Reviewerly/exHarmony huggingface dataset:
Resolving hand‐object occlusion for mixed reality with joint deep learning and model optimization

Abstract By overlaying virtual imagery onto the real world, mixed reality facilitates diverse applications and has drawn increasing attention. Enhancing physical in‐hand objects with a virtual appearance is a key component for many applications that require users to interact with tools such as surgery simulations. However, due to complex hand articulations and severe hand‐object occlusions, resolving occlusions in hand‐object interactions is a challenging topic. Traditional tracking‐based approaches are limited by strong ambiguities from occlusions and changing shapes, while reconstruction‐based methods show a poor capability of handling dynamic scenes. In this article, we propose a novel real‐time optimization system to resolve hand‐object occlusions by spatially reconstructing the scene with estimated hand joints and masks. To acquire accurate results, we propose a joint learning process that shares information between two models and jointly estimates hand poses and semantic segmentation. To facilitate the joint learning system and improve its accuracy under occlusions, we propose an occlusion‐aware RGB‐D hand data set that mitigates the ambiguity through precise annotations and photorealistic appearance. Evaluations show more consistent overlays compared with literature, and a user study verifies a more realistic experience.
A Novel Weight Update Rule of Online Transfer Learning

Transfer learning aims to enhance performance in a target domain by exploiting useful information from related source domains. Transfer learning is important for many applications where the target domain instances are difficult to obtain while many data are available in source domains. Online transfer learning is established as an effective technology in many applications where the target data is received in an online manner. However, most existing methods ignore that target domain model does not have any prior knowledge when combined with source domain models. It is inappropriate that using conventional weight update rules to combine target domain model with source domain models. In this paper, we put forward a novel weight update rule for online transfer learning. Specifically, we prove the shortcomings of the previous methods and propose an effective improvement method which does not require a large amount of computation. Extensive experiments verify that our method can outperform several state-of-the-art methods on real-world data sets.
Noise-resistant phase gates with amplitude modulation

We propose a simple scheme for implementing fast arbitrary phase gates and employ pulse modulation to improve the gate robustness against different sources of noise. Parametric driving of a cavity is introduced to induce Rabi interactions between the cavity and qutrits, and then a two-qubit arbitrary phase gate is constructed by designing proper logical states. On this basis, we implement amplitude-shaped gates to obtain enhanced resilience to control errors in gate time and frequency detuning. By virtue of specifically designed logical states, the scheme displays intrinsic resistance to the energy relaxations of qutrits. Furthermore, we show that the gate robustness against cavity decay can be enhanced significantly with amplitude modulation.
A novel visualization system of using augmented reality in knee replacement surgery: Enhanced bidirectional maximum correntropy algorithm

Abstract Background and Aim Image registration and alignment are the main limitations of augmented reality (AR)‐based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. Methodology Markerless image registration method was used for AR‐based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera‐based tracking by filling border occlusion in right‐to‐left direction and non‐border occlusion from left‐to‐right direction. Results This study has improved video precision to 0.57–0.61 mm alignment error. Furthermore, with the use of bidirectional points, that is, forward and backward directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4–11.74 frames per second. Conclusions It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by the movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non‐Gaussian noise. The proposed AR system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels and so forth.
A Captcha-Based Graphical Password With Strong Password Space and Usability Study

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based n ×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( 2.4 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.
Automatic Malignant Thyroid Nodule Recognition in Ultrasound Images based on Deep Learning

