Miracle-dz/UniFAR-FTUs huggingface dataset:
TAGraph: Knowledge Graph of Threat Actor

Understanding of cybersecurity threat landscape especially information about threat actor is a challenging task as these information are usually hidden and scattered. The online news had became one of the popular and important source of information for cybersecurity personnels to understand about the activities conducted by these threat actors. In this paper, we propose a framework to create knowledge graph of threat actor by building ontology of threat actor and named entity recognition system to extract cybersecurity-related entities. The resulting ontology and model can be used to automatically extract cybesecurity-related entities from an article and create knowledge graph of threatactor.
Bio-Inspired Tendon-Driven Finger Design With Isomorphic Ligamentous Joint

This paper suggests a novel type of bio-inspired finger design in that the joints that compose the finger mimic the features of human finger joints. They have isomorphic structures but different movements, further, there is no friction between bones in the joint, and the joint has a compliance property in all the directions thanks to the elasticity of ligaments. In the proposed joints, a number of strings substitute the articular capsule and collateral ligaments based on the concept of a tensegrity structure. Ultimately, a compliant robotic finger based on the ligamentous structure of the human is proposed. In addition, adjusting the fixed positions of the strings without any structural change, the joint can have a rest position yielded from the elasticity of the strings, and thereby, the tendon is only needed to drive the finger. Especially, since the finger does not have any mechanical rotating parts such as bearings and bushes, it can freely operate even underwater. As the proposed finger design is a type of underactuated mechanism, it is able to realize active flexion, passive extension, and passive adduction and abduction.
Authentication & Encryption Based Security Services in Blockchain Technology

Without any involvement of third party, block chain has shown tremendous potential in establishing secure links with IoT.This technology can be widely used without using any centralized authority. By combining both the technologies throughput of the system can be enhanced. Due to its various applications it is being widely used in almost every field which makes it vulnerable to various attacks. In this paper, blockchain architecture along with its key properties has been discussed. Later part of paper comprises of various security principles such as confidentiality, integrity and availability, various attacks on network along with its countermeasures.
Hard Disk Drives Failure Detection Using A Dynamic Tracking Method

Hard disk drives (HDDs) are the core components of data center in IT companies. A breakdown of HDD may cause horrible data loss and great economic loss. Therefore, failure prediction for HDDs is significant to avoid loss and make a data backup plan in advance. Existing prediction methods always focus on a fixed threshold to distinguish whether a HDD is healthy or not, and these methods neglect the problem of multi-stage degradation phenomenon of HDDs. To solve these problems, this paper proposes a dynamic tracking method for HDD failure prediction based on a switchable state stochastic process model. By utilizing Rao-Blackwellized particle filter, the model estimates and parameters are updated by newly available data. To improve model ability, a sensitive health indicator is constructed from SMART attributes based on multiple regression analysis. Then, based on the statistical property of the tracking residuals, the dynamic failure threshold is designed to realize the online prediction of HDD failure. Furthermore, experiments of proposed method are carried out in a real-life data set. The results show the validity of the proposed method.
A Cost/Worth Analysis Framework for Reliability Enhancement of Multi-Microgrid Distribution Systems

In this paper, a cost/worth analysis framework is presented for determination of optimal reliability enhancement plan in multi-microgrid (MMG) distribution systems. In such systems, multiple microgrids exist within the distribution grid, and they are operated in close coordination in order to enhance the reliability and efficiency of the whole system. In this context, it is assumed that conventional and renewable distributed generation (DG) units are to be installed, and the optimal capacity of DG unit is determined such that distribution system annual social costs are minimized. Annual social costs include annualized investment costs, annual operating costs, annual revenues as well as annual customer interruption costs (CIC). Therefore, estimation procedure of different cost terms are introduced. Moreover, an appropriate framework based on sequential Monte Carlo Simulation (MCS) approach is presented for calculation of CIC. According to this framework, different operating modes of MGs/sections are identified and operating measures are simulated. The presented framework is implemented on a test system, various DG installation scenarios are investigated and the results are discussed.
IoT and Microservice Architecture for Multimobility in a Smart City

In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for general-purpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.
Relative Pose Estimator based on Semi-Dense Homography for UAV Landing on Moving Target using Monocular Camera

Autonomous landing has been studied on various types of landing targets. In particular, relative pose estimation between a moving landing target and a UAV is important for stable landing in various environments. Most of the studies on UAV landing for a moving landing target used templates given in advance and feature-based methods to estimate the relative pose between the UAV and the landing target. However, the feature-based methods are vulnerable to ambiguity due to the symmetric shape of the helipad or camera noise. In this paper, we propose the algorithm that accurately estimates pose by applying the direct method. The algorithm does not need to use a given template image, which enables landing in various environments. Since the algorithm does not use the feature-based method, there is no concern about the ambiguity. To evaluate the proposed algorithm, we collect our dataset and confirm that the proposed algorithm is more accurate than the feature-based method.
Wireless Noise Monitoring and Fault Diagnosis for Substation Based on Ubiquitous Power Internet of Things and Deep Learning

