Reviewerly/exHarmony huggingface dataset:
A New Language and Input–Output Hidden Markov Model for Automated Audit Inquiry

This article presents a mathematical coding language to express dynamic interactions between auditors and client personnel. Then, an input-output hidden Markov model is presented that represents clients as well as auditors, and models the coupled system. The calibrated model can be used to design optimal automated auditors, and can also be used to perform analysis of client inquiry responses. A case study is performed using data collected with subjects simulating auditor-client communications in a controlled environment. We also discuss the details of model calibration, validations, and significance of results.
Benchmarking Object Detection Networks for Image Based Reference Detection in Document Images

In this paper we study the performance evaluation of state-of-the-art object detection models for the task of bibliographic reference detection from document images. The motivation of evaluating object detection models for the task in hand is inspired from how human perceive a document containing bibliographic references. Humans can easily distinguish between different references just by exploiting the layout with a glimpse of an eye, without understanding the content. Existing state-of-the-art systems for bibliographic reference detection are purely based on textual content. By contrast, we employed four state-of-the art object detection models and compared their performance with state-of-the-art text based reference extraction models. Evaluations are performed on the publicly available dataset (ICONIP) for image based reference detection, containing 455 scanned bibliographic documents with 8766 references from Social Sciences books and journals. Evaluation results reveal the superiority of image based methods for the task of reference detection in document images.
Intermittent Renewable Energy Source (IRES) model of solar energy in Cipayung microgrid system

Abstract Indonesia has a great solar energy potential because its location is in a tropical area. One significant characteristic of solar energy is intermittent. Integration of intermittent renewable energy source (IRES) especially solar energy into electrical power grid can cause significant impact in power system stability. This study aims to model the IRES characteristic, profile, and time duration classification based on collected data in Cipayung microgrid system. The result shows the characteristic of IRES consist of two things, a sudden increase and suddenly decrease of solar source. These characteristic profile model is made by a polynomial and logarithmic regression analysis using mathematical equation. A condition where the availability of solar energy is immediately at a maximum or at a lowest point of zero has never been found. The very fast IRES classification occurs the most often with 0.5 to 1.0 minutes of time duration. System operator can use this information to prevent the system from collapsing and even having a blackout.
Control Effector Unsaturation Modification to the Cascading Generalized Inverse Control Allocation Algorithm

Control allocation has sufficiently progressed such that it is used in front-line fighter aircraft such as the F-18 Super Hornet and the F-35 Joint Strike Fighter. Published literature shows the F-35 utilizes nonlinear Dynamic Inversion in conjunction with an effector blender that incorporates the Cascading Generalized Inverse control allocation algorithm. While the Cascading Generalized Inverse algorithm is one of the premier generalized inverse methods, it does suffer from three deficiencies. In particular, it suffers from an inability to achieve some desired outcomes, it intermittently provides non-optimal solutions and generally fails to preserve moment direction near maximal achievable moments. An effector unsaturation method based on a Scalar Difference Quadratic was first introduced and implemented on the iterative Prediction Method control allocation algorithm which was shown to consistently achieve optimal (weighted) control allocation solutions throughout the entire attainable moment set while preserving desired moment direction. In this paper, the shortcomings of the Cascading Generalized Inverse algorithm are addressed by augmenting the baseline algorithm with Scalar Difference Quadratic unsaturation identification and location at each iteration. Numerical case studies demonstrate that the Modified Cascading Generalized Inverse algorithm resolves the aforementioned deficiencies.
Handwritten Gujarati script recognition with image processing and deep learning

The motive behind writing this paper is to throw light on the proposed application which can be used for detecting and recognizing Gujarati handwritten scripts using image processing and machine learning techniques. It emphasizes the key technologies involved in this process. There is a lot of variation in the handwriting of people and the curves involved in the characters of the Gujarati language and therefore possess a challenge in the process. The paper features all the important phases in character recognition and detection process namely image acquisition, preprocessing, segmentation, classification and recognition, and post-processing. It also emphasizes the key aspects like the designing a neural network suitable for the challenging task of handwritten character recognition in Gujarati scripts, training and testing that model and fine-tuning various hyper-parameters to get the best accuracy. The paper can be referred by researchers and technology enthusiasts to develop systems for Gujarati script recognition. The paper aims to present and deal with special properties associated with Gujarati script.
Efficient Privacy Preserving and Dynamic Public Auditing for Storage Cloud

