Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Turkish Reviews on Google Play Store

The development of technology and the increase in shared data volume has made the analysis performed on this data valuable. Users expressing their opinions as comments have also been invaluable to companies. In this study, approximately two million Turkish user reviews that are made on Google Play are collected and evaluated. The comments were analyzed as ratings out of five and positive-negative states. For this data, encoder-based BERT model, which has been popular recently in deep learning methodologies, has been fine-tuned and used. The effects of different sizes of training data on success were observed. Classification achievements are presented with tables and graphics. In order to compare the performance of the Turkish BERT model against prior text classification studies in the literature, a similar smaller amount of training data is used.
Top-k Graph Summarization on Hierarchical DAGs

Directed acyclic graph (DAG) is an essentially important model to represent terminologies and their hierarchical relationships, such as Disease Ontology. Due to massive terminologies and complex structures in a large DAG, it is challenging to summarize the whole hierarchical DAG.
Central bank digital currency

This chapter aims to employ the most up-to-date literature to shed light on the aims, working mechanisms and likely macroeconomic impact of Central Bank Digital Currency (CBDC). It describes payment systems to distributed ledger technology (DLT)-based ones. The chapter explains the working mechanisms of CBDC, providing some real-world examples. It discusses some of the potential benefits and macroeconomic risks associated with CBDC. A payment system is a collection of technologies, laws and contracts that allow payments to occur and determine when a payment effects a settlement. The volume of electronic payments processed by central banks’ clearinghouses has been rising. DLT-based payment systems have some potential benefits, and they also present some challenging aspects. Token-based retail CBDCs are meant to be the digital version of cash. Hence, the general public would be granted direct access to central banks’ liabilities. Turning to monetary policy, CBDCs’ effects on policy rates largely depend on CBDC design.
Local Differential Privacy for Data Streams

The dynamic change, huge data size, and complex structure of the data stream have made it very difficult to be analyzed and protected in real-time. Traditional privacy protection models such as differential privacy which need to rely on the trusted servers or companies, and this will increase the uncertainty of protecting streaming privacy. In this paper, we propose a new privacy protection protocol for data streams under local differential privacy and w-event privacy, which makes it possible to keep up-to-date statistics over time, and it is still available when the third parties are untrusted. We use sliding window to collect the data streams in real-time, finding out the occurrence of significant moves, capturing the latest data distribution trend, and releasing the perturbed data streams report in time. This protocol provides a provable privacy guarantee, reduces computation and storage costs, and provides valuable statistical information. The experimental results of real datasets show that the proposed method can protect the privacy of the data streams and provide available statistical data at the same time.
A Simple Framework of Smart Geriatric Nursing considering Health Big Data and User Profile

The National Bureau of Statistics of China shows that the population over 65 years old in China exceeds 166 million accounting for 11.93% of the total population by the end of 2018. The importance and severity of taking care of the elderly are becoming increasingly prominent. High-quality and meticulous care for the daily life of the elderly needs helpful and advanced sciences and technologies. Smart geriatric nursing is a must. Basing on the professional knowledge of geriatric nursing, this paper proposes a framework of smart geriatric nursing which consists of three aspects of smart nursing: smart geriatric nursing in physical health using biosensor and advanced devices, smart geriatric nursing in mental health based on user profile, and smart geriatric nursing for daily life based on big data in health. The deployment of the proposed method relies on the technologies of the Internet of Things (IoT), user profile system, big data, and many other advanced information technologies. The framework of methods can provide a useful reference for the systematic technical scheme of smart geriatric nursing in an aging society.
Learning Analytics in Augmented Reality : Blueprint for an AR / xAPI Framework

Learning Analytics in flat web platforms is rather commonplace, but recording learner performance from immersive real-world experience still poses a challenge. With this study, we break ground and show that Learning Analytics for Augmented Reality are possible and we outline how an according system can be implemented. We describe a novel framework for competency-based workplace Learning Analytics and document the required components with regards to software, hardware, and their configuration. Thereby, we discuss key design decisions and lessons learned from the implementation of the framework, drawing from the experience of the EU project Learning Analytics in Augmented Reality (LAAR), which provided the environment and context for this work. Most notably, this included testing the technologies in realistic scenarios in vocational training of stage technicians. Finally, we conclude the paper with a summary of the work done and an outlook on possible extensions for researchers wishing to build on our work.
The Laboratory-Based HySpex Features of Chlorite as the Exploration Tool for High-Grade Iron Ore in Anshan-Benxi Area, Liaoning Province, Northeast China

