Reviewerly/exHarmony huggingface dataset:
Deep Feature Fusion for Mitosis Counting

Each woman living in the United States has about 1 in 8 chance of developing invasive breast cancer. The mitotic cell count is one of the most common tests to assess the aggressiveness or grade of breast cancer. In this prognosis, histopathology images must be examined by a pathologist using high-resolution microscopes to count the cells. Unfortunately, this can be an exhaustive task with poor reproducibility, especially for non-experts. Deep learning networks have recently been adapted to medical applications which are able to automatically localize these regions of interest. However, these region-based networks lack the ability to take advantage of the segmentation features produced by a full image CNN which are often used as a sole method of detection. Therefore, the proposed method leverages Faster RCNN for object detection while fusing segmentation features generated by a UNet with RGB image features to achieve an F-score of 0.508 on the MITOS-ATYPIA 2014 mitosis counting challenge dataset, outperforming state-of-the-art methods.
Data Augmentation for English-Vietnamese Neural Machine Translation: An Empirical Study

The translation quality of machine translation systems depends on the parallel corpus used for training, including the quantity and quality of the corpus. However, building a highquality and large-scale parallel corpus is complex and expensive, particularly for a specific domain parallel corpus. Therefore, data augmentation techniques are widely used in machine translation. The back-translation method is simple and effective when the input is monolingual text. In practice, monolingual texts can be collected from different sources, in which sources from websites often have errors in grammar and spelling, sentence mismatch, freestyle, etc. Therefore, the quality of the output translation is reduced, leading to a low-quality parallel corpus generated by back-translation. In this study, we propose a method for improving the quality of monolingual texts for back-translation. Moreover, we supplemented the data by pruning translation table. We experimented with an English-Vietnamese neural machine translation using the IWSLT2015 dataset for training and testing in the legal domain. The results showed that the proposed method can effectively augment parallel data for machine translation, thereby improving translation quality. In our experimental cases, the BLEU score increased by 16.37 points compared with the baseline system.
NeoCube

In this work, we consider metadata-based exploration of media collections using the M3 data model, to support multimedia analytics applications. We propose a new metadata-server implementation based on the Neo4j graph database system, and compare it to the existing, heavily-optimised server based on a relational database system. We show that the graph-based implementation performs well for interactive metadata-space retrieval, albeit not as well as the optimised relational implementation. The graph-based implementation also allows for very efficient updates to the metadata collection, however, which are practically impossible in the optimised relational implementation.
A news picture geo-localization pipeline based on deep learning and street view images

Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined, but only a small fraction of them are geotagged. The visual content of the news image hints at clues of the geographical location because they are usually taken at the site of the incident, which provides a prerequisite for geo-localization. This paper proposes an automated pipeline based on deep learning for the geo-localization of news pictures in a large-scale urban environment using geotagged street view images as a reference dataset. The approach obtains location information by constructing an attention-based feature extraction network. Then, the image features are aggregated, and the candidate street view image results are retrieved by the selective matching kernel function. Finally, the coordinates of the news images are estimated by the kernel density prediction method. The pipeline is tested in the news pictures in Hong Kong. In the comparison experiments, the proposed pipeline shows stable performance and generalizability in the large-scale urban environment. In addition, the performance analysis of components in the pipeline shows the ability to recognize localization features of partial areas in pictures and the effectiveness of the proposed solution in news picture geo-localization.
Research on Intelligent Anomaly Detection Algorithm of Launch Vehicle Telemetry Data

