Reviewerly/exHarmony huggingface dataset:
Specular Highlights Detection Using a U-Net Based Deep Learning Architecture

Different lighting conditions surrounding an object can cause specular reflections, resulting in specular highlights in the captured image. These can interfere with image processing algorithms, leading to false interpretation of results. Hence, applications that demand consistent object appearance require that such highlights are detected and localized for subsequent processing. Using a slightly modified U-Net architecture, we propose a semantic segmentation model to localize specular highlights. To evaluate the segmentation performance, the IoU metric is used. This measures how good the segmentation prediction matches the corresponding reference annotation by dividing their intersection by their union (IoU). The model achieved a frequency-weighted and mean IoU of 0.83 and 0.75 respectively when tested on a benchmark dataset. Furthermore, the proposed network was also trained and tested on a custom dataset, focusing on flash light reflections in an underground tunnel environment. For this custom dataset, the model achieved a frequency-weighted and mean IoU of 0.98 and 0.80 respectively.
RETRACTED: Application of 3Ds Max and virtual reality technology in 3D submarine scene modeling

With the advancement and augmented reality innovation, its applications are getting increasingly far-reaching. The 3D show innovation appropriate to this work is the checking of submarine pipelines. It is a basic database "submarine pipeline" that reconstructs the submarine pipeline, shows a PC in a virtual scene of the submarine pipeline dependent on augmented reality, and encompasses submarine topography displays related data. And associated terrain and landscape integrated database engine "using rendering with 3D graphics Surveillance and collection of submarine pipelines. Similarly, mappings can mainly use their derivatives in 2D map views with GIS software digital elevation models (such as tilt or shadow grids). As a result, submarine research lacks an on-site research phase of classic works on land, allowing clear visualization and appreciation of the study subject. Development of traditional educational theories based on FPGAs and neural networks (NN). A summary of your experience when taught. In reality, it's easy to find a universal educational model.
Tangle-Free Exploration with a Tethered Mobile Robot

Exploration and remote sensing with mobile robots is a well known field of research, but current solutions cannot be directly applied for tethered robots. In some applications, tethers may be very important to provide power or allow communication with the robot. This paper presents an exploration algorithm that guarantees complete exploration of arbitrary environments within the length constraint of the tether, while keeping the tether tangle-free at all times. While we also propose a generalized algorithm that can be used with several exploration strategies, our implementation uses a modified frontier-based exploration approach, where the robot chooses its next goal in the frontier between explored and unexplored regions of the environment. The basic idea of the algorithm is to keep an estimate of the tether configuration, including length and homotopy, and decide the next robot path based on the difference between the current tether length and the shortest tether length at the next goal position. Our algorithm is provable correct and was tested and evaluated using both simulations and real-world experiments.
A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images

In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed. First, an adversarial learning framework is designed to generate speckled-to-speckled (S2S) image pairs from the same scene in the situation where only single speckled SAR images are available. Then, the S2S SAR image pairs are employed to train a modified despeckling Nested-UNet model using the Noise2Noise (N2N) strategy. Moreover, an iterative version of the PSD method (PSDi) is also presented. Experiments are conducted on both synthetic speckled and real SAR data to demonstrate the superiority of the proposed methods compared with several state-of-the-art methods. The results show that our methods can reach a good tradeoff between feature preservation and speckle suppression.
Strategic tree rewriting in attribute grammars

This paper presents strategy attributes, a seamless integration of strategic term rewriting into attribute grammars. Strategy attributes are specified using rewrite rules with strategies that control their application. The rules can reference contextual information held in attributes on the trees being rewritten. This use of attributes leads to rewriting on decorated trees instead of undecorated terms. During rewriting, attributes are (lazily) computed on new trees to ensure they are correct with respect to their defining equations. Attributes and strategic rewriting can each be used where most appropriate, thus avoiding the cumbersome aspects of each.
A Niche Particle Swarm Optimization-Perks and Perspectives

Optimization is a method for searching the best candidate solution to lessen or expand the value of the objective problem. Broadly speaking algorithms can be orgabized into four main classes, i.e. biology-based algorithms, physics-based algorithms, sociology-based algorithms, and human intelligence-based algorithms. Swarm-intelligence (SI) based algorithms appeared as a commanding family of optimization techniques. The paper aims to commence a brief review of meta-heuristic algorithms especially Particle swarm optimization (PSO) and its sister variants in short. The understudy paper covers all important aspects of swarm intelligence PSO with deep insight learning for practitioners and scholars.
Performance Ratio Analysis Using Experimental Combining Historical Weather Data for Grid-Connected PV Systems

Along with the development of new technology, photovoltaic (PV) systems are increasingly used widely. To assess the quality of PV systems using the performance ratio (PR), to calculate this index, we need to use pyranometers with high accuracy, and of course, its price is also expensive. This article presents an alternative method for calculating the PR of a grid-connected PV system (GCPVS) based on the amount of measurement by the experiment electricity and weather history data. In addition, the article analyzes the PR for GCPVS with and without a single-axis solar tracker. The results show that the PR for GCPVS with a single-axis solar tracker is larger than the none-system. On a sunny day, the GCPVS with a single-axis tracker obtained a total in-plane solar irradiance and generated more power into the grid than the GCPVS without a single-axis tracker in the morning until early noon, and almost noon to late afternoon. The largest PR received 79.8%, an average of 66.0% and 69.2% for the GCPVS without and with a single-axis solar tracker, respectively. The analysis also shows that the PR of the GCPVS is much influenced by the temperature of the environment, which will be the premise for further studies.
A Compact Coprocessor for the Elliptic Curve Point Multiplication over Gaussian Integers

