Reviewerly/exHarmony huggingface dataset:
Texture Analysis Based on Structural Co-occurrence Matrix Improves the Colorectal Tissue Characterization

Colorectal cancer causes the deaths of thousands of people worldwide according to the World Health Organization. Automatic tissue recognition of histopathological images is essential for early disease diagnosis. Most research consists of employing texture descriptors to capture features that identify tumor samples. However, accurate multi-class classification is a challenge due to the complexity of colorectal tissue images. Recently, researchers have shown that the analysis of texture structural patterns degraded by image filtering provides valuable features for pre-diagnosis in several medical applications. Here we propose an approach to automatically classify eight types of colorectal tissues using Structural Co-occurrence Matrix. We carried on experiments on 5000 tissue patches from a public dataset to evaluate our algorithm, considering two scenarios: structural differences as a single descriptor, and combined with other characteristics. We found that our strategy improves the state-of-the-art, achieving, accuracy: 91.30%, precision: 91.41%, sensitivity: 91.31%, specificity: 98.76% e F1-score: 91.31%.
Reversible and recoverable authentication method for demosaiced images using adaptive coding technique

This paper proposes a reversible authentication scheme for demosaiced images with the capability to approximately recover original contents of tampered parts. The existing methods protect the marked images to a large extent; however, they cannot detect some intentional modifications, or lack the self-recovery capability of tampered regions. The proposed method uses the most significant bits (MSBs) of sampled components to generate recovery codes, and embeds them into the least significant bits (LSBs) of rebuilt components. The MSBs of rebuilt components are adjusted to reduce the embedding distortion. The authentication codes are generated by hashing the MSBs of adjusted rebuilt components, and are embedded into their LSBs. By analyzing the extracted authentication and recovery codes, the tampered regions can be localized, and the original contents can be approximately recovered. If the marked image is untampered, the original demosaiced image can be exactly reconstructed. Experimental results demonstrate that the proposed method not only provides an excellent marked image quality but also achieves a satisfactory detection and recovery results.
TOPSIS Method on Selection of New Employees’ Acceptance

A hospital is a labor-intensive organization that requires many personnel and professions from various disciplines. Employees are one of the determinants of the success of a company. One way that agencies use to obtain qualified employees is by making a selection at the time of employee recruitment. The problem that is often encountered is that part-time employee selection is calculated objectively so that it is less efficient and accurate in the selection process. Decision support systems with the TOPSIS method are considered suitable to assist the personnel section of the public Hospital in selecting prospective part-time employees. TOPSIS is a multi-criteria decision-making method based on a concept where the chosen alternative has the closest distance from a positive ideal solution and also has the farthest distance from a negative ideal solution. The completion criteria applied to the selection of employees include the results of written tests, psychological tests, health tests, and interviews. The results of the tests show that the TOPSIS method can be used well in selecting and sorting from the largest to the smallest value with an accuracy rate of 85% in the amount of 20 data.
Using Galois Connection to Hierarchical Classifying Chunks in Ontology-Based E-learning System

This article is dedicated to using Galois connections to hierarchically classify chunks in ontology-based e-learning systems. The main idea of chunk-based approach for course ontology creation has been described. Also, described what benefits can be obtained using Galois connection to hierarchical classifying chunks in ontology-based e-learning systems. This article demonstrates that how to formalize the educational content dynamically and how to select suitable items effectively are two main problems in the current researches of the ontology-based e-learning systems. A new approach of monitoring the interaction between students and chunk-based content is proposed, which is based on hierarchical classification of chunks their sets and sub-sets. This article proposed a new approach that can detect and recommend suitable education material which is connected to chunks to easily and efficiently rebuild the individual learning pathway of students. As a result, has been shown that the proposed approach of the using Galois connection can effectively formalize the educational materials, concepts and chunks, and also create the hierarchical classification of them. According to this classification it is easy to update a student's individual learning pathway.
Adaptively Secure Efficient (H)IBE over Ideal Lattice with Short Parameters

