Reviewerly/exHarmony huggingface dataset:
Comparison of scanned administrative document images

In this work the methods of comparison of digitized copies of administrative documents were considered. This problem arises, for example, when comparing two copies of documents signed by two parties in order to find possible modifications made by one party, in the banking sector at the conclusion of contracts in paper form. The proposed method of document image comparison is based on a combination of several ways of image comparison of words that are descriptors of text feature points. Testing was conducted on public Payslip Dataset (French). The results showed the high quality and the reliability of finding differences in two images that are versions of the same document.
Virtual machine placement mechanisms in the cloud environments: a systematic review

Purpose Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future. Design/methodology/approach Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms. Findings The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance. Research limitations/implications This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP. Practical implications The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area. Originality/value The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.
'The Ghosts of Roller Disco', a Choreographed, Interactive Performance for Robotic Roller Skates

The project investigates how interactions with complex (biologically inspired swarming) behaviors of multiple robots are understood by human participants within a performative and dramaturgical system. Nonanthropomorphic robots in the form of roller skates are used in innovative ways by creating social formations from their movements, for example a leader and followers in a conga line. Synchronized audio signals and speech-like sonic structures are used in innovative ways by influencing and engaging the participant's interactions with the robots. Localization data of the robots in space is mapped to control the surround sound and lighting within the space. This is used to enhance audience immersion and engagement within the interactive performance work.
Yi Characters Recognition Based on Tesseract-OCR

OCR(optical character recognition) is a general image recognition technology, which has been widely used in Tibetan and Mongolian minority characters recognition, but it is seldom used for Yi characters recognition. In order to promote the exchange of Yi and Chinese characters, this paper proposed the Yi characters recognition system based on OCR . This method uses the open source Tesseract-OCR engine. The system uses the jTeesBox Editor to extract the frame of the Yi characters, obtain the feature of the font, thus correct the characters, and then train the input Yi characters through Tesseract-OCR. In this paper, 900 pictures of Yi characters are used for training. The experimental results show that the recognition accuracy of the method reaches 85.9% , which has the good effect.
Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system, such as projectile's trajectory estimation and control. While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined, which may result in filtering divergence. As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model, we improve the weighted least squares method (WLSM) with minimum model error principle. Invariant embedding method is adopted to solve the cost function including the model error. With the knowledge of measurement data and measurement error covariance matrix, we use gradient descent algorithm to determine the weighting matrix of model error. The uncertainty and linearization error of model are recursively estimated by the proposed method, thus achieving an online filtering estimation of the observations. Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
An Optimal Image Dehazing Technique Using Dark Channel Prior

Haze is a common phenomenon in digital images and it shows poor visibility and contrast. The images may be degraded due to the influence of atmospheric light or due to the poor quality of sensors. Hazy images may cause blur in edges or may miss many details in an image. To solve these problems the current study proposes an efficient dehazing technique using dark channel algorithm. The method defines a new optimized transmission map algorithm for selection of a dehazing parameter to remove the haze from the image. This algorithm is compared with other algorithms and results show that haze is removed from the image by retaining some amount of haze which gives the image a natural look.
Integrating GeoGebra into Space Geometry in College

Pre-service mathematics teacher students often find geometrical concepts that are difficult to understand.This results in a decrease in interest in geometry.The purpose of this study was to examine the impact of using 'GeoGebra' on the mathematics achievement of pre-service mathematics teacher students in space geometry learning.The study was a quasi-experimental design.A total of 56 students were selected from the mathematics education program of Tidar University, a state university located in Magelang, Central Java, Indonesia.The experimental group ( 27) was taught about the slice plane in the prism and pyramid using GeoGebra while the control group (29) was taught using traditional teaching methods.The research data were obtained from a test consisting of six questions and questionnaire consists of nine items.At the end of the treatment, students' mathematics achievement was measured using a post-test.The data were then analyzed using an independent T-test.The results showed that GeoGebra was an effective tool for teaching and learning space geometry in college.
A novel oversampling method based on SeqGAN for imbalanced text classification

Nowadays, investors prefer to learn the latest trends of stock market through the Internet, grasp the best time to buy and sell stocks, and simultaneously release their comments during the trading process through various online media. Therefore, many researchers utilize NLP approaches to extract sentiment tendency of investors from online comments. However, since there exists noise in corpora related to stock, when extracting emotional tendency from these corpora by using text classification, it is inevitable to face the imbalanced dataset problem, which is harmful to traditional machine learning classifiers. Our research innovatively proposes a preprocessing method based on SeqGAN algorithm, in order to solve the imbalanced classification problem. We carry out experiments on different corpora, and results show that compared with other four oversampling methods, the oversampling method based on SeqGAN can improve the performance of text classifier at best both in binary class and multiclass text classification, which proves to be an effective method for preprocessing text data.
Printed Odia Symbols for Character Recognition: A Database Study

