Reviewerly/exHarmony huggingface dataset:
The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things

The Internet of Things (IoT) is playing a vital role in shaping today?s technological world, including our daily lives. By 2025, the number of connected devices due to the IoT is estimated to surpass a whopping 75 billion. It is a challenging task to discover, integrate, and interpret processed big data from such ubiquitously available heterogeneous and actively natural resources and devices. Cluster analysis of IoT-generated big data is essential for the meaningful interpretation of such complex data. However, we often have very limited knowledge of the number of clusters actually present in the given data. The problem of finding whether clusters are present even before applying clustering algorithms is termed the assessment of clustering tendency. In this article, we present a set of useful visual assessment of cluster tendency (VAT) tools and techniques developed with major contributions from James C. Bezdek. The article further highlights how these techniques are advancing the IoT through large-scale IoT implementations.
Anomaly Detection on Event Logs with a Scarcity of Labels

Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.
Design and analysis of a noise-suppression zeroing neural network approach for robust synchronization of chaotic systems

Robust synchronization of chaotic systems with time-varying external disturbances is a hot topic in the field of science and engineering. In view of the negative influence of complex noise on the synchronization of chaotic systems, a noise-suppression zeroing neural network (NSZNN) is designed and proposed to effectively resist time-varying external disturbances. Compared with existing zeroing neural network models only for bounded noise, the proposed (NSZNN) model has consistent robustness for both bounded and unbounded noises. Furthermore, the design process, theoretical analysis and numerical verification of the NSZNN are presented in detail. Both theoretical and numerical results show that the NSZNN has better synchronization control performance of chaotic systems under bounded and unbounded noises, as compared with existing zeroing neural network models.
XAI-Driven Explainable Multi-view Game Cheating Detection

Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.
Category-aware Graph Neural Networks for Improving E-commerce Review Helpfulness Prediction

Helpful reviews in e-commerce sites can help customers acquire detailed information about a certain item, thus affecting customers' buying decisions. Predicting review helpfulness automatically in Taobao is an essential but challenging task for two reasons: (1) whether a review is helpful not only relies on its text, but also is related with the corresponding item and the user who posts the review, (2) the criteria of classifying review helpfulness under different items are not the same. To handle these two challenges, we propose CA-GNN (Category Aware Graph Neural Networks), which uses graph neural networks (GNNs) to identify helpful reviews in a multi-task manner --- we employ GNNs with one shared and many item-specific graph convolutions to learn the common features and each item's specific criterion for classifying reviews simultaneously. To reduce the number of parameters in CA-GNN and further boost its performance, we partition the items into several clusters according to their category information, such that items in one cluster share a common graph convolution.We conduct solid experiments on two public datasets and demonstrate that CA-GNN outperforms existing methods by up to 10.9% in AUC. We also deployed our system in Taobao with online A/B Test and verify that CA-GNN still outperforms the baseline system in most cases.
MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern

Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets.
Fine grained sentiment polarity classification using augmented knowledge sequence-attention mechanism

Sentiment Analysis is a channel by which automated feedback analysis can be processed effectively and efficiently. The reviews reachable are not only useful for customers buying a product or service but also the manufacturers and industrialists to formulate their production and marketing strategies as well as government organizations to access the views of the citizens. Recommender systems, market researches, and predictions on various social media platforms can be made more practical and comprehensive by sentiment analysis. It has evolved in numerous ways starting from the coarsely grained analysis. But when we need to find intricate details scrunched into a single statement or so, aspect-level sentiment analysis is the way. The uprising of deep learning approaches has opened doors in various applications, including Aspect-based Sentiment Analysis. These networks are computationally strong and can be trained easily. However, since they lag in recognizing semantic complexities, various Natural Language Processing techniques have been joined into the neural models. Researchers have been creative with inventing various novel models, combining a myriad of neural networks and attention mechanisms to improve aspect detection and sentiment polarity identification. The promise that this field provides because of its independent nature compels scientists to delve into the topic. In this work, fine grained analysis is not only processed for the aspect and aspect word detection but also for polarity and its intensity analysis. Our Contributions include the enriched input embedding with token, orientation, grammatical function, field and intensity components in the embedding stage, refined pattern extraction with convolutional kernels and improved performance using attention mechanism in the latter stages. Our experimental results reveal that, our procedural methodology has brought out an optimal enhanced performance compared to the near closer designs.
Multi-Leader Multi-Follower Stackelberg Game in Mobile Blockchain Mining

The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. We study two practical scenarios: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">connected</i> (to the CSP) or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">standalone</i> , which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.
CAPath: 3D-Printed Interfaces with Conductive Points in Grid Layout to Extend Capacitive Touch Inputs