Identity-based encryption (IBE), and its hierarchical extension (HIBE), are interesting cryptographic primitives that aim at the implicit authentication on the users’ public keys by using users’ identities directly. During the past several decades, numerous elegant pairing-based (H)IBE schemes were proposed. However, most pairing-related security assumptions suffer from known quantum algorithmic attacks. Therefore, the construction of lattice-based (H)IBE became one of the hot directions in recent years. In the setting of most existing lattice-based (H)IBE schemes, each bit of a user’s identity is always associated with a parameter matrix. This always leads to drastic but unfavorable increases in the sizes of the system public parameters. To overcome this issue, we propose a flexible trade-off mechanism between the size of the public parameters and the involved computational cost using the blocking technique. More specifically, we divide an identity into l′ segments and associate each segment with a matrix, while increasing the lattice modulo slightly for maintaining the same security level. As a result, for the setting of 160-bit identities, we show that the size of the public parameters can be reduced by almost 89.7% (resp. 93.8%) while increasing the computational cost by merely 5.2% (resp. 12.25%) when l′ is a set of 16 (resp. 8). Finally, our IBE scheme is extended to an HIBE scheme, and both of them are proved to achieve the indistinguishability of ciphertexts against adaptively chosen identity and chosen plaintext attack (IND-ID-CPA) in the standard model, assuming that the well-known ring learning with error (RLWE) problem over the involved ideal lattices is intractable, even in the post-quantum era.
Fuzzy Cognitive Map-Based Genetic Algorithm for Community Detection

One of the most elemental operations concerning the analysis of properties of a network is community detection. It is the process of decomposition of a given network into groups of densely connected nodes that tend to share some similar properties. A wide variety of algorithms to identify the communities in complex networks exists. In this paper, an intelligent genetic algorithm (GA)-based approach to identify communities has been proposed. The efficiency of the solution that resulted from the genetic algorithm depends on the setting appropriate values for the various parameters involved. As a means to reduce the convergence time of the genetic algorithm, a fuzzy cognitive map (FCM) is used. The knowledge derived from the FCM is used to populate the initial population reducing the randomness of the algorithm. The potency of the algorithm is evaluated on various weighted and unweighted benchmark networks.
Digital and Digitalized Economy in EMs: A Focus on Turkey

Covid-19 still pressures global economies. Pandemic has seriously damaged both macro and micro indicators of countries. Economies try to accelerate their efforts towards a digital new normal in order to preserve their activities. Decreasing trust in monetary authorities and tools as a side effect of global financial crisis, decreasing demand for cash as a precaution towards virus, increasing demand for fast payment, increasing search for yield, search for a trustless, cost saving, peer to peer financial system accelerates the progress of creative destructors. The way to leapfrog developed countries requires benefiting more from digitalization. Governments, central banks, financial institutions and corporations that are aware of this swift transformation are in an effort to adapt the system to take the lead. This study aims to explore leading game changers, potential use cases and their potential impacts on EMs with a special focus on Turkey.
A Wise Up Visual Robot Driven by a Self-taught Neural Agent

This paper presents a biological inspired robot capable of learning by itself high level Tic-Tac-Toe playing policies and then use this knowledge to advantageously compete with humans. The robot comprises a robotic arm, an artificial vision system and a self-motivated neural agent which has the capability to explore in a simulated ambient, new forms of game episodes that conduce toward bigger rewards. During the training phase a three terms reinforcement learning scheme is proposed, where the agent memory resources are sustained by adviser neural sub-networks, noise-balanced trained as to satisfy the look for future conditions in the control optimization predicted by the Bellman equation. In the operating phase the components merge into a wised up robot, with look ahead capacities, that mimic the abilities of ingenious human players. The achieved look ahead robotic intelligence could be useful in other complex robotic mechanisms.
Fast Build Top-k Lightweight Service-Based Systems

This work aims to solve the problem of automatically building top-k (the number of suggested results) lightweight service-based-systems (LitSBSs) based on user-given keywords and QoS constraints. Top-k LitSBSs are those service-based systems (SBSs) that optimally match the query keywords, contain the minimum number of component services and satisfy the user-given QoS constraints. A LitSBS is easy to be managed, executed, monitored, debugged, deployed and scaled. Its QoS performance (e.g. communication overhead, risk of failure and connection latency) is usually better than the non-lightweight SBSs in a similar network environment. We design a database-driven algorithm (called LitDB) to solve the problem. We conduct comprehensive experiments to demonstrate the time efficiency of LitDB. In addition, we use real mashup examples from programmableWeb to show its practicality.
Modification Affine Cipher Transform Digraph to Squared the value of ‘n’ in Text Security

