Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Semantic Sensitive Feature Profiling of IoT Devices

Billions of Internet of Things (IoT) devices are being adopted in our daily life as personal wearables, home automation agents, medical appliances, etc. Many domains of their use nowadays rely on the privacy and security of these devices—critical infrastructure, healthcare, logistics, manufacturing. In this paper, we aim to establish a standardized framework, which does not require access to physical devices yet allows to profile security and privacy-sensitive functionality in both existing and upcoming IoT products, based on semantic analysis of discovered technical information. We develop a software tool for automatic feature profiling of IoT devices and present case studies on two real-world IoT devices—a fitness tracker, Garmin Forerunner 230, and a voice-controlled home assistant, Amazon Echo Dot second generation and further provide comparative results analysis.
Nature-Inspired Processes and Structures: New Paradigms to Develop Highly Bioactive Devices for Hard Tissue Regeneration

Material scientists are increasingly looking to natural structures as inspiration for new-generation functional devices. Particularly in the medical field, the need to regenerate tissue defects claims, since decades, biomaterials with the ability to instruct cells toward formation and organization of new tissue. It is today increasingly accepted that biomimetics is a leading concept for biomaterials development. In fact, there is increasing evidence that the use of biomedical devices showing substantial mimicry of the composition and multi-scale structure of target native tissues have enhanced regenerative ability. As a relevant example, biomimetic materials have high potential to solve degenerative diseases affecting the musculoskeletal system, namely, bone, cartilage and articular tissues, which is of pivotal importance for most of human abilities, such as walking, running, manipulating, and chew-ing. In this respect, the adoption of nature-inspired processes and structures is an emerging fabrication concept, uniquely able to provide biomaterials with superior biological performance. The chapter will give an overview of the most recent results obtained in the field of hard tissue regeneration by using 3D biomaterials obtained by nature-inspired approaches. The main focus is given to porous hydroxyapatite-based ceramic or hybrid scaffolds for regeneration of bone and osteochondral tissues in neurosurgery and orthopedics.
Local Bi-Directional Differential Exciation Co-Occurrence Pattern for Face Image Retrieval

This article presents a novel feature descriptor named as local bi-directional differential excitation co-occurrence pattern (LBiDECoP) for face image retrieval. The proposed method aims at extracting salient features present in face images using differential excitation and unique sampling strategy. The proposed feature descriptor fuses the human perception of pattern into feature extraction using differential excitation which mimics human visual system. Further, LBiDECoP encodes the co-occurrence of similar ternary edges for enriched discriminative power. The performance of LBiDECoP descriptor has been tested for image retrieval on two benchmark and challenging face image databases such as AT&T and FEI. The experimental results demonstrate that LBiDECoP outperforms the well known LTCoP, CSLBCoP, RICLBP and FDLBP descriptors in terms of average precision and average recall rates.
The digital ingredients of donation-based crowdfunding. A data-driven study of Leetchi projects and social campaigns

ABSTRACT Crowdfunding platforms have seen increasing success over the last few years. While crowdfunding by counterparts has been extensively studied, only a very few works have focused on donation-based crowdfunding. The authors analyze Leetchi money-pots and study the impact of project presentations and Twitter campaigns on the success of fundraising. They propose a data-driven approach to determine the social strategies applied by the projects’ owners and their relevance to the success of the crowdfunding process. The authors extracted and computed a set of quantitative indicators using data mining and graph analysis techniques, including the project page and Tweets content, Twitter promoters and message diffusion process. The authors evaluate the predictive power of 70 features on levels of success using a dataset of 1,415 projects with 50,119 project-related tweets. The paper provides recommendations concerning key indicators to be followed and potential strategies to apply when attempting to raise funds on donation platforms.
Design, Fabrication and Testing of a 3D Printed FBG Pressure Sensor

