Reviewerly/exHarmony huggingface dataset:
Professional marginalization of servicemen: pedagogical conditions of warning in the educational process of military universities

We considered the current and insufficiently developed problem of professional marginalism by domestic teachers in the aspect of creating pedagogical conditions in the educational process of military universities to prevent it. Based on the analysis of philosophical, sociological, psychological literature, we specified the concept of “professional marginalism of servicemen”, gave its characterization as a dangerous psychological phenomenon, gave a comparative characteristic of the concepts of “professional burnout” and “deviant behavior”. To identify pedagogical conditions for preventing professional marginalism, we considered it as an antipode of the professional identity of military specialists. In view of the fact that the formation of the professional identity of future officers in the educational process of military universities is carried out during the implementation of educational tasks in classrooms and in extracurricular activities, we presented pedagogical conditions, the creation of which contributes to the prevention of professional marginalism of specialists engaged in military professional activities. The presented research results have a scientific novelty, since in this aspect the professional marginalism of servicemen has not yet been investigated, and we give not only a concretization of the key concept, but also the identified pedagogical conditions. The opportunities that arise in connection with the creation in the educational process of military universities of pedagogical conditions that prevent professional marginalism of military personnel as a dangerous psychological phenomenon determine the practical significance of the research and its results. Further development of the problem will contribute to the creation of a holistic concept of the development of professional identity and the prevention of professional marginalism of servicemen.
Comparison of CNN Architectures using RP Algorithm for Burst Signal Detection

Recently, convolutional neural networks (CNNs) achieved remarkable success in various fields, especially computer vision and image processing. However, it is not known what type of CNN architecture is the best fit for the detection or classification of communication signals. In this work, we compare the three of CNN architecture in a burst signal detection task. The three CNN architectures are compared to their detection performance and computational complexity. The 9-layer CNN is shown to achieve a similar performance of 12-layer CNN on overall environments. The performance of the 7-layer CNN model is worse than that of the other two types of CNN architectures, except in terms of the computational complexity at low SNR.
Weighted Majority Voting by Statistical Performance Analysis on Ensemble Multiclassifier

Ensemble classifier method uses several base classifiers to predict a new test instance, while weighted majority voting has a scheme providing different weight values using several measurement parameters. However, the determination of the appropriate weight value to obtain an adequate ensemble model is a critical issue. This study, therefore, proposed a novel weighted majority voting scheme involving five base classifiers based on ensemble learning, including Random Forest, Decision Tree (C.45), Gradient Boosting Machine, XGBosst, and Bagging. The weighting scheme was formulated by analyzing the base classifier performance measured from the parameters of accuracy, recall, precision, and F Measure. The experiments were conducted using public datasets and umbilical cord data owned and the results showed the proposed method has the ability to improve performance in comparison with the base classifier and methods from previous studies with the best recorded in umbilical cord dataset with an average accuracy of 86.1%, a precision of 86%, a recall of 86%, and an F measure of 86%.
Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud

For centuries, the concept of a smart, autonomous learning machine has fascinated people. The machine learning philosophy is to automate the development of analytical models so that algorithms can learn continually with the assistance of accessible information. Machine learning (ML) and deep learning (DL) methods are implemented to further improve an application's intelligence and capacities as the quantity of the gathered information rises. Because IoT will be one of the main sources of information, data science will make a significant contribution to making IoT apps smarter. There is a rapid development of both technologies, cloud computing and the internet of things, considering the field of wireless communication. This chapter answers the questions: How can IoT intelligent information be applied to ML and DL algorithms? What is the taxonomy of IoT's ML and DL and profound learning algorithms? And what are real-world IoT data features that require data analytics?
How to Aggregate Acoustic Delta Features for Deep Speaker Embeddings

Speaker verification based on deep speaker embeddings (DSE) network outperformed traditional i- vectors systems. Afterward, to improve the performance, various researches have been conducting and data augmentation methods are one of them. In this paper, we focus on acoustic delta features augmentation and their aggregation methods for DSE networks, X-vectors and MobileVoxNet. For CNN-based MobileVoxNet, we re-design the architecture to aggregate delta features in deeper layer with squeeze and excitation (SE) module. Experimental results show that the proposed methods achieve performance improvement compared to not using delta features on the VoxCeleb1 test dataset. We also compare the number of computations and parameters of models to analyze efficiency of the proposed methods.
Deep Pedestrian Distance Estimation from Single-shot Image

