Reviewerly/exHarmony huggingface dataset:
QTN: Quaternion Transformer Network for Hyperspectral Image Classification

Numerous state-of-the-art transformer-based techniques with self-attention mechanisms have recently been demonstrated to be quite effective in the classification of hyperspectral images (HSIs). However, traditional transformer-based methods severely suffer from the following problems when processing HSIs with three dimensions: (1) processing the HSIs using 1D sequences misses the 3D structure information; (2) too expensive numerous parameters for hyperspectral image classification tasks; (3) only capturing spatial information while lacking the spectral information. To solve these problems, we propose a novel Quaternion Transformer Network (QTN) for recovering self-adaptive and long-range correlations in HSIs. Specially, we first develop a band adaptive selection module (BASM) for producing Quaternion data from HSIs. And then, we propose a new and novel quaternion self-attention (QSA) mechanism to capture the local and global representations. Finally, we propose a new and novel transformer method, i.e., QTN by stacking a series of QSA for hyperspectral classification. The proposed QTN could exploit computation using Quaternion algebra in hypercomplex spaces. Extensive experiments on three public datasets demonstrate that the QTN outperforms the state-of-the-art vision transformers and convolution neural networks.
Bridging Research Fields: An Empirical Study on Joint, Neural Relation Extraction Techniques

Information systems that have to deal with natural language text are often equipped with application-specific techniques for solving various Natural Language Processing (NLP) tasks. One of those tasks, extracting entities and their relations from human-readable text, is relevant for downstream tasks like automated model extraction (e.g. UML diagrams, business process models) and question answering (e.g. in chatbots). In NLP the rapidly evolving research field of Relation Extraction denotes a family of techniques for solving this task application-independently. Thus, the question arises why scientific publications about information systems often neglect those existing solutions. One supposed reason is that for reliably selecting an appropriate technique, a comprehensive study of the available alternatives is required. However, existing studies (i) cannot be considered complete due to irreproducible literature search methods and (ii) lack validity, since they compare relevant approaches based on different datasets and different experimental setups. This paper presents an empirical comparative study on domain-independent, open-source deep learning techniques for extracting entities and their relations jointly from texts. Limitations of former studies are overcome (i) by a rigorous and well-documented literature search and (ii) by evaluating relevant techniques on equal datasets in a unified experimental setup. The results $$^{1}$$ show that a group of approaches form a reliable baseline for developing new techniques or for utilizing them directly in the above mentioned application scenarios( $$^{1}$$ Our code and data: https://github.com/JulianNeuberger/re-study-caise .).
STEM Education in the Spanish Context: Key Features and Issues

The recent introduction of STEM education in Spanish schools has been quite heterogeneous. Being a decentralized educational system, schools in different Spanish regions have followed different strategies to introduce this new perspective. In this chapter, we share three different cases of STEM programs focusing on its teacher education: the IndagaSTEAM Escuela project in Andalucia, the STEAMCat in Catalonia and the MaSDiV project at both national and international levels. Through the narration of these case studies, we characterize different ways of understanding STEM education and show how each STEM approach has been used to inform school practices and teachers’ professional development efforts. Contested aspects in STEM education such as which level of integration is desirable, what the role of disciplinary content knowledge is in STEM, or how much focus to accord to the development of key competencies, among others, emerged in these different accounts. The impact of the programs on students’ competences and teachers’ beliefs and practices as well as its effect on teachers’ self-efficacy to implement the previously discussed STEM education model are also discussed.
Delayed evolutionary game clustering-based recommendation algorithm via latent information and user preference

Recommendation system is widely used because of its personalized service. It helps users obtain satisfactory results due to ambiguous expression in search engines. However, with the increasing number of users and items, most of the existing recommendation algorithms have problems of cold start, sparse data and high complexity. To address the above issues, this paper applies the rating information and attribute information of users and items to better represent the preferences of users and the activity of items by enriching the available information. Furthermore, we propose a novel game-based evolutionary clustering method to divide interest communities for users, which not only reduces the complexity of recommendation, but also takes full account of users’ preferences. In addition, the impact of delayed information transmission on experimental performance is considered in the game-based evolutionary clustering. Since the calculation of similarity plays a significant role in finding the nearest neighbors of target users in the community, we propose a new similarity measurement strategy based on user preferences. Finally, the effectiveness of the proposed algorithm is verified by ablation experiment and comparison experiments. The experimental results illustrate that our algorithm outperforms the existing excellent algorithms.
LBCC-Hung: A load balancing protocol for cloud computing based on Hungarian method

