Reviewerly/exHarmony huggingface dataset:
Two-axis two-spin squeezed states

This paper examines the properties of the states generated by the two-spin generalization of the two-axis countertwisting Hamiltonian. The spin squeezing produces correlations for arbitrary spin directions, and can violate the Bell-CHSH inequality with quadratic spin correlators
CAB-IoT: Continuous authentication architecture based on Blockchain for internet of things

Raising incidents of security threats among active sessions is an increasing concern in IoT environment. Continuous authentication was introducing to be superior to traditional authentication schemes by constantly verify users' identities on an ongoing basis and spot the moment at which an illicit attacker seizes control of the session. However, several challenges remain unsolved. This research aims to investigate the power of Blockchain technology to provide real-time and non-intrusive continuous authentication for the IoT environment. Accordingly, a distributed and scalable continuous authentication solution based on Blockchain technology called CAB-IoT was proposed. It enabled fog nodes layer that tackles the limitations of IoT resources by providing localized processing of heavy continuous authentication-related tasks for a group of IoT devices. Besides, CAB-IoT introduced a trust module that depends on the face recognition machine learning model to detect outliers and abnormal access. Moreover, mutual authentication between end-users and fog nodes is also designed, as well as secure communication between the authenticated nodes. The results demonstrate a lightweight continuous authentication solution that achieved the desired balance between security and performance requirements where it was observed in a real-world environment for truly performance results. Security analysis and attack analysis are also considered during the evaluation.
Deep Real-Time Anomaly Detection for Connected Autonomous Vehicles

Connected and autonomous vehicles (CAV) are expected to change the landscape of the automotive market. They are autonomous decision-making systems that process streams of observations coming from different external and on-board sensors. CAV like any other cyber-physical objects are prone to signal interference, hardware deterioration, software errors, power instability, and cyber-attacks. To avoid these anomalies which can be fatal, it is mandatory to design a robust real-time technique to detect them and identify their sources. In this paper, we propose a deep learning approach which consists of hierarchic models to firstly extract the signal features using an LSTM auto-encoder, then perform an accurate classification of each signal sequence in real-time. In addition, we investigated the impact of the model parameter tuning on the anomaly detection and the advantage of channel boosting through three scenarios. The model achieves an accuracy of 95.5% and precision of 94.2%.
A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction

Recurrent neural network (RNN) has become popular for human motion prediction thanks to its ability to capture temporal dependencies. However, it has limited capacity in modeling the complex spatial relationship in the human skeletal structure. In this work, we present a novel diffusion convolutional recurrent predictor for spatial and temporal movement forecasting, with multi-step random walks traversing bidirectionally along an adaptive graph to model interdependency among body joints. In the temporal domain, existing methods rely on a single forward predictor with the produced motion deflecting to the drift route, which leads to error accumulations over time. We propose to supplement the forward predictor with a forward discriminator to alleviate such motion drift in the long term under adversarial training. The solution is further enhanced by a backward predictor and a backward discriminator to effectively reduce the error, such that the system can also look into the past to improve the prediction at early frames. The two-way spatial diffusion convolutions and two-way temporal predictors together form a quadruple network. Furthermore, we train our framework by modeling the velocity from observed motion dynamics instead of static poses to predict future movements that effectively reduces the discontinuity problem at early prediction. Our method outperforms the state of the arts on both 3D and 2D datasets, including the Human3.6M, CMU Motion Capture and Penn Action datasets. The results also show that our method correctly predicts both high-dynamic and low-dynamic moving trends with less motion drift.
ModCon: a model-based testing platform for smart contracts

Unlike those on public permissionless blockchains, smart contracts on enterprise permissioned blockchains are not limited by resource constraints, and therefore often larger and more complex. Current testing and analysis tools lack support for such contracts, which demonstrate stateful behaviors and require special treatment in quality assurance. In this paper, we present a model-based testing platform, called ModCon, relying on user-specified models to define test oracles, guide test generation, and measure test adequacy. ModCon is Web-based and supports both permissionless and permissioned blockchain platforms. We demonstrate the usage and key features of ModCon on real enterprise smart contract applications.
Fuzzy Logic Controller to control the position of a mobile robot that follows a track on the floor

