Reviewerly/exHarmony huggingface dataset:
Discrimination between Handwritten and Computer-Generated Texts using a Distribution of Patch-Wise Font Features

Recently developed deep learning techniques allow us to generate images of handwritten-like texts that closely resemble a target writer's actual handwriting. Although these models are applicable to useful systems (e.g. communication tools for hand-impaired people), they also could be misused for document forgery by a malicious user. To cope with this problem, in this paper, we propose a text-independent method for discriminating between the computer-generated texts (CGTs) and actual handwritten texts (HWTs). Our proposed method only takes a single text image and recognizes whether the image is a CGT or a HWT. Characters in HWT images have various shapes even when the same writer writes the same sentence. This property is difficult to perfectly mimic even by state-of-the-art CGT generation methods. To capture this difference between HWTs and CGTs, we use the distribution of patch-wise font features. The proposed procedure for discriminating HWTs and CGTs is as follows: First, we divide a given text image into several patches and classify each patch into one of pre-determined standard font classes. Then we compute the histogram of the standard fonts, which is finally fed into the recognizer that discriminates between HWTs and CGTs. In our experiments, the proposed method achieved more than 96% of discrimination accuracy, which demonstrates the effectiveness of the proposed method.
Statistical Machine Learning for Transliteration: Transliterating names between Sinhala, Tamil and English

In this paper, we focus on building models for transliteration of personal names between the primary languages of Sri Lanka-namely Sinhala, Tamil and English. Currently, a Rule-based system has been used to transliterate names between Sinhala and Tamil. However, we found that it fails in several cases. Further, there were no systems available to transliterate names to English. In this paper, we present a hybrid approach where we use machine learning and statistical machine translation to do the transliteration. We built a parallel trilingual corpus of personal names. Then we trained a machine learner to classify names based on the ethnicity as we found it is an influencing factor in transliteration. Then we took the transliteration as a translation problem and applied statistical machine translation to generate the most probable transliteration for personal names. The system shows very promising results compared with the existing rule-based system. It gives a BLEU score of 89 in all the test cases and produces the top BLEU score of 93.7 for Sinhala to English transliteration.
Automated Malaria Diagnosis Using Microscopic Images

In accordance with the necessity, there is an increase in demand for medical images in diagnostic applications, the use of proper enhancement methods are inevitable. Medical imaging is a challenging field where robust and cost-effective approaches are yet to be developed for screening of diseases. Regular screening for Malaria is necessary since it can be a life-threatening disease to the group of people. Thus risk management can be done with pre-diagnosis. The identification of malarial parasites is usually done based on the symptoms of the patients. In this conventional method, there is limited access to experts in microbiology and pathology particularly in rural area practice and this restricts the diagnostic facilities. This paper presents a novel automatic diagnosis of malaria through microscopic images with the help of image processing. The paper deals with the existing systems for malarial screening and the detection methods of the disease. The proposed system quantifies and classifies the red blood cells infected by Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale and Plasmodium malariae in thin blood smears. The experimentation was conducted in MATLAB in which image acquisition, pre-processing including segmentation, classification and a combination of other image processing techniques were performed.
A Metaphorical Study Of Variants Of Recurrent Neural Network Models For A Context Learning Chatbot

In this paper, different types of recurrent neural networks such as GRU, bi-directional LSTM and a single forward pass LSTM available for question answering systems are explored, so that it can be used in a high level API to be able to create a chatbot interface for a web application. The networks are compared on the Facebook bAbi dataset to test the question answering functionality on 20 different types tasks available in the dataset. The results from existing models were compared and it is observed that the bi-directional LSTM and the GRU performed better in few tasks, however the single forward pass LSTM performed the best for majority of tasks in the dataset.
Action Recognition using Convolutional Neural Networks with Joint Supervision

Mapping the depth video into an optimally representation in two-dimensional space are of vital importance for depth video based human action understanding. Meanwhile, such representation will lost some useful information inevitably, a feature learning approach not only separable but also discriminative are essential for action recognition task from such representation. This paper presents a new method for action recognition base on convolutional neural networks with joint supervision which shares the merits of both representation as mentioned above and convolutional neural networks. The advantages of our method come from (i) The whole procedure of our method is done automatically no matter the generation of representation or deeply feature learned; (ii) The deeply feature using the proposed deep architectures to learned has high discriminative capacity to improve the accuracy of action recognition effectively compared with handcrafted features. We conduct experiments on two challenging datasets: MSRDailyActivity3D and SYSU 3D HOI. Experimental results show that our method outperform previous methods based on hand-crafted features. Our method also achieves superior performance to the state-of-the-art on these datasets.
The Messaging Model Design Based Blockchain and Edge Computing for the Internet of Things

