Reviewerly/exHarmony huggingface dataset:
Leveraging Deep Learning Approaches for Patient Case Similarity Evaluation

One of the fundamental problems in Health Informatics is evaluating the clinical similarity between two patients for treatment recommendation. Retrieving clinical records of existing patients who are potentially similar to a newly arrived patient could help a physician in faster diagnosis and recommending informed treatment options, especially in the case of areas where specialist medical care is scarce. In Western countries, patient records are extensively stored in the form of Electronic Health Records (EHR), which are created manually by human experts, which can take a lot of time and is a cost-intensive process. In developing countries like India, patient records are increasingly being stored in digital formats and often contain diverse, heterogeneous, unstructured reports of patients. These can be potentially utilized for designing patient similarity assessment and recommendation systems. In this paper, we propose a patient similarity evaluation framework built on two supervised learning models—Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU). Our method creates an optimal patient representation for existing patients by aggregating reports collected over the duration of treatment, to overcome the loss of temporal information, for which a cohort of 16,723 patients across 8 disease categories was used. Both the models (CNN and GRU) learn by passing through the records of a patient chronologically and achieve an accuracy of 97.60 and 93.62%, respectively, on standard EHR dataset like MIMIC-III.
Correlation-Guided Network for Fine-Grained Classification of Glomerular lesions in Kidney Histopathology Images

Chronic Kidney Disease has become a worldwide public health problem which demands careful assessments by pathologists. In this paper, we propose a novel architecture for fine-grained classification of glomerular lesions in renal pathology images sampling from patients with IgA nephropathy. The adversarial correlation loss is innovatively presented to guide a parallel convolutional neural network. In this well- designed loss function, bias between the prediction and the label was take into account while the relationship among different categories is well-aligned. Glomerular lesions in this study are divided into five subcategories, Neg (Negative samples such as tubule and artery), SS (sclerosis involving a portion of the glomerular tuft), GS (sclerosis involving 100% of the tuft), C (build-up of more than two layers of cells within Bowman's space, often with fibrin and collagen deposition) and NOA (none of above). Our model with 93.0% accuracy and 92.9% Fl-score for these five categories has proved superior to other models through experimental results.
Improving Quality of Friendly Progressive Visual Secret Sharing using Essential Shares

Friendly Progressive Visual Secret Sharing (FPVSS) shares the secret image using meaningful shares to n users such that $k(k \leq n)$ or more than k users can reconstruct the secret image progressively without computations. Existing FPVSS have the problems such as residual trace of cover image, poor quality of meaningful shares and reconstructed image. Hence this paper implements FPVSS using essential shares to overcome the problems of existing work. In addition to that, the proposed scheme can also be used in hierarchy secret sharing. The simulation result shows the effectiveness of proposed scheme.
A Simple Two-Dimensional Location Embedding for Passive Infrared Motion-Sensing based Home Monitoring Applications

Pervasive computing based home-monitoring has attracted increasing interest over the past years, especially regarding applications in the growing population of older adults. Applications include safety, monitoring chronic conditions like dementia, or providing preventive information about changes in health and behavior. Commonly used components of such systems are inexpensive and low-power passive infrared motion sensing units, usually placed in distinct locations of an older adult's apartment. To efficiently analyse the resulting data the majority of procedures expect the resulting sensor data to be encoded in a vector space. However, most common vector space encodings are based on orthogonal representations of the sensor locations and thus lead to loss of information as the sensors are placed in a 3D-space. In this work we introduce an embedding of sensor-locations in a 2D-space based on multidimensional scaling, without knowledge of the physical position of the sensors. We evaluate this embedding, using two different algorithms and compare it to commonly used baselines in different tasks. All evaluations are carried out on a real-world home-monitoring data-set.
CloudMeter: A Tool to Evaluate and Select the Desired VMs

Nowadays, as more and more developers choose to deploy their work on the cloud, diverse workloads are being run in the cloud. Recognizing the diversity, cloud providers offer a rich selection of VMs with a large configuration space. The choice of cloud service can have a big impact on performance and cost. However, there are few guidelines given by the cloud providers. Choosing the right cloud service is difficult for users only with configuration information. Besides, there lacks a comparison between different cloud providers. To aid in the selection of VMs, we present CloudMeter, a tool to continuously run benchmarks on VMs of different cloud providers and grade each VM. With the grade, we recommend appropriate cloud service according to the requirement of cloud users. We also use CloudMeter to draw significant insights into cloud services among different cloud providers.
Grammatical Parallelism of Russian Prepositional Localization and Temporal Constructions

