Miracle-dz/UniFAR-FTUs huggingface dataset:
MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge

Existing systems to support the daily decisiontaking process carried out by health professionals need to be used independently to perform different text mining subtasks. In practice, there are few systems that unify all the subtasks into an unique framework, easing therefore the clinical work by automating complex clinical tasks such as the detection of clinical alerts as well as clinical information coding. In this sense, the MiNerDoc system is proposed, whose main objective is to support clinical decision-taking process by analysing tons of textual clinical reports in an unified framework. MiNerDoc performs two basic functions that are of great importance in the medical field: detection of risk factors based on the recognition of five medical entities (Disease, Pharmacologic, Region/Part Body, Procedure/Test, Finding/Sign), and automatic prediction of standardized diagnostic codes (MeSH descriptors). A major feature of MiNerDoc is it includes external knowledge sources such as MetaMap and UMLS to terminologically and semantically enrich the interpretation of clinical texts. Some study cases are considered in this work to demonstrate the power of MiNerDoc.
Enhanced Absorption of Solar Cells by Graphene-Based Nano-Antenna Array

A novel graphene based nano-antenna array is —1designed to increase the absorption of sunlight in silicon wafers of solar cells. The unit cell of array is composed of two coupled particles. Particles are graphene nano-bar and nano square ring placed at the middle of a silicon substrate with the thickness of 30nm. The former performs as electric and the latter as magnetic resonating particles at visible light frequency range. First, the two resonating particles are considered solely. It is shown that their absorption is about 40% at wavelength near 643 and 626nm respectively. Next they are considered together and the coupling is investigated. It is observed that plasmonic hybridization represents how graphene particles behave next to each other at visible region. An absorption of almost 80% of incident light is achieved by resonating particles. It is also shown that metallic nano-particles do not affect the electromagnetic characteristic.
A New Method for Retinal Vascular Segmentation based on the GMM Algorithm

The structure and morphology of retinal blood vessels contain many clinical information. Given the low contrast and uneven distribution of gray colors on the fundus image, achieving an accurate retinal vascular segmentation remains a challenge. To address this problem, the fundus image is initially enhanced via adaptive histogram equalization and bottom-hat transform. Afterward, the Gaussian mixture model is used to classify the enhanced images into three classes, namely, the background cluster, the vessel cluster, and the retinal disc cluster. Finally, denoising is performed. The experimental results reveal that the aforementioned approach can generate more accurate retinal vascular segmentation images compared with those obtained by other methods when applied on DRIVE dataset and STARE dataset.
Impact of SIR Estimation on Feedback Reduction During Heavy Crowd Events in 4G/5G Networks

Crowd events such as large-scale cultural entertainment events, urban festivals, sport events, in particular, football world cups, cause a high traffic load to the mobile networks, because a large number of users compete simultaneously to access the radio resources. During such heavy events, the uplink traffic is high and hence the network becomes uplink-limited, in which the Channel Quality Indicator (CQI) feedback received from the users to the Base Station (BS) consumes much of the uplink resources. In practical scenarios, CQI feedback strategies should be able to reduce the signalling overhead while satisfying certain performance bounds of Block Error Ratio (BLER), as an indicator of the connection quality. In this paper, we aim to reduce the feedback overhead in a multi-BS scenario. We cast the problem of Signal-to-Interference Ratio (SIR) estimation by exploiting the theory of Gaussian Process Regression (GPR), which takes the advantage of macroscopic shadow fading correlation properties. Our results demonstrate that with GPR and with the consideration of the interfering BS, an efficient reduction in feedback is achieved.
Signboard Detection and Text Recognition Using Artificial Neural Networks

A sub-field of Artificial Intelligence (AI) is machine learning (ML) which allows the computer to learn and adopt new rules. With the ML algorithm, the computer can identify samples of the observations, figures and other formats, which may describe the model or other structure. The machine observes various events from the world and presents things with/without explaining the pre-programmed rules. This paper presents the recognition of scene text from the outside environment focusing signboards. A framework for text detection and recognition of text from the natural environment is presented. Advance steps from paper-based text recognition; the text recognition from natural scenes are divided into several levels. Firstly, the image is captured from the outside environment with a smart device, followed by detection of the edges of a signboard. The next phase is the detection of text and the recognition of the text into two languages such as Urdu and English. Final phase uses Artificial Neural Network for the classification and recognition of the text extracted from the natural scenes or an outside environment. Experimental results have been generated on the developed image database created as the part of this research. The effort is a multilingual and produces output in Urdu and English. The system performs well as compared to traditional recognition systems and delivers an overall 85%accuracy in image results.
PodBot: A New Botnet Detection Method by Host and Network-Based Analysis