In the edge network of the Internet of Things (IoT), the integration of blockchain and edge computing provides reliable and secure manners for large scale of services. Blockchain insures security with its distributed storage and consensus mechanisms, meanwhile edge computing brings forth large resources. However, facing communication, computing, caching and other issues, the integration is arduous to deploy. In this paper, we focus on solving the communication dilemmas in the integration and establish a messaging model for IoT systems, designing a two-layer edge computing architecture with different types of blockchain running on it. Based the practical application of BUAA vehicle monitoring system, we set up new edge nodes, redefine the data access through the blockchain and define the selection of the servers. Compared to the centralized cloud, the hybrid messaging protocol is proved to be more effective. The results show that the messaging model can run well on the application, transforming the IoT cloud center into a distributed mode. Under the security of blockchain and the computing power supported by edge computing, the advantages of distributed systems can be fully utilized.
On variable selection in matrix mixture modelling

Summary Finite mixture models are widely used for cluster analysis, including clustering matrix data. Nowadays, high‐dimensional matrix observations arise in a variety of fields. It is known that irrelevant variables can severely affect the performance of clustering procedures. Therefore, it is important to develop algorithms capable of excluding irrelevant variables and focusing on informative attributes in order to achieve good clustering results. Several variable selection approaches have been proposed in the multivariate framework. We introduce and study a variable selection procedure that can be applied in the matrix‐variate context. The methodological developments are supported by several simulation studies and application to real‐life data sets, with good results.
Gibbs Sampling of Measurement Partitions and Associations for Extended Multi-Target Tracking

In this paper we propose a novel approach to handle the extended target association problem in multi-target tracking for conjugate priors like the <tex>$\delta$</tex>-GLMB, LMB, PMBM and MBM. By introducing dependencies between partition cells we are able to employ a Gibbs sampler to simultaneously sample from partitions of the measurement set and association mappings. This formulation allows for a reduction of the approximation error as well as a more efficient implementation.
Identity Management: State of the Art, Challenges and Perspectives

Passwords are still the primary means for achieving user authentication online. However, using a username-password combination at every service provider someone wants to connect to introduces several possibilities for vulnerabilities. A combination of password reuse and a compromise of an iffy provider can quickly lead to financial and identity theft. Further, the username-password paradigm also makes it hard to distribute authorized and up-to-date attributes about users; like residency or age. Being able to share such authorized information is becoming increasingly more relevant as more real-world services become connected online. A number of alternative approaches such as individual user certificates, Single Sign-On (SSO), and Privacy-Enhancing Attribute-Based Credentials (P-ABCs) exist. We will discuss these different strategies and highlight their individual benefits and shortcomings. In short, their strengths are highly complementary: P-ABC based solutions are strongly secure and privacy-friendly but cumbersome to use; whereas SSO provides a convenient and user-friendly solution, but requires a fully trusted identity provider, as it learns all users' online activities and could impersonate users towards other providers. The vision of the Olympus project is to combine the advantages of these approaches into a secure and user-friendly identity management system using distributed and advanced cryptography. The distributed aspect will avoid the need of a single trusted party that is inherent in SSO, yet maintain its usability advantages for the end users. We will sketch our vision and outline the design of Olympus' distributed identity management system.
A Topology Based Differential Privacy Scheme for Average Path Length Query

Differential privacy is heavily used in privacy protection due to it provides strong protection against private data. The existing differential privacy scheme mainly implements the privacy protection of nodes or edges in the network by perturbing the data query results. Most of them cannot meet the privacy protection requirements of multiple types of information. In order to overcome these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both network vertices and edge weights. Firstly, by describing APL, the reasons for choosing this attribute as the query function are analyzed. Secondly, global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Finally, the relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.
Tighter polygamy relations for a superposition of the generalized W class state and vacuum beyond qubits

