Reviewerly/exHarmony huggingface dataset:
Dense Receptive Field Network: A Backbone Network for Object Detection

Although training object detectors with ImageNet pre-trained models is very common, the models designed for classification are not suitable enough for detection tasks. So, designing a special backbone network for detection tasks is one of the best solutions. In this paper, a backbone network named Dense Receptive Field Network (DRFNet) is proposed for object detection. DRFNet is based on Darknet-60 (our modified version of Darknet-53) and contains a novel architecture named Dense Receptive Field Block (DenseRFB) module. DenseRFB is a densely connected mode of RFB and can form much denser effective receptive fields, which can greatly improve the feature presentation of DRFNet and keep its fast speed. The proposed DRFNet is firstly tested with ScratchDet for fast evaluation. Moreover, as a pre-trained model on ImageNet, DRFNet is also tested with SSD. All the experiments show that DRFNet is an effective and efficient backbone network for object detection.
Identifying Topical Coverages of Curricula using Topic Modeling and Visualization Techniques: A Case of Digital and Data Curation

Digital/data curation curricula have been around for a couple of decades. Currently, several ALA-accredited LIS programs offer digital/data curation courses and certificate programs to address the high demand for professionals with the knowledge and skills to handle digital content and research data in an ever-changing information environment. In this study, we aimed to examine the topical scopes of digital/data curation curricula in the context of the LIS field. We collected 16 syllabi from the digital/data curation courses, as well as textual descriptions of the 11 programs and their core courses offered in the U.S., Canada, and the U.K. The collected data were analyzed using a probabilistic topic modeling technique, Latent Dirichlet Allocation, to identify both common and unique topics. The results are the identification of 20 topics both at the program- and course-levels. Comparison between the program- and course-level topics uncovered a set of unique topics, and a number of common topics. Furthermore, we provide interactive visualizations for digital/data curation programs and courses for further analysis of topical distributions. We believe that our combined approach of a topic modeling and visualizations may provide insight for identifying emerging trends and co-occurrences of topics among digital/data curation curricula in the LIS field.
A Smörgåsbord of Typos: Exploring International Keyboard Layout Typosquatting

Typosquatting is the malicious practice of registering domains that result from typos made when users try to visit popular domains. Previous works have only considered the US English keyboard layout, but of course other layouts are widely used around the world. In this paper, we uncover how typosquatters are also targeting communities that use these other layouts by examining typo domains on non-US English keyboards for 100 000 popular domains. We find that German users are the most targeted, with over 15 000 registered typo domains. Companies such as Equifax and Amazon have defensively registered such domains but are often incomplete; moreover, other major companies ignore them altogether and allow malicious actors to capitalize on their brand. Parking domains or advertising them for sale remains the most popular monetization strategy of squatters on at least 40% of registered domains, but we also see more harmful practices, such as a scam website that spoofs a local newspaper. This proves that domain squatters also consider typos on non-US English keyboards to be valuable, and that companies should be more alert in claiming these domains.
Incremental Learning Techniques Within a Self-updating Approach for Face Verification in Video-Surveillance

Data labelling is still a crucial task which precedes the training of a face verification system. In contexts where training data are obtained online during operational stages, and/or the genuine identity changes over time, supervised approaches are less suitable. This work proposes a face verification system capable of autonomously generating a robust model of a target identity (genuine) from a very limited amount of labelled data (one or a few video frames). A self-updating approach is used to wrap two well known incremental learning techniques, namely Incremental SVM and Online Sequential ELM. The performance of both strategies are compared by measuring their ability to unsupervisedly improve the model of the genuine identity over time, as the system is queried by both genuine and impostor identities. Results confirm the feasibility and potential of the self-updating approach in a video-surveillance context.
An Event Based Detection of Internal Threat to Information System

