Reviewerly/exHarmony huggingface dataset:
Style Permutation for Diversified Arbitrary Style Transfer

Arbitrary neural style transfer aims to render a content image in a randomly given artistic style using the features extracted from a well-trained convolutional neural network.Existing style transfer algorithms have demonstrated astonishing results.However, the generated images suffer from loss of content details, non-uniform stroke patterns, and limited diversity.In this article, we focus on improving the diversity of the stylized images.We propose a light-weighted yet efficient method named style permutation (SP) to tackle the limitation of the diversity without harming the original style information.The core of our style permutation algorithm is to multiply the deep image feature maps by a permutation matrix.Compared with state-of-the-art diversified style transfer methods, our style permutation algorithm offers more flexibility.Also, we present qualitative and quantitative analysis and theory explanations of the effectiveness of our proposed method.Experimental results show that our proposed method could generate diverse outputs for arbitrary styles when integrated into both WCT (whitening and coloring transform)-based methods and AdaIN (adaptive instance normalization)-based methods.
Chaos-based Video Steganography Method in Discrete Cosine Transform Domain

Due to the technological advancements in digital communication, the amount of multimedia content over the internet has increased manifold in past decade. This has renewed the internet of researchers in the area of privacy and secure communication. This paper presents a secure and robust video steganography method in discrete cosine transform (DCT) domain. In order to enhance the security of the proposed algorithm, the frame selection process is randomized and the secret data are pre-treated using Arnold’s cat map. The secret data are embedded in the middle band DCT coefficient using two pseudo random sequences. These sequences are generated using a chaotic map. We analyze the proposed algorithm in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), multi-scale structural similarity index (MSSIM) and video quality metric (VQM). The evaluation has been done on 107 video sequences. The experimental results demonstrate that the algorithm maintains acceptable video quality. The robustness of the proposed method is tested under Gaussian and salt and pepper noise attack using correlation between original and recovered images. The proposed algorithm is able to recover 90.60% data without error under salt and pepper noise ([Formula: see text]) attack and 87.23% data correctly under Gaussian noise attack with mean [Formula: see text] and variance [Formula: see text].
Environmental Education in Grammar Learning Process for Junior High School students by using Multifunctional English Learning Media (MELDe)

Abstract The teaching and learning process is no longer what it used to be. If the learning process is not adapted to the times, students will not be interested and the learning process will be ineffective, likewise with learning English in Indonesia, especially in grammar learning process. The inadequacy of learning media has decreased student enthusiasm and motivation in learning grammar. Previous researchers have relied primarily on one learning model without combining it with other models. This situation can actually lead the students’ tedium because there is no combination of several learning models. Therefore, this paper presents MELDe (Multifunctional English Learning Devices) as a smart solution to overcoming this problem. This research used Research and Development Methodology with 20 islamic junior high school students randomly in Pekanbaru as the samples. The research has distributed the questionnaire and MELDe learning media demo video via Google Form and Youtube to obtain the data needed. The data analysis technique used qualitative data in a descriptive form that described the practicality of MELDe. The practicality observation for MELDe, which used the ADDIE model, aimed to observe the practicality of this learning media as an English learning tool, especially on grammar material. All scores in the assessment aspect have been calculated and got an average score of 77.2% in the 'practical' category. Therefore, MELDe is stated to be ‘practical’ for junior high school students in learning English grammar and can be boosted up the enthusiasm of students in learning process.
Embedded Deep Learning Solution for Person Identification and Following with a Robot

This research presents a robust embedded system for following a person, making use of a pipeline of convolutional neural networks. Besides, it features an optical tracking system for supporting the inferences of the neural networks, allowing to determine the position of a person using an RGBD camera. The system is deployed using ROS, and runs in a NVIDIA Jetson TX2, an embedded SoM (System-on-Module), capable of performing computationally demanding tasks onboard, and coping with the complexity required to run a robust tracking and following algorithm. The board is attached to a robotic mobile base, which receives velocity commands to move the system towards the target person.
ARcheoBox

Museum visitors can often be distanced from directly engaging with historical artefacts due to their fragile nature. ARcheoBox is a novel interface that lets users physically pick up a digitized version of a real artefact and inspect it closely from all angles through a screen. The system combines augmented reality and tangible interactions to remove barriers between visitors and artefacts. We piloted our prototype with eight archaeological experts at a local exhibition centre. The results indicate that this is a promising way to remove barriers between humans and historical artefacts, and that it allows expert users to inspect, understand and interact with these artefacts in a way not previously possible.
Implementing Marked-Based Augmented Reality in Discrete industry - A Case Study

