Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Classification Framework for Pressure Ulcer Tissues Based on 3D Convolutional Neural Network

Pressure ulcer (PU) is a clinical pathology of localized deterioration to the underlying tissues as well as to the skin, which is generated by friction and pressure. A trustworthy diagnosis of PU, which is supported by accurate assessment, is critical to have effective therapy and save the patient's life. In this paper, we propose an automatic classification framework to segment and classify various tissues to help in diagnosis and treatment of PU. The proposed framework consists of two main stages, which are region of interest (ROI) extraction and tissue segmentation stages. The main idea is to extract various models and features from PU RGB images and supply them to multi-path 3D convolution neural network (CNN) to segment slough, necrotic eschar, and granulation tissues to help in assessing the status of PU. ROI is extracted by supplying three different color models to the CNN, which are RGB, HSV, and YCbCr. Then, the PU tissues are classified by providing four various models to the 3D CNN. These models are the original RGB image, the smoothed image with a pre-selected Gaussian kernel, and the 1st-order models of prior and current visual appearance. The framework was trained and tested on 100 color RGB PU images. The classification accuracy was evaluated using the area under the curve (AUC), the percentage area distance (PAD), and Dice similarity coefficient (DSC). The obtained preliminary results have AUC of 96%, PAD of 10%, and DSC of 93%. These experimental results are promising and can lead to an accurate assessment of the PU status.
Knowledge Extraction from Bangla Documents: A Case Study

Numerous resolutions and agendas of abundant official meetings are composed in Bangla language. In this article, we have demonstrated a system that automatically discovers and extracts these decisions and agendas from the increasingly vast pool of official Bangla digital documents like Big Data and allow extraction, detection, and analysis of the documents. A set of different rule base approaches is used to recognize and extract the decisions of the resolutions of Bangla documents which are presented here. Then with different parameters, the results of the procedure are discussed, classified and analyzed where 86% accuracy is achieved for a sample Dataset to detect decisions in the documents.
A 200 MHz Bandwidth GaAs Switch-Mode Supply Modulator

We report a wide-bandwidth and high-efficiency supply modulator for an envelope tracking power amplifier. We fabricated a GaAs switching power amplifier monolithic microwave integrated circuit (MMIC), the switching frequency of which is 2 GHz, and measured its performance as a supply modulator. A delta-sigma modulation is used to generate digital input signals for the MMIC. To reduce the switching loss, we added an extra feed-back path to a conventional delta-sigma modulator from the output of the comparator to the input of the accumulator. The supply modulator shows a switching efficiency of 66% and an average output power of 30.5 dBm with a 200 MHz bandwidth orthogonal frequency division multiplexing (OFDM) envelope signal.
Fuzzy Logic for Performance Analysis of AES and Lightweight AES

the Internet of Things is being more and more adopted for applications in almost every application of today’s business. The ability to integrate the features of the web to the features of Internet of Things systems has led to more flexibility in managing data from almost any location and almost any device platform. The need to secure the exchanged data has made users have some hesitation towards comfortably adopting these technologies, so developers enhanced security measures through encryption, and creating some "lightweight" version of efficient cryptosystems. Having a decision system that guides the user on the best version of a ciphering algorithm means having a more efficient IoT based secured system. The proposed system uses fuzzy logic to provide indications to which variation of the algorithm to use for the exchanges messages, with certainty levels that would assist the user in choosing the most suitable ciphering algorithm.
The Lexical and Syntactic Analyzers of the Translator for the EI Language

This paper contains the task of implementation of functionally-imperative programming language EI, the composition and structure of the its translator, a brief description of the lexical and syntactic rules of the language, the algorithms and the basic functions of the lexical and syntactic analyzers. It describes fragments of formal definitions of lexic and syntax of the El-Ianguage that was used for the automated construction of lexical and syntactic analyzers of the compiler in C++ using the client-server package Webtranslab. It describes also the basic algorithms of lexical analyzer (scanner), which in this version of the compiler performs macroprocessing, files including, deleting all the insignificant character sequences and transformation of correct words of language into an internal representation - tokens. We consider the structure and functions of a parser that performs a descending analysis. This parser constructed in the form of a stack automaton with multi-states controlled by the current input token and the current state fields. The composition of the state cell of the automaton and the algorithms of its operation are described, as well as the mechanisms for performing the operations that are embedded in the grammar and intended to implement the functionality of the semantic analyzer and code generator. The current state of realization of two constituent parts of the translator for EI-language - scanner and parser - is described.
Enhancing Knowledge Graph Completion with Positive Unlabeled Learning

