Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Machine Learning Based Analyses on Compressed Firmware

As Internet of Things (IoT) applications are getting attention these days, the importance of firmware security is also growing. However, it is not straightforward to analyze the bugs or vulnerabilities that reside in firmware. One of the major challenges is to detect information about hardware architectures of compressed firmware. Traditional analysis tools make use of static signatures embedded in the compressed binary code of firmware. However, signature extraction needs the careful elaboration of experts, and it is not always even possible. In this paper, we introduce our experience in analyzing the hardware information of compressed firmware. Since it is not possible to use the semantic information of compressed binary code, we adopt machine learning technologies for this purpose. Despite various difficulties, we have positive experimental results.
Using collaborative robots as a tool for easier programming of industrial robots

Programming industrial robots using traditional jogging via teach pendant is a time-consuming task that requires extensive training. More intuitive and faster task programming is often possible using kinesthetic teaching. Although this feature is available on many commercial collaborative robots, it is rarely available on traditional industrial robots. In this paper we propose a framework for allowing tasks to be instructed using a collaborative robot via kinesthetic teaching, and afterwards deployed to a traditional industrial robot. The frame- work consists of a physical modular concept for robot exchange, and a online programming software tool called Universal Industrial Interface. To assess the framework, a feasibility study is conducted where an industrial relevant task is rst programmed using a collaborative manipulator, and afterwards deployed on an industrial manipulator.
An Intelligent Operation and Maintenance System for Power Consumption Based on Deep Learning

With the advent of the era of power big data, power companies can easily access more and more user power data, but they are not making full use of such massive datasets to reflect their value. Due to the huge amount of data collected by the power system, the total amount of abnormal data collected cannot be ignored. These abnormal data cause great interference to subsequent data analysis and maintenance; It’s also important to find how to identify user’s abnormal behaviour based on the power dataset. In this paper, a smart operation and maintenance system based on deep learning is proposed. Based on the composite mathematical statistics method, the data is efficiently reviewed and cleaned. The isolated forest algorithm is used to quickly identify the users with abnormal power consumption and GAN is used to help evaluate this model. This system can help power companies efficiently complete data review and anomaly identification, and can integrate data information from multiple dimensions to guide the actual maintenance and verification.
AMCNet: Attention-Based Multiscale Convolutional Network for DCM MRI Segmentation

For patients with dilated cardiomyopathy (DCM), fast and accurate diagnosis is important to save lives. MRI is a non-invasive, effective medical imaging method that allows doctors to diagnose DCM. However, manual and semi-automatic segmentation is subjective, non-reproducible and time-consuming task. In this paper, a new attention-based convolutional encoder-decoder network is proposed to automatically segment my-ocardium in DCM, which assisting the doctor to quickly diagnose. In the proposed method, the attention mechanism module is used, which is able to fully highlight useful features that facilitate segmentation while suppress useless features that are not conducive to segmentation. Combining with the multi-scale convolution, our encoder-decoder network can accurately segment the my-ocardium in DCM. We verified our approach on 1155 myocardial MRI. Our network achieves the most advanced segmentation performance on the cardiac DCM dataset. Experiment results demonstrate the effectiveness of the proposed method.
An Attention-based Deep Model for Automatic Short Answer Score

In traditional education scenario, scoring assignment is done by human teachers and is time-consuming and laborious. In online education scenario, it is impracticable by human scoring, so automatic scoring is applied broadly. There are two categories of scoring questions: one is short text answer with reference answer, and the other is essay scoring without reference answer. In this work we focus on the former. Existed automatic scoring methods for short text answer used handy craft features which suffered from low accuracy. In order to overcome the problem, we propose a deep learning based method with attention mechanism to automatic scoring for assignment or exam on online education scenario. The method combines pre-trained embedding word vector and RNN model with attention to learn answer vector, and then learned response answer vector and reference answer vector are fed into logistic regression model to predict response answer’s score. Experimental results show that our proposed model achieves a relative 10% increase in performance compared with baseline model.
An exploratory study to identify factors that influence the use of Electronic Patient Journey Boards in Queensland Health

This research paper provides preliminary findings on views expressed by users regarding the use of Electronic Patient Journey Boards (EPJBs) in Queensland Health. Initial data were collected through a qualitative approach in order to understand the views of health professionals regarding EPJBs. This was achieved through interviews, brainstorming sessions and focus groups held with healthcare professionals who have used the EPJB and those who will be using EPJBs in the future. These qualitative data were analysed through the lens of three key critical variables, namely people, processes and technology. The preliminary findings show that these three variables are critical for the success of the use and implementation of EPJBs in the healthcare domain. Furthermore, this research paper was also able to identify factors that will have a significant influence on the implementation of a technology in a healthcare setting. This study is limited t o Queensland Health and needs further research to test the findings of the study, in order to apply the findings more generally. The analysis of data provides an initial blueprint for the implementation of EPJBs more widely.
Hoggorm: a Python Library for Explorative Multivariate Statistics

hoggorm is a python library for explorative analysis of multivariate data that implements statistical methods typically used in the field of chemometrics (Tormod Næs & Martens, 1988). Although hoggorm shares some statistical methods with the Python library scikit-learn for machine learning, it follows the chemometrics paradigm for data analysis where great attention is paid to understanding and interpretation of the variance in the data.
A Quality of Experience Hexagram Model for Mobile Network Operators' Multimedia Services and Applications

