Reviewerly/exHarmony huggingface dataset:
Necessary conditions for convergence of CNNs and initialization of convolution kernels

Despite the great success of deep learning in many fields such as computer vision, natural language processing, and information retrieval, there are relatively few studies on the convergence of deep convolutional neural networks (CNNs), and there is a lack of theoretical studies on the necessary conditions for the convergence of CNNs. The initialization of the convolution kernel of CNNs is an important factor in whether the network can converge. However, the existing initialization methods do not analyze the influence of their methods on the convergence performance of CNNs and did not analyze the conditions of their application, and thus the performance is not the best in different network models. In this work, the computational process of both forward and backward propagation of CNNs is considered as a mapping in linear normed space, and thus a necessary condition for CNNs stable converge is proposed. According to this necessary condition of convergence, we first derive initialization formulas for plain networks applicable to any activation function, and derive the initialization method of plain networks whose activation function is ReLU and PReLU. Secondly, the necessary conditions for convergence of CNNs proposed in this work can explain the mathematical reasons why the BN contribute to the training of CNNs, and this problem has always been an active research topic. Finally, we find that the learning rate and the initialization of the convolutional kernel jointly affect the convergence performance of the network. Based on the plain networks convolution kernel initialization method, we derive the convolution kernel initialization method of network with BN layer related to the learning rate. In order to verify the effectiveness of the proposed initialization method, we test it on the CIFAR-10 and CIFAR-100 datasets, and performed image classification with four network models (VGG-19, ResNet-110, DenseNet-100 and WideResNet28-10), and the experimental results showed that the initialization method proposed in this work improved the accuracy of image classification.
FCP-Net: A Feature-Compression-Pyramid Network Guided by Game-Theoretic Interactions for Medical Image Segmentation

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep convolutional neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have fully explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multi-receptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques.
SCA-Net: A Spatial and Channel Attention Network for Medical Image Segmentation

Automatic medical image segmentation is a critical tool for medical image analysis and disease treatment. In recent years, convolutional neural networks (CNNs) have played an important role in this field, and U-Net is one of the most famous fully convolutional network architectures among many kinds of CNNs for medical segmentation tasks. However, the CNNs based on U-Net used for medical image segmentation rely only on simple concatenation operation of multiscale features. The spatial and channel context information is easily missed. To capture the spatial and channel context information and improve the segmentation performance, in this paper, a spatial and channel attention network (SCA-Net) is proposed. SCA-Net presents two novel blocks: a spatial attention block and a channel attention block. The spatial attention block (SAB) combines the multiscale information from high-level and low-level stages to learn more representative spatial features, and the channel attention block (CAB) redistributes the channel feature responses to strengthen the most critical channel information while restraining the irrelevant channels. Compared with other state-of-the-art networks, our proposed framework obtained better segmentation performance in each of the three public datasets. The average Dice score improved from 88.79% to 92.92% for skin lesion segmentation, 94.02% to 98.25% for thyroid gland segmentation and 87.98% to 91.37% for pancreas segmentation compared with U-Net. Additionally, the Bland–Altman analysis showed that our network had better agreement between automatic and manually calculated areas in each task.
Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on delay-bounded applications in distributed GDCs (DGDCs) and performs cost and energy-effective scheduling of multiple heterogeneous applications verifying delay bound constraints of different tasks. DGDCs' operational cost minimization problem is formulated and successfully optimized using an innovative modified Firefly Algorithm (mFA). Real-life data trace-driven experiments are conducted to evaluate the effectiveness of the proposed mFA in solving this problem. High performance task scheduling results are obtained. The operational cost of each GDC is minimized, the utilization of solar and wind renewable energy from the different geographical locations is maximized while delay bound constraints of all tasks are strictly met. Compared to Bat Algorithm, Simulated-annealing Bat Algorithm and basic firefly algorithm, mFA can produce a schedule that outperforms its peers' drastically in terms of operational cost of DGDCs. Moreover, mFA finds more rapidly both global or local optima than its peers. It succeeds to meet all equality and inequality constraints at all time slots while its peers may sometimes fail to find satisfactory solutions at some particular time slots.
NFTCert: NFT-Based Certificates With Online Payment Gateway

