Reviewerly/exHarmony huggingface dataset:
Offline Character recognition on Segmented Handwritten Kannada Characters

Optical character recognition (OCR) is the conversion of pictures of typed or handwritten characters into machine encoded characters. We chose to work on a subfield of OCR, namely offline learning of handwritten characters. Kannada script is agglutinative, where simple shapes are concatenated horizontally to form words. This paper presents a comparative study between different machine learning and deep learning models on Kannada characters. A Convolutional Neural Network (CNN) was chosen to show that handcrafted features are not required for recognizing classes to which characters belong to. The CNN beats the accuracy score of previous models by 5%.
Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning

The bulk synchronous parallel (BSP) is a celebrated synchronization model for general-purpose parallel computing that has successfully been employed for distributed training of machine learning models. A prevalent shortcoming of the BSP is that it requires workers to wait for the straggler at every iteration. To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement. The proposed model offers more flexibility and adaptability during the training phase, without sacrificing on the accuracy of the trained model. We also propose an efficient method that materializes the model, named ZIPLINE. The algorithm is tunable and can effectively balance the trade-off between quality of convergence and iteration throughput, in order to accommodate different environments or applications. A thorough experimental evaluation demonstrates that our proposed ELASTICBSP model converges faster and to a higher accuracy than the classic BSP. It also achieves comparable (if not higher) accuracy than the other sensible synchronization models.
Sequential Recommendations on Board-Game Platforms

The game market is an increasingly large industry. The board-game market, which is the most traditional in the game market, continues to show a steady growth. It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games. Despite its importance, no study has been performed on board-game recommendation systems. In this study, we propose a method to build a deep-learning-based recommendation system using large-scale user data of an online community related to board games. Our study showed that new games can be effectively recommended for board-game users based on user big data accumulated for a long time. This is the first study to propose a personalized recommendation system for users in the board-game market and to introduce a provision of new large datasets for board-game users. The proposed dataset shares symmetric characteristics with other datasets and has shown its ability to be applied to various recommendation systems through experiments. Therefore, the dataset and recommendation system proposed in this study are expected to be applied for various studies in the field.
Generative Oversampling with a Contrastive Variational Autoencoder

Although oversampling methods are widely used to deal with class imbalance problems, most only utilize observed samples in the minority class and ignore the rich information available in the majority class.In this work, we use an oversampling method that leverages information in both the majority and minority classes to mitigate the class imbalance problem.Experimental results on two clinical datasets with highly imbalanced outcomes demonstrate that prediction models can be significantly improved using data obtained from this oversampling method when the number of minority class samples is very small.
Blockchain-Based Smart Contract for E-Bidding System

Electronic bidding systems have become widespread since the advent of the internet and mobile phones. In the Electronic bidding systems, the seller will sell an item and many buyers will bid for that item and the highest bidder will get the item. One of the main issue with this E-Bidding system is the introduction of third-party mainly a company or set of companies which will develop and host either the website or smartphone application. The Buyers and the Sellers have to trust this company because all the bidding process will be handled by the company. The company can manipulate the bidding process if it wants. So to avoid the trust issues blockchain based electronic bidding system is introduced in this paper. In this model, there is no need for third party. Smart Contract will handle all the bidding transactions. Since blockchains are known for its integrity this system makes sure that the integrity of the bidding process is preserved.
Recurrent Convolutions for Causal 3D CNNs

Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations in videos, by adding to pre-existing 2D CNNs a third, temporal dimension. Such 3D CNNs, however, are anti-causal (i.e., they exploit information from both the past and the future frames to produce feature representations, thus preventing their use in online settings), constrain the temporal reasoning horizon to the size of the temporal convolution kernel, and are not temporal resolution-preserving for video sequence-to-sequence modelling, as, for instance, in action detection. To address these serious limitations, here we present a new 3D CNN architecture for the causal/online processing of videos. Namely, we propose a novel Recurrent Convolutional Network (RCN), which relies on recurrence to capture the temporal context across frames at each network level. Our network decomposes 3D convolutions into (1) a 2D spatial convolution component, and (2) an additional hidden state 1 × 1 convolution, applied across time. The hidden state at any time t is assumed to depend on the hidden state at t - 1 and on the current output of the spatial convolution component. As a result, the proposed network: (i) produces causal outputs, (ii) provides flexible temporal reasoning, (iii) preserves temporal resolution. Our experiments on the largescale large Kinetics and MultiThumos datasets show that the proposed method performs comparably to anti-causal 3D CNNs, while being causal and using fewer parameters.
Cascading Modular U-Nets for Document Image Binarization