Cloud Computing can be defined as enabling computing resources to whoever want remotely upon demand. This necessarily requires using virtualization. A virtual machine (VM) is an emulation of a computer that runs in virtualization software. Tasks coming from different users are passed to the virtual machines to be processed. This paper studies the load balancing problem among various VMs; to ensure that the network resources are distributed in a fair way between various clients. This paper proposes solving the load balancing problem using a combinatorial optimization approach named the Hungarian method. The proposed protocol is named LBCC-Hung (Load Balancing Protocol for Cloud Computing Based on Hungarian Method). Using simulation, the performance of LBCC-Hung is measured and compared with two well-known methods; MIN-MIN and First Come First Serve FCFS methods. The simulation results proved that LBCC-Hung overperforms the others two protocols, in terms of both the Makespan and the throughput and the virtual machine utilization deviation.
Smart Tech is all Around us – Bridging Employee Vulnerability with Organizational Active <scp>Trust‐Building</scp>

Abstract Public and academic opinion remains divided regarding the benefits and pitfalls of datafication technology in organizations, particularly regarding their impact on employees. Taking a dual‐process perspective on trust, we propose that datafication technology can create small, erratic surprises in the workplace that highlight employee vulnerability and increase employees’ reliance on the systematic processing of trust. We argue that these surprises precipitate a phase in the employment relationship in which employees more actively weigh trust‐related cues, and the employer should therefore engage in active trust management to protect and strengthen the relationship. Our paper develops a framework of symbolic and substantive strategies to guide organizations’ active trust management efforts to (re‐)create situational normality, root goodwill intentions, and enable a more balanced interdependence between the organization and its employees. We discuss the implications of our paper for reconciling competing narratives about the future of work and for developing an understanding of trust processes.
Learning the degradation distribution for medical image superresolution via sparse swin transformer

High-resolution (HR) medical images can provide rich details, which are important for discovering subtle lesions to make diagnoses. Convolutional neural networks (CNNs) are widely used in this field, but struggle to model long-range dependencies. Although transformer-based methods have improved in this respect, this method requires large quantities of data. Unfortunately, large quantities of low-resolution (LR) and HR medical image pairs may not always be available. In addition, most medical image superresolution (SR) methods are deterministic, while the degradation in real scenarios is stochastic. To address these problems, we introduce a probabilistic degradation model that combines natural and medical images for training. This design alleviates the problem of insufficient medical image pairs and learns the degradation process of the natural scene. In addition, we propose a new medical image SR model that consists of CNNs and the Swin Transformer structure to excavate both local and global semantic features. Moreover, to reduce computational stress, the spherical locality-sensitive hashing (SLSH) module is employed in the nonlocal attention (NLA) mechanism to form the ENLA module. This design enables the proposed Sparse Swin Transformer (SSFormer) model to generate HR medical images without extensive training images. Experiments on diverse datasets (natural images and medical images) demonstrate that the proposed method is robust and effective, qualitatively and quantitatively outperforming other medical image SR methods. Code is available at https://github.com/codehxj/SSFormer.
Hierarchical path planner for unknown space exploration using reinforcement learning-based intelligent frontier selection

Path planning in unknown environments is extremely useful for some specific tasks, such as exploration of outer space planets, search and rescue in disaster areas, home sweeping services, etc. However, existing frontier-based path planners suffer from insufficient exploration, while reinforcement learning (RL)-based ones are confronted with problems in efficient training and effective searching. To overcome the above problems, this paper proposes a novel hierarchical path planner for unknown space exploration using RL-based intelligent frontier selection. Firstly, by decomposing the path planner into three-layered architecture (including the perception layer, planning layer, and control layer) and using edge detection to find potential frontiers to track, the path search space is shrunk from the whole map to a handful of points of interest, which significantly saves the computational resources in both training and execution processes. Secondly, one of the advanced RL algorithms, trust region policy optimization (TRPO), is used as a judge to select the best frontier for the robot to track, which ensures the optimality of the path planner with a shorter path length. The proposed method is validated through simulation and compared with both classic and state-of-the-art methods. Results show that the training process could be greatly accelerated compared with the traditional deep-Q network (DQN). Moreover, the proposed method has 4.2%–14.3% improvement in exploration region rate and achieves the highest exploration completeness.
LiDAR Technology for Human Activity Recognition: Outlooks and Challenges

