Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigation and Improving SHA-1 Standard Using Collision Detection Approach

This paper introduces a collision detection methodology and an improved version of Secure Hash Algorithm (SHA-1) standard. The proposed work helps to protect weak primitives from any possible collision attack. Two designs are implemented to help protect and improve SHA-1 standard. The first design employs near collision detection approach that was proposed by Marc Stevens. The second design is the proposed work that employs two block calculation schemes. Both designs are tested and verified for examples of collided messages. The designs can detect the collision probability and produce a different hash for weak messages that are susceptible to collision attack.
Random Bin Picking with Multi-view Image Acquisition and CAD-Based Pose Estimation

Due to the recent development of industrial automation, using vision based techniques to estimate the pose of workpieces for random bin picking application is a future trend. The common method for 3D pose estimation is to use the CAD model and feature points. However, the CAD-based method has a high degree of flexibility in the assembly line, and it is not easy to acquire all necessary features of the workpieces. In this work, we use two depth cameras to capture the 3D scene, and propose a CAD-based multi-view pose estimation algorithm. First, RANSAC and an outlier filter are adopted for noise removal and object segmentation. We use a voting scheme for preliminary pose estimation, followed by the ICP algorithm to derive a more precise target pose. Finally, with disturbance detection, the robot arm can grip the objects without rescanning for each operation. A complete system for 3D scene acquisition using structured light cameras, 3D pose estimation and robot arm control is developed for the pick-and-place task. Experiments are carried out in the real scene environment to demonstrate the feasibility of the proposed technique.
Monocular SLAM and Obstacle Removal for Indoor Navigation

Visual Simultaneous Localization and Mapping (SLAM) is one of the hot topics in computer vision. For the past few years, the AI and deep learning technology research have been widespread used in self-driving technology and surveillance system etc., gaining more and more attention from researchers and public media. The combination of AI technology and robot perception is inevitably going to be a trend. This paper aims at removing the obstacle to enhance the SLAM system performance that based on popular open source framework ORB-SLAM2 in dynamic environment. Moving objects will bring noise in camera pose estimation, besides, when in re-localization, the robot returns to the previous place finding the previous landmark mismatches because of its movement. The system will be confused and misdirected. A novel approach is proposed to remove the obstacle in real environment by using convolutional neural network (CNN) to generate a segmentation mask of obstacle object so as to eliminate the interference by moving object. Our experiment result shows an impressive outcome of practical use and benchmark dataset test.
Development of Shoulder Exoskeleton Toward BMI Triggered Rehabilitation Robot Therapy

Since exoskeletons show potential for rehabilitation therapy, many scientists have been designing upper extremity exoskeletons. Unfortunately, few have successfully provided a shoulder exoskeleton for severe impairment. Toward Brain-Machine-Interface (BMI) rehabilitation robot therapies for severe upper extremity impairment, this paper introduces a shoulder exoskeleton robot with a modular joint and an off-board modular actuator. We applied a Modular Exoskeletal Joint (MEJ) to a shoulder exoskeleton that was driven by Pneumatic Artificial Muscles (PAMs) transmitted by a Bowden cable. Our objective is generating passive movements triggered by BMI. Since large torque has to be generated for assist a whole arm, we newly designed a more powerful Nested-cylinder PAMs (NcPAMs) than our previous work. As a proof of the concept of the mechatronics design, we show the tracking performance of the periodic trajectory of a joint angle with both human and mannequin arms as simulated impairments. Our result shows that the tracking error is sufficiently small in all of the conditions and that our developed shoulder exoskeleton is an adequate substitute for flexion/extension movements.
An Exploration of Deep Transfer Learning for Food Image Classification

Image classification is an important problem in computer vision research and is useful in applications such as content-based image retrieval and automated detection systems. In recent years, extensive research has been conducted in this field to classify different types of images. In this paper, we investigate one such domain, namely, food image classification. Classification of food images is useful in applications such as waiter-less restaurants and dietary intake calculators. To this end, we explore the use of pre-trained deep convolutional neural networks (DCNNs) in two ways. First, we use transfer learning and re-train the DCNNs on food images. Second, we extract features from pre-trained DCNNs to train conventional classifiers. We also introduce a new food image database based on Australian dietary guidelines. We compare the performance of these methods on existing databases and the one introduced here. We show that similar levels of accuracy are obtained in both methods, but the training time for the latter is significantly lower. We also perform a comparison with existing methods and show that the methods explored here are comparably accurate to existing methods.
A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments

Non-line-of-sight (NLOS) propagation of radio signals can significantly degrade the performance of ultra-wideband localization systems indoors, it is hence crucial to mitigate the NLOS effect to enhance the accuracy of positioning. The existing NLOS mitigation algorithms to improve localization accuracy are either by compensating range errors through NLOS identification and mitigation methods for ranging or by using dedicated localization techniques. However, they are only applicable to some specific scenarios due to some special assumptions or the need of a priori knowledge, such as thresholds and distribution functions. Another disadvantage is that they neither have the capability to evaluate the magnitude of NLOS effect nor take account of the residual NLOS range errors during location estimation. To remedy these problems, this paper proposes a less environment-dependent and a priori knowledge-independent NLOS identification and mitigation method for ranging which is able to determine the specific NLOS channel. Based on the identified channel information, a rule is developed to select appropriate NLOS ranges for location estimation. Meanwhile, an equality constrained Taylor series robust least squares (ECTSRLS) technique is proposed to suppress residual NLOS range errors by introducing robustness to Taylor series least squares method. All these constitute our FCE-ECTSRLS NLOS mitigation algorithm. The performance of the proposed algorithm is compared with four existing NLOS mitigation algorithms by both static and mobile localization experiments in a harsh indoor environment. Experimental results have demonstrated that the proposed FCE-ECTSRLS algorithm outperforms the other four algorithms significantly.
A Two-Level Planning Framework for Mixed Reality Interactive Narratives with User Engagement

We present an event-based interactive storytelling system for virtual 3D environments that aims to offer free-form user experiences while constraining the narrative to follow author intent. The characters of our stories are represented as smart objects, each having their own state and set of capabilities that they expose to the virtual world. Our narratives are represented as a collection of branching stories, where narrative flow is controlled by author-defined states. A user model is employed to evaluate the user's engagement with smart objects and events, based on proximity, interaction patterns and visibility to the user. A two-level online planning system is designed to find the best narrative trajectory along pre-authored stories, according to the user model, and to generate a story sequence to the best trajectory with Monte Carlo Tree Search. We present the capabilities of our interactive storytelling system on an example story and describe the adaptations required for modeling user engagement in AR and VR applications.
Securely Connecting IoT Devices with LoRaWAN

IoT devices are usually connected wirelessly to the Internet. Due to the low energy resources, special new protocols are used. One such protocol is Long Range Wide Area Network (LoRaWAN). It was developed with the aim of sending small amounts of data to the Internet as energy-efficiently and robustly as possible. Transmission security played a major role in the specification of LoRaWAN. Wireless LoRaWAN devices communicate via radio with gateways, which send the data packets to a network server connected to the Internet. The network server has interfaces to be connected to existing IoT platforms and applications. Wireless transmission allows a potential attacker many additional attack vectors. We assess the security mechanisms defined in the LoRaWAN specification and describe own research to show, whether those security mechanisms are sufficient. For this, we explain typical attacks on LoRaWAN radio-based networks. We further show which precautions are necessary not to undermine these measures and whether additional security measures beyond the specification may be necessary. An important contribution of this paper is to present the key features of the LoRaWAN specification in a more generally understandable manner.
Historical Manuscript Dating using Textural Measures

Historical manuscript dating is a challenging task that requires extensive domain knowledge regarding varying scripts, writing styles and instruments involved. Increased digitization of historical documents for preservation and analysis purposes have encouraged the relevant community to design techniques for computerized dating of historical manuscripts. This paper presents a performance based comparative analysis of popular textural features for document dating purposes. Outcomes of a comprehensive series of experiments demonstrate that a combined textural features-based approach is more effective than employing individual texture features. A 345-dimensional feature vector is extracted by using a combination of individual features from Gabor filters, Uniform Local Binary Patterns and Histogram of Local Binary Patterns, whichisthenfedtoaLinear Discriminant Analysis (LDA) classifier. Performance evaluation of our proposed scheme on the Medieval Paleographical Scale (MPS) dataset gives a reduced Mean Absolute Error of 20.13.
Elastic Time Series Motifs and Discords

The recent proposal of the Matrix Profile (MP) has brought the attention of the time series community to the usefulness and versatility of the similarity joins. This primitive has numerous applications including the discovery of time series motifs and discords. However, the original MP algorithm has two prominent limitations: the algorithm only works for Euclidean distance (ED) and it is sensitive to the subsequences length. Is this work, we extend the MP algorithm to overcome both limitations. We use a recently proposed variant of Dynamic Time Warping (DTW), the Prefix and Suffix Invariant DTW (PSI-DTW) distance. The PSI-DTW allows invariance to warp and spurious endpoints caused by segmenting subsequences and has a side-effect of supporting the match of subsequences with different lengths. Besides, we propose a suite of simple methods to speed up the MP calculation, making it more than one order of magnitude faster than a straightforward implementation and providing an anytime feature. We show that using PSI-DTW avoids false positives and false dismissals commonly observed by applying ED, improving the time series motifs and discords discovery in several application domains.
Multiagent Coordination Systems Based on Neuro-Fuzzy Models with Reinforcement Learning