In this article, we present generalized polygamy relations for a superposition of the generalized W class state and vacuum. This class of states were shown to satisfy the strong monogamy relations in terms of the squared convex roof extended negativity [14]. Here we first present a polygamy relation in terms of the β-th power of the linear entanglement of assistance when β∈(0,1] for the superposition of the generalized W class state and vacuum. Then we present a tighter polygamy relation for the class of states in terms of the linear entanglement of assistance under some constraints. At last, we present a class of mixed states in higher dimensional systems satisfying the tighter polygamy relations we present.
Consequences of Dependent and Independent Variables based on Acceptance Test Suite Metric Using Test Driven Development Approach

The fundamental of software development was Test-Driven Development but the individual tests must be carried out previously the production code. To research, the consequence of test-driven development on product code quality and developer productivity was the destination of this paper. This system builds the acceptance test suite metric of regression analysis to assess the impact of the process on dependent variables and independent variables. This paper's results observed the positive effect of external quality over function of the number tests, and slightly decrease the effect of developer productivity over function of the number of tests. TDD can affect advance software products' quality, also mend programmers' productivity. TDD undertook to help the delivery of high-quality products, both operational (fewer bugs) and technical perspective (cleaner code) while improving developers' productivity. TDD affects to less defects and fewer debugging period which correct code can be certified by writing tests first and thus serving the developer get a finer understanding of the software requirements.
Performance of the NCV Kalman Filter with ECAE for Tracking Maneuvering Targets

When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter, the selection of the process noise variance is complicated by the fact that the process errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. Due to this model mismatch, the NCV Kalman filter is a biased estimator for an accelerating target. In recent years, the deterministic tracking index was introduced and used to develop a relationship between the maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MMSE) in position. A lower bound on the process noise variance was also expressed in terms of the maximum acceleration and deterministic tracking index. In this paper, the NCV model Kalman filter with exponentially-correlated acceleration errors (ECAE) is studied. While the model mismatch of the NCV Kalman filter with ECAE is less than that for the NCV Kalman filter with white noise errors for a constant acceleration target, it is a biased estimator. Expressions for the MMSE of an NCV Kalman filter with ECAE tracking a constant acceleration target are developed and those expressions are verified via Monte Carlo simulations.
Phenomenological Intuition in the Artistic Interpretation of History

The logics of intuitionism, which has been elaborated on the premises of phenomenology as an alternative to the formal logics, reveals common features with eidetic reduction while making up preconditions for the formation of eidetic images. It is the relation “part — whole” that becomes the foundation of intuition being common with totallogy in philosophy and meronymy in linguistics. Here, in opposite to the elements of a class in formal logics, the parts are not indifferent but are rather different in function beforehand while determining the division of an object into closed cycles. The main means of intuition, i.e. the creation of cycles and the disclosure of manifold connections (“diagonalization”), conform to the properties of historical reality which is marked with totality and uniqueness and is represented as the “possible worlds” that are remote from immediate observation due to the historical distance. Of particular importance is such peculiarity of historical stuff as that of closed nature which imparts the property of a self-referential text to history. That evokes the problems of fatalism reflected in theatre as a “freedom — fate” dilemma. As to the theatrical interpretation of history, the scope of “episode — epoch” cycles plays here the important role where the transition is provided with the subject of action as the vehicle of intentionality. The intuition in theatrical interpretation works through the disclosure of manifold connections among separate episodes, in particular, through the searches of the prototypes of dramatis personae. The epoch’s subject of action is reconstructed in theatre as the vehicle of intentionality. Episodes correspond with the epoch as a whole in the eidetic image of an epoch.
A novel recommender algorithm based on graph embedding and diffusion sampling

Summary With the rapid increase in e‐commerce data, recommender systems (RSs) have become the most prevalent methods for providing recommended services in various commercial platforms. Deep learning–based recommender methods improve recommendation results by learning latent representations; however, most cannot capture the correlations between items and ignore additional information such as time information, which leads to suboptimal suggestions. To improve recommendation accuracy, we propose a novel recommender algorithm based on graph embedding and diffusion sampling (graph2vec). Our improved model constructs a graph based on users' behavior histories and embeds the graph to a low‐dimensional vector space with a deep learning approach. To obtain more accurate embedding results, we use a revised sampling method based on information diffusion theory to capture both the depth and breadth information of a graph. Then, we recommend the top‐N items to the target user depending on the final representation vectors. Experiments are carried out with real‐world datasets to demonstrate the superior performance of graph2vec. The results show that browse‐based graph construction and diffuse‐based graph embedding help improve the recommender accuracy of the new model compared with that of the selected state‐of‐the‐art models.
Automatic Word Embeddings-Based Glossary Term Extraction from Large-Sized Software Requirements