India is the second most populated country in the world with 34.33% inhabitants being between 15 and 24 years old. On the other hand, engineering institutions in India produce only 3% of `industry-ready engineers'. Manufacturers are having difficult to find right candidates to fulfill their job requirements. Even if they find one, they are very expensive and demand more. Industry 4.0 solutions help to fix these problems. This paper discusses several elements of Industry 4.0 and how it could be utilized by the industries. An empirical study of implementing augmented reality (AR) in a pump assembly line is explained in this paper. It also discusses the steps involved in creating cloud-based AR and stand-alone AR. The case study company reported that 96% of the users' recommended AR experience helped them to assemble the pump in ease and able to complete within the specified time. AR made the users 100% error-proof assembly however it cannot be used to replace a skilled worker.
Fixed-point tracking of English reading text based on mean shift and multi-feature fusion

English text is difficult to recognize under the interference of blurred background, so it is necessary to improve the fixed-point tracking technology of English text. Based on machine learning algorithms, this paper studies the fixed-point tracking model of English reading text based on mean shift and multi-feature fusion. The target tracking algorithm based on mean shift obtains the description of the target model and the candidate model by calculating the pixel feature probability in the target area and the candidate area. Then, it uses the similarity function to measure the similarity between the initial frame target model and the current candidate model, selects the candidate model that maximizes the similarity function, and obtains the target model mean offset vector. Finally, it continuously iteratively calculates the offset vector based on this vector, and finally converges to the true position of the target, thereby achieving the effect of tracking. In general, it is verified that the model constructed in this paper works well through control experiments.
A Novel Approach for E-mail Classification Using FastText

The upward trend of communication through emails has made the task of handling mails efficiently very vital. As the number of email users is growing considerably each day, the volume of mails to each user is also enlarging. It mostly requires an intelligent classification system for multi-folder categorization of emails to save time and manual labor. Several studies and experiments done till date have been proved to provide a great extent of automation to this classification task. However, multi-folder classification task has always been a little more challenging than others, especially with emails, because of the large number of the possible classes. This paper suggests an approach with machine learning for multi-class categorization of emails in a simple text classification method. We used our own dataset to build a text classifier with fastText, keeping different features of each class into consideration. The results from our proposed method were also compared to the performance of methods like Convolutional Neural Network with the same dataset and fastText was observed to perform better.
Exploring the Potential of Fast Delta Encoding: Marching to a Higher Compression Ratio

Delta compression (or called delta encoding) is a data reduction technique capable of calculating the differences (i.e., delta) among the very similar files and chunks, and is thus widely used for optimizing synchronization replication, backup/archival storage, cache compression, etc. However, delta compression is costly because of its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches, are either at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this paper, we propose Gdelta, a fast delta encoding approach with a high compression ratio, that improves the delta encoding speed by employing an improved fast Gear-based rolling hash for scanning fine-grained words, and a quick array-based indexing scheme for word-matching, and then, after word-matching, further batch compressing the rest to improve the compression ratio. Our evaluation results driven by six real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 2X~4X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10%~120%.
Robust Deep Neural Network Model for Identification of Malaria Parasites in Cell Images

Malaria is a common mosquito-borne disease that is transmitted through humans by mosquito bites. Severe cases of it lead to death. In the labs, tests are performed on the thick and thin blood smears to determine whether there is any presence of malaria parasites. Counting the number of parasitized and normal cells is a tremendously trivial and important task that relies mostly on the proficiency of the observer. Recent advancement in computer-aided diagnosis systems has enabled it to use sophisticated machine learning and deep learning algorithms. We propose the usage of CNN models for the classification of the cell images as CNN is capable of identifying many complex features and labeling them appropriately. Here we have implemented a variant of CNN called VGG16 model for the identification. In order to improve these models' robustness from adversarial attacks like from fast sign gradient attack, we have trained our model with adversarial images with correct labels to ensure accurate classification even when adversarial images are used for classification. Our model provides 95.96% accuracy before adversarial training and 95.79% accuracy after adversarial training. For adversarial images, we acquire 93.38% accuracy.
Modeling and simulation of hierarchical task allocation system for energy-aware HPC clouds

