Reviewerly/exHarmony huggingface dataset:
Everyone is A Forensic Artist: Sketch-to-Photo Transformation for Human Face

There are a lot of crimes happening all over the world every day. Among the criminal acts, homicide is the type of crime with a large number of victims. Occassionally there are witnesses who see the incident and remember the face of the criminal. Thus the police ask them to sketch to find out the suspect. Since the human face is the most significant and informative part of the human body, the sketch of the face is used to identify the suspect with high certainty. However, the suspects may change their facial features by makeup, such as putting on glasses or dyeing hair. If a sketch is converted into photographic images with modified facial features flexibly, the investigation of crime might accelerate effectively. Recent research has shown the techniques that transform a sketch of a human face into a photographic image or change the style of a human face according to the designated facial features. However there has not yet been an integrated architecture to transform pencil sketches to photographic images directly with desired facial features. In addition there is no fast and automatic evaluation approach that consider multiple metrics of images jointly. It is hence difficult to optimize and select the setting among a few alternatives efficiently. With the limitation we propose the Forensic GAN a network of performing the sketch-to-photo transformation and image manipulation according to the designated attributes. A voting mechanism with multiple metrics including PSNR SSIM SCC and ERGAS for fast evaluating the image quality jointly was proposed. Accordingly the training settings such as the loss function and the number of epochs can be optimized and selected based on the quality of the synthesized images. The performance of the Forensic GAN was tested and its potential for the real forensic task has been verified with synthesized pencil images and real hand-drawing sketches
Fast 3D measurement method based on improved digital image correlation using grid-based feature extraction

In this paper, we propose an enhanced IC-GN algorithm that selects multiple seed points based on the grid to facilitate parallel processing of global points, enabling highly accurate 3D measurements with high computational efficiency.
The Evaluation of an Online English Course for Tourism Workers in Ninh Binh from Learners’ Perspective

his article presents the results of a study designed to measure the evaluation of learners on an online English course. A survey questionnaire was distributed to 20-course attendees, who are tourism workers in Ninh Binh province. The results of the survey reveal learners’ positive attitudes towards (1) the course content, (2) the course approach, (3) the course materials, (4) the course management, and (5) the perceived course outcomes. Besides, semi-structured interviews were carried out to pinpoint the difficulties learners encountered throughout the course. Based on the preliminary analyses of this questionnaire form, suggestions for a more efficient application of this online course are proposed.
A new ideal secret sharing scheme based on a tree

One of the methods used in order to protect a secret K is a secret sharing scheme. In this method, the secret K is distributed among a finite set of participants P by a special participant called the dealer, assumed to be honest, in such a way that only predefined subsets of participants can recover the secret after collaborating with their secret shares. The construction of secret sharing schemes has received a considerable attention of many researchers whose main goal was to improve the information rate. In this paper, we focused on the hierarchical secret sharing. Related to the previous works, we propose a novel simpler construction of a secret sharing scheme, where the access structure is a tree according to the hierarchical concept of companies illustrated through its organization chart. We prove that the proposed scheme is ideal by showing that the information rate equals 1. In order to show the efficiency of the proposed scheme, we discuss all possible kinds of attacks and prove that the security is ensured. Finally, we include a detailed didactic example of a small company organization chart.
PupilFace: A Cascaded Face Detection and Location Network Fusing Attention

Although the development of uncontrolled face detection and location technology have made great progress, there are some problems needing to be solved in more complicated situation, such as massive occlusion and pose variation. In this paper, we propose a robust one-stage face detection and location network named PupilFace. It can locate faces of different sizes at the pixel level in complex scenarios. Specifically, we have made contributions in the following three aspects: (1) Using a lightweight backbone, we can not only detect images of dense faces, but also mark facial landmarks in pictures of various scale. In this paper, the pictures are difficult to detect because of massive occlusion or tiny faces. On the WIDER FACE hard test set, PupilFace performs better than other state-of-the-art networks. (2) The addition of the attention module–Hard Efficient Channel Attention (HECA), proposed by us, enhances the connection between the feature channels and improves the detection performance without reducing the dimension. The parameters and computations of HECA, against the parameters and computations of MobileNetV2 are 9 vs. 3.34M and 5.1e−4 GFLOPs vs. 0.32 GFLOPs. (3) We can employ varying-depths backbones accordingly to different detection and location tasks, so the model can be popularized in different fields. Extra annotations and code have been made available at: https://github.com/Ideal-maths/PupilFace.
Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data