As the most common malignancy in the endocrine system, thyroid cancer is usually diagnosed by discriminating the malignant nodules from the benign ones using ultrasonography, whose interpretation results primarily depends on the subjectivity judgement of the radiologists. In this study, we propose a novel cascade deep learning model to achieve automatic objective diagnose during ultrasound examination for assisting radiologists in recognizing benign and malignant thyroid nodules. First, the simplified U-net is employed to segment the region of interesting (ROI) of the thyroid nodules in each frame of the ultrasound image automatically. Then, to alleviate the limitation that medical training data are relatively small in size, the improved Conditional Variational Auto-Encoder (CVAE) learning the probability distribution of ROI images is trained to generate new images for data augmentation. Finally, ResNet50 is trained with both original and generated ROI images. As consequence, the deep learning model formed by the trained U-net and trained Resnet-50 cascade can achieve malignant thyroid nodule recognition with the accuracy of 87.4%, the sensitivity of 92%, and the specificity of 86.8%.
Improved VLSI Architecture of Dual-CLCG for Pseudo-Random Bit Generator

In this paper, improved VLSI architecture of dual coupled linear congruential generator (CLCG) for pseudorandom bit generation is proposed to enhance the timing performance with minimum area overhead. To improve its performance, a splitting structure of the adder circuit rather than a single adder is used in linear congruential generator (LCG). Its effect on the design of a dual-CLCG is observed to compute some of the essential performance parameters such as maximum frequency of bit generation, minimum period of clock signal, initial clock latency, output to output latency and area complexity. For Spartan-3E XC500E FPGA based synthesis of proposed architecture with 8, 16 and 32-bit word length, the ISE design suite provided by Xilinx is used.
A Comparative Analysis of AlexNet and GoogLeNet with a Simple DCNN for Face Recognition

Deep neural networks have achieved great success in many fields like bioinformatics, computer vision, automatic machine translation, etc. DCNN plays vital role in face recognition and outperforms human performance. In recent years, many pre-trained DCNNs were developed by researchers for object detection and recognition. In this work, a simple DCNN with very few layers is compared with two pre-trained networks: AlexNet which has eight layers and GoogLeNet which has 22 layers. Faces of the given image are detected using all three networks, training and testing performances are also studied. At the end of the study, it is observed that GoogLeNet learns the features and get trained very soon with less iteration, AlexNet achieved 100% accuracy after 30th iteration and simple DCNN also achieves 100% accuracy but goes nearly 42 iterations. For analysis, faces94 dataset is used.
&lt;b&gt;Improved Epicentral Distance Estimations for Railway Earthquake Early Warning&lt;/b&gt;

Accurate, rapid estimation of epicentral distance, Δ, is essential for earthquake early warning systems. To improve Δ estimation, new methods to calculate the empirical relationship between the amplitude growth rate parameter, C, and Δ are investigated. Using orthogonal regression is most appropriate for application to EEW systems for Japanese high-speed trains. Evaluation using a K-NET dataset, of earthquake epicenters up to 200 km from the recording station, showed that the proposed method reduces maximum error from 546.2 km to 209.8 km. The percentage of correct estimations, defined as estimates within ±30% of the measured epicentral distance, is increased from 38.0% to 55.3%.
Cross-environment activity recognition using word embeddings for sensor and activity representation

Cross-environment activity recognition in smart homes is a very challenging problem, specially for data-driven approaches. Currently, systems developed to work for a certain environment degrade substantially when applied to a new environment, where not only sensors, but also the monitored activities may be different. Some systems require manual labeling and mapping of the new sensor names and activities using an ontology. Ideally, given a new smart home, we would like to be able to deploy the system, which has been trained on other sources, with minimal manual effort and with acceptable performance. In this paper, we propose the use of neural word embeddings to represent sensor activations and activities, which comes with several advantages: (i) the representation of the semantic information of sensor and activity names, and (ii) automatically mapping sensors and activities of different environments into the same semantic space. Based on this novel representation approach, we propose two data-driven activity recognition systems: the first one is a completely unsupervised system based on embedding similarities, while the second one adds a supervised learning regressor on top of them. We compare our approaches with some baselines using four public datasets, showing that data-driven cross-environment activity recognition obtains good results even when sensors and activity labels significantly differ. Our results show promise for reducing manual effort, and are complementary to other efforts using ontologies.
Neurochaos Inspired Hybrid Machine Learning Architecture for Classification