The paper presents a hierarchical approach to the problem of energy-aware task allocation in a distributed HPC system consisting of several computing clusters. The solution involves the control of clusters and the use of network traffic engineering methods to minimize overall energy consumption. The advantages of the hierarchical approach are twofold: first entities controlling parts of computing infrastructure may decide how much of their responsibility is delegated to the coordinator, next the computational complexity of related mathematical programming problems may be reduced, and their solution may be done in parallel. The mathematical programming problem of task allocation is defined in the centralized form first. After discussing its computation complexity and related organizational difficulties, it is decomposed to form a hierarchy. Finally, heuristics for allocation are proposed and verified via numerical simulation.
Specialized Collection “For Library Professionals” on the National Electronic Library’s Portal

The article highlights the first stage of creating a specialized collection (section) “For Library Professionals” on the portal of the National Electronic Library (NEL). The last decade’s trend has been a reduction in the number of titles of professional library publications, and a drop in their average circulation. In these conditions, the issue of providing library and information specialists with relevant scientific, practical and reference literature has become urgent. One of the ways to solve the problem is creating electronic collections of professional library literature. The article analyzes the Russian State Library’s experience in this area since the early 2000s. Currently, the special collection “For Library Professionals” is available on the basis of the National Electronic Library, a key library and information project of recent years. The collection has been accessible since July 2019. At the beginning of 2020, this section included about 1,5 thousand documents of different types (monographs, indexes, textbooks and manuals, collected scientific articles, proceedings of scientific conferences, periodical materials, etc.). At the end of 2 019, a survey w as conducted on the feasibility of maintaining a professional library collection in the NEL, which was attended by about 700 professionals in the area. The article analyzes the results of this survey, which indicate the demand for such electronic collections of professional library literature and show the priority topics demanded by library employees and expectations in the field of search capabilities.
Matrix Expression of Convolution and Its Generalized Continuous Form

In this paper, we consider the matrix expression of convolution, and its generalized continuous form. The matrix expression of convolution is effectively applied in convolutional neural networks, and in this study, we correlate the concept of convolution in mathematics to that in convolutional neural network. Of course, convolution is a main process of deep learning, the learning method of deep neural networks, as a core technology. In addition to this, the generalized continuous form of convolution has been expressed as a new variant of Laplace-type transform that, encompasses almost all existing integral transforms. Finally, we would, in this paper, like to describe the theoretical contents as detailed as possible so that the paper may be self-contained.
Short-term Solar Irradiance Forecasting Based on Multi-Branch Residual Network

For having a stable and reliable smart grid system, an accurate short-term solar irradiance forecasting is necessary. The challenges arise while solar energy has variable and fluctuating nature due to complex weather conditions, temperature, and meteorological factors. Machine learning and deep learning techniques besides traditional time-series forecasting methods are considered to be effective tools to have precise short-term solar forecasting system, while they are analyzing the time-series solar data at single resolution and unable to capture their sudden and short variations. In this paper, we propose a novel deep architecture consisting multi-branch residual network (ResNet) to model the solar irradiance data at different resolutions and extract hierarchical features to improve the forecasting accuracy. We evaluate the performance of the proposed model relative to other deep learning models, ResNet, and long short-term memory (LSTM), using seventeen years of data from twelve different sites in Philadelphia. Numerical results show the state-of-the-art performance on half-day-ahead forecasting.
A Relational Program Logic with Data Abstraction and Dynamic Framing

Dedicated to Tony Hoare. In a paper published in 1972, Hoare articulated the fundamental notions of hiding invariants and simulations. Hiding: invariants on encapsulated data representations need not be mentioned in specifications that comprise the API of a module. Simulation: correctness of a new data representation and implementation can be established by proving simulation between the old and new implementations using a coupling relation defined on the encapsulated state. These results were formalized semantically and for a simple model of state, though the paper claimed this could be extended to encompass dynamically allocated objects. In recent years, progress has been made toward formalizing the claim, for simulation, though mainly in semantic developments. In this article, hiding and simulation are combined with the idea in Hoare’s 1969 paper: a logic of programs. For an object-based language with dynamic allocation, we introduce a relational Hoare logic with stateful frame conditions that formalizes encapsulation, hiding of invariants, and couplings that relate two implementations. Relations and other assertions are expressed in first-order logic. Specifications can express a wide range of relational properties such as conditional equivalence and noninterference with declassification. The proof rules facilitate relational reasoning by means of convenient alignments and are shown sound with respect to a conventional operational semantics. A derived proof rule for equivalence of linked programs directly embodies representation independence. Applicability to representative examples is demonstrated using an SMT-based implementation.
A modified firefly algorithm based on neighborhood search