In this paper, we propose a deep learning-based distance estimation method from a single-shot image. In the proposal, we model the estimation as a regression problem, and estimate the distance between a pedestrian and a camera by using three main features; size of bounding box, image blur and image features. By using KITTI dataset, we evaluate the accuracy of the proposed model.
Real-Time Train Speed Prediction using Ip-Camera Based On Time Difference

In 2014, PT. KAI has added Remote locomotive speed detection which is useful for increasing travel safety. The indicator will turn red if the speed of the monitored train exceeds the predetermined speed. This monitoring can only focus on overspeed only, while it needs to be given specifically at places such as train intersections, and stops. In This, a security system will be made for Monitoring Train Speed at certain points, such as train gates, intersections, and stations. In this case, the Real-Time Train Speed Prediction using IP-Camera is an effective and affordable solution to be applied. This system uses IP-camera to capture the input image and an SBC to process the image to produce the output direction and speed data. The Camera is used to detect the train, then the speed is calculated based on the different frames in the image input.
Bounded Multi-agent Reasoning: Actualizing Distributed Knowledge

The idealizations resulting from the use of Kripke semantics in Epistemic Logic are inherited by formalizations of group epistemic notions. For example, distributed knowledge (DK) is often taken to reflect the potential knowledge of a group: what agents would know if they had unbounded means of communication and deductive ability. However, this does not specify whether/how this potential can be actualized, especially since real people are not unbounded reasoners. Inspired by experiments on group reasoning, we identify two dimensions of actualizing DK: communication and inference. We build a dynamic framework with effortful actions accounting for both, combining impossible-worlds semantics and action models, and we provide a method for extracting a sound and complete axiomatization.
Agile Accelerator Program

The agile accelerator program takes place in a Brazilian technology park, as a collaboration between a university and a world-renowned technology company, specialized in agile development and consulting. This partnership has 8-year long with the main goal of preparing undergraduate students to work in high-performance agile teams. This partnership created a culturally rich environment for student learning while influencing other companies to follow the same initiative within this technology park. We conducted a Case Study aiming to characterize this partnership (explaining how it works) and the resulting program, understanding the benefits to the program students. Our results point out the importance of the kind of partnership that provides an immersive learning environment to students, where students can learn empirically, with real projects and real stakeholders and how important it was for the program's former students to enter the job market. This successful enhanced students' training program on agile software development through the blending of culture between institutions can be of inspiration to those interested in aiming to bridge the gap between academia and industry.
Automation System for Six-minute Walk Test Using RFID Technology

The main objective of this work was to develop a low-cost monitoring system based on radio frequency identifi-cation (RFID) to automatize six-minute walk tests (6MWT), an aerobic fitness test used in cardiopulmonary patients including those who have recovered from COVID-19. The advantages of RFID systems have been widely applied in the health area and in the field of physical fitness tests, reducing human errors often occurred due to the difficulty of tracking and monitoring continuously. We propose an automatic monitoring system based on RFID in which it is possible to perform the test on several patients simultaneously. The results demonstrate that the proposed system offers the ability to monitor multiple users simultaneously, accurately and quickly, in addition to reducing the manual work performed by test administrators.
Implementation of Convolutional Neural Network with Co-design of High-Level Synthesis and Verilog HDL

In recent years, Convolutional Neural Networks(CNNs) have been widely adopted for image classification and target recognition. As one of CNN's main hardware implementation platforms, FPGA has its advantages of high flexibility, excellent trade-off between performance and power, but still has the problems of complex developing processes and poor adaptability for various algorithm models. Therefore, a high-performance and fast hardware implementation architecture adaptation to the CNNs is presented in the paper. The hardware architecture is designed with co-design of High-Level Synthesis(HLS) and Verilog HDL, which simplifies the design process and ensures performance. And it adopts the variables parameterization to deal with the problem of pool model adaptability. With row-by-row calculation, the circuits adopt the layered parallelism to ensure the flexibility of convolution and the pipeline parallel calculation to improve the speed. The paper achieves an overall average 359.7GOP/s for the AlexNet on Xilinx ZCU104 platform.
Moving Toward BIBFRAME and a Linked Data Environment

