Reviewerly/exHarmony huggingface dataset:
Exploiting deep learning and augmented reality in fused deposition modeling: a focus on registration

Abstract The current study aimed to propose a Deep Learning (DL) based framework to retrieve in real-time the position and the rotation of an object in need of maintenance from live video frames only. For testing the positioning performances, we focused on intervention on a generic Fused Deposition Modeling (FDM) 3D printer maintenance. Lastly, to demonstrate a possible Augmented Reality (AR) application that can be built on top of this, we discussed a specific case study using a Prusa i3 MKS FDM printer. This method was developed using a You Only Look Once (YOLOv3) network for object detection to locate the position of the FDM 3D printer and a subsequent Rotation Convolutional Neural Network (RotationCNN), trained on a dataset of artificial images, to predict the rotations’ parameters for attaching the 3D model. To train YOLOv3 we used an augmented dataset of 1653 real images, while to train the RotationCNN we utilized a dataset of 99.220 synthetic images, showing the FDM 3D Printer with different orientations, and fine-tuned it using 235 real images tagged manually. The YOLOv3 network obtained an AP (Average Precision) of 100% with Intersection Over Unit parameter of 0.5, while the RotationCNN showed a mean Geodesic Distance of 0.250 (σ = 0.210) and a mean accuracy to detect the correct rotation r of 0.619 (σ = 0.130), considering as acceptable the range [ r − 10, r + 10]. We then evaluate the CAD system performances with 10 non-expert users: the average speed improved from 9.61 (σ = 1.53) to 5.30 (σ = 1.30) and the average number of actions to complete the task from 12.60 (σ = 2.15) to 11.00 (σ = 0.89). This work is a further step through the adoption of DL and AR in the assistance domain. In future works, we will overcome the limitations of this approach and develop a complete mobile CAD system that could be extended to any object that presents a 3D counterpart model.
IDENTIFICATION OF ENTRANT’S ABILITIES ON THE BASIS OF SUGENO-TYPE FUZZY INFERENCE SYSTEMS

In the conditions of effective training in aviation for dispatchers and pilots, it requires the use of infocommunication systems capable of working under conditions of fuzzy uncertainty in real time. The functioning of such systems is based on fuzzy inference systems. However, the development and implementation of these systems requires the creation of fuzzy knowledge bases. Therefore, special attention in this study is paid to the creation of a system of fuzzy inferences and the formation of a fuzzy knowledge base of this system. The result is a lozenge-type fuzzy inference system. The fuzzy knowledge base of the system contains the rules according to which, based on the results of test computer game problems of varying complexity, a conclusion is formed about the applicant’s ability to acquire knowledge and skills in a certain specialty.When developing the rules, both the results of passing different levels of professionally oriented computer test games were taken into account, and the interest of dispatchers and pilots was taken into account. Therefore, the proposed fuzzy rules of the knowledge base of the fuzzy inference system make it possible to assess not only the ability of the controller or pilot to solve certain problems. This dependence of the input dataset on time allows the implementation of a fuzzy inference system of the Sugeno type, using clear input data in the formation of inferences.
Analysis of Air Temperature Trends as a Climate Change Indicator for Alexandria (Egypt)

This work provides the results of the analyzed long-term trends for three air temperature categories, namely: average (Tavg), maximum (Tmax) and minimum (Tmin) on monthly, annual and seasonal bases for Alexandria. The aim was to examine possible climate changes in this famous City based on the results obtained from the analyzed trends. The study was based on examining (1) linear regression approaches, (2) trend magnitudes, (3) Mann-Kendall trend test and (4) extreme air temperature events. The air temperature data from Alexandria International Airport station were used over a period of 65 years (1957-2021). For all analyzed time series, the results showed statistically significant positive trends. The exception was for the monthly mean Tmax during January, February and December (negative trends), and March (No trend). The H1 hypothesis prevails for the three temperature categories, on different basis. Over the period of investigation, both the annual mean Tavg and Tmin rose at a rate of +0.02 °C/yr, while annual mean Tmax rose at a rate of + 0.008°C/ yr. In winter, the annual mean Tavg and Tmin had increasing trends at a rate of +0.02 °C/yr. The winter Tmax had a feeble increasing trend (almost constant), at a rate of only +0.0003 °C/yr. In summer, the three air temperature categories increased at a rate of +0.02 °C/yr, each. Extreme air temperature times were specified in this study on all bases for the three temperature categories. The findings of this study are thought to be a reliable indicator of the presence of climate change in Alexandria. Keywords: Alexandria, air temperature, climate change, trend analysis, extremes
ANALYSING THE WEB LOG FILES TO DISCOVER USER PATTERN USING MACHINE LEARNING ALGORITHMS