Optical Character Recognition (OCR) is a popular tool which helps in converting a character image to a text form. It is also recognized as one of the most popular devices to digitize the printed documents. The main challenge in Odia character recognition is to capture a set of symbols which have been used in the printing technology. There are many letters and symbols with similar structure, and also different printing materials follow different font styles. Again, there are allographs which can be used while writing in Odia. So, it is a challenge to classify the orthographs and allographs accurately. It is a fact that no standard database can be found in Odia for training and testing purposes. The main goal of this paper is to develop a database for complete set of printed Odia character symbols and then recognize using SVM classifier through a graphical user interface and the DWT is specifically used to extract the features of the character image and process them in a scientific manner.
Asynchronous Multitask Reinforcement Learning with Dropout for Continuous Control

Deep reinforcement learning is sample inefficient for solving complex tasks. Recently, multitask reinforcement learning has received increased attention because of its ability to learn general policies with improved sample efficiency. In multitask reinforcement learning, a single agent must learn multiple related tasks, either sequentially or simultaneously. Based on the DDPG algorithm, this paper presents Asyn-DDPG, which asynchronously learns a multitask policy for continuous control with simultaneous worker agents. We empirically found that sparse policy gradients can significantly reduce interference among conflicting tasks and make multitask learning more stable and sample efficient. To ensure the sparsity of gradients evaluated for each task, Asyn-DDPG represents both actor and critic functions as deep neural networks and regularizes them using Dropout. During training, worker agents share the actor and the critic functions, and asynchronously optimize them using task-specific gradients. For evaluating Asyn-DDPG, we proposed robotic navigation tasks based on realistically simulated robots and physics-enabled maze-like environments. Although the number of tasks used in our experiment is small, each task is conducted based on a real-world setting and posts a challenging environment. Through extensive evaluation, we demonstrate that Dropout regularization can effectively stabilize asynchronous learning and enable Asyn-DDPG to outperform DDPG significantly. Also, Asyn-DDPG was able to learn a multitask policy that can be well generalized for handling environments unseen during training.
Multiple-image encryption based on optical scanning holography using orthogonal compressive sensing and random phase mask

A method is proposed for multiple-image encryption based on optical scanning holography (OSH) using a random phase mask (RPM) and orthogonal compressive sensing (CS). It can destroy the linearity of the traditional OSH system and possess a superior security. On the one hand, images are preferentially preprocessed by utilizing the orthogonal CS to provide a single-layer section for OSH. Therefore, the defocus noise as well as information leakage can be controlled effectively in decryption. On the other hand, each image can be extracted separately without the others’ contents. In addition, the use of RPM can bring about a more ulterior distribution to the cyphertext, which may be better than the case without the RPM. The use of orthogonal modulation matrices and RPM can provide the additional key spaces to guarantee the security of this holography cryptosystem. Simulations and discussions are also made on the cyphertext characteristics as well as the ability of resisting occlusive attack.
M-WebQuest Development: Reading Comprehension of Senior High School Students in Indonesia

M-WebQuest is a teaching model adapted from WebQuest created firstly by Bernie Dodge, where its development is used ADDIE model. This research aims to develop M-WebQuest to improve reading comprehension attainment of senior high school students Grade XI in Pekanbaru, Riau – Indonesia. In gaining the feasibility of the developed M-WebQuest, 3 experts, 10 English teachers and 30 students were involved. Based on the given questionnaire analysis, the experts agree on the feasibility of created M-WebQuest in terms of technical, content, material unit, and impacts of using M-WebQuest on students and teachers. The English teachers and the students are also agree on the feasibility of created M-WebQuest in terms of interface screen, texts, and content. Overall, it can be concluded that M-WebQuest can be used for the learning activities of English Reading Comprehension subject
Bridged Variational Autoencoders for Joint Modeling of Images and Attributes

Generative models have recently shown the ability to realistically generate data and model the distribution accurately. However, joint modeling of an image with the attribute that it is labeled with requires learning a cross modal correspondence between image and attribute data. Though the information present in a set of images and its attributes possesses completely different statistical properties altogether, there exists an inherent correspondence that is challenging to capture. Various models have aimed at capturing this correspondence either through joint modeling of a variational autoencoder or through separate encoder networks that are then concatenated. We present an alternative by proposing a bridged variational autoencoder that allows for learning cross-modal correspondence by incorporating cross-modal hallucination losses in the latent space. In comparison to the existing methods, we have found that by using a bridge connection in latent space we not only obtain better generation results, but also obtain highly parameter-efficient model which provide 40% reduction in training parameters for bimodal dataset and nearly 70% reduction for trimodal dataset. We validate the proposed method through comparison with state of the art methods and benchmarking on standard datasets.
Virtual Desktop Infrastructure (VDI) Deployment Using OpenNebula as a Private Cloud