The growing use of smart phones equipped with a variety of sensors makes them an ideal place for criminal activities and attacks such as bots. Unfortunately, the existing methods have made detection either by using network traffic or just using static analysis. In this paper, a method with a tool called PODBot has been introduced. The detection is done based on both application features and network traffic analysis. PODBot was evaluated on a set of botnets from well-known types and it could accurately detect over 87% in high risk and 96% in very high risk. In addition, in qualitative comparisons of similar tasks, due to the combination of the detection methods, it has features that improve the method relative to the previous methods.
Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks

Diabetic retinopathy occurs when the blood vessels inside the retina are damaged as a result of diabetes. Early diagnosis and treatment of this disease is crucial to avoid blindness. Analysis of retinal images such as funduscopy, ultrasonography, and optical coherence tomography (OCT) is typically used in the diagnosis of diabetic retinopathy. In recent years, various automated techniques including deep learning have been used for this purpose. In this paper, we explore how to use deep transfer learning for the diagnosis of diabetic retinopathy using OCT images. We retrain existing deep learning models for this task and investigate how a retrained model can be optimized. We demonstrate that using an optimized pre-trained model as a feature extractor and training a conventional classifier on these features is an effective way to diagnose diabetic retinopathy using OCT images. We show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and training time.
A Weight-Based Data Pre-processing Mechanism for Improving the Accuracy of the Massive Traffic Flow Prediction

One of the major research domains of intelligent transportation systems (ITSs) is applying certain parameters, such as traffic flow volume and speed, to predict conditions on the freeway. However, the time series dataset cannot be directly applied to a machine learning algorithm, as the original time series dataset must first be transformed. The traditional transformation method is the sliding window, and a dataset transformed in this manner only generates the relation between continuous time periods. Therefore, we propose a weight-based data pre-processing mechanism for improving the Accuracy of the massive traffic flow prediction, in which the dataset is transformed by combining the historical and current continuous data. In this approach, the transformed dataset has a certain amount of weight between two kinds of data, with the nearest data having more weight than historical data. Using this method to improve traffic flow prediction accuracy, and applying three kinds of machine learning algorithms to validate the effects, the experimental results demonstrate that our method is superior to the traditional one.
Efficient Wide Range Telecom Wavelength Conversion in Hydrogenated-Amorphous Silicon-Polymer Waveguide

In this paper, we present a highly efficient second harmonic generation in near-infrared region, from λ = 1.55 μm to λ=0.775 μm, using hydrogenated-amorphous silicon slot waveguide coated with a nonlinear polymer. The necessary phase matching condition is fulfilled through quasi-phase matching by periodically poling the nonlinear polymer filled the slot region of the waveguide. A conversion efficiency of P2ωlPω = 12 % is predicted for a low pump power of 10 mW in a propagation distance of 2.4 mm. This result is comparable to the previously reported values but with a smaller device length and lower pump power.
Applying Private Information Retrieval to Lightweight Bitcoin Clients

Lightweight Bitcoin clients execute a Simple Payment Verification (SPV) protocol to verify the validity of transactions related to a particular user. Currently, lightweight clients use Bloom filters to significantly reduce the amount of bandwidth required to validate a particular transaction. This is despite the fact that research has shown that Bloom filters are insufficient at preserving the privacy of clients' queries. In this paper we describe our design of an SPV protocol that leverages Private Information Retrieval (PIR) to create fully private and performant queries. We show that our protocol has a low bandwidth and latency cost; properties that make our protocol a viable alternative for lightweight Bitcoin clients and other cryptocurrencies with a similar SPV model. In contract to Bloom filters, our PIR-based approach offers deterministic privacy to the user. Among our results, we show that in the worst case, clients who would like to verify 100 transactions occurring in the past week incurs a bandwidth cost of 33.54 MB with an associated latency of approximately 4.8 minutes, when using our protocol. The same query executed using the Bloom-filter-based SPV protocol incurs a bandwidth cost of 12.85 MB; this is a modest overhead considering the privacy guarantees it provides.
Deep Learning Face Hallucination via Attributes Transfer and Enhancement