There is a tremendous amount of data available over the World Wide Web in the form of web documents, and that amount is growing daily.The purpose of web mining is to extract useful information from web documents.There are three types of web mining: web content mining, web structure mining, and web usage mining.Web usage mining involves mining web log data from the World Wide Web to extract useful information.It is useful for applications such as e-commerce, terrorism, fraud detection, identifying criminal activities, web design, etc. Web Usage Mining applications and tools will be explained in detail in this paper.
Multiscale Approach for Bounded Deformation Image Registration

Deformable image registration is a very important topic in the field of image processing. It is widely used in image fusion and shape analysis. Generally speaking, image registration models can be divided into two categories: smooth registration and non-smooth registration. During the last decades, many smooth registration models (i.e., diffeomorphic registration) were proposed. However, image with strong noise may lead to discontinuous deformation, which cannot be modelled by smooth registration. To simulate this kind of deformation, some non-smooth registration models were also proposed. However, numerical algorithms for these models are easily trapped into a local minimum because of the nonconvexity of the object functional. To overcome the local minimum of the object functional, we propose a multiscale approach for a non-smooth registration model: the bounded deformation (BD) model. The convergence of the approach is shown, and numerical tests are also performed to show the good performance of the proposed multiscale approach.
DEVELOPMENT OF A VIDEO PROCESSING MODULE FOR THE TASK OF AIR OBJECT RECOGNITION BASED ON THEIR CONTOURS

The subject of research in the article is the module of automatic segmentation and subtraction of the background, which is created, based on the sequential application of methods of image preprocessing and modified method of interactive segmentation of images and implemented in the system of optical monitoring of the air situation. The aim of the work is to develop an image segmentation module to increase the efficiency of recognition of an air object type on a video image in the system of visual monitoring of the air environment by means of qualitative automatic segmentation. To solve this problem, a modified interactive algorithm in the mode of automatic selection of an object in the image, which allows more accurately, without the participation of the operator, to determine the foreground pixels of the image for further recognition of the type of airborne object. The following tasks are solved in the article: the analysis of existing methods of binarization of color images for semantic segmentation of images, which are used in image recognition systems; the development of a pipeline of methods for automatic segmentation of images in the system of optical monitoring of the air environment. In the work, the following methods are used: methods of digital image processing, methods of filtering and semantic segmentation of images, methods of graph analysis. The following results are obtained: the results of image processing with the proposed module of segmentation and background subtraction confirm the performance of the module procedures. The developed pipeline of methods included in the module demonstrates correct segmentation in 93% of test images in automatic mode without operator participation, which allows us to conclude about the effectiveness of the proposed module. Conclusions: The implementation of the developed module of segmentation and background subtraction for the system of optical monitoring of the air environment allowed to solve the problem of segmentation of video images for further recognition of aerial objects in the system of optical monitoring of the air environment in automatic mode with a high degree of reliability, thus increasing the operational efficiency of this system.
Research on secure workload execution scheme in heterogeneous cloud environment

&lt;span lang="EN-US"&gt;The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customer’s specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment.&lt;/span&gt;
Phuket mascot design from based on cultural value

&lt;span lang="EN-US"&gt;Designing with cultural, wisdom, way of life, beliefs, and other values that have been passed down through generations. Let's produce a creative product that is entertaining and varied. One of them is to design a mascot to be used in the media as a character and representation of something, and to imply that this mascot will be able to act on behalf of those representatives. It is clear that Phuket has an exceptional and valuable cultural capital. In addition, there are numerous traditions, rituals, beliefs, customs, arts, and civilizations that are indigenous to the local population. It was thought that this study did a good job of figuring out how to use cultural values to make characters by applying design theory or design science to creativity. It also did a good job of figuring out how applicable the methods and design approaches were.&lt;/span&gt;
Research on the Innovative Development of University Library Subject Services Under the Background of Big Data

