Reviewerly/exHarmony huggingface dataset:
Multi-domain Document Layout Understanding Using Few-Shot Object Detection

We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors. We will open-source the code, trained models, source and target datasets upon acceptance.
The role of the analytic hierarchy process (AHP) algorithm in health care services

Abstract Improvement in quality health care has now become a must that both developed and developing countries work on improving their health care system, especially in midwifery emergencies, using information systems. UNICEF has developed a midwifery referral system with its program known as Expanding Maternal and Newborn Survival to speed up the referring process. AHP is one of the information system procedures that support decision making to speed up the determination of health care facilities. This research discusses some journal articles by conducting a detailed analysis of their preliminary studies, research objectives, achievement indicators, variables, and methods used. It is expected that readers can briefly learn some benefits of AHP in health care. This research employed the PRISMA protocol guidelines. Chosen journal articles are full papers published in reputable journals. Inclusion and exclusion criteria were also implemented in choosing proper journal articles for this systematic literature review. It is found that the implementation of AHP in any type of health care service has proven to be very effective. It is expected that this literature review can serve as a reference for the implementation of AHP in health care based on practice evidence.
Testing of Code-Based Pseudorandom Number Generators for Post-Quantum Application

This article presents the results of studies of design pseudorandom sequence generators using code-based cryptosystems. The resistance of this generators type is based on the complexity of solving the syndrome decoding problem, which allows to provide resistance to classic cryptanalysis and cryptanalysis using quantum computing. The article describes the principles of constructing a classic Fisher-Stern generator and proposes a new approach to design a code-based generator that can be applied in the post-quantum period. Heuristic testing of the presented generators was also conducted in terms of a period length, a sequence generation speed, and a resistance of the generators, which revealed the Fisher-Stern generator deficiency. It is a difference between a theoretical and a practical period of the generated sequences, which is eliminated in the proposed generator by adding two components: a linear feedback shift register and an adder.
Dynamic String Alignment.

We consider the problem of dynamically maintaining an optimal alignment of two strings, each of length at most n, as they undergo insertions, deletions, and substitutions of letters. The string alignment problem generalizes the longest common subsequence (LCS) problem and the edit distance problem (also with non-unit costs, as long as insertions and deletions cost the same). The conditional lower bound of Backurs and Indyk [J. Comput. 2018] for computing the LCS in the static case implies that strongly sublinear update time for the dynamic string alignment problem would refute the Strong Exponential Time Hypothesis. We essentially match this lower bound when the alignment weights are constants, by showing how to process each update in 𝒪(n) time. When the weights are integers bounded in absolute value by some w=n^{𝒪(1)}, we can maintain the alignment in 𝒪(n ⋅ min {√ n,w}) time per update. For the 𝒪(nw)-time algorithm, we heavily rely on Tiskin’s work on semi-local LCS, and in particular, in an implicit way, on his algorithm for computing the (min,+)-product of two simple unit-Monge matrices [Algorithmica 2015]. As for the 𝒪(n√n)-time algorithm, we employ efficient data structures for computing distances in planar graphs.
“Work in Progress: Creating an E-learning Environment for Teaching Foreign Languages to Engineering Students”

The article touches upon the problem of developing an e-learning environment (video and Ted.Talks) for teaching foreign languages to large groups of engineering students. The purpose of our experiment was to prove the efficacy of using Video and Ted.Talks both in boosting students’ motivation for learning English in an engineering program and in enriching the vocabulary of future engineers with special engineering terms, consequently enhancing the students’ knowledge in their engineering field. The research was conducted by applying the following methods: experiments, observation and introspection. Two stages of working with video material were developed. This method proved to be effective in teaching English in large groups of students specializing in an engineering program. Students acquired important knowledge in the sphere of their future activity which they may later apply to real situations and projects.
Sublinear-Space Lexicographic Depth-First Search for Bounded Treewidth Graphs and Planar Graphs

