Reviewerly/exHarmony huggingface dataset:
Multi-Label Classification of ICD-10 Coding &amp; Clinical Notes Using MIMIC &amp; CodiEsp

ICD-10, is the 10th revision of the International Classification of Diseases (ICD) coding standard used to get proper treatment and charged accordingly for any medical service. Clinical notes for patients’ assessment and treatment are captured by the physicians in free-form texts. Abbreviations and/or misspellings of words in this free-form text creates ambiguity and leads to the complexity and errors within the medical billing and coding process. In our study, multi-label classification experiments are conducted using preprocessing and deep learning techniques against two known corpora: the MIMIC-III (Medical Information Mart for Intensive Care) and CodiEsp. Clinical notes use abbreviation normalization and misspelled words are corrected. Logical hierarchy of lower-level ICD codes (labels) are converted to their ICD Chapters (first-level hierarchy). Our study performs several experiments with our preprocessed training datasets against several deep learning models. Our results showed that the deep learning attention mechanism is effective in enhancing ICD-10 predictions and the HAN GRU yields the best in F1 measures and 10-fold cross validation.
Qubit Transmission Techniques

Quantum technology has advanced and has found its application in various fields. Research efforts are been made in realization of quantum networks which would enable transmission of quantum information in terms of qubits. Qubit transmission has unique challenges due to quantum properties. This study proposes to use CNOT gates for qubit transmission, and also based on the discussion and experiments conducted in the study a comparison between quantum teleportation, quantum entanglement swapping and CNOT gates is made for qubit transmission in a quantum network.
GiGAN: Gate in GAN, could gate mechanism filter the features in image-to-image translation?

Image-to-image translation techniques have been used in many different fields and have obtained remarkable performance in recent years. However, in many image-to-image translation tasks, only certain parts of the image need to be converted instead of the whole image. Traditional GAN-based methods often reconstruct the entire image, which may lead to artifacts and low-quality results. To address this issue, we propose a novel model, named GiGAN: Gate in GAN, which utilizes special Residual Blocks embedded with Gate Cells to filter and extract the features for facial attribute transfer and facial expression synthesis tasks. Specifically, we treat the intermediate feature from source domain to target domain as a sequence, and introduce the gate mechanism into this sequential task. To achieve this, we introduce the convolutional layers into gate cell and modify the stream in traditional gate cell to suit for image-to-image translation task. We designed two types of methods based on reusing parameters in the residual blocks or not, namely GiGAN-reuse and GiGAN-non-reuse. Experimental results and quantitative evaluations show that our model has superior performance against state-of-the-arts. And ablation studies demonstrate the effectiveness of our method. Furthermore, visualization of the features in Gate Cells shows that Gate Mechanism can filter the features in image-to-image translation effectively.
ELICITING CHILDREN'S EXPECTATIONS FOR HAND PROSTHESES THROUGH GENERATIVE DESIGN TOOLS

Abstract When designing assistive devices for children with disabilities, designers mostly consider technical and functional aspects and overlook factors that affect their usage from children’s perspective. Therefore, in most cases, assistive devices do not fully meet the needs of the children and may create a negative effect on children’s well-being. To explore the opinion’s of children using 3D printed hand prosthetics in Country-Xx, individual generative sessions were conducted with four children with limb deficiency. Generative tools are used to engage children and encourage them to express themselves in relation to prosthesis use. The main aim was to investigate how children provide design relevant information that may help designers to achieve improved assistive devices that support children’s physical, emotional and social wellbeing. Children’s feedback related to prosthetic usage categorized under two topics; expectations of children for prosthesis use and children’s priority expectation for the prosthesis. The findings are argued to be useful and usable by NGOs, product designers and design researchers who work with children with disabilities.
An Algorithm for Ranking Hospitals based on Intuitionistic Fuzzy Sets and Sentiment Analysis

Understanding opinions about products offered by big online providers, for instance, TripAdvisor, is relatively easy because the features assessed by the user about hotels are well-known (food, room, desk, sleep quality, etc.). Nonetheless, in the health domain, many times the user provides free-text reviews which are not clearly focused on a few specific features. The present study proposes a methodology for recommending hospitals according to textual opinions which describe the quality of the offered services. First, it detects hospital aspects, which represent the hospital services, by a Latent Dirichlet Allocation-based approach following the criteria of a quality model called SERVQUAL. The polarity of those aspects is computed and modelled by intuitionistic fuzzy sets. Depending on the user preferences or his/her attitude, the aspects are aggregated to finally rank the alternative hospitals following a Multicriteria Decision Making algorithm (PROMETHEE II). The methodology has been tested using a large collection of free-text reviews on hospitals, which contain information about their offered services, obtaining interesting results.
Condition-Based Monitoring and Anomaly Detection of Industrial Equipment using Autoencoder

