Reviewerly/exHarmony huggingface dataset:
Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction

Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners.
An efficient autonomous vehicle navigation scheme based on LiDAR sensor in vehicular network

Recently, autonomous vehicles navigation (AVN) attracted many researches trying to improve road traffic without the human intervention. One of the main challenges in AVN is allowing a vehicle to discover its moving trajectory with a reduced computational complexity. To cope with this issue, we propose in this paper a new simulated annealing algorithm to discover an optimal trajectory when the vehicle encounters an obstacle using LiDAR perception. The found trajectory is then sent to a roadside unit (RSU), which communicates this discovery to other nodes in the network for further use. During its navigation, the vehicle perceives the environment by a LiDAR sensor to detect an eventual obstacle and launches an optimal path discovery to reach the final destination in a reduced time. The results obtained showed the effectiveness of our proposal to find an optimal route compared to Dijkstra algorithm.
Importance-aware personalized learning for early risk prediction using static and dynamic health data

Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.Data were from 6367 patients with Peptic Ulcer Bleeding between 2007 and 2016. This article develops a novel End-to-end Importance-Aware Personalized Deep Learning Approach (eiPDLA) to achieve accurate early clinical risk prediction. Specifically, eiPDLA introduces a long short-term memory with temporal attention to learn sequential dependencies from time-stamped records and simultaneously incorporating a residual network with correlation attention to capture their influencing relationship with static medical data. Furthermore, a new multi-residual multi-scale network with the importance-aware mechanism is designed to adaptively fuse the learned multisource features, automatically assigning larger weights to important features while weakening the influence of less important features.Extensive experimental results on a real-world dataset illustrate that our method significantly outperforms the state-of-the-arts for early risk prediction under various settings (eg, achieving an AUC score of 0.944 at 1 year ahead of risk prediction). Case studies indicate that the achieved prediction results are highly interpretable.These results reflect the importance of combining static and dynamic health data, mining their influencing relationship, and incorporating the importance-aware mechanism to automatically identify important features. The achieved accurate early risk prediction results save precious time for doctors to timely design effective treatments and improve clinical outcomes.
Wasserstein-2 Generative Networks

We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance). The algorithm uses input convex neural networks and a cycle-consistency regularization to approximate Wasserstein-2 distance. In contrast to popular entropic and quadratic regularizers, cycle-consistency does not introduce bias and scales well to high dimensions. From the theoretical side, we estimate the properties of the generative mapping fitted by our algorithm. From the practical side, we evaluate our algorithm on a wide range of tasks: image-to-image color transfer, latent space optimal transport, image-to-image style transfer, and domain adaptation.
Steganography algorithm based on modified EMD-coded PU partition modes for HEVC videos

Abstract As High Efficiency Video Coding (HEVC) is a worldwide popular video coding standard, the steganography of HEVC videos has gained more and more attention. Prediction unit (PU) is one of the most important innovative modules of HEVC; thus, PU partition mode-based steganography is becoming a novel branch of HEVC steganography. However, the embedding capacity of this kind of steganography is limited by the types of PU partition modes. To solve the problem, modified exploiting modification direction (EMD)-coded PU partition mode-based steganography is proposed in this paper, which can hide a secret digit in a (2 n + x − 1)-ary notational system in a pair of PU partition modes and thus enlarging the capacity. Furthermore, two mapping patterns for PU partition modes are analyzed, and the one that performs the better is selected as the final mapping pattern. Firstly, 8 × 8- and 16 × 16-sized PU partition modes are recorded according to the optimal mapping pattern in the video encoding process. Then, PU partition modes are modified by using the proposed method to satisfy the requirement of secret information. Finally, the stego video can be obtained by re-encoding the video with the modified PU partition modes. Experimental results show that the embedding capacity can be significantly enlarged, and compared with the state-of-the-art work, the proposed method has much larger capacity while keeping high visual quality.
Dawning of Progressive Web Applications (PWA): Edging Out the Pitfalls of Traditional Mobile Development