Knowledge graphs have proven to be incredibly useful for many artificial intelligence applications. Although typical knowledge graphs may contain a huge amount of facts, they are far from being complete, which motivates an increasing research interest in learning statistical models for knowledge graph completion. Learning such models relies on sampling appropriate number of negative examples, as only the positive examples are contained in the data set. However, this would introduce errors or heuristic biases which restrict the sampler to visit other potentially reliable negative examples for better prediction models. In this paper, we present a novel perspective on skillfully selecting the negative examples for knowledge graph completion. We develop a two-stage logistic regression filter under the positive-unlabeled learning (PU learning) framework, which enables an automatic and iterative refinement of the negative candidate pools. We then contrast positive examples with the resulting negative ones based on the improved embedding-based models. In particular, we work with a cost-sensitive loss function by weighting the semantic differences between negative examples and particular positive ones. This weighting scheme reflects the importance of predicting the preferences between them correctly. In experiments, we validate the effectiveness of negative examples in refining and weighting schemes, respectively. Besides this, our proposed prediction model also outperforms the state-of-the-art methods on two public datasets.
Fast, Robust, and Accurate Image Denoising via Very Deeply Cascaded Residual Networks

Patch based image modelings have shown great potential in image denoising. They mainly exploit the nonlocal self-similarity (NSS) of either input degraded images or clean natural ones when training models, while failing to learn the mappings between them. More seriously, these algorithms have very high time complexity and poor robustness when handling images with different noise variances and resolutions. To address these problems, in this paper, we propose very deeply cascaded residual networks (VDCRN) to build the precise relationships between the noisy images and their corresponding noise-free ones. It adopts a new residual unit with an identity skip connection (shortcut) to make training easy and improve generalization. The introduction of shortcut is helpful to avoid the problem of gradient vanishing and preserve more image details. By cascading three such residual units, we build the VDCRN to deploy deeper and larger convolutional networks. Based on such a residual network, our VDCRN achieves very fast speed and good robustness. Experimental results demonstrate that our model outperforms a lot of state-of-the-art denoising algorithms quantitively and qualitively.
Reverse Engineering the Behaviour of Twitter Bots

Recent research has shown significant success in the detection of social bots. While there are tools to distinguish automated bots from regular user accounts, information about their strategies, biases and influence on their target audience remains harder to obtain. To uncover such details, e.g., to understand the role of bots in political campaigns, we address three questions: Can we describe the behaviour of a bot (when and how a bot takes actions) by a set of understandable rules? How can we express bias and influence? Can we extract such information automatically, from observations of a bot? In this paper, we present an approach to reverse engineering the behaviour of Twitter bots to create a visual model explaining their actions. We use machine learning to infer a set of simple and general rules governing the behaviour of a bot. We propose the notion of differential sentiment analysis to provide means of understanding the behaviour with respect to the topics on its network in relation to both its sources of information (friends) and its target audience (followers). Respectively, this provides insights into their bias and the influence aimed at their target audience. We evaluate our approach using prototype bots we created and selected real Twitter bots. The results show that we are successful in correctly describing the behaviour of the bots and potentially useful in understanding their impact.
Joint Head Pose Estimation with Multi-task Cascaded Convolutional Networks for Face Alignment

In the past decades, face alignment has been studied widely, but it has long been impeded by the problem of pose variation. Recent studies show that pose information used as additional source of information can help address the above problem. In this paper, we adopt a multi-task cascaded CNNs based framework for simultaneous face detection, dense face alignment and fine head pose estimation. Especially, our framework exploits the inherent correlation between face alignment and fine head pose estimation to boost up landmark detection robustness in the case of various poses. Experiments show that our method not only demonstrates real-time performance for face detection, dense face alignment and fine head pose estimation, but also outperforms most state-of-the-art methods for face alignment on the challenging 300-W benchmark. Especially in the case of large pose variations, it achieves outstanding results.
Coding Gain Optimized 8-Point DST with Fast Algorithm for Intra-frames in Video Coding

In modern video codecs, such as HEVC and VP9, intra-frame blocks are decorrelated using DCT or DST. The optimal separable transform for intra prediction residual blocks has been determined to be a hybrid transform composed of the DCT and the odd type-3 DST (ODST-3), independent of block size. However, the ODST-3 has no fast algorithm like the DCT. Hence its use in HEVC and VP9 has been limited to only 4×4 blocks. For larger blocks such as 8×8 or 16×16, HEVC replaces the optimal ODST-3 with the conventional DCT while VP9 replaces it with the even type-3 DST (EDST-3), both of which have fast algorithms. The EDST-3 has better coding gain than the DCT but it has still a coding gain loss with respect to the optimal ODST-3. This paper attempts to optimize some parameters of the EDST-3 to reduce this coding gain loss while still retaining the fast algorithm. In particular, the 8-point EDST-3 is represented as a cascade of Givens rotations and some rotation angles are optimized to reduce the coding gain loss with respect to the optimal 8-point ODST-3. By replacing only the 8-point EDST-3 in VP9 with this optimized transform, while leaving other transforms with different sizes unchanged, average Bjontegaard-Delta bitrate savings of −0.13% are achieved with respect to the standard VP9 codec.
Code Acceleration Using Memristor-Based Approximate Matrix Multiplier: Application to Convolutional Neural Networks

