Reviewerly/exHarmony huggingface dataset:
Selecting efficient VM types to train deep learning models on Amazon SageMaker

The cloud has become a popular environment for running Deep Learning (DL) applications. Public cloud providers charge by the amount time the resources are actually used, with the price by hour depending on the configuration of the chosen cloud instance. Instances are usually provided in the form of a VM that gives access to a certain hardware configuration, and may also come with a pre-configured software environment. More advanced, and theoretically faster, VMs are usually more expensive, but may not necessarily provide the best performance for all applications. Therefore, in order to choose the best instance (or VM type), users must consider the relative performances (and consequent cost) of different VMs when running their specific target application. Taking this into account, we propose a model to estimate the relative performance and cost of training deep learning applications running in different VM instances. This model is built upon observations derived from the performance profile of executions of three different DL applications, on 12 different public cloud instances. We argue that this model is a valuable tool for cloud users looking for optimal VM types to train their deep learning applications on the cloud.
Traceable and Privacy-Preserving Non-Interactive Data Sharing in Mobile Crowdsensing

Data sharing is one of the key technologies, which provides the practice of making data collected from a crowd of mobile devices available to others using a cloud infrastructure, known as mobile crowdsensing (MCS). However, the collected data may contain sensitive information, and sharing them in public clouds without proper protection could cause serious security problems, such as privacy leakage, unauthorized access, and secret key abuse. To address the above issues, in this paper, we propose a Traceable and privacy-preserving non-Interactive Data Sharing (TIDS) scheme in mobile crowdsensing. Specifically, to achieve privacy-preserving fine-grained data sharing, an attribute-based access policy is generated by a data owner without interacting with data users in the TIDS. Furthermore, we design a ciphertext conversion mechanism to support flexible data sharing. Also, by utilizing traceable Ciphertext-Policy Attribute-Based Encryption (CP-ABE), TIDS supports a trusted authority to trace malicious users who abuse their secret keys without incurring additional computational overhead. Security analysis demonstrates that TIDS can protect the confidentiality of the outsourced data. Experimental results show that TIDS can achieve efficient data sharing in mobile crowdsensing applications.
Graphic Interpretation of Surfaces with the Support of Augmented Reality as a Training Complement in Engineering Studies

Currently, some difficulty has been detected in engineering students when interpreting graphical representations of surfaces. The combined use of new technological tools together with mobile devices involves a very interesting option to support academic training in drawing subjects. This paper presents the ARTID (Augmented Reality for Teaching, Innovation and Design) tool used for the representation of mechanical components in engineering studies, which has allowed the development of various teaching materials for the representation of surfaces in the Dihedral system. For this purpose, QR codes are used to accompany the drawings of different surface exercises, increasing students' interest by using mobile devices to visualize in 3D the models studied. The response of the students to the use of these complementary contents has been very satisfactory in the Industrial Design II course of the Degree in Mechanical Engineering at the Technical University of Cartagena. The evaluations have been collected in a questionnaire to address the conclusions of the experience.
Two and multiple categorization of breast pathological images by transfer learning

Breast cancer is the most common cancer in women worldwide. By using artificial intelligence technology to assist doctors in pathological diagnosis can effectively improve the efficiency of cancer diagnosis. However, the computer-aided diagnosis (CAD) has the problems of long training time for large-resolution pathological pictures and insufficient data that can be marked for training. In this paper, a transfer learning model is developed for the pathological diagnosis of breast cancer to overcome those problems. Four common deep learning models (VGGnet, Resnet, Densenet, Mobilenet) were adopted to train breast pathology images under four different resolutions (40X, 100X, 200X, 400X). A transfer learning framework was established to distinguish benign and malignant breast pathology and their subtypes. The accuracy of the two-classification model can reach 91.24&#x0025; at the best magnification (200X), and the average accuracy is 89.31&#x0025;. At the same time, the multi-classification model for the eight subtypes of pathological sections also achieved quite satisfied results. It is indicated that the presented transfer learning framework has great potential for exploring the CAD of breast cancer.
Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms

Abstract Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people.
On Improving SimBlock Blockchain Simulator