In the field of launch vehicles, high launch density, heavy work intensity and large data volume have brought the traditional automatic anomaly detection with incomplete coverage, and complex design of criterion, which influencing performance evaluation of launch vehicle. In order to fully mine the data variation law implied in the massive telemetry data, the intelligent anomaly detection algorithm is proposed and designed as a beneficial supplement to the traditional anomaly detection algorithm, and improve the anomaly detection coverage and efficiency. With the telemetry data generated by the liquid launch vehicle taken as the research object, and the intelligent anomaly detection algorithm is compared with the traditional algorithm. It is shown that the algorithm proposed adapts to the anomaly detection scenarios with frequency anomaly and frame loss, which may be difficult for traditional algorithms to design anomaly detection criteria, and the average anomaly detection performance is improved by nearly 30&#x0025; and the variance is reduced by 85&#x0025;, providing a reference for improving anomaly detection system and applying intelligent anomaly detection algorithms in aerospace.
Memory-efficient Very Large Scale Integration Architecture of 2D Algebraic-integer-based Daubechies Discrete Wavelet Transform

Low memory requirement and reduced latency have been two major concerns in the implementation of the 2D Daubechies discrete wavelet transform.In this study, a multilevel 2D Daubechies-4 (Daub-4) wavelet filter architecture based on an algebraic integer (AI) is investigated.This architecture can improve the traditional Daub-4 very large scale integration (VLSI) architecture design and reduce the number of adders in a 1D Daub-4 filter module architecture.The is because the AI-based multilevel architecture does not require any multipliers and can improve the efficiency of accurate calculations.In addition, to solve the problem of the large transpose memory generated by multimedia chip design, we examine the uses of N × N image inputs in the calculation of the Daub-4 filter by importing them into the interlaced read scan algorithm.This investigated architecture not only reduces the size of the transpose memory from N 2 to 10 or 21 (in the Daub-4 and Daub-6 modes, respectively) but also speeds up the sensing and reading of signals and the calculations.We also show that when a fieldprogrammable gate array is combined with the investigated system, it can enhance the implementation of 2D multilevel AI-based Daub-4 and Daub-6 VLSI architectures.Latency represents time from signal input to output, Ta and Tm: numbers of adders and multipliers that have passed, respectively, CT: time required to calculate image.
Blockchain-Aided Searchable Encryption-Based Two-Way Attribute Access Control Research

In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.
Machine Learning Approach Based on Fuzzy Logic for Industrial Temperature Regulation

In recent times, Fuzzy logic has entered as a superior control methodology for processes that are mathematically difficult to model. Fuzzy-based system focuses on systems that use knowledge-based techniques to support human decision-making, learning and action. In this paper, a Fuzzy knowledge-based controller (FKBC) has been proposed based on Fuzzy logic knowledge using linguistic variables representation and inference formalism. The proposed FKBC is a unique closed loop Fuzzy logic controller (FLC) structure having a small rule base with an efficient realization and can be easily implemented in existing industrial temperature controllers. This includes dealing with unknown and probably variable time delays, operating at very different temperature set points without returning.
Morphological and Otsu’s Technique Based Mammography Mass Detection and Deep Neural Network Classifier Based Prediction

Breast cancer is a leading cause of death among women. The death rate is reduced when this disease is detected early with the help of mammography. Deep learning is a method that radiologists use and request to help them make more accurate diagnoses and enhance their outcome predictions. This work presents a novel strategy comprising a pre-processing method and a mix of morphological and multi-thresholding using Otsu's technique based segmentation technique, which was tested on the Mini-MIAS dataset of 322 images. For Speed-Up Robust Features (SURF) selection, the inbuilt feature extraction is done utilizing multiple colour and texture features approaches. At the classification level, a new layer is added that performs 70 percent training and 30 percent testing of the deep neural network. There are two primary steps in the training phase: (1) Develop a model for dividing breast tissue into dense and non-dense categories. (2) Develop a model for classifying breast regions into mass and non-mass. The results show that the accuracy rate of the proposed automated DL approach is higher than that of other state-of-the-art models. The average accuracy (ACC) rates of the three types of cancer, i.e., normal, benign, and malignant cancer, utilizing the suggested method are 91 percent, 94 percent, and 93 percent, respectively, according to experimental results.
Statistical Keyboard Decoding

