Reviewerly/exHarmony huggingface dataset:
An innovative approach for real-time network traffic classification

The growing demand for high-speed transmission rates in recent years attracted research in new mechanisms for network traffic characterization and classification. Their inadequate treatment degrades the performance of important operational schemes, such as Network Survivability, Traffic Engineering, Quality of Service (QoS), and Dynamic Access Control, among others. The most common methods for traffic classification are Deep Packet Inspection (DPI) and port based classification. However, those methods are becoming obsolete, as increasingly more traffic is being encrypted and applications are using dynamic ports or ports originally assigned to other popular applications. This paper presents a classification module for video streaming traffic, based on machine learning, as a solution for network schemes that require adequate real-time traffic treatment. The module adopts a new approach for the relaxation of the hypothesis of independence between the attributes of the Naive Bayes algorithm. The results show that the proposed module is a promising alternative to be applied in real-time scenarios.
Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing

Multi-scale permutation entropy (MPE) has been proven to be an effective nonlinear dynamic analysis tool for complexity and irregularity evaluation of rolling bearing. Nevertheless, MPE still has some issues that need to be addressed. First, the coarse grained process used in MPE will shorten the length of time series and result in mode information loss, especially for short time series. Second, different patterns of a symbol cannot be distinguished by permutation entropy and MPE. Inspired by the thought of composite coarse graining and weighted permutation entropy, the composite multi-scale weighted permutation entropy (CMWPE) methodology is proposed in this paper. Compared with MPE, CMWPE preserves much more useful information by adding the weighted factor and using composite coarse graining to optimize the process of coarse-gained time series, where multiple time series information is considered for the same scale factor. The simulation synthetic signals are used to demonstrate the effectiveness of CMWPE and the results show that CMWPE has less dependence on data length and the estimated entropy values are much more stable than the other existing methods. Based on CMWPE, a new intelligent fault diagnosis scheme for rolling bearing is proposed with combination of extreme learning machine. Finally, the proposed fault diagnosis method is applied to two diagnostic cases of rolling bearing and the results verified the effectiveness and superiority of the proposed approach to MPE and MWPE.
Multi‐Party Semi‐Quantum Key Distribution Protocol With Four‐Particle Cluster States

Abstract The application of semi‐quantum conception can provide unconditional secure communication for communicators without quantum capabilities. A semi‐quantum key distribution (SQKD) protocol based on four‐particle cluster states is put forward, which can achieve key distribution among one quantum party and two classical parties simultaneously. Furthermore, this protocol can be expanded to the χ‐party () communication scheme. Compared with the existing multi‐party SQKD protocol, the proposed protocol and the extended one own more excellent time efficiency and qubit efficiency. The security of the proposed SQKD protocol under ideal circumstances is validated while the key rate under non‐ideal conditions is calculated.
Self-adaptive mutation differential evolution algorithm based on particle swarm optimization

Differential evolution (DE) is an effective evolutionary algorithm for global optimization, and widely applied to solve different optimization problems. However, the convergence speed of DE will be slower in the later stage of the evolution and it is more likely to get stuck at a local optimum. Moreover, the performance of DE is sensitive to its mutation strategies and control parameters. Therefore, a self-adaptive mutation differential evolution algorithm based on particle swarm optimization (DEPSO) is proposed to improve the optimization performance of DE. DEPSO can effectively utilize an improved DE/rand/1 mutation strategy with stronger global exploration ability and PSO mutation strategy with higher convergence ability. As a result, the population diversity can be maintained well in the early stage of the evolution, and the faster convergence speed can be obtained in the later stage of the evolution. The performance of the proposed DEPSO is evaluated on 30-dimensional and 100-dimensional functions. The experimental results indicate that DEPSO can significantly improve the global convergence performance of the conventional DE and thus avoid premature convergence, and its average performance is better than those of the conventional DE, PSO and the compared algorithms. Moreover, DEPSO is applied to solve arrival flights scheduling and the optimization results show that it can optimize the sequence and decrease the delay time.
Blockchain as the Database Engine in the Accounting System