Affine Cipher is the development of Classic Caesar cipher that multiplies plain text before shifting operation. Affine cipher has two parameters namely key a, and b. Key ‘a’ is multiplies the plain text. It must have an inverse with Great Common Divisor (GCD) from the number of characters. The Problems of Affine Cipher Algorithm is the number of characters available and the least possible of key ‘a’ that can be used, so it is considered to be a classic technique that is easy to hack. Generally, Affine Cipher is used to encrypt the plaintext of character ‘A to Z’ so that the number of keys ‘a’ is only 12 digits from 26 digits (n) is available. Development of affine had increased the key ‘a’ into 128 of 256 digits (n) with 8 bit of ASCII. Digraph Transform is the modification of text encryption. It able to square the value of n. Usually, Text is encrypted by one character. In Digraph Transform, text is encrypted by two characters. For two characters, multiplying integer of the first character by number of characters, then add integer of the second character.
OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections

This paper presents the Online Adaptive Fuser: OAFuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises are estimated with steady-state filters to obtain combined measurement noise and process noise estimators for all sensors in order to obtain state estimation with a linear Minimum Mean Square Error (MMSE) estimator and accelerating the system's performance. The proposed adaptive tracking and fusion system was tested based on high fidelity simulation data and several real-world scenarios for automotive radar, where ground truth data is available for evaluation. We demonstrate the proposed method's accuracy and efficiency in a challenging, highly dynamic scenario where our system is benchmarked with Multiple Model filter in terms of error statistics and run time performance.
The genesis of forensic research of documents as the branch of forensic technology

The history of the falsification of various documents for illegal purposes accompanies the entire history of human civilization, so the study of the evolution of methods of documenting information and related methods of forgery is one of the methods of improving forensic knowledge. The examination of documents similarly has a long history and is directly related to the emergence and spread of writing.&#x0D; At the present stage of development of forensic document research, the achievements of various sciences are actively used to reveal the patterns in the field of forensic technology and its improvement. In particular, the study of the anatomical characteristics of writing and motor skills during the analysis of certain elements of handwriting; physiological processes that form the basis of movement control and reproduce written features or their combinations. The coordination of movements in the distribution of manuscripts according to the pace of writing is separately studied. Factors influencing the formation of written speech are determined by conducting experiments. Internal and external factors are taken into account, which allow to reconstruct the psycho-physiological features of the person who wrote the document, without which the establishment of its authorship is extremely problematic. Data of physiology and psychology help in the process of substantiating the stability of handwriting elements, drawing parallels between psycho-physiological characteristics of a person and certain graphic features.&#x0D; Considering the acquired historical experience, modern forensic research can be divided into three categories: 1) handwriting, where the main purpose is to establish the executor of the document; 2) auto-research – finding out the authorship of the text of the document; 3) technical and forensic, which allow to accurately establish the remaining components of the document.&#x0D; Forensic examination of documents has passed a long way in historical evolution. In its modern form, it was developed in the second half of the XX century due to a combination of theoretical principles and special methods borrowed from the natural and technical sciences. It allowed to determine as accurately as possible the technical features of producing a document or its part thereof, identifying the used tools and materials.
Evaluation of Mixed-Radix Digit Computation Techniques for the Three Moduli RNS {2<sup> <i>n</i> </sup>−1, 2<sup> <i>n</i> </sup>, 2<sup> <i>n</i>+1</sup>−1}

In this brief, we evaluate two techniques for obtaining mixed-radix digits using mixed-radix conversion technique, for the residue number system (RNS) moduli set {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sup> -1}. These are needed for comparison of two numbers in residue form, base extension to modulus 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+e</sup> and reverse conversion. The complete architectures are derived to estimate the hardware resource requirement and computation time for deriving the mixed-radix digits. The proposed techniques are also compared with designs based on other techniques available in literature. The trade-offs regarding hardware resource requirement, computation time and power dissipation are brought out.
Refactoring Recommendations Based on the Optimization of Socio-Technical Congruence