In recent years, cloud computing has gained lots of importance and is being used in almost all applications in terms of various services. One of the most widely used service is storage as a service. Even though the stored data can be accessed from anytime and at any place, security of such data remains a prime concern of storage server as well as data owner. It may possible that the stored data can be altered or deleted. Therefore, it is essential to verify the correctness of data (auditing) and an agent termed as Third Party Auditor (TPA) can be utilised to do so. Existing auditing approaches have their own strengths and weakness. Hence, it is essential to propose auditing scheme which eliminates limitations of existing auditing mechanisms. Here we are proposing public auditing scheme which supports data dynamics as well as preserves privacy. Data owner, TPA, and cloud server are integral part of any auditing mechanism. Data in the form of various blocks are encoded, hashed, concatenated and then signature is calculated on it. This scheme also supports data dynamics in terms of addition, modification and deletion of data. TPA reads encoded data from cloud server and perform hashing, merging and signature calculation for checking correctness of data. In this paper, we have proposed efficient privacy preserving and dynamic public auditing by utilizing Merkle Hash Tree (MHT) for indexing of encoded data. It allows updating of data dynamically while preserving data integrity. It supports data dynamics operations like insert, modify and deletion. Several users can request for storage correctness simultaneously and it will be efficiently handled in the proposed scheme. It also minimizes the communication and computing cost. The proposed auditing scheme is experimented and results are evaluated considering various block size and file size parameters.
A Survey for 2D and 3D Face Alignment

Face alignment, which is critical for many face applications such as face recognition, is to detect facial landmarks automatically. Existing methods can be classified into two categories: 2D solution and 3D solution. There are many advanced methods in 2D solution to optimize the detection on the image plane. 3D solution employs a 3D dense face model for 3D facial landmarks estimation. Though the two solutions have made great progress, there are limitations hindering face alignment accuracy's further improvement. 2D solution lacks the ability to process faces in large poses like profile face, while 3D solution is not superior in alignment of common-angle images like near-front faces. In this paper, a comparative survey is presented to provide a holistic understanding of both 2D and 3D face alignment, which may inspire more advanced solutions.
Poisson Noise Removal Using Multi-Frame 3D Block Matching

The 3D block matching (BM3D) filter belongs to the state-of-the-art techniques for eliminating additive white Gaussian noise from single-frame images. There exist four multi-frame extensions of BM3D as of today. In this work, we combine these extensions with a variance stabilising transformation (VST) for eliminating Poisson noise. Our evaluation reveals that the extension which retains the original noise model of the noisy images and additionally has a comprehensive connectivity of 2D and temporal image information at both pixel and patch levels, gives the best results. Additionally, we find a surprising change in performance of one the four extensions due to the specific application of the VST. Finally, we also introduce a simple low-pass filtering as a preprocessing step for the best performing extension. This can give rise to a significant additional improvement of 0.94 dB in the output according to the peak signal to noise ratio.
Research on netizens’ emotion evolution in emergency based on machine learning

Abstract [Purpose/significance] By analyzing the emotional evolution of Weibo users after emergencies, we can find out the law and potential risks of public opinion evolution and provide instruction for the government to control and guide network public opinion. [Method/process] We proposed an analysis model of public opinion evolution based on Emotional Analysis and GBRT. With the help of Python, a web crawler was developed to collect Weibo comments. After that, a Naive Bayesian Classifier was used for emotional analysis. According to public emotion and the number of comments, we divided the evolution process into fever period, persistence period, incubation period and extinction period. Statistical and visualization methods were used to study the evolution characteristics of word cloud, emotional tendency and age groups. Finally, correlation analysis and GBRT were used to predict each individual’s emotions. [Results/conclusion] Taking the dangerous chemical explosion accident in Tianjin as an example, we can validate our model. Results shows that the model can reasonably divide the evolutionary stages, find out the law of public opinion evolution in different stages, and accurately predict users’ emotional tendency.
Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process.
Ignis: An efficient and scalable multi-language Big Data framework