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BadmintonDB

This paper introduces BadmintonDB, a new badminton dataset for training models for player-specific match analysis and prediction tasks, which are interesting challenges. The dataset features rally, strokes, and outcome annotations of 9 real-world badminton matches between two top players. We discussed our methodologies and processes behind selecting and annotating the matches. We also proposed player-independent and player-dependent Naive Bayes baselines for rally outcome prediction. The paper concludes with the analysis performed on the experiments to study the effects of player-dependent model on the prediction performances. We released our dataset at https://github.com/kwban/badminton-db.
SVAT: Secure outsourcing of variant annotation and genotype aggregation

Abstract Background Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions. Results We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. SVAT uses homomorphic encryption to encrypt the data at the client-side. The data always stays encrypted while it is stored, in-transit, and most importantly while it is analyzed. SVAT makes use of a vectorized data representation to convert annotation and aggregation into efficient vectorized operations in a single framework. Also, SVAT utilizes a secure re-encryption approach so that multiple disparate genotype datasets can be combined for federated aggregation and secure computation of allele frequencies on the aggregated dataset. Conclusions Overall, SVAT provides a secure, flexible, and practical framework for privacy-aware outsourcing of annotation, filtering, and aggregation of genetic variants. SVAT is publicly available for download from https://github.com/harmancilab/SVAT .
Lessons Learned from ASVSpoof and Remaining Challenges

Although speech technology reproducing an individual's voice is expected to bring new value to entertainment, it may cause security problems in speaker recognition systems if misused. In addition, there is a possibility of this technology being used for telephone fraud and information manipulation. Recognizing the importance of this issue, we have been working on speech anti-spoofing countermeasures since 2010, including building large-scale speech databases and organizing a series of ASVspoof challenges to evaluate the detectors on the shared database. This presentation will summarize the essential findings and lessons we have learned recently [1] and present the remaining challenges we are currently facing and the results we have achieved to date [2-4]. Examples of the lessons include a) sensitivity to hyper-parameters and features in deep learning-based countermeasure models and the importance of designing a network structure and learning loss that are stable even under different conditions, and b) effectiveness of ensemble learning of multiple models trained on different types of acoustic features and ineffectiveness of ensemble learning of different network structures using similar acoustic features. The ongoing research topics include 1) front-end features that are robust to domain and channel mismatches [2], 2) how to automatically expand the countermeasure database in a situation where new speech synthesis methods are being invented regularly [3], and 3) detection of partial synthetic regions to provide evidence for XAI anti-spoofing countermeasures [4]. Through these new attempts, the importance of studying the issue of speech anti-spoofing countermeasures from various angles, in addition to reducing EERs, will be illustrated.
The Platform for Czech National Qualifications Framework in Cybersecurity

The scarcity of cybersecurity experts negatively affects the overall state of cybersecurity among all relevant stakeholders in the private and public sectors. The efforts to close the cybersecurity skills gap are unfortunately limited by the lack of a sufficiently broad and granular taxonomy. In this article, we introduce our work on the Czech National Qualifications Framework in Cybersecurity as a possible remedy for the said problem. Firstly, we shortly describe other relevant projects and initiatives. Further, we focus on the development process of the Czech Framework, its components and their interactions, and subsequently we focus on the Platform and its functions. This interactive platform offers other stakeholders (and future users) an effective access to the framework and its further utilization. It also simplifies and allows for an effective administration and update of a framework on this level of granularity.
Grundy numbers of impartial three-dimensional chocolate-bar games