In this paper, a fiber Bragg grating (FBG) pressure sensor was designed and fabricated using 3D printing method for the measurement of vertical pressure. The raw material used for the fabrication of this new pressure sensor was Polylactic Acid (PLA). The effectiveness of embedding FBG sensors inside printed PLA material was first examined taking into account different infill densities. It is found that the FBG sensor can be successfully embedded into PLA material without sacrificing its sensing performance. The change of infill density has no influence on both peak and residual wavelength change of FBG sensors during and after the printing process. All FBG sensors printed inside PLA prototype with different infill density values exhibit almost the same peak and residual wavelength rise values in the printing process. In addition, the higher the density value, the longer the time required to finish printing process. A simple FBG pressure sensor was designed and fabricated according to the successful experience of embedding FBG sensors into printed PLA. Measurement performance of this FBG pressure sensor was examined in laboratory cyclic loading tests. Calibration tests indicate that the maximum measured vertical pressure of this new pressure sensor was 2000 kPa with a measurement sensitivity of 13.22 kPa/pm.
Text Recognition on Khmer Historical Documents using Glyph Class Map Generation with Encoder-Decoder Model

In this paper, we propose a handwritten text recognition approach on word image patches extracted from Khmer historical documents. The network consists of two main modules composing of deep convolutional and multi-dimensional recurrent blocks. We utilize the annotated information of glyph components in the word image to build a glyph class map which is to be predicted by the first module of the network call glyph class map generator. The second module of the network encodes the generated glyph class map and transform it into a context vector which is to be decoded to produce the final word transcription. We also adapt an attention mechanism to the decoder to take advantage of local contexts which are also provided by the encoder. Experiments on a publicly available dataset of digitized Khmer palm leaf manuscripts called SleukRith set are conducted.
Recovering Graph Signals from Folded Samples

Graph sampling approach allows a multidimensional signal generated on a graph to be represented by the signal at a smaller set of sampled nodes. Currently, self-reset analog-to-digital converters (ADCs) are used to sample high dynamic range signals resulting in modulo-operation based folded signal at the sampled nodes. In this paper, we study the problem of signal recovery from the folded samples of a graph signal. We present a theoretical graph sampling rate that is sufficient and necessary for successful reconstruction of the graph signals from the folded samples. It is shown that the entire signal can be recovered by using an additional sampling node, with a different folding rate, while using the Shannon's rate for the signal at each node. An integer programming based formulation has been proposed to recover the lost information on the folding number at the sampled nodes. Furthermore, we propose a scalable sparse optimization based recovery method for graph signals satisfying certain conditions. The proposed algorithm is presented to emphasize that in our spatio-temporal sampling scenario, the inherent relationship among the graph nodes should be exploited in addition to the temporal correlation in the graph signal at different nodes to recover the signal.
Performance Characterization of Absolute Scale Computation for 3D Structure from Motion Reconstruction

Structure from Motion (SfM) 3D reconstruction of objects and environments has become a go-to process, when detailed digitization and visualization is needed in the energy and production industry, medicine, culture and media. A successful reconstruction must properly capture the 3D information and it must scale everything to the correct scale. SfM has an inherent ambivalence to the scale of the scanned objects, so additional data is needed. In this paper we propose a lightweight solution for computation of absolute scale of 3D reconstructions by using only a real-time kinematic (RTK) GPS, in comparison to other custom solutions, which require multiple sensor fusion. Additionally, our solution estimates the noise sensitivity of the calculated scale, depending on the precision of the positioning sensor. We first test our solution with synthetic data to find how the results depend on changes to the capturing setup. We then test our pipeline using real world data, against the built-in solutions used in two state-of-the-art reconstruction software. We show that our solution gives better results, than both state-of-the-art solutions.
Predicting CyberSecurity Incidents using Machine Learning Algorithms: A Case Study of Korean SMEs

The increasing amount and complexity of cyber security attacks in recent years have made text analysis and data-mining based techniques an important factor in detecting security threats. However, despite the popularity of text and other data mining techniques, the cyber security community has remained somehow reluctant in adopting an open approach to security-related data. In this paper, we analyze a dataset that has been collected from five Small and Medium companies in South Korea, this dataset represents cyber security incidents and response actions. We investigate how the data representing different incidents collected from multiple companies can help improve the classification accuracy and help the classifiers in distinguishing between different types of incidents. A model has been developed using text mining methods, such as n-gram, bag-of-words and machine learning algorithms for the classification of incidents and their response actions. Experimental results have demonstrated good performance of the classifiers for the prediction of different types of response and malware.
ControlChain: A new stage on the IoT access control authorization