Internal threat is an important issue for the information systems of an organization. To deal with this problem, organizations often formulate regulations and rules to regulate the behavior of employees and prevent them from causing production risks. However, how to effectively detect violations of the rules in the production process is challenging. In this paper, we propose an event based internal threat detection method. Firstly, we establish a detection model for regulation violation by representing rules and regulations as complex events and design a rule engine to detect if these complex events occur and discover the violations of rules. Then the logs generated during product are used for activating the rule reasoning. Finally, the rule violation will be reported to the supervisor for further investigation. The experiment on the real production processes shows the method is effective and efficient to detect internal threats and can be used at major production sites.
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans

Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification.Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications.Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance.In realworld scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task.This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited.In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task.Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.
THE METHODS OF DEVELOPING COMPUTATIONAL THINKING OF STUDENTS WHILE STUDYING THE COURSE "NUMERICAL METHODS" BASED ON BLENDED LEARNING

The work is devoted to the consideration of improving the quality of teaching students the discipline “Numerical methods” through the development of the cognitive component of computational thinking based on blended learning. The article presents a methodology for the formation of computational thinking of mathematics students, based on the visualization of algorithmic design schemes and the activation of the cognitive independence of students. The characteristic of computational thinking is given, the content and structure of computational thinking are shown. It is argued that a student with such a mind is able to manifest himself in his professional field in the best possible way. The results of the application of the technique are described. To determine the level of development of the cognitive component of computational thinking, a diagnostic model has been developed based on measuring the content, operational and motivational components. It is shown that the proposed method of developing computational thinking of students, taking into account the individual characteristics of students’ thinking, meaningfully based on the theoretical and practical aspects of studying the discipline, increases the effectiveness of learning the course “Numerical methods”. The materials of the article are of practical value for teachers of mathematical disciplines who use information and telecommunication technologies in their professional activities.
Video-Based Marathi Sign Language Recognition and Text Conversion Using Convolutional Neural Network

The communication between the deaf people and the hearing community is the challenging task. To overcome this barrier automatic sign language recognition plays an important role. It helps to remove the communication barrier between them. A Convolutional Neural Network (CNN) based approach for Marathi sign language is presented in this paper to help understand and interpret the hand gestures made for Marathi alphabets. This system using CNN is an automated process of constructing the handcrafted feature from gesture images. The system is able to recognize 25 Marathi sign language (MSL) alphabets with a testing accuracy of 99.28%.
Mirror binocular calibration method based on sole principal point

In three-dimensional vision measurements, single-camera mirror binocular stereo vision systems composed of a camera and mirrors have become popular, owing to their high flexibility and compactness. However, the calibration of these systems is different from traditional two-camera stereo vision systems, because there is no rigid-body transformation between the actual camera and the virtual camera. A calibration method for single-camera mirror binocular vision system is proposed, which can establish the rigid-body transformation by rotating the image based on the sole principal point. The comparative experiments showed that the proposed method yielded a higher accuracy than the traditional method.
Comparison of aplanatic and real lens focused spots in the framework of the local plane interface approximation

Tight focusing of various beams is widely used for microscopy, optical tweezers, lithography, optical storage, etc. In literature, the investigation of the tightly focused spot is very often based on an ideal lens, which is aplanatic, by the Debye–Wolf integral. In this work, we formulate the aplanatic lens by interpreting the Gaussian reference sphere as a fictitious surface. In this manner, the formulation of an ideal lens is analogous to the one of a real lens by local plane interface approximation, which is well known. It is straightforward to interpret. And furthermore, we compare the tight focusing of differently polarized beams via ideal and real lenses with circular and annular apertures. We find the focal spot by a well-designed real lens is in good agreement with that of the ideal lens in the case of perfect alignment. But the appearance of misalignment distorts the focal spot. The deformed focal spot is more sensitive in the case of the annular aperture compared with the circular aperture. It is in good agreement with the experimental results in literature. An investigation of the tolerance of misalignment of a specific lens system is also performed.
Querying the Network