Most of the relevant Big Data processing frameworks (e.g., Apache Hadoop, Apache Spark) only support JVM (Java Virtual Machine) languages by default. In order to support non-JVM languages, subprocesses are created and connected to the framework using system pipes. With this technique, the impossibility of managing the data at thread level arises together with an important loss in the performance. To address this problem we introduce Ignis, a new Big Data framework that benefits from an elegant way to create multi-language executors managed through an RPC system. As a consequence, the new system is able to execute natively applications implemented using non-JVM languages. In addition, Ignis allows users to combine in the same application the benefits of implementing each computational task in the best suited programming language without additional overhead. The system runs completely inside Docker containers, isolating the execution environment from the physical machine. A comparison with Apache Spark shows the advantages of our proposal in terms of performance and scalability.
Part-Based Feature Aggregation Method for Dynamic Scene Recognition

Existing methods for dynamic scene recognition mostly use global features extracted from the entire video frame or a video segment. In this paper, a part-based method is proposed for aggregating local features from multiple video frames. A pre-trained Fast R-CNN model is used to extract local convolutional layer features from the regions of interest (ROIs) of training images. These features are then clustered to locate representative parts. A set cover problem is formulated to select the discriminative parts, which are further refined by fine-tuning the Fast R-CNN. Local convolutional layer features and fully-connected layer features are extracted using the fine-tuned Fast R-CNN model, and then aggregated separately from a video segment to form two feature representations. They are concatenated into a global feature representation. Experimental results show that the proposed method outperforms several state-of-the-art features on two dynamic scene datasets.
Aurora: A Robust and Trustless Verification and Synchronization Algorithm for Distributed Ledgers

Current consumer grade hardware running a full DLT node takes a long time to synchronize with the network and requires a lot of resources. Nodes that have never before synchronized with the network require significant resources to verify entire ledger history. The initial synchronization assumes inherent trust in the bootstrap nodes, which is a point of centralization. In the case when bootstrap nodes cannot be trusted, a new node might find itself in a malicious cluster of nodes that can either make the node waste resources before realizing that it has been exposed to malicious data, or subvert it into thinking that it has downloaded the longest chain. The Aurora algorithm is able to detect the existence of malicious cliques with a certain plausibility when a new node is attempting to synchronize with the network, and will try to exit cliques identified as malicious. If the node is unable to exit a malicious clique, the node will halt its operation. Furthermore, the same algorithm can be applied to verify if a specific transaction has been included into a referent block with a certain plausibility without the need to download the entire chain (or the header chain), or to trust centralized block explorers. Thus, the Aurora algorithm has the potential to significantly improve the scalability of DLT by reducing the time necessary for initial blockchain synchronization. Furthermore, it strengthens the trustless environment by incentivizing users not willing to run a full or even a light node to partly verify transaction inclusion without relying on third party entities.
Performance of the Runge-Kutta methods in solving a mathematical model for the spread of dengue fever disease

Dengue fever disease has been a big problem in Indonesia. In this paper, we solve a mathematical model for the spread of dengue fever disease using the Runge-Kutta methods. We provide some cases to show the performance of the Runge-Kutta methods in solving the model. In particular, the Runge-Kutta methods to be applied are the Euler and the Heun methods. We focus on researching the performance of these methods in solving the model.
Development of a Prognostic Model of the User’s Information Image Using Automated Tools for Processing Data from Social Networks

This article presents a first phase’s results of research project, dedicated to developing prognostic models of User’s Image with the automated Social Networks data processing methods. A pilot study carried out a comparative analysis of automatic methods for analyzing textual and visual data, which constituting a User’s informational image. We used a correlation analysis of data, which was obtained by using parsing methods and the results of psycho diagnostic research. As a result, multiple relationships with the socio-psychological characteristics of the respondents were identified. Based on obtained data, we have concluded about acceptable predictive capabilities of automated analysis.
Scalable NAS with factorizable architectural parameters