With the advancement of technology and unlimited access to information through the Internet, traditional teaching models are evolving to become centralized in the student. The objective is to offer personalized education that overcomes any spatial-temporal barrier, achieving a continuous transfer of knowledge. However, under this scenario arises the need for equipment with high performance hardware and software. On the one hand, educational institutions, in most cases, do not have enough capital to migrate to new infrastructures; while, on the other hand, students encounter problems linked to licensing and lack of own resources. Overall, this translates into an impediment for teachers to design innovative activities. Hence, the concept of virtual desktop infrastructure (VDIs) is born, which relies on cloud computing techniques to provide access to remote machines pre-configured according to different storage, processing, network and software requirements. This paper presents an open source VDI solution based on OpenNebula, since the costs associated with using the services of large cloud providers could represent a considerable investment. Among the tests carried out, a pilot experiment stands out that demonstrates the benefits of deploying local VDIs, as well as a comparison of the savings they represent in relation to commercial solutions.
A Model for Incorporating an Automatic Speech Recognition System in a Noisy Educational Environment

Automatic speech recognition technology has a high potential for improving the learning experience of students in an educational setting. This paper addresses some of the key theoretical areas involved in developing automatic speech recognition systems for educational use; namely the applications of the technology in education, prominent feature extraction and noise cancellation techniques used with audio speech data as well as some of the recent neural network based machine learning models capable of keyword spotting or continuous speech recognition. Following the theoretical background, a model for a strategy of incorporating an automatic speech recognition system in a noisy educational environment is proposed. The model is not only used to generate a lesson transcript, but also to associate named keywords identified during the lesson with the course content the lesson is based upon. Ultimately, this model is used to present a topical lesson overview to students which allows them to monitor the key topics and their prevalence as communicated in the lesson while having a transcript of what was communicated to refer back to for review purposes.
Blockchain use Cases in Financial Services for Improving Security

Blockchain in today's world is digital ledger of transactions not only recording transaction but also recording everything that is of value, from the evolution of blockchain there have been dramatic changes in the financial sector and around the globe that how people carry out financial transactions, Blockchain is considerably new technology and has great potential for revolutionizing the whole global economy. In recent years Blockchain has been in the news because of bitcoin, the rise and fall of bitcoin, the market crash along with the regulations that Indian government are trying to implement to banned bitcoin in the country, this kind of steps is what becomes a hurdle for this interestingly advance technology to grow and make room for it's ability to power decentralized economy with this idea of decentralized economy government of various nations are scared that they will lose control of their economy but instead that government should have done is that they should study the implications of blockchain and benefits associated with it and how this can benefit to help eradicate corruptions and within the organization Blockchain has Proved to be Groundbreaking technology with its potential in the current, Blockchain is unique and has been implemented in many industries. Thus, blockchain use in banking will definitely result in a better banking system.
Content-Based Attention Network for Person Image Generation

This paper proposes a novel method for person image generation with arbitrary target pose. Given a person image and an arbitrary target pose, our proposed model can synthesize images with the same person but different poses. The Generative Adversarial Networks (GANs) are the major part of the proposed model. Different from the traditional GANs, we add attention mechanism to the generator in order to generate realistic-looking images, we also use content reconstruction with a pretrained VGG16 Net to keep the content consistency between generated images and target images. Furthermore, we test our model on DeepFashion and Market-1501 datasets. The experimental results show that the proposed network performs favorably against state-of-the-art methods.
Enabling Prediction via Multi-Layer Graph Inference and Sampling

In this work we propose a novel method to efficiently predict dynamic signals over both space and time, exploiting the theory of sampling and recovery of band-limited graph signals. The approach hinges on a multi-layer graph topology, where each layer refers to a spatial map of points where the signal is observed at a given time, whereas different layers pertain to different time instants. Then, a dynamic learning method is employed to infer space-time relationships among data in order to find a band-limited representation of the observed signal over the multi-layer graph. Such a parsimonious representation is then instrumental to use sampling theory over graphs to predict the value of the signal on a future layer, based on the observations over the past graphs. The method is then tested on a real data-set, which contains the outgoing cellular data traffic over the city of Milan. Numerical simulations illustrate how the proposed approach is very efficient in predicting the calls activity over a grid of nodes at a given daily hour, based on the observations of previous traffic activity over both space and time.
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift

Despite the prominent success of deep learning (DL) in medical imaging for tasks such as computer-aided detection and diagnosis, the field faces a number of challenging problems. An important issue is that of mismatch of data distributions between different data sources, also known as a distribution shift. Distribution shifts may also be present between different subpopulations or subgroups. Distribution shifts that are not easily detectable can prevent the successful deployment of DL models in medical imaging. We use variational inference to create subsets of a given dataset while enforcing artificial distribution shifts between these subsets, thus creating subsets with different characteristics that represent different pseudo “data sources”. By training and testing ROI-based malignant/benign lesion classification models over these pseudo data sources, we evaluate the extent to which distribution shift could deteriorate the performance of popular DL models. We show that distribution shift indeed poses a serious concern for malignant/benign lesion classification in mammography, and we show that the algorithmically created pseudo data sources may not correspond to any recorded clinical or image characteristics. This study shows a potential method for evaluating deep learning algorithms for robustness against distribution shifts. Furthermore, our technique can serve as a benchmark method for development of new models which aim to be robust to distribution shift.