Reviewerly/exHarmony huggingface dataset:
Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this work, we propose a novel model to extract transactional behaviors of users and learn new transactional behavioral representations for credit card fraud detection. Considering the characteristics of transactional behaviors, two time-aware gates are designed in a recurrent neural net unit to learn long- and short-term transactional habits of users, respectively, and to capture behavioral changes of users caused by different time intervals between their consecutive transactions. A time-aware-attention module is proposed and employed to extract the behavioral information from their consecutive historical transactions with time intervals, which enables the proposed model to capture behavioral motive and periodicity inside their historical transactional behaviors. An interaction module is designed to learn more comprehensive and rational representations. To prove the effectiveness of the learned transactional behavioral representations, experiments are conducted on a large real-world transaction dataset and a public one. The results show that the learned representation can well distinguish fraudulent behaviors from legitimate ones, and the proposed method can improve the performance of credit card fraud detection in terms of various evaluation criteria over the state-of-the-art methods.
LDP-IDS: Local Differential Privacy for Infinite Data Streams

Streaming data collection is essential to real-time data analytics in various IoTs and mobile device-based systems, which, however, may expose end users' privacy. Local differential privacy (LDP) is a promising solution to privacy-preserving data collection and analysis. However, existing few LDP studies over streams are either applicable to finite streams only or suffering from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel $w$-event LDP paradigm to provide practical privacy guarantee for infinite streams at users end, and adapting the popular budget division framework in centralized differential privacy (CDP). By constructing a unified error analysi for LDP, we first develop two adatpive budget division-based LDP methods for LDP-IDS that can enhance data utility via leveraging the non-deterministic sparsity in streams. Beyond that, we further propose a novel population division framework that can not only avoid the high sensitivity of LDP noise to budget division but also require significantly less communication. Based on the framework, we also present two adaptive population division methods for LDP-IDS with theoretical analysis. We conduct extensive experiments on synthetic and real-world datasets to evaluate the effectiveness and efficiency pf our proposed frameworks and methods. Experimental results demonstrate that, despite the effectiveness of the adaptive budget division methods, the proposed population division framework and methods can further achieve much higher effectiveness and efficiency.
High-Capacity Framework for Reversible Data Hiding in Encrypted Image Using Pixel Prediction and Entropy Encoding

While the existing reserving room before encryption (RRBE) based reversible data hiding in encrypted image (RDHEI) schemes can achieve decent embedding capacity, the capacity of the existing vacating room by encryption (VRBE) based schemes is relatively low. To address this issue, this paper proposes a generalized framework for high-capacity RDHEI for both the RRBE and VRBE cases. First, an efficient embedding room generation algorithm (ERGA) is designed to produce large embedding room using pixel prediction and entropy encoding. Then, we propose two RDHEI schemes, one for RRBE, another for VRBE. In the RRBE scenario, the image owner generates the embedding room with ERGA and encrypts the preprocessed image using stream cipher with two encryption keys. Then, the data hider locates the embedding room and embeds the additional encrypted data. In the VRBE scenario, the cover image is encrypted by an improved block modulation and permutation encryption algorithm, where the spatial redundancy in the plain-text image is greatly preserved. Then, the data hider applies ERGA on the encrypted image to generate the embedding room and conducts data embedding. For both schemes, receivers with different authentication keys can conduct either error-free data extraction or error-free image recovery. The experimental results show that the two proposed schemes outperform many state-of-the-art RDHEI schemes. Besides, they can ensure high security level, where the original image can be hardly discovered from the encrypted version before or after data hiding by unauthorized users.
Dynamic commonsense knowledge fused method for Chinese implicit sentiment analysis

Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis.
Discrete and Parameter-Free Multiple Kernel <i>k</i>-Means

The multiple kernel k -means (MKKM) and its variants utilize complementary information from different sources, achieving better performance than kernel k -means (KKM). However, the optimization procedures of most previous works comprise two stages, learning the continuous relaxation matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. Even worse, most existing MKKM methods overlook the correlation among prespecified kernels, which leads to the fusion of mutually redundant kernels and bad effects on the diversity of information sources, finally resulting in unsatisfying results. To address these issues, we elaborate a novel Discrete and Parameter-free Multiple Kernel k -means (DPMKKM) model solved by an alternative optimization method, which can directly obtain the cluster assignment results without subsequent discretization procedure. Moreover, DPMKKM can measure the correlation among kernels by implicitly introducing a regularization term, which is able to enhance kernel fusion by reducing redundancy and improving diversity. Noteworthily, the time complexity of optimization algorithm is successfully reduced, through masterly utilizing of coordinate descent technique, which contributes to higher algorithm efficiency and broader applications. What's more, our proposed model is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Lastly, extensive experiments conducted on a number of real-world datasets illustrated the effectiveness and superiority of the proposed DPMKKM model.
A Time Series Forecasting Approach Based on Nonlinear Spiking Neural Systems

Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear spiking mechanism of biological neurons. NSNP systems have a nonlinear structure and the potential to describe nonlinear dynamic systems. Based on NSNP systems, a novel time series forecasting approach is developed in this paper. During the training phase, a time series is first converted to frequency domain by using a redundant wavelet transform, and then according to the frequency data, an NSNP system is automatically constructed and adaptively trained in frequency domain. Then, the well-trained NSNP system can automatically generate sequence data for future time as the prediction results. Eight benchmark time series data sets and two real-life time series data sets are utilized to compare the proposed approach with several state-of-the-art forecasting approaches. The comparison results demonstrate availability and effectiveness of the proposed forecasting approach.
A New Automated Polyp Detection Network MP-FSSD in WCE and Colonoscopy Images Based Fusion Single Shot Multibox Detector and Transfer Learning

Small polyp region detection in wireless capsule endoscopy (WCE) images is a challenging task in computer vision owing to two major problems: its variation in terms of shape, texture, and size, and the low illumination in the gastrointestinal tract. This study proposes a multiscale pyramidal fusion single-shot multibox detector network (MP-FSSD) to detect small polyp regions in WCE or colonoscopy frames, or both, with respect to the precision-vs-speed trade-off as the base architecture. We investigated deep transfer learning by transferring knowledge to polyp images, thereby enabling the extraction of highly representative features and contextual information from the FSSD. First, an edge-pooling layer was embedded in the shallow part of the network. Subsequently, the feature maps from different layers and scales were transformed to match their sizes. A concatenation module was introduced to integrate the feature maps from different layers, which were delivered to the next layer, followed by downsampling blocks to generate new pyramidal layers. Finally, the feature maps were fed to the multibox detectors to predict the final detection results. Experimentally, we maintained the same hyperparameters for both datasets for a fair comparison. The proposed MP-FSSD network exceeded FSSD by 3.62% in terms of mean average precision (mAP). The testing speed of 62.5 FPS is superior to that of the competitor detection methods. The proposal demonstrates that deep learning has much room for development in the field of gastrointestinal image detection.
A Multiwatermarking Scheme for Verifying Medical Image Integrity and Authenticity in the Internet of Medical Things

With the advent of a fifth-generation mobile network and developments in technologies such as the Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records, particularly medical images, for successful diagnosis, treatment, and management of different ailments. In this study, a multiwatermarking scheme is proposed for medical images based on quantum random walk and the brain storm optimization algorithm. A logo image used to verify medical image integrity is embedded in regions of interest, and text data are embedded in regions of non-interest to conceal private hospital and patient information. This process improves the accuracy of medical image verification and helps to ensure authenticity. A series of experiments were conducted to validate the capacity, security, robustness, and imperceptibility of the proposed multiwatermarking scheme.
Nonlinear optimization and support vector machines

Abstract Support vector machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.
A taxonomy study on securing Blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues

Blockchain technology has taken on a leading position in today’s industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. However, with the integration of Blockchain technology into different industrial applications, many application designs, security, and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific application, security and privacy challenges of targeted applications with limited security enhancement solutions, there is still a need for a comprehensive research study on the application design, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a comprehensive and state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on potential application design, security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues of integrating Blockchain technology into industrial applications that help design secure Blockchain-based applications as future directions.
Siamese Implicit Region Proposal Network With Compound Attention for Visual Tracking

Recently, siamese-based trackers have achieved significant successes. However, those trackers are restricted by the difficulty of learning consistent feature representation with the object. To address the above challenge, this paper proposes a novel siamese implicit region proposal network with compound attention for visual tracking. First, an implicit region proposal (IRP) module is designed by combining a novel pixel-wise correlation method. This module can aggregate feature information of different regions that are similar to the pre-defined anchor boxes in Region Proposal Network. To this end, the adaptive feature receptive fields then can be obtained by linear fusion of features from different regions. Second, a compound attention module including a channel and non-local attention is raised to assist the IRP module to perform a better perception of the scale and shape of the object. The channel attention is applied for mining the discriminative information of the object to handle the background clutters of the template, while non-local attention is trained to aggregate the contextual information to learn the semantic range of the object. Finally, experimental results demonstrate that the proposed tracker achieves state-of-the-art performance on six challenging benchmark tests, including VOT-2018, VOT-2019, OTB-100, GOT-10k, LaSOT, and TrackingNet. Further, our obtained results demonstrate that the proposed approach can be run at an average speed of 72 FPS in real time.
Wrist-Worn Hand Gesture Recognition While Walking via Transfer Learning