The library metadata of the twenty-first century is moving toward a linked data model. BIBFRAME, which stands for Bibliographic Framework Initiative, was launched in 2011 with the goal to make bibliographic descriptions sharable and interoperable on the web. Since its inception, BIBFRAME development has made remarkable progress. The focus of BIBFRAME discussions has now shifted from experimentation to implementation. The library community is collaborating with all stakeholders to build the infrastructure for BIBFRAME production in order to provide the environment where BIBFRAME data can be easily created, reused, and shared. This chapter addresses library community's BIBFRAME endeavors, with the focus on Library of Congress, Program for Cooperative Program, Linked Data for Production Phase 2, and OCLC. This chapter discusses BIBFRAME's major differences from the MARC standard with the hope of helping metadata practitioners get a general understanding of the future metadata activity. While the BIBFRAME landscape is beginning to take shape and its practical implications are beginning to develop, it is anticipated that MARC records will continue to be circulated for the foreseeable future. Upcoming multistandard metadata environments will bring new challenges to metadata practitioners, and this chapter addresses the required knowledge and skills for this transitional and multistandard metadata landscape. Finally, this chapter explores BIBFRAME's remaining challenges to realize the BIBFRAME production environment and asserts that BIBFRAME's ultimate goal is to deliver a value-added next-web search experience to our users.
At Your Command! An Empirical Study on How Laypersons Teach Robots New Functions

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 subjects. The analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality; two corresponding trigrams are among the most frequent. On the contrary, more than one third (37%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. The full corpus is publicly available: http://dx.doi.org/10.21227/zecn-6c61.
Prior based Single Image Dehazing using Decision Image

The quality of outdoor images may degrade due to dust, fog, and other tiny particles present in the environment. This degradation in images can limit the performance of many computer vision applications such as self-driving car, surveillance camera systems, satellite imaging etc. Thus, dehazing of such low quality images remains as a very important step for many computer vision applications. This paper proposes a simple and efficient way of dehazing a single image based on color attenuation prior and the notion of a dark channel. The proposed research work will first find the ambient light using decision image and dark channel and then computes the transmission by using the color attenuation prior (CAP). CAP uses a depth map to compute the transmission. In the later stage, the transmission is refined using the bilateral filtering and restore scene radiance using the refined transmission map and ambient light. Experimental analysis shows that our methodology enhances the quality of the image, which got degraded due to hazy weather.
Detection of Misbehaviors in Clone Identities on Online Social Networks

The account registration steps in Online Social Networks (OSNs) are simple to facilitate users to join the OSN sites. Alongside, Personally Identifiable Information (PII) of users is readily available online. Therefore, it becomes trivial for a malicious user (attacker) to create a spoofed identity of a real user (victim), which we refer to as clone identity. While a victim can be an ordinary or a famous person, we focus our attention on clone identities of famous persons (celebrity clones). These clone identities ride on the credibility and popularity of celebrities to gain engagement and impact. In this work, we analyze celebrity clone identities and extract an exhaustive set of 40 features based on posting behavior, friend network and profile attributes. Accordingly, we characterize their behavior as benign and malicious. On detailed inspection, we find benign behaviors are either to promote the celebrity which they have cloned or seek attention, thereby helping in the popularity of celebrity. However, on the contrary, we also find malicious behaviors (misbehaviors) wherein clone celebrities indulge in spreading indecent content, issuing advisories and opinions on contentious topics. We evaluate our approach on a real social network (Twitter) by constructing a machine learning based model to automatically classify behaviors of clone identities, and achieve accuracies of 86%, 95%, 74%, 92% & 63% for five clone behaviors corresponding to promotion, indecency, attention-seeking, advisory and opinionated.
Constructive Dynamic Logic of Relation Changers

This paper proposes an intuitionistic generalization of van Benthem and Liu’s dynamic logic of relation changers, where relation changers are dynamic operators which rewrite each agent’s accessibility relation. We employ Nishimura’s Kripke semantics for a constructive propositional dynamic logic to define the semantics of relation changers. A sound and complete axiomatization of the constructive dynamic logic of relation changers is provided. Moreover, we follow Hatano et al.’s approach to provide a different semantics for dynamic logic of relation changers, where relation changers are regarded as bounded morphisms. This alternative semantics leads us to a semantic completeness proof of the axiomatization for the original semantics, which does not require a reduction strategy based on recursion axioms.
Adversarial Input Detection Using Image Processing Techniques (IPT)

