Reviewerly/exHarmony huggingface dataset:
A comparison of real-time 4K/UltraHD connected component labelling architectures

This work presents a comparison of hardware architectures realising a connected component labelling (CCL) algorithm in reprogrammable logic. The architectures are capable of processing a video stream with 4K/UltraHD resolution at 60 frames per second in real-time. The modules were verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.
The role of the managerial competences of the teachers in the primary education

This article treats the managerial skills and abilities of the teachers in primary education. To be a successful manager the teacher must have both theoretical knowledge and ambition, perseverance, tenacity. To work constructively with pupils and their parents is an art, a talent developed through experience. A teacher-manager in the primary classes must plan the actions, then achieve the goals set. The success is assured by the active involvement in children and school‟s life. The teacher-manager represents the link between the educational institution and the society.
Applying Smart Document Technology for Teaching and Research

The technology of “smart” documents for teaching and research has been developed, which is distinguished by the use of extensible libraries of programs in the VBA language and interaction with services in the Python language, which increases the productivity of solving educational problems in the field of technical systems management, informatics and data analysis. Examples of the application of the developed technology in the educational process are given.
Computer System for Processing Industrial Information for Controlling the Production of Multi-Assortment Polymeric Films

The results of the development of a computer system for processing big data and expert knowledge of multi-assortment production of polymeric films have been presented. The computer system is an effective tool that helps operators to make decisions on the polymeric film production control in a routine mode and in the event of a defect. It is adjusted to the characteristics of polymeric film production and allows making advices on the choice of controlling actions at the main stages of production, ensuring the specified quality of the polymeric film (in the routine mode) and entering quality indices into the regulatory restrictions (in the event of a film defect). The system integrates production data obtained from various sources (a SCADA system, a video monitoring system, a thickness gauge, etc.). The information ware of the system includes a bank of data and knowledge on polymeric film production characteristics. Mathematical models are used to calculate quality indices of intermediates (extrudate) and products (polymeric film) that are not monitored in production. Data mining methods are used to compress, cluster complete sets of measured and calculated data, and predict consumer characteristics of the film. Interfaces of managerial production staff are used to visualize the results of industrial information processing (trends in production parameters, 3D graphs of the dependencies of quality indices on controlling actions, areas for quality indices, advices on control). The results of testing the system according to the data of the productions of rigid polymeric films for pharmaceutical and food packaging on the extrusion-calendering lines of factories in Russia and Germany have confirmed the system operability. The effectiveness of the system application is due to increased efficiency of decision-making on control and ensuring resource saving in production by improving the quality of the polymeric film and reducing non-returnable waste (defective film).
Identify Diabetic Retinopathy-related Clinical Concepts Using Transformer-based Natural Language Processing Methods

Diabetic retinopathy (DR) is a leading cause of blindness in American adults. If detected early, DR can be treated to preventing further damage causing blindness, therefore, early detection is very important for the treatment of DR. There is an increasing interest in developing Al technologies to help early detection of DR using electronic health records (EHR). The detailed diagnoses information documented in image reports is a valuable resource that could help detect lesions from the medical image, thus helping early detection of DR. In this study, we examined two state-of-the-art transformer-based natural language processing models, including BERT and RoBERTa, to extract DR-related concepts from clinical narratives. We identified four different categories of DR-related clinical concepts including lesions, eye parts, laterality, and severity, developed annotation guidelines, annotated a DR-corpus of 536 image reports, and trained four transformer-based NLP models for clinical concept extraction. The experimental results show that the BERT model pretrained with the MIMIC III dataset achieved the best strict/lenient F1-score of 0.9503 and 0.9645, respectively.
Individual and contextual factors associated with data sharing in the social sciences

This secondary analysis of restricted-use data from the LEADS Database examines individual and contextual factors associated with social scientists’ propensity to share data. Prior literature primarily examines simple bivariate relationships between individual and contextual factors and data sharing. This study improves on this literature by considering multiple such factors simultaneously among U.S. principal investigators in the social sciences using structural equation modeling. By examining this full set of predictors, we explained a high proportion of variance (R2 = .69) in researchers’ propensity to share data. Ethics-related barriers, favorable disciplinary attitudes toward data sharing, discipline, and academic rank all significantly predict propensity to share data. Also, favorable disciplinary attitudes toward data sharing were negatively associated with both perceived time- and ethics-related barriers to data sharing. These findings, although contradictory compared to some prior research, have implications for educators and may inform efforts to promote data sharing in the social sciences.
Image Colorization Based on VAE-GAN and the Mixture Density Model