The embracement of Ubiquitous Power Internet of Things(UPIoT) and Deep Learning(DL) technology provides a brand new stage for the development of power system. Based on the combination of UPIoT, DL, IEC 61850 and ZigBee wireless telecommunication technology, this article proposes a wireless noise monitoring and fault diagnosis scheme for digital substation. The ZigBee technology and the wireless SOC chip is implemented on the data collecting part of the wireless monitoring telecommunication system and the AWA6218CD sound level meter is utilized as the noise sensor. The real-time environmental noise monitoring value could be displayed and recorded on the PC software so that by learning the mapping between the noise feature and the substation equipment fault information through DL algorithm, intelligent fault diagnosis could be realized.
Low-Power Wide-Area Networks: Design Goals, Architecture, Suitability to Use Cases and Research Challenges

Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology’s ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal’s importance to those applications as Low, Moderate, or High. Using these priorities and each technology’s suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies.
Unexpected Collision Avoidance Driving Strategy Using Deep Reinforcement Learning

In this paper, we generated intelligent self-driving policies that minimize the injury severity in unexpected traffic signal violation scenarios at an intersection using the deep reinforcement learning. We provided guidance on reward engineering in terms of the multiplicity of objective function. We used a deep deterministic policy gradient method in the simulated environment to train self-driving agents. We designed two agents, one with a single-objective reward function of collision avoidance and the other with a multi-objective reward function of both collision avoidance and goal-approaching. We evaluated their performances by comparing the percentages of collision avoidance and the average injury severity against those of human drivers and an autonomous emergency braking (AEB) system. The percentage of collision avoidance of our agents were 78.89% higher than human drivers and 84.70% higher than the AEB system. The average injury severity score of our agents were only 8.92% of human drivers and 6.25% of the AEB system.
Novel DTN Mobility-Driven Routing in Autonomous Drone Logistics Networks

Drones have become prevalent for the delivery of goods by many retail companies such as Amazon and Dominos. Amazon has an issued patent that describes how drones scan and collect data on their fly-overs while dropping off packages. In this context, we propose a path optimization algorithm for a drone multi-hop communications network that can carry and forward data in addition to its primary function of parcel deliveries. We argue that traditional Delay Tolerant Networking (DTN) based protocols may not be efficient for this purpose. Therefore, this paper proposes a new DTN-based algorithm that optimizes drone flight paths in conjunction with optimized routing to deliver both parcels and data in a power efficient way and within the shortest possible time. We propose a heuristic algorithm called Weighted Flight Path Planning (WFPP) that priorities the data packets in an exchange pool in order to create an optimized path for the drones. Our approach is to determine a weight for each packet based on the packet’s remaining time to live, priority, size, and location of the packet’s destination. When two drones meet each other, they exchange the high weighted packets. Simulation studies show that WFPP delivers up to 25% more packets compared with EBR, EPIDEMIC, and a similar path planning method. Also, WFPP reduces the data delivery delays by up to 66% while the overhead ratio is low.
Multi-aspect Mining of Complex Sensor Sequences

In recent years, a massive amount of time-stamped sensor data has been generated and collected by many Internet of Things (IoT) applications, such as advanced automobiles and health care devices. Given such a large collection of complex sensor sequences, which consists of multiple attributes (e.g., sensor, user, timestamp), how can we automatically find important dynamic time-series patterns and the points of variation? How can we summarize all the complex sensor sequences, and achieve a meaningful segmentation? Also, can we see any hidden user-specific differences and outliers? In this paper we present CUBEMARKER, an efficient and effective method for capturing such multi-aspect features in sensor sequences. CUBEMARKER performs multi-way summarization for all attributes, namely, sensors, users, and time, and specifically it extracts multi-aspect features, such as important time-series patterns (i.e., time-aspect features) and hidden groups of users (i.e., user-aspect features), in complex sensor sequences. Our proposed method has the following advantages: (a) It is effective: it extracts multi-aspect features from complex sensor sequences and enables the efficient and effective analysis of complicated datasets; (b) It is automatic: it requires no prior training and no parameter tuning; (c) It is scalable: our method is carefully designed to be linear as regards dataset size and applicable to a large number of sensor sequences. Extensive experiments on real datasets show that CUBEMARKER is effective in that it can capture meaningful patterns for various real-world datasets, such as those obtained from smart factories, human activities, and automobiles. CUBEMARKER consistently outperforms the best state-of-the-art methods in terms of both accuracy and execution speed.
Internet of Medical Things (IoMT) Assisted Vertebral Tumor Prediction Using Heuristic Hock Transformation Based Gautschi Model–A Numerical Approach