The university library is an important institution responsible for disseminating knowledge. In order to improve the service level of the library, the subject service model has been adopted in recent years, but there are still some deficiencies in the construction of this service. By analyzing the current situation of the development of subject services in university libraries, this paper further analyzes the effective path of innovative subject services in university libraries under the background of big data.
Research Related to Teaching Chinese Language and Culture in U.S. Classrooms: A Guide to Teaching Foreign Languages in U.S. Elementary and Secondary Schools

Language and Children: A Guide to Teaching Foreign Languages in American Elementary and Secondary Classrooms is a classic second language teaching series co-authored by two language educators, Helena Cotton and Carol Ann Dahlberg. The book reflects the latest advances in language classroom research and is the only comprehensive guide to foreign language classroom methods for K-12 and college students, using language learning objectives as the guiding principles for classroom rules and instruction. [Cheng Liang: "The Golden Key to Second Language Acquisition: A Review of the American Guide to Teaching Foreign Language in Elementary and Secondary Schools," Journal of Jiamusi Vocational College, 2019 (05)] Through careful reading, I have gained deeper thoughts and knowledge about various aspects of immersion teaching, teaching integration of language and culture, and teaching communication. Because I am very interested in "how to realize the organic integration of language and culture teaching in second language teaching classrooms," I focused on Chapter 9, "Experiencing Culture in the Classroom: Interaction of Language, Culture and Curriculum," and came up with a series of reflections, including insights into children's cultural teaching, academic associations, thoughts on overseas Chinese cultural I have a series of reflections, including insights on teaching children's culture, academic associations, thoughts on teaching Chinese culture overseas, and reflections on some of the contents of the book.
The Application of Graph Embedding Based on Random Walk

In the historical process of scientific development, computers have a lofty position, and in recent years, graph embedding algorithms and models are one of the most popular subjects. A large number of similar data structures are indistinguishable by humans, but graph embedding can quickly compare and analyze these data structures. Existing research on random walk-based graph embedding methods is very rich. In order to summarize and classify the status quo of the more mature classical models and compare and integrate them, many different classical models are discussed in this paper. Based on different models, the problems solved, algorithm ideas, strategies, advantages, and disadvantages of the models are discussed in detail, and the application performance of some models is evaluated. DeepWalk model, Node2Vec model, HARP model are three graph embedding models based on the classical random walk model. Calculations for different data can occur by generating different node sequences. The three most important models in attribute random walk models are TriDNR model, GraphRNA model and FEATHER model. The model that only targets the information data in the shallow network is no longer suitable for the rapidly developing network. Attribute random walk models can handle data in deeper networks. At the end of this paper, the full text is summarized and the future prospect of this field is made.
Research on the process of university student resources mining and applicationbased on resource Service center

In view of the current situation of student resources mining and application, this paper establishes the overall framework of university student resources mining and application, optimizes and designs the process and specific actions of university student resources mining and application based on resource service center, and specifically studies the construction of supporting environment based on resource service center.
Hierarchical Phrase-based Sequence-to-Sequence Learning

We describe a neural transducer that maintains the flexibility of standard sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference. Our approach trains two models: a discriminative parser based on a bracketing transduction grammar whose derivation tree hierarchically aligns source and target phrases, and a neural seq2seq model that learns to translate the aligned phrases one-by-one. We use the same seq2seq model to translate at all phrase scales, which results in two inference modes: one mode in which the parser is discarded and only the seq2seq component is used at the sequence-level, and another in which the parser is combined with the seq2seq model. Decoding in the latter mode is done with the cube-pruned CKY algorithm, which is more involved but can make use of new translation rules during inference. We formalize our model as a source-conditioned synchronous grammar and develop an efficient variational inference algorithm for training. When applied on top of both randomly initialized and pretrained seq2seq models, we find that both inference modes performs well compared to baselines on small scale machine translation benchmarks.
Decreasing the Commissioning Time of Optical Multi-Camera Inspection Systems by Simulating Surface Coverage Using the Example of Formed Bipolar Plates