This paper presents the research and development of a hybrid neuro-fuzzy model for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-GC). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.
Structural Edge Detection: A Dataset and Benchmark

Edge detection is a fundamental problem in computer vision community. In this paper, we propose a novel concept for edge detection called Structural Edge. The Structural edges include occluding contours of objects as well as orientation discontinuities in surfaces that define the 3D structure of objects and their environments. This contrasts the semantic edge which is only the boundary between semantic areas. While existing edge detection methods focus on either semantic boundaries or low-level gradients, we focus on the structural edge. To achieve that, in this paper, we propose the structural edge dataset along with a benchmark. The structural edge dataset contains 600 images of natural indoor and outdoor scenes. The structural edges are labeled manually and validated by eye-tracking data from 10 participants with overall 20 trials. Later, we use the dataset to benchmark the existing edge detection methods. We benchmark both the learning based and non-learning based methods and draw the conclusion that existing methods cannot fully solve the structural edge detection. We encourage new research to exploit the proposed task.
A Framework for Crowd-Based Causal Analysis of Open Data

Many organizations provide open data, and important insights can be gained by analyzing such data. Analysis of the potential causal relationships is a complex task. We have developed a framework for analyzing causal relationships that combines the intelligence of the crowd with state-of-the-art machine learning methods. The proposed framework takes into account the effect of possible confounding in causal analysis by collecting explanations of the correlation between variables. The validity of the collected explanations is tested using a causal discovery workflow including a conditional independence test step and a causal direction inference step. Application of this framework to data obtained from the World Bank Data website and open government data sources revealed several interesting causal relationships. The results demonstrate that the proposed framework can efficiently perform causal analysis of open data.
Simplified Deep-Learning-based decoders for linear block codes

Deep-learning technology proliferates in a wide spectrum of applications. Modern communications applications require innovative solutions that can deliver near optimum performance in diverse and evolving environments. This paper proposes several substantial simplifications to deep-learning networks applied to the decoding of linear block codes. All proposed techniques reduce computational and interconnection complexity required for the inference in a deep-learning network over prior art. The proposed techniques build on inducing or exploiting sparsity in the trained network. Complexity savings of 60% to more than 80% are achieved without any practical degradation on decoding performance, quantified as coding gain.
Eccentricity Based Quantification of Retinal Vascular Tortuosity For Early Detection of Diabetes and Diabetic Retinopathy

Diabetic retinopathy is the main cause for the loss of vision among working age adults. Therefore, early detection of diabetic retinopathy is vital for the prevention of blindness. Vascular tortuosity (amount of twists and turns) is an early indicator of diabetes and diabetic retinopathy compared to other signs observed in the retinal image. We propose a novel tortuosity index to quantify retinal vascular tortuosity including dilations and elongation due to vascular augmentation caused by diabetes in addition to conventional characteristics; curvature and twists. In the proposed method, the tortuosity of a blood vessel is quantified using the eccentricity of the pixels of its skeleton. Retinal images of 110 voluntary participants (72 healthy subjects, 5 Type I diabetic, 28 Type II diabetic and 5 diabetic retinopathy patients) were acquired using a retinal camera to study the capabilities of the proposed tortuosity index. The derived tortuosity values using the novel method were significantly different among healthy, Type II diabetic and diabetic retinopathy patients (p-value < 0.001). While tortuosity values of diabetic retinopathy patients were significantly higher than all other participants, the healthy group showed less tortuosity than others. When compared to age, diabetes was identified as the predominant factor which causes the increase in retinal vascular tortuosity.
A Differential Low-Power Voltage-Clamped ISFET Topology for Biomedical Applications

Over the past few years, ion-sensitive field-effect transistors (ISFETs) have played a major role in chemical detection systems. This paper presents an architecture for an ultra-low power CMOS pH sensor suitable for biomedical applications. The design uses a differential ISFET readout circuit operating at 0.9V power supply. The minimum supply voltage and minimum power consumption are achieved by operating the MOSFETs in subthreshold regions. The novelty of this design lies in using different size sensing gate areas in a differential voltage clamping ISFET topology. The ISFET model is derived from experimental measurements. Simulation results of the circuit in a 0. 5µm standard CMOS process show that the designed differential ISFET provides an average sensitivity −49mV/pH with ISFET sensing areas of 80µm×80µm and 10µm×10µm over a 1-14pH range with 2.3nW of power.
Letter Segmentation and Modifier Detection in Printed Sinhala Signage

