Reviewerly/exHarmony huggingface dataset:
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of registration or label fusion procedures. In this work, we design a novel cross-modality MAS framework, which uses available atlases from a certain modality to segment a target image from another modality. To boost the computational efficiency of the framework, both the image registration and label fusion are achieved by well-designed deep neural networks. For the atlas-to-target image registration, we propose a bi-directional registration network (BiRegNet), which can efficiently align images from different modalities. For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image. SimNet can learn multi-scale information for similarity estimation to improve the performance of label fusion. The proposed framework was evaluated by the left ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets, respectively. Results have shown that the framework is effective for cross-modality MAS in both registration and label fusion https://github.com/NanYoMy/cmmas.
Safety Boundary Extraction Using FCM and Prediction Using ELM for Aero-engine Performance Parameters

The safety boundary of Aero-engine performance parameters is one of the essential criteria for measuring aero-engine performance. However, due to the differences among individuals and discrepancies among the working environments, the fixed theoretical boundary is no longer sufficient for engineering needs. In this paper, a method based on fuzzy C-means (FCM) and Extreme Learning Machine (ELM) is proposed to extract and predict the safety boundary for aero-engine performance parameters. Firstly, the residuals between the predicted values and the actual values are used as the quantitative basis to extract the safe boundary. And then the ELM algorithm is used to forecast the safety boundary for next period of time. The method mentioned in this paper enhances the accuracy and generalization of safety boundary due to improvement for specific situations. The effectiveness of this method has been verified by simulation case.
Model for Educational Free Software Integration into Artificial Intelligence Teaching and Learning

Artificial Intelligence is a branch of Computer Science with complex subjects. A trend in the teaching and learning of Artificial Intelligence is to use software where students modify the source code. However, teachers do not use the freedoms of free software in their classrooms. Based on a previous review, it is difficult to see methodological indications to use or create new programs with these characteristics for other teaching-learning processes. The goal of this work is to develop a model of integration of educational free software to the teaching-learning process of Artificial Intelligence. The authors use several research methods: historical-logical, analysis and synthesis, functional-structural-systemic and modeling. The model has three fundamental components. The conceptual component represents the principles and their theoretical foundations. The structural component represents the main elements that intervene in the integration. The instrumental component constitutes the materialization of the model and contains three stages: preparation, execution and evaluation. The authors used the focus group technique, a pre-experiment and Iadov’s satisfaction technique to validate the model. These methods made it possible to verify the relevance of the model, its good acceptance by the teachers and the acceptable satisfaction of the students after the first application of the model.
MultiView Mannequins for Deep Depth Estimation in 360º Videos

We estimate depth maps from real-life monocular 360º VR videos using a Deep Neural Net architecture trained on 3D point-based renderings of people (called depth “mannequins”) captured with a Multi36 camera setup and processed with a combined pipeline of AI Instance Segmentation, Structure from Motion and Multi View Stereo methods.
MSANet: A Multi-Scale Attention Module

Multi-scale representation ability is one of key criteria for measuring convolutional neural networks (CNNs) effectiveness. Recent studies have shown that multi-scale features can represent different semantic information of original images, and a combination of them would have positive influence on vision tasks. Many researchers are investigated in extract the multi-scale features in a layerwise manner and equipped with relatively inflexible receptive field. In this paper, we propose a multi-scale attention (MSA) module for CNNs, namely MSANet, where the residual block comprises hierarchical attention connections and skip connections. The MSANet improves the multi-scale representation power of the network by adaptively enriching the receptive fields of each convolutional branch. We insert the proposed MSANet block into several backbone CNN models and achieve consistent improvement over backbone models on CIFAR-100 dataset. To better verify the effectiveness of MSANet, the experimental results on major network details, i.e., scale, depth, further demonstrate the superiority of the MSANet over the Res2Net methods.
One-Shot Algebraic Effects as Coroutines

This paper presents a translation from algebraic effects and handlers to asymmetric coroutines, which provides a simple, efficient and widely applicable implementation for the former. Algebraic effects and handlers are emerging as main-stream language technology to model effectful computations and attract attention not only from researchers but also from programmers. They are implemented in various ways as part of compilers, interpreters, or as libraries. We present a direct embedding of one-shot algebraic effects and handlers in a language which has asymmetric coroutines. The key observation is that, by restricting the use of continuations to be one-shot, we obtain a simple and sufficiently general implementation via coroutines, which are available in many modern programming languages. Our translation is a macro-expressible translation, and we have implemented its embedding as a library in Lua and Ruby, which allows one to write effectful programs in a modular way using algebraic effects and handlers.
Discriminative Spectral Regression Metric Learning in Unconstrained Face Verification