The smart and autonomous learning and recognition of human activities will certainly lead to an incredible progression for several applications and services in public healthcare, education, entertainment, safety and security, and more. With the recent advances in artificial intelligence, signal processing, and computational capabilities, light detection and ranging (LiDAR) technology can play an instrumental role to revamp current human activity recognition (HAR) systems. In this magazine, we investigate the potential of using LiDAR sensors as a novel technology enabling complex and real-time HAR applications. We first overview the HAR categories and the existing state-of-the-art technologies: video-based cameras, depth sensors, wearable devices, and WiFi. Afterward, we delve into the integration of LiDAR technology to perform HAR applications. Then, we discuss the advantages and drawbacks of utilizing LiDAR technology for HAR and compare it with the existing techniques. Finally, we discuss the challenges that need to be addressed to enable advanced LiDAR-based HAR applications.
Security Verification Software Platform of Data-efficient Image Transformer Based on Fast Gradient Sign Method

Recently, research using knowledge distillation in artificial intelligence (AI) has been actively conducted. In particular, data-efficient image transformer (DeiT) is a representative transformer model using knowledge distillation in image classification. However, DeiT's safety against the patch unit's adversarial attacks was not verified. Furthermore, existing DeiT research did not prove security robustness against adversarial attacks. In order to verify the vulnerability of adversarial attacks, we conducted an attack using the fast gradient sign method (FGSM) targeting the DeiT model based on knowledge distillation. As a result of the experiment, an accuracy of 93.99% was shown in DeiT verification based on Normal data (Cifar-10). In contrast, when verified with abnormal data based on FGSM (adversarial examples), the accuracy decreased by 83.49% to 10.50%. By analyzing the vulnerability pattern related to adversarial attacks, we confirmed that FGSM showed successful attack performance through weight control of DeiT. Moreover, we verified that DeiT has security limitations for practical application.
Cross-efficiency evaluation method for non-homogeneous parallel network systems with imprecise data

The existing parallel DEA models assume that all decision making units (DMUs) should contain identical subsystems. This paper proposes a cross-efficiency evaluation method to handle the non-homogeneous DMUs (NHDMUs) in the parallel network system, where the non-homogeneity of DMUs is shown in that the input and output sets of subsystems and the internal structure of DMUs are not the same. The method considers the scenario wherein quantitative and qualitative data exist in the inputs and outputs simultaneously. To do this, first, the NHDMUs are divided into different mutually exclusive unit groups according to the subsystems in which DMUs participate. Then, several interval cross-efficiency models are constructed for the cross-efficiency evaluation of DMUs in the same group and the cross-efficiency evaluation of DMUs with overlapping internal outlines in different groups to obtain the interval cross-efficiencies of NHDMUs. Further, the evidence theory and grey relation analysis method are used to determine a set of weights that aggregate the cross-efficiency matrix. Finally, the proposed method is discussed using an application case of talent introduction performance. The results show that the proposed method can improve the rationality and distinguishability of efficiency evaluation and find the reasons for the performance decline of a DMU.
Secure <scp>Internet of Thing</scp> based data communication in blockchain model using novel teaching‐learning optimized fuzzy approach

Abstract The potential uses in the Internet of Things (IoT), blockchain is gaining prominence. It is particularly skillful at storing data in immutable blocks, which is connected to its protected peer‐to‐peer architecture in the face of a growing challenge of transaction authorization in industrial and service‐provider applications. IoT is prolonged to several additional applications as a result of its integration with various technologies, allowing direct engagement with our individual and professional lives. The major aim of this study is to provide the security based IoT data communication in blockchain using novel techniques. The device's security is a significant concern. However, on‐demand security solutions have issues since devices have limited processing and energy resources. IoT devices do not allow for the use of large, costly systems. In order to address these issues, we present a novel optimized fuzzy architecture for a blockchain‐based IoT device data exchange and communication in this research. The TLBO method is used to improve decision‐making capabilities, while the Mamdani fuzzy inference system makes the majority of the decisions. To construct the two input variables (drop ratio and integrated trust level) and reduce the dimensionality of the input information, the TLBO method was used. We examine and illustrate the efficacy of our method with another state‐of‐art method. Based on the experiment, we have obtained 1.8, 2.1, 2.8, 3.2 and 3.8 ms computational time based on the number of users from 2 to 10, respectively. The proposed method attains 96% accuracy and 95.4% F‐score results than other previous methods.
Real-time scene classification of unmanned aerial vehicles remote sensing image based on Modified GhostNet