In this paper we present a part of corpus-driven semantico-grammatical ontological description of Russian prepositional constructions. The main problem of a prepositional ontology is its inner controversy because the ontological structure presupposes logical analysis of concepts, however, prepositions are usually interpreted as non-lexical grammatical language elements. In our understanding, this is an ontology of lexico-grammatical relations that are implemented in prepositional constructions. We demonstrate the ontological structure for semantic rubrics of temporal and locative syntaxemes extracted through the elaborated technique for processing corpus statistics of prepositional constructions in modern Russian texts. Common and contrastive traits between this two topmost semantic domains are shown.
Handwritten Hindi Word Generation to enable Few Instance Learning of Hindi Documents

Handwritten Text Recognition (HTR) of Hindi Documents is a challenging research problem of interest which could enable digitization of millions of official documents. Due to challenges in character segmentation, Segmentation-free Word Recognition is the preferred approach. Lack of a large, diverse Hindi Handwritten Word dataset for pre-training deep learning architectures is a pressing issue. In this paper, we propose a novel way of generating diverse Handwritten Hindi Word images using only Handwritten Hindi Characters and further analyze its effectiveness in enabling Few Instance Learning of Handwritten Hindi Documents.
Proposal of Multimodal Program optimization Benchmark and Its Application to Multimodal Genetic Programming

Multimodal program optimizations (MMPOs) have been studied in recent years. MMPOs aims at obtaining multiple optimal programs with different structures simultaneously. This paper proposes novel MMPO benchmark problems to evaluate the performance of the multimodal program search algorithms. In particular, we propose five MMPOs, which have different characteristics, the similarity between optimal programs, the complexity of optimal programs, and the number of local optimal programs. We apply multimodal genetic programming (MMGP) proposed in our previous work to the proposed MMPOs to verify their difficulty and effectiveness, and evaluate the performance of MMGP. The experimental results reveal that the proposed MMPOs are difficult and complex to obtain the global and local optimal programs simultaneously as compared to the conventional benchmark. In addition, the experimental results clarify mechanisms to improve the performance of MMGP.
Single Image Reflection Removal for Privacy Protection using Deep CNN

It is a problem that the interior of the room and the photographer are reflected by windows and glass at taking photographs. In order to take good images of the outdoors from indoors through glass, it is necessary to take measures to prevent these reflections. However, if the already captured image contains reflections, the method is needed to remove the reflections from the image. We believe that the most important mission of Reflection Removal is to protect privacy, and we will train CNN specializing in human image reflection removal and verify their performance. We improve human removal performance by learning a network using a database that always contains humans in the reflection layer.
Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases

Most existing data mining algorithms focused on mining the information from the static database. In this paper, the principle of pre-large is used to update the newly discovered HAUIs and reduce the time of the rescanning process. To further improve the performance of the suggested algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIs. The experimental results show the proposed algorithm has excellent performance and good potential to be applied in real applications.
Text Sentiment Transfer Methods by Using Sentence Keywords

Text sentiment transfer models modify sentence sentiments while retaining their semantic content. The main challenge is to separate sentiment-independent content information from the semantic information of the sentence. The previous works usually expect to utilize the model encoder to infer the sentence-level representation that removed sentiment information. However, the strength of the models’ abilities to reconstruct text is difficult to control which resulting encoder infer sentence-level sentiment-independent content embedding failed. In this paper, we address this challenge by using word-level representation. We first use the POS-Tagging technique to tag the part of speech of word sequence, then extracting content keywords by three schemes and use them as input, rather than the entire sentence, to obtain a purer word-level sentiment-independent content representation. In this way, the model does not require to infer the sentiment-independent representation, which avoids the instability of the adversarial training process. Experiments show that our method achieves the state-of-the-art performance and is also effective in long text sentiment transfer tasks.
Development of Hybrid Neural Networks

The main complexity of using neural networks (NN) is the “dimension curse”. In the case of increasing the size of the inputs and the number of layers, the complexity of the network and, accordingly, the learning time increases exponentially, the result is far from optimal.
A CNN Hardware Accelerator in FPGA for Stacked Hourglass Network