The IoT is changing the way we interact with the world. Very soon, almost all of our daily tasks will be made through self intelligent systems embedded in devices scattered all around us. Their mission is to turn our cities, transportation systems, buildings, homes, and bodies in smart environments. These environments will bring us more comfort, improve our performance, increase our profits, and take away time‐consuming tasks. However, besides its great benefits, the IoT is also a big source of concerns, mainly because a good part of its devices will handle private and confidential information. Recently, cases of successful IoT invasions only worsen this scenario and show us that the today's adopted access control systems need to be replaced by more efficiently and secure ones. To overcome these access control problems, in this work, we present the ControlChain. The ControlChain is an access control authorization architecture that is heavily based on Blockchain technology. We also demonstrate the viability of the ControlChain through the E‐ControlChain, a proof‐of‐concept developed to run over the Ethereum network. Our proposals follows the IoT tendency requirements and are user‐transparent, user‐friendly, fully decentralized, scalable, fault tolerant, and compatible with a wide range of today's access control models already used in the IoT. Finally, we also make a cost and a performance analysis of E‐ControlChain, using a Raspberry Pi as an IoT device.
An Efficient Green-Aware Architecture for Virtual Machine Migration in Sustainable Vehicular Clouds

Virtual Machines (VMs) are used to increase flexibility and to reuse available resources in a vehicular cloud. These VMs allow the use of an environment capable of executing processes without the need for physical resources. Due to the mobility characteristics of vehicles in a vehicular cloud, these VMs can be migrated to different physical hosts. Furthermore, this migration of VMs can occur due to the need for load balancing, the scarcity of cloud resources, and the need to optimize the energy consumption of the vehicular cloud infrastructure. Establishing a mobile cloud migration policy becomes a major challenge due to the characteristics of the vehicular networks. In this paper, we propose an architecture for VMs migration in vehicular clouds based on the Energy-Aware paradigm. We also propose a new migration policy of VMs based on the energy expenditure of this migration, considering the energy expenditure of the VMs in the cloud and in the cloudlet, as well as the energy consumption of the communication between cloudlets, clouds, and vehicles. Simulation results show that the proposed policy achieved a reduction in the system's power consumption of about 10 percent. Furthermore, the proposed approach also reduced the number of drop migration of VMs by 5 percent.
A View-invariant Framework for Fast Skeleton-based Action Recognition using a Single RGB Camera

View-invariant action recognition using a single RGB camera represents a very challenging topic due to the lack of 3D information in RGB images. Lately, the recent advances in deep learning made it possible to extract a 3D skeleton from a single RGB image. Taking advantage of this impressive progress, we propose a simple framework for fast and view-invariant action recognition using a single RGB camera. The proposed pipeline can be seen as the association of two key steps. The first step is the estimation of a 3D skeleton from a single RGB image using a CNN-based pose estimator such as VNect. The second one aims at computing view-invariant skeleton-based features based on the estimated 3D skeletons. Experiments are conducted on two well-known benchmarks, namely, IXMAS and Northwestern-UCLA datasets. The obtained results prove the validity of our concept, which suggests a new way to address the challenge of RGB-based view-invariant
Vessel Profile Indicators using Fuzzy Logic Reasoning and AIS

Early vessel profiling and risk assessment is a critical component of advanced maritime tracking systems, required by a number of maritime stakeholders including custom controls, port authorities, coastguards and others. This paper reports on the development of a fuzzy logic reasoning tool for generating maritime vessel profile indicators through the Automatic Identification System (AIS). The report describes the need and the underlying statistical methods applied, which are based on Fuzzy Logic Reasoning, for finding potential profile indicators and classifying vessels to a degree of “risk”, thus requiring further examination and monitoring. Under conservative assumptions, some preliminary results about the probabilities and boundaries of potential indicators are presented and discussed. Keywords-AIS; Maritime Domain Awareness; Anomaly Detection; Fuzzy Logic System.
Robust Adaptive and Sliding Mode Synchronization of Uncertain Chaotic Systems

Parameter uncertainty of chaotic systems brings the complexity to the design of control law in many past research papers. In order to simplify the design of adaptive PID composite synchronous rule further and use the robust control method to deal with uncertainty conveniently, two kind of robust adaptive and nonlinear sliding mode synchronization algorithms are proposed for uncertain chaotic systems in this paper. The design strategy of the algorithm lies in using the boundedness of chaotic systems. The unknown information of driven system and response system is limited to be a given uncertainty range described by a bounded function. The robust adaptive controller is designed to complete synchronization. At last, it can be seen from the simulation that chaotic systems can achieve synchronization and eliminate the vibration phenomenon at the same time. The only disadvantage is that the precision of synchronization is limited by the control gain.
Building and Studying a Password Store that Perfectly Hides Passwords from Itself