We review Bayesian models developed for understanding how the microbiome varies within a population of interest. We focus on integrative analyses, where the goal is to combine microbiome data with other available information to identify significant associations between taxa and a set of predictors. For this, we describe a general class of hierarchical Dirichlet-multinomial regression models that use spike-and-slab priors for the selection of the significant associations. We further illustrate extensions that accommodate various parameterizations of a predictor’s prior probability of inclusion and the phylogenetic structure of microbiome data. We discuss advantages and limitations of the methods with respect to current approaches used in the microbiome community, demonstrate an R package, MicroBVS, which enables researchers to apply these methods to microbiome data in practice, and present results on the analysis of real datasets.
The development of the chemical mineralogy in Russia in the late 18th - early 19th century in the works of V. M. Severgin

The late 18th and early 19th centuries saw a clear differentiation of sciences. First of all, general mineralogy split into oryctognosy (mineralogy-proper), geognosy (geology), and geogeny (geotectonics). Paleontology became a discipline of its own a bit later, as well as crystallography. Fossils and rock formations were excluded from mineralogy. The concept of a 'mineral' became clearer. The verbal description of minerals was complemented with their accurate crystallographic measurements, the one-circle reflecting goniometer was invented, and crystallographic methods became crucial for mineral diagnostics. The period between the 1790es and the 1830es is characterized by the rapid development of chemical concepts, the discovery of many chemical elements, and the study of physical and chemical properties of well-known and newly-discovered minerals. V.M. Severgin played a key role in the establishment of chemical mineralogy in Russia. Severgin wrote numerous works in mineralogy and chemistry and published translated works by foreign scientists complemented with his many contributions. As a disciple of M.V. Lomonosov's ideas, he proved that mineralogy must be based on chemistry in his works. Severgin provided one of the first definitions of chemical mineralogy as a subject that deals with the decomposition of minerals into their components, their compounding, and changes they undergo due to chemical processes. The works of V.M. Severgin are of historical value and they can be seen as a source of learning about the history of mineralogy and chemistry in our country. Some ideas proposed by V.M. Severgin remain relevant to this day. These include the following: 1 the chemical mineralogy; 2 the applied study of paragenetic mineral associations; 3 the use of many mineralogical and chemical terms coined by the scientist.
Creative Interventions in Corporate Museums and the Transformation of a Company’s Communication Space

Modern corporate museums need to use creativity in the processing of all components of the museum space when interacting with target audiences. Corporate museums play an important role as a custodian and translator of the organization’s values to the external and internal environment. The methodological basis of the work was the concept of P. Bourdieu about four types of capital, with an emphasis on cultural capital as a means of forming a single communication space of the organization and translating of the organization’s values. In the article we study distinctive characteristics of corporate museums and functional dimensions of a corporate museum subbrand, using T. Gad’s model. The article presents the results of a sociological study of corporate museums. These results enable to come to conclusion about trends of development of capitalisation of values of corporate museums and of communication opportunities of museum space which are designed to enhance the communication effects of the company’s brand.
A Novel Scrambled Data Hiding Approach Towards Securing Sensitive Data

Our world is making its way toward complete digitization, with more and more data going to the Internet. Thus, the security of the data has become one of our utmost responsibilities. Strong and secure cryptography and steganography techniques have become the need of the hour to defend against modern adversaries. In this article, a novel scrambled data hiding approach is proposed to secure the sensitive information used in communications. The lacuna in the existing data hiding approaches is the loss of originality in the sensitive data to be attached, leading to poor information quality. This issue is addressed in the proposed approach by encrypting the data using the triple data encryption standard (DES) algorithm. The scrambled data are hidden in the audio file using the least significant bit (LSB) embedding. Experiments were conducted, and the proposed approach secured the sensitive information significantly without the loss of information quality. Furthermore, the secured information is retrieved by only the authenticated users with high quality.
Cross-modality Attention Method for Medical Image Enhancement

In this paper, we propose a cross-modality attention method (CMAM) for medical image enhancement especially in magnetic resonance imaging, which typically addresses the issue of exploiting the clean feature by generative model. To realize this goal, we distill the complementary information directly from different modalities of raw input images for reliable image generation. More specifically, our method integrates local features with exploiting the semantic high-order dependencies and thus explores attentional fields for robust feature representation learning. To evaluate the effectiveness of our CMAM, we conduct folds of experiments on the standard benchmark Brats 2019 dataset and experimental results demonstrate the effectiveness of CMAM.
‘Continuous’ and ‘Discontinuous’ in German History Studies - Diagnosis and Prospects of the 2019-2020 Research Responding to the Crisis -