Over the years, there has been a constant increase in the demand for mobile software due to the constant increase in the number of smart phones. Mobile developers have the liberty to adopt different development architectures or strategies which includes the native app, mobile web app, hybrid app and the new Progressive Web App (PWA). PWA which combines the features of the native and web development strategies emerged as a better alternative to other development approaches due to additional benefits such as offline capability, background synchronization and so on despite several concerns that have been raised towards the efficiency of PWAs. Hence, this research work aims at performing a comparative study on the existing mobile development architectures using the Systematic Literature Review (SLR) technique, performing feature comparison on the native, hybrid and PWA architecture and finally argues for the PWA development architecture based on the comparisons. The comparison will aid researchers and development firm in understanding the concept of PWA thereby motivating them to adopt this strategy for further development.
Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.
Ultra-Short-Term Solar PV Power Forecasting Method Based on Frequency-Domain Decomposition and Deep Learning

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of power grid and the optimal operation of PV power station itself. However, due to various meteorological factors, the photovoltaic power has great fluctuations. To improve the refined ultra-short-term forecasting technology of PV power, this paper proposes an ultra-short-term forecasting model of PV power based on optimal frequency-domain decomposition and deep learning. First, the amplitude and phase of each frequency sine wave is obtained by fast Fourier decomposition. As the frequency demarcation point is different, the correlation between the decomposition component and the original data is analyzed. By minimizing the square of the difference that the correlation between low-frequency components and raw data is subtracted from the correlation between high-frequency components and raw data, the optimal frequency demarcation points for decomposition components are obtained. Then convolutional neural network is used to predict low-frequency component and high-frequency component, and final forecasting result is obtained by addition reconstruction. Finally, the paper compares forecasting results of the proposed model and the non-spectrum analysis model in the case of predicting the 1 hour, 2 hours, 3 hours, and 4 hours. The results fully show that the proposed model improves forecasting accuracy.
BioExpDNN: Bioinformatic Explainable Deep Neural Network

In recent years, machine learning is applied in the bioinformatics and medical fields to analyze relationships among biological features and behaviors. However, it is difficult to discover the significant features of large-scale datasets. A novel feature extraction method called bioinformatic explainable deep neural network (BioExpDNN) is proposed to filter the critical features with strong influences on the dataset and to explain the interaction of features. In the practical experiments, this study adopted three biomedical science datasets from the UCI (University of California, Irvine) Machine Learning Repository: (1). Cryotherapy Data Set (CDS) contains 6 attributes and 2 classes (i.e., recovery and non-recovery); (2). Cervical Cancer Behavior Risk Data Set (CCBRDS) consists of 18 attributes and 2 classes (i.e., cervical cancer patient and healthy body); (3). Heart Failure Clinical Records Data Set (HFCRDS) includes 12 clinical attributes and 2 classes (i.e., death and life). In comparison results, extracted features were considered as inputs of a classifier based on deep neural network for classification. The classification accuracy was selected as an evaluation factor to evaluate the performance of feature extraction methods. The experimental results showed that the classification accuracies of CDS, CCBRDS, and HFCRDS were 92.59%, 100%, and 78.9%, respectively.
PENGEMBANGAN BUKU CERITA BERGAMBAR BERBASIS KARAKTER UNTUK PEMBELAJARAN MEMBACA SISWA SD KELAS IV