In this paper presents satisfactory preliminary results to use a simple mathematical analysis is proposed to convert the fuzzy range into a real actual range of velocity. The robot main controller is a single microcontroller that controls all the functionality of the robot. A fuzzy logical framework is used to control the angular momentum of left and right wheels. The fuzzy rules works on one variable that is LED position through the use of 6 sensors placed on the front of the robot to give the greatest amount of flexibility when turning and avoiding obstacles. The rules are based on connection the fuzzy variables with the operator “and”. As a conclusion, one photoelectric switch get activated lets the wheel of the opposite side get raised in the speed. The raise of speed depends on the location of the photoelectric switch; an edge located photoelectric switch causes a maximum raise of speed on the opposite wheel. This study objectives to present and analyze what is known as control system engineering through the implementation of fuzzy logic, down to achieving the following points like, introduce a problem that requires the implementation a fuzzy logic controller, demonstrate theoretically the behavior plus response under fuzzy logic control and prove the efficiency of fuzzy logic controller. The results in this study showed a high positive and that the proposed solution is able to drive the robot inside curved paths with high efficiency, maneuver freely at different speeds and avoid obstacles without any collision.
Robust linearly constrained extended Kalman filter for mismatched nonlinear systems

Summary Standard state estimation techniques, ranging from the linear Kalman filter (KF) to nonlinear extended KF (EKF), sigma‐point or particle filters, assume a perfectly known system model, that is, process and measurement functions and system noise statistics (both the distribution and its parameters). This is a strong assumption which may not hold in practice, reason why several approaches have been proposed for robust filtering, mainly because the filter performance is particularly sensitive to different model mismatches. In the context of linear filtering, a solution to cope with possible system matrices mismatch is to use linear constraints. In this contribution we further explore the extension and use of recent results on linearly constrained KF for robust nonlinear filtering under both process and measurement model mismatch. We first investigate how linear equality constraints can be incorporated within the EKF and derive a new linearly constrained extended KF (LCEKF). Then we detail its use to mitigate parametric modeling errors in the nonlinear process and measurement functions. Numerical results are provided to show the performance improvement of the new LCEKF for robust vehicle navigation.
Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other contexts random search (RS) has shown similar results to grid search, while being less computationally-expensive. In this paper, we investigate to what extent the random search hyper-parameter tuning approach affects the accuracy and stability of support vector regression (SVR) in SEE. Results were compared to those obtained from ridge regression models and grid search-tuned models. A case study with four data sets extracted from the ISBSG 2018 repository shows that random search exhibits similar performance to grid search, rendering it an attractive alternative technique for hyper-parameter tuning. RS-tuned SVR achieved an increase of 0.227 standardized accuracy (SA) with respect to default hyper-parameters. In addition, random search improved prediction stability of SVR models to a minimum ratio of 0.840. The analysis showed that RS-tuned SVR attained performance equivalent to GS-tuned SVR. Future work includes extending this research to cover other hyper-parameter tuning approaches and machine learning algorithms, as well as using additional data sets.
Earthquake disaster avoidance learning system using deep learning

The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 s on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96% in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.
A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.
RDPC: Secure Cloud Storage with Deduplication Technique

Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.
A machine learning approach for regional geochemical data: Platinum-group element geochemistry vs geodynamic settings of the North Atlantic Igneous Province

Whilst traditional approaches to geochemistry provide valuable insights into magmatic processes such as melting and element fractionation, by considering entire regional data sets on an objective basis using machine learning algorithms (MLAs), we can highlight new facets within the broader data structure and significantly enhance previous geochemical interpretations. The platinum-group element (PGE) budget of lavas in the North Atlantic Igneous Province (NAIP) has been shown to vary systematically according to age, geographic location and geodynamic environment. Given the large multi-element geochemical data set available for the region, MLAs were employed to explore the magmatic controls on these shifting concentrations. The key advantage of using machine learning in analysis is its ability to cluster samples across multi-dimensional (i.e., multi-element) space. The NAIP data set is manipulated using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) techniques to increase separability in the data alongside clustering using the k-means MLA. The new multi-element classification is compared to the original geographic classification to assess the performance of both approaches. The workflow provides a means for creating an objective high-dimensional investigation on a geochemical data set and particularly enhances the identification of metallogenic anomalies across the region. The techniques used highlight three distinct multi-element end-members which successfully capture the variability of the majority of elements included as input variables. These end-members are seen to fluctuate in prominence throughout the NAIP, which we propose reflects the changing geodynamic environment and melting source. Crucially, the variability of Pt and Pd are not reflected in MLA-based clustering trends, suggesting that they vary independently through controls not readily demonstrated by the NAIP major or trace element data structure (i.e., other proxies for magmatic differentiation). This data science approach thus highlights that PGE (here signalled by Pt/Pd ratio) may be used to identify otherwise localised or cryptic geochemical inputs from the subcontinental lithospheric mantle (SCLM) during the ascent of plume-derived magma, and thereby impact upon the resulting metallogenic basket.
Energy-Efficient Acceleration of Deep Neural Networks on Realtime-Constrained Embedded Edge Devices

