Reviewerly/exHarmony huggingface dataset:
Multi-category classification with label noise by robust binary loss

In recent times, deep learning models have achieved state-of-the-art performances in image classification. However, the classification and generalization ability of most achievements have highly relied on the availability of large-scale accurate labeled training data, which are time-consuming, laborious, and expensive to collect. Moreover, in the generalizing process of deep learning models, the noisy labels are the challenges for multi-category classification. Hence, it is essential to explore effective methods that can efficiently and correctly train deep models under label noise to conduct multi-category classification. This paper proposes using robust binary loss functions to train deep models under label noise to address this problem. Specifically, we suggest handling the K-class classification task by using K binary classifiers, which can be completed by a joint adoption of multi-category large margin classification approaches, e.g., Pairwise-Comparison (PC) or One-versus-All (OVA). We also theoretically demonstrate that our method is inherently tolerant to label noise by using symmetric binary loss functions in multi-category classification tasks. Moreover, we designed a truncated CCE loss to combine with the proposed losses to improve the learning ability. Finally, we test our method on three different benchmark datasets with different types of label noise. The experimental results have clearly confirmed the effectiveness of our method, which can reduce the negative effect of noisy labels and improve the generalization ability.
Development of 21st century skills through virtual exchange

This study investigates what skills university teacher candidates from the United States developed from their participation in a virtual exchange with Turkey that aided in their ability to cope with a critical situation such as the Covid-19 epidemic. To trace the impact of the crisis on teacher candidates' perceptions as well as the evidence of development of these skills, the qualitative data, reflection questionnaires and video recordings, of interactions were analyzed. The findings include commentary on virtual collaboration, virtual interaction, social impact, and social responsibility that occurred during the six-week exchange.
Attention Mechanism Based ResNeXt Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is becoming increasingly important in modern wireless communication. In this paper, we proposed a novel integrative approach for AMC based on feature and deep learning. The time-frequency spectrograms are extracted by short-time Fourier transform (STFT) on the received communication signals, which are used as the inputs of the deep learning (DL) network. The ResNeXt network is designed as the backbone, and two dual attention mechanism modules and customized classification module are incorporated. ResNeXt introduces a new dimension named Cardinality, making ResNeXt own excellent feature extraction ability. The dual attention mechanism module combines the channel attention and spatial attention modules to enhance the salient features and suppress the redundant features. Furthermore, the customized classification header improves the robustness of the classifier. Experimental results on the RadioML2016.10B dataset demonstrate its high accuracy and robust performance compared with other state-of-the-art techniques, surpassing them by 2% to 10% in terms of accuracy.
The influence of sustainable innovation on financial entrepreneurship performance: Growth and prediction in an emerging market

This study aims to perceive the effect of financial entrepreneurship performance (FEP) over sustainable innovation (SI) disclosure in an emerging market. Jordanian banks are tested based on a multiple regression analysis for the periods 2008 and 2018 and a time series forecasting webinar analysis for the period from 2019 to 2029 based on data ranging from 2008 to 2018. Innovation is indicated through disclosed intangible assets (IA), and items related to research and development (R&amp;D) costs. As organizations anticipate stability by concentrating on technological awareness to influence higher innovative performance (Guo, Guo, Zhou, &amp; Wu, 2020), this study came to converse the relationships between previous literature variables; Hussain (2015) as well as Lassala, Apetrei, and Sapena (2017) revealed through the regression models that there is a relationship between FEP and SI. Meanwhile, bank FEP is directed by return on assets (ROA) and return on equity (ROE). Results reveal that bank FEP affects SI disclosure in a positive manner for the period 2008 and at a higher significant level than 2018. In the meantime, the growth prediction analyses divulge that both ROA and ROE are expected to decrease rapidly within a coming couple of years and then increase promptly.
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for the development of KGs. Knowledge Graph Embeddings (KGEs) embedding entities and relations of a KG into continuous vector spaces, have been proposed for these reasoning tasks and proven to be efficient and robust. But the plausibility and feasibility of applying and deploying KGEs in real-work applications has not been well-explored. In this paper, we discuss and report our experiences of deploying KGEs in a real domain application: e-commerce. We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems. While non existing KGE could meet all these desiderata, we propose a novel one, an explainable knowledge graph attention network that make prediction through modeling correlations between triples rather than purely relying on its head entity, relation and tail entity embeddings. It could automatically selects attentive triples for prediction and records the contribution of them at the same time, from which explanations could be easily provided and transferable rules could be efficiently produced. We empirically show that our method is capable of meeting all three desiderata in our e-commerce application and outperform typical baselines on datasets from real domain applications.
GCN-ETA: High-Efficiency Encrypted Malicious Traffic Detection