Staked hourglass network is a widely used deep neural network model for body pose estimation. The essence of this model can be roughly considered as a combination of Deep Convolutional Neural Networks (DCNNs) and cross-layer feature map fusion operations. FPGA gains its advantages in accelerating such a model because of the customizable data parallelism and high on-chip memory bandwidth. However, different with accelerating a bare DCNN model, stacked hourglass networks introduce implementation difficulty by presenting massive feature map fusion in a first-in-last-out manner. This feature introduces a larger challenge to the memory bandwidth utilization and control logic complexity on top of the already complicated DCNN data flow design. In this work, an FPGA accelerator is proposed as a pioneering effort on accelerating the stacked hourglass model. To achieve this goal, we propose an address mapping method to handle the upsample convolutional layers and a network mapper for scheduling the feature map fusion. A 125 MHz fully working demo on Xilinx XC7Z045 FPGA achieves a performance of 8.434 GOP/s with a power efficiency of 4.924 GOP/s/W. Our system is 296× higher than the compared Arm Cortex-A9 CPU and 3.2× higher power efficiency, measured by GOP/s/W, than the GPU implementation on Nvidia 1080Ti.
A Command and Control System for Air Defense Forces with Augmented Reality and Multimodal Interaction

Abstract This work proposes a framework for the command and control system of air defense forces based on augmented reality technology. The system uses Microsoft HoloLens hardware as a carrier to realize the holographic display of battlefield situation information. The system can help the commander to perceive and control the situation on the battlefield in an efficient manner, in order to reduce the decision-making time of the commander. Commanders can efficiently interact with the system through gestures and voice, deploy simulated military units in the system through gestures, and send commands to weapon systems through the system. At the same time, the system also supports multi-person real-time collaborative operation, which enables the joint decision-making and sharing of commanders. This system is of great significance to improve operational efficiency and reliability, and at the same time provides an open platform for operational process planning, deduction, simulation, and verification evaluation.
Control of a Mobile Robot Formation Using Artificial Potential Functions

This chapter presents three control algorithms for a group of differentially-driven mobile robots: based on linearization, persistent excitation and vector field orientation method. All the presented algorithms have been extended by the authors with a collision avoidance mechanism based on the artificial potentials functions. For each of them, stability analysis was conducted using Lyapunov method. Both stability analysis and numerical verification of the effectiveness of the control methods were carried out in two stages: first, the case of a single robot moving in the environment with static circle-shaped obstacles was considered, then the same algorithm was extended to multi-robot version. The robots were executing synchronous motion while avoiding inter-agent collisions.
Is the use of mobile phones good for your neck? Text neck syndrome as an awareness of the existing threat – literature review

Introduction.SMS neck syndrome is a problem that threatens the physical and mental health of modern mobile phone users.It is difficult for today's population to function without mobile devices, therefore the problem of using mobile phones concerns many aspects of a social, health and psychological nature.People who use phones excessively potentially increase the risk of developing musculoskeletal problems.Using smartphones contributes to incorrect body posture, which in turn causes neck and back pain.Objective.The aim of the study based on a literature review was to present the threats arising from the use of mobile phones, and to identify the main health problems of mobile device users.Brief description of the state of knowledge.The literature review included scientific literature published in the period between 2011-2019.The resources of three bibliographic databases were searched: Medline (Medical Literature Analysis and Retrieval System Online), Web of Science, and PubMed.On analyzing the literature in the field of psychology, pedagogy and sociology, it can be seen that in Poland there is a lack of intercultural screening tools, which would be quick and easy method to identify groups of young people at risk by using a mobile phone, who by engaging in this activity experience various problems of social, health or mental nature.Conclusions.This literature review confirms the need for public health education programmes to inform people about the physical dangers of excessive smartphone use.Smartphone addiction can significantly impair the function and cause pain of the cervical spine.
A Principal Components Analysis of Grid Differentiation Measures

Many different cognitive differentiation measures have been developed since the foundation of Personal Construct Psychology in 1955. A relatively small number of studies have explored the component...
Development and Research of a Terminal Controller for Marine Robots

Marine robots (unmanned surface vehicles) are actively used for various civil applications. One of the interesting tasks – automatic transportation of goods and people along rivers, seas and oceans – is being solved now. Control of the ship’s movement at the modern technological level allows to implement complex modes of operation with good speed and accuracy. However, if unmanned ship is used in ports and canals, it is very important to ensure that the task is performed both automatically and within certain time limits. These two requirements are specific for so-called terminal control that authors try to implement for marine robots taking the problem of surface vehicle docking as an example. The proposed solution consists of two stages and include obstacle avoidance method. Procedure of terminal control synthesis is provided as well as movement algorithm. Experiments were based upon the mathematical model used in real surface mini vessel “Neptune” and include simulation of docking in several different scenes with obstacles. Method of obstacle avoidance, proposed in this paper, is based on unstable mode. The unstable mode can be used directly but can also be used in a hybrid version that includes intelligent analysis of current situation and unstable mode of movement. In general, the unstable mode is based on a bionic approach; in particular, such an approach to avoiding obstacles can be observed in the behavior of fish.
Discriminative Features Fusion with BERT for Social Sentiment Analysis

