Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Mobile Crowdsensing With Differential and Distortion Location Privacy

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and infer urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we propose a novel location obfuscation mechanism combining <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> <italic>-differential-privacy</italic> and <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> <italic>-distortion-privacy</italic> in Sparse MCS. More specifically, differential privacy bounds adversaries’ relative information gain regardless of their prior knowledge, while distortion privacy ensures that the expected inference error is larger than a threshold under an assumption of adversaries’ prior knowledge. To reduce the data quality loss incurred by location obfuscation, we design a differential-and-distortion privacy-preserving framework with three components. First, we learn a <italic>data adjustment</italic> function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an <italic>optimal location obfuscation</italic> function. The linear program aims to minimize the uncertainty in data adjustment under the constraints of <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-differential-privacy, <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>-distortion-privacy, and evenly-distributed obfuscation. We also design an <italic>approximated</italic> method to reduce the required computation resources. Third, we propose an <italic>uncertainty-aware inference</italic> algorithm to improve the inference accuracy for the obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to the state-of-the-art methods with the same level of privacy protection; the approximated method incurs < 3% additional quality loss than the optimal method, but only needs < 1% of the computation time.
Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning

Malicious insider attacks represent one of the most damaging threats to networked systems of companies and government agencies. There is a unique set of challenges that come with insider threat detection in terms of hugely unbalanced data, limited ground truth, as well as behaviour drifts and shifts. This work proposes and evaluates a machine learning based system for user-centered insider threat detection. Using machine learning, analysis of data is performed on multiple levels of granularity under realistic conditions for identifying not only malicious behaviours, but also malicious insiders. Detailed analysis of popular insider threat scenarios with different performance measures are presented to facilitate the realistic estimation of system performance. Evaluation results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders in unseen data with a high accuracy. Specifically, up to 85% of malicious insiders are detected at only 0.78% false positive rate. The system is also able to quickly detect the malicious behaviours, as low as 14 minutes after the first malicious action. Comprehensive result reporting allows the system to provide valuable insights to analysts in investigating insider threat cases.
Quantifying Outsourcing Risk Arising from Product Interdependencies in Supply Networks

Recent studies have shown that the topological structure of complex supply networks has a direct impact on the robustness of these networks against disruptions. Majority of these studies have characterised topology aggregated at the firm level. We argue that this approach, while valuable, lacks specificity when used to understand the impact of disruptions on individual product lines. In this study, we propose a novel, product-specific perspective to investigate how disruptions in supply networks can impact the final assembly of products. In particular, we consider the interdependencies between the individual products contained within the portfolios of each company embedded in a network of outsourcing relationships. We first show how such product-interdependencies can be estimated from firm level relationships, and next, we show how the estimated interdependencies can be used to quantify the outsourcing risk associated with a given company's product portfolio.
Magneto-visual-inertial Dead-reckoning: Improving Estimation Consistency by Invariance

Tractable algorithms used for 6DOF visual-inertial odometry have decades-long history of estimation consistency issues. Those arise in particular in two well-studied filters: namely the EKF-SLAM and MSCKF. Recently, strong theoretical works linked the error-state of these filters with their consistency properties; these results led to the synthesis of far more consistent filters. In previous works, we have shown that using similar filter for the fusion of magneto-inertial sensors with optical ones improved classical visual-inertial navigation systems. The consistency of those novel magneto-visual-inertial filters were, however, not addressed until now. This work does. We apply invariance theory findings to the specific case of magneto-inertial odometry and magneto-visual-inertial odometry for the synthesis of a filter with interesting consistency properties. We describe thoroughly such an invariant filter, implement it and conduct experiments on carefully captured data from real sensor. By comparing the results of non-invariant, observability-constrained and invariant versions of the filter, we find that the invariant version (i) shows an error estimate that is consistent with observability of the system, (ii) is applicable in case of unknown heading at initialization, (iii) improves long-term behavior of the filter and (iv) exhibits a lower normalized estimation error. We experiment on challenging scenarios for regular visual-inertial pedestrian navigation systems.
Sparse Channel Estimation for Spatial Non-Stationary Massive MIMO Channels