A vertebral tumor is a category of spinal tumor which influence the bones or vertebrae of the spine in the human body system. The Spinal tumors that start inside the spinal string or the covering of the spinal line (dura) are called spinal rope tumors. Furthermore, the tumors that influence the vertebrae have frequently spread (metastasized) from the malignant growths in different parts of the body which is erratic with increasing the precision outcomes with the presence of medical instruments. Moreover, there are a few types of tumors that start inside the bones of the spine, for instance, such as chordoma, chondrosarcoma, osteosarcoma, plasmacytoma and Ewing’s sarcoma. A vertebral tumor can influence the neurological capacity by pressing on the spinal string or nerve roots closer. In this paper, the vertebral tumor is analysed using Heuristic Hock Transformation Based Gautschi’s-Blur Model (HHTGM) as a numerical approach. As these tumors developed inside the bone, they may likewise lead to torment, vertebral breaks or spinal precariousness which has been further examined in this research using Internet of Medical things (IoMT) Platform. Whether harmful or not, a vertebral tumor can be hazardous and cause perpetual incapacity has been tentative and experimentally analysed in accordance with HLC, CTM, LM, and CFRM methods, moreover HHTGM in this research explored the best results with 95.8% efficiency and 98.66 % of precision when compared with these methods.
Architecture Design and Application of Global Renewable Energy Reserves Evaluation and planning Platform

It proceeds from environmental protection and energy sustainable development angle, the development and utilization of renewable energy has gradually improved. However, the global renewable energy is unevenly distributed in space, and it’s complicated to develop and use affected by the local social economy, resource condition. The platform use internet, cloud computing, big data, GIS, and fully integrate the specific regional resource condition, political and economic situation, industry development trends, ecological environment to build the integrated database of the global renewable energy reserves evaluation and planning, research on site selection of renewable energy planning It can be predicted that the system will improve the overall planning level of renewable energy development and utilization, and serve the sustainable development of economy and society.
A Simple and Fast Solution for Fault Simulation Using Approximate Parallel Critical Path Tracing

Due to the growing complexity of today’s digital circuits, the speed of fault simulation has become increasingly important. Although critical path tracing (CPT) is faster than conventional methods, it is not fast enough for fault simulation of complex circuits with a large number of faults and tests. Exact stem analysis is the most important obstacle in accelerating the CPT method. The simplification of stem analysis eliminates time-consuming computations and makes the CPT method more parallelizable. An approximate and bit-parallel CPT algorithm is proposed for ultrafast fault simulation for both stuck-at-fault (SAF) and transition delay fault (TDF) models. Time linearity, speedup, and accuracy of the proposed algorithm are examined and evaluated using ISCAS85, ISCAS89, and ITC99 benchmark circuits. In order to assess the accuracy, the false-positive and false-negative detection of faults are counted for each benchmark circuit. The experimental results reveal considerable speedup as well as acceptable accuracy of the proposed approach in comparison with the traditional methods and commercial fault simulators.
3D Input Convolutional Neural Networks for P300 Signal Detection

P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants “A” and “B” for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.
Motion Primitive Recognition on Human Guided Robotic Arm using Machine Learning

This paper proposed a novel intuitive teaching technologies by reconstructing the recorded motion information during human guided robotic arm. A learning algorithm is proposed in this paper to recognize the motion primitives according to therbligs definition. The hybrid sensing interface is used to record and modified the positional trajectory, force/torque, and gripper information. Furthermore, an extended Kalman filter is used to pre-process the data and estimate the velocity and acceleration profile as motion features. The motion features, output data via the hybrid sensing interface, is finally used to recognize the target therblig by proposed cascade support vector machine. The experimental results show that the proposed method can recognize the motion features into therbligs correctly and efficiently. The recognition results can be further used to reconstruct an assembly operation.
A Hierarchical Storage System for Industrial Time-Series Data

The increasing interest among manufacturers in monitoring and analyzing industrial systems is generating a problem related to the considerable costs associated with the storage of captured data. This paper presents a three-level hierarchical architecture for time-series data storage on cloud environments that helps to decrease those costs. In the first level, new raw time-series data is stored for a short-period of time (e.g., one day) on electronic non-volatile storage such as solid-state drives (SSDs) that provide fast access for real time visualization of the latest data. In the second level, recent time series are stored for a medium-period of time (e.g., one week) on magnetic hard disk drives (HDDs) that are lower-cost devices with slower data transfer speed. In the third level, a reduced representation of the time series obtained by applying time-series reduction techniques are stored in HDDs, for a longer period of time (e.g., one year). Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future use of the data in different processes.The architecture has been implemented by using the top Database Management System from three different categories: Wide column store, Time series DBMS and Graph DBMS. It has been tested using industrial time series coming from a real manufacturing environment, and with three different types of queries proposed by domain experts. The performance results regarding storage space, storage costs and query time processing are shown on the paper.
A Novel Image Fusion Scheme for FTV View Synthesis Based on Layered Depth Scene Representation & Scale Periodic Transform

This paper presents a novel image fusion scheme for view synthesis based on a layered depth profile of the scene and scale periodic transform. To create a layered depth profile of the scene, we utilize the unique properties of scale transform considering the problem of depth map computation from reference images as a certain shift-variant problem. The problem of depth computation is solved without deterministic stereo correspondences or rather than representing image signals in terms of shifts. Instead, we pose the problem of image signals being representable as scale periodic function, and compute appropriate depth estimates determining the scalings of a basis function. The rendering process is formulated as a novel image fusion in which the textures of all probable matching points are adaptively determined, leveraging implicitly the geometric information. The results demonstrate superiority of the proposed approach in suppressing geometric, blurring or flicker artifacts in rendered wide-baseline virtual videos.