The need for sentiment analysis in social networks is increasing. In recent years, many studies have shifted from author sentiment research to reader sentiment research. However, the use of words that hinders sentiment analysis is very diverse. In this paper, we provide a model that combines the latest and most recent contextual text embedding technology and feature selection to more accurately detect the emotional intent of an article. We named it DF2BERT (Discriminative Features Fusion with Bert), and extensively applied datasets in different languages and different text classification tasks to validate our method, and compared it with several well-known approaches. Experimental results show that our model can effectively predict sentiment behind the text which outperform comparisons.
Parallel Mining of Partial Periodic Itemsets in Big Data

Partial Periodic itemsets are an important class of regularities that exist in a temporal database. A Partial Periodic itemset is something persistent and predictable that appears in the data. Past studies on Partial Periodic itemsets have been primarily focused on centralized databases and are not scalable for Big Data environments. One cannot ignore the advantage of scalability by using more resources. This is because we deal with large databases in a real-time environment and using more resources can increase the performance. To address the issue we have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in a number of machines.
The Effect of Students’ Expectancy-Value Awareness of Physical Education Class on Their Grit and Challenge

The purpose of this study is to investigate the effect of students’ expectancy-value awareness of physical education class on their grit and challenge. For this, middle and high school students in Gyeonggi-do were established as a population, and 319 students were selected through convenience sampling. A multiple regression analysis was conducted with the collected data using the SPSS 21.0 statistical program, and the following results were obtained: First, the students" expectancy-value awareness of physical education class had a statistically significant effect on perseverance of effort among the sub-factors of grit, while it did not have a significant effect on consistency of interest. Second, among the sub-factors of grit, perseverance of effort had a statistically significant effect on challenge, while consistency of interest did not. Finally, the students" expectancy-value awareness of physical education class had a statistically significant effect on challenge.
Strain Measurement of Medical Textile Using 2D Digital Image Correlation Method

Medical textile plays an important role in the technical textiles sector as one of the most rapidly growing sectors in the technical textile market. The textile materials should have some adequate mechanical properties to be useful as medical textile. Tensile strength presents one of the basic mechanical properties used to describe textile specimens. Standardized tensile testing procedures on textile specimens were commonly used in the past. The aim of this paper was to measure in-plane strain field on the tensile medical textile specimen using 2D Digital Image Correlation method (2D-DIC). 2D-DIC is a non-contact optical method for accurate displacement and strain full-field measurement. In this study, two medical cotton textiles, with density 120 and 130 g/m2, were used to create three specimens for each material. Each specimen was placed in the tensile testing machine and measured until the break. During the tensile testing, camera was automatically recording full-field displacement in X and Y directions. Textile 1 and Textile 2 showed significant differences in point distance values, despite the small deviation in densities (less than 10%). Mean value of the elongation for Textile 1 is more than a double than the elongation for Textile 2, although the difference for mean value of Maximum force if negligible. Also, it has been showed that 2D-DIC can play significant role for measurement in textile mechanical properties measurement.
Application of the Improved PSO-Based Extended Domain Method in Engineering

The standard particle swarm optimization (PSO) algorithm is the boundary constraints of simple variables, which can hardly be directly applied in the constrained optimization. Furthermore, the standard PSO algorithm often fails to obtain the global optimal solution when the dimensionality is high for unconstrained optimization. Thus, an improved PSO-based extended domain method (IPSO-EDM) is proposed to solve engineering optimization problems. The core idea of this method is that the original feasible region is expanded in the constrained optimization which is transformed into the unconstrained optimization by combining the ergodicity of chaos optimization and the evolutionary variation to realize global search. In addition, to verify the effectiveness of the IPSO-EDM, an unconstrained optimization case study, four constrained optimization case studies, and one engineering example are investigated. The results indicate that the computational accuracy of the IPSO-EDM is comparable to that provided by the existing literature, and the computational efficiency of the IPSO-EDM is significantly improved. Meanwhile, this method has conspicuous global search ability and stability in engineering optimization.