Reviewerly/exHarmony huggingface dataset:
Applicability of Machine Learning Methods to Multi-label Medical Text Classification

Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction. In this work we investigate the applicability of several machine learning models and classifier chains (CC) to medical unstructured text classification. The experimental study was performed on a corpus of 11671 manually labeled Russian medical notes. The results showed that using CC strategy allows to improve classification performance. Ensemble of classifier chains based on linear SVC showed the best result: 0.924 micro F-measure, 0.872 micro precision and 0.927 micro recall.
Asynchronous Semantic Background Subtraction

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.
Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images

Convolutional neural network (CNN)-based denoisers are recently shown to overwhelm the denoising performances of the conventional prior- or optimization-based methods, particularly for the additive white Gaussian noise (AWGN) case. However, those typical CNN-based denoisers had two main limitations: (1) the superb denoising performances for AWGN do not necessarily carry over to the signal-dependent real-noise cases and (2) the clean source images are required as targets for supervised training. Both limitations are critical in practical real-noise denoising setting, since neither the noise characteristics nor the clean source images are readily available for supervised training. While some recent work proposed CNN denoisers that are trained solely with noisy images, they still fall short of the classical baselines, e.g., BM3D, for blind real-noise denoising settings, in which nothing is known about the noise and only noisy images are available for training. In this paper, we propose Blind Pixelwise Affine Image DEnoiser (BP-AIDE), which tackles above two limitations by combining variance stabilizing transformation (VST) technique with a recently developed unbiased estimate of the mean-squared error (MSE) for a pixelwise affine denoiser. As a result, we show BP-AIDE can be blindly trained solely with single real-noise corrupted images and, to the best of our knowledge, first outperforms both CNN-based and conventional denoisers for the same setting, on several real-world noisy image benchmark datasets.
Automatic ship detection in SAR Image based on Multi-scale Faster R-CNN

Abstract Automatic ship detection of synthetic aperture radar (SAR) images has been widely used in maritime surveillance. SAR images have the characteristics of all-weather, all-day detection. Therefore, many object detection methods ranging from traditional to deep learning techniques have been proposed. However, the objects in large-scale remote sensing images are relatively small, and objects are often appeared at different scales. What’s more, the current ship detection methods are insensitive to small-scale vessels. To solve these problems, a novel multi-scale ship detection method based on a Multi-scale Faster R-CNN network in SAR images is proposed in this paper. Firstly, a multi-scale network is used to decompose the SAR images into a pyramid structure and extract the features. Then, the region proposal network (RPN) is performed using the feature map for each layer to get the proposals that contains ship targets. Finally, these proposals are fed to the classification and regression network to obtain the final detection results. Multi-scale Faster R-CNN achieves the mean average precision(mAP) score 0.986 on the dataset of SAR-Ship-Dataset, which indicates that the proposed method has high detection accuracy and low missing rate.
Worldwide evaluation and correction of irradiance measurements from personal weather stations under all-sky conditions

Personal weather stations (PWS) are gaining popularity since they provide open meteorological data with high spatial resolution. However, a number of factors may affect the quality of the measurements of these stations. PWS provide irradiance measurements using silicon-photodiode sensors, which are a low-cost and lower-maintenance option compared to thermopile-type pyranometers. Silicon-photodiode sensors present, however, several limitations related to the spectral bandwidth, the device's temperature and other affecting factors, which increase the error in the measurements. This article evaluates the accuracy of irradiance measurements from PWS under all-sky conditions. We propose a calibration method to reduce the uncertainty in the measurements based on the solar zenith angle, the temperature of the device and the clear-sky index. The proposed calibration model is evaluated in 30 personal weather stations from different producers located in 5 climate zones worldwide over several years. An average reduction of the relative mean bias error from 18.4% to 2.8% is achieved based on 5-min instantaneous irradiance samples for clear-sky instances and it is within a ±5% tolerance for all-sky conditions, where other metrics are also improved 1–4%. This study helps understanding the nature of uncertainty of irradiance measurements in this increasingly used data source and provides a practical calibration method to increase accuracy of PWS data.
Zeroth-order finite similitude and scaling of complex geometries in biomechanical experimentation