Neural Architecture Search (NAS) replaces manually designed networks with automatically searched networks and has become a hot topic in machine learning and computer vision. One key factor of NAS, scaling up the search space by adding operators, could help bring about more possibilities for effective architectures. However, existing works require high search costs and are prone to make confused selections caused by competition among similar operators. This paper presents a scalable NAS that utilizes a factorized method, which improves existing art in the following aspects. (1) Our work explores a broader search space. We construct an ample search space through the cartesian product of activation operators and regular operators. (2) Our work experiences a limited computation burden even though searching in a more extensive search space. We factorize a search space into two subspaces and adopt different architectural parameters to control corresponding subspaces. (3) Our work avoids competition among similarly combined operators (e.g., ReLU & sep-conv-3x3 and ReLU6 & sep-conv-3x3 and PReLU & sep-conv-3x3). That is because our architectural parameters are optimized sequentially. Experimental results show that our approach achieves SOTA on CIFAR10 (2.38% test error) and ImageNet (23.8% test error). Furthermore, the excellent performance of 35.8% (AP) and 55.7% (AP50) on COCO further proves the superiority of our factorized method.
Multilingual Data Analysis to Classify Sentiment Analysis for Tweets Using NLP and Classification Algorithm

The analysis of sentiments consists in identifying and classifying the opinions, attitudes, and sentiments of people expressed in original sentences. The advancement of social media, different critics, forum discussions, blogging, and working on social networks can be divided into different ways. Users who generate huge amounts of sentiment data on the website are in large quantities in the form of tweets and status updates. The sentiment analysis of this data is useful for market analysis and product research organizations. They are increasingly using public opinions in these media for their decision-making. In this paper, we propose an approach for analyzing the sentiment or opinion in an efficient manner. For this, we have proposed a technique that focuses multilingual data analysis to classify sentiment analysis for the tweets.
Fractals2019: Combinatorial Optimisation with Dynamic Constraint Annealing

Fractals2019 started as a new experimental entry in the RoboCup Soccer 2D Simulation League, based on Gliders2d code base, and advanced to become a RoboCup-2019 champion. We employ combinatorial optimisation methods, within the framework of Guided Self-Organisation, with the search guided by local constraints. We present examples of several tactical tasks based on the Gliders2d code (version v2), including the search for an optimal assignment of heterogeneous player types, as well as blocking behaviours, offside trap, and attacking formations. We propose a new method, Dynamic Constraint Annealing, for solving dynamic constraint satisfaction problems, and apply it to optimise thermodynamic potential of collective behaviours, under dynamically induced constraints.
Trading User Data: A Blockchain Based Approach

User data has become a valuable economic resource for many companies. While on the one hand user data may help companies improve their services and gain a better understanding of potential customers, on the other hand, the proliferation of user data is usually accompanied by a loss of user privacy. In the case of platforms like Facebook, Twitter, and Google, users are essentially trading their personal information for services and are often unaware of the potential consequences this trade may have on their private life. This paper presents an approach that allows users to store their data on their own smartphone and have full control over their data, allowing them to specify which companies or organizations obtain their personal data. At the same time, the blockchain technology will be used to realize a system for reliable negotiations involving user data without a central entity. Our approach makes it impossible for either the data provider or data collector to perform manipulations or cheat once a data exchange contract has been signed.
Nepali Image Captioning

Neural networks have seen a surge in applications ranging image recognition, time series prediction, and image captioning among others. The task of image captioning combines two fields of machine learning - computer vision and natural language processing. Several works have been done for image captioning in dominant languages like English and Mandarin with impressive results. The challenge, however, increases for Nepali, a language with a complex grammatical structure. With inherently complex grammar, inputting an image and generating the description of the image in Nepali with correct grammar is hard to achieve. Also, a standard data set does not exist for such tasks in Nepali. This work on image captioning in Nepali is the first of its kind that establishes a baseline for tasks of this sort and aims to encourage other researchers to pursue this line of research. For this, it makes public the data set that was generated during this process so as to provide a standard data set for future works. This work builds on top of the model proposed by [24] and generates image descriptions in Nepali. Describing the contents of an image in Nepali has several applications. Additionally, this work serves as a gateway to more challenging problems such as a video descriptor system and image search for search engines in Nepali. This work utilizes two encoder-decoder architectures, one with visual attention and another without visual attention. We empirically show the loss and perplexity of model performances using different optimizers. The captions generated are agreeable and coherent with the images in general and leave room for improvements in the future.