Bitcoin-based smart city services are an ever increasing up-and-coming reality. For these services, simulating the Bitcoin blockchain is important to parametrize the system and tailor the costs and the economic incentives. In this regard, SimBlock simulator is the current state-of-the-art tool for blockchain simulations. Unfortunately, based on an up-to-date parametrization, SimBlock turns out not to simulate the mining of blocks. Furthermore, it does not simulate the incentive mechanism. These limitations strongly confine SimBlock's effective usage towards evaluating Bitcoin-based services relevant to many application contexts, including smart cities. To overcome these limitations, we propose an improved SimBlock's implementation. Upon it, we assess whether SimBlock can abstract the current Bitcoin blockchain. The experimental analysis shows that the proposed implementation can effectively simulate the current Bitcoin blockchain. Though, introducing relay network modelling in SimBlock should even improve the accuracy of the simulation.
Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof-of-principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light-microscopy level. The proposed system used image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. The final model combined the Gini feature importance set and linear discriminant analysis classifier. Six morphologic (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness greater than ten, glomerular tuft thickness greater than three, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were, together, the best performing biomarkers. Accuracies of 77% and 87% were obtained for glomerular and patient-level classification, respectively. Computational methods, using explainable glomerular biomarkers, have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.
LURAT: a Lightweight Unsupervised Automatic Readability Assessment Toolkit for Second Language Learners

In second language acquisition, assessing the readability of texts is essential for many educational applications. Hence, the development of artificial intelligence tools to automatically assess readability with little or no human supervision is required owing to the high cost of manual readability labeling by educational experts, who must carefully read and assess the texts to perform this task. Prior unsupervised approaches have manually searched textual features that correlate well with readability labels, such as perplexity scores of large language models. However, these features do not capture the language knowledge of second language learners as the target users. To this end, we propose a novel unsupervised approach that captures their knowledge. Our key idea is to establish word difficulty features that accurately capture the language knowledge of second language learners. We analyze vocabulary test results to obtain and utilize the probability that a typical language learner knows a given word. By sacrificing syntactically complicated textual features, our approach enables lightweight classifiers that reflect learners’ vocabulary knowledge. In the experiments, our assessor, which was trained on vocabulary tests without costly readability labels, outperformed the perplexity-based assessors of large neural language models.
Exploration of Public Emotion Dynamics in Japan from Twitter Data during COVID-19

The COVID-19 has affected human lives in many ways throughout the globe. Several recent studies indicated that it has greatly impacted people&#x0027;s income. Many people have become jobless and many business entities have already closed especially in the travelling, tourism, entertainment, and restaurant sectors. Anecdotal evidences suggest that the pandemic has severely affected mental health issues. However, systematic studies for tracking public worries towards health and economy due COVID-19, regarded as hWorry and eWorry, over time are still lacking. In this study, several supervised machine learning models have been applied to a collection of public tweets to explore the mentioned worries. Experimental analysis with a set of 4072 tweets spanning six-month from January 2020 to June 2020 have discriminated tweets into hWorry and eWorry classes with 61&#x0025; accuracy. By applying a lexicon-based approach to the classified tweets, our approach reveals how the four selected emotions, i.e., fear, anger, sadness, and trust propagated over time. While the fear and trust emotions showed dominant temporal patterns in both classes, the average anger and sadness emotions were stronger in the e Worry class as compared to those in the h Worry class suggesting the necessity of more viable economic policies to overcome corona calamity.
An evaluation of Cassandra NoSQL database on a low-power cluster

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices, create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL distributed databases such as Cassandra have been used for unstructured data storage and to increase horizontal scalability and high availability. In this paper, we evaluated Cassandra on a low-power low-cost cluster of commodity Single Board Computers (SBC). The cluster has 15 Raspberry Pi v3 nodes with Docker Swarm orchestration tool for Cassandra service deployment and ingress load balancing over SBCs. Experimental results demonstrated that hardware limitations impacted workload throughput, but read and write latencies were comparable to results from other works on high-end or virtualized platforms. Despite the observed limitations, the results show that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity and high availability.
A new ORB feature matching image algorithm based on Non-maximum suppression method and Retinal sampling model

Traditional oriented FAST and rotated BRIEF (ORB) feature matching algorithm has mismatch problems in complex environment. To solve the problem, a new ORB feature matching algorithm is proposed. First, Non-maximum suppression method is used to eliminate the feature points and the retained feature points are described by retinal sampling model. Then, the feature points are matched by Hamming distance. Finally, a progressive sampling consensus (PROSAC) algorithm performs matching pair purification and calculates the matrix transformation. Experimental results show that the matching accuracy of the proposed ORB algorithm in complex illumination environment is greatly improved compared with the traditional ORB algorithm. It has been demonstrated that the proposed ORB algorithm possesses strong robustness and real-time performance.
Dealing with different conceptions of pollution in the Gene Regulation Knowledge Commons