This development research with the design of Research and Development (R & D) is motivated by the lack of children's reading interest and is not supported by any quality children's reading material or stories.Most of the story books that are developing in the market are children's story books which are based on pictures and are fictional in nature so that they cannot stimulate children's thinking patterns.Even though it still contains moral values and educational values that can be emulated, the story can only be used as a means of entertainment.The picture story book is a unique and interesting medium by combining images and text in a creative form.The purpose of this study was to describe the need for picture book development, describe the characteristics of the needs for book development, to obtain the results of book product testing so that it is suitable for use as learning to read elementary students.This research is a development research with seven stages of research, starting from needs analysis to limited product testing.The research was conducted in three sample schools in the city of Purwokerto, namely SD N 2 Karanglewas Lor, SD N 1 Bantarsoka, SD N 1 Pasir Kidul.The data instruments used include: observation guidelines, interview guidelines, questionnaires and expert assessment instruments.The results of this study indicate that the product is good and feasible as a learning resource.Furthermore, based on the results of the product effectiveness test in learning the use of attractive images, clear, communicative sentences, it also makes it easier for students to understand the material / content of the book.
Mish: A Self Regularized Non-Monotonic Activation Function.

We propose $\textit{Mish}$, a novel self-regularized non-monotonic activation function which can be mathematically defined as: $f(x)=x\tanh(softplus(x))$. As activation functions play a crucial role in the performance and training dynamics in neural networks, we validated experimentally on several well-known benchmarks against the best combinations of architectures and activation functions. We also observe that data augmentation techniques have a favorable effect on benchmarks like ImageNet-1k and MS-COCO across multiple architectures. For example, Mish outperformed Leaky ReLU on YOLOv4 with a CSP-DarkNet-53 backbone on average precision ($AP_{50}^{val}$) by 2.1$\%$ in MS-COCO object detection and ReLU on ResNet-50 on ImageNet-1k in Top-1 accuracy by $\approx$1$\%$ while keeping all other network parameters and hyperparameters constant. Furthermore, we explore the mathematical formulation of Mish in relation with the Swish family of functions and propose an intuitive understanding on how the first derivative behavior may be acting as a regularizer helping the optimization of deep neural networks. Code is publicly available at this https URL.
Development of an Eye-Gaze Input System With High Speed and Accuracy through Target Prediction Based on Homing Eye Movements

In this study, a method to predict a target on the basis of the trajectory of eye movements and to increase the pointing speed while maintaining high predictive accuracy is proposed. First, a predictive method based on ballistic (fast) eye movements (Approach 1) was evaluated in terms of pointing speed and predictive accuracy. In Approach 1, the so-called Midas touch problem (pointing to an unintended target) occurred, particularly when a small number of samples was used to predict a target. Therefore, to overcome the poor predictive accuracy of Approach 1, we developed a new predictive method (Approach 2) using homing (slow) eye movements rather than ballistic (fast) eye movements. Approach 2 overcame the disadvantage (inaccurate prediction) of Approach 1 by shortening the pointing time while maintaining high predictive accuracy.
Detecting spam e-mails using stop word TF-IDF and stemming algorithm with Naïve Bayes classifier on the multicore GPU

&lt;span&gt;A spam filter is a program which is used to identify unwanted emails and prevents those messages from getting into a user's mail. The study was focused on how the algorithms can be applied on a number of e-mails consisting of both ham and spam e-mails. First, the working principle and steps which are followed for implementation of stop words, TF-IDF and stemming algorithm on NVIDIA’s Tesla P100 GPU are discussed and to verify the findings by executing of Naïve Bayes algorithm. After complete training and testing of the spam e-mails dataset taken from Kaggle by using the proposed method, we got a high training accuracy of 99.67% and got a testing accuracy of about 99.03% on the multicore GPU that boosted the speed of execution of training time period and testing time period which is improved of training and testing accuracy around 0.22% and 0.18% respectively when compared to that after applying only Naïve Bayes i.e. conventional method to the same dataset where we found training and testing accuracy to be 99.45% and 98.85% respectively. Also, we found that training time taken on GPU is 1.361 seconds which was about 1.49X faster than that taken on CPU which is 2.029 seconds. And the testing time taken on GPU is 1.978 seconds which was about 1.15X faster than that taken on CPU which is 2.280 seconds.&lt;/span&gt;
Automated Classification of Evidence of Respect in the Communication through Twitter

Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is now possible to automatically detect and counter hate speech in textual data spreading rapidly, for example, in social media. However, recently another approach to tackling the roots of disrespect was proposed, it is based on the concept of promoting positive behavior instead of only penalizing hate and disrespect. In our study, we followed this approach and discovered that it is hard to find any textual data sets or studies discussing automatic detection regarding respectful behaviors and their textual expressions. Therefore, we decided to contribute probably one of the first human-annotated data sets which allows for supervised training of text analysis methods for automatic detection of respectful messages. By choosing a data set of tweets which already possessed sentiment annotations we were also able to discuss the correlation of sentiment and respect. Finally, we provide a comparison of recent machine and deep learning text analysis methods and their performance which allowed us to demonstrate that automatic detection of respectful messages in social media is feasible.
Quantum Computing Fundamentals

This chapter introduces the fundamental concepts and rules of quantum computing. In parallel, it develops an initial, easy-to-understand codebase in Python for building and simulating small-scale quantum circuits and algorithms.
Visibility-aware Multi-view Stereo Network

Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration, resulting in erroneous cost aggregation from occluded pixels. In this paper, we explicitly infer and integrate the pixel-wise occlusion information in the MVS network via the matching uncertainty estimation. The pair-wise uncertainty map is jointly inferred with the pair-wise depth map, which is further used as weighting guidance during the multi-view cost volume fusion. As such, the adverse influence of occluded pixels is suppressed in the cost fusion. The proposed framework Vis-MVSNet significantly improves depth accuracies in the scenes with severe occlusion. Extensive experiments are performed on DTU, BlendedMVS, and Tanks and Temples datasets to justify the effectiveness of the proposed framework.
Dense Residual Generative Adversarial Network for Rapid Rain Removal

Single-image rain removal always was one of the difficulties in the environment perception task. Usually, it has two paths to solve this problem: data-driven solutions and model-based solutions. Due to the benefits of convenient, learning features automatically and rapidly, data driven solutions has attracted tremendous interests. However, the time consumed per frame is still hard to match the requirement of high real-time performance, especially for high speed unmanned platform. In this article, we propose a fast dense residual generative adversarial network (FDRN), which can remove rain and reduce computation time consumption, the de-raining time of each frame only consumes 0.02s. We enhanced the data of original rainy images, put it into the generator network which is composed of long short-term memory networks (LSTM) and a newly designed dense residual network (DRN). The feature map in generator and discriminator is extracted to calculate the loss function and guide the direction of training. We selected 1500 pairs of synthetic images from existed datasets to train our network. And in order to test our method's de-raining ability realistically, we also selected 147 real-world rainy images from existed datasets. Experiments on both synthetic and real-world rainy images demonstrate that the proposed method can achieve competitive results to some existing methods in performance and effectiveness.
Petri Net based modeling and analysis for improved resource utilization in cloud computing

The cloud is a shared pool of systems that provides multiple resources through the Internet, users can access a lot of computing power using their computer. However, with the strong migration rate of multiple applications towards the cloud, more disks and servers are required to store huge data. Most of the cloud storage service providers are replicating full copies of data over multiple data centers to ensure data availability. Further, the replication is not only a costly process but also a wastage of energy resources. Furthermore, erasure codes reduce the storage cost by splitting data in n chunks and storing these chunks into n + k different data centers, to tolerate k failures. Moreover, it also needs extra computation cost to regenerate the data object. Cache-A Replica On Modification (CAROM) is a hybrid file system that gets combined benefits from both the replication and erasure codes to reduce access latency and bandwidth consumption. However, in the literature, no formal analysis of CAROM is available which can validate its performance. To address this issue, this research firstly presents a colored Petri net based formal model of CAROM. The research proceeds by presenting a formal analysis and simulation to validate the performance of the proposed system. This paper contributes towards the utilization of resources in clouds by presenting a comprehensive formal analysis of CAROM.