Implementing &ldquo;first-time-right&rdquo; production processes increases a production line&rsquo;s sustainability and minimizes rejects. Multi-camera systems are a key element since they can quickly detect defects without contact. However, it is still a time-consuming challenge to determine the correct number and position of cameras to achieve gapless surface monitoring of complex components. This proposal aims to develop a new software tool that automatically calculates and visualizes surface coverage by using bipolar plates as an example. With this method, 100% surface coverage inspections become feasible, and the cost of commissioning multi-camera inspection systems can be significantly decreased.
Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis

We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we novelly generate congruent image--report pairs employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the generated report will adequately explain a medical image, while a report-generated image that effectively characterizes the text visually should (sufficiently) resemble the original. The aim of the work is to generate trustworthy and faithful explanations for the outputs of a model diagnosing chest x-ray images by pointing a human user to similar cases in support of a diagnostic decision. Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset
A Rigorous Study Of The Deep Taylor Decomposition

Saliency methods attempt to explain deep neural networks by highlighting the most salient features of a sample. Some widely used methods are based on a theoretical framework called Deep Taylor Decomposition (DTD), which formalizes the recursive application of the Taylor Theorem to the network's layers. However, recent work has found these methods to be independent of the network's deeper layers and appear to respond only to lower-level image structure. Here, we investigate the DTD theory to better understand this perplexing behavior and found that the Deep Taylor Decomposition is equivalent to the basic gradient$\times$input method when the Taylor root points (an important parameter of the algorithm chosen by the user) are locally constant. If the root points are locally input-dependent, then one can justify any explanation. In this case, the theory is under-constrained. In an empirical evaluation, we find that DTD roots do not lie in the same linear regions as the input - contrary to a fundamental assumption of the Taylor theorem. The theoretical foundations of DTD were cited as a source of reliability for the explanations. However, our findings urge caution in making such claims.
kogito: A Commonsense Knowledge Inference Toolkit

In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interface to interact with natural language generation models that can be used for hypothesizing commonsense knowledge inference from a textual input. In particular, kogito offers several features for targeted, multi-granularity knowledge generation. These include a standardized API for training and evaluating knowledge models, and generating and filtering inferences from them. We also include helper functions for converting natural language texts into a format ingestible by knowledge models - intermediate pipeline stages such as knowledge head extraction from text, heuristic and model-based knowledge head-relation matching, and an ability to define and use custom knowledge relations. We make the code for kogito available at https://github.com/epfl-nlp/kogito along with thorough documentation at https://kogito.readthedocs.io.
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation

Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), and Feedforward Neural Network (FFNN) with structured data. Our empirical investigation examines the qualitative behavior of model performance as the HPs vary, quantifies the importance of each HP for different ML algorithms, and stability of the performance near the optimal region. Based on the findings, we propose a set of guidelines for efficient HP tuning by reducing the search space.
ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-training

Multilingual pre-trained models exhibit zero-shot cross-lingual transfer, where a model fine-tuned on a source language achieves surprisingly good performance on a target language. While studies have attempted to understand transfer, they focus only on MLM, and the large number of differences between natural languages makes it hard to disentangle the importance of different properties. In this work, we specifically highlight the importance of word embedding alignment by proposing a pre-training objective (ALIGN-MLM) whose auxiliary loss guides similar words in different languages to have similar word embeddings. ALIGN-MLM either outperforms or matches three widely adopted objectives (MLM, XLM, DICT-MLM) when we evaluate transfer between pairs of natural languages and their counterparts created by systematically modifying specific properties like the script. In particular, ALIGN-MLM outperforms XLM and MLM by 35 and 30 F1 points on POS-tagging for transfer between languages that differ both in their script and word order (left-to-right v.s. right-to-left). We also show a strong correlation between alignment and transfer for all objectives (e.g., rho=0.727 for XNLI), which together with ALIGN-MLM's strong performance calls for explicitly aligning word embeddings for multilingual models.
Continuity of Powerspaces Structures in Directed Spaces

Powerspaces of directed spaces play an important role in modeling the semantics of nondeterministic functional programming languages. The notions of upper,lower and convex powerspace of a directed space are defined by the way of free algebras[25]. In this paper, We study the continuity of power structures of directed spaces and show that the directed lower powerspaces, directed upper powerspaces and directed convex powerspaces of continuous spaces are continuous spaces.