Reviewerly/exHarmony huggingface dataset:
A Flexible Approach to Multi-party Business Process Execution on Blockchain

In modern business scenarios, more and more organisations have to deal with the critical requirements of trustworthiness and flexibility, when collaborating in multi-party business processes. This calls for new kinds of systems able to manage collaborative processes in untrusted and dynamic environments. Concerning the collaborative perspective, the Business Process Management discipline has provided effective and standardised solutions for a long time, now. Regarding the trustworthiness perspective, blockchain is advocated as one of the most prominent technologies to guarantee trust in a multi-party setting. However, while the immutability of blockchain provides transparent and secure proof of past business interactions, it hinders the flexibility of the business process execution, as the business logic regulating the process execution is immutably stored in the blockchain. On the other hand, flexibility is a property that is becoming crucial in such a setting due to the high dynamism of the business scenarios. In fact, it permits to modify a process at run-time to deal with internal or external changes. In this paper, we face this issue by proposing an architecture for the flexible blockchain-based execution of multi-party business processes. In our approach, business processes are modelled by BPMN choreography diagrams translated into code, whose execution state is then stored in the blockchain. Flexibility is achieved by decoupling the business process's logic from its execution state, thus allowing run-time changes to the process execution without losing the fundamental properties of trust provided by the blockchain. To show the effectiveness of our approach, we provide a prototypical implementation, called FlexChain, and we use it on a case study from the healthcare application domain. The results obtained by the analysis of cost for the reported case study show the feasibility of the approach. In particular, major costs to sustain relate to one-time operations, such as the deployment and the run-time update of the model, while the most frequent actions are quite efficient.
Generative Adversarial Super-Resolution at the edge with knowledge distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution, called EdgeSRGAN1. We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications.
An active learning-based incremental deep-broad learning algorithm for unbalanced time series prediction

Time series are a kind of streaming data, which are chaotic and sequential. As real-world time series data are often not available at once and drift with time growth, Incremental Learning (IL) is well suited for Time Series Prediction (TSP). Most previous incremental TSP algorithms are limited by the assumption of data balance. However, real-world time series data are often unbalanced, with long-tailed distribution and other characteristics resulting in the failure of IL algorithms. In this paper, a balanced-driven Active Learning (AL) strategy is proposed to deal with data imbalance problems in IL processes. What's more, by integrating the advantages of Deep Learning (DL) and the Broad Learning System (BLS), a novel Deep-Broad Learning (DeepBL) network with its incremental learning algorithm is proposed. The proposed Active Learning-based Incremental Deep-Broad Learning (AI_DeepBL) algorithm is applied to real-world univariate and multivariate time series datasets and achieves superior performance compared with classical and state-of-the-art TSP algorithms.
Growth of Cyber-crimes in Society 4.0

In the fast-paced Information and communication technology, cyber-crimes are also evolving and growing very fast thereby increasing damage of the organizations and individuals universally. This paper is an attempt to get an overview of the different trends of cyber-crimes, to spread awareness about the cyber-crimes among people to increase security of the people of Delhi and NCR from cyber-crimes. Since Internet has become a basic need of life in metro cities today for almost every individual, increased dependence on Internet has led to the rise of cyber-crime and one of the best ways of protection from cybercrimes is its awareness. The paper intends to understand the level and intensity of awareness about various cyber-crimes present in the era of Society 4.0 in capital of India. The paper also identifies the importance of being acquainted with the effects of cyber-crime and awareness of the methods of prevention.
From Softmax to Nucleusmax: A Novel Sparse Language Model for Chinese Radiology Report Summarization

The Chinese radiology report summarization is a crucial component in smart healthcare that employs language models to summarize key findings in radiology reports and communicate these findings to physicians. However, most language models for radiology report summarization utilize a softmax transformation in their output layer, leading to dense alignments and strictly positive output probabilities. This density is inefficient, reducing model interpretability and giving probability mass to many unrealistic outputs. To tackle this issue, we propose a novel approach named nucleusmax. Nucleusmax is able to mitigate dense outputs and improve model interpretability by truncating the unreliable tail of the probability distribution. In addition, we incorporate nucleusmax with a copy mechanism, a useful technique to avoid professional errors in the generated diagnostic opinions. To further promote the research of radiology report summarization, we also have created a Chinese radiology report summarization dataset, which is freely available. Experimental results showed via both automatic and human evaluation that the proposed approach substantially improves the sparsity and overall quality of outputs over competitive softmax models, producing radiology summaries that approach the quality of those authored by physicians. In general, our work demonstrates the feasibility and prospect of the language model to the domain of radiology and smart healthcare.
MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation

Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MixCode that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MixCode on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRep1, and Refactory), and seven model architectures (including two pretrained models CodeBERT and GraphCodeBERT). Experimental results demonstrate that MixCode outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness.
A comprehensive review of State-of-The-Art methods for Java code generation from Natural Language Text

Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules and the necessity of a deep understanding of the semantic aspect of the programming language. Many works tried to tackle this task using either RNN-based, or Transformer-based models. The latter achieved remarkable advancement in the domain and they can be divided into three groups: (1) encoder-only models, (2) decoder-only models, and (3) encoder-decoder models. In this paper, we provide a comprehensive review of the evolution and progress of deep learning models in Java code generation task. We focus on the most important methods and present their merits and limitations, as well as the objective functions used by the community. In addition, we provide a detailed description of datasets and evaluation metrics used in the literature. Finally, we discuss results of different models on CONCODE dataset, then propose some future directions.
An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network

Due to its importance regarding the integration, economic dispatch, and operation of PV smart grid systems, infrastructure planning, and budgeting, the accurate forecasting of photovoltaic (PV) power generation has drawn increasing research and industry attention. However, the instability, intermittence, and randomness of solar irradiance impose difficulties on the short-term economic dispatch of a smart integrated power, grid and significantly increase the risks arising from PV generation in a power system, exposing PV generators to potential additional costs. A deep convolutional neural network (CNN)-based method can effectively improve the performance of PV generation point prediction and probabilistic interval prediction by efficiently extracting nonlinear features at each frequency. Nevertheless, existing deep learning (DL) studies have mostly focused on more complex network topologies and data decomposition algorithms, ignoring the importance of simultaneously forecasting the PV power produced over multiple temporal periods. To solve the described challenge, we propose a novel two-stage DL approach for PV generation prediction. The end-to-end trained model used in the proposed PV generation forecasting method is an effort to combine the existing methods and avoid the separation of entire task, such as single time-scale PV generation forecasting, independent point forecasting, and probabilistic interval forecasting. A temporal signal decomposition technique called variational mode decomposition (VMD) is employed in the first stage to construct association mappings from fine-grained features to images. In the second stage, an innovative capsule CNN (ACCNet) is proposed to obtain very short-term multihorizon ahead output power predictions for seven different PV systems based on polycrystalline, monocrystalline, cadmium telluride (CdTe) thin-film, amorphous, copper indium gallium diselenide (CIGS) thin-film, heterojunction with intrinsic thin layer (HIT) hybrid, and concentrated photovoltaic (CPV) technologies. The input parameters for each system include solar radiation (diffuse/global horizontal radiation (DHR/GHR) and radiation diffuse/global tilted (RDT/RGT)) and ambient temperature, while the output parameter is the power output of each PV system. The proposed model is validated with the historical datasets of a PV system downloaded from the Desert Knowledge Precinct in Central Australia (DKASC) homepage. The performance of the developed method is proven in detail based on seven different PV systems over multiple data periods, and the experimental results reveal that the proposed approach displays significant improvements and robustness in point forecasting and probabilistic forecasting tasks. We will release the source code to ensure reproducibility and facilitate future work. Our model is open-sourced at https://github.com/YunDuanFei/ACCNet.
Detecting insurance fraud using supervised and unsupervised machine learning

Abstract Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary insurance claim data. Furthermore, we conduct a field experiment in cooperation with an insurance company to investigate the performance of each approach in terms of identifying new fraudulent claims. We derive several important findings. Unsupervised learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on different input information. Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.
IoT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments

The Internet of Things (IoT) technology with wearable devices provides a promising solution that enables con-tinuous monitoring of health parameters. Non-invasive sensors enable remote health monitoring and improve many aspects of life quality. The study explores advances in IoT and Wearable Devices through a conceptualization of health monitoring observer networks. The paper proposes an architecture inspired by a formal description of human perception, which has been successfully employed in environmental monitoring and can be adapted to healthcare for better integration and comprehension of patient health data. The proposed architecture involves a three-layer ap-proach Sensors, Integration and Association and Application and Diagnostic, similar to the architecture used in forest fire monitoring. The Sensors layer describes state-of-the-art cutting-edge equipment and related sensors for gathering information. The second layer, Integration and Association propose an ontology and a potential solution for integrat-ing sensor information from the device to the cloud. Furthermore, the third layer, Application and Diagnostic gives a review of used machine learning algorithms for detecting and diagnosing various diseases. Finally, a case study of elderly health monitoring using wearable devices is presented, discussing the methodology for economic value calcu-lation to decrease the cost of efficient health management and improve quality of life. The use of a health monitoring observer network not only improves quality of life, but also reduces the cost of health monitoring, making it more sustainable for the general community.
A Multitask Deep Learning Approach for Sensor-Based Human Activity Recognition and Segmentation