This paper presents the robustness of the proposed metric learning formulation, dubbed Discriminative Spectral Regression Metric Learning in offering a simplistic solution for measuring the Mahalanobis metric to solve unconstrained face verification problems. It takes advantage of distance metric learning on pairs of doublets by adopting the merit of the quadratic kernel function in the verification task. To be specific, the spectral graph analysis and the linear discriminant analysis are unified into the distance metric learning process for better exploitation of the intrinsic discriminant structure of face data. The proposed formulation is evaluated with four benchmarked constrained and unconstrained face datasets, with different tuning parameters under the restricted protocol. The promising result of 89.07% verification rate evinces the effectiveness and feasibility of the proposed formulation in unconstrained face verification compared to the state-of-the-art methods.
A Feasibility Study of Service Level Agreement Compliance for Start-Ups in Cloud Computing

Cloud Computing is an emerging field where the contributions from industry and academia add value to it. In recent years, cloud start-ups have become common and their revenue has been increasing many folds taking them to next level in business. Profits generated by start-ups and ease of doing business have welcomed more business avenues into cloud. As start-ups play a notable role in cloud business, it has become essential to analyze their business and the factors driving them. While analyzing, the factors like SLA and virtual machine migrations come into picture. This paper is dedicated for the detailed analysis on those and try to bring a better understanding on virtual machine migration algorithm with respect to cloud start-ups.
An Information Fusion Positioning Algorithm Based on Extended Dempster-Shafer Evidence Theory

Due to a variety of noise interference, received signal strength indicator (RSSI)-based fingerprint data are often accompanied by uncertain factors. In order to solve the problem of positioning with high precision and accuracy in a complex indoor environment, this study designs a fingerprint positioning algorithm based on extended Dempster-Shafer evidence inference. First, a recognition framework is built to design a basic probability distribution function. Then a new evidence combination rule is proposed to assign different trust levels to the signal strength messages received from multiple sources, and the final position is obtained by converging the RSSI values. Finally, simulation experiments are conducted to show that the proposed algorithm is more valuable for improving the accuracy and accuracy of indoor positioning.
Impartial Sensor Tasking via Forecasted Information Content Quantification

The consistently increasing population of space debris in orbit about the Earth is becoming an increasingly unavoidable problem, necessitating a proper handling of the problem of sensor tasking to effectively and efficiently obtain observations of space objects to maintain catalogs of their positions and velocities. A variety of approaches have been explored to achieve this in an autonomous fashion, assisting with space traffic management. This work presents new statistical analysis of the Kullback–Leibler divergence justifying the use of its expected value in generating sensor schedules. This divergence’s use as an objective measure is extended to simultaneously consider, in an impartial manner, multiple observations of potentially varying quality and type into a common space, namely the information space, by mapping them to an arbitrary reference time. This time mapping is then shown to exhibit improvements in performance not only with respect to the estimate at the reference time, but over the entire tracking interval.
Vulnerability Assessment of Water in Landfill Site and Environs: A Case Study of Olusosun in Lagos, Nigeria

This study assessed the vulnerability of landfill site in Olusosun, Lagos Nigeria using modified DRASTIC (DRASTIC L) model in a geographical information system environment. It also analyzed water samples with a view to determining the amount of trace metals concentrate present. Water samples were collected from different Boreholes and Wells with a radius of 500m to the landfill site. The modified DRASTIC (DRASTIC L) based on eight parameters such as Depth to water, Net recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, Hydraulic conductivity and Distance to landfill site. The vulnerability index was calculated using a sum overlay of the eight parameters. ArcGIS 10.2 software was used to integrate all these parameters together to obtain Boreholes and Wells vulnerable zones I areas. The results showed that out of a total of 228.38 hectares, only about 47.46 hectare was observed to be within the low vulnerable zone having a DRASTIC index range between 113 – 136; while about 130.65 hectares were found to be in the moderately vulnerable zone with a DRASTIC index ranging between 136 and 144. About 50.28 hectares were within the high vulnerability zone having a DRASTIC index range between 144 and 163.Atomic Absorption Spectroscopy (AAS) was used for to test the concentrations of the heavy metals in the water samples collected and found that aside the presence of other heavy metals, Chromium was found to be present in most Wells and Boreholes measuring between 0.08 mg/L and 0.43 mg/L which is above the World Health Organization Standard of 0.05 mg/L for drinking water. This study concluded that the groundwater is contaminated and the level of pollution is directly related to the distance from the landfill.
Using Chatbot for Augmenting the Design of Virtual Lab Experiments

