Reviewerly/exHarmony huggingface dataset:
Dynamically adaptive and diverse dual ensemble learning approach for handling concept drift in data streams

Abstract Concept drift refers to the change in data distributions and evolving relationships between input and output variables with the passage of time. To analyze such variations in learning environments and generate models which can accommodate changing performance of predictive systems is one of the challenging machine learning applications. In general, the majority of the existing schemes consider one of the specific drift types: gradual, abrupt, recurring, or mixed, with traditional voting setup. In this work, we propose a novel data stream framework, dynamically adaptive and diverse dual ensemble (DA‐DDE) which responds to multiple drift types in the incoming data streams by combining online and block‐based ensemble techniques. In the proposed scheme, a dual diversified ensemble‐based system is constructed with the combination of active and passive ensembles, updated over a diverse set of resampled input space. The adaptive weight setting method is proposed in this work which utilizes the overall performance of learners on historic as well as recent concepts of distributions. Further a dual voting system has been used for hypothesis generation by considering dynamic adaptive credibility of ensembles in real time. Comparative analysis with 14 state‐of‐the‐art algorithms on 24 artificial and 11 real datasets shows that DA‐DDE is highly effective in handling various drift types.
Usability and User Experience of Interactions on VR-PC, HoloLens 2, VR Cardboard and AR Smartphone in a Biomedical Application

The Augmented (AR), Mixed (MR) and Virtual Reality (VR) technologies, here subsumed as “xR”, have matured in the last years. The research focus of this paper is to study interaction on four different xR platforms to improve medical applications in the future. Often medical applications could be created either as VR or AR scenario. It may depend on multiple factors which xR platform should be used in a specific use case. Therefore, it is interesting to know if there are any differences in usability or user experience between those. There are various manipulation possibilities that can be done with a virtual object in xR. These major interaction possibilities are explored here: addressing, grasping, moving and releasing of objects. We use a specific virtual scenario to assess user experience and usability of the aforementioned interactions on the four xR platforms. We compare differences and commonalities of the way the interactions are achieved on all platforms. A study with twenty-one participants gives insight on three main aspects: First, even though the used four platforms have very different interaction capabilities, they can be suitable for biomedical training tasks. Second, all four platforms result in high usability and user experience scores. Third, the HoloLens 2 was the most attractive device but usability and user experience stayed lower in some aspects than even the very simple VR Cardboard.
Design and Testbed Experiments of Public Blockchain-Based Security Framework for IoT-Enabled Drone-Assisted Wildlife Monitoring

In recent years, the Internet of Things (IoT) enabled drones, also called as unmanned aerial vehicles (UAVs), are widely used in many applications ranging from military to civilian applications, such as wildlife monitoring. Since the drones provide a risk-free as well as low-cost facility in order to quickly and persistently monitor natural circumstances at high spacial temporal resolution, they help in wildlife monitoring research. Due to wireless communication nature, the communication among the deployed drones in their respective flying zones and the IoT smart devices installed in animal bodies, and also among the drones and their respective ground station server (GSS), is susceptible to various passive and active attacks. To mitigate these issues, we propose a public blockchain based access control implementation for wild-life monitoring purpose. The application of both access control and blockchain at the same time not only protects various attacks, but also maintains immutability, transparency as well as decentralization properties. Next, we simulate the proposed security framework for the blockchain part to measure the total computational time needed to add a varied number of blocks in a blockchain and also a varied number of transactions per block. Finally, a practical testbed experiment has been implemented to show the feasibility of the proposed framework.
Twitter sentiment analysis using Machine Learning and Hadoop: A comparative study

Nowadays Big Data is one of the most commonly used terms. As the name suggest it is an enormous amount of data which is very difficult to handle. Twitter is a very famous social networking sites and a very good source of Big Data. The main objective of Twitter Sentiment analysis is to find a way to discover what people like, what they prefer or how they feel about a particular topic or product. We can find out sentiment analysis in web mining, data mining; it is a part of Natural Language processing. This paper adds to arrangement of tweets into one or the other positive or negative utilizing Machine Learning procedures like Naïve Bayes, Logistic regression model, Support vector machine and an another platform Hadoop. We observe that the accuracy of the machine learning methods varies from one dataset to another. We also observe that Apache Spark is faster and more efficient than HDFS for dynamic dataset.
Anomaly Detection and Classification to enable Self-Explainability of Autonomous Systems