[Context and Motivation] Requirements glossary defines specialized and technical terms used in a requirements document. A requirements glossary helps in improving the quality and understandability of requirements documents. [Question/Problem] Manual extraction of glossary terms from a large body of requirements is an expensive and time-consuming task. This paper proposes a fundamentally new approach for automated extraction of glossary terms from large-sized requirements documents. [Principal Ideas/Result] Firstly, our technique extracts the candidate glossary terms by applying text chunking. Next, we apply a novel word embeddings based semantic filter for reducing the number of candidate glossary terms. Since word embeddings are very effective in identifying terms that are semantically very similar, this filter ensures that only domain-specific terms are present in the final set of glossary terms. We create a domain-specific reference corpus for home automation by Wikipedia crawling and use it for computing the semantic similarity scores of candidate glossary terms. We apply our technique to a large-sized requirements document, i.e., a CrowdRE dataset with around 3000 crowd-generated requirements for smart home applications. Semantic filtering reduces the number of glossary terms by 92.7%. To evaluate the quality of our extracted glossary terms we manually create the ground truth data from CrowdRE dataset and use it for computing precision and recall. Additionally, we also compute the requirements coverage of these extracted glossary terms. [Contributions] Our detailed experiments show that word embeddings based semantic filtering can be very useful for extracting glossary terms from a large body of requirements.
Mathematical formulation of quantum circuit design problems in networks of quantum computers

In quantum circuit design, the question arises how to distribute qubits, used in algorithms, over the various quantum computers, and how to order them within a quantum computer. In order to evaluate these problems, we define the global and local reordering problems for distributed quantum computing. We formalise the mathematical problems and model them as integer linear programming problems, to minimise the number of SWAP gates or the number of interactions between different quantum computers. For global reordering, we analyse the problem for various geometries of networks: completely connected networks, general networks, linear arrays and grid-structured networks. For local reordering, in networks of quantum computers, we also define the mathematical optimisation problem.
Efficient algorithms for mining maximal high-utility itemsets

To overcome the limitation of high-utility itemset mining, a more compact, lossless and concise representation of high utility itemsets (HUIs) have been proposed, such as closed HUIs (CHUIs) or maximal HUIs (MHUIs). In this paper, we present algorithms to efficiently mine MHUIs from transactional databases. The proposed algorithms utilize the many pruning techniques to significantly reduce the search space and increase the performance of the MHUIs mining process.
Automatic Counting of Infected White Blood Cells Using Multi-Level Thresholding

The identification of leukocytes is the primary step to analyze diseases such as leukemia which are ordinarily performed by pathologists utilizing a light binocular. These are very time consumption, monotonous, expensive and require specialists. In this way, computer assisted symptomatic framework that helps pathologists within the demonstrative strategy can be exceptionally viable. White blood cell (WBC) fragmentation is ordinarily the primary stage in computer-assisted diagnostic framework development. This paper proposed an automated method for segmenting and counting white blood cell from microscopic images. For this purpose, Otsu technique was used in segmentation of WBCs from a microscopic image, Counting WBCs and diagnoses the patient case. The proposed system achieves an accuracy of 98.2%.
XDF-REPA: A Densely Labeled Dataset toward Refined Pronunciation Assessment for English Learning

Currently, most computer assisted pronunciation training (CAPT) systems focus on overall scoring or mispronunciation detection. In this paper, we address the issue of refined pronunciation assessment (RPA), which aims at providing more refined information to L2 learners. To meet the major challenge of the lack of densely labeled data, we present the XDF-REPA dataset, which is freely available to the public. The dataset contains 19,213 English word utterances by 18 Chinese adults, among which 4,200 audio clips from 9 speakers are densely labeled by 3 linguists with intended phoneme, actually uttered phoneme, phoneme score for each phoneme, and an overall score for the word as well. To reduce the difference between annotators, scoring rules combining subjectivity and objectivity are defined. To demonstrate the usage of the dataset and provide a baseline for other researchers, a prototype system for RPA is developed and described in the paper, which adopts a DNN-HMM based acoustic model and a variant of Goodness of Pronunciation (GOP) to yield all the corrective feedbacks needed for RPA. Experimental results show error detection accuracy varies from 80.1% to 85.1% for different subsets and linguists, and accuracy of actually-uttered-phoneme recognition varies from 70.9% to 80.8% for different subsets and linguists.