We introduce a novel approach to password management, called SPHINX, which remains secure even when the password manager itself has been compromised. In SPHINX, the information stored on the device is theoretically independent of the user's master password. Moreover, an attacker with full control of the device, even at the time the user interacts with it, learns nothing about the master password – the password is not entered into the device in plaintext form or in any other way that may leak information on it. Unlike existing managers, SPHINX produces strictly high-entropy passwords and makes it compulsory for the users to register these passwords with the web services, which defeats online guessing attacks and offline dictionary attack upon service compromise. We present the design, implementation and performance evaluation of SPHINX, offering prototype browser plugins, smartphone apps and transparent device-client communication. We further provide a comparative analytical evaluation of SPHINX with other password managers based on a formal framework consisting of security, usability, and deployability metrics.
Calibration of Two 3D Sensors with Perpendicular Scanning Directions by using a Piece of Paper

It is difficult to find the 3D transformation relationship between two 3D sensors when the scanning directions of the two sensors has very large angle, for example 90 degrees or more. In this paper, we propose a very simple and efficient calibration method to get 3D transformation between two 3D sensors using a piece of white-colored paper. A piece of white-colored paper is folded in a quadrangular pyramid shape. The paper calibration object is placed on the transparent acryl board to get the shape of the object from two 3D sensors whose scanning direction is about 90 degree. The performance of the proposed calibration method is verified through 3D model reconstruction experiments. The calibration error between two sensors is less than 0.5 mm.
Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External Applications

Recently, Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. PDS offers individuals the capability to keep their data in a unique logical repository, that can be connected and exploited by proper analytical tools, or shared with third parties under the control of end users. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data when stored into the PDS. In contrast, in this paper we aim at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. The proposed P-PDS is based on preliminary results presented in [1] , where it has been demonstrated that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. In this paper, we have deeply revised the learning process in order to have a more usable P-PDS, in terms of reduced effort for the training phase, as well as a more conservative approach w.r. t. users privacy, when handling conflicting access requests. We run several experiments on a realistic dataset exploiting a group of 360 evaluators. The obtained results show the effectiveness of the proposed approach.
Shade : a differentially private wrapper around Apache Spark

Enterprises usually provide strong controls to prevent external cyberattacks and inadvertent leakage of data to external entities. However, in the case where employees and data scientists have legitimate access to analyze and derive insights from the data, there are insufficient controls and employees are usually permitted access to all information about the customers of the enterprise including sensitive and private information. Though it is important to be able to identify useful patterns of one’s customers for better customization and service, customers’ privacy must not be sacrificed to do so. We propose an alternative a framework that will allow privacy preserving data analytics over big data. In this paper, we present an efficient and scalable framework for Apache Spark, a cluster computing framework, that provides strong privacy guarantees for users even in the presence of an informed adversary, while still providing high utility for analysts in an interactive wrapper. The framework, titled Shade, includes two mechanisms SparkLAP, which provides Laplacian perturbation based on a user’s query and SparkSAM, which uses the contents of the database itself in order to calculate the perturbation. We show that performance of Shade is substantially better than earlier differential privacy systems without loss of accuracy, particularly when run on datasets small enough to fit in memory, and find that SparkSAM can even exceed performance of an identical non-private Spark query. Thesis Supervisor: Lalana Kagal Title: Principal Research Scientist
Generation of 3D Building Models from City Area Maps

In this paper, we propose a pipeline that converts buildings described in city area maps to 3D models in the CityGML LOD1 standard. The input documents are scanned city area maps provided by a city authority. The city area maps were recorded and stored over a long time period. This imposes several challenges to the pipeline such as different font styles of typewriters, handwritings of different persons, varying layout, low contrast, damages and scanning artifacts. The novel and distinguishing aspect of our approach is its ability to deal with these challenges. In the pipeline we, firstly, identify and analyse text boxes within the city area maps to extract information like height and location of its described buildings. Secondly, we extract the building shapes based on these locations from an online city map API. Lastly, using the extracted building shapes and heights, we generate 3D models of the buildings.