While the importance of autonomous systems in our daily lives and in the industry increases, we have to ensure that this development is accepted by their users. A crucial factor for a successful cooperation between humans and autonomous systems is a basic understanding that allows users to anticipate the behavior of the systems. Due to their complexity, complete understanding is neither achievable, nor desirable. Instead, we propose self-explainability as a solution. A self-explainable system autonomously explains behavior that differs from anticipated behavior. As a first step towards this vision, we present an approach for detecting anomalous behavior that requires an explanation and for reducing the huge search space of possible reasons for this behavior by classifying it into classes with similar reasons. We envision our approach to be part of an explanation component that can be added to any autonomous system.
2D vision-based tracking algorithm for general space non-cooperative objects

Space non-cooperative object visual tracking (SNCOVT) with monocular or binocular camera is one of the most significant tasks for on-orbit services. In recent years, most of vision-based trackers utilized in spacecraft are based on hand-crafted features (e.g. point, edge and color), which are vulnerable to the dynamic and harsh environment in space. Although various correlation filter and deep learning based generic object trackers with high performance have been proposed in tracking community. To verify the availability of those generic trackers in aerospace domain, we present a moderate and simulated space non-cooperative object visual tracking dataset, which contains 60 binocular video sequences with manual annotations, 54742 frames in total. Meanwhile, a new evaluation protocol that is more appropriate to the practical scenario and some novel metrics are adopted into our SNCOVT evaluation toolkit. We implement extensive monocular and binocular experiments with state-of-the-art trackers on our dataset. Finally, we make a comprehensive analysis to those algorithms and experiment results for future SNCOVT research.
Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys

Federated learning is a privacy preserving machine learning paradigm which trains model on distributed datasets. Homomorphic encryption can be used to protect local model parameters during model aggregation. However, most homomorphic encryption only supports single key. To enhance security, we should allow different clients encrypt their local models by different keys. In this paper, we propose a secure neural network in federated learning and support multiple keys. A double-trapdoor encryption scheme is adapted. We rely on two non-colluding cloud servers. The clients upload the encrypted local models to the first cloud server, which can be decrypted by the second server using one trapdoor. To avoid privacy leakage, the first cloud server runs ciphertext perturbation before sending data to the second server. The aggregated model will be sent back to clients for decryption individually. Benchmark dataset is used for evaluation and the experimental results show that our scheme is competitive in terms of accuracy with some sacrifice of efficiency.
RGB Image Encryption using Hill Algorithm and Chaos System

Abstract The development of modern technology in recent years, makes the data security very big issue. To secure our data from unauthorized person, encryption is used before transmission. Various sensitive information is using encryption such as: e-mail messages, banking transactions, credit information and military operations. The encrypted data can be in form of text, image, voice, and video. In this paper, a proposed Hill algorithm with chaos system is used to encrypt colour RGB (Red, Green and Blue) image. The key of Hill algorithm is a square matrix. The selection of matrix key numbers is very important. Therefore, chaos system is used to generate sequences of numbers for Hill key matrix. These sequences have a property of randomness that make it very difficult for guessing. Finally, the proposed system is simulated using MATLAB, and a test image is encrypted and decrypted using proposed system successfully.
Surrogate Model-Based Explainability Methods for Point Cloud NNs

In the field of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point cloud neural networks have become a popular research direction in recent years. So far, however, there has been little discussion about the explainability of deep neural networks for point clouds. In this paper, we propose a point cloud-applicable explainability approach based on a local surrogate model-based method to show which components contribute to the classification. Moreover, we propose quantitative fidelity validations for generated explanations that enhance the persuasive power of ex- plainability and compare the plausibility of different existing point cloud-applicable explainability methods. Our new explainability approach provides a fairly accurate, more semantically coherent and widely applicable explanation for point cloud classification tasks. Our code is available at https://github.com/Explain3D/LIME-3D
Supervised Machine Learning and Deep Learning Classification Techniques to Identify Scholarly and Research Content