We propose a 3D-printed interface, CAPath, in which conductive contact points are in a grid layout. This structure allows not only specific inputs (e.g., scrolling or pinching) but also general 2D inputs and gestures that fully leverage the "touch surface." We provide the requirements to fabricate the interface and implement a designing system to generate 3D objects in the conductive grid structure. The CAPath interface can be utilized in the uniquely shaped interfaces and opens up further application fields that cannot currently be accessed with existing passive touch extensions. Our contributions also include an evaluation for the recognition accuracy of the touch operations with the implemented interfaces. The results show that our technique is promising to fabricate customizable touch-sensitive interactive objects.
Empowering Blockchain in Vehicular Environments With Decentralized Edges

In order to enable emerging vehicular Internet of Things (IoT) applications, including fully autonomous driving, more efforts should be done in collecting driving experiences in different road situations. This requires the exchange of information between vehicles as each vehicle has very limited experience. Due to the decentralized feature of vehicular environment, an efficient management of collaborative behaviors among the vehicles becomes particularly important. Blockchain has been attracting great interest recently because it provides a way to reach consensus in decentralized systems. However, existing blockchain systems assume high communication capabilities for vehicles, which is difficult to achieve in a decentralized vehicular environment. Existing studies also assume the existence of networking infrastructure, such as roadside units (RSU). In this paper, we propose a scheme to empower blockchain in vehicular environments without depending on the existing networking infrastructure. The proposed scheme uses a distributed clustering approach to select some vehicles as edge nodes, and the edge nodes maintain the blockchain used to record transactions in a decentralized way. The proposed scheme employs a distributed approach that guides vehicle clustering with the consideration of multiple metrics based on a fuzzy logic algorithm. By using the edge nodes, the proposed scheme solves the communication problem of maintaining a blockchain in a totally decentralized vehicular environment. We use computer simulations to clarify the performance of the proposed scheme in terms of communication performance by comparing it with existing baselines.
Video Super-Resolution Reconstruction Based on Deep Learning and Spatio-Temporal Feature Self-Similarity

To address the problems in the existing video super-resolution methods, such as noise, over smooth and visual artifacts, which are caused by the reliance on limited external training or mismatch of internal similarity patch instances, this study proposes a novel video super-resolution reconstruction algorithm based on deep learning and spatio-temporal feature similarity (DLSS-VSR). The video super-resolution reconstruction mechanism with the joint internal and external constraints is established utilizing the complementary advantages of both external deep correlation mapping learning and internal spatio-temporal nonlocal self-similarity prior constraint. A deep learning model based on deep convolutional neural network is constructed to learn the nonlinear correlation mapping between low-resolution and high-resolution video frame patches. A novel spatio-temporal feature similarity calculation method is proposed, which considers both internal video spatio-temporal self-similarity and external clean nonlocal similarity. For the internal spatio-temporal feature self-similarity, we improve the accuracy and robustness of similarity matching by proposing a similarity measure strategy based on spatio-temporal moment feature similarity and structural similarity. The external nonlocal similarity prior constraint is learned by the patch group-based Gaussian mixture model. The time efficiency for spatio-temporal similarity matching is further improved based on saliency detection and region correlation judgment strategy, which achieves a better tradeoff between super-resolution accuracy and speed. Experimental results demonstrate that the DLSS-VSR algorithm achieves competitive super-resolution quality compared to other state-of-the-art algorithms in both subjective and objective evaluations.
User Experience Goals for Designing Industrial Human-Cobot Collaboration

Pleasurable user experience (UX) is an essential design target in human-technology interaction. It helps to fulfil users' expectations and to complete tasks successfully, in both everyday life and work contexts. In the industrial context, collaborative robots (cobots) are developed to reduce users' workload. In this context, the human workers and cobots share tasks and the workspace. Traditionally, design of such machinery has focused on functionality and safety. One approach that focuses on designing for broader aspects of pleasurable user experience is experience-driven design (EDD). EDD allows UX designers to identify and design for certain experiences using experience goals. In this study, we explored experience goals suitable for the design of human-cobot interaction. We observed and interviewed 22 participants in a laboratory setup, where they performed a collaborative task with Franka Panda cobot. As a result, we identified four prominent UX goals: fellowship and sympathy; inspiration; safety and trust; and accomplishment.
BANet: A Balanced Atrous Net Improved From SSD for Autonomous Driving in Smart Transportation

Object detection for autonomous driving in smart transportation systems requires comprehensive consideration of accuracy, speed and sensitivity for detecting multi-objects. The one-stage algorithm, Single Shot MultiBox Detector (SSD), can basically satisfy the above requirements. However, there are still rooms for improvement of the overall precision due to its dissatisfactory detection rate of small objects, which are abundant in autonomous driving scenarios. In order to solve the inherent defect problem of one-stage algorithm in processing the extreme foreground-background class imbalance encountered during training of dense detectors, we propose a Balanced Atrous Net (BANet) to significantly improve the performance of the SSD for autonomous driving. The BANet combines the atrous convolution and the feature fusion to improve the network structure of the SSD, thereby expanding receptive field and enriching semantic information in shallow layers. Meanwhile, a new loss that alleviates the class imbalance is designed to replace the standard cross entropy loss in the original algorithm. The experimental results show that the BANet outperforms the original SSD in mAP on KITTI with a relatively high speed retained.
Prioritization Based Taxonomy of DevOps Challenges Using Fuzzy AHP Analysis