Current research of gene regulatory mechanisms is increasingly dependent on the availability of high-quality information from manually curated databases. Biocurators undertake the task of extracting knowledge claims from scholarly publications, organizing these claims in a meaningful format and making them computable. In doing so, they enhance the value of existing scientific knowledge by making it accessible to the users of their databases. In this capacity, biocurators are well positioned to identify and weed out information that is of insufficient quality. The criteria that define information quality are typically outlined in curation guidelines developed by biocurators. These guidelines have been prudently developed to reflect the needs of the user community the database caters to. The guidelines depict the standard evidence that this community recognizes as sufficient justification for trustworthy data. Additionally, these guidelines determine the process by which data should be organized and maintained to be valuable to users. Following these guidelines, biocurators assess the quality, reliability, and validity of the information they encounter. In this article we explore to what extent different use cases agree with the inclusion criteria that define positive and negative data, implemented by the database. What are the drawbacks to users who have queries that would be well served by results that fall just short of the criteria used by a database? Finally, how can databases (and biocurators) accommodate the needs of such more explorative use cases?
Nuclei Segmentation on Histopathology Images of Breast Carcinoma

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.
Structurally re-parameterized rotation detector for arbitrary-oriented objects in high-resolution remote sensing images

Horizontal bounding boxes are inflexible for precisely locating geospatial objects with arbitrary orientations in high-resolution remote sensing images. Recently, rotation detectors with oriented bounding boxes have been found to have a positive effect on the detection of arbitrary-oriented objects. However, this method usually suffers from the requirement of a heavy network structure to learn orientation information. Therefore, a structurally re-parameterized rotation detector (SRep-RDet) for arbitrary-oriented objects in high-resolution remote sensing images is proposed. (1) A structurally re-parameterized backbone network, RepVGG-B1g2, is introduced to the detector based on RetinaNet to decouple the training-time multi-branch and inference-time single-path architecture. (2) The multiscale features of RepVGG-B1g2 are refined by using a lightweight channel attention structure. (3) The multiscale features are fused by structurally re-parameterizing a portion of the convolution layers in the feature pyramid network (FPN). (4) Arbitrary-oriented objects are detected by using a rotation detector with oriented bounding boxes. The experimental results on DOTA and HRSC2016 datasets achieve competitive performance.
Handling of Nonstandard Spelling in GRAC

GRAC is a large reference corpus of Ukrainian spanning over 200 years. The system of morphological analysis used to mark up the corpus was originally designed only for modern language. Meanwhile, the corpus includes texts that sometimes differ significantly from modern ones. Orthographic systems different from the modern standard have been used throughout the history of the Ukrainian language, including regional and diaspora publications. The article describes the algorithms used in the corpus to handle old orthographies and some other cases of nonstandard spelling, and discusses the prospects for their development. The developed tools have been made available online to the NLP and CL community.
Context-Aware Music Recommender Systems for Groups: A Comparative Study

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.
Fault-tolerant quantum reversible full adder/subtractor: Design and implementation

In quantum computers, all the heavy computations are done through quantum processors. ALU is one of the most significant parts of every processor responsible for performing logical operations and primary binary operations. All of the arithmetic computations in ALU utilize adder/subtractor to produce the expected result. Fault-tolerant circuit designs have found increasing application in quantum computers regarding the noise sensitivity of qubits and the faulty environment in which they operate. Additionally, reversible quantum circuits can prevent heat circulation and information lost throughout the high-rate binary calculations. The primary subject is the development of digital reversible fault-tolerant quantum circuits with higher performance and more efficiency regarding the number of inputs and outputs employed by quantum gates, depth, and delay. Full adders and subtractors are highly significant in different parts of quantum digital circuits and are used widely in multipliers, dividers, ALUs, and floating-point operations. In this paper, applying the parity-preserving method and the properties of Boolean algebra two novel reversible fault-tolerant designs are proposed that can detect every single or the odd number of errors. Also, both proposed designs are capable to perform addition and subtraction operations by changing the control signal(ctr). Our offered designs can reduce energy loss in the form of emitted heat in circuits, reduce the cost of building quantum circuits, reduce computation time, and detect errors in case of happen. Furthermore, comprehensive simulations for each possible combination of inputs and corresponding outputs are done to prove the full functionality and accurate performance of the proposed designs.