Face hallucination technique aims to generate high-resolution (HR) face images from low-resolution (LR) inputs. Even though existing face hallucination methods have achieved great performance on the global region evaluation, most of them cannot reasonably restore local attributes, especially when ultra-resolving tiny LR face image (16×16 pixels) to its larger version (8×upscaling factor). In this paper, we propose a novel attribute-guided face transfer and enhancement network for face hallucination. Specifically, we first construct a face transfer network, which upsamples LR face images to HR feature maps, and then fuses facial attributes and the upsampled features to generate HR face images with rational attributes. Finally, a face enhancement network is developed based on generative adversarial network (GAN) to improve visual quality by exploiting a composite loss that combines image color, texture and content. Extensive experiments demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art.
A Low Phase Noise Class-C Oscillator Using an Improved Resonator

In this paper, a low phase noise class-C oscillator is presented. The phase noise reduction is achieved by using an improved resonator instead of conventional parallel LC resonator. Moreover, compared to the conventional class-C oscillators, the start-up problem is compromised and consequently, the oscillation amplitude is also increased. The proposed class-C oscillator is simulated in 0.18 μm CMOS technology. The simulated phase noise is −134.2 dBc/Hz at 1 MHz offset frequency. The oscillation frequency and the power consumption of the oscillator are 2.7 GHz and 26.8 mW, respectively.
Classification System Vocal Cords Disease Using Digital Image Processing

This paper presents the study of vocal cords disease classification using digital image processing. Stroboscopy and laryngoscopy are common tools used by doctors to observe the state of the vocal cords directly. The vocal cords disease can be characterized by characters change the shape of glottis contour on the vocal cords. There are six classifications of vocal cords, normal, paralysis, nodule, papilloma, cyst, and granuloma which present in this paper. Before the classification process, extraction image vocal cords to get the characteristics or information of objects in the image. In this study used feature descriptions with the Speeded Up Robust Features (SURF) algorithm and shape measurement to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get binary image this study used a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, Chan-Vese algorithm. The results of this study can optimize glottis contour extraction and results of the classification training process using K-Nearest Neighbor get an accuracy of 96.7%.
An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.
CO Gas Sensing Behavior of Free-standing ZnO Nanowires

In this work, a conductometric gas sensor based on free-standing Zinc oxide (ZnO) nanowires with on-top gold (Au) interdigitated electrodes was fabricated. ZnO nanowires were grown through hydrothermal approach. The fabricated sensor was tested for carbon monoxide (CO) gas sensing, which the results showed response value of 3.2 towards 100 ppm CO with reasonable recovery times at working temperature of 400 °C. Morphology and crystallography of the nanowires were investigated by scanning electron microscopy (SEM) and X-Ray Diffraction (XRD) measurements. According to the XRD and SEM results, ZnO nanowires were C-axis preferential with mean diameter of 100 nm and mean length of 1300 nm.
A Novel Locomotion Controller Based on Coordination Between Leg and Spine for a Quadruped Salamander-Like Robot

In this paper, a quadruped salamander-like robot with the capability of traversing complex terrain is developed, which presents not only leg joints used in traditional legged robots, but also spine joints, so as to increases its flexibility. To coordinate spine and leg joints to balance the robot's center of gravity, a novel control method is proposed, which consists of an inverse kinematics-based control, a bio-inspired mechanism, and a coordination law. Specifically, the inverse kinematics is utilized to calculate the control for the legs, and biological inspiration is employed for the control of the spine, while the coordination between the legs and the spine is ensured by the utilization of the static stability principle. Some typical experiments, including walking straight, turning, and avoiding obstacles, are performed for the developed quadruped salamander-like robot, with the collected results convincingly demonstrating the effectiveness of the proposed control method and the developed robot.
Effect of CO2 Concentration on Uptake and Assimilation of Inorganic Carbon in the Extreme Acidophile Acidithiobacillus ferrooxidans