Neuromorphic computing systems are biologically inspired with an aim to understand the rich structure and behaviour of biological neural networks so that novel learning architectures can be designed in both software and hardware. Traditional machine learning and deep neural network architectures are only weakly inspired from the human brain. In this work, we propose a novel `neurochaos' inspired hybrid machine learning architecture for classification. Specifically, we extract four `neurochaos' features - firing time, firing rate, energy and entropy of the chaotic neural firing from the neurons in the ChaosNet architecture (which we have recently proposed). These are used to train a Support Vector Machine linear classifier. Such a hybrid approach yields superior performance in the low training sample regime on synthetically generated and real-world datasets. Our proposed method could be viewed as a novel application of chaos as a kernel trick and has the potential for combining with other machine learning algorithms.
Clustering tendency assessment for datasets having inter-cluster density variations

Clustering tendency assessment, i.e., determining if a dataset has any inherent clusters, and if so, how many clusters, k to seek is a crucial pre-clustering task. The visual assessment of tendency (VAT) and improved visual assessment of tendency (iVAT) algorithms provide a visual way to assess cluster tendency of a dataset by reordering the pairwise dissimilarity matrix so that potential clusters are displayed as dark blocks along the diagonal in the image of the reordered dissimilarity matrix. VAT and iVAT, being distance-based schemes, fail to perform well for datasets consisting of clusters characterized by different density levels. In this paper, we introduce two new members of the VAT family of algorithms: Locally Scaled VAT (LSVAT) and Locally Scaled iVAT (LS-iVAT), which produces better iVAT images for data having inter-cluster density variations. Numerical experiments comparing the proposed novel approach with baseline VAT/iVAT as well as spectral clustering and density-based clustering algorithms establish that LS-VAT and LS-iVAT are superior to the comparable algorithms in terms of clustering quality.
Sharing Beyond Peer-to-peer Trading: Collaborative (Open) Business Models as a Pathway to Smart Circular Economy in Electricity Markets

This paper performs an analysis on emergent peer-to-peer (p2p) electricity trading markets utilizing the resource-based theory based on the sharing economy concept to identify possible opportunities for assets/data sharing amongst existing and emerging market players. First, the study introduces actors in the future electricity market and identifies their objectives. Second, valuable resources of each actor are recognized and categorized under physical and digital groups. Third, interested actors in each resource and benefits they can gain by having access to the resource are discussed. Last, a matrix of sharing opportunities amongst the actors in the electricity market is built and the current state of sharing is compared with the possibilities in the future. This paper provides and serves as a benchmark for implementing the sharing economy concept in future design of electricity markets. It highlights sharing opportunities as a means for circular economy in the electricity market. Implementing the identified sharing opportunities requires more collaborative (open) business models.
Detection of Road Artefacts Using Fuzzy Adaptive Thresholding

In this paper the authors are proposing approximate method for road artefacts detection and their location by analyzing acceleration values recorded in the car during driving over the road fragment using the smartphone mounted in the car. The new method called F-THRESH has been introduced, which is adaptively adjusting threshold for road artefacts detection by the fuzzy system means, allowing for outlier detection in chaotic time streams. First, the road quality is being calculated, then the difference between the current data point and mean acceleration is calculated and those two values are used as the input for the fuzzy system, which is calculating threshold to classify data point as an outlier. The proposed method has been compared to the previously implemented method and has an accuracy over 94% with 1.3% of False Positive Rate for the same problem which makes it a great candidate to be implemented in the IoT Edge scenarios, for reducing amount of data being sent to the cloud analyzing system.
Predicting users’ behavior using mouse movement information: an information foraging theory perspective