This paper comprehensively reviews the achievements of German history research in Korea during the last two years (2019-2020). First, the research results during that period decreased slightly in terms of the total number of published papers amid the impact of Covid-19, but it showed unprecedented great progress in monographs including books and translations. Second, if we look at the research in the field classified as ‘continuous’, it leads to a further expansion of the scope of research in the field of transnational history, while in history education and public history a large number of research papers have also been published. In addition, certain research results were also found in the field of political history and studies related to German unification. Third, looking at the research fields grouped as ‘discontinuous’, although many researchers did not participate, meaningful research results were still obtained in social movement history, corporate history, economic history, urban history, etc. Good papers have been published in studies of the early modern era, including the history of emotions. One of the characteristics of Korean studies on German history is that it responds to the demands of the times, and it is quite unfortunate that there are too few research results in women/gender history and environmental history, which are representative fields that require such a role. Finally, in the context of a new historical interpretation caused by the coronavirus crisis, it is expected that German history researchers will take the lead.
Spark-Based Sentiment Analysis of Tweets Using Machine Learning Algorithms

Analysis of sentiment is an important aspect of machine learning and has found its vivid application in marketing and sales, political campaigns, etc. analysis of product reviews, company reviews have become an integral part of modern-day marketing, but it is difficult to process so many product reviews and ratings hence to cumulate all these we can use a neutral platform, social media. This paper addresses these problems of sentiment analysis of tweets; using machine learning techniques and big data analytics. We implement spark framework to achieve the map-reduced model. The result of the analysis shows that Support Vector Machine outperforms the other algorithms while considering both time consumption and accuracy.
Balanced Q-learning: Combining the influence of optimistic and pessimistic targets

The optimistic nature of the Q−learning target leads to an overestimation bias, which is an inherent problem associated with standard Q−learning. Such a bias fails to account for the possibility of low returns, particularly in risky scenarios. However, the existence of biases, whether overestimation or underestimation, need not necessarily be undesirable. In this paper, we analytically examine the utility of biased learning, and show that specific types of biases may be preferable, depending on the scenario. Based on this finding, we design a novel reinforcement learning algorithm, Balanced Q-learning, in which the target is modified to be a convex combination of a pessimistic and an optimistic term, whose associated weights are determined online, analytically. Such a balanced target inherently promotes risk-averse behavior, which we examine through the lens of the agent's exploration. We prove the convergence of this algorithm in a tabular setting, and empirically demonstrate its consistently good learning performance in various environments.
Relaxation of Some Confusions about Confounders

This work is about observational causal discovery for deterministic and stochastic dynamic systems. We explore what additional knowledge can be gained by the usage of standard conditional independence tests and if the interacting systems are located in a geodesic space.
Soccer Field Registration Based on Geometric Constraint and Deep Learning Method

Registering a soccer field image into the unified standard template model image can provide preconditions for semantic analysis of soccer videos. In order to complete the task of soccer field registration, the soccer field marker lines need to be detected, which is a challenging problem. In order to solve the problem, we propose a soccer field registration framework based on geometric constraint and deep learning method. We construct a multi-task learning network to realize marker lines detection, homography matrix calculation and soccer field registration. Firstly, the input image is preprocessed to remove the background and occlusion areas and extract the marker lines to obtain the edge map of the soccer field; then, we extract the features on the edge map and on the standard template model image to calculate the homography matrix. In this paper, a two-stage deep training network is proposed. The first stage mainly completes the soccer field marker lines detection and the initial calculation of the homography matrix. The second stage mainly completes the optimization of the homography matrix using geometric constraint, which can provide more accurate homography matrix calculation. We propose to integrate the geometric constraint of the marker lines into the multi-task learning network, in which structural loss is constructed to import prior information such as the shape and direction of the marker lines of the soccer field. We evaluate our method on the World Cup dataset to show its performance against the state-of-the-art methods.
Early Detection System of Cataract using Haar-like Feature-Based Cascade Classifiers

research-article Share on Early Detection System of Cataract using Haar-like Feature-Based Cascade Classifiers Authors: Pandu Dewabrata University of Brawijaya, Indonesia University of Brawijaya, IndonesiaView Profile , Yuita Arum Sari Faculty of Computer Science, Brawijaya University, Indonesia Faculty of Computer Science, Brawijaya University, IndonesiaView Profile , Hera Dwi Novita University of Brawijaya, Indonesia University of Brawijaya, IndonesiaView Profile , Samsul Arifin University of Brawijaya, Indonesia University of Brawijaya, IndonesiaView Profile Authors Info & Claims SIET '21: 6th International Conference on Sustainable Information Engineering and Technology 2021September 2021 Pages 168–172https://doi.org/10.1145/3479645.3479672Published:03 November 2021Publication History 0citation62DownloadsMetricsTotal Citations0Total Downloads62Last 12 Months54Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Scene-Aware Ensemble Learning for Robust Crowd Counting