In recent years, U-Net has achieved good results in various image processing tasks. However, conventional U-Nets need to be re-trained for individual tasks with enough amount of images with ground-truth. This requirement makes U-Net not applicable to tasks with small amounts of data. In this paper, we propose to use "modular" U-Nets, each of which is pre-trained to perform an existing image processing task, such as dilation, erosion, and histogram equalization. Then, to accomplish a specific image processing task, such as binarization of historical document images, the modular U-Nets are cascaded with inter-module skip connections and fine-tuned to the target task. We verified the proposed model using the Document Image Binarization Competition (DIBCO) 2017 dataset.
Developing a social media-based Chatbot for English learning

Abstract The increasing number of foreign companies offering job vacancies in Indonesia is a blessing for new graduates. However, they could not use the opportunity due to their limited English skill. This condition may occur because there is a lack of human resource providing direct, gradual, and continuous assistance to improve their English skills. Nowadays, social media are very popular among various groups of people but they are mostly used for information exchanges. This study aims to develop an application inside Facebook as an English learning media to help students learn English more efficiently. This application is in the form of a Chatbot (an answering robot) as a substitute for a teacher to help those who have difficulties in learning English. The Chatbot integrated in the social media is expected to provide an easier adoption as the users are already familiar with its interface and navigation. This application has been applied to students of Telkom Vocational High School Malang and D3 English Study Program, State Polytechnic of Malang. With this application, students can learn English independently and utilize a rich collection of information and organized exercise items to get help or find more challenges whenever needed to avoid learning boredom.
Differential Privacy-Based Genetic Matching in Personalized Medicine

Genetic matching in personalized medicine is becoming more popular in cloud computing, whereby a cloud server performs genetic matching from the genetic data outsourced by a gene provider (e.g., a genetic lab) and an authorized party (e.g., a doctor) for diagnosing the patients’ diseases. Due to sensitive privacy, we should protect genetic data before outsourcing it to the untrusted cloud. However, traditional differential privacy schemes do not support genetic matching and ciphertext methods hinder data availability. In this article, we propose a differential privacy-based genetic matching (DPGM) scheme to achieve effective genetic matching and protect genetic privacy. Specifically, DPGM first uses a DP-based EIGENSTRAT (DPE) algorithm to construct a published sequence that contains significantly noisy single-nucleotide polymorphisms (SNPs) associated with diseases, thereby ensuring outsourced genetic data privacy. Second, DPGM adopts a DP-based N-order Markov (DPNM) algorithm to generate a noisy query sequence, which considers query privacy and the similarity between the noisy query and the actual query. Finally, DPGM calculates the longest common subsequence (LCS) based on a dynamic programming algorithm, which achieves effective matching results. Detailed theoretical analysis proves that our DPGM scheme achieves <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy. Extensive experiments over actual genetic datasets demonstrate that our scheme achieves high efficiency and data utility.
Influence of method selection on clustering analyses of point-like features: Examples from three zones of distributed volcanism

Most regions that have experienced spatially distributed volcanism also present a clustered structure of eruptive vents. Attempts to identify patterns from the spatial distribution of vents have resorted to various methods. The influence that specific methods might have on the identified patterns, however, has remained largely unexplored. In this work I assess the ability of common “out-of-the-box” algorithms to yield consistent results. The geological significance of those groupings is also tested. The results of those tests indicate that method selection exerts a strong influence, and therefore introduces undesired biases on the identification of groups on zones of distributed volcanism. The results reported below indicate that it is almost impossible to capture the fine structure of volcanic clusters by focusing on a single value that “best” describes spatial distribution. Nevertheless, an alternative approach that could reduce the negative influence of such method-dependence is outlined. This method searches for core-groups as the membership identified on each group by more than one algorithm. Despite the limited ability of this method to reproduce pre-existing geologic or tectonic partitions, it provides grounds for a more robust interpretation of results than the use of only one out-of-the box method in isolation.
FPNet: Customized Convolutional Neural Network for FPGA Platforms