In this paper, we demonstrate the feasibility of building a memristor-based approximate accelerator to be used in cooperation with general-purpose <inline-formula> <tex-math notation="LaTeX">$\times 86$ </tex-math></inline-formula> processors. First, an integrated full system simulator is developed for simultaneous simulation of any multicrossbar architecture as an accelerator for <inline-formula> <tex-math notation="LaTeX">$\times 86$ </tex-math></inline-formula> processors, which is performed by coupling a cycle accurate Marss <inline-formula> <tex-math notation="LaTeX">$\times 86$ </tex-math></inline-formula> processor simulator with the Ngspice mixed-level/mixed-signal circuit simulator. Then, a novel mixed-signal memristor-based architecture is presented for multiplying floating-point signed complex numbers. The presented multiplier is extended for accelerating convolutional neural networks and finally, it is tightly integrated with the pipeline of a generic <inline-formula> <tex-math notation="LaTeX">$\times 86$ </tex-math></inline-formula> processor. To validate the accelerator, first it is utilized for multiplying different matrices that vary in size and distribution. Then, it is used as an accelerator for accelerating the tiny-dnn, an open-source C++ implementation of deep learning neural networks. The memristor-based accelerator provides more than <inline-formula> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> speedup and energy saving for a <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> matrix-matrix multiplication, with an accuracy of 90%. Using the accelerated tiny-dnn for the MNIST database classification more than <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> speedup and energy saving along with 95.51% pattern recognition accuracy is achieved.
IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents

At present time, drowsy driving has become one of the major issues of the traffic collision. According to statistics, a large number of road accidents occur due to drowsy driving which results in severe injuries and deaths. For this reason, various studies were done in designing systems that can examine the driver fatigue and alert him beforehand, thus preventing him to fall asleep behind the wheel and cause an accident. Some traditional approaches used vehicle-based measures to design their system, however, such measurements are highly influenced by the structure of the road, type of vehicle and the driving skill. Other approaches used psychological measures for their system that tend to provide better accuracy in monitoring the drowsiness of the driver. However, such techniques are usually intrusive as electrodes are required to be placed on the head and body. Furthermore, there are few existing researches in which subjective measurements are used as the input for the system, but, such methods can distract the driver and lead to an ambiguous result. In this paper, we proposed a system that is absolutely nonintrusive and real-time. Our proposed system used the eye closure ratio as input parameter to detect the drowsiness of the driver. If the eye closure ratio deteriorates from the standard ratio, the driver is alerted with the help of a buzzer. For our system, a Pi camera is used to capture the images of the driver's eye and the entire system is incorporated using Raspberry-Pi.
Influence of defatted mango kernel seed flour addition on the rheological characteristics and cookie making quality of wheat flour

Abstract Defatted mango kernel seed flour was added to wheat flour for the production of cookies. The protein contents of the wheat‐mango kernel flour blend were optimized using optimal mixture design of response surface methodology. The farinograph analysis had acceptable mixing profile at 25%‐defatted mango kernel flour incorporation, after which the mixing profile was adversely affected. The pasting temperature was between 65.8 and 75.6°C; peak viscosity ranged between 448 and 521 BU while the final viscosity ranged between 594 and 709 BU. The flour blends had promising dynamic rheological characteristics; at 15% incorporation of defatted mango kernel flour, G’ was greater than G’’ while a stage was reached when G’ was equal to G’’ at 25% incorporation. At 50% incorporation, G’’ was greater than G’. The cookies produced showed high (>70%) quality in terms of physical properties, color, and sensory evaluation at 25% incorporation of defatted mango flour.
Influence of Word Normalization and Chi-Squared Feature Selection on Support Vector Machine (SVM) Text Classification