This study was motivated by surprising gaps in the current knowledge of microbial inorganic carbon (Ci) uptake and assimilation at acidic pH values (pH < 3). Particularly striking is the limited understanding of the differences between Ci uptake mechanisms in acidic versus circumneutral environments where the Ci predominantly occurs either as a dissolved gas (CO2) or as bicarbonate (HCO3-), respectively. In order to gain initial traction on the problem, the relative abundance of transcripts encoding proteins involved in Ci uptake and assimilation was studied in the autotrophic, polyextreme acidophile Acidithiobacillus ferrooxidans whose optimum pH for growth is 2.5 using ferrous iron as an energy source, although they are able to grow at pH 5 when using sulfur as an energy source. The relative abundance of transcripts of five operons (cbb1-5) and one gene cluster (can-sulP) was monitored by RT-qPCR and, in selected cases, at the protein level by Western blotting, when cells were grown under different regimens of CO2 concentration in elemental sulfur. Of particular note was the absence of a classical bicarbonate uptake system in A. ferrooxidans. However, bioinformatic approaches predict that sulP, previously annotated as a sulfate transporter, is a novel type of bicarbonate transporter. A conceptual model of CO2 fixation was constructed from combined bioinformatic and experimental approaches that suggests strategies for providing ecological flexibility under changing concentrations of CO2 and provides a portal to elucidating Ci uptake and regulation in acidic conditions. The results could advance the understanding of industrial bioleaching processes to recover metals such as copper at acidic pH. In addition, they may also shed light on how chemolithoautotrophic acidophiles influence the nutrient and energy balance in naturally occurring low pH environments.
Process Mining of Logged Gaming Behavior

Video gaming takes a significant position in contemporary social life. It also constitutes as an elegant scientific model to study social behavior. By applying this model, behavioral patterns and their underlying physiological processes can be precisely observed at millisecond resolutions, e.g. with functional brain imaging. To date, a detailed behavioral analysis on an event-level was only performed in a few studies. Process mining (PM) is an efficient methodology to assess and systematize these complex behavioral patterns. We describe the application of PM to semi-naturalistic behavior during gameplay of a first-person shooter (FPS). Behavioral data were collected from 18 participants performing three 10-min gaming sessions of a customized FPS during functional brain imaging. An event log recorded during gameplay represented 10 types of game events in chronological order. In total 54 sessions were entered in the analysis serving as process indicators (cases). ProM software (version 6.8 TU Eindhoven, NL) provided data summary, heuristic and fuzzy PM. Event summaries revealed that being under attack early on prevented environmental exploration and resulted in shorter behavioral cycles, in contrast to other behaviors such as navigation and active attacking. Furthermore, the Fuzzy Miner generated a general overview of the player behavior by defining gaming pattern sequences. Upon further investigation, the Heuristic Miner revealed examples of how PM could highlight important patterns in player behavior. These patterns reflect the uninfluenced behavior ('free will') of the player and the player's tendency towards aggressive or defensive behavior. PM was used to analyze and systematize behavioral patterns in complex semi-naturalistic behavior during game play and showed capability to give an overview of this behavior and then further describe detailed information about them. This can allow for more in-depth investigations of interactive social behavior in the future. Furthermore, the capability of inspecting behavioral cycle characteristics (length, performance) in varied situations enables the use of PM as a testing tool for the preparation of game related paradigms in functional brain imaging. Finally, the description of such patterns and their alterations in psychiatric disorders may be applied to investigate therapeutic targets for social impairments.
A Feature Enginering Framework for Short-term Earthquake Prediction Based on AETA Data

In this work, a feature engineering framework is proposed for short-term earthquake prediction based on the Multi-Component Seismic Monitoring System (AETA)’ precursor data and historic seismic events. For the typical electromagnetic disturbance (ED) diurnal periodic waveform which synchronize with sun rise and sun set, an auto-recognition algorithm with three parts is designed and corresponding features are extracted. To extract non-liner feature, Higuchi Fractal Dimension algorithm is used for the ED data. The popular seismic precursor detection method-sliding interquartile range method is also used for the ED data feature extraction. Based on historic seismic events, a value and b value of Gutenberg-Richter Law are extracted using least square fitting method. Besides, the daily mean value of geo-sound time-series data is calculated as a feature. Based on the feature engineering framework, short-term earthquake prediction algorithm and model will be designed in the future work.
Encoding Complexity Control for Live Video Applications: An Interpretable Machine Learning Approach

In this paper, we propose an interpretable machine learning-based complexity control method for efficiently im-plementing HEVC on live video applications with different computing capacities and limited powers. Specifically, a complexity allocation method is designed to reasonably assign the complexity resources. Then, a multi-accuracy Coding Unit (CU) decision model is obtained by interpret-ably adjusting the parameters to efficiently and flexibly achieve a tradeoff between encoding complexity and rate distortion performance. Finally, a coding tree unit-level complexity control method is proposed to select appropri-ate accuracy of the CU decision model for making the en-coding complexity approach the target. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of accuracy and encoding efficiency.