Anshan-Benxi area in Liaoning province is an important banded iron formations (BIFs) ore-mining district in China. Chlorite is widely distributed in this area, which is related to BIFs and high-grade iron ore, respectively. A fast and convenient method to identify the type and spatial distribution of different chlorites is crucial to the evaluation of high-grade iron ore in this area. Qidashan iron mine is a typical BIFs deposit, and its BIFs-related high-grade iron ore reserves are the second largest in the area. In this paper, the laboratory-based HySpex-320m hyperspectral imaging was used to study the wall rock in Qidashan iron mine. A hyperspectral imaging processing model was established for mineral identification, mineralogy mapping, and chlorite spectral features extraction. The results show that the wavelength positions of OH, Fe-OH, and Mg-OH absorptions of chlorite in the altered wall rock of high-grade iron ore are between 1400 and 1410, 2260 and 2265, and 2360 and 2370 nm, respectively, which are longer than those around BIFs. The relationship between cations in the octahedral layer of chlorite and the wavelengths of OH, Fe-OH, and Mg-OH indicates that Mg and Mg/(Mg + Fe) are inversely related to these wavelengths, whereas Fe is positively related. The wavelengths appear to be weakly influenced by AlVI. Since the bandpass of hyperspectral imaging systems is usually less than 10 nm, these chlorite wavelength differences can be used as a favorable tool for the high-grade iron ore exploration and the iron resources evaluation in the Anshan-Benxi area.
Compression of Deep Learning Models for NLP

In recent years, the fields of NLP and information retrieval have made tremendous progress thanks to deep learning models like RNNs and LSTMs, and Transformer[35] based models like BERT[9]. But these models are humongous in size. Real world applications however demand small model size, low response times and low computational power wattage. We will discuss six different types of methods (pruning, quantization, knowledge distillation, parameter sharing, matrix decomposition, and other Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this tutorial is very timely. We will organize related work done by the 'deep learning for NLP' community in the past few years and present it as a coherent story.
Fast 4D elastic group-wise image registration. Convolutional interpolation revisited

Background and Objective:This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. Methods:Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. Results:The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation. Conclusions:Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.
Long-term Solar Radiation Forecasting using a Deep Learning Approach-GRUs

A long-term solar generation forecasting is an important issue in a microgrid design. Solar generation forecasting mainly depends on solar radiation forecasting. In this paper, A Deep Learning approach using Gated Recurrent Units (GRUs) is proposed for forecasting of a year-ahead hourly and daily solar radiation. The proposed GRU model is compared with the state of the art methods like Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Regression (SVR), Feed Forward Neural Network (FFNN) models and numerical model. Its effectiveness for long-term solar radiation forecasting over other methods is verified.
Automatic Complex Word Identification Using Implicit Feedback from User Copy Operations

Complex Word Identification (CWI) is one of the key components of lexical text simplification. This paper proposes a new approach to CWI on websites, based on tracking what web users copy to their clipboards. Users may copy to the clipboard words that they are not familiar with or that make the text difficult to understand, in order to search for more information on the internet. Accordingly, this study examines the hypothesis that word copying on a website is an indicator of word complexity. Copied words on a sample website are compared to uncopied words using three simple word complexity indicators: number of syllables, number of characters, and general word frequency. The results show that copied words are more likely to be evaluated as complex than uncopied words and words that are copied more frequently are more likely to be evaluated as complex than words that are copied less frequently, by all three indicators. Consequently, word copying on a website can be considered a novel CWI indicator. Unlike traditional CWI indicators, which are based on static word features, this new indicator provides a different approach by targeting complex words based on dynamic user behavior. Therefore, simplifying these complex words might be particularly helpful to the readers. Further work should evaluate using this word copying indicator in complete CWI and text simplification implementations.
SFNN: Semantic Features Fusion Neural Network for Multimodal Sentiment Analysis

Detecting sentiment in online reviews is a key task, and effective analysis of sentiment in online reviews is the foundation of applications such as user preference modeling, consumer behavior monitoring, and public opinion analysis. In previous studies, the sentiment analysis task mainly relied on text content and ignored the effective modeling of visual information in comments. This paper proposes a neural network SFNN based on semantic feature fusion. The model first uses convolutional neural networks and attention mechanism to obtain the effective emotional feature expressions of the image, and then maps the emotional feature expressions to the semantic feature level. Then, the semantic features of the visual modal is combined with the semantic features of the text modal, and finally the emotional polarity of the comment is effectively analyzed by combining the emotional features of the physical level of the image. Feature fusion based on semantic level can reduce the difference of heterogeneous data. Experimental results show that our model could achieve better performance than the existing methods in the benchmark dataset.
Regularized Hierarchical Quadratic Program for Real-Time Whole-Body Motion Generation