Nowadays, academic certificates are still widely issued in paper format. Traditional certificate verification is a lengthy, manually intensive, and sometimes expensive process. In this paper, we propose a novel NFT-based certificate framework called NFTCert, which enables the establishment of links between a legitimate certificate and its owner through a Blockchain. In this paper, we describe the implementation of the NFTCert framework, including schema definition, minting, verification, and revocation of NFT-based certificates. We also introduce a payment gateway into the minting process, which enables NFTCert to be used by a wider audience. Therefore, participants of NFTCerts do not need to rely on cryptocurrency for transactions. All in all, the proposed framework is designed to achieve usability, authenticity, confidentiality, transparency, and availability properties when it is compared to existing Blockchain-based systems.
AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks

Graph Neural Networks (GNNs) have demonstrated great power in many network analytical tasks. However, graphs (i.e., networks) in the real world are usually text-rich, implying that valuable semantic information needs to be carefully considered. Existing GNNs for text-rich networks typically treat text as attribute words alone, which inevitably leads to the loss of important semantic structures, limiting the representation capability of GNNs. In this paper, we propose an end-to-end adaptive semantic architecture of graph convolutional networks, namely AS-GCN, which unifies neural topic model and graph convolutional networks, for text-rich network representation. Specifically, we utilize a neural topic model to extract the global topic semantics, and accordingly augment the original text-rich network into a tri-typed heterogeneous network, capturing both the local word-sequence semantic structure and the global topic semantic structure from text. We then design an effective semantic-aware propagation of information by introducing a discriminative convolution mechanism. We further propose two strategies, that is, distribution sharing and joint training, to adaptively generate a proper network structure based on the learning objective to improve network representation. Extensive experiments on text-rich networks illustrate that our new architecture outperforms the state-of-the-art methods by a significant improvement. Meanwhile, this architecture can also be applied to e-commerce search scenes, and experiments on a real e-commerce problem from JD further demonstrate the superiority of the proposed architecture over the baselines.
Twisted Reed–Solomon Codes

In this article, we present a new construction of evaluation codes in the Hamming metric, which we call twisted Reed-Solomon codes.Whereas Reed-Solomon (RS) codes are MDS codes, this need not be the case for twisted RS codes.Nonetheless, we show that our construction yields several families of MDS codes.Further, for a large subclass of (MDS) twisted RS codes, we show that the new codes are not generalized RS codes.To achieve this, we use properties of Schur squares of codes as well as an explicit description of the dual of a large subclass of our codes.We conclude the paper with a description of a decoder, that performs very well in practice as shown by extensive simulation results.
Federated Transfer Learning-Based IDS for the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes; with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users’ data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient’s healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes.
Sequential graph collaborative filtering

Understanding collaborative and sequential information plays a major role in next-item recommendation. Although previous models have achieved considerable results, their capabilities have been limited by the basic paradigm of characterizing each user’s profile with only his/her sequential behaviors. This paper considers collaborative information over users to solve the problem. Specifically, we first reorganize all interactions as a multiplex directed heterogeneous graph (named sequential graph) to depict each user’s sequential behaviors and all users’ collaborative interactions. Second, we propose a generic vectorization algorithm to address the challenge of multiplex edges. The algorithm can implement arbitrary graph attention networks on complex graphs without graph simplification. Finally, we propose a novel sequential graph attention network (SGAT) that controls information to attentively propagate through directed heterogeneous edges. Based on the sequential graph and the vectorization algorithm, SGAT infers each user’s dynamic preference by capturing high-order sequential information among items and exploiting both latitudinally and longitudinally collaborative information over users. Extensive experiments and ablation studies on various datasets demonstrate the superiority of SGAT. The sequential graph provides an alternative way to organize users’ sequential behaviors and the vectorization algorithm can promote the development of graph mining by retaining topological structures of complex graphs.
Mobile Learning Applications for Refugees: A Systematic Literature Review

The proliferation of mobile devices in everyday life since the end of the 20th century has led to mobile applications for educational purposes and the creation of the research field of mobile learning. Despite the extended research interest on the effectiveness of this field, there is limited research on mobile learning for various social groups, such as refugees, students with learning difficulties and disabilities. Due to the unprecedented number (over one hundred million) of refugees during the second decade of the 21st century worldwide, many NGOs (Non-Governmental Organizations) and UN (United Nations) initiatives have proposed leveraging mobile learning for refugee educational needs. This research article focuses on mobile learning for refugee education. Namely, the present systematic literature review results from 2015 to 2020 will give a concrete picture of the recently existing mobile learning apps for refugees and their characteristics. According to the research findings, 15 characteristics were collected out of 14 applications. According to prior literature, areas of agreement or discrepancies in the field were found. Two new -to previous literature- characteristics were revealed: interwoven psychological and educational features and refugees’ cultural features in the apps. The summarization and categorization of the app’s characteristics aim to contribute to mobile learning research and impact game developers, educators, and NGOs according to refugee needs. The limitations of this study and issues for further exploration will also be discussed in the last sections.
Extending On-chain Trust to Off-chain -- Trustworthy Blockchain Data Collection using Trusted Execution Environment (TEE)