The Convolutional Neural Network (CNN) has been widely adopted in various applications, which include object detection mobile robot vision, image search engine, etc. And due to the computing-intensive and memory-intensive features of CNN models, specialized hardware accelerators, like Application-Specific-Integrated-Circuit (ASIC) and Field Programmable Gate Arrays (FPGA) have been widely utilized in edge devices. Among all the neural network specialized hardware accelerators, an FPGA accelerator stands out for its flexibility, short time-to-market, and energy efficiency. Previous works about FPGA accelerator designs are mostly changing hardware configuration to fit the CNN model structure. In this paper, we propose an automated neural network architecture search approach utilizing reinforcement learning to design customized convolutional neural network model for specialized FPGA platforms.
Quantifying non-Gaussianity of bosonic fields via an uncertainty relation

While Gaussian states and associated Gaussian operations are basic ingredients and convenient objects for continuous-variable quantum information, it is also realized that non-Gaussianity is an important resource for quantum information processing. The characterization and quantification of non-Gaussianity have been widely studied in the past decade, with several significant measures for non-Gaussianity introduced. In this work, by exploiting an information-theoretic refinement of the conventional Heisenberg uncertainty relation and a physical characterization of Gaussian states as minimum uncertainty states, we introduce an easily computable measure for non-Gaussianity of bosonic field states in terms of the Wigner-Yanase skew information. Fundamental properties, as well as intuitive meaning, of this measure are unveiled. The concept is illustrated by prototypical non-Gaussian states, and compared with various existent measures for non-Gaussianity. Its merit and physical significance are elucidated.
Dynamic Density Topological Structure Generation for Real-Time Ladder Affordance Detection

This paper presents a method with dynamic density topological structure generation for low-cost real-time vertical ladder detection from 3D point cloud data. Dynamic Density Growing Neural Gas (DD-GNG) is proposed to generate a dynamic density of the topological structure. The density of the structure and the number of nodes will be increased in the targeted object area. Feature extraction model is required to classify suspected objects for being processed in the next time process. After that, rungs of the vertical ladder is processed using an inlier-outlier method. Thus, the ladder detection model represents the ladder with a set of nodes and edges. Next, affordance detection is processed for detecting the feasible grasped location. To validate the effectiveness of the proposed method, a series of experiments are conducted on a 4-legged robot with a non-GPU board for real-time vertical ladder detection and climbing to validate the effectiveness of the proposed method. Results show that our proposed method able to detect and track the ladder structure in real-time with a much lower computational cost. The affordance of the ladder provides safety information for robot grasping.
Object Rearrangement with Nested Nonprehensile Manipulation Actions

This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector's trajectories.
Two Hand Indian Sign Language dataset for benchmarking classification models of Machine Learning

Currently, a lot of research is going in the field of sign language recognition. Recognition of gesture poses a serious challenge to the system due to inconsistent illuminance and background conditions, different skin colours of the hand and each person has his/her own trait of making the gesture. It gets even more difficult with Two Hand Indian Sign Language (THISL) due to the representation of gesture with both hands. There is no proper THISL dataset available to the public. So, we present a THISL dataset consisting of 26 gestures each representing the English alphabet. This dataset consists of 50x50 images of total 9100 in which each gesture is made of 350 images and it is divided into two parts, training and test. The training set consists of 7020 images and the test set consists of 2080 images. In this paper, THISL dataset is validated on various classification models of machine learning and overall accuracy of 91.72% is achieved. This dataset serves a very good purpose for benchmarking machine learning algorithms and it is freely available to people on request to authors.
An Analysis of Fraudulent Financial Reporting Using the Fraud Diamond Theory Perspective: An Empirical Study on the Manufacturing Sector Companies Listed on the Borsa Istanbul

