Reviewerly/exHarmony huggingface dataset:
Deep Learning Application of Automated Facemask Classification and Physical-Distancing Detection

COVID-19 adversity has stopped the global economy and most of the countries in the entire world are still in locked down even though there is now a vaccine available to fight the COVID-19 diseases. Many nations including the Philippines proclaimed national emergencies to combat the further spread and to reduce the infections brought about by the COVID-19 virus. People are now getting vaccinated with the COVID-19 yet everyone is still in doubt when this catastrophe will end because the virus has evolved in many variants which is more infectious and deadly compared with the original virus. To fight this disease, health authorities implements wearing of facemask, avoiding crowds, cleaning and washing of hands often and staying at least 1-meter apart. This study aims to detect and classify facemask according to the types of facemask they are wearing and detects the physical distancing observe by the person in the area. Deep Learning is a family of Artificial Intelligence and Machine Learning that emulates the human brain in processing information and creates patterns to be use for decision-making. Dataset uses 4,000 images of the person wearing a facemask and not wearing a facemask and the type of facemask they are wearing including the surgical mask, N95 mask, and cloth mask. A real-time video is used to analyze the observation of physical distancing. The images were trained using the MobileNet model and recorded an accuracy rate of 94.50% during training. The trained model has effectively classified persons wearing a cloth mask, surgical mask, N95 mask, person not wearing a facemask, and detected the persons observing the physical distancing protocol. The study can be implemented in real-time to prevent the spread of COVID-19 by identifying the persons wearing the mask, classify the type of mask they are wearing, and detecting the physical distancing protocols.
Classification of Histopathological Images by Spatial Feature Extraction and Morphological Methods

The high accuracy of the computerized analysis of histopathological images is very important in the detection of cancerous cells. Thanks to the images with high accuracy, early diagnosis will be made with the detection of cancerous cells. Four different types (benign, normal, in situ carcinoma, invasive carcinoma) classification performances will be analyzed by applying various methods to cancer cells. At the beginning of the studies, the BACH data set was obtained, then the desired and usable parts were tried to be extracted with image processing methods. After obtaining data and images of different sizes, their features were extracted with different algorithms (HOG, GLCM, EMP, SIFT, SURF, LBP), and then the accuracy of classifications was examined with RF, KNN, SVM machine learning algorithms and transfer learning algorithm ResNet.
Assessing the Limits of Privacy and Data Usage for Web Browsing Analytics

Web browsing analytics provides insights on the websites that users access, which affects their privacy. Although this analysis might be seen as an easy task, different problems, such as encryption, the tangled web, with several domains visited at the same time in a single web page, or IP addresses of a cloud provider shared by several sites, make it a though job. However, despite these issues, users' privacy is still unaccomplished, as we show in this work. We provide a novel approach that only takes into account the IP addresses that the user has connected to without performing any reverse DNS lookup. We use this sequence of addresses as an input of a neural network, which is able to identify accurately which was the website actually visited among Alexa's World Top 500 most visited domains. Moreover, we have also studied other factors, such as the dependence on the DNS server used to resolve the visited IP addresses, the accuracy for the top domains (e.g., Google, YouTube, Facebook, etc.), data augmentation to improve our results, or the impact on packet sampling. In this last case, we conclude that, using only a 10% of the packets, we can identify the visited website with an accuracy of 93%, whereas it can be over 97% if there is no packet sampling and we use data augmentation.
Bounded Model Checking of Speculative Non-Interference

Spectre, a hardware vulnerability that breaks the isolation between applications, has received ample attention in recent years. Spectre-style attacks exploit speculative execution to leak information through micro-architectural side-channels, breaking down abstractions software developers relied on for decades. As these attacks are based on fundamental optimization techniques present in most modern micro-processors, salvation seems to lie in software-based countermeasures for now. Comprehensive software mitigation, however, has proved to be an exceptionally challenging task with ample of room for failure. To support the automated analysis of mitigation attempts, we present a technique that relies on Bounded Model Checking to detect violations of non-interference in speculative executions. Since off-the-shelf software model checking tools are nescient of micro-architectural state, we base our effort on an operational semantics of speculative executions of micro-assembly code. Our semantics is parameterized with micro-architectural components (such as the cache or the branch predictor), allowing for precise models of various side-channels. We evaluate our approach on widely used benchmark instances, report the detection of a zeroday vulnerability in the Linux kernel, and demonstrate that our approach is more exhaustive than symbolic simulation (with comparable computational effort).
Bayesian Network Based Method for Support Parameter Design of Soft Rock Tunnel