Unmanned Aerial Vehicles (UAVs) play an important role in remote sensing image classification because they are capable of autonomously monitoring specific areas and analyzing images. The embedded platform and deep learning are used to classify UAV images in real-time. However, given the limited memory and computational resources, deploying deep learning networks on embedded devices and real-time analysis of ground scenes still has challenges in actual applications. To balance computational cost and classification accuracy, a novel lightweight network based on the original GhostNet is presented. The computational cost of this network is reduced by changing the number of convolutional layers. Meanwhile, the fully connected layer at the end is replaced with the fully convolutional layer. To evaluate the performance of the Modified GhostNet in remote sensing scene classification, experiments are performed on three public datasets: UCMerced, AID, and NWPU-RESISC. Compared with the basic GhostNet, the Floating Point Operations (FLOPs) are reduced from 7.85 MFLOPs to 2.58 MFLOPs, the memory is reduced from 16.40 MB to 5.70 MB, and the predicted time is improved by 18.86%. Our modified GhostNet also increases the average accuracy (Acc) (4.70% in AID experiments, 3.39% in UCMerced experiments). These results indicate that our Modified GhostNet can improve the performance of lightweight networks for scene classification and effectively enable real-time monitoring of ground scenes.
A survey on Relation Extraction

With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information. Information Extraction (IE) can extract meaningful information from text sources and present it in a structured format. The sub-tasks of IE include Named Entity Recognition (NER), Event Extraction, Relation Extraction (RE), Sentiment Extraction, Opinion Extraction, Terminology Extraction, Reference Extraction, and so on. One way to represent information in the text is in the form of entities and relations representing links between entities. The Entity Extraction task identifies entities from the text, and the Relation Extraction (RE) task can identify relationships between those entities. Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system.
Diagnosing heart disease by a novel hybrid method: Effective learning approach

In this paper, the effective classifier Extreme Learning Machines (ELM), Enhanced Fast Learning Networks (EFLN), Support Vector Machines (SVM), and Decision Trees (DT) for early Heart Disease (HD) diagnostics have been investigated. Using the optimal parameters for the proposed method, ELM, EFLN, SVM, and DT are considered for early diagnosis of heart disease. The performance of the proposed method has been evaluated versus the Accuracy (ACC), Sensitivity (SE), and Specificity (SP) HD dataset. This study also proposed a new effective Hybrid Model, by combining the Particle Swarm Optimization Algorithm (PSOA) with the most effective Learning classifiers. The proposed hybrid diagnostic method records ACC through ten runs for a 10-fold cross-validation (CV). According to the results, the proposed method shows an ACC of 93% for (PSO-ELM), 96% (PSO_EFLN), 91% (PSO-SVM) and 93% (PSO-DT).
Semantic-based Adaptation of Quality of Experience in Web Multimedia Streams

Video streaming accounts for the majority of worldwide Internet traffic, and HTTP-based multimedia has the largest share among technologies and protocols. The wide availability of mobile devices and wireless broadband networks currently leads to wider heterogeneity of fruition contexts and frequent condition changes during a streaming session. MPEG-DASH is the reference standard for Dynamic Adaptive Streaming over HTTP: a provider defines several representations for a segmented multimedia source, with different bit rates, allowing a client to dynamically select the best one based on current conditions, and to download the corresponding sequence of segments for smooth playback. MPEG-DASH does not mandate specific bit rate adaptation schemes; conventional approaches are divided in buffer-based, bandwidth-based and hybrid. Nevertheless, Quality of Experience (QoE) can be influenced by many additional factors. This paper proposes a novel QoE adaptation approach based on dynamic ontology-based annotation of streaming context and mobile matchmaking with DASH representation profiles in a Web Ontology Language (OWL) fragment, exploiting a WebAssembly port of an embedded reasoning engine. The proposed framework enables adaptation based not only on network status, but also on client device capabilities, ambient conditions and multimedia content type. A case study validates the proposal, while early experiments support its sustainability.