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.
Facing Radical Digitalization: Capturing Teachers’ Transition to Virtual Classrooms Through Ideal Type Experiences

In 2020, a global pandemic changed the educational landscape overnight and caused an abrupt transition to virtual classrooms. This study aims to gain increased knowledge of teachers’ experiences of facing such radical digitalization through ideal types. The data include a teacher survey with 1109 respondents from 15 high schools in Sweden, containing both fixed and open-ended response types. Educational affordances and digital competence are used as analytical lenses. The results show distinct differences regarding teachers’ perception of how teaching in a virtual classroom has worked and whether they and their students have developed their digital competence during this period. We present four ideal types: a) the enthusiast, b) the skeptic, c) the pessimist, and d) the affirmative, which capture the essence of teachers’ multifaceted experiences, actions, and affordances perceived in the transition to virtual classrooms. Contributions include theorizing about teachers’ encounters with radical cases of digitalization.
Random Sketching for Neural Networks With ReLU

Training neural networks is recently a hot topic in machine learning due to its great success in many applications. Since the neural networks' training usually involves a highly nonconvex optimization problem, it is difficult to design optimization algorithms with perfect convergence guarantees to derive a neural network estimator of high quality. In this article, we borrow the well-known random sketching strategy from kernel methods to transform the training of shallow rectified linear unit (ReLU) nets into a linear least-squares problem. Using the localized approximation property of shallow ReLU nets and a recently developed dimensionality-leveraging scheme, we succeed in equipping shallow ReLU nets with a specific random sketching scheme. The efficiency of the suggested random sketching strategy is guaranteed by theoretical analysis and also verified via a series of numerical experiments. Theoretically, we show that the proposed random sketching is almost optimal in terms of both approximation capability and learning performance. This implies that random sketching does not degenerate the performance of shallow ReLU nets. Numerically, we show that random sketching can significantly reduce the computational burden of numerous backpropagation (BP) algorithms while maintaining their learning performance.
H2GNN: Hierarchical-Hops Graph Neural Networks for Multi-Robot Exploration in Unknown Environments

Multi-robot coarse-to-fine exploration in unknown environments makes great sense in many application fields like search and rescue. For different stages of the task, robots need to extract information from the environment discriminately, which can improve their decision-making capability. To this end, we present the Hierarchical-Hops Graph Neural Networks (H2GNN) to enable robots to targetedly integrate the key information of the graph-represented environment, which distinguishes the importance of information from different hops around robots based on the multi-head attention mechanism. And in order to improve the efficiency of cooperation, we utilize multi-agent reinforcement learning (MARL) to help robots to learn collaborative strategies implicitly. We conduct experiments to verify our proposed method in a simulation environment, and the experimental results demonstrate that the H2GNN significantly improves the multi-robot exploration performance in unknown environments.
Sign Language Recognition Based on R(2+1)D With Spatial–Temporal–Channel Attention

Previous work utilized three-dimensional (3-D) convolutional neural networks (CNNs) tomodel the spatial appearance and temporal evolution concurrently for sign language recognition (SLR) and exhibited impressive performance. However, there are still challenges for 3-D CNN-based methods. First, motion information plays a more significant role than spatial content in sign language. Therefore, it is still questionable whether to treat space and time equally and model them jointly by heavy 3-D convolutions in a unified approach. Second, because of the interference from the highly redundant information in sign videos, it is still nontrivial to effectively extract discriminative spatiotemporal features related to sign language. In this study, deep R(2+1)D was adopted for separate spatial and temporal modeling and demonstrated that decomposing 3-D convolution filters into independent spatial and temporal convolutions facilitates the optimization process in SLR. A lightweight spatial–temporal–channel attention module, including two submodules called channel–temporal attention and spatial–temporal attention, was proposed to make the network concentrate on the significant information along spatial, temporal, and channel dimensions by combining squeeze and excitation attention with self-attention. By embedding this module into R(2+1)D, superior or comparable results to the state-of-the-art methods on the CSL-500, Jester, and EgoGesture datasets were obtained, which demonstrated the effectiveness of the proposed method.