In massive MIMO systems, the channel shows a spatial non-stationarity that has a significant impact on the design of sparse channel estimation scheme based on compressive sensing, due to the large size of antenna array. In this letter, the block matching pursuit (BMP)-based two-stage sparse channel estimation scheme is proposed for spatial non-stationary (SNS) channels. In this scheme, the BMP is introduced to search for the support blocks and the threshold-based denoising method is utilized to refine the supports of non-zero elements. In order to evaluate our scheme, we provide a simple SNS channel model modified from the 3GPP 3D channel model to capture the delay-domain special sparse characteristic. Simulation results demonstrate that the proposed scheme can recover channels effectively and achieve better performance than comparison methods.
Stabilizing Distributed Model Predictive Control using the Consensus Form of ADMM

In distributed model predictive control (DMPC) problems, stability assurance is a demanding issue since the well-known terminal cost and constraint technique is not readily adaptable to the distributed schemes. An alternative to the complex procedure of calculating a separable terminal cost function and constraint set for stabilization is to quantify a minimum length for the DMPC prediction horizon. It is known that with such a stabilizing horizon length the DMPC cost bears a relationship to the infinite-horizon cost through a so-called performance factor. The stopping criterion of the distributed optimization algorithm, which for the sake of smooth handling of the coupling constraints is usually a duality-based algorithm, can then be assessed by determination of two scalars: an upper bound for the cost of the next sampling period and a lower bound for the optimal cost of the current one. Since in duality-based algorithms feasibility can only be attained in the limit of iterations, the constraint tightening technique can be deployed to determine the former scalar. However, the tightening technique impinges upon computation of the latter one. This problem is already conquered in the literature for gradient-based methods. By contrast, in the current paper the consensus form of ADMM is utilized to deal with the DMPC problem and an iterative algorithm is introduced to determine the aforementioned lower bound with the aim of stabilizing DMPC. Simulation results reveal that this algorithm outperforms the existing gradient-based methods in terms of required number of iterations and communications.
Sentiment Analysis Using Naïve Bayes Algorithm: With Case Study

Data is the new oil. It becomes valuable only when mined appropriately. Huge amounts of money is invested in the Data segment in the company only because the companies know its importance. Apart from profit generating information, the companies also need to know the customers' opinions of about the measures implemented. Text mining is the discovery of valuable information from the text in a text file. Online Text mining relates to gathering the information from social media posts such as tweets and blogs. Sentiment Analysis is the computational treatment of opinion, sentiment and subjectivity [4]. Sentiment Analysis of this information extends to the emotions behind this information which assists in selecting appropriate steps. In this paper we chose to mine the opinion of people regarding the two most important measures taken by the Government of India – Demonetization and Goods and Services Tax (GST).
Simulators, Emulators, and Test-beds for Internet of Things: A Comparison

In the last few years, the world has seen extensive growth in the field of IoT in both research & deployment and adoption of the Internet of Things (IoT). The number of IoT devices is increasing multi-fold each year. So, the state of IoT is under rigorous research & deployment by several researchers across the globe to develop new algorithms, techniques, and protocols to secure IoT networks further. In this paper, we compare sixteen simulators (IOTSim, IoTIFY, Bevywise-IoT, etc), four emulators (Cooja, NetSim, MAMMotH, NCTUns 6.0), and six test-beds (MBTAAS, FIT IoT-LAB, etc) for IoT on the basis of Scope, Type, Programming language, IoT layers, Scale of Operation, IoT Standards, API Integration, Cyber Resilience, Service Domain, and Security Measures. Its comparison helps for modeling techniques, proper simulation, and testing for algorithm validation/proof-of-concept implementation in IoT. This comparison will help to support researchers and developers for choosing suitable tools for their application. We have also discussed Vertical & Horizontal Analysis of existing Simulators, Emulators, and Test-beds. This will help in testing the research ideas before moving towards live implementation.
Time-optimal control for the hybrid double integrator with state-driven jumps

The problem of steering the state of a double integrator from a given initial condition to the origin in minimum time and in the presence of point-wise constraint on the control input has been thoroughly characterized in the case of purely continuous-time systems. The objective of this paper consists in extending the theory to the hybrid setting, namely with the double integrator dynamics potentially undergoing state-driven jumps. In particular, the optimal solution is characterized and compared with the purely continuous one, namely assessing potential advantages of a hybrid scheme in controlling the state of a double integrator to the origin. Moreover, we first construct the optimal solution in the case of a prescribed number of jumps before reaching the origin and then we provide sufficient conditions ensuring that the overall minimum-time solution undergoes a finite number of jumps.
A Review on Vision Based Control of Autonomous Vehicles Using Artificial Intelligence Techniques