The lexicographic depth-first search (Lex-DFS) is one of the first basic graph problems studied in the context of space-efficient algorithms. It is shown independently by Asano et al. [ISAAC 2014] and Elmasry et al. [STACS 2015] that Lex-DFS admits polynomial-time algorithms that run with O(n)-bit working memory, where n is the number of vertices in the graph. Lex-DFS is known to be P-complete under logspace reduction, and giving or ruling out polynomial-time sublinear-space algorithms for Lex-DFS on general graphs is quite challenging. In this paper, we study Lex-DFS on graphs of bounded treewidth. We first show that given a tree decomposition of width O(n^(1-e)) with e > 0, Lex-DFS can be solved in sublinear space. We then complement this result by presenting a space-efficient algorithm that can compute, for w ≤ √n, a tree decomposition of width O(w √nlog n) or correctly decide that the graph has a treewidth more than w. This algorithm itself would be of independent interest as the first space-efficient algorithm for computing a tree decomposition of moderate (small but non-constant) width. By combining these results, we can show in particular that graphs of treewidth O(n^(1/2 - e)) for some e > 0 admits a polynomial-time sublinear-space algorithm for Lex-DFS. We can also show that planar graphs admit a polynomial-time algorithm with O(n^(1/2+e))-bit working memory for Lex-DFS.
A hybrid model for class noise detection using k-means and classification filtering algorithms

Real data may have a considerable amount of noise produced by error in data collection, transmission and storage. The noisy training data set increases the training time and complexity of the induced machine learning model, which led to reduce the overall performance. Identifying noisy instances and then eliminating or correcting them are useful techniques in data mining research. This paper investigates misclassified instances issues and proposes a clustering-based and classification filtering algorithm (CLCF) in noise detection and classification model. It applies the k-means clustering technique for noise detection, and then five different classification filtering algorithms are applied for noise filtering. It also employs two well-known techniques for noise classification, namely, removing and relabeling. To evaluate the performance of the CLCF model, several experiments were conducted on four binary data sets. The proposed technique was found to be successful in classify class noisy instances, which is significantly effective for decision making system in several domains such as medical areas. The results shows that the proposed model led to a significant performance improvement compared with before performing noise filtering.
Broad Multi-Parameter Dimensioning of Magnetocaloric Systems Using Statistical Learning Classifiers

Prototyping innovative energy devices is a complex multivariable dimensioning problem. For the case of magnetocaloric systems, one aims to obtain an optimized balance between energy conversion performance, useful power generated and power consumed. In these devices, modeling is entering a mature phase, but dimensioning is still time consuming. We have developed a technique that dimensions any type of magnetocaloric system by training statistical learning classifiers that are used to simulate the computation of a very large number of systems with different combinations of parameters to be dimensioned. We used this method in the dimensioning of a magnetocaloric heat pump aiming at optimizing the temperature span, heating power, and coefficient of performance, obtaining an f-score of 95%. The respective classifier was used to mimic over 940 thousand computed systems. The gain in computation time was 300 times that of computing numerically the system for each combination of parameters.
Bounding the fidelity of quantum many-body states from partial information

We formulate an algorithm to lower bound the fidelity between quantum many-body states only from partial information, such as the one accessible by few-body observables. Our method is especially tailored to permutationally invariant states, but it gives nontrivial results in all situations where this symmetry is even partial. This property makes it particularly useful for experiments with atomic ensembles, where relevant many-body states can be certified from collective measurements. As an example, we show that a $\xi^2\approx-6\;\text{dB}$ spin squeezed state of $N=100$ particles can be certified with a fidelity up to $F=0.999$, only from the measurement of its polarization and of its squeezed quadrature. Moreover, we show how to quantitatively account for both measurement noise and partial symmetry in the states, which makes our method useful in realistic experimental situations.
Cybersecurity Education and Skills: Exploring Students’ Perceptions, Preferences and Performance in a Blended Learning Initiative

Designing a cybersecurity course for a big cohort of students from the different educational background is a challenging job. Examined in this study are the perceptions, preferences and performance of students who have participated in a strategic blended learning initiative aimed at preparing students for their working lives. For this purpose, both self-reported and observational data were collected from 115 students who voluntarily registered for the pilot run of the course. Self-reported data was used to measure students’ preferences as well as perceptions related to satisfaction, engagement, convenience, interaction and views on learning. Observational data measuring students’ performance was directly extracted from the collaborative learning platform on which the course was hosted. The results show that overall students liked the blended design of the course. They were satisfied with the format of the course, they felt engaged, and most of them secured good grades. Moreover, no significant difference in perceptions and preferences were found when controlled for gender, educational discipline, and overall performance, showing that the blended design of the course was accepted across the board.
Assessing text mining algorithm outcomes