Walking, one of the most common daily activities, causes unwanted movement artifacts which can significantly deteriorate hand gesture recognition accuracy. However, traditional hand gesture recognition algorithms are typically developed and validated with wrist-worn devices only during static human poses, neglecting the critical importance of dynamic effects on gesture accuracy. Thus, we developed and validated a signal decomposition approach via empirical mode decomposition to accurately segment target gestures from coupled raw signals during dynamic walking and a transfer learning method based on distribution adaptation to enable gesture recognition through domain transfer between dynamic walking and static standing scenarios. Ten healthy subjects performed seven hand gestures during both walking and standing experiments while wearing an IMU wrist-worn device. Experimental results showed that the signal decomposition approach reduced the gesture detection error by 83.8%, and the transfer learning approach (20% transfer rate) improved hand gesture recognition accuracy by 15.1%. This ground-breaking work demonstrates the feasibility of hand gesture recognition while walking via wrist-worn sensing. These findings serve to inform real-life and ubiquitous adoption of wrist-worn hand gesture recognition for intuitive human-machine interaction in dynamic walking situations.
Opposition-based ant colony optimization with all-dimension neighborhood search for engineering design

Abstract The ant colony optimization algorithm is a classical swarm intelligence algorithm, but it cannot be used for continuous class optimization problems. A continuous ant colony optimization algorithm (ACOR) is proposed to overcome this difficulty. Still, some problems exist, such as quickly falling into local optimum, slow convergence speed, and low convergence accuracy. To solve these problems, this paper proposes a modified version of ACOR called ADNOLACO. There is an opposition-based learning mechanism introduced into ACOR to effectively improve the convergence speed of ACOR. All-dimension neighborhood mechanism is also introduced into ACOR to further enhance the ability of ACOR to avoid getting trapped in the local optimum. To strongly demonstrate these core advantages of ADNOLACO, with the 30 benchmark functions of IEEE CEC2017 as the basis, a detailed analysis of ADNOLACO and ACOR is not only qualitatively performed, but also a comparison experiment is conducted between ADNOLACO and its peers. The results fully proved that ADNOLACO has accelerated the convergence speed and improved the convergence accuracy. The ability to find a balance between local and globally optimal solutions is improved. Also, to show that ADNOLACO has some practical value in real applications, it deals with four engineering problems. The simulation results also illustrate that ADNOLACO can improve the accuracy of the computational results. Therefore, it can be demonstrated that the proposed ADNOLACO is a promising and excellent algorithm based on the results.
Digitalization of waste management: Insights from German private and public waste management firms

Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste management. While these advances promise an increasingly digitalized future for collecting, sorting, and recycling waste material, little is known about the current extent of digitalization by waste management firms. Available studies focus on firms' digitalization intentions, largely neglecting the level of actual adoption of digital technologies, and do not differentiate the level of digitalization alongside different steps of the waste management value chain. Our study reports on a cross-sectional descriptive survey that captures current digitalization efforts and strategies of 130 public and private waste management firms in Germany. We analyze their levels of digitalization along with different steps of the waste management value chain, explore their different objectives, approaches, and transformational measures with regard to digitalization. Our findings reveal that while the perceived importance of digitalization in the waste management sector continues to grow, the actual adoption of advanced digital technologies falls notably behind intentions reported in 2016 and 2017. We explore the reasons for this gap, point out so far largely ignored research opportunities, and derive recommendations for waste management firms and associations.
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning

In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium access control (MAC) protocol in a multiple access scenario. In this framework, the BS and UEs are reinforcement learning (RL) agents that need to learn to cooperate in order to deliver data. The network nodes can exchange control messages to collaborate and deliver data across the network, but without any prior agreement on the meaning of the control messages. In such a framework, the agents have to learn not only the channel access policy, but also the signaling policy. The collaboration between agents is shown to be important, by comparing the proposed algorithm to ablated versions where either the communication between agents or the central critic is removed. The comparison with a contention-free baseline shows that our framework achieves a superior performance in terms of goodput and can effectively be used to learn a new protocol.
Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article.