Based on the concise analysis of the influence of support parameters to stability and deformation of surrounding rock mass in soft rock tunnel, Bayesian network and corresponding uncertainty reasoning principle has been introduced to develop an expert system for selection of support parameters of soft rock tunnel with large span. By combining prior knowledge of domain experts with worksite data recorder, we get the posterior probability density of most nodes. The expert system was applied to compare several schemes about the support parameters of one tunnel. The field practices proved that the expert system has good applicability. The Bayesian network can reason the incomplete input information by using the prior knowledge of domain experts and worksite data recorder.
Visual Language for Modeling Cohesion in Hybrid Intelligent Multi-agent Systems

The paper is devoted to the construction of a visual language (alphabet, syntactic and semantic rules) for representing the cohesion of agents of a cohesive hybrid intelligent multi-agent system. The feature of such a system is the presence of mechanisms for the formation of the cohesion of agents, i.e. automatic negotiation by agents of goals, domain models, as well as the development of a problem-solving protocol. Solving problems with such a system is a complex multi-stage process, without visualization of which it is impossible to ensure user confidence in its results. In this regard, the construction of the visual language that provides a figurative representation of the processes occurring in the system, in particular, the emergence and dynamics of the cohesion of agents, is a relevant problem.
Data Analytics Approach in Virtual Educational Space

Modern educational spaces integrate data from the Cyber-Physical-Social System (CPSS) space, which can be analyzed using various techniques. This enables the implementation of intelligent applications and services. The report proposes an approach for modeling learners, based on an analysis of their behavior in the virtual educational space. The four-step approach is applied for Data analysis of students from Burgas free university in the discipline Business Intelligence. The results can be used to understand the application and impact of technology in modern educational spaces.
Gabor Dictionary of Sparse Image Patches Selected in Prior Boundaries for 3D Liver Segmentation in CT Images

The gray contrast between the liver and other soft tissues is low, and the boundary is not obvious. As a result, it is still a challenging task to accurately segment the liver from CT images. In recent years, methods of machine learning have become a research hotspot in the field of medical image segmentation because they can effectively use the “gold standard” personalized features of the liver from different data. However, machine learning usually requires a large number of data samples to train the model and improve the accuracy of medical image segmentation. This paper proposed a method for liver segmentation based on the Gabor dictionary of sparse image blocks with prior boundaries. This method reduced the number of samples by selecting the test sample set within the initial boundary area of the liver. The Gabor feature was extracted and the query dictionary was created, and the sparse coefficient was calculated to obtain the boundary information of the liver. By optimizing the reconstruction error and filling holes, a smooth liver boundary was obtained. The proposed method was tested on the MICCAI 2007 dataset and ISBI2017 dataset, and five measures were used to evaluate the results. The proposed method was compared with methods for liver segmentation proposed in recent years. The experimental results show that this method can improve the accuracy of liver segmentation and effectively repair the discontinuity and local overlap of segmentation results.
A Discussion of Strategies for recording Babbitt’s <i>Composition for Twelve Instruments</i>

This article talks about the strategies used and the problems encountered in the author's process of making a recording of Milton Babbitt's Composition for Twelve Instruments. In order to achieve an accurate and compelling recording on a restrictive budget, each gesture of the piece was recorded individually and later assembled into a coherent whole on the computer. Various issues of rhythm, pitch, dynamics, and phrasing needed to be evaluated and addressed.
Parameter Design for Group Method Data Handling (GMDH) using Taguchi in Software Effort Estimation

Abstract Recently, the use of data-driven models is becoming increasingly impactful but has proven to offer best prediction with less knowledge of the geological, hydrological, and physical process behaviour and criteria. A Group Data Handling Model (GMDH) is one of the sub-model common neural network data driven. It was first developed for complex systems with a modelling and recognition algorithm. GMDH is known as a self-organizing heuristic modelling approach. For solving modelling issues involving multiple inputs to single output data, it is very successful. While the GMDH model has been implemented in many modelling fields, some modifications in terms of parameter design have been given little attention. In other respects, Dr. Genichi Taguchi suggested that the Taguchi method for improving the process or product design with the help of significant parameter levels that influence the delivery of the product. In this paper, we evaluated the behaviour of GMDH model based on numbers of neuron per layer, hidden layer, alpha, and train ratio parameters using Taguchi method. Cocomo and Kemerer datasets are used to test our hypothesized scenarios. The result shows that number of neurons, layer and train ratio are the important parameters that affects the performance of the GMDH model.
JCDL 2021 Tutorial on Systemic Challenges and Computational Solutions on Bias and Unfairness in Peer Review