This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning

Image Captioning (IC), the task of automatic generation of image captions, has attracted attentions from researchers in many fields of computer science, being computer vision, natural language processing and machine learning in recent years. This paper contributes to research on Image Captioning task in terms of extending dataset to a different language - Vietnamese. So far, there has been no existed Image Captioning dataset for Vietnamese language, so this is the foremost fundamental step for developing Vietnamese Image Captioning. In this scope, we first built a dataset which contains manually written captions for images from Microsoft COCO dataset relating to sports played with balls, we called this dataset UIT-ViIC (University Of Information Technology - Vietnamese Image Captions). UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images. Following that, we evaluated our dataset on deep neural network models and did comparisons with English dataset and two Vietnamese datasets built by different methods. UIT-ViIC is published on our lab website ( https://sites.google.com/uit.edu.vn/uit-nlp/ ) for research purposes.
Towards a Context-Aware Knowledge Model for Smart Service Systems

The advancement of the Internet of things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present a context-aware knowledge model for smart service systems that organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system and Network of service systems. The context-aware knowledge model for smart service systems integrates all the information and knowledge related to smart services, knowledge components and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. To demonstrate the approach, a case study about a chatbot as a smart service for customer support is presented.
An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting

OCR post-processing is an essential step to improve the accuracy of OCR-generated texts by detecting and correcting OCR errors. In this paper, the OCR texts are resulted from an OCR engine which is based on the attention-based encoder-decoder model for unconstrained Vietnamese handwriting. We identify various kinds of Vietnamese OCR errors and their possible causes. Detailed statistics of Vietnamese OCR errors are provided and analyzed at both character level and syllable level, using typical OCR error characteristics such as error rate, error mapping/edit, frequency and error length. Furthermore, the statistical analyses are done on training and test sets of a benchmark database to infer whether the test set is the appropriate representative of the training set regarding the OCR error characteristics. We also discuss the choice of designing OCR post-processing approaches at character level or at syllable level relying on provided statistics of studied datasets.
Efficient implementation of complete and nondestructive Bell-state measurement for trapped ions with reverse engineering

Abstract In this paper, we put forward a protocol for the complete and nondestructive Bell-state measurement of trapped ions. We show that the four different Bell states of two information carriers can be distinguished using two auxiliary ions with the initial Bell states unchanged after the procedures. The parameter selections are discussed based on the experimentally available parameters, and the robustness against the errors and decoherence are analyzed. The results show that the protocol works well with the selected parameters, and possesses robustness against errors and decoherence. Since the laser pulses to drive the transitions of ions in the procedures are designed via reverse engineering, the control parameters can be flexibly selected under certain boundary conditions, which provides the possibility to find corrective parameters to compensate the errors caused by the decoherence. We hope the protocol can be useful for the Bell-state based quantum information tasks.
A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.
UnifyDR: A Generic Framework for Unifying Data and Replica Placement

The advent of (big) data management applications operating at Cloud scale has led to extensive research on the data placement problem. The key objective of data placement is to obtain a partitioning (possibly allowing for replicas) of a set of data-items into distributed nodes that minimizes the overall network communication cost. Although replication is intrinsic to data placement, it has seldom been studied in combination with the latter. On the contrary, most of the existing solutions treat them as two independent problems, and employ a two-phase approach: (1) data placement, followed by (2) replica placement. We address this by proposing a new paradigm, CDR, with the objective of c ombining d ata and r eplica placement as a single joint optimization problem. Specifically, we study two variants of the CDR problem: (1) CDR-Single, where the objective is to minimize the communication cost alone, and (2) CDR-Multi, which performs a multi-objective optimization to also minimize traffic and storage costs. To unify data and replica placement, we propose a generic framework called UnifyDR, which leverages overlapping correlation clustering to assign a data-item to multiple nodes, thereby facilitating data and replica placement to be performed jointly. We establish the generic nature of UnifyDR by portraying its ability to address the CDR problem in two real-world use-cases, that of join-intensive online analytical processing (OLAP) queries and a location-based online social network (OSN) service. The effectiveness and scalability of UnifyDR are showcased by experiments performed on data generated using the TPC-DS benchmark and a trace of the Gowalla OSN for the OLAP queries and OSN service use-case, respectively. Empirically, the presented approach obtains an improvement of approximately 35% in terms of the evaluated metrics and a speed-up of 8 times in comparison to state-of-the-art techniques.
AnnaBellaDB: Key-Value Store Made Cloud Native

