Reviewerly/exHarmony huggingface dataset:
The costructure–cosemantics adjunction for comodels for computational effects

Abstract It is well established that equational algebraic theories and the monads they generate can be used to encode computational effects. An important insight of Power and Shkaravska is that comodels of an algebraic theory $\mathbb{T}$ – i.e., models in the opposite category $\mathcal{S}\mathrm{et}^{\mathrm{op}}$ – provide a suitable environment for evaluating the computational effects encoded by $\mathbb{T}$ . As already noted by Power and Shkaravska, taking comodels yields a functor from accessible monads to accessible comonads on $\mathcal{S}\mathrm{et}$ . In this paper, we show that this functor is part of an adjunction – the “costructure–cosemantics adjunction” of the title – and undertake a thorough investigation of its properties. We show that, on the one hand, the cosemantics functor takes its image in what we term the presheaf comonads induced by small categories; and that, on the other, costructure takes its image in the presheaf monads induced by small categories. In particular, the cosemantics comonad of an accessible monad will be induced by an explicitly-described category called its behaviour category that encodes the static and dynamic properties of the comodels. Similarly, the costructure monad of an accessible comonad will be induced by a behaviour category encoding static and dynamic properties of the comonad coalgebras. We tie these results together by showing that the costructure–cosemantics adjunction is idempotent , with fixpoints to either side given precisely by the presheaf monads and comonads. Along the way, we illustrate the value of our results with numerous examples drawn from computation and mathematics.
Categorization of Videos Based on Text Using Multinomial Naïve Bayes Classifier

After the dawn of the Internet, we are surrounded by a world filled with multimedia content, especially video. Two type of videos every Indian loves and watches a lot are News and Cricket. News and cricket are two very different types of category of videos and thus helping to annotate and categorize these two type of videos will be valuable. We have presented a method to categorize videos on the basis of textual content in them and as the text in a Cricket Video is generally quite significantly different from that of a News Video, the categorization of videos is done in two parts, the first being Text Detection and Extraction, where Edge Detection, Thresholding, Dilation, Contour Detection, and Optical Character Recognition (OCR) are used for the exposure of the part of Videos which has text in them and extract the text present in it. The second stage is that of Text Categorization, using Term Frequency (TF) and Inverse Document Frequency (IDF) and applying Multinomial Naive Bayes Classifier to classify the text into a category.
The Study of Observation in Physics Classes through XR Technologies

XR (eXtended Reality) technologies play an increasing role in education. Physics education suggests great opportunities for the practical realization of XR teaching. Observation is one of the most important research method used by physics. The primary role of an XR technology user is to be an observer. This makes it easy to adopt them for physics learning. By this reason XR technologies turn out in the center of the physics teachers' attention. The paper deals with the methodological and practical aspects of XR observations in physics classes. It describes definitions of observation, the procedure of observations, types of XR observations, tools of XR observations, labworks based on XR observations, the opportunity of making simplest observational XR managers by students, and the students’ opinion about this version of XR technology. The wide availability of XR technologies allows us to apply them in physics classes right now. Both students and teachers can use it easily.
Bert-based Siamese Network for Semantic Similarity

Abstract As a key technology in the field of natural language processing, text matching is widely used in many tasks such as question answering systems, search, recommendation, and advertising. Aiming at the insufficient feature extraction capabilities of traditional Siamese-based text matching methods, a fast matching method based on large-scale pre-training model BSSM (BERT based Siamese Semantic Model) is proposed. The proposed method uses a pre-training model to encode two texts separately, which interact the representation vectors of the two texts to obtain attention weights and generate new representation vectors, so that the new and old representation vectors can be pooled and aggregated, and finally two representation vectors are concatenated in some strategy and sent to the prediction layer for prediction. Experimental results on the AFQMC and LCQMC datasets show that the proposed method not only improves the accuracy rate, but also improves the computational efficiency.
A teaching assistant system for big data analysis

Abstract With the rapid development of big data analysis, cloud computing and mobile computing, the demand of teaching auxiliary processing and students’ online learning in colleges and universities depends more and more on information system. According to the latest view of pedagogy, teaching evaluation is gradually turning to the "accompanying" education which is data-based and tracking the whole teaching process. In order to improve the current situation that the function of single teaching assistant system is limited, the information island caused by the expansion of multiple teaching auxiliary systems, and the situation that process data is not paid attention to, this research develops a complete educational administration and teaching auxiliary system, so as to promote the collection, processing and analysis of more, more detailed and higher frequency teaching real-time data.
A Hardware/Software Framework for the Integration of FPGA-based Accelerators into Cloud Computing Infrastructures