Real-time Condition-based Monitoring (CbM) of wire manufacturing equipment of a partner facility involves the manual process of listening to the sound pressure of the equipment by the personnel assigned to it. This is to prevent further damage and to mitigate costs by monitoring the earliest signs of defects in the form of anomalous sound. We augmented the facility’s CbM system by deploying an acoustic recorder and by building an autoencoder that is trained using the normal sound pressure of the wire extruding machine. This paper discusses a process for sound pressure acquisition, data pre-processing and preparation, feature extraction, anomaly detection, model evaluation, and case studies of downtime incidents. The objective of this paper is to automate the monitoring of the condition of the equipment and to find possible symptoms of unhealthy sound pressure prior to the reported downtimes. A comparative analysis of density score and reconstruction error, our chosen anomaly detection techniques, is presented in this paper.
Twenty years of Frame Interpolation for Retiming in the Movies

Frame interpolation is the process of synthesising a new frame in-between existing frames in an image sequence. It has emerged as a key algorithmic module in motion picture effects since its use at a large scale in the making of the movie “The Matrix”. This paper presents a review and a new unified view of the classical algorithms used to create inbetween frames, representing most of the last 20 years of their evolution. This is used to benchmark the recent Deep Learning algorithms against two of the best industrial retimers available. A significantly expanded dataset of 140,000 frames is used for testing. In the context of high resolution material we find that techniques relying principally on DNNs do not clearly outperform the classical ideas. It is only with the emergence of hybrid approaches since 2019 that we see DNNs adding significantly to the performance in this space. Despite the hype surrounding DNNs, we find that there is still something left to do.
StyleTune: Interactive Style Transfer Enhancement on Mobile Devices

We present StyleTune, a mobile app for interactive style transfer enhancement that enables global and spatial control over stroke elements and can generate high fidelity outputs. The app uses adjustable neural style transfer (NST) networks to enable art-direction of stroke size and orientation in the output image. The implemented approach enables continuous and seamless edits through a unified stroke-size representation in the feature space of the style transfer network. StyleTune introduces a three-stage user interface, that enables users to first explore global stroke parametrizations for a chosen NST. They can then interactively locally retouch the stroke size and orientation using brush metaphors. Finally, high resolution outputs of 20 Megapixels and more can be obtained using a patch-based upsampling and local detail transfer approach, that transfers small-scale details such as paint-bristles and canvas structure. The app uses Apple’s CoreML and Metal APIs for efficient on-device processing.
Audio-based Data Hiding using All-in Modulo of Difference

The development of technology has provided incredible achievements in the information and communication environment. Nevertheless, it also triggers cybercrime, disrupting the security and convenience of sharing information over the internet. Moreover, some activities require a highly secure process to transfer data, restricting public users from accessing it. In this research, a new method of audio-based data hiding is extended. This method uses the modulus function and the difference in sample values as a means of inserting a secret message. In more detail, the evaluation results show that the payload capacity can reach nearly 4 bits per audio sample. In addition, the PSNR value can reach 116.54 dB.
Cooperative Task Assignment of Multi-UAV in Road-network Reconnaissance Using Customized Genetic Algorithm

Cooperative reconnaissance is an important task scenario of multi-UAV cooperative mission. The complex environments require UAVs to move along the road-network and complete reconnaissance of the road in mission scenarios such as urban warfare, precision agriculture, logistics and transportation. In this paper, considering the road-network constraints during the task execution, the task assignment problem of multi-UAV cooperative road-network reconnaissance is defined, and the model of multi-UAV road-network reconnaissance traveling salesman problem (MRRTSP) is established. Furthermore, a customized genetic algorithm for road-network reconnaissance (CGA-RR) is used to solve the task assignment problem. Simulation results show the effectiveness of the algorithm, which can achieve the optimal multi-UAV task assignment.
Large-scale visual search and similarity for e-commerce