Encrypted network traffic is the principal foundation of secure network communication, and it can help ensure the privacy and integrity of confidential information. However, it hides the characteristics of the data, increases the difficulty of detecting malicious traffic, and protects such malicious behavior. Therefore, encryption alone cannot fundamentally guarantee information security. It is also necessary to monitor traffic to detect malicious actions. At present, the more commonly used traffic classification methods are the method based on statistical features and the method based on graphs. However, these two methods are not always reliable when they are applied to the problem of encrypted malicious traffic detection due to their limitations. The former only focuses on the internal information of the network flow itself and ignores the external connections between the network flows. The latter is just the opposite. This paper proposes an encrypted malicious traffic detection method based on a graph convolutional network (GCN) called GCN-ETA, which considers the statistical features (internal information) of network flows and the structural information (external connections) between them. GCN-ETA consists of two parts: a feature extractor that uses an improved GCN and a classifier that uses a decision tree. Improving on the traditional GCN, the effect and speed of encrypted malicious traffic detection can be effectively improved and the deployment of the detection model in the real environment is increased, which provides a reference for the application of GCN in similar scenarios. This method has achieved excellent performance in experiments using real-world encrypted network traffic data for malicious traffic detection, with the accuracy, AUC, and F1-score exceeding 98% and more than 1,300 flows detected per second.
Semantics and canonicalisation of SPARQL 1.1

We define a procedure for canonicalising SPARQL 1.1 queries. Specifically, given two input queries that return the same solutions modulo variable names over any RDF graph (which we call congruent queries), the canonicalisation procedure aims to rewrite both input queries to a syntactically canonical query that likewise returns the same results modulo variable renaming. The use-cases for such canonicalisation include caching, optimisation, redundancy elimination, question answering, and more besides. To begin, we formally define the semantics of the SPARQL 1.1 language, including features often overlooked in the literature. We then propose a canonicalisation procedure based on mapping a SPARQL query to an RDF graph, applying algebraic rewritings, removing redundancy, and then using canonical labelling techniques to produce a canonical form. Unfortunately a full canonicalisation procedure for SPARQL 1.1 queries would be undecidable. We rather propose a procedure that we prove to be sound and complete for a decidable fragment of monotone queries under both set and bag semantics, and that is sound but incomplete in the case of the full SPARQL 1.1 query language. Although the worst case of the procedure is super-exponential, our experiments show that it is efficient for real-world queries, and that such difficult cases are rare.
Attention-based Feature Interaction for Efficient Online Knowledge Distillation

Existing online knowledge distillation (KD) methods solve the dependency problem of the high-capacity teacher model via mutual learning and ensemble learning. But they focus on the utilization of logits information in the last few layers and fail to construct a strong teacher model to better supervise student networks, leading to the inefficiency of KD. In this work, we propose a simple but effective online knowledge distillation algorithm, called Attentive Feature Interaction Distillation (AFID). It applies interactive teaching in which the teacher and the student can send, receive, and give feedback on an equal footing, ultimately promoting the generality of both. Specifically, we set up a Feature Interaction Module for two sub-networks to conduct low-level and mid-level feature learning. They can alternately transfer attentive features maps to exchange interesting regions and fuse the other party’s map with the features of self-extraction for information enhancement. Besides, we assign a Feature Fusion Module, in which a Peer Fused Teacher is formed to fuse the output features of two sub-networks to guide sub-networks and a Peer Ensemble Teacher is established to accomplish mutual learning between the two teachers. Integrating Feature Interaction Module and Feature Fusion Module into a unified framework takes full advantage of the interactive teaching mechanism and makes the two sub-networks capture and transfer more fine-grained features to each other. Experimental results on CIFAR-100 and ImageNet ILSVRC 2012 real datasets show that AFID achieves significant performance improvements compared with existing online KD and classical teacher-guide methods.
Transfer reinforcement learning via meta-knowledge extraction using auto-pruned decision trees

