the theoretical real and imaginary parts of a signal sample in the complex domain.Thus, it may be helpful for a classifier to regard and treat the modulated signal as a complex data array representing beneficial mutual information between the two real data arrays.In this paper, we propose two types of fully complex convolutional neural network (CNN) and residual neural network (ResNet) classifiers that deal with the complex data instead of the two separated data.First, we organize and define the core complex operations for implementing the complex DL classifiers.Next, the architectures of the proposed classifiers are realized by applying the structural optimizations and the regularization technique.Then, the various aspects of the classifiers' performance are analyzed and explained for providing comprehensive and deep understandings: (a) the effectiveness of the complex signal handling, (b) the in-depth evaluation of classification accuracy, (c) the impact of the data size on performance, and (d) the computational complexity.The experimental results show that the proposed classifiers can provide the faster learning speed, the higher optimization, and the better generalization with sacrificing acceptable learning and classification costs.Especially, our approaches remarkably improve the performance on the mutually correlated modulation types (i.e., the phase-related modulations: PSK, APSK, and QAM) even in the less-scale datasets.
End-to-End Real-Time Obstacle Detection Network for Safe Self-Driving via Multi-Task Learning

Semantic segmentation and depth estimation lie at the heart of scene understanding and play crucial roles especially for autonomous driving. In particular, it is desirable for an intelligent self-driving agent to discern unexpected obstacles on the road ahead reliably in real-time. While existing semantic segmentation studies for small road hazard detection have incorporated fusion of multiple modalities, they require additional sensor inputs and are often limited by a heavyweight network for real-time processing. In this light, we propose an end-to-end Real-time Obstacle Detection via Simultaneous refinement, coined RODSNet ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SAMMiCA/</uri> RODSNet) which jointly learns semantic segmentation and disparity maps from a stereo RGB pair and refines them simultaneously in a single module. RODSNet exploits two efficient single-task network architectures and a simple refinement module in a multi-task learning scheme to recognize unexpected small obstacles on the road. We validate our method by fusing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cityscapes</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lost and Found</i> datasets and show that our method outperforms previous approaches on the obstacle detection task, even recognizing the unannotated obstacles at 14.5 FPS on our fused dataset ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2048\times 1024$ </tex-math></inline-formula> resolution) using RODSNet <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-<inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula></monospace> . In addition, extensive ablation studies demonstrate that our simultaneous refinement effectively facilitates contextual learning between semantic and depth information.
Bayesian mixture of gaussian processes for data association problem

We address the data association problem and propose a Bayesian approach based on a mixture of Gaussian Processes (GPs) having two key components, the assignment probabilities and the GPs. In the proposed approach, the two key components are simultaneously updated according to observations through an efficient Expectation-Maximization (EM) algorithm that we develop. The proposed approach is thus more adaptive to the observations than the existing approaches for data association. To validate the performance of the proposed approach, we provide experimental results with real data sets as well as two synthetic data sets. We also provide a theoretical analysis to show the effectiveness of the Bayesian update.
Supervised graph classification for chiral quantum walks

Particle transport in quantum systems, which can be modeled by quantum walks on graphs, demonstrates a faster propagation advantage over the corresponding transport in classical systems. As known from several graph examples, achieving further advantages is possible by adding directional control in quantum walks. One way to introduce directional bias is via time-reversal symmetry breaking that can be achieved with chiral quantum walks, where complex phases are added to the edge weights. However, it is not known for which complex phases values and on which graphs quantum transport can be enhanced. Therefore, the classification of graph properties remains an open problem. Here we tackle this graph classification problem with a graph convolutional neural network trained on a set of simulated examples. We find that chiral quantum-walk dynamics leads to almost always faster transport on hypercubes compared to nonchiral dynamics. We connect our paper to physical implementations of quantum walks in superconducting qubits and optical waveguides. Our results open the possibility and flexibility of experimental implementations in demonstrating quantum-walk advantage.
Gender-Related Differences in Online Comment Sections: Findings From a Large-Scale Content Analysis of Commenting Behavior