Abstract This paper examines the prediction that blockchain technology will transform accounting and the profession because transactions recorded on a blockchain can be aggregated into financial statements and confirmed as true and accurate. We argue that blockchain technology affects the database engine of the accounting information system (AIS) through digitisation of the current paper‐based validation process. In a blockchain‐based AIS, accountants will no longer be the central authority but will remain the preparer of financial reports required by regulations; they will continue to influence policies such as the choice and accreditation of validators and serve as validators of last resort. Audit evidence still needs to be gathered for rendering of an audit opinion in a blockchain‐based AIS. While digitisation of the validation process reduces the error rate and lowers the cost of vouching and tracing, and immutability of blockchain data reduces the incentive and opportunities for fraud, a blockchain‐based AIS alone does not guarantee that financial reports are true and fair. Lower error rates and reduced incentives for accounting fraud in a blockchain‐based AIS are expected to improve audit quality. This prediction will need to be empirically tested when blockchain‐based AIS become available. Using the three‐tier architecture of the AIS, this paper addresses the gap in the literature that misses how characteristics of blockchain technology can influence the implementation of a blockchain‐based AIS with related implications for the accounting profession.
Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements

A hybrid forecasting methodology to predict one-minute averaged solar irradiance one to ten minutes in advance is presented and evaluated. The methodology combines the use of all-sky images and irradiance measurements which are both processed in real time to produce the forecast. Pre-existing image processing techniques are locally adapted to estimate the mean motion of clouds, which is used to predict the future sun disk cover by clouds. Then, the predicted cloud information is converted into a solar irradiance estimate using the proposed model which uses real time measurements to extract its parameters for prediction. The validation of the method is done with a sample of 5238 forecasting time points, spread over a six-month period. The forecast uncertainty is assessed separately for clear, cloudy and partly cloudy days, revealing important characteristics of the model's performance under the different conditions. Under partly cloudy and highly variable conditions, positive forecasting skills with respect to regular persistence are achieved above forecasting horizons of two minutes, with a peak performance of 11.4% for forecasting horizons of six and ten minutes. The proposed model also outperforms a smart persistence model for all time horizons under these sky conditions. The model's ramp detection index (RDI, as defined in Chu et al. (2015)) is also evaluated for high and moderate ramps, achieving RDI indexes between 55 and 62% and between 43 and 49% for high and moderate ramps, respectively. These results show that in challenging highly variable solar irradiance conditions the proposed model is suitable for the very short term solar resource forecasting.
Kapur’s entropy based optimal multilevel image segmentation using Crow Search Algorithm

Image segmentation is an essential part of image analysis, which has a direct impact on the quality of image analysis results. Thresholding is one of the simplest and widely used methods for image segmentation. Thresholding can be either bi-level, which involves partitioning of an image into two segments, or multilevel, which partitions an image into multiple segments using multiple thresholds values. This paper focuses on multilevel thresholding. A good segmentation scheme through multilevel thresholding identifies suitable threshold values to optimize between-class variance or entropy criterion. For such optimizations, nature inspired metaheuristic algorithms are commonly used. This paper presents a Kapur’s entropy based Crow Search Algorithm (CSA) to estimate optimal values of multilevel thresholds. Crow Search Algorithm is based on the intelligent behavior of crow flock. Crow Search Algorithm have shown better results because of less number of parameters, no premature convergence, and better exploration–exploitation balance in the search strategy. Kapur’s entropy is used as an objective function during the optimization process. The experiments have been performed on benchmarked images for different threshold values (i.e. 2, 4, 8, 16, 32 thresholds). The proposed method has been assessed and performance is compared with well-known metaheuristic optimization methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO) and Cuckoo Search (CS). Experimental results have been evaluated qualitatively and quantitatively by using well-performed evaluation methods namely PSNR, SSIM, and FSIM. Computational time and Wilcoxon p-type value also compared. Experimental results show that proposed algorithm performed better than PSO, DE, GWO, MFO and CS in terms of quality and consistency.
DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network