Transfer reinforcement learning (RL) has recently received increasing attention to make RL agents have better learning performance in target Markov decision problems (MDPs) by using the knowledge learned in source MDPs. However, it is still an open and challenging problem to improve the transfer capability and interpretability of RL algorithms. In this paper, we propose a novel transfer reinforcement learning approach via meta-knowledge extraction using auto-pruned decision trees. In source MDPs, pre-trained policies are firstly learned via RL algorithms using general function approximators. Then, a meta-knowledge extraction algorithm is designed with an auto-pruned decision tree model, where the meta-knowledge is learned by re-training the auto-pruned decision tree based on the data samples generated from the pre-trained policies. The state spaces of meta-knowledge are determined by estimating the uncertainty of state–action pairs in pre-trained policies based on the entropy value of leaf nodes. In target MDPs, according to whether the state is in the state set of meta-knowledge, a hybrid policy is generated by integrating the meta-knowledge and the policies learned on the target MDPs. Based on the proposed transfer RL approach, two meta-knowledge-based transfer reinforcement learning (MKRL) algorithms are developed for MDPs with discrete action spaces and continuous action spaces, respectively. Experimental results in several benchmark tasks show that the MKRL algorithm outperforms other baselines in terms of learning efficiency and interpretability in the target MDPs with generic cases of task similarity.
Graph Picture of Linear Quantum Networks and Entanglement

The indistinguishability of quantum particles is widely used as a resource for the generation of entanglement. Linear quantum networks (LQNs), in which identical particles linearly evolve to arrive at multimode detectors, exploit the indistinguishability to generate various multipartite entangled states by the proper control of transformation operators. However, it is challenging to devise a suitable LQN that carries a specific entangled state or compute the possible entangled state in a given LQN as the particle and mode number increase. This research presents a mapping process of arbitrary LQNs to graphs, which provides a powerful tool for analyzing and designing LQNs to generate multipartite entanglement. We also introduce the perfect matching diagram (PM diagram), which is a refined directed graph that includes all the essential information on the entanglement generation by an LQN. The PM diagram furnishes rigorous criteria for the entanglement of an LQN and solid guidelines for designing suitable LQNs for the genuine entanglement. Based on the structure of PM diagrams, we compose LQNs for fundamental <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>-partite genuinely entangled states.
A hybrid machine learning system to impute and classify a component-based robot

Abstract In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components.
Digital forensic analysis methodology for private browsing: Firefox and Chrome on Linux as a case study

The web browser has become one of the basic tools of everyday life. A tool that is increasingly used to manage personal information. This has led to the introduction of new privacy options by the browsers, including private mode. In this paper, a methodology to explore the effectiveness of the private mode included in most browsers is proposed. A browsing session was designed and conducted in Mozilla Firefox and Google Chrome running on four different Linux environments. After analyzing the information written to disk and the information available in memory, it can be observed that Firefox and Chrome did not store any browsing-related information on the hard disk. However, memory analysis reveals that a large amount of information could be retrieved in some of the environments tested. For example, for the case where the browsers were executed in a VMware virtual machine, it was possible to retrieve most of the actions performed, from the keywords entered in a search field to the username and password entered to log in to a website, even after restarting the computer. In contrast, when Firefox was run on a slightly hardened non-virtualized Linux, it was not possible to retrieve any browsing-related artifacts after the browser was closed.
Selective attention to lesson‐relevant contextual information promotes 3‐ to 5‐year‐old children's learning