The need for high computing power has increased enormously in recent years, particularly in the field of image signal processing and machine learning applications, very powerful computing systems are required. It has been shown that homogeneous architectures in data centers work very inefficiently regarding these special applications, showing high latency of the response times and providing a very poor power efficiency. In order to integrate FPGA (Field Programming Gate Array)- as well as GPU-based accelerators into cloud computing infrastructures as compute nodes we present a generic hardware/software framework for using heterogeneous computing systems. A real industrial image processing application shows the acceleration achieved.
Deep Dive in tf.keras

Keras is a high-level neural networks API that runs on top of TensorFlow. Last many years, you were using Keras API with TensorFlow running at the backend. With TensorFlow 2.x, this has changed. TensorFlow has now integrated Keras in tf.keras API. The tf.keras is the TensorFlow’s implementation of the Keras API specification. This change is mainly done to bring consistency in using Keras with TF. It also resulted in taking advantage of several TensorFlow features such as eager execution, distributed training and others while using Keras. The latest Keras release as of this writing is 2.3.0. This release adds support for TensorFlow 2.x and is also the last major release of multi-backend Keras. Henceforth, you will be using only tf.keras in all your deep learning applications. You have already used tf.keras in chapter 2 while getting started on TensorFlow. This chapter will take you deeper in the use of tf.keras.
Using Deep Learning to Recognize Handwritten Thai Noi Characters in Ancient Palm Leaf Manuscripts

Extracting knowledge from ancient palm leaf manuscripts is essential for historians and other scholars who would like to access accumulated knowledge in the Thai Noi language manuscripts. In the absence of Thai Noi language readers, computer technologies play an important role in fulfilling this need. This research aims to apply deep learning approaches to recognize Thai Noi characters written in palm leaf manuscripts. The experiments were carried out by firstly collecting the page images of the manuscripts archived in the Museum of Art and Culture of Loei. Then the page images were preprocessed by converting to grayscale. To recognize Thai Noi characters, four convolutional neural network models based on inception and mobilenet networks namely Inception-v3, Inception-v4, MobileNetV1, and MobileNetV2 were evaluated. Handwritten Thai Noi characters were segmented from the grayscale images based on 26 Thai Noi characters. In this process, 100 images of each character were segmented and the whole dataset contained 2,600 images. Two image augmentation methods were applied to increase the amount of training data. Three experiments were carried out with three different datasets based on a 10-fold cross-validation design. The results indicate that MobileNetV1 outperformed other models in all experiments with an accuracy rate higher than 90%, while MobileNetV2 showed an interesting performance, which was almost equivalent to MobileNetV1 in the last experiment.
“ADVANCED COURSE ON SOFTWARE ENGINEERING” AS THE FIRST MODEL FOR TRAINING OF SOFTWARE ENGINEERS

Designing a mobile-oriented environment for professional and practical training requires determining the stable (fundamental) and mobile (technological) components of its content and determining the appropriate model for specialist training. In order to determine the ratio of fundamental and technological in the content of software engineers’ training, a retrospective analysis of the first model of training software engineers developed in the early 1970s was carried out and its compliance with the current state of software engineering development as a field of knowledge and a new the standard of higher education in Ukraine, specialty 121 “Software Engineering”. It is determined that the consistency and scalability inherent in the historically first training program are largely consistent with the ideas of evolutionary software design. An analysis of its content also provided an opportunity to identify the links between the training for software engineers and training for computer science, computer engineering, cybersecurity, information systems and technologies. It has been established that the fundamental core of software engineers’ training should ensure that students achieve such leading learning outcomes: to know and put into practice the fundamental concepts, paradigms and basic principles of the functioning of language, instrumental and computational tools for software engineering; know and apply the appropriate mathematical concepts, methods of domain, system and object-oriented analysis and mathematical modeling for software development; put into practice the software tools for domain analysis, design, testing, visualization, measurement and documentation of software. It is shown that the formation of the relevant competencies of future software engineers must be carried out in the training of all disciplines of professional and practical training.
Creating an Augmented Reality Website with Three.js and the WebXR API