After a not-so-brief interruption about writing smart contracts, we will review in-depth the different ways to connect to the Ethereum network to retrieve data. We will cover different connection methods, as well as patterns for listening to changes, and put it all together in a sample application for monitoring transactions of an ERC20 token.
Training a Neural Network

This chapter gives an overview of the techniques for training a deep neural network. Here, we briefly discuss
Keyphrase Extraction from Modern Standard Arabic Texts Based on Association Rules

Keywords or Keyphrases constitute a very important kind of concepts which can be extracted from texts. They reflect the semantic contained in these texts and are useful in many tasks of Information Retrieval, Text mining and Natural Language Processing. Their extraction is a challenging problem to which researchers have an active interest. In this paper, an approach based on the Association Rules model is described for extracting keyphrases from modern standard Arabic texts. The experiments done and the results obtained are promising: the performance values of the proposed system (in terms of precision, recall and f-score) are higher than 60% and can exceed 70%.
Modeling Quantum Devices and the Reconstruction of Physics in Practical Systems

Modeling quantum devices is to find a model according to quantum theory that can explain the result of experiments in a quantum device. We find that usually we cannot correctly identify the model describing the actual physics of the device, regardless of the experimental effort, given a limited set of operations. According to sufficient conditions that we find, correctly reconstructing the model requires either a particular set of pure states and projective measurements or a set of evolution operators that can generate all unitary operators.
Authorship Attribution of Arabic Articles

With the huge size and large diversity of web content and the appearance of more social media platforms and blog websites, more people are contributing content of varying quality. Many users prefer to keep themselves anonymous when posting material to the web, which resulted in more pieces of text: articles, blogs, essays and emails being published under assumed identities or have no known author. This may result in copyright and other legal issues and thus the need for good authorship attribution systems. The problem may be more acute for Arabic texts due to restrictions, actual and perceived, on electronic content publication and the prevailing social norms. In this paper we study the issue of Arabic author attribution (AAA) concerned with designating a particular author of an Arabic (MSA) article from among a given set of potential authors. Many features were taken into consideration for training and testing our models for AAA. We studied the effects of features like part of speech (PoS) tags, stylistic issues like punctuation marks usage and sentence characteristics, word types and word diversity. In general, PoS features, word n-grams features and rare words proved to be the most informative for our task. We also investigated the effect of factors like number of potential authors, number of articles per author, and the size of text chunks used and we report on the results.
Towards a Semantic Matchmaking Algorithm for Capacity Exchange in Manufacturing Supply Chains

Within supply chains, companies have difficulties in finding suppliers outside their known supplier pool or geographical areas. The EU project MANUSQUARE aims to deploy a marketplace to match supply and demand of supply chain resources to facilitate accurate and efficient matchmaking. To this end, a semantic matching algorithm has been developed as one of the key enablers of such a marketplace. The algorithm exploits formal descriptions of resources provided by an ontology developed in the project and will later be extended to incorporate additional data from different endpoints. This paper describes the main components of the semantic matching algorithm, which on the basis of the formally described supply chain resources returns a ranked list of relevant suppliers given a customer query. The paper further describes a comparative evaluation of a set of common semantic similarity techniques that was conducted in order to identify the most appropriate technique for our purpose. The results from the evaluation show that all four techniques perform pretty well and are able to distinguish relevant suppliers from irrelevant ones. The best performing technique is the edge-based technique Wu-Palmer.
Clinical Application of a Real-Time Telepathology System for Frozen Section Diagnosis in Comparison With Optical Microscope