Deep learning for sensor-based human activity recognition (HAR) has been a focus of research in recent years. Sensor data stream segmentation is a core element in HAR, which has currently been treated as an independent preprocessing task, usually with a fixed-size window. This has led to two critical problems, namely the multi-class window problem caused by possible multiple activities within a fixed-size window and the fluctuation of prediction results due to noisy data and over-segmentation. To address these research challenges, in this paper, we conceive a novel Multi-Task deep learning approach to segmenting and recognizing human activity simultaneously. Specifically, we propose a multi-scale window method based on feature sequence generation to overcome the multi-class window problem. We develop a novel boundary offset prediction algorithm to adjust a windows boundary to tackle the over-segmentation issue. In addition, We design a multi-task framework to streamline and optimize the activity recognition and segmentation tasks simultaneously. We conduct extensive experiments on eight benchmark datasets to evaluate the proposed framework and associated methods. Initial results show that our approach outperforms the performance of current state-of-the-art HAR methods.
Social network trust relationship environment based advanced ovarian cancer treatment decision-making model: An approach based on linguistic information with experts’ multiple confidence levels

Advanced ovarian cancer is the deadliest gynecologic cancer in World. The survival rate is not significant, and it depends on how well cancer responds to treatment. Generally, the treatment decision depends on Gynecologic oncology experts, which treatment is best for the patient. However, a Gynecologic oncology expert’s limited rationality and personality traits strongly impact a patient’s treatment decision. In addition, according to the American Cancer Society, a patient’s treatment decision may change if Gynecologic oncology experts change. To solve this kind of medical problem, we design a group decision-making model (GDM) based on linguistic preference relations (LPRs) with multiple confidence levels like confidence and doubting levels of experts to analyze experts’ treatment decision opinions digitally for improving the survival rate of patients. In a GDM, consistency analysis and obtaining expert weights are two keys. Hence, this paper design an LPRs with confidence and doubting levels (LPRs-CDLs) based GDM model under a unique consistency improvement algorithm and Pythagorean linguistic trust relations social network analysis driven expert weight determination. First, we define the additive consistency of LPRs-CDLs based on minimum confidence and maximum doubting levels. Second, a consistency reaching process (CRP) algorithm with convergence theorem is offered to improve the consistency of experts’ opinion decisions. Third, develop a trust propagation model based on Pythagorean linguistic trust relations under a social network environment for expert’s weights determination. Then, a social network trust relationship-based GDM model with LPRs-CDLs information is presented to the advanced ovarian cancer patient’s treatment final decision. Finally, we show the rationality of this study with some comparative analysis.
A novel adaptive algorithm of particle swarm optimization based on the human social learning intelligence

A novel adaptive algorithm of particle swarm optimization is proposed in this paper, where the learning strategy is designed by simulating features of human social learning intelligence. By the principles of this strategy, the swarm is first adaptively grouped into a number of subswarms based on the fitness levels of particles, as well as no need of fixing the population sizes as done in the existing algorithms. Then, the particles in each subswarm constitute three different types classified by their evolutionary levels, which adopt different learning modes defined by distinct adaptive inertia weighting rules and dynamical learning mechanisms. Consequently, all the particles play their respective roles in promoting the global optimization performance of our algorithm, just like all the individuals in society. Numerical tests of our algorithm are conducted by solution of the standard benchmark test problems from the CEC 2014 and CEC 2017 test suites. The test results show the advantages of the proposed learning strategy in this paper, and demonstrate that the overall performance of our algorithm outperforms both of the other compared PSO variants and the other state-of-the-art evolutionary algorithms, in terms of global search ability, solution accuracy and convergence speed. Statistical tests also validate the promising performance of our algorithm.
A survey on location and motion tracking technologies, methodologies and applications in precision sports

Sports involve commonly players and equipment of high dynamics. Their location and motion data are essential for sports digitalization-related applications, such as from monitoring player skill level to even media presentation of sports. Location and motion data of players and equipment are important elements for precision sport analytics. With the popularization of the Internet of things, a variety of sports location and motion tracking technologies have advanced in terms of sensors and tracking methodologies to achieve ubiquitous coverage from outdoors to indoors, higher precision, and applicability for increasing sports scenarios. This paper presents a systematic survey on location and motion tracking technologies that have been applied in sports to drive various sports-related analytics and applications. First, several wearable sensor-based technologies are introduced with their principles, advantages and shortages in sports, and the location and motion tracking methods of each technology. Then, vison-based technology and its methodologies that detect, track, and recognize objects including human and equipment in sports are illustrated. Furthermore, sports-related data analytics and applications are presented using location and motion data of human and equipment. Data privacy and ethics are also focused based on the location and motion tracking technologies. Finally, challenges and further development of location and motion tracking in precision sports are discussed. The comprehensive survey provides a survey about state-of-the-art of location and motion sensors and methodologies, as well future trends of these technologies, which are beneficial for developing forthcoming precision sports.