The Internet Archive (IA), one of the largest open-access digital libraries, offers 28 million books and texts as part of its effort to provide an open, comprehensive digital library. As it organizes its archive to support increased accessibility of scholarly content to support research, it confronts both a need to efficiently identify and organize academic documents and to ensure an inclusive corpus of scholarly work that reflects a "long tail distribution," ranging from high-visibility, frequently-accessed documents to documents with low visibility and usage. At the same time, it is important to ensure that artifacts labeled as research meet widely-accepted criteria and standards of rigor for research or academic work to maintain the credibility of that collection as a legitimate repository for scholarship. Our project identifies effective supervised machine learning and deep learning classification techniques to quickly and correctly identify research products, while also ensuring inclusivity along the entire long-tail spectrum. Using data extraction and feature engineering techniques, we identify lexical and structural features such as number of pages, size, and keywords that indicate structure and content that conforms to research product criteria. We compare performance among machine learning classification algorithms and identify an efficient set of visual and linguistic features for accurate identification, and then use image classification for more challenging cases, particularly for papers written in non-Romance languages. We use a large dataset of PDF files from the Internet Archive, but our research offers broader implications for library science and information retrieval. We hypothesize that key lexical markers and visual document dimensions, extracted through PDF parsing and feature engineering as part of data processing, can be efficiently extracted from a corpus of documents and combined effectively for a high level of accurate classification.
Visual Object Detection and Tracking for Internet of Things Devices Based on Spatial Attention Powered Multidomain Network

Internet of Things (IoT) has brought changes in many fields by joining physical space with the cyber space. The IoT devices are becoming increasingly complex. With the rapid deployment of cameras, tasks in IoT like visual information are more important, but IoT devices have limited computing resources, including power, computing ability, storage, etc. Some tasks that might be perfectly normal to perform on a computer would be rather challenging on an IoT device. Therefore, how to maintain acceptable performance while minimizing resources is becoming a more consequential part in IoT. In this article, we aim to solve the problem of object detection and tracking in IoT while minimizing resources. The traditional algorithms need to use a convolutional neural network (CNN) to identify different objects in each frame, and then determine the tracking target from identified objects, which typically requires a lot of computing resources. By incorporating spatial attention, and multidomain network, we proposed a novel algorithm named as spatial attention powered multidomain network (SA-MDNet). By adding the spatial attention mechanism to the original MDNet model, and using multiclass cross-entropy loss, we are able to distinguish the background and the target in different video sequences effectively and efficiently. This novel algorithm achieves similar performance on the OTB 50/100/2013 data sets compared to several state-of-art models, while uses only a fraction of the memory compared to MDNet.
Dynamics of Time-Delayed Optoelectronic Oscillators With Nonlinear Amplifiers and Its Potential Application to Random Numbers Generation

Time-delayed dynamical systems generally feature smooth nonlinear transfer functions in the feedback loop, such as polynomial or sinusoidal functions. As a consequence, the complexity of their dynamical behavior mainly originates from the time-delay. In this paper, we explore the opposite case where the nonlinear transfer function is complex ( cos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (sinh)), and therefore, non-smooth. We perform a bifurcation analysis of the system, and evidence that this novel type of time-delayed system can display a chaotic behavior characterized by positive maximum Lyapunov exponent and quasi-maximal entropy. The high entropy behavior of the system combined with post-processing are used to generate random numbers for small values of the feedback gain with an overall bit rate up to 1.478 Gb/s. Our theoretical results are in excellent agreement with experimental measurements, performed with an optoelectronic oscillator involving a complex transfer function designed ad hoc.
Humans in the Loop: Cybersecurity Aspects in the Consumer IoT Context