Superior network Quality of Experience (QoE) is important for Mobile Network Operators (MNO) as it ensures they increase profit margins, attract new customers and differentiate themselves from the competition by providing better quality guarantees. In this paper, we propose a QoE hexagram model that comprises six Key Quality Indicators (KQI). In this model, we introduced an additional KQI, Terminal Quality. Other new metrics like Packet Corruption Rate and Service Access Time were also incorporated. Furthermore, several experiments were conducted by introducing disturbances using the NetEm tool. The QoE value obtained from our model is an indication of the overall acceptability of the applications and services as perceived subjectively by the end users.
DBA Companion: a Tool for Database Analysis

Extracting information about data semantics from existing databases is essential in any re-engineering process. Several sources of information can be relevant for tackling this task, e.g. physical schema, database extension or application programs. In this paper, we present a tool called DBA Companion which can be an help to deal with the understanding of existing relational databases. The tool is based on the notion of agree sets. Agree sets allow to devise a framework dealing with a wide range of design problems. Agree sets are seen as a common data centric step of several algorithms useful for database analysis. These algorithms are: functional and approximate dependency inference, minimal key inference, example relation generation and normal form tests. We show how the framework is integrated in a tool which can provide some help to database administrators or analysts. We consider two problems: functional dependency inference and approximate functional dependency inference.
Moderating the influence of a prime: can self-focused attention inhibit response preparation behaviour in an interpersonal context?

Research into the effects of focused attention on prime-to-behaviour effects indicates that heightened self-focus can eliminate stereotype and trait activation. The present research investigated the influence of focused attention on prime-to-behaviour effects after the exposure to a threatening social group (‘hoodies’). In particular it was predicted that focusing on an irrelevant target after ‘hoodie’ priming would result in response preparation behaviour whereas self-focusing would inhibit it. Results revealed that self-focusing does indeed inhibit response preparation behaviour during an interpersonal context but failed to show the opposite effect for non self-focusing. This suggests that purely focusing attention away from prime related material is sufficient to produce inhibitory effects.
Hybrid mmWave MIMO Systems Under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-Aided Configuration

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.
Dynamically Reconfigurable RF-NoC with Distance-Aware Routing Algorithm

With the growing number of cores per processor, on-chip communications are becoming a critical issue. Conventional wired mesh Networks-on-Chips (NoC) are reaching their scalability limit, paving the way for new interconnect technologies such as Radio Frequency (RF), Optics and 3D. This paper presents a new routing mechanism based on the WiNoCoD architecture, a hierarchical RF interconnect with dynamic bandwidth allocation for Chip Multi Processors (CMP). WiNoCoD consists of three levels of hierarchy with an RF NoC at the highest level, a wired mesh NoC and a crossbar at the lowest level. In this paper, we propose a new organisation for the manycore supporting the RF-NoC, and introduce a new routing algorithm that can choose between wired and RF networks in order to optimize the use of RF. We show that the new architecture improves performance in terms of latency and saturation. Platform simulations show a latency reduction for lower transaction rates of up to 10% compared to the original WiNoCoD architecture and up to 30% compared to a wired mesh NoC. Furthermore, smart routing pushes back the saturation threshold up to 20 %.
Deep Learning-based framework for Autism functional MRI Image Classification

The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.
Trust-Oriented Live Video Distribution Architecture

Due to the recent popularization of live video distributions, live video distributors such as YouTubers have attracted great attention. Some of them shoot videos of themselves using smartphones while moving and distribute the videos via the Internet. One of the major consideration points of live video distributions on the Internet is trust between distributors and viewers, since various public concerns such as threats or attacks to distributors occur in live video distribution situations with no trust. To solve these concerns and enable safety live video distributions on the Internet, we propose a novel live video distribution architecture. In trusty situations, the systems under our proposed architecture add more video captions to give various information to the viewers. Otherwise, the systems add more effects to hide information. We also define three policies for distributors to manage trust.
Novel chaotic map‐based privacy‐preserving authenticated key agreement scheme without the electricity service provider involvement

Devising proper security measures to protect the usage reports of customers against unauthorized accesses is of prime importance. Authenticated key agreement schemes are one of the solutions, which are extensively used to provide secure data reading from the smart meters. Over the past decade, a number of key agreement protocols have been proposed for the smart meters that are covered by the smart grid data communication network. However, there are only two schemes, which have been presented quite recently particularly for the isolated smart meters. Since these schemes rely on the Internet and for each key agreement, the electricity service provider needs to involve, in this paper, we propose a novel anonymous authenticated key agreement scheme that is free from the electricity service provider entanglement. Further, the proposed scheme takes the advantage of substantial reduction in the communication overhead. The security of the presented scheme has been formally validated using the well‐known ProVerif tool and random oracle model and its efficiency has been analyzed in comparison to the related ones. The outcome indicates the superiority of the proposed scheme to be employed for the isolated smart meters.
Pair-HMM accelerator based on non-cooperative structure