The DevOps (development and operations) is a collaborative software development environment which offers the continues development and deployment of quality software project within short time.The DevOps practices are not yet mature enough, and the software organizations hesitate to adopt it.This study aims: 1) to explore the DevOps challenges by conducting systematic literature review (SLR) and to get the insight of industry experts via questionnaire survey study and 2) to prioritize the investigated challenges using fuzzy analytical hierarchy process (FAHP).The study findings provide the set of critical challenges faced by the software organizations while adopting DevOps and a prioritization-based taxonomy of the DevOps challenges.The application of FAHP is novel in this research area as it assists in addressing the vagueness of practitioners concerning the influencing factors of DevOps.We believe that the finding of this study will serve as a body of knowledge for real world practitioners and researchers to revise and develop the new strategies for the successful implementation of DevOps practices in the software industry.
Speaker Anonymization for Personal Information Protection Using Voice Conversion Techniques

As speech-based user interfaces integrated in the devices such as AI speakers become ubiquitous, a large amount of user voice data is being collected to enhance the accuracy of speech recognition systems. Since such voice data contain personal information that can endanger the privacy of users, the issue of privacy protection in the speech data has garnered increasing attention after the introduction of the General Data Protection Regulation in the EU, which implies that restrictions and safety measures for the use of speech data become essential. This study aims to filter the speaker-related voice biometrics present in speech data such as voice fingerprint without altering the linguistic content to preserve the usefulness of the data while protecting the privacy of users. To achieve this, we propose an algorithm that produces anonymized speeches by adopting many-to-many voice conversion techniques based on variational autoencoders (VAEs) and modifying the speaker identity vectors of the VAE input to anonymize the speech data. We validated the effectiveness of the proposed method by measuring the speaker-related information and the original linguistic information retained in the resultant speech, using an open source speaker recognizer and a deep neural network-based automatic speech recognizer, respectively. Using the proposed method, the speaker identification accuracy of the speech data was reduced to 0.1-9.2%, indicating successful anonymization, while the speech recognition accuracy was maintained as 78.2-81.3%.
Mosaic Privacy-Preserving Mechanisms for Healthcare Analytics

The Internet of Things (IoT) has propelled the evolution of medical sensing technologies to greater heights. Thus, traditional health systems have been transformed into new data-rich environments. This provides an unprecedented opportunity to develop new analytical methods and tools towards a new paradigm of smart and interconnected health systems. Nevertheless, there are risks pertinent to increasing levels of system connectivity and data accessibility. Cyber-attacks become more prevalent and complex, leading to greater likelihood of data breaches. These events bring sudden disruptions to routine operations and cause the loss of billions of dollars. Adversaries often attempt to leverage models to learn a target's sensitive attributes or extrapolate its inclusion within a database. As healthcare systems are critical to improving the wellbeing of our society, there is an urgent need to protect the privacy of patients and minimize the risk of model inversion attacks. This paper presents a new approach, named Mosaic Gradient Perturbation (MGP), to preserve privacy in the framework of predictive modeling, which meets the requirement of differential privacy while mitigating the risk of model inversion. MGP is flexible in fine-tuning the trade-offs between model performance and attack accuracy while being highly scalable for large-scale computing. Experimental results show that the proposed MGP method improves upon traditional gradient perturbation to mitigate the risk of model inversion while offering greater preservation of model accuracy. The MGP technique shows strong potential to circumvent paramount costs due to privacy breaches while maintaining the quality of existing decision-support systems, thereby ushering in a privacy-preserving smart health system.
Incremental Few-Shot Meta-learning via Indirect Discriminant Alignment

We propose a method to train a model so it can learn new classification tasks while improving with each task solved. This amounts to combining meta-learning with incremental learning. Different tasks can have disjoint classes, so one cannot directly align different classifiers as done in model distillation. On the other hand, simply aligning features shared by all classes does not allow the base model sufficient flexibility to evolve to solve new tasks. We therefore indirectly align features relative to a minimal set of "anchor classes". Such indirect discriminant alignment (IDA) adapts a new model to old classes without the need to re-process old data, while leaving maximum flexibility for the model to adapt to new tasks. This process enables incrementally improving the model by processing multiple learning episodes, each representing a different learning task, even with few training examples. Experiments on few-shot learning benchmarks show that this incremental approach performs favorably compared to training the model with the entire dataset at once.