The prediction of users’ behavior is essential for keeping useful information on the web. Previous studies have used mouse cursor information in web usability evaluation and designing user-oriented search interfaces. However, we know fairly to a small extent pertaining to user behavior, specifically clicking and navigating behavior, for prolonged search session illustrating sophisticated search norms. In this study, we perform extensive analysis on a mouse movement activities dataset to capture every users’ movement pattern using the effects of information foraging theory (IFT). The mouse cursor movement information dataset includes the timing and positioning information of mouse cursors collected from several users in different sessions. The tasks vary in two dimensions: (1) to determine the interactive elements (i.e., information episodes) of user interaction with the site; (2) adopt these findings to predict users’ behavior by exploiting the LSTM model. Our model is developed to find the main patterns of the user’s movement on the site and simulate the behavior of users’ mouse movement on any website. We validate our approach on a mouse movement dataset with a rich collection of time and position information of mouse pointers in which searchers and websites are annotated by web foragers and information patches, respectively. Our evaluation shows that the proposed IFT-based effects provide an LSTM model a more accurate interpretative exposition of all the patterns in the movement of the users’ mouse cursors across the screen.
A Mixed Gaussian Membership Function Fuzzy CMAC for a Three-Link Robot

This research produces a mixed Gaussian membership function (GMF) fuzzy cerebellar model articulation controller (CMAC) for a three-link robot. A mixed GMF is created using the current and the previous GMFs on each layer of CMAC to detect errors efficiently, so a mixed GMF fuzzy CMAC (MGMFFC) is able to train parameters efficiently and constructs the MGMFFC structure automatically. A Lyapunov cost function and the gradient descent techniques are utilized to get the adaptive with guaranteed system's stable. Simulation studies for a three-link robot show that the MGMFFC attains favorable tracking performance.
Sensor Selection for Activity Classification at Smart Home Environments

As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.
Anomaly detection in large-scale networks: A state-space decision process

A new data fusion and network analytics framework is proposed that is based on the topology of large-scale networks and the stochastic dependencies between nodes, edges, and sensor data. The framework can transform real-time sensor data collected from disparate sources in a network to detect the location of anomalies and the nodes that are impacted by the detected anomalies. By intelligently fuzing multidimensional sensor data based on the topology of a large-scale network, this article also contributes to big data analytics for network systems. We will show that the proposed framework not only brings computational benefits, but also results in better anomaly estimates leading to lower false alarm rates and higher detection rates.
Anomaly Detection Mechanism for Solar Generation using Semi-supervision Learning Model

Solar is an important energy resource at present, and thus how to generate power efficiently by using solar is the crucial research topics in next generation power system. Among these research topics, managing and maintaining the solar panels for avoiding the situation which cannot generate power due to damage is also an interesting issue. Because the cost of developing the solar plant is expensive and needing the extra-cost to maintain solar, how to maintain the solar panels effectively is another important issue. In this study, an anomaly detection mechanism with using the semi-supervision learning model is proposed to pre-identify whether the solar panel will occur the abnormal events or not. In the anomaly detection mechanism, this study uses the clustering algorithm to filter the normal events, and then adopts the neuron network model, Autoencoder, to develop the classificator. This study takes the data collected from a 500kW solar power plant to train models and verify the feasibility of the proposed anomaly detection mechanism.
Data Compression and Visualization Using PCA and T-SNE

This paper examines two commonly used data dimensionality reduction techniques, namely, PCA and T-SNE. PCA was founded in 1933 and T-SNE in 2008, both are fundamentally different techniques. PCA focuses heavily on linear algebra while T-SNE is a probabilistic technique. The goal is to apply these algorithms on MNIST dataset and to see how they practically work and what conclusions we could draw from their application. The objective is to reduce the dimension of the data while retaining most of the information. We perform both these techniques and make a comparison between them by observing the results. We note the behavior of the reduced components obtained from both techniques, by visualizing it in 2-dimensional space. Upon further research and application, it became apparent that the data dimensionality reduction is sensitive to the parameter settings and must be fine-tuned carefully to be successful.