This paper presents a hardware management technique that enables energy-efficient acceleration of deep neural networks (DNNs) on realtime-constrained embedded edge devices. It becomes increasingly common for edge devices to incorporate dedicated hardware accelerators for neural processing. The execution of neural accelerators in general follows a host-device model, where CPUs offload neural computations (e.g., matrix and vector calculations) to the accelerators for datapath-optimized executions. Such a serialized execution is simple to implement and manage, but it is wasteful for the resource-limited edge devices to exercise only a single type of processing unit in a discrete execution phase. This paper presents a hardware management technique named NeuroPipe that utilizes heterogeneous processing units in an embedded edge device to accelerate DNNs in energy-efficient manner. In particular, NeuroPipe splits a neural network into groups of consecutive layers and pipelines their executions using different types of processing units. The proposed technique offers several advantages to accelerate DNN inference in the embedded edge device. It enables the embedded processor to operate at lower voltage and frequency to enhance energy efficiency while delivering the same performance as uncontrolled baseline executions, or inversely it can dispatch faster inferences at the same energy consumption. Our measurement-driven experiments based on NVIDIA Jetson AGX Xavier with 64 tensor cores and eight-core ARM CPU demonstrate that NeuroPipe reduces energy consumption by 11.4% on average without performance degradation, or it can achieve 30.5% greater performance for the same energy consumption.
A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly bound the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.
Neural Topic Modeling by Incorporating Document Relationship Graph

Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.In this paper, we propose Graph Topic Model (GTM), a GNN based neural topic model that represents a corpus as a document relationship graph.Documents and words in the corpus become nodes in the graph and are connected based on document-word cooccurrences.By introducing the graph structure, the relationships between documents are established through their shared words and thus the topical representation of a document is enriched by aggregating information from its neighboring nodes using graph convolution.Extensive experiments on three datasets were conducted and the results demonstrate the effectiveness of the proposed approach.
Multi robots interactive control using mixed reality

The paper presents a framework for interactive control of multi robots using mixed reality interfaces. The proposed approach can operate with both single and multi-robot systems, including interaction with industrial manipulators, mobile robots, and unmanned aerial vehicles (UAV). The human–robot interface allows both interactive and physical interaction with the robot. Particular attention is paid to a visualisation of the robots' intent during control. The presented framework has Robotic Operation System (ROS) as a core that can be connected to any robot and mixed reality interface. This allows us to make framework scalable and connect several different mixed reality devices and multiple heterogeneous robots. In the implementation part we connected our framework with Microsoft HoloLens, collaborative robot KUKA IIWA, mobile platform Plato, UAV designed on the basis of dji f450. The proposed framework was tested experimentally with real experimental setups mentioned above. The results showed the capabilities of the mixed reality system for interactive control of different robots' type.
HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction

Duplicate bug reports often exist in bug tracking systems (BTSs). Almost all the existing approaches for automatically detecting duplicate bug reports are based on text similarity. A recent study found that such approaches may become ineffective in detecting duplicates in bug reports submitted after the just-in-time (JIT) retrieval, which is now a built-in feature of modern BTSs (e.g., Bugzilla). This is mainly because the embedded JIT feature suggests possible duplicates in a bug database when a bug reporter types in the new summary field, therefore minimizing the submission of textually similar reports. Although JIT filtering seems effective, a number of bug report duplicates remain undetected. Our hypothesis is that we can detect them using a semantic similarity-based approach. This paper presents HINDBR, a novel deep neural network (DNN) that accurately detects semantically similar duplicate bug reports using a heterogeneous information network (HIN). Instead of matching text similarity alone, HINDBR embeds semantic relations of bug reports into a low-dimensional embedding space where two duplicate bug reports represented by two vectors are close to each other in the latent space. Results show that HINDBR is effective.
Quantum Circuits for Dynamic Runtime Assertions in Quantum Computation

In this paper, we propose quantum circuits for runtime assertions, which can be used for both software debugging and error detection. Runtime assertion is challenging in quantum computing for two key reasons. First, a quantum bit (qubit) cannot be copied, which is known as the non-cloning theorem. Second, when a qubit is measured, its superposition state collapses into a classical state, losing the inherent parallel information. In this paper, we overcome these challenges with runtime computation through ancilla qubits, which are used to indirectly collect the information of the qubits of interest. We design quantum circuits to assert classical states, entanglement, and superposition states.
Ramp-based twin support vector clustering

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other plane-based clustering methods. The non-convex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.