Scaled experimentation provides an alternative approach to full-scale biomechanical (and biological) testing but is known to suffer from scale effects, where the underlying system behaviour changes with scale. This phenomenon is arguably the overriding principal obstacle to the many advantages that scaled experimentation provides. These include reduced costs, materials and time, along with the eschewal of ethical compliance concerns with the application of substitute artificial materials as opposed to the use of hazardous biological agents. This paper examines the role scale effects play in biomechanical experimentation involving strain measurement and introduces a formulation that overtly captures scale dependencies arising from geometrical change. The basic idea underpinning the new scaling approach is the concept of space scaling, where a biomechanical experiment is scaled by the metaphysical mechanism of space contraction. The scaling approach is verified and validated with finite-element (FE) models and actual physical-trial experimentation using digital image correlation software applied to synthetic composite bone. The experimental design aspect of the approach allows for the selection of three-dimensional printing materials for trial-space analysis in a complex pelvis geometry. This aspect takes advantage of recent advancements in additive manufacturing technologies with the objective of countering behavioural distorting scale effects. Analysis is carried out using a laser confocal microscope to compare the trial and physical space materials and subsequently measured using surface roughness parameters. FE models were constructed for the left hemipelvis and results show similar strain patterns (average percentage error less than 10%) for two of the three trial-space material combinations. A Bland–Altman statistical analysis shows a good agreement between the FE models and physical experimentation and a good agreement between the physical-trial experimentation, providing good supporting evidence of the applicability of the new scaling approach in a wider range of experiments.
Towards a Model to Improve Boolean Knowledge Mapping by Using Text Mining and Its Applications

The textual document set has become an important and rapidly growing information source especially for the health sector. Many efforts are made to cope with medical text explosion and to obtain useful knowledge from it, and also predict diseases and anticipate the cure. Text mining and natural language processing are fast-growing areas of research, with numerous applications in medical, pharmaceutical, and scientific avenues. Text knowledge management oversees the storage, capture, and sharing of knowledge encoded in hospital reports, primarily chronic disease records. The main objective of this article is to present the design of a model to improve Boolean knowledge mapping by knowledge extraction from medical reports dealing with epidemiological surveillance. This model that the authors have developed in this article is conducted in two major phases. The first is the preprocessing phase that produces an index of words which is the vector binary representation in order to generate the categorization model based on the Boolean modeling inspired by the Boolean knowledge management guided by data mining (BKMDM) method. In the second phase, with the data mining techniques, they exploit the vector binary representation to improve and refine the Boolean knowledge mapping of SEMEP. They examine experiment performance of the proposed model and compare it with other results such as tacit and explicit knowledge of SEMEP. Finally, knowledge mapping can be used for decision making by health specialists or can help in research topics for improving the health system.
Extending the RISC-V Instruction Set for Hardware Acceleration of the Post-Quantum Scheme LAC

The increasing effort in the development of quantum computers represents a high risk for communication systems due to their capability of breaking currently used public-key cryptography. LAC is a lattice-based public-key encryption scheme resistant to traditional and quantum attacks. It is characterized by small key sizes and low arithmetic complexity. Recent publications have shown practical post-quantum solutions through co-design techniques. However, for LAC only software implementations were explored. In this work, we propose an efficient, flexible and time-protected HW/SW co-design architecture for LAC. We present two contributions. First, we develop and integrate hardware accelerators for three LAC performance bottlenecks: the generation of polynomials, polynomial multiplication and error correction. The accelerators were designed to support all post-quantum security levels from 128 to 256-bits. Second, we develop tailored instruction set extensions for LAC on RISC-V and integrate the HW accelerators directly into a RISC-V core. The results show that our architecture for LAC with constant-time error correction improves the performance by a factor of 7.66 for LAC-128, 14.42 for LAC-192, and 13.36 for LAC-256, when compared to the unprotected reference implementation running on RISC-V. The increased performance comes at a cost of an increased resource consumption (32,617 LUTs, 11,019 registers, and two DSP slices).
Experimental Quantum Key Distribution Secure Against Malicious Devices