There is a surge in the development of decision-oriented analysis tools intended to extract actionable information from text. These tools integrate various text-mining methods that were performance tested in a manner that was often biased toward the new system. Those tests primarily utilised descriptive measurement criteria and test datasets that are inconsistent with most business corpora. We propose and test a user-oriented judgment approach that allows testing under controlled customer-oriented corpora and generates effect size measures. To illustrate the approach, customer relations data was analysed by latent semantic analysis and latent Dirichlet analysis with results evaluated by prospective business analysts. Reporting includes comparisons of results with published literature. While the research centres on the context-region text-mining systems, literature comparisons include word-embedding methods. The analysis concludes that none of the systems reviewed possess a repeatable statistical advantage over the others. Instead, distribution attributes, algorithm configuration, and the evaluation task drive results.
Deep Learning With TensorFlow

This chapter aims to acquaint the users with key parts of TensorFlow and some basic ideas about deep learning. In particular, users will figure out how to perform fundamental calculations in TensorFlow and implementation of deep learning using TensorFlow. This chapter intends to gives a straightforward manual for the complexities of Google's TensorFlow framework that is easy to understand. The basic steps for the installation and setup of TensorFlow will also be discussed. Starting with a simple “Hello World” example, a practical implementation of deep learning problem to identify the handwritten digits will be discussed using MNIST dataset. It is only possible to understand deep learning through substantial practical examples. For that reason, the authors have included practical implementation of deep learning problems that motivates the readers to plunge deeply into these examples and to get their hands grimy trying different things with their own ideas using TensorFlow because it is never adequate to perceive algorithms only theoretically.
Parallel techniques for speckle noise reduction in medical ultrasound images

Parallel computing is used to accelerate the computations in the reduction of speckle noise in medical ultrasound images. A new hybrid algorithm for speckle noise reduction in medical ultrasound images is proposed and compared to an existing efficient method. The method combines in an efficient manner the advantages of several denoising filters using local and non-local information. A comparative study is carried out using quantitative and qualitative measures and showing the competitiveness of the proposed method. Both methods are parallelized using OpenMP, and a hybrid combination of MPI and OpenMP. The parallel implementations are compared to the sequential ones, obtaining significative values of speedup.
A Tractor Formation Coverage Path Planning Method Based on Rotating Calipers and Probabilistic Roadmaps Algorithm

Severe labor shortages and rising daily wages are forcing farmers to use heavy tractors to sow seeds, which increases efficiency and saves labor costs. But driving heavy tractors on soil for growing plants causes permanent damage to the soil, leading to crop failures. In order to reduce the damage to soil fertility, a formation of several light tractors can replace a single heavy tractor for field operations. However, multiple tractors require multiple operators, which adds to labor costs. In order to solve the above problems, this paper presents an algorithm for coverage path planning of tractors formation based on the Rotating Calipers and Probabilistic Roadmaps. First, the algorithm uses five light tractors to form a reverse “V” formation, which not only reduces damage to soil fertility, but also ensures operational efficiency. In addition, this algorithm can achieve autonomous path coverage and obtain time-optimal coverage trajectory, which lays a foundation for realizing autonomous operation of tractors and reduces labor cost. And this method can be used to coverage path planning of both convex and concave polygons.
Image encryption using dynamic DNA encoding and pixel scrambling using composite chaotic maps

Abstract In this digital world, encryption plays an important role in various domains. Securing the information is the main goal when transfer of information takes place. Image encryption is a very important part of this as it applies to various domains like medical, multimedia, defence etc. A new method for image encryption is proposed here taking into account the “confusion-diffusion” structure. While working with secure data, requirements like fast computation, compression and processing are important issues. Deoxyribonucleic Acid (DNA) encoding has the capability to cope up with this requirement. In this paper a new algorithm is proposed based on composite chaos map for pixel scrambling and hence image encryption. First the algorithm takes the image and XORs with a composite chaotic map which further goes into dynamic DNA encoding followed by pixel scrambling which results in the image being very random thus it becomes less prone to attacks. The method discussed performs efficiently as shown in the experimental results.
An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal

In recent years, sparse representation theory and low-rank approximation model have been widely used in signal and image processing fields. In the study of natural image denoising, non-local similarity method can enhance the correlation of grouped image blocks, and a low rank prior is used to devise a bilateral sparse representation of the image matrix, consequently achieving the purpose of removing additive white Gaussian noise. However, problems such as how to select thresholds after a singular value shrinkage, and how to eliminate image artifacts in removing noise especially with high levels, have not been resolved. In this paper, a low rank adaptive singular value thresholding (ASVT) denoising algorithm based on singular value decomposition(SVD) is proposed. Our method uses random matrix and asymptotic matrix reconstruction theory to scientifically select the threshold of singular value thresholding. At the same time, a dual-domain filtering method is used to process the visual artifacts after image denoising by ASVT, which we call collaborative singular value thresholding (CSVT) algorithm. The experimental results show that the proposed algorithm has a certain improvement in subjective visual effects, and objective quantitative indicators compared with some related advanced denoising algorithms.
Geological mapping using Random Forests applied to Remote Sensing data: a demonstration study from Msaidira-Souk Al Had, Sidi Ifni inlier (Western Anti-Atlas, Morocco)

Geological mapping plays a very important role in the exploration of oil, mineral and water resources, as well as in identifying and monitoring natural hazards. It is an indispensable means for the economic development of the country. Remote sensing data provides critical support by reducing the costs and increasing the precision. This research work evaluates the use of Random Forests, a supervised machine learning algorithm, for geological mapping of the Msaidira-Souk Al Had region, a part of the sidi Ifni inlier situated in southern Morocco. By integrating the spectral and textural features of Sentinel-2A with the morphometric attributes of Digital Elevation Model (DEM) of ALOS/PALSAR. The experiment revealed that the overall accuracy reaches ≈ 91% while the kappa coefficient is 88%. As the final result of this research, the Random Forest method is an effective tool that geoscientists can use to produce a new map or to update existing geological maps.
Deep Audio Steganalysis in Time Domain

Digital audio, as well as image, is one of the most popular media for information hiding. However, even the state-of-the-art deep learning model still has a limitation for detecting basic LSB steganography algorithms that hide secret messages in time domain of WAV audio. To advance audio steganalysis based on deep learning, deep audio steganalysis, in time domain of lossless audio format, we have developed a convolutional neural network that incorporates bit-plane separation, weight-standardized convolution, and channel attention. Training through payload curriculum learning and testing for six steganography methods demonstrated that our proposed model is superior to the other two deep learning models, achieving state-of-the-art performance. We expect our approach will provide insights to create a breakthrough for deep audio steganalysis.
An Efficient Private Set Intersection Protocol for the Cloud Computing Environments

Private set intersection (PSI) is a hot topic in cryptography and has a wide range of applications such as privacy preserving personal properties matching and data mining. Many PSI protocols have been presented, which discussed the efficiency and formal security. However, there are few PSI protocols for the cloud computing environments, where the cloud server offers effective computations and storage capabilities to clients and performs the PSI computations for clients. In this paper, we propose an efficient PSI protocol for the cloud computing environments, which is based on homomorphic encryption, pseudorandom function, set polynomial representation, and point-value form of polynomials. Our PSI protocol enjoys the following advantages: (a) our protocol gives a natural secure data storage; (b) normal channels are suitable to our protocol; (c) the communication and computation complexity are linear to the dataset size; (d) our protocol provides a formal detailed simulated-based security proof; (e) our protocol is a scalable protocol that could be extended to multiple clients conveniently, and a formal simulated-based security proof is provided in details.
Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances

Artificial intelligence (AI), particularly deep learning (DL), which involves automated feature extraction using deep neural networks, is expected to be used increasingly often by clinicians in the near future. AI can analyze medical images and patient data at a level not possible by a single physician; however, the resulting parameters are difficult to interpret. This so-called “black box” problem causes opaqueness in DL. The aim of the present study is to help realize the transparency of black box machine learning for digital pathology (DP). To achieve this aim, we review the “black box” problem and the limitations of DL for DP, and attempt to reveal a paradigm shift in DP in which diagnostic accuracy is surpassed to achieve explainability. DL in medical fields such as DP still has considerable limitations. To interpret and apply DL effectively in DP, sufficient expertise in computer science is required. Moreover, although rules can be extracted using the Re-RX family, the classification accuracy is slightly lower than that using whole images trained by a convolutional neural network; thus, to establish accountability, one of the most important issues in DP is to explain the classification results clearly. Although more interpretable algorithms seem likely to be more readily accepted by medical professionals, it remains necessary to determine whether this could lead to increased clinical effectiveness. For the acceptance of AI by pathologists and physicians in DP, not only quantitative, but also qualitative algorithmic performance, such as rule extraction, should be improved.