We concluded the previous exercise by launching a virtual reality scene in an Oculus Quest through the browser. While the exercise accomplished its modest aims—to broadcast a Three.js scene from the browser to an XR device—it didn’t offer much in terms of interactivity between a user and their scene. In this chapter we will begin to explore the role spatial tracking plays in the WebXR API. By providing convenient abstractions for complex matrix multiplication, the WebXR API and its affiliated spatial-tracking modules allow XR developers to create immersive experiences that make the most of the mobility so integral to the essence of the Web.
We are watching them as they post: a mirror into the Nigerian Police Force use of social media and challenges affecting its adoption in policing

Most available studies on the police use of social media and challenges focus on developed countries. This research aims to better understand how the Nigeria Police Force (NPF) use social media and the challenges to the adoption of social media for policing in Nigeria. Our research answers the following questions: how does the NPF use social media? And what challenges hinder the adoption of social media for policing? We relied on in-depth interviews of forty-five police officers and findings show that NPF uses social media for surveillance and intelligence gathering, community engagement, and image making, and the force is faced with financial constraints, untrained personnel, and preponderance of online fake information, privacy issue and leaking of ongoing investigation as challenges. We argue that for the NPF to effectively use social media there is urgent need to address the challenges raised by the officers.
Interferometric grazing incidence test of rough steep convex spherics: experimental data analysis

Grazing incidence interferometry has been applied to plane, cylindrical, acylindrical, and general rod-like surfaces using diffractive beam splitters. Here, in a first step towards measuring aspherics, we demonstrate that also rough convex steep rotationally symmetric spherics can be measured along one meridian in a single step using diffractive beam splitters and phase shifting techniques. The measurement of rough surfaces is possible, i.e., without the need to polish the surfaces, due to the large effective wavelength ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>λ<!-- λ --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>eff</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>≈<!-- ≈ --></mml:mo> <mml:mn>10</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mspace width="thinmathspace" /> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mtext>m</mml:mtext> </mml:math> ) of the test. We include, besides the short recapitulation of the measurement principle and experimental setup, a presentation of the measured data of one small meridian region for the special case of spherical objects under test. The subsequent data analysis combined with suitable simulations focuses on elimination of the misalignment aberrations from the results caused by specimen displacements in the setup.
Fundamentals of Generalized and Extended Graph-Based Structural Modeling

The subject of the paper is connected to defining data structures, which are or can be used in metamodeling and modeling disciplines. A new and general notion of Extended Graph Generalization has been introduced. This notion enables to represent arbitrarily complex such the structures. A way of introducing constraints, which allows to reduce this general form to any well known structure has been introduced as well. As the result of the extension and generalization mechanisms applied to the original graph definition any form of graph generalization exceeding well-known structures can be defined. Moreover, the way of associating any form of data to each such structure has been defined. Notions introduced in the paper are intended to be used while defining novel family of metamodels.
MULTICULTURAL EDUCATION AS THE BASIS OF PROFESSIONAL LITERACY OF THE STUDENTS

T. Asten1, A. Rynkevich2, A. Karpova3 1Don State Technical University (RUSSIAN FEDERATION) 2Leonov Moscow Region University of Technology (RUSSIAN FEDERATION) 3Financial University under the Government of the Russian Federation (RUSSIAN FEDERATION)
Lifting Sequential Effects to Control Operators

Sequential effect systems are a class of effect system that exploits information about program order, rather than discarding it as traditional commutative effect systems do. This extra expressive power allows effect systems to reason about behavior over time, capturing properties such as atomicity, unstructured lock ownership, or even general safety properties. While we now understand the essential denotational (categorical) models fairly well, application of these ideas to real software is hampered by the variety of source level control flow constructs and control operators in real languages. We address this new problem by appeal to a classic idea: macro-expression of commonly-used programming constructs in terms of control operators. We give an effect system for a subset of Racket’s tagged delimited control operators, as a lifting of an effect system for a language without direct control operators. This gives the first account of sequential effects in the presence of general control operators. Using this system, we also re-derive the sequential effect system rules for control flow constructs previously shown sound directly, and derive sequential effect rules for new constructs not previously studied in the context of source-level sequential effect systems. This offers a way to directly extend source-level support for sequential effect systems to real programming languages.
What Kind of Learning Is Machine Learning?