In this study, we used SVM for text classification. There is stemming or Iemmatization word normalization with the addition of Chi-squarefeature selection on the classification that we made. There are also pre-processing data being performed, namely stopwords removal and tokenize. We used BBC dataset containing 2,225 documents and 5 categories. There are 21,813. features resulting from the use of stemming and 31,007 features resulting from the use of lemmatization. Each feature represents the number of words that come out in the document. We used confusion matrix to evaluate the results of text clasification. SVM text classification performance using stemming enhanced by Chi-squared (method 1) get better results than using lemmatization enhanced by Chi-squared (method 2). The best performance was obtained using 80% feature reduction where method 1 received a precision value of 95%, a recall value of 95%, and an accuracy value of 95.05%. Method 2 only received a precision value of 93%, a recall value of 93%, and an accuracy value of 93.24% using the same amount of feature reduction.
Secure Three-Factor Remote user Authentication for E-Governance of Smart Cities

With the advancements in the Information and communication Technologies, people's living are becoming more sophisticated and easier. Information and communication Technologies is the core of modern computing. Among its several applications, smart cities playa major role in the upliftment of the society's well-being. E-governance in the smart cities bridges the gap between the public and the government and hence plays a major role in the modern era. There arises some security concerned issues with the rapid growth of e-governance applications. To ensure its security, authentication must be more secure and must allow only the legitimate users to access the services. In this paper, a robust authentication scheme is proposed to overcome the drawbacks of the existing schemes. It utilises only lightweight XOR, one way and perceptual hash operations for authorisation. Also multiple factors such as User's Identity, Password and Biometrics are used for better scalability and security. The proposed scheme is validated with well known Automated Validation of Internet Security Protocols and Applications tool. The security analysis concludes that the scheme is resistant to various attacks.
Regularization approach for an immersion-based observer design

This paper is about immersion-based observer design for nonlinear systems. Starting from the fact that direct immersion may result in stability issues, which may in turn affect the observer performance, a method is proposed to overcome such problems, by somehow ‘stabilizing’ the transformation (or ‘regularizing’ it). A couple of examples are presented to illustrate the effectiveness of the method, including the challenging case of state and parameter estimation in a speed sensorless induction machine.
Improving Torque and Speed Estimation Accuracy by Conjoint Parameter Identification and Unscented Kalman Filter Design for Induction Machines

Any model-based control of induction machines (IM) is sensitive to incorrect model parameters and measurement errors when utilized in an open-loop torque or speed-sensorless drive. Thus, various publications are available which address the IM's parameter identification as well as the flux- and speed-observer design. Classically, this is handled as a separated two-step procedure. However, measurement uncertainty negatively affects the IM identification result and, consequently, the uncertainty regarding the IM's parameter is then adversely affecting the observer design. In contrast to that, this contribution is proposing a conjoint observer design and machine identification process by one global optimization. As the observer tuning and machine identification have the same objective, i.e. being able to control a given drive with highest speed and torque accuracy, there is no compelling reason to separate them from each other. Hence, more degrees of freedom are given to the optimization process. Whereas an unscented Kalman filter (UKF) is applied as a flux- and speed-observer, sensor noise is taken into account due its internal working principle. Experimental test bench evaluations prove the feasibility of the proposed method, however, the achievable estimation accuracy is worse compared to a similar approach using an extended Kalman filter instead of the UKF.
A Fusion Strategy for the Single Shot Text Detector

In this paper, we propose a new fusion strategy for scene text detection. The system is based on a single fully convolution network, which outputs the coordinates of text bounding boxes at multiple scales. We improve the performance of text detection by combining a fusion strategy. This strategy obtains precise text bounding boxes according to the confidence of candidate text boxes. It exhibits promising robustness and discriminative power by fusing text boxes. Experimental results on ICDAR2011 and ICDAR2013 datasets indicate the effectiveness and robustness of the proposed fusion strategy with an F-measure of 87%, which outperforms the base network 2%.
Hybrid-Based Facial Expression Recognition Approach for Human-Computer Interaction

Human-Computer Interaction represents an important component in each device designed to be used by humans. Moreover, improving interaction leads to a better user experience and effectiveness of the designed device. One of the most intuitive ways of interaction remains emotions since they allow to understand and even predict the human behavior and react to it. Nevertheless, emotion recognition still challenging since emotions might be complex and subtle. In this paper, we introduce a new hybrid-based approach to identify emotions through facial expressions. We combine two different feature types that are geometric-based (from facial fiducial points) and appearance-based (from Discrete Wavelet Transform coefficients). Each one provides specific information about the six basic emotions to identify. Furthermore, we propose to use a multi-class Support Vector Machine architecture for classification and Extremely Randomized Trees as feature selection technique. Carried experimentation attests to the effectiveness of our approach since it yields 96.11%, 91.79% and 99.05% with three benchmark facial expression datasets namely JAFFE, KDEF and RaFD.