Optical Character Recognition (OCR) uses digital image processing concepts to extract text from images and convert them into machine-readable characters. Image segmentation is used in OCR to select sub images that register individual letters in the input image. Different approaches have attempted to effectively segment letters of different languages in both written and printed materials. In the context of Sinhala language, the alphabet consists of symbols that are complex and vary in shape and dimensions. Isolating each letter or modifier in a Sinhala text image is a challenge due to such features. This research proposes a methodology for the segmentation of printed Sinhala letters and detection of unconnected modifiers, which uses spatial arrangement of the Sinhala words and prior knowledge about the shape of Sinhala letters and modifiers. This paper also evaluates the results of the proposed algorithm and proposes further extensions to improve the goal of letter segmentation.
Extraordinary Ferromagnetic Coupling and Magnetodielectric Phenomena in NiO Nanoparticles

We intend to understand the ferromagnetism and magnetodielectric (MD) phenomenon in NiO nanoparticles (NPs) with average diameter ~23 nm synthesized by a chemical route. Magnetic studies revealed that NiO NPs are weakly ferromagnetic (FM). Notably, temperature-dependent magnetization studies show a bifurcation of the FC–ZFC curves, suggesting a competition between FM and antiferromagnetic (AFM) interactions with the blocking temperature ( $T_{B}$ ) at ~210 K. In addition, we observed field-dependent exchange bias effect in the NPs. Furthermore, MD studies revealed that the changes in the dielectric constant and loss induced by the magnetic field are strongly frequency dependent, which originates from the combined effect of extrinsic Maxwell–Wagner polarization along with magnetoresistance. It was found that the magnetoimpedance (MI) changes sign from negative at low frequencies to positive at higher frequencies of an excitation signal. Cole–Cole studies showed that the negative MI at low frequencies arises from FM surface/grain boundary, and positive MI at higher frequencies originates from AFM core of NiO NPs.
Effective Color Components for Pupil Diameter Measurement of Brown Eye Using a Visible-Light Camera

The pupil diameter variation are used for various intelligent system. The measurement of pupil diameter is generally performed using eye images taken with a near-infrared camera. However, the near-infrared camera is not mounted on a general PC or mobile devices. Therefore, if the pupil diameter can be measured using a visible-light camera mounted on a general PC or mobile devices, the system using pupil diameter variation can be generalized. In this paper, we investigate the effective color components for pupil detection using the visible-light camera under visible-light conditions. From the experimental results, the effective color components for pupil detection and pupil diameter measurement are R in RGB color space and H in HSV color space. In addition, as a result of measuring the pupil diameter with R and H color components, the accuracy of the visible-light camera is almost same as that of the near-infrared camera.
Stochastic Attitude Synchronization

Attitude synchronization in the presence of noisy communications is an important problem in control, finding applications in the robotics and aerospace domains. The underlying Lie group geometry of SO (3) combined with the stochastic aspects pose fundamental difficulties in designing robust controllers. Even in Euclidean space, noise causes catastrophic problems for consensus algorithms. In this work we employ the Cayley representation of SO (3), to analyze some deterministic synchronizing controllers and show that they either become unweildy or fail in the presence of noise. Then we propose a new controller and show it is stable in the presence of noise and admits a principled design procedure.
Vehicle Counting Quantitative Comparison Using Background Subtraction, Viola Jones and Deep Learning Methods

In this paper, vehicle counting is investigated using various machine methods on four datasets. Vehicle counting is needed to complete the data required for short term predictions using highway traffic model, which is in turn, applicable for road design and usage planning. The goal of this research is to show that automatic car counting using machine methods, can be obtained from utilizing existing CCTV image data or from better cameras. Then by applying quantitative evaluation, F1 and precision scores are obtained, so that a few recommendations can be given. Through numerous simulations, F1 scores ranging from 0.32 to 0.75 have been successfully obtained for one low resolution dataset, using Background Subtraction and Viola Jones methods, on existing CCTV data. It has been found also that Viola Jones method can improve F1 score, by about 39% to 56%, over Back Subtraction method. Furthermore, the use of Deep Learning especially YOLO has provided good results, with F1 scores ranging from 0.94 to 1 and its precision ranges from 97.37% to 100% involving three datasets of higher resolution. (Abstract)