Comment sections below news articles are public fora in which potentially everyone can engage in equal and fair discussions on political and social issues. Yet, empirical studies have reported that many comment sections are spaces of selective participation, discrimination, and verbal abuse. The current study complements these findings by analyzing gender-related differences in participation and incivility. It uses a sample of 303,342 user comments from 14 German news media Facebook pages. We compare participation rates of female and male users as well as associations between the users’ gender, the incivility of their comments, and the incivility of the adjacent replies. To determine the incivility of the comments, we developed a Supervised Machine Learning Model (classifier) using pre-trained word embeddings and word// frequency features. The findings show that, overall, women participate less than men. Comments written by female authors are more civil than comments written by male authors. Women’s comments do not receive more uncivil replies than men’s comments and women are not punished disproportionately for communicating uncivilly. These findings contribute to the discourse on gender-related differences in online comment sections and provide insights into the dynamics of online discussions.
A blockchain-adaptive contractual approach for multi-contracting organizational entities

Every organization relies on a designed contract to manage the rules that guide and coordinate the activities within each organization. However, traditional and contracts have many challenges. This has created the need for automated digital contracts, which are largely used within organizations. Although these contracts provide their own sets of advantages, they still have a single point of failure and still rely on trusted parties that cannot enforce rules. This has caused the introduction of blockchain smart contracts to help organizations collaborate in a decentralized manner. However, most contracting approaches are still influenced by traditional contracting schemes and cannot leverage the blockchain’s features. This has made contracts designed over the blockchain network inefficient and difficult to manage. Existing contractual approaches also do not account for the full advantages of blockchain smart contracts due to a lack of focus in this area. The blockchain and its contracting mechanism are unique, and thus, a unique perspective must be brought in designing a contracting scheme that works efficiently with the blockchain and its inherent smart contracts. In this work, we develop a unique contractual approach that is adaptive to the blockchain and leverages its features in ensuring an efficient contracting scheme that is able to solve the major operations within organizations. We introduce a novel concept of contracting among blockchain networks using existing contract theory concepts to understand better how participants can work together across multiple organizations. We create a novel multi-contracting scheme for multiple parties to collaborate and work together efficiently between multiple organizations, ensuring the completion of projects and contract solidification. We develop a complex interlinked system of contracts to make contacts among multiple organizations easily traceable and manageable. We enhance this with a unique contract consolidation scheme to avoid redundancy. We utilize our proposed scheme to develop a uniquely transparent and fair workflow within organizations such as recruitment and employment leveraging blockchain-based oracle nodes. We extend this work to outsourced and sub-outsourced projects on the blockchain where an arbitration scheme is developed in multi-arbitration and multi-contracting scenarios. We also consider in our case in ex-facto situations. We tested our proposed blockchain-based idea over existing blockchain networks. Our results prove the feasibility and efficiency of our proposed idea.
A comparative driving safety study of mountainous expressway individual tunnel and tunnel group based on eye gaze behavior

The traffic environment of a tunnel group is more complex than that of a single tunnel, which increases the driving risk. The eye gaze behavior of drivers can be used to evaluate driving safety and comfort. To analyze the fixation characteristics of drivers in a single tunnel and tunnel group of mountainous expressways, an actual vehicle test is conducted. The test area has a total length of 160 km and 38 tunnels, including 8 tunnel groups and 16 single tunnels. In the test, the difference in the gaze time of five drivers between single tunnels and tunnel groups is compared. The k-means method is used to cluster driver's gaze points dynamically. Based on the Markov theory, the attributes related to gaze transfer are obtained. The results show that when tunnels are of short or medium length, there is no significant difference in the gaze time and gaze point transfer between the tunnel group and a single tunnel. In contrast, when tunnels have long or extra-long length, the repeated fixation probability and the two-step transition probability of looking back of a driver in a tunnel group are higher than those in a single tunnel. The design and management method of a single tunnel cannot be directly used, especially for extra-long tunnels located at the back of a tunnel group with a long upstream tunnel length and a short interval distance from the upstream tunnel. Therefore, it is necessary to focus on the design and management methods of tunnel groups.
Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching

Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task.
Short-term Solar Irradiance Prediction from Sky Images with a Clear Sky Model

Integrating the solar power into the power grid system while maintaining its stability is essential for utilising such type of clean energy widely. It renders the solar irradiance (determining the solar power) forecasting a critical task. This paper tackles the problem of solar irradiance prediction from a history of sky image sequence. Most existing machine learning methods directly regress the solar irradiance values from a historical image sequence and/or solar irradiance observations. By contrast, we propose a novel deep neural network for short-term solar irradiance forecasting by leveraging a clear sky model. In particular, we build our network structure on the vision transformer to encode the spatial as well as the temporal information in the sky video sequence. We then aim to predict the solar irradiance residual from the learned representation by explicitly using a clear sky model. We evaluated our approach extensively on the existing benchmark datasets, such as TSI880 and ASI16. Results on the nowcasting task, namely estimation of the solar irradiance from the observations, and the forecasting task, which is up to 4-hour ahead-of-time prediction, demonstrate the superior performance of our method compared with existing machine learning algorithms.
Hierarchical Imitation Learning via Subgoal Representation Learning for Dynamic Treatment Recommendation

Dynamic Treatment Recommendation (DTR) is a sequence of tailored treatment decision rules which can be grouped as individual sub-tasks. As the reward signals in DTR are hard to design, Imitation Learning (IL) has achieved great success as it is effective in mimicking doctors' behaviors from their demonstrations without explicit reward signals. As a patient may have several different symptoms, the behaviors in doctors' demonstrations can often be grouped to handle individual symptoms. However, a single flat policy learned by IL is difficult to mimic doctors' demonstrations with such hierarchical structure, where low-level behaviors are switching from one symptom to another controlled by high-level decisions. Due to this observation, we consider Hierarchical Imitation Learning methods as good solutions for DTR. In this paper, we propose a novel Subgoal conditioned HIL framework (short for SHIL), where a high-level policy sequentially sets a subgoal for each sub-task without prior knowledge, and the low-level policy for sub-tasks is learned to reach the subgoal. To get rid of prior knowledge, a self-supervised learning method is proposed to learn an effective representation for each subgoal. More specifically, we carefully designed to encourage diverse representations among different subgoals. To demonstrate that SHIL is able to learn meaningful high-level policy and low-level policy that accurately reproduces complex doctors' demonstrations, we conduct experiments on a real-world medical data from health care domain, MIMIC-III. Compared with state-of-the-art baselines, SHIL improves the likelihood of patient survival by a significant margin and provides explainable recommendation with hierarchical structure.
An effective intrusion-resilient mechanism for programmable logic controllers against data tampering attacks

Programmable Logic Controllers (PLCs) play a prominent role in critical infrastructures, such as power grid, transportation, and petrochemical industry. Suffering from data tampering attacks, PLCs are fragile as the forged data could cause significant damage to industrial machines and human safety. However, less attention has been paid to develop resilience mechanisms for automatically recovering PLCs systems from anomalous states under malicious attacks. In this work, we present the first resilience approach for protecting PLCs against data tampering attacks. The key observation we leverage is that the programmable characteristic of a PLC enables a defender to ensure its data integrity by embedding security mechanism into its control logic. In particular, we design a novel data authentication mechanism to generate and authenticate the message digest of the communication data between PLCs. An anomaly alert will be triggered if the data authentication fails. The execution on received malicious commands is disabled to avoid detrimental effect and keep the system stable. Furthermore, to recover real data from malicious modifications, a data sender is required to encrypt data using the proposed RC5-based data recovery algorithm and re-transmit it. The data authentication and recovery approaches can be implemented on the programmable logic circuit of PLCs through code updating, which requires no alterations to its current hardware architecture. We present the prototype of our resilience scheme and conduct real-world experiments to validate its efficacy under 12 typical attack scenarios. Our results show that our approach achieves 97.4% of accuracy in data authentication and 98.1% of success rate in abnormal state recovery. Finally, we assess the practicality of the proposed mechanism in terms of execution delay.