The cloud-native paradigm has become a well-known approach to ensure the elasticity and reliability of applications running in the cloud. One recurrent motif is the stateless design of applications, which aims to decouple the life-cycle of application states from the life-cycle of individual application instances. Application data is written to and read from cloud databases, deployed close to the application code to ensure low latency bounds on state access. When applying a stateless design, the performance of the cloud service is often limited by the cloud database. In order not to become a bottleneck, database instances are distributed on multiple hosts, and strive to ensure data locality for all application functions. However, the shared nature of certain states, and the inevitable dynamics of the application workload necessarily lead to inter-host data access. If the service is geographically distributed, this is even across data centers and edge servers resulting in a significant delay. To minimize the service performance loss due to the stateless design of applications, we propose a latency and access pattern aware state storage method, called state-layer, that can be easily applied in any kind of key-value store with the ability of deciding where to store replicas in the cluster and measure networking/computing delay. By adapting our solution to Anna, a key-value store from academia, we show the proposed state-layer is ideal to use as a cloud database for storing application states. To foster further research in this area, we make our proof-of-concept solution open-source.
Vector-to-Sequence Models for Sentence Analogies

We solve sentence analogies by generating the solution rather than identifying the best candidate from a given set of candidates, as usually done. We design a decoder to transform sentence embedding vectors back into sequences of words. To generate the vector representations of answer sentences, we build a linear regression network which learns the mapping between the distribution of known and expected vectors. We subsequently leverage this pre-trained decoder to decode sentences from regressed vectors. The results of experiments conducted on a set of semantico-formal sentence analogies show that our proposed solution performs better than a state-of-the-art baseline vector offset method which solves analogies using embeddings.
A Rapid Recognition Method for Pedestrian Abnormal Behavior

In recent years, with the development of deep learning technology, human behavior recognition has gradually become a very challenging and important topic in the field of computer vision. Human behavior recognition algorithms generally exploit temporal domain information such as optical flow and LSTM, or utilize 3D convolution, leading to large calculation and slow recognition speed, which is difficult to meet the requirements of speed and cost in practical applications. Therefore, this paper proposes a rapid recognition method that can quickly recognize the abnormal behavior of pedestrians. We extract the human skeleton information and remove the background to obtain a feature image containing only the human skeleton information, and then the Multi-scale information Fusion recognition Network (MFN) designed in this paper is used to recognize the abnormal behavior of pedestrians. We have achieved an accuracy rate of 96.3% in the self-built skeleton-based pedestrian abnormal behavior data set and the amount of calculation has dropped significantly.
Human behavior recognition based on attention mechanism

In recent years, human behavior recognition has become a research hotspot in the field of computer vision. Compared with static images, human behavior recognition is based on video analysis. The video contains two parts of information in the spatial and temporal domains. The spatiotemporal two-stream convolutional neural network model gives the same weight to different spatial and temporal cues in the parameter training stage, which seriously affects the distinction of features. In response to this shortcoming, this paper proposes to introduce attention mechanism in the spatiotemporal dual-flow network model. This method can effectively add a weight to the static image features and the dense optical flow features between frames, pay attention to the beneficial regions in the feature information and then distinguish the feature information, so as to achieve more accurate behavior recognition. Experiments were conducted on the UCF-101 human behavior video dataset, and the results prove the effectiveness of the spatiotemporal two-stream convolutional neural network with attention mechanism introduced in the human behavior recognition task.
Bibliometric Analysis of the Tertiary Study on Agile Software Development using Social Network Analysis

This study aims to examine the systematic literature reviews published on the Agile Software Development subject between 2013 and 2018 and to examine the citation relationships among the studies within the scope of the tertiary study with the help of social network analysis. In this study, the publications within the scope were visualized with VOSviewer and Gephi social network analysis tools, and the relations between publishing institutions, countries were revealed. In citation analysis; according to the total link strength, it is seen that UK, Spain and Slovenia are at the forefront at institution and country level. Brazil has the highest citation value and provides the link between the two large clusters obtained in the analysis. In the bibliographic coupling analysis, the five most active countries at the country level were Brazil, Germany, Finland, Malaysia and Pakistan. When the same analysis is made at the institution level, the top five institutions are in Brazil, Switzerland, Peru and Pakistan. The findings of the study indicate that developing countries have more studies on the subject and that the cited publications are mostly from developed countries; European countries seem to be more collaborative based on citation analysis yet developing countries such as Brazil and Malaysia have also relations with them; the number of publications is not directly proportional to the citations.
Eight things to know about business research data

Business researchers are using increasingly large and unique datasets on a wide range of topics. For librarians without prior experience with large-scale business and finance data it can be overwhelming to figure out where to even start. This article surveys eight things someone interested in providing more data-related business research support should know as they get started, such as the types of data used, data acquisition, and merging datasets. Because providing data support to business researchers is one way for librarians to provide value-added services, this article attempts to help others start working more with data.
Small Samples of Multidimensional Feature Vectors

A small sample of multidimensional feature vectors appears when the number of features is much greater than the number of objects (feature vectors). For example, such circumstances appear typically in genetic data sets. In such cases, feature clustering can become a useful tool in classification or prognosis tasks. Feature clustering can be performed through the minimization of the convex and piecewise linear (CPL) criterion functions.