In the field of radar data processing, traditional maneuvering target-tracking algorithms assume that target movements can be modeled by pre-defined multiple mathematical models. However, the changeable and uncertain maneuvering movements cannot be timely and precisely modeled because it is difficult to obtain sufficient information to pre-define multiple models before tracking. To solve this problem, we propose a deep learning maneuvering target-tracking (DeepMTT) algorithm based on a DeepMTT network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent maneuvering targets. To this end, we first build a LArge-Scale Trajectory (LAST) database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed to track the maneuvering targets using a DeepMTT network, which consists of three bidirectional long short-term memory layers, a filtering layer, a maxout layer and a linear output layer. The simulation results verify that our DeepMTT algorithm outperforms other state-of-the-art maneuvering target-tracking algorithms.
ECCAuth: A Secure Authentication Protocol for Demand Response Management in a Smart Grid System

The devices in smart grids (SG) transfer data to a utility center (UC) or to the remote control centers. Using these data, the energy balance is maintained between consumers and the grid. However, this flow of data may be tampered by the intruders, which may result in energy imbalance. Thus, a robust authentication protocol, which supports dynamic SG device validation and UC addition, both in the local and global domains, is an essential requirement. For this reason, ECCAuth: a novel elliptic curve cryptography-based authentication protocol is proposed in this paper for preserving demand response in SG. This protocol allows establishment of a secret session key between an SG device and a UC after mutual authentication. Using this key, they can securely communicate for exchanging the sensitive information. The formal security analysis, informal security analysis, and formal security verification show that ECCAuth can withstand several known attacks.
Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Abstract The question: “What is an appropriate role for AI?” is the subject of much discussion and interest. Arguments about whether AI should be a human replacing technology or a human assisting technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should play, and as with many other professional areas, the exact role of AI in education is not easy to predict. Here, we argue that one potential role for AI in education is to provide opportunities for human intelligence augmentation, with AI supporting us in decision‐making processes, rather than replacing us through automation. To provide empirical evidence to support our argument, we present a case study in the context of debate tutoring, in which we use prediction and classification models to increase the transparency of the intuitive decision‐making processes of expert tutors for advanced reflections and feedback. Furthermore, we compare the accuracy of unimodal and multimodal classification models of expert human tutors' decisions about the social and emotional aspects of tutoring while evaluating trainees. Our results show that multimodal data leads to more accurate classification models in the context we studied.
A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time.In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution.The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.
Sensitive and Energetic IoT Access Control for Managing Cloud Electronic Health Records

Electronic health records (EHRs) replaced the old paper-based systems to make patient data more accurate, reliable, and more accessible.Yet, the EHRs system requires high transmission cost, energy, and waste of time for both doctors and patients.Furthermore, EHRs security presents a serious issue threatening the patient's privacy.Most of the third-party hosting systems have some issues related to the users' privacy and data security.Hence, it is necessary to restrict the access control policies and develop efficient mechanisms for cloud-based EHRs data.In this paper, a sensitive and energetic access control (SE-AC) mechanism is proposed for managing the cloud-hosted EHRs and providing a fine-grained access control even in critical situations.The proposed mechanism ensures the confidentiality of the patient's data, where only authorized individuals to have permission to be able to edit or review certain of the patient's data.Each EHR data is encrypted by the managing authority before submitting to the cloud storage.The requesting user can get dynamically changing permissions based on authentication and context attributes.In addition, seven major aspects have been quantified to assess the operation of any access control that could be deployed in the Internet-of-Thing (IoT).The security analysis indicates that the SE-AC mechanism is secure and will prevent any unauthorized access.The results show exceptional compatibility and performance with different setups and configuration.
DeepTriage