Background: The imbalance between the increasing demand of highly specialized service and the reduction of specialists able to release this service is a global challenge for Pathology. This situation applies also to the setting of intra-operatory diagnostic: here the broad presence of Surgical divisions contrasts with the contraction of Pathology departments, progressively concentrated in few hospitals. The use of e-pathology device, such as remote-control microscopes, offers a possible solution to this imbalance. Aim: To prove the non-inferiority of function of a remote-control, real-time microscope named Nano-Eye Device (NED) with the optical microscope (OM) for intra-operatory histological diagnosis. Methods: The study was designed into two phases: discovery and validation. During the discovery phase features influencing the process of adaptation to NED were investigated in detail, focusing on the turnaround time (TAT). Validation phase investigated the diagnostic concordance between NED and OM; as well as sensitivity, specificity, and accuracy of NED in intra-operatory histological diagnosis. Results: During the discovery phase 250 cases were examined. TAT of NED was longer than that of OM (112 ± 89.8 vs. 36 ± 37.9 s) and influenced by the difficulty of the specimen, age of pathologist and the type of the specimen. In the validation phase (185 cases) TAT of NED reduced significantly to 92 ± 86.3 s (p: 0.01). NED showed a concordance rate of 98% with OM; the sensitivity (95.65%), specificity (100%), and diagnostic accuracy (98.87%) of NED were equal to that of OM. NED failed to work in 6% during the discovery phase and 4% in the validation. Conclusions: Taken as a whole, the functionality of NED is comparable to OM. It can be the alternative choice for hospital lacking on-site pathology services and one of the tool of e-pathology.
Managing Blockchain Projects with Agile Methodology

Blockchain technology is one of the latest and recent famous technologies in the digital world. Most organizations, especially financial institutions observed and felt the changes that were created in cryptocurrency markets. As blockchain is the hosted environment and the backbone of the crypto currencies like Bitcoin, Litecoin and Ethereum they believe they should adopt some of their provided services on this technology. Many entities in the region are interested to be pioneers on providing services on recent and widely trusted and accepted technologies like blockchain. Researchers and interested personnel expected that, blockchain technology will be capable to minimize the requirement of multiple participants in many businesses. Business processes will need to be reengineered on the firm. In addition, it is expected to reduce required time and resources to complete a transaction. Managing projects on blockchain technology will be different from managing other projects as these types of project will require the ability to change and a lot of preparations for unexpected. This paper highlights briefly this technology mentioning its concepts, types and phases. In addition, it is listed how agile project management could suite the management of blockchain applications.
The South African land cover change detection derived from 2013_2014 and 2017_2018 land cover products

The appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.
A dependency-based approach to word contextualization using compositional distributional semantics

We propose a strategy to build the distributional meaning of sentences, which is mainly based on two types of semantic objects: context vectors associated with content words and compositional operations driven by syntactic dependencies. The compositional operations of a syntactic dependency make use of two input vectors to build two new compositional vectors representing the contextualized sense of the two related words. Given a sentence, the iterative application of the dependencies results in as many contextualized vectors as content words the sentence contains. At the end of the compositional semantic process, we do not obtain a single compositional vector representing the semantic denotation of the whole sentence (or of the root word), but one contextualized vector for each constituent word of the sentence. Our method avoids the troublesome high-order tensor representations of approaches relying on category theory, by defining all words as first-order tensors (i.e. standard vectors). Some corpus-based experiments are performed to both evaluate the quality of the compositional vectors built with our strategy, and to compare them to other approaches on distributional compositional semantics. The experiments show that our dependency-based compositional method performs as (or even better than) the state-of-the-art.
Infrared and visible face fusion recognition based on extended sparse representation classification and local binary patterns for the single sample problem

While near infrared and visible fusion recognition has been actively researched in recent years, most theoretical results and algorithms concentrate on the sufficient training samples setting. This paper focuses on the general fusion method when there are insufficient training samples with one pair of near-infrared and visible face images. Compared with existing methods, the proposed method requires neither sufficient samples nor the training step. To get a robust and time-efficient fusion model for unconstrained face recognition in the single sample situation, two models are proposed to fuse the local binary pattern based descriptors and the sparse representation based classification: the first fusion model directly fuses the representation error, while the second fusion model is an accelerated version that learns from a cross-spectral dictionary. Experiments are performed on the HITSZ LAB2 database, and the experiment results showed that the proposed fusion model extracted the complementary features of near-infrared and visible-light images. The fusion face recognition method had superior performance to state of the art fusion methods.