In recent years, there has been substantial research scope for Autonomous Unmanned Ground Vehicles (AUGV) which is a burning topic for researchers and scientists. The concept of AUGV had been introduced way back to the 1920s and at that time it was mainly applied in war fronts, such as in Teletanks, Infantry Tanks, Goliath tracked mine etc. during 2nd world war. After the introduction of Artificial Intelligence (AI) techniques, AUGV has been used in different fields of applications, such as in transport systems, control systems, space science, manufacturing, etc. All the works demonstrated in these applications were mainly on vision-based obstacle avoidance for autonomous ground vehicles. In some of the work, LIDAR based camera embedded at the top of the vehicle for acquiring images which are directly connected to the GPS monitoring system has been implemented. Some of the evolutionary and swarm intelligence based optimization techniques, such as Particle Swarm Optimization (PSO) have been successfully applied for this interesting and challenging problem. Moreover, hybridization of variants of neural networks, probabilistic based model, heuristic optimization techniques has been effectively implemented in AUGV. The basic motto of these nature-inspired techniques is to complete the tasks, such as optimizing the path, optimizing the system parameters and controller, etc., required for both static as well as dynamic obstacles avoidance.
Effect of Altered OCT Image Quality on Deep Learning Boundary Segmentation

Deep learning methods provide a platform to segment boundaries within the retina and choroid in OCT images of the posterior eye, with the ultimate goal of having a robust model that works well across a wide range of different datasets. However, since most studies of deep learning methods use datasets exhibiting similar image quality for both training and evaluation, the effect of varied image quality on such methods is not normally explored in the context of OCT image segmentation. An understanding of the effects of image quality factors is vital to determine the robustness of the methods and their ability to be applied in clinical practice where images exhibiting a range of different qualities are encountered. This study examined a range of factors that can affect standard OCT image quality and determined how and why the performance of an existing neural network based segmentation method can subsequently degrade as a result. Three image quality factors (noise, contrast reduction, and gamma correction) all had a negative impact upon performance, while more robust performance was maintained in the presence of both JPEG and JPEG2000 image compression. Improving the method’s robustness to each of these degradations is also demonstrated with marked performance improvements identified by applying a fine-tuning approach to the network. This study improves our understanding of the effect of OCT image degradation on neural network performance, the effect that fine-tuning with poor-image quality data has on the network and highlights the benefit and importance of training resilient models using data augmentation.
Friend Recommendation Based on Multi-Social Graph Convolutional Network

Friend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks. However, most of the existing friend recommendation methods use user attributes or a single social network, while rarely integrating multiple social relationships to enhance the representation. This paper focuses on integrating various social relationships to guide the representation learning, and further generating personalized friend recommendations. We design an end-to-end framework based on multiple social networks to learn the potential features of users and construct a friend recommendation model named Multi-Social Graph Convolutional Network (MSGCN). It learns the features of higher-order neighbors from multiple social networks to enrich the representation of the target user based on the improved graph convolution neural network. In particular, some graph fusion strategies by adjusting and fusing the Laplace matrix of the graph are designed to integrate social relationships. Finally, we use Bayesian theory to transform friend recommendation into a sorting problem for personalized recommendation. The experimental results show that the proposed model outperforms the state-of-the-art methods.
Design of a Robust Logic Gate using Magnetic Tunnel Junction

In the era of big data, limited communication bandwidth poses a great challenge for the conventional Von- Neumann architecture. Moreover, significant data movement between memory and processor to handle ever growing data set further degrade the system performance. To address this issue the most efficient way is to perform computation within the memory. This promising solution of integrating logic within the memory avoids expensive data transfers between memory and processor thereby resulting in higher performance and energy efficiency. Therefore, in this paper emerging non-volatile memory such as Magnetic Random Access Memory (MRAM) is explored as one of the most promising candidates to compute within the memory. It offers several additional advantages such as zero standby leakage power consumption and instant on capability. This work presents the structure of Computational Random-Access Memory (CRAM) and design of universal logic gates such as NAND and NOR. Next, to increase the reliability of these gates a novel technique is proposed which significantly reducing the functional error probability.
Optimal Waveform for Fast Entrainment of Weakly Forced Quantum Nonlinear Dissipative Oscillators