Visual search and similarity can aid an e-commerce platform by providing appropriate recommendations where semantic labeling and associated metadata does not always exist. In this work, we detail the specifics of our system that powers visually similar recommendations. While a common approach leverages learned representation from common classification tasks using DNN's, the crux of the problem are the labels and ontology that is applied in order for the DNN to functionally perform. Our proposed approach in production for a variety of products is to supply these recommendations based on a defined taxonomy through a hierarchy that has been carefully curated while additionally scaled up through our platform's natural crowd-sourcing interface. To scale the use of these taxonomies in production, we quantization schemes for retrieving approximate nearest neighbors after applying base transformations on the images using Apache Beam and Tensorflow Transform. The nearest neighbor retrievals are based on using a ResNet model architecture, trained from scratch on 3000+ classes. These are trained daily in a distributed fashion and optimizing data throughput. Finally, in order to verify the appropriateness, we use an extensive human evaluation pipeline and quality control. In this work, we share our product design learnings from the various attempts/experiments we conducted for a successful launch.
Path classification by stochastic linear recurrent neural networks

Abstract We investigate the functioning of a classifying biological neural network from the perspective of statistical learning theory, modelled, in a simplified setting, as a continuous-time stochastic recurrent neural network (RNN) with the identity activation function. In the purely stochastic (robust) regime, we give a generalisation error bound that holds with high probability, thus showing that the empirical risk minimiser is the best-in-class hypothesis. We show that RNNs retain a partial signature of the paths they are fed as the unique information exploited for training and classification tasks. We argue that these RNNs are easy to train and robust and support these observations with numerical experiments on both synthetic and real data. We also show a trade-off phenomenon between accuracy and robustness.
Digital Transformation in the Energy Industry

In recent years, digital transformation has begun to significantly affect the business landscape, disrupting existing business models. In the midst of this revolution, oil and gas retail companies are experiencing a strong transformation of the whole industry, which is also driving the transformation of their processes, assets, and people. In this chapter, the authors explore how oil and gas retail companies are trying to redefine their business models by providing end-users with a wide range of smart and connected solutions. The main purpose of this work is to analyse, in the face of the digital transformation era, the potential that new technologies can unleash in mature and commoditized industries such as the one of oil and gas retail. In particular, the present work focuses on the digital transformation strategy of fuel retail companies based on the improvement of customer experience.
Generating Audio Adversarial Examples with Ensemble Substituted Models

The rapid development of machine learning technology has prompted the applications of Automatic Speech Recognition(ASR). However, studies have shown that the state-of-the-art ASR technologies are still vulnerable to various attacks, which undermines the stability of ASR destructively. In general, most of the existing attack techniques for the ASR model are based on white box scenarios, where the adversary uses adversarial samples to generate a substituted model corresponding to the target model. On the contrary, there are fewer attack schemes in the black-box scenario. Moreover, no scheme considers the problem of how to construct the architecture of the substituted models. In this paper, we point out that constructing a good substituted model architecture is crucial to the effectiveness of the attack, as it helps to generate a more sophisticated set of adversarial examples. We evaluate the performance of different substituted models by comprehensive experiments, and find that ensemble substituted models can achieve the optimal attack effect. The experiment shows that our approach performs attack over 80% success rate (2% improvement compared to the latest work) meanwhile maintaining the authenticity of the original sample well.
Analysis of the Global Solar Radiation and Solar Energy in Ruse, Bulgaria

In the present study is performed a harmonic analysis of the global solar radiation for the region of Ruse, Bulgaria. The study is based on radiation data obtained from a meteorological station in the campus of the University of Ruse. Initially, basic statistical analysis was performed. Thereafter, the constant term and the first three harmonics were obtained for the average daily solar radiation in each month of the year. The obtained results showed that for the region of Ruse the average solar radiation could well be approximated with only its constant term and first component. The obtained results could be used as input data in PV and solar simulation software.
Data-Driven Technical Debt Management: Software Engineering or Data Science Challenge?