While theories of human learning have proliferated in the last century, machine learning is a rather less reflexive enterprise. What conception of learning do the techniques of machine learning—especially in its recent connectionist forms—imply or induce? To answer this question, we explore the perspectives of the Soviet cultural-historical psychologist Lev Vygotsky, contrasting his socially-grounded understandings of mediated concept learning and the “zone of proximal development” with the methodologies of supervised and unsupervised machine learning. Such a comparison highlights the dependence of machine learning on microgenesis (repetitive, behaviorist training processes) and phylogenesis (the architectural “evolution” of models) at the expense of ontogenesis (the lifelong, interactional development of an individual in society), and thus provides new insights into the fundamental limits of contemporary artificial intelligence.
A Study of Residual Adapters for Multi-Domain Neural Machine Translation

Domain adaptation is an old and vexing problem for machine translation systems. The most common and successful approach to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the model unchanged. This has the additional merit to leave the baseline model intact and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea using two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model and open perspective to also make adapted models more robust to label domain errors.
Object Tracking by Detection using YOLO and SORT

Over the past two decades, computer vision has received a great deal of coverage. Visual object tracking is one of the most important areas of computer vision. Tracking objects is the process of tracking over time a moving object (or several objects). The purpose of visual object tracking in consecutive video frames is to detect or connect target objects. In this paper, we present analysis of tracking-by-detection approach which include detection by YOLO and tracking by SORT algorithm. This paper has information about custom image dataset being trained for 6 specific classes using YOLO and this model is being used in videos for tracking by SORT algorithm. Recognizing a vehicle or pedestrian in an ongoing video is helpful for traffic analysis. The goal of this paper is for analysis and knowledge of the domain.
Convolutional Neural Network for First Break Picking in Land Seismic Surveys

Summary Current methods of automated first break picking used in seismic data processing software are mostly based on the unsupervised algorithms. Due to the variety of the near-surface structures, the behavior of the signal in the regions of first arrivals may vary from shot to shot. This results in manual parameters selection for different seismic exploration regions. As a result, it takes a lot of time and effort of a seismologist to properly complete this stage. In this work to overcome this problem, we propose a supervised method based on the popular nowadays convolutional neural network. Such approach, however, raises another challenge - CNNs require a large amount of well-labeled data.
Smooth Dark Channel Prior Technique for Image Dehazing Applications

Image dehazing algorithm can restore a hazed image back to a haze-free image. Dark channel prior (DCP) is an efficient technique to estimate an airlight owing to its effectiveness. However, DCP causes considerable computation burdens, limiting its hardware efficiencies. To overcome this limitation, a smooth DCP (SDCP) technique is proposed in this study. The experimental results show that this study can save computation burdens by 70.9% with a minor loss of 1.03% on the accuracy of airlight estimation. Consequently, this study can greatly reduce computation burdens of DCP while keeping an accurate airlight estimation.
Improved application of transfer learning in network traffic classification

Abstract When using machine learning for traffic classification, there is such an assumption: the training data and the test data are independently and identically distributed. However, in reality, the assumption that the flow characteristics obey the same distribution may no longer hold because of conceptual drift or regional changes. Existing models will not be able to effectively classify new traffic. The transfer learning method TrAdaBoost has achieved great success in traffic classification and other aspects, but there are some problems, such as too much attention to the difficult-to-classify instances in the target domain, and failure to consider the wrong-classified instances in the source domain. In this study, the method of introducing weight correction factors in TrAdaBoost is used to make the iteration of weights more reasonable, and the effectiveness of this method is proved through theoretical analysis and experiments.
Quantized deep neural network empowering an IM-DD link running in realtime on a field programmable gate array

We demonstrate a communication system whose receiver DSP is an INT5 quantized 9-layer neural network directly extracting information bits processing 1.08 Tb/s of raw data delivering 12.28 Gb/s at a BER of 4×10-5 running in real-time on an FPGA.
Machine Learning Forensics

Machine learning (without human interference) can collect, analyze, and process data. In the case of cyber security, this technology helps to better analyze previous cyber-attacks and develop respective defense responses. This approach enables an automated cyber defense system with a minimum-skilled cyber security force. There are high expectations for machine learning (ML) in cyber security, and for good reasons. With the help of ML algorithms, we can sift through massive amounts of security events looking for anomalies, deviations from normal behavior that are often indicative of malicious activity. These findings are then presented to the analyst for review and vetting, and the results of his determination fed back into the system for training. As we process more and more data through the system, it evolves: it learns to recognize similar events and, eventually, the underlying traits of malicious behavior that we're trying to detect. This chapter explores machine learning forensics.