Accounting-based financial scandals caused by fraudulent financial reports negatively affect the financial markets and cause loss of confidence in investors. Financial reporting quality needs to be improved in order to build and maintain trust in financial markets. To increase the quality of financial reports, fraudulent financial reporting risks should be defined. At this point, regulators, practitioners, and researchers are in constant search.There are improved approaches to the detection of financial reporting frauds in the literature. Many studies have been conducted on the “Fraud Triangle Theory” and the “Fraud Diamond Theory” approaches. The Fraud Triangle Theory argues that while fraudulent action is taking place in defining the elements of press, rationalization, and opportunity, the Fraud Diamond Theory approach argues that in order to achieve these three elements, the capability to carry out a fraud in individuals must be improved.In this study, it is aimed to investigate the effect of Fraud Diamond elements on fraudulent financial reports. For the scope of the research, data of 26 companies from Manufacturing Industry enterprises operating in BORSA ISTANBUL between 2013 and 2017 were used. Financial reports of the companies are divided into two groups: (1) Fraudulent Financial Reports and (2) Non-Fraud Financial Reports. The hypotheses developed within the scope of the research were tested using the Logistic Regression analysis in IBM SPSS Statistic 20 program.As a result of the study, it has been determined that there is a negative correlation between borrowing level, asset profitability, independent audit firm, auditor exchanges and institutionalization level, and fraudulent financial reports. It was understood that the change in assets and the size of the audit committee did not have any effect on the fraudulent financial reports.
Exploiting the local temporal information for video captioning

Typical video captioning methods are developed based on the encoder-decoder architecture. To better exploit the local temporal information, e.g., details about objects and their corresponding actions, we propose a reinforcement learning based method to predict the adaptive sliding window size sequentially for better event exploration. More specifically, we introduce the single Monte-Carlo sample to approximate the gradient of reward-based loss function. And the self-critical strategy is employed to estimate baseline reward to diminish the variance of gradients. Moreover, temporal attention is utilized to selectively focus on a subset of temporal frame representations while generating each word. In addition, to better initialize the decoder’s state, we utilize the motion features extracted by 3D CNNs with mean pooling to endow the decoder with the prior knowledge of the entire video. To evaluate the proposed method, experiments are performed on three public benchmark datasets: Microsoft Video Description Corpus (MSVD), MSR Video to Text challenge (MSR-VTT) and Charades. The experimental results demonstrate the effectiveness of our method by comparing with state-of-the-art methods.
Vision‐based approach for predicting the probability of vehicle–pedestrian collisions at intersections

Road accidents impose serious problems on society. Possible collisions between vehicles and pedestrians must be detected before they occur so that a timely warning may be issued. By using the vision-based approach, this study presents an effective and efficient algorithm to estimate the vehicle–pedestrian collision probability at intersections. The real-time trajectories and movement parameters (position, speed, acceleration or direction) of vehicles and pedestrians are obtained based on state-of-the-art detection and tracking algorithm which include background subtraction method, faster regions with convolutional neural networks and optical flow method. To find the appropriate time to identify the latent collision risk for calculating the collision probability, this study defines the critical time based on different collision patterns of perception-reaction failure and evasive action failure. In addition, based on discrete acceleration and discrete angle, the authors get different extended trajectories which can include most situation when the conflict happened. Trajectories generation probability are given by the discrete choice probability model based on the Logit model to get the accurate collision probability. Real-world video data is implemented to demonstrate the approach. This proposed collision prediction method can provide some important results for designing the intelligent pedestrian signal timing schemes at intersections.
Article Segmentation in Digitised Newspapers with a 2D Markov Model

Document analysis and recognition is increasingly used to digitise collections of historical books, newspapers and other periodicals. In the digital humanities, it is often the goal to apply information retrieval (IR) and natural language processing (NLP) techniques to help researchers analyse and navigate these digitised archives. The lack of article segmentation is impairing many IR and NLP systems, which assume text is split into ordered, error-free documents. We define a document analysis and image processing task for segmenting digitised newspapers into articles and other content, e.g. adverts, and we automatically create a dataset of 11602 articles. Using this dataset, we develop and evaluate an innovative 2D Markov model that encodes reading order and substantially outperforms the current state-of-the-art, reaching similar accuracy to human annotators.