The goal of this article is to find an optimal and robust solution for on-line hierarchical least-squares optimization subject to both equality and inequality constraints. We focus on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face a singularity. The mixed problem of a regularization and inequality-constrained hierarchical optimization is not fully discussed due to the mathematical complexity. We address this problem by formulating a regularized hierarchical quadratic programming. The solution is obtained in a unified and computationally efficient manner by leveraging a singular value decomposition and an active set method. At the same time, we concentrate on the realization of the proposed algorithm as a practical means of real-time whole-body motion generation. The effectiveness of the algorithm is validated through extensive numerical simulations and experimental tests of a rescue robot successfully executing manipulation missions in a highly unstructured environment.
Alert Generation on Detection of Suspicious Activity Using Transfer Learning

The system aims to give CCTV cameras the ability to detect suspicious activity, without human intervention. The goal of this paper is to identify suspicious activity for Surveillance and alert the shop owners when suspicious activity is detected. Electronic Article Surveillance (EAS) systems are widely used in today's retail stores, but this system is not capable enough as the shoplifters can easily remove the tag or label from the product. Hence, this system aims to take real-time videos from CCTV as an input and pass it to the CNN model created with the help of transfer learning and detect `Shoplifting', `Robbery' or `Break-In' in the store and notify it to the owners as soon as it occurs. Finally the main motive is to provide a system that detects suspicious activities without human intervention and generates alert, thus making a huge revolution in today's surveillance system.
Ship Target Detection Algorithm Based on Improved YOLOv3

In view of the increasingly complex maritime traffic, the low level of intelligence in safety supervision, the low recognition rate and poor real-time performance of current ship detection algorithms. In this paper, we propose an improved YOLOv3 algorithm, which can better detect ship targets on the sea. First, by replacing IOU in YOLOv3 with Complete_IOU, keeps the prediction box close to the ground truth center point and increases the recall rate when non-maximum suppression (NMS) is used; The prior anchor box size is redesigned using K-means++ clustering algorithm and evenly distributed to the corresponding prediction scale; In the loss function design, the previously improved Complete_IOU loss is used to replace the mean square error (MSE) loss, which solves the inconsistency of optimization and scale sensitivity. And improves the confidence loss function to solve the problem of positive and negative sample imbalance. Comparing this algorithm with other target detection algorithms on ship dataset, the experimental results show that the mean accuracy (mAP) of this algorithm on ship images is 89.84%, which is 4.57%, 7.23% and 3.75% higher than Faster R-CNN, SSD and traditional YOLOv3 algorithms respectively, and the frame rate is 30fps, which enables real-time target detection. Experiments show that this method can detect ship target accurately in real time which meets the needs of practical application.
Website Category Classification Using Fine-tuned BERT Language Model

The contents on the Word Wide Web is expanding every second providing web users a rich content. However, this situation may cause web users harm rather than good due to its harmful or misleading information. The harmful contents can contain text, audio, video, or image that can be about violence, adult contents, or any other harmful information. Especially young people may readily be affected with these harmful information psychologically. To prevent youth from these harmful contents, various web filtering techniques, such as keyword filtering, Uniform Resource Locator (URL) based filtering, Intelligent analysis, and semantic analysis, are used. We propose an algorithm that can classify websites, which may contain adult contents, with 67.81% (BERT) accuracy among 32 unique categories. We also show that a BERT model gives higher accuracy than both the Sequential and Functional API models when used for text classification.
Pose Flow Learning From Person Images for Pose Guided Synthesis

Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on either hard-coded spatial transformations or 3D body modeling. They often overlook complex non-rigid pose deformation or unmatched occluded regions, thus fail to effectively preserve appearance information. In this article, we propose a pose flow learning scheme that learns to transfer the appearance details from the source image without resorting to annotated correspondences. Based on such learned pose flow, we proposed GarmentNet and SynthesisNet, both of which use multi-scale feature-domain alignment for coarse-to-fine synthesis. Experiments on the DeepFashion, MVC dataset and additional real-world datasets demonstrate that our approach compares favorably with the state-of-the-art methods and generalizes to unseen poses and clothing styles.
Impact of Teleconsultation on Patients With Type 2 Diabetes in the Brazilian Public Health System: Protocol for a Randomized Controlled Trial (TELEconsulta Diabetes Trial)