Summary Firefly algorithm (FA) is a new member of intelligent optimization algorithms. Several studies demonstrated that FA could successfully deal with several engineering and theoretical optimization problems. Nevertheless, there are some weak points in FA, such as parameter dependence and high computational complexity. To overcome the above issues, this paper proposes a modified FA based on neighborhood search (MFANS). In MFANS, there are three modifications. First, a modified attraction strategy is employed to reduce the complexity. Then, a neighborhood search method is used to search around the best neighborhood solutions. Third, the step parameter is dynamically adjusted. Performance of MFANS is compared with two improved FAs. Results show the effectiveness of MFANS.
Extend, Push, Pull: Smartphone Mediated Interaction in Spatial Augmented Reality via Intuitive Mode Switching

We investigate how smartphones can be used to mediate the manipulation of smartphone-based content in spatial augmented reality (SAR). A major challenge here is in seamlessly transitioning a phone between its use as a smartphone to its use as a controller for SAR. Most users are familiar with hand extension as a way for using a remote control for SAR. We therefore propose to use hand extension as an intuitive mode switching mechanism for switching back and forth between the mobile interaction mode and the spatial interaction mode. Based on this intuitive mode switch, our technique enables the user to push smartphone content to an external SAR environment, interact with the external content, rotate-scale-translate it, and pull the content back into the smartphone, all the while ensuring no conflict between mobile interaction and spatial interaction. To ensure feasibility of hand extension as mode switch, we evaluate the classification of extended and retracted states of the smartphone based on the phone's relative 3D position with respect to the user's head while varying user postures, surface distances, and target locations. Our results show that a random forest classifier can classify the extended and retracted states with a 96% accuracy on average.
Attentive Feedback Feature Pyramid Network for Shadow Detection

Shadow detection is one of the most challenging issues in computer vision. Inspired by the great success of the convolutional neural network (CNN) for the problem of image restoration, learned features have been widely adopted for shadow detection. However, most existing methods still suffer from ambiguities driven by black-colored objects, which are not actually shaded, as well as the background clutter. In this letter, we propose the attentive feedback feature pyramid network (AFFPN) for shadow detection in a single image. The key idea of the proposed method is to extract shadow-relevant features based on multiple feedback modules, which are defined in the feature pyramid network. Specifically, attentive features extracted from each level of the encoder are progressively refined via connections between feedback modules from high-level to low-level layers for learning properties of shadow more accurately. Experimental results on benchmark datasets show that the proposed method is effective for shadow detection under complicated real-world environments. The code and model are publicly available at: https://github.com/JinheeKIM94/AFFPN_release.
WITHDRAWN: Selection of most significant variables to detect fraud in financial statements

In this work, a methodology is proposed to detect fraud in financial statements. The financial statements of Indian companies were used as the dataset. The models were trained on undersampled and oversampled datasets. A total of 38 models were trained on undersampled and oversampled datasets. Top metrics like accuracy, sensitivity and precision were used to select the top performing models in prediction of fraud in test dataset. The methodology to select the top performing models is explained in detail. The most significant features from the top performing models are extracted and common features are selected. The common features can be used to detect the fraud in financial statements quickly in practice.
Co-Designing Accessible Science Education Simulations with Blind and Visually-Impaired Teens

Design thinking is an approach to educational curriculum that builds empathy, encourages ideation, and fosters active problem solving through hands-on design projects. Embedding participatory “co-design” into design thinking curriculum offers students agency in finding solutions to real-world design challenges, which may support personal empowerment. An opportunity to explore this prospect arose in the design of sounds for an accessible interactive science-education simulation in the PhET Project. Over the course of three weeks, PhET researchers engaged blind and visually-impaired high-school students in a design thinking curriculum that included the co-design of sounds and auditory interactions for the Balloons and Static Electricity (BASE) sim. By the end of the curriculum, students had iterated through all aspects of design thinking and performed a quantitative evaluation of multiple sound prototypes. Furthermore, the group’s mean self-efficacy rating had increased. We reflect on our curriculum and the choices we made that helped enable the students to become authentic partners in sound design.