Chocolate-bar games are variants of the Chomp game. Let Z≥0 be a set of nonnegative numbers, and let x, y, z ∈ Z≥0. A three-dimensional chocolate bar is comprised of a set of 1 × 1 × 1 cubes with a “bitter” or “poison” cube at the bottom of the column at position (0, 0). For u, w ∈ Z≥0 such that u ≤ x and w ≤ z, the height of the column at position (u, w) is min(F(u, w), y) + 1, where F is an increasing function. We denote such a chocolate bar as CB(F, x, y, z). Two players take turns to cut the bar along a plane horizontally or vertically along the grooves, and eat the broken pieces. The player who manages to leave the opponent with a single bitter cube is the winner. In a prior work, we characterized the function f for a two-dimensional chocolatebar game such that the Sprague-Grundy value of CB(f , y, z) is y ⊕ z. In this study, we characterize the function F such that the Sprague-Grundy value of CB(F, x, y, z) is x ⊕ y ⊕ z.
Exponential Sailfish Optimizer-based generative adversarial network for image annotation on natural scene images

Generally, automatic image annotation can offer semantic graphics for recognizing image contents, and it creates a base for devising various techniques, which can search images in a huge dataset. Although most existing techniques mainly focus on resolving annotation issues through sculpting tag semantic information and visual image content, it ignores additional information, like picture positions and descriptions. The established Exponential Sailfish Optimizer-based Generative Adversarial Networks are therefore used to provide an efficient approach for image annotation (ESFO-based GAN). By combining Exponentially Weighted Moving Average (EWMA) and Sailfish Optimizer (SFO), the ESFO is a newly created design that is used to train the GAN classifier. Additionally, the Grabcut is presented to successfully do image annotation by extracting the background and foreground images. Additionally, DeepJoint segmentation is used to divide apart the images based on the background image that was extracted. Finally, image annotation is successfully accomplished with the aid of GAN. As a result, image annotation uses the produced ESFO-based GAN's subsequent results. The developed approach exhibited enhanced outcomes with maximum F-Measure of 98.37%, maximum precision of 97.02%, and maximal recall of 96.64%, respectively, using the flicker dataset.
Principles of Secure Processor Architecture Design

This chapter concludes the book by presenting the principles of secure processor architecture design which have been derived based on the observations of best practices for secure processor design. It then gives a short description of how the secure design principles affect the standard computer architecture principles. It next overviews threats to the assumptions that have been discussed at the end of each chapter. It also presents common pitfalls and fallacies of secure processor architecture design. It ends with a list of challenges that secure processor architects can encounter, a list of future trends, and a brief note on the art and science of secure processor architecture design.
Data analysis with MATLAB® or Python

This chapter, Data Analysis with MATLAB or Python, is a brief introduction to MATLAB and Python as a data analysis environments and scripting languages. It is meant to teach the reader barely enough to understand the scripts in the book and to begin to start using and modifying them. While both MATLAB and Python are fully featured programming language, this book is not a treatise on computer programming. It teaches scripting mainly by example and avoids long discussions on programming theory.
Consent Verification Monitoring

Advances in personalization of digital services are driven by low-cost data collection and processing, in addition to the wide variety of third-party frameworks for authentication, storage, and marketing. New privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act, increasingly require organizations to explicitly state their data practices in privacy policies. When data practices change, a new version of the policy is released. This can occur a few times a year, when data collection or processing requirements are rapidly changing. Consent evolution raises specific challenges to ensuring GDPR compliance. We propose a formal consent framework to support organizations, data users, and data subjects in their understanding of policy evolution under a consent regime that supports both the retroactive and non-retroactive granting and withdrawal of consent. The contributions include (i) a formal framework to reason about data collection and access under multiple consent granting and revocation scenarios, (ii) a scripting language that implements the consent framework for encoding and executing different scenarios, (iii) five consent evolution use cases that illustrate how organizations would evolve their policies using this framework, and (iv) a scalability evaluation of the reasoning framework. The framework models are used to verify when user consent prevents or detects unauthorized data collection and access. The framework can be integrated into a runtime architecture to monitor policy violations as data practices evolve in real time. The framework was evaluated using the five use cases and a simulation to measure the framework scalability. The simulation results show that the approach is computationally scalable for use in runtime consent monitoring under a standard model of data collection and access and practice and policy evolution.