The fabrication of quantum key distribution (QKD) systems typically involves several parties, thus providing Eve with multiple opportunities to meddle with the devices. As a consequence, conventional hardware and/or software hacking attacks pose natural threats to the security of practical QKD. Fortunately, if the number of corrupted devices is limited, the security can be restored by using redundant apparatuses. Here, we report on the demonstration of a secure QKD setup with optical devices and classical postprocessing units possibly controlled by an eavesdropper. We implement a 1.25 GHz chip-based measurement-device-independent QKD system secure against malicious devices on both the measurement and the users' sides. The secret key rate reaches 137 bps over a 24 dB channel loss. Our setup, benefiting from a high clock rate, miniaturized transmitters, and a cost-effective structure, provides a promising solution for widespread applications requiring uncompromising communication security.
The evolution of information dissemination, communication media and technology in Malaysia

Abstract Information plays an integral part of human life. With the availability of data and information, people could learn and comprehend about beneficial substances in life. Knowledge and dissemination of information have flourished today as the development of technology has intensified how dissemination of information from traditional to modern means. With this increasingly evolving mass media, it has also inadvertently affected the distribution of information, expansively and flexible. From this study, we can observe the evolution of the dissemination of information that began with printed materials to digital materials. Massive user growth has been evident from the method of reading printed materials, to the use of radio, television, Internet and mobile phone. The degree of tendency of using these methods is also comparable in terms of reliability of the information gained and accessibility of the audience through the media. The interdependent use of the Internet and mobile phones has outperformed other media, especially in terms of information search, knowledge trustworthiness and audience accessibility.
Light weight convolutional models with spiking neural network based human action recognition

Though deep learning networks have proven ability to perform video analytics in complex environments, there is an increased attention towards the development of compact networks which would facilitate edge processing and the result of which have yielded high performance compressed deep learning net works such as, MobileNet, PWCNet and BindsNet. In the work proposed herein, a dual network configuration is used for human action recognition, wherein, the MobileNet captures the spatial appearance of the action sequences and the PWCNet is used to extract the motion vectors. A novel Spiking Neural Network (SNN) based configuration is used as the classifier and the SNN implementation is based on BindsNet. The proposed configuration is experimentally validated on challenging datasets, viz., HMDB51 and UCF101. The experimental results demonstrate that the proposed work is superior to the state-of-the-art techniques and comparable in few cases.
Variable metric evolution strategies by mutation matrix adaptation

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful evolutionary algorithms. CMA-ES incrementally learns the variable metric by evolving a full covariance matrix. Yet, it suffers from high computational overload. In this paper, we propose two efficient variants of CMA-ES, termed mutation matrix adaptation (MMA-ES) and exponential MMA-ES (xMMA-ES). These variants are derived by taking the first order approximation of the update of the covariance matrix in CMA-ES. Both variants avoid the computational costly matrix decomposition while keeping the simplicity of the update scheme of CMA-ES. We analyze the properties and connections of MMA-ES and xMMA-ES to other variants of evolution strategies. We have experimentally studied the proposed algorithms’ behaviors and performances. xMMA-ES and MMA-ES generally outperform or perform competitively to CMA-ES. We have investigated the performance of MMA-ES with the BiPop restart strategy on the BBOB benchmarks. The experimental results validate the performance of the proposed algorithms.
Authorship identification on limited samplings

The internet has changed the way that many people access written works. Books and articles, of various lengths, in several formats can be bought and accessed online, both legally and illegally. Texts in even shorter form are originating through forums, SMS, blogs, emails, and social media. Automating the process of determining the authorship of posted texts would help combat online piracy of copyrighted text and plagiarism. In addition, authorship identification could help detect fraudulent email messages from dangerous sources and combat cyberattacks by identifying authentic sources. We experiment with several machine learning algorithms on a limited set of public domain literature to identify the most efficient method of authorship identification using the least amount of samples. Different sized data sets are created by 5 predefined rounds of random sampling of 1500 word blocks on a total of 28 text books from a corpus of 7 authors. Traditional methods of authorship identification, such as Naive Bayes, Artificial Neural Network, and Support Vector Machine are implemented in addition to using a modern Deep Learning Neural Network for classification. Thirteen stylometric features are extracted ranging from character based, word based, and syntactic features. Our model consistently showed that Support Vector Machine out performs other classification methods.
Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers

The last decade observed a significant research effort directed towards maneuverability and safety of mobile robots such as smart wheelchairs. The conventional electric wheelchair can be equipped with motorized omnidirectional wheels and several sensors serving as inputs for the controller to achieve smooth, safe, and reliable maneuverability. This work uses the decentralized algorithm to control the motion of omnidirectional wheelchairs. In the body frame of the omnidirectional wheeled wheelchair there are three separated independent components of motion including rotational motion, horizontal motion, and vertical motion, which can be controlled separately. So, each component can have its different sub-controller with a minimum tracking error. The present work aims to enhance the mobility of wheelchair users by utilizing an application to control the motion of their attained/unattained smart wheelchairs, especially in narrow places and at hard detours such as 90˚ corners and U-turns, which improves the quality of life of disabled users by facilitating their wheelchairs’ maneuverability. Two approaches of artificial intelligent-based controllers (PD-Fuzzy-P and GA-PID controllers) are designed to optimally enhance the maneuverability of the system. MATLAB software is used to simulate the system and calculate the Mean Error (ME) and Mean Square Error (MSE) for various scenarios in both approaches, the results showed that the PD-Fuzzy-P controller has a faster convergence in trajectory tracking than the GA-PID controller. Therefore, the proposed system can find its application in many areas including transporting locomotor-based disabled individuals and geriatric people as well as automated guided vehicles.
Stability of internal states in recurrent neural networks trained on regular languages

We provide an empirical study of the stability of recurrent neural networks trained to recognize regular languages. When a small amount of noise is introduced into the activation function, the neurons in the recurrent layer tend to saturate in order to compensate the variability. In this saturated regime, analysis of the network activation shows a set of clusters that resemble discrete states in a finite state machine. We show that transitions between these states in response to input symbols are deterministic and stable. The networks display a stable behavior for arbitrarily long strings, and when random perturbations are applied to any of the states, they are able to recover and their evolution converges to the original clusters. This observation reinforces the interpretation of the networks as finite automata, with neurons or groups of neurons coding specific and meaningful input patterns.
Assimilating Disruptive Technology: A New Approach of Learning Science in Engineering Education

Technology, if embedded in education efficiently, can do miracles. It can motivate the learners to participate actively at learning platforms and make the learning process more effective. Earlier research has recognized the problem that learning would merely be a passive process if the use of technology does not encourage critical thinking, conception or meta cognition. Since its inception augmented reality(AR) has exhibited a great potential in improving the learning to have active engagement, increased interaction and critical intervention by the students. This is so because this advanced technology provides the learner a natural experience while interacting the real and the virtual environments seamlessly. Merging AR in education, therefore, has caught the researchers’ attention as it provides the learners an immersive and realistic experience. This research work investigates the effect of Augmented Reality(AR) in electronics, electrical and science education on university level students. This paper aims to elaborate the understanding of ohm’s law using AR technology. A marker based AR application namely AROhm has been developed which can make the students learn in a Hands-on augmented way to provide them a noval and concrete learning experience. 20 participants(students) were chosen from first year engineering and the subject chosen is BEEE(Basics of electrical & electronics Engineering). Two groups namely treatment and control group were made and data collection has been done to obtain qualitative as well as quantitative results of the application. AROhm has been proved cost effective and simply requires a laptop or an ordinary desktop. T- test is used for measuring the effects of AR technology over traditional learning. Written feedback has been taken from both the participant groups. Pros and Cons of the application as evolved from the students as well as teachers were discussed.
An improved salp optimization algorithm inspired by quantum computing

Abstract Salp Swarm Algorithm (SSA) is a novel optimization algorithm which is widely used in engineering problems. An improved SSA inspired by quantum computing is proposed in this paper. The principles of quantum computing, such as qubits and quantum states, are introduced into the original SSA in order to overcome the defect of trapping into local optimum easily. Instead of updating the salp position directly, the quantum angle related to the quantum state is updated to increase the diversity of states. Two multidimensional benchmark functions are used to verify the proposed improved SSA, the result shows that the introduction of quantum computing can successfully prevent the SSA from falling into the local optimum and increase the accuracy.