Peer review is the backbone of scientific research and determines the composition of scientific digital libraries. Any systemic issues in peer review - such as biases or fraud - can systematically affect the resulting scientific digital library as well as any analyses on that library. They also affect billions of dollars in research grants made via peer review as well as entire careers of researchers. The tutorial will discuss various systemic issues in peer review via insightful experiments, several computational solutions proposed to address these issues, and a number of important open problems. A detailed writeup on the topics of this tutorial as well as a complete list of references is available in [1].
Chinese Text Similarity Calculation Model Based on Multi-Attention Siamese Bi-LSTM

Measuring text similarity is a key research area in natural language processing technology. In this research, we proposed a multi-attention Siamese bi-directional long short-term memory (MAS-Bi-LSTM) to calculate the semantic similarity between two Chinese texts. The novel model used Bi-LSTM as the basic framework of the Siamese network, introduced a multi-head attention mechanism to capture the key features of the text, and used the Manhattan distance to calculate the similarity. Experiments were conducted on the large-scale Chinese question matching corpus dataset. Results showed that our model can achieve higher accuracy compared with other comparable models. The F1 value of our model reached 0.8070. The contribution of this research is to use the multi-head attention mechanism to re-weight the semantic features, and explore the influence of different pre-training corpus, distance formulas and heads of multi-attention on the model.
Object and Contour Detection with an Architecture-Fusion Network

We present a new, simple, flexible framework for object and contour detection called Architecture-Fusion Network (AFN), which fuses two basic deep learning architecture by sharing partly common stages to detect objects and their contours simultaneously and does not complicate their respective network architectures. In this paper, we train four AFN instances end-to-end on the modified PASCAL VOC 2012 dataset and effectively reduce the misrecognition of both background edges and internal edges inside objects without compromising object detection precision. Compared with each network itself, the tests on the refined PASCAL-val dataset show our instances increase Optimal Dataset Scale (ODS) by a range of 1.5% to 5.3% on object contour detection with object detection precision fluctuating slightly. Furthermore, each AFN instance has fewer parameters than the state-of-the-art network RCN. Compared with RCN on the same training dataset(RCN-VOC), the best Average Precision (AP) of our AFN instances could be up to 9.7% higher while its ODS descends only 0.9%.
SOSE-Based E-Government Systems: A Systematic Literature Review

Purpose: E-government is a means of providing services by facilitating communication between government and citizens, government and businesses and among levels of government in a robust, flexible and efficient manner. Service-Oriented Software Engineering (SOSE) is a recognized approach of developing robust, flexible and efficient E-government system by transformation of existing systems. However, several challenges and issues in SOSE-Based E-government systems need to be addressed to attend the desired goal. This paper used systematic literature review (SLR) to identify, classify and analyze primary studies published in the research community from 2010 to 2021 that focused on SOSE approach for E-government systems. Methodology/Approach: A review protocol was applied to gather 61 relevant primary studies that were critically analyzed, classified and evaluated. Findings/Result: The results shows that research was active on this topic within the reviewed period. The review’s record shows that the highest number of publication of 10 was in 2010 and lowest number of publication of 2 was in 2016. Furthermore, interoperability received the highest attention by researchers with 22 publications, whereas security received the lowest attention with 2 publications in the reviewed period. It is recommended that further research be conducted on modelling in view of the heterogeneous and dynamic nature of business operations in governance. In addition, further research is required toward SOSE security for protection of E-government systems. Originality/Value: A new research direction identified on SOSE-Based E-Government System Paper Type: Review Research Paper.
Arranging resource amount after automatic GPU offloading

Heterogeneous hardware other than a small-core central processing unit (CPU) such as a graphics processing unit (GPU), field-programmable gate array (FPGA), or many-core CPU is increasingly being used. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, we have proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once written code, in accordance with the hardware to be placed. However, although it has been considered to convert the code according to the offload devices, there has been no study to set the amount of resources appropriately after automatic conversion. In this paper, as a new element of environment-adapted software, we examine a method of optimizing the amount of resources of the CPU and offload device in order to operate the application with high cost-performance. Through the offloading of multiple existing applications, we confirm that the proposed method can meet the user’s request and set an appropriate amount of resources.
Dual Enhancement for Multi-Label Learning with Missing Labels