We consider entrainment of a quantum nonlinear dissipative oscillator to a periodically modulated harmonic driving in the semiclassical regime. We derive the optimal waveform of the periodic amplitude modulation by extending a classical optimization scheme to the semiclassical phase equation approximately describing the quantum oscillatory dynamics. Specifically, we consider optimization of the waveform for fast entrainment of quantum nonlinear oscillators and show that the optimal waveform yields faster entrainment to the driving signal than the simple sinusoidal waveform. We argue that the optimization of waveforms provides better performance when the phase sensitivity function of the limit cycle has stronger high-harmonic components. The theoretical results are verified by using the quantum van der Pol model, which is a typical model of the quantum nonlinear dissipative oscillator.
Vehicular Traffic Management Based on Traffic Engineering for Vehicular Ad Hoc Networks

Urbanization causes many problems to human mobility, since people living in the cities tend to increase the vehicular traffic flow. City road infrastructure does not increase faster than the number of vehicles, thus causing traffic congestion in dense urban centers. Besides travel delay, vehicular traffic congestion results in serious problems to human beings (e.g., health problems due to stress), to the planet (e.g., increase in pollution) and to the economy (e.g., waste of a large amount of money due to time spent in traffic jams). In order to improve vehicular traffic flow in dense urban areas, this paper presents a new Vehicular Traffic Management Service based on Traffic Engineering theory, called Re-RouTE. The Re-RouTE service relies on the density of vehicles in roads and applies the flow-density macroscopic traffic engineering model to identify congested routes. Roads are represented by a weighted graph, which is then used to discover routes without traffic jams and with a small increase in the travel distance. The main goal of Re-RouTE is to reduce traffic jams while increasing the global vehicular traffic flow of the road network. Moreover, the service was designed to reduce traffic jams instead of moving them to a different road/area. Simulation results show the ability of Re-RouTE to improve travel time, travel distance, speed and the number of messages transmitted when compared to a literature solution.
Automatic Counting of Infected White Blood Cells Using Multi-Level Thresholding

The identification of leukocytes is the primary step to analyze diseases such as leukemia which are ordinarily performed by pathologists utilizing a light binocular. These are very time consumption, monotonous, expensive and require specialists. In this way, computer assisted symptomatic framework that helps pathologists within the demonstrative strategy can be exceptionally viable. White blood cell (WBC) fragmentation is ordinarily the primary stage in computer-assisted diagnostic framework development. This paper proposed an automated method for segmenting and counting white blood cell from microscopic images. For this purpose, Otsu technique was used in segmentation of WBCs from a microscopic image, Counting WBCs and diagnoses the patient case. The proposed system achieves an accuracy of 98.2%
Semantic Inference for Cyber-Physical Systems with Signal Temporal Logic

Formal specification plays crucial roles in the rigorous verification and design of cyber-physical systems (CPS). The challenge of getting high-quality formal specifications is well documented. This challenge is further exacerbated in CPS with artificial-intelligence- or machine-learning-based components. This paper presents a problem called ‘semantic inference’, the goal of which is to automatically translate the behavior of a CPS to a formal specification written in signal temporal logic (STL). To reduce the potential combinatorial explosion inherent to the problem, this paper adopts a search strategy called agenda-based computation, which is inspired by natural language processing. Based on such a strategy, the semantic inference problem can be formulated as a Markov decision process (MDP) and then solved using reinforcement learning (RL). The obtained formal specification can be viewed as an interpretable classifier, which, on the one hand, can classify desirable and undesirable behaviors, and, on the other hand, is expressed in a human-understandable form. The performance of the proposed method is demonstrated with a case study.
Rigidity in Non-Euclidean Frameworks for Formation Control: The Manhattan Metric

This paper considers the formation control problem when the target is a rigid framework with respect to inter-agent ℓ1-distance measurements. We present a rigidity theory for frameworks under the ℓ1 norm and show that, under this type of rigidity, infinitesimally rigid frameworks are uniquely determined up to a translation. Based on the characterizations of the theory, we propose a distributed control law and prove local exponential stability to the target formation. We also present results on the non-target equilibriums and convergence speed of the control law, supporting these claims with illustrative examples.