Modern deep learning models for the computer vision domain are vulnerable against adversarial attacks. Image prepossessing technique based defense against malicious input is currently considered obsolete as this defense is not effective against all types of attacks. The advanced adaptive attack can easily defeat pre-processing based defenses. In this paper, we proposed a framework that will generate a set of image processing sequences (several image processing techniques in a series). We randomly select a set of Image processing technique sequences (IPTS) dynamically to answer the obscurity question in testing time. This paper outlines methodology utilizing varied datasets examined with various adversarial data manipulations. For specific attack types and dataset, it produces unique IPTS. The outcome of our empirical experiments shows that the method can efficiently employ as processing for any machine learning models. The research also showed that our process works against adaptive attacks as we are using a non-deterministic set of IPTS for each adversarial input.
Extracting clinical information from chest x-ray reports: A case study for Russian language

In this paper, we analyze possible approaches for diagnosis identification in Russian medical reports. Firstly, we introduce the main problems of raw Russian medical reports preprocessing. Secondly, focusing on the embedding extraction method, we analyzed several publicly available models and discovered that the use of BERT model is a promising instrument for this task. Performing the first attempt to build the NLP system for the Russian medical report classification based on the embeddings extraction method, we formulated the main weaknesses that limit the use of the existing publicly available Russian NLP models in the medical-text domain. Having no labeled data available, we evaluate each model visually, analyzing embeddings representation in 2D field retrieved by dimensionality reduction using t-SNE. We assume that a good model will be able to place reports that describe the same diagnosis close to each other, while moving reports with distinct diagnoses far from each other, forming clusters. Finally, we proposed several ways of possible future research that, as we believe, will improve the results achieved in this field so far.
ESGAN: Edge Loss and Spatial Convolution Generative Adversarial Network for Image Inpainting

Deep learning-based algorithms have a wide application in image inpainting because of their special capabilities in synthesizing new texture structures. However, the generated images are usually blurry with artificial textures. This paper, to solve this problem, this paper introduces spatial convolution to expand the receptive field of the convolution kernel under the same computational complexity. It also proposes edge loss to emphasize the role of object shape in weight optimization. Experiments are conducted in Place2 with irregular masks, and the results show that comparing with most other existing algorithms, our proposed new network model has achieved a better performance in model scale and PSNR, SSIM, etc.
A novel reversible data hiding based on adaptive block‐partition and payload‐allocation method

A reversible data hiding method based on partition is researched because it can effectively reduce shifting distortion in given embedding capacity. However, traditional partition is to divide an image into equal-sized blocks, which cannot be divided reasonably according to the content of the image. In order to achieve dynamic partition and effectively utilize the complexity of the image, this paper proposes a novel reversible data hiding based on adaptive block-partition and payload-allocation method. In this technique, instead of equal partition, adaptive block-partition is proposed to establish multiple histograms by dividing the cover image into several image blocks of different sizes dynamically, and the image blocks of different sizes are processed successively through multiple sorting and implement adaptive payload allocation according to complexity, then the data is embedded into two sides of prediction-error histograms to effectively reduce the shifting distortion. Experimental results show that the proposed method is superior to the state-of-the-art traditional fixed-sized blocking-based reversible data hiding methods.
User Location and Collaborative based Recommender System using Naive Bayes Classifier and UIR Matrix

The world is filled with information and getting the right information is a challenging task for internet users and online buyers. Recommender system helps internet users to get their information in a short span of time. It acts as an information extraction system that works behind users to perform their search easier. The recommender system comes under user's content or item based search, similar users browsing behavior called collaborative and combination of both known as a hybrid. Here collaborative-based approach is adopted which recommends items to their users based on their past browsing behavior. In this article, the User-Item-Rating matrix is formulated concerning user personal profile, rating of the product, and reviews given by the users during their previous browsing history. In this research, user location is considered as an important attribute to group similar users. It also attempts to suppress the scalability and sparsity problems of the traditional collaborative filtering approach. So, the User-Item-Rating (UIR) matrix has considered the location, ratings and reviews for future recommendation. The Navie Bayes classifier algorithm is used to provide accurate topmost recommendations to internet users. The data set is taken from the MovieLens and IMDb database. The accuracy of the recommender system is measured based on the main metric f-measure. The experimental result has proven the improvement of the recommender system with the mentioned added attributes.