Although the Brazilian Unified Health System (SUS) offers universal health coverage, access to quality care is often limited by social inequality and location. Although telemedicine has been shown to be an important tool in the efforts to overcome this problem, because it can provide access to specialist care and break the geographical barriers to health care, there are no national studies demonstrating its use in public health.This study aims to test the hypothesis that remote consultation can be as effective as standard face-to-face consultation for type 2 diabetes mellitus in the Brazilian public health system and to assess the associated costs related to teleconsultation in public health scenarios, for patients referred from Primary Health Care units of the SUS for specialist care.This is a pragmatic, phase 2, unicentric, open-label, noninferiority, blinded allocation, data-blinded, centrally randomized clinical trial. The inclusion criteria will be adults, both sexes, ≥18 years old, glycated hemoglobin (HbA1c) ≥8%. Outcomes will be evaluated by assessing symptoms, laboratory exams, anthropometric measurements, blood pressure, adverse events, and satisfaction level for 6 months. The costs of the teleconsultation will be assessed using the time-driven activity-based costing (TDABC) method to compare the costs with the face-to-face consultations. The noninferiority margin was set at 0.5%. Assuming an SD of 1.3% for both groups, true difference between the means of zero, and a type I error level of 5% (one-sided), it was estimated that 117 individuals per group would be necessary to achieve 90% power. Statistical analysis of the efficacy will be done using intention-to-treat and per-protocol approaches.The results from this trial will be reported according to the CONSORT guidelines. The trial was approved by the institutional review board on October 5, 2019. Data collection started in January 2019 and is expected to finish in 2022. At the time of manuscript submission, 18 participants were recruited.Our expectations are that providing remote access to health care will result in improvements in the health and quality of life of patients with type 2 diabetes and reduce costs and that both patients and clinicians will benefit from and be satisfied with this technology.Registro Brasileiro de Ensaios Clínicos RBR-8gpgyd; https://ensaiosclinicos.gov.br/rg/RBR-8gpgyd.DERR1-10.2196/23679.
Stochastic Modeling of Distance to Collision for Robot Manipulators

Evaluating distance to collision for robot manipulators is useful for assessing the feasibility of a robot configuration or for defining safe robot motion in unpredictable environments. However, distance estimation is a time-consuming operation, and the sensors involved in measuring the distance are always noisy. A challenge thus exists in evaluating the expected distance to collision for safer robot control and planning. In this work, we propose the use of Gaussian process (GP) regression and the forward kinematics (FK) kernel (a similarity function for robot manipulators) to efficiently and accurately estimate distance to collision. We show that the GP model with the FK kernel achieves almost 70 times faster distance evaluations compared to a standard geometric technique, and up to 18 times more accurate evaluations compared to other regression models, even when the GP is trained on noisy distance measurements. We employ this technique in trajectory optimization tasks and observe 13 times faster optimization than with the noise-free geometric approach yet obtain similar optimized motion plans. We also propose a confidence-based hybrid model that uses model-based predictions in regions of high confidence and switches to a more expensive sensor-based approach in other areas, and we demonstrate the usefulness of this hybrid model in an application involving reaching into a narrow passage.
Generating Clarifying Questions in Conversational Search Systems

Asking a clarifying question can be a key element improving the performance of information seeking systems, particularly conversational search systems due to their limited bandwidth interfaces. While generating and asking clarifying questions is important; get-ting an answer for the clarifying question is also essential as a clarifying question without an answer is useless. Therefore, as the first step in current research, we analysed human-generated clarifying questions in a Community Question Answering website as a sample of conversation. This helped us to gain a better insight into how users interact with clarification. We investigated the clarifying questions in terms of whether they add any information to the question and the accepted answer. We further discovered the patterns and types of such clarifying questions. The next phase of this research will then generate clarifying questions in conversational search systems. We will then employ neural network models to generate clarifying questions to maximise clarification in questions. The proposed model will be trained using the MIMICS data collection in addition to our collected dataset. We will also attempt to consider the recognised patterns from the analysis conducted in the first step to enhance the chance that a user will interact with the clarifying questions. Finally, we will aim to minimise the interaction between the search system and the user to reduce the risk of dropping the conversation by the user due to asking too many clarifying questions.