There is a variety of conversational chatbots that aid in education and they aid students in the process of learning. Similarly, many online tools exist to aid teaching. However, there is a scarcity of tools that help in experimental design and hence to bridge the gap, this paper introduces a conversational agent (chatbot) to augment the design of virtual lab experiments. The chatbot was implemented to answer queries and to help the teachers while using SDViCE tool. The data analysis and results of the research carried on the instructors reveals that chatbots will be useful for designing of virtual lab experiments. The research question we are trying to answer is - Whether the engineering instructors find the chatbot helpful while designing virtual laboratory experiment in the online SDVIcE tool?
Maximum Likelihood Estimation for Disk Image Parameters

We present a novel technique for estimating disk parameters (the center and the radius) from its 2D image. It is based on the maximal likelihood approach utilizing both edge pixels coordinates and the image intensity gradients. We emphasize the following advantages of our likelihood model. It has closed-form formulae for estimating the parameters, therefore, requiring less computational resources than iterative algorithms. The likelihood model naturally distinguishes outer and inner annulus edges. The proposed technique was evaluated on both synthetic and real data.
Designing a shaped balloon machine with a waterwheel mechanism

This paper explores the mechanism design of a balloon machine representing a vending machine where consumers can select their desired shapes, as well as avoid bursting caused by manual inflation. Inside the machine, balloons are placed on the mechanism designed based on a waterwheel which rotates at a constant speed generated by motion among different sizes of gears. When rotating to the corresponding shaped balloon, the inflator will move under the opening to pull it out from the display point and start to inflate the balloon for the set number of seconds. After the inflation is completed, the glass door will open and the customer can take out their balloon. The main purpose of our development and design of the balloon machine is to improve the general balloon blowing and it is easy to prevent over-exposure and blast. The balloon machine can automatically inflate and detect the blowing time to improve the shortcomings of the balloon’s blast. In this way, it can effectively decrease sudden explosions and the number of frightened people.
Multi-hop Syntactic Graph Convolutional Networks for Aspect-Based Sentiment Classification

Sentiment analysis is widely applied to online and offline applications such as marketing, customer service and social media. Aspect-based sentiment classification is a fine-grained sentiment analysis that identifies the sentiment polarity of a specific aspect in a given sentence. In order to model syntactical constraints and word dependencies in a sentence, graph convolutional network (GCN) has been introduced for aspect-based sentiment classification. Though achieved promising results, GCN becomes less effective when the aspect term is far from the key context words on the dependency tree. To tackle this problem, we propose a Multi-hop Syntactic Graph Convolutional Networks model, in which a syntactic graph convolutional network is constructed according to transmission way of information in the sentence structure. Then a multi-range attention mechanism is applied to deepen the number of layers of the model to aggregate further information on the dependency tree. Experiments on benchmarking collections show that our proposed model outperforms the state-of-the-art methods.
Language Modeling for Journalistic Robot based on Generative Pretrained Transformer 2

The language model is typically represented as an unsupervised distribution estimate from a set of examples, each consisting of symbol sequences, and it could predict over sequences of words. We demonstrate the language model based on Generative Pretrained 2 will have a readable generated article for the journalistic robot. Nowadays, there is some trending of journalistic in Indonesia, freedom of the press, and it enables every journalist to make unprofessional news on the media. The problem affects the raise of journalist numbers who have lack journalistic knowledge and increases the amount of inappropriate news content in Indonesia. Therefore, to improve the quality of news produced by the mass media in Indonesia, a journalistic robot is needed to produce news content by the guidelines and the journalistic code of ethics. This research uses language modeling based on GPT-2 to generate articles. The program has four primary steps: building dataset, fine tuning GPT-2, modeling the trained data, and create articles. Furthermore, this research will add an Indonesian model for GPT-2 since the main purpose of this research is Indonesian articles. This paper proposes GPT-2 to be applied to news contents and calculate the result with BLEU scores to check if the results are readable content. These findings show that the proposed model is capable of generating a readable article after trained by 110 Indonesian articles with an excellent score of BLEU.
An efficient circle-type multiparty quantum key agreement protocol with single particles