In this article, we summarize experience with data-driven technical debt management that we gained through several industry research projects. We report challenges and their consequences, propose solutions, and sketch improvement directions.
An Acquaintance to Text-Steganography and its Methods

Abstract In this research paper discussion will be going on upon text steganography. As the word ‘steganography’ is very well known amongst human beings, considering that, this research paper will highlight the term ‘Text Steganography’. Defining steganography, that an individual can hide data into/within or behind text, images, audio, and video would not be inappropriate. Digital steganography is a modern approach and necessity of digital security system. Digital Text steganography is a distinct factor of digital steganography. Various types of Text steganography will have been discussed further.
Information Leakage Metrics for Adversaries with Incomplete Information: Binary Privacy Mechanism

Maximal leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data, or some arbitrary function of the private data, when presented with the legitimate user’s publicly disclosed information. However, this definition of maximal leakage implicitly assumes that the privacy mechanism, as well as the prior probability of the original data, are entirely known to the attacker. In reality, this assumption is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which she can access the corresponding un-distorted data. In this scenario, maximal leakage no longer has an operational meaning. To address this problem, in this paper, we propose two novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism – we call them maximal subjective leakage and maximal objective leakage, respectively. For a simple, binary scenario, we demonstrate how to find the optimal privacy mechanism that minimizes the system’s information leakages.
Extended set of superpixel features

Superpixel-based image processing and analysis methods usually use a small set of superpixel features. Expanding the description of superpixels can improve the quality of processing algorithms. In the paper, a set of 25 basic superpixel features of shape, intensity, geometry, and location is proposed. The features meet the requirements of low computational complexity in the process of image superpixel segmentation and sufficiency for solving a wide class of application tasks. Applying the set, we present a modification of the well-known approach to the superpixel generation. It consists of fast primary superpixel segmentation of the image with a strict homogeneity predicate, which provides superpixels preserving the intensity information of the original image with high accuracy, and the subsequent enlargement of the superpixels with softer homogeneity predicates. The experiments show that the approach can significantly reduce the number of image elements, which helps to reduce the complexity of processing algorithms, meanwhile the expanded superpixels more accurately correspond to the image objects.
Differentially Private Web Browsing Trajectory over Infinite Streams

Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>w</mi> <mo>,</mo> <mi>n</mi> </mrow> </mfenced> </math> -differential privacy to protect any spatial-temporal sequence occurring in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>w</mi> </math> successive timestamps and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>n</mi> </math> -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.
Security on mobile cloud computing using cipher text policy and attribute based encryption scheme

Mobile Cloud Computing (MCC) is a new paradigm that has been emerged by the advances in the Cloud Computing for Mobile devices to access Cloud services. The data security challenges against the data thefting, deleting, corrupting, or exploiting are existed as the data storage and access to/from the cloud has been most popular. In order to handle these data security issues in the cloud and to provide protection for the data, some innovative encryption methods have been developed. One of such encryption methods is Attribute-Based Encryption (ABE) that has a more control on the data in cloud as which is accessed by whom. Since from the recent years, many of academic researchers have paying a widespread of attention on using advanced cryptographic techniques to securely store, process, also share data over the untrusted cloud environment. One of the promising cryptographic techniques which can solve the open challenge of regulating the fine-grained access control of important data over the distributed cloud is the Cipher text Policy – Attribute Based Encryption (CP-ABE). Therefore, a survey of work is presented in this paper on different CP-ABE methods for the secure mobile cloud computing architecture. This paper presents some works that are focused on secure data storing, accessing and sharing using CP-ABE methods over the untrusted cloud environments. All the works that are studied are analyzed and compared with each other in the results to find the best out of it.
Algorithm of stitching and fusion image in PCB analysis

The article proposes an approach that allows merging a series of images obtained using an electron microscope and fusion data with a camera that records data in the far-infrared spectrum (thermal images). The proposed approach is implemented based on algorithms for stitching images obtained in the visible spectrum. Based on the data on the calibration parameters of the pair cameras, the images obtained by the thermal imaging camera are stitched. The frame overlap is between 30% and 50%. The data received by the thermal imaging camera is noisy and requires primary data processing. To filter thermal imaging images, a multi-criteria method is used in work. The parameters of the method are analyzed in parallel on both pairs of the image. They are used for the subsequent identification of the search boundaries of objects and interframe communication points. For data obtained in the visible spectrum, a simplification algorithm is applied to increase data analysis speed. As test data, we used a combination of images obtained by an electron microscope (a maximum approximation is 300x, the color depth is 8bit, the resolution is 1024x768) and a thermal imaging camera (image resolution is 320x240 pixels).