For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of bug triaging. Automatic bug triaging is formulated as a classification problem, which takes the bug title and description as the input, and maps it to one of the available developers. A major challenge in doing this is that the bug description usually contains a combination of unstructured text, code snippets, and stack traces making the input data highly noisy. The existing bag-of-words (BOW) models do not consider the semantic information in the unstructured text.
COCO-CN for Cross-Lingual Image Tagging, Captioning, and Retrieval

This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with manually written Chinese sentences and tags. For effective annotation acquisition, we develop a recommendation-assisted collective annotation system, automatically providing an annotator with several tags and sentences deemed to be relevant with respect to the pictorial content. Having 20 342 images annotated with 27 218 Chinese sentences and 70 993 tags, COCO-CN is currently the largest Chinese-English dataset that provides a unified and challenging platform for cross-lingual image tagging, captioning, and retrieval. We develop conceptually simple yet effective methods per task for learning from cross-lingual resources. Extensive experiments on the three tasks justify the viability of the proposed dataset and methods. Data and code are publicly available at https://github.com/li-xirong/coco-cn.
MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences

Human gesture recognition has become a pillar of today’s intelligent Human-Computer Interfaces as it typically provides more comfortable and ubiquitous interaction. Such expert system has a promising prospect in various applications, including smart houses, gaming, healthcare, and robotics. However, recognizing human gestures in videos is one of the most challenging topics in computer vision, because of some irrelevant environmental factors like complex background, occlusion, lighting conditions, and so on. With the recent development of deep learning, many researchers have addressed this problem by building single deep networks to learn spatiotemporal features from video data. However, the performance is still unsatisfactory due to the limitation that the single deep networks are incapable of handling these challenges simultaneously. Hence, the extracted features cannot efficiently capture both relevant shape information and detailed spatiotemporal variation of the gestures. One solution to overcome the aforementioned drawbacks is to fuse multiple features from different models learned on multiple vision cues. Aiming at this objective, we present in this paper an effective multi-dimensional feature learning approach, termed as MultiD-CNN, for human gesture recognition in RGB-D videos. The key to our design is to learn high-level gesture representations by taking advantages from Convolutional Residual Networks (ResNets) for training extremely deep models and Convolutional Long Short-Term Memory Networks (ConvLSTM) for dealing with time-series connections. More specifically, we first construct an architecture to simultaneously learn the spatiotemporal features from RGB and depth sequences through 3D ResNets which are then linked to a ConvLSTM to capture the temporal dependencies between them, and we show that they better combine appearance and motion information effectively. Second, to alleviate distractions from background and other variations, we propose a method that encodes the temporal information into a motion representation, while a two-stream architecture based on 2D-ResNets is then employed to extract deep features from this representation. Third, we investigate different fusion strategies at different levels for blending the classification results, and we show that integrating multiple ways of encoding the spatial and temporal information leads to a robust and stable spatiotemporal feature learning with better generalization capability. Finally, we perform different experiments to evaluate the performance of the investigated architectures on four kinds of challenging datasets, demonstrating that our approach is particularly impressive where it outperforms prior arts in both accuracy and efficiency. The obtained results affirm also the importance of embedding the proposed approach in other intelligent systems application areas.
MMKG: Multi-modal Knowledge Graphs

We present Mmkg, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. We validate the utility of Mmkg in the $$\mathtt {sameAs}$$ link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.
Use cases for Blockchain in the Energy Industry Opportunities of emerging business models and related risks

This paper focuses on use cases for blockchain architectures with the goal of identifying major issues to study further in future research works. The paper will also consider aspects related to energy consumption of blockchain architectures, and risks and opportunities of emerging business models while ensuring a reliable distribution network and security of supply.
Optimizing subgraph queries by combining binary and worst-case optimal joins

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multi-way intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost . The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal subplans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and the effectiveness of the adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.