Attending to distracting or competing information is typically considered detrimental to learning, but the presence of competing information can also facilitate learning when it is relevant to ongoing task goals. Educational settings often contain contextual elements such as classroom decorations or visual aids to enhance student learning. Despite this, most research examining effects of contextual information on children's learning has only utilized lesson-irrelevant stimuli. While this research has shown that increased looking to task-irrelevant information hinders learning, the extent to which looking to lesson-relevant information can benefit children's learning is unknown. We addressed this question by examining 3- to 5-year-old children's attention to and learning from lesson-relevant contextual information. We recorded children's eye movements as they viewed video science lessons while lesson-relevant and -irrelevant images appeared in the periphery. We assessed learning based on improvements in content knowledge following the video lessons and separately measured selective attention skills using the Track-It task. Children overall spent more time looking at lesson-relevant versus -irrelevant images, and those with more initial knowledge of the lesson topics or more advanced selective attention skills showed increased preferential looking to the relevant images. This increased preferential looking to lesson-relevant images related to more effective learning during trials in which both relevant and irrelevant images were present. These results suggest that the effects of competing contextual information on early learning depend on the relationship between information content and task goals, as well as children's ability to actively select task-relevant information from their environment.
ADS-Lead: Lifelong Anomaly Detection in Autonomous Driving Systems

Autonomous Vehicles (AVs) are closely connected in the Cooperative Intelligent Transportation System (C-ITS). They are equipped with various sensors and controlled by Autonomous Driving Systems (ADSs) to provide high-level autonomy. The vehicles exchange different types of real-time data with each other, which can help reduce traffic accidents and congestion, and improve the efficiency of transportation systems. However, when interacting with the environment, AVs suffer from a broad attack surface, and the sensory data are susceptible to anomalies caused by faults, sensor malfunctions, or attacks, which may jeopardize traffic safety and result in serious accidents. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADS-Lead</monospace> , an efficient collaborative anomaly detection methodology to protect the lane-following mechanism of ADSs. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADS-Lead</monospace> is equipped with a novel transformer-based one-class classification model to identify time series anomalies (GPS spoofing threat) and adversarial image examples (traffic sign and lane recognition attacks). Besides, AVs inside the C-ITS form a cognitive network, enabling us to apply the federated learning technology to our anomaly detection method, where the vehicles in the C-ITS jointly update the detection model with higher model generalization and data privacy. Experiments on Baidu Apollo and two public data sets (GTSRB and Tumsimple) indicate that our method can not only detect sensor anomalies effectively and efficiently but also outperform state-of-the-art anomaly detection methods.
QR Code-Based Student Attendance System

Student attendance in higher education institutions is a factor that impacts academic performance. Some higher education institutions have penalised students for poor attendance records. Most universities, however, have not implemented an automated attendance-taking system to deter poor attendance, leaving the task instead to instructors who manually record students' attendances into the system; a time-consuming and tedious undertaking, particularly, with a large number of students. This study primarily aimed to propose a Quick Response (QR) code-based attendance system, complete with several features to prevent attendance cheating. Students at the Centre for American Education, Sunway University tested a mobile application designed to scan QR codes, specifically generated for each classroom. The system checked three categories of data: subject class hour, registered mobile device, and geolocation. Once the information was verified, the student's attendance was recorded into the system. The proposed system succeeded in overcoming limitations encountered in the university's existing student attendance-taking system.
Pedestrian Detection with Multi-View Convolution Fusion Algorithm

In recent years, the pedestrian detection technology of a single 2D image has been dramatically improved. When the scene becomes very crowded, the detection performance will deteriorate seriously and cannot meet the requirements of autonomous driving perception. With the introduction of the multi-view method, the task of pedestrian detection in crowded or fuzzy scenes has been significantly improved and has become a widely used method in autonomous driving. In this paper, we construct a double-branch feature fusion structure, the first branch adopts a lightweight structure, the second branch further extracts features and gets the feature map obtained from each layer. At the same time, the receptive field is enlarged by expanding convolution. To improve the speed of the model, the keypoint is used instead of the entire object for regression without an NMS post-processing operation. Meanwhile, the whole model can be learned from end to end. Even in the presence of many people, the method can still perform better on accuracy and speed. In the standard of Wildtrack and MultiviewX dataset, the accuracy and running speed both perform better than the state-of-the-art model, which has great practical significance in the autonomous driving field.