With the growing popularity of the consumer Internet of Things devices in the society, there is an urgent need to understand the associated security and privacy risks. Currently, the consumer electronics (CE) community largely ignore the humans in the loop, while framing cybersecurity solutions in the IoT context. In this work, we stress on the need toward a human-centric approach to cybersecurity by shifting focus from “humans as a problem” to “humans as a solution.” We propose a Security & Privacy Preserving framework for illustrating how a human-centric approach can be initiated, what are its important components, and how security and privacy can be preserved with a human focus. We hope that the findings will help the CE community to realize the importance of a human-centric approach.
Short-Term Electric Load Forecasting With Sparse Coding Methods

Short-term load forecasting is a key task for planning and stability of the current and future distribution grid, as it can significantly contribute to the management of energy market for ancillary services. In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting problem on a substation level. In this context, sparse representation theory can provide parsimonial predictive models, which become attractive mainly due to their ability to successfully model the input space in a self-learning manner, by interacting between theory, algorithms, and applications. Several techniques are implemented, incorporating numerous dictionary learning and sparse decomposition algorithms, and a hierarchical structured model is proposed. The concept of sparsity in each case is embedded throughout the utilization of different regularization forms which include the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tree</sup> norms. The observed superiority of the proposed theory, especially the one which embeds the atoms and corresponding coefficients in a tree structure, stems from the construction of the dictionary so as to represent efficiently the ambient electricity signal space and the consequent extraction of sparse basis-vectors. The performance of each model is evaluated using real hourly load measurements from a high voltage/medium voltage (HV/MV) substation and compared with that of widely used machine learning methods. The provided analytical results, verify the effectiveness of hierarchical sparse representation in short-term load forecasting applications, in terms of common accuracy indices.
Single-camera 3D-DIC system based on a fiber bundle

The three-dimensional digital image correlation (3D-DIC) method featuring two synchronized cameras has been widely used in the field of experimental mechanics to measure full-field 3D shape and deformation. However, the requirement of two synchronized cameras often limits the application of the regular 3D-DIC method. This study proposes a single-camera 3D-DIC system based on a fiber bundle (FB), aiming to make full use of the flexibility of the FB and further broaden the range of application of the 3D-DIC method. The multi-channel FB enables the proposed method to photograph the test object from two different angles with a single camera. In order to verify the effectiveness of the proposed method, a three-point bending deformation measurement experiment and a cylinder 3D reconstruction experiment were carried out. For comparison, the regular 3D-DIC system was used to measure the same specimens. Factors affecting the accuracy of the proposed method were analyzed and the corresponding optimization methods were given. As the proposed method can isolate the imaging sensor from harsh environment, it demonstrates significant potential in determining 3D deformation in intense electromagnetic interference environment and under water.
Service Robots, Customers, and Service Employees: What Can We Learn from the Academic Literature and Where are the Gaps?

Purpose – Robots are predicted to have a profound impact on the service sector. The emergenceof robots has attracted increasing interest from business scholars and practitioners alike. In this article, we undertake a systematic review of the business literature about the impact of service robots on customers and employees with the objective of guiding future research. Design/methodology/approach – We analyzed the literature on service robots as they relate to customers and employees in business journals listed in the Financial Times top 50 journals plus all journals covered in the cross-disciplinary SERVSIG literature alerts.Findings – The analysis of the identified studies yielded multiple observations about the impact of service robots on customers (e.g., overarching frameworks on acceptance and usage of service robots; characteristics of service robots and anthropomorphism; and potential for enhanced and deteriorated service experiences) and service employees (e.g., employee benefits such as reduced routine work, enhanced productivity and job satisfaction; potential negative consequences such as loss of autonomy and a range of negative psychological outcomes; opportunities for human-robot collaboration; job insecurity; and robot-related upskilling and development requirements). We also conclude that current research on service robots is fragmented, is largely conceptual in nature, and focused on the initial adoption stage. We feel that more research is needed to build an overarching theory. In addition, more empirical research is needed, especially on the long(er)- term usage service robots on actual behaviors, the well-being, and potential downsides and (ethical) risks for customers and service employees.Research limitations – Our review focused on the business and service literature. Future work may want to include additional literature streams, including those in computer science, engineering, and information systems.Originality/value – This article is the first to synthesize the business and service literature on the impact of service robots on customers and employees.