Software development is known to be a social activity that involves developers, project managers, and stakeholders. Recent studies have proved a direct relation between social and technical aspects, e.g., poor coordination among developers may lead to an increase of technical debt in source code. The so-called socio-technical congruence measures the level of coordination existing in an organization at their different levels. In this late-breaking idea paper, we propose a novel way to employ the socio-technical congruence in the context of source code quality improvement: we design a community-based refactoring recommendation approach that aims at optimizing socio-technical congruence while keeping into account the source code dependencies among the components of a software project. A search-based algorithm is employed to this purpose and we envision the novel approach to be suitable for providing Extract Class and Extract Package refactoring recommendations.
Hardness of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e631" altimg="si10.svg"><mml:mi>k</mml:mi></mml:math>-anonymous microaggregation

k-anonymous microaggregation of data in Rd with d≥2 is shown to be NP-hard for all k≥4, extending a previous result for the case k=3 only. The proof uses similarities between microaggregation and the k-means problem. A reduction of Planar 3-SAT to the k-means clustering problem is adapted to 4-anonymous clustering. Then this construction is extended to arbitrary k≥4.
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training. The network only needs the low-resolution (LR) ASL image itself for training and the T1-weighted image as the anatomical prior. No training pairs or pre-training are needed. A low-pass filter guided item was added as an additional loss to suppress the noise interference from the LR ASL image. After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer. Performance of the proposed method was evaluated by comparing the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using normal-resolution (NR) ASL image (5.5 min acquisition) and high-resolution (HR) ASL image (44 min acquisition) as the ground truth. Compared to the nearest, linear, and spline interpolation methods, the proposed method recovers more detailed structure information, reduces the image noise visually, and achieves the highest PSNR and SSIM when using HR ASL image as the ground-truth.
Designing a Culturally Inspired Mobile Application for Cooperative Learning

Cooperative learning is shown to build positive relationships among students and improve one’s achievement. In most cases, studies focus on improving the type of learning technologies and other associated challenges pertaining to infrastructural needs. Little attention is given to understand specific knowledge that could enable advancement in learning, particularly in places that are culturally diverse with traditional knowledge. If cultural underpinnings are not carefully considered, it may hinder students to collaborate. This work adopted a user-centered approach to gain user needs and requirements to design and test a mobile application that fosters cooperative learning. The study also discovered the importance of having cultural elements and the need for having a system that recognizes and supports cooperative learning.
FCAD: Feature-based Clipped Representation for Time Series Anomaly Detection

Since time series are a ubiquitous type of data, more and more researchers are investing in time series data mining in recent years. It is generally known that time series have the characteristics of large volume, high dimensionality and ever-increasing. Thus, dimensionality reduction is usually the first step in time series data mining. Over the years, amount of high-level representation approaches have been proposed. In this paper, we propose a novel bit level approximation of time series data, called Feature-based Clipped Representation (FCR), and a similarity measure for FCR which lower bounds the Euclidean distance is introduced. Finally, we propose an anomaly detection approach for time series, called FCAD, which is a novel anomaly detection approach based on FCR and the corresponding similarity measure. Extensive experiments have been conducted to demonstrate the advantages of FCAD in time series anomaly detection.
Handwritten Digit Recognition using Image Preprocessing and CNN

Handwritten digit recognition, is a technique of identifying and enlisting the recognized digit, that uses neural networks, deep learning and machine learning. The applications and demand of handwritten digit recognition systems such as zip code recognition, car number plate recognition, robotics, banks, mobile applications and numerous more, are soaring every day. It can be done through numerous approaches, but convolutional neural network is considered one of the best methods. The special neural network uses multilayer architecture for identification and classification. Although the accuracy factor can be increased, based on image preprocessing, in this paper we discuss how the accuracy of the system can be increased for better handwritten digit recognition, using convolutional neural networks, image preprocessing; binarization, resizing, rotation. The accuracy rate obtained is 99.33%.