Crowd counting models usually suffer from sharp fluctuations when trained on one specific dataset but tested on the other one with huge scene deviation. To alleviate this problem, we propose a Scene-aware Ensemble Learning CNN model (SEL-CNN) for robust cross-scene crowd counting. Firstly, crowd scenes are divided into three levels: low, middle, and high level, and then one three-parallel-branch based ensemble learning framework is developed to regress density maps for three-level scenes, respectively. Secondly, one scene-aware branch is introduced to learn global weight parameters, which performs as a scene adaptive weighting scheme that dynamically fuses three outputs to generate a final prediction map. Moreover, a Combination-Division-Iteration-based three-stage training strategy is also applied to ensure the learning efficiency of the proposed multi-branch CNN model. Extensive intra-dataset and cross-dataset experiments demonstrate the advantages of our method in terms of scene adaptability and robustness.
Compression of YOLOv3-spp Model Based on Channel and Layer Pruning

Hands is an important medium for human-computer interaction, and it is important for computers to detect human hands in real time. However, the detection algorithm based on deep learning has complex network and requires enormous computation power therefore cannot be deployed to mobile terminals with limited power and memory. To solve this issue, a pruning algorithm combining channel and layer pruning is proposed to compress the network model. The method applies L1 regularization to the scaling factor of BN layer to enhance the network sparsity, and then prunes the channel with smaller scaling factor. Specifically, for a shortcut layer, the previous DBL is evaluated, the γ value of each layer is sorted, and the smallest layer is removed. Two groups of experiments were carried out. One compared the effect of the channel pruning and combination of channel and layer pruning, respectively. The other group compared the effect of choosing different thresholds. The results showed that the size of the original model was reduced by 97%, while its inference speed is increased by 1.7 times. Meanwhile, the pruning barely caused loss of precision. Thus, the proposed approach enables hand detection models to be deployed in mobile terminals and make such algorithms more practical.
A novel key and image concealing with static-dynamic pattern using modified periodic table

This research contribution reports a new symmetric encryption algorithm for ciphering the unique identification numbers (12- digit Aadhaar, Ration, Mobile, and region numbers in India) and biometric images. In this algorithm, existing unique number identifying a particular person are fully and partially utilized to produce secret key and cipher image respectively. For verification and authentication, person unique numbers are divided into two parts in the secret key generation. In the first part, the Aadhaar number is preprocessed with out-shuffle and coupling methods. In the second part, the remaining unique numbers are used for authentication. Finally, both parts are replaced with atomic symbols of periodic table property and extended ASCII values select depends on the day of the week to generate the complete secret key. On the other hand, biometric images split as small images and shuffle them with the dynamic pattern. Scrambling the pixel positions of shuffled images are accomplished by tree traversal algorithm and a novel technique. Meanwhile, 8-bit binary conversion of shuffled image values and cyclic shift process is performed. Even though the key space is less, the authentication and verification process eliminates the prediction of secret keys. Experimental result of the proposed encryption e has shown that various attacks and security is assured. The performance of the proposed encryption scheme is better than existing approaches.
Towards End-to-End Embroidery Style Generation: A Paired Dataset and Benchmark

Despite the numerous developments in image to image translation, further improvement and applications are limited by monotonous datasets, especially the lack of the datasets with aligned image pairs. In this paper, we present the first dedicated paired embroidery dataset, which contains 9k sets of the input sketches and the corresponding output embroidery images. Specialized on this dataset, we propose a new image-based generative approach as the benchmark for automatically generating a preview of the image produced by an expert on an embroidery machine. The key idea is to segment the input image and use the two-stage generation. The first stage trains a sub-generator function within each segmentation region, while the second stage fine-tunes the output of the first stage with a global generator function. Experimental results demonstrate that our proposed benchmark handles this task best, in terms of both qualitative and quantitative measures.