An efficient circle-type multiparty quantum key agreement protocol utilizing two non-orthogonal bases single particles is proposed. In our protocol, each participant performs one of two simple unitary operations to encode and additional unitary operations to protect his privacy. Both outside attacks and participant attacks can be perfectly resisted. Moreover, our protocol has a high efficiency up to [Formula: see text], where [Formula: see text] is the number of the participants, and it can be easily implemented with current quantum technologies as it needs only single particle measurements.
The Concave n-Square Salient Wood Image-based Quality Assessment

We make an offer of a state-of-the-art method of the deep salient wood image-based quality assessment (DS-WIQA) for no-reference image appraisal. We explore a five-layer deep convolutional neural network (DCNN) for the salient wood image map. The DS-WIQA uses the concave n-square method. The outcomes allow that DS-WIQA model has a greater achievement on Zenodo and Lignoindo datasets, respectively. We appraise a salient wood image map by extracting in small wood image patches. The DS-WIQA has an admirable performance of other recent methods on Zenodo and Lignoindo datasets, respectively. DS-WIQA outdoes other recent techniques by 14.29% and 19.96% more advanced than other techniques with respect to SROCC and LCC measurement, respectively. DS-WIQA shows up to be more significant than the other DCNN methods.
Image Caption Model Based on Multi-Head Attention and Encoder-Decoder Framework

In recently, image caption tasks are solved by using the LSTM to generate description. However, the model only accords image features and is hard to learn existing syntactic features, thereby lead to generate inaccurate description. In this paper, an image captioning model based on multi-head attention mechanism is presented. Specifically, the proposed model adopts Encoder-Decoder framework. A five-layer ResNet is used in Encoder module to extract image features. Multi-head attention layer and full connection feed forward layer are added to Decoder module. In addition, to capture the order of extracting feature sequences, the position-coded is used as a determining factor While calculating multi-head self-attention. Compared With the other current models based on various visual attention mechanisms, experimental results show that the proposed model has better performance.
K-Means and DNN-Based Novel Approach to Human Identification in Low Resolution Thermal Imagery

In this chapter, a human detection system based on unsupervised learning method K-means clustering followed by deep learning approach You Only Look Once (YOLO) on thermal imagery has been proposed. Generally, images in the visible spectrum are used to conduct such human detection, which are not suitable for nighttime due to low visibility, hence for evaluation of our system. Hence, long wave infrared (LWIR) images have been used to implement the proposed work in this chapter. The system follows a two-step approach of generating anchor boxes using K-means clustering and then using those anchor boxes in 252 layered single shot detector (YOLO) to predict proper boundary boxes. The dataset of such images is provided by FLIR company. The dataset contains 6822 images for training purposes and 757 images for the validation. This proposed system can be used for real-time object detection as YOLO can achieve much higher rate of processing when compared to traditional method like HAAR cascade classifier in long wave infrared imagery (LWIR).
Experiments on Iris Recognition Using Partially Occluded Images

In this paper we present some experiments we have made on iris recognition and retrieval on partial occluded images using color features. Our experiments were done on two known iris databases, UBIRIS and UPOL, on HSV color space, using as classifier k-NN (k-Nearest Neighbors). For the k-NN method, three classic measures, Canberra, Euclidean and Manhattan, and two new measures were used in our work. In experiments performed in image retrieval we were interested to see if the color indexing methods we have tested can be used as a simple, fast filter, whose output can be then processed by methods more computationally expensive, with new color or texture indexing methods that can improve the robustness of finding iris images with a similar occluded one.
On Image Compression for Mobile Robots Using Feed-Forward Neural Networks

In mobile robots control, the vision system is placed in the feedback loop, and it requires the ability to extract the necessary information for multiple real-time image and video processing tasks. Image compression techniques are useful in vision systems for large data streaming, like image transmission, archival and retrieval purposes. Neural networks (NN) are widely used in image processing, for solving different issues, with different NN topology, training and testing sets selection, and learning algorithms. Aspects of grayscale image compression for vision system in mobile robots using artificial NNs are discussed in this paper. Several feed-forward neural networks (FFNN) are analyzed, using different structures, input dimensions, neuron numbers, and performance criteria. The goal of the paper is to study the behavior of low-complexity FFNN models for grayscale image compression, with good compression rate and small enough errors for the vision system purposes in mobile robots.