Pair Hidden Markov Model (Pair-HMM) forward algorithm is gaining increasing popularity in biological research tools. We propose a novel non-cooperative structure of Pair-HMM forward algorithm accelerator on Field Programmable Gate Array (FPGA). We employ a task-level parallel scheme in the structure. We design the non-cooperative Processing Element (PE) to complete Pair-HMM forward algorithm independently. Our three-layer tree topology improves the scalability for di ﬀ erent FPGAs. Compared to previous works, our structure reduces the idle cycles which occurs in the systolic array structure and the PE ring structure. Compared with the PE ring, our implementation on Arria 10 can achieve 1.19 × speedups.
Nonlinear control strategy for a cost effective myoelectric prosthetic hand

The loss of a limb tremendously impacts the life of the affected individual. In the past decades, researchers have been developing artificial limbs that may return some of the missing functions and cosmetics. However, the development of dexterous mechanisms capable of mimicking the function of the human hand is a complex venture. Even though myoelectric prostheses have advanced, several issues remain to be solved before an artificial limb may be comparable to its human counterpart. Moreover, the high cost of advanced limbs prevents their widespread use among the low-income population. This dissertation presents a strategy for the low-level of control of a cost effective robotic hand for prosthetic applications. The main purpose of this work is to reduce the high cost associated with limb replacement. The presented strategy uses an electromyographic signal classifier, which detects user intent by classifying 4 different wrist movements. This information is supplied as 4 different pre-shapes of the robotic hand to the low-level of control for safely and effectively performing the grasping tasks. Two proof-ofconcept prototypes were implemented, consisting on five-finger underactuated hands driven by inexpensive DC motors and equipped with low-cost sensors. To overcome the limitations and nonlinearities of inexpensive components, a multi-stage control methodology was designed for modulating the grasping force based on slippage detection and nonlinear force control.
Solving Fragment Assembly Problem using hybrid 2-Stage Algorithm

Deoxyribonucleic acid (DNA) is the carrier of the hereditary characteristics of an organism. It also holds promise for predicting fatal diseases. These predictions are essential as they enable prevention rather than cure. However, such detection techniques would be improbable without comprehending the complete genome of an organism. Through our work we tried to unravel the genome code by assembling DNA fragments using our 2-stage hybrid algorithm which employs Ant Colony Optimization and deft Local Search technique to maximize the DNA fragment overlap count. In memory of our families and teachers who dared to believe in us.
A novel method to detect foreground region using morphological operations with block based enhancement for underwater images

Automation of detecting the Foreground Region (FR) or Shape of the object is essential in several computer vision, object recognition applications and poses several challenges in case of underwater images. Although Synthetic Sonar Images produce better quality images scattering of light, color distortion and poor lighting conditions are the few characteristics that effects the natural scene of the captured image. A novel technique for extracting the foreground region from a low quality underwater image is presented in this paper. We have decomposed the image in to multiple levels based on discrete wavelet transforms (DWT) for improving the sharpness or to reduce the fogginess in the image in order to get the clear image. Subsequently, to determine the sharpness of the local patches in the image a block based SSI algorithm is presented. Finally, the segmentation is performed by computing the binary gradient mask with the Sobel edge detection algorithm along with morphological operations. The proposed method is fast, extracting the accurate foreground regions and also detect the smallest particles present in the image. The results are qualitatively compared with the improved fuzzy c-means clustering (FCM), Otsu’s Threshold and FCM thresholding by considering the static background images.
Cloud-based Reversible Dynamic Secure Steganography Model for embedding pathological report in medical images

ABSTRACT Over past decades, several research works have been published in the direction of hiding confidential data that includes only the patients personal details in medical images. The medical images and respective pathological reports are stored separately in the storage device. Storing medical images and pathological reports separately occupy more storage compared to storing pathological reports Stego in medical images. This research identified pixel space available in the medical image for storing other relevant secret information. In this technique, a new algorithm is proposed for generating secret keys. The secret keys are utilized to segment the medical image into five regions: (i) one Region of Interest (ROI) sub-image and (ii) four non-Region of Interest sub-images. From the four non-Region of Interest sub-images, a particular non-ROI region sub-image is randomly selected from the secret key matrix. The pathological report is embedded into the preprocessed selected non-ROI region sub-image of the medical image and generated the Stego medical image. The Stego medical image and secret key are communicated using various network communication applications to the user for deStego the original X-ray image and pathological report at the user end. The research work confirmed the quality of the medical image using MSE and PSNR values.