The goal of multi-label learning with missing labels (MLML) is assigning each testing instance multiple labels given training instances that have a partial set of labels. The most challenging issue is to complete the missing labels by leveraging input signals and given labels. Existing methods usually complete missing entries by utilizing label correlation, which, however, overlook knowledge from the object itself. To tackle this problem, we propose a dual enhancement framework that enhances missing entries from the label- perspective and instance-perspective. First, we develop a Long Short-Term Memory (LSTM) network that utilizes sequential label correlations to complete missing labels. Second, we expand the training dataset via a MixUp procedure to further enhance the label confidence. Extensive experiments on three multi-label image datasets clearly verify the superiority of our proposed method to the baselines.
Cloud detection of space-borne video remote sensing using improved Unet method

Cloud detection is important for the application of space-borne video remote sensing. Video data of Chinese Jilin-1 is detected through migration learning and improved Unet with fully connected conditional random field. Due to the interference of fast movement of cloud targets and satellite platform jitter in video satellite remote sensing, it is difficult for Unet network depth to effectively extract the context characteristics of cloud targets, and effect of segmentation and cloud detection is poor. To solve the problem of missing cloud target extraction features, this paper uses the VGG16 pretraining model as the backbone network of the context path, and refines the segmentation results using the fully connected conditional random field (Fully Connected / Dense CRF) to improve cloud boundary pixel localization. The results show that the proposed algorithm can effectively improve the model segmentation accuracy, where the accuracy and intersection ratio reach 92.6% and 90.9%. The proposed network has strong generalization and high practical application value.
Different Testing Results on SVM with Double Penalty Parameters

The Support Vector Machine proposed by Vapnik is a generalized linear classifier which makes binary classification of data based on the supervised learning. SVM has been rapidly developed and has derived a series of improved and extended algorithms, which have been applied in pattern recognition, image recognition, etc. Among the many improved algorithms, the technique of regulating the ratio of two penalty parameters according to the ratio of the sample quantities of the two classes has been widely accepted. However, the technique has not been verified in the way of rigorous mathematical proof. The experiments based on USPS sets in the study were designed to test the accuracy of the theory. The optimal parameters of the USPS sets were found through the grid-scanning method, which showed that the theory is not accurate in any case because there is absolutely no linear relationship between ratios of penalty parameters and sample sizes.
Benchmarking Monocular Depth Estimation Models for VR Content Creation from a User Perspective

Exploring monocular images and videos in 3D in Virtual Reality (VR) requires reliable methods for estimating depth as this has not been captured in the original footage. There are monocular depth estimation methods that address this requirement by using neural network models to predict depth values for each pixel. However, as this is a quickly moving field and the state-of-the-art is constantly evolving, how do we choose the best depth estimation model for 3D content creation for VR? It can be difficult to interpret the widely used benchmark metrics and how they relate to the quality of the created 3D/VR content.In this paper, we explore a user-centred approach to evaluating depth estimation models with regard to 3D content creation. We look at evaluating these models based on content created with these models that an end-user might see, rather than just their immediate output (depth maps) that does not directly contribute to users&#x2019; perceptual experience. In particular, we investigate the relationship between commonly used depth estimation metrics, image similarity metrics applied to synthesised novel viewpoints, and user perception of quality and similarity of these novel view-points. Our results suggest that the standard depth estimation metrics are indeed good indicators of 3D quality, and that they correspond well with human judgements and image similarity metrics on novel viewpoints synthesised from a range of sources of depth data. We also show that users rate a state-of-the-art depth estimation model as almost visually indistinguishable from the outputs derived from ground truth sensor data.
Local Binary Pattern with Neighborhood Relationship (LBPNR) -A novel approach for Texture Classification

Texture is inclusive in natural images and also a prime feature of the availability of virtually all natural surfaces. Classification of Texture has accepted noticeable attention during the past decades. A fruitful classification of texture analysis provides a valuable representation of image texture. Many sophisticated methods have been developed for expressing image texture. Application of Local Binary Pattern (LBP) in texture classification is one of the prominent methods. Modifying this to a new method namely Local Binary Pattern with Neighborhood Relationship (LBPNR) is proposed here for texture classification. It determines two binary patterns where the first binary pattern obtains by considering the relationships of a central pixel with its neighboring pixels and the 2nd one comes from the relationships among neighboring pixels. Ultimately the final binary pattern drives from the exclusive OR operation of these two binary patterns. By applying this method a unique local region can be boldly distinguished from all others and yields an improvement in the classification process. In this paper, we used Brodatz Dataset including 16 texture classes where everyone contains eight 256 × 256 root images. The proposed model outperformed with a classification rate of 93.27% than that of other methods in this domain.