A diverse image colorization method based on VAE-GAN and mixture density network is proposed by this paper. The VAE-GAN is used to learn the color representation of images, while the mixture density network is to model multimodal distribution of color with learned representation, so that we can draw samples from the distribution and decode them with VAE-GAN to achieve diverse colorization. In comparison with vanilla VAE, the VAE-GAN is trained with adversarial loss and perceptual loss on the discriminator, which ensures better color representation learned by the model and more consistent color result.
Research on Trajectory Optimization of Quadrotor Flighting through Narrow Gap

Aiming at the problem of quadrotors flighting through narrow gaps, applying imitation learning technology, this paper proposes an optimization scheme that combines path planning and attitude adjustment. After recalculating the traverse trajectory in real time, the approach trajectory is re-planned and the attitude is adjusted to optimize the trajectory. This scheme can realize that the quadrotor traverse the gap with any positions and attitudes quickly, and solve the problem caused by gap loss, attitude changed significantly and traverse attitude difficult to reach, which obviously reduces the difficulty of subsequent control.
Selecting classifiers to ensure the quality and reliability of pattern recognition at class intersection

The article poses and solves the problem of selecting classifiers for the case when the given classes intersect in the space of initial properties of objects. The value of the limiting dimension of the classifier space is determined, taking into account the volume of the learning sample, the probability of errors made when separating classes and recognizing new objects. Procedures are given for determining the classifiers of three types and selecting among them the ones that satisfy the predetermined error when separating classes, and the predetermined error probability and reliability when recognizing new objects. An algorithm and software were developed based on the proposed procedures. Computational experiments on a computer and the results were presented in the form of decision rules used for object recognition.
Semantic segmentation of remote sensing images based on deep learning methods

Remote sensing image segmentation has always been an important research direction in the field of remote sensing image processing, and it is a key step in the further understanding and analysis of remote sensing images. Image semantic segmentation is the process of classifying each pixel to form several sub-regions with respective characteristics, and extracting the objects of interest among them. However, due to the complex boundary and scale difference of the remote sensing image, the traditional algorithm can not meet the actual needs well, resulting in low segmentation accuracy. In order to further improve the accuracy of remote sensing image segmentation, this paper combines deep convolutional neural network with remote sensing image, based on the U-Net, firstly compares the model's segmentation accuracy under different learning strategies, and introduces a new learning strategy to improve the learning effect of the model; secondly, in the loss function part of the model, a new compound loss function is proposed to speed up the convergence of the network and improve the segmentation accuracy. Based on full experimental research on the WHDLD remote sensing image dataset, the results show that the improved method has 1.5% accuracy improvement compare to the U-Net.
Research on the Construction of "Theory and Practice Integration" Professional Classroom Based on Information Technology

"Theory and practice integration " teaching mode is a kind of teaching mode introduced by vocational education institutions according to the characteristics of personnel training. It has changed the shortcomings of previous theory and practice, and has obvious advantages in improving students' post ability. Based on information technology, "Theory and practice integration" professional classroom is the carrier of "Theory and practice integration" teaching. Through the demand analysis of "Theory and practice integration" professional classroom, this paper puts forward the construction scheme of professional core curriculum "theory and practice integration" professional classroom, which is conducive to the construction of students' theoretical knowledge and the mastery of practical skills, so as to improve the quality of professional personnel training.
Multivariate Mean Comparison Under Differential Privacy

The comparison of multivariate population means is a central task of statistical inference . While statistical theory provides a variety of analysis tools, they usually do not protect individuals' privacy. This knowledge can create incentives for participants in a study to conceal their true data (especially for outliers), which might result in a distorted analysis. In this paper, we address this problem by developing a hypothesis test for multivariate mean comparisons that guarantees differential privacy to users. The test statistic is based on the popular Hotelling's $$t^2$$ -statistic, which has a natural interpretation in terms of the Mahalanobis distance. In order to control the type-1-error, we present a bootstrap algorithm under differential privacy that provably yields a reliable test decision. In an empirical study, we demonstrate the applicability of this approach.
Improving Representation Learning Incorporating Neighborhood Context and Relational Paths for Knowledge Graph Completion

Incompleteness is the most common problem in knowledge graphs and it has prompted many researchers to propose methods to automatically infer missing facts in knowledge graphs, which has gradually developed from the initial TransE, TransH, and DistMult to the current advanced ConvKB. Most of the existing KG completion methods only consider the direct relationship between two entities, namely, the interactive information within one triple, and ignore the connectivity of the knowledge graph structure. In this paper, we develop a convolutional neural network-based model PathConvKB that incorporates neighborhood context and relational paths for representation learning. We adopt a multi-layer message passing scheme proposed by PathCon to iteratively aggregate messages from K-hop neighbor edges of entities. Also, we design a weighted path representation learning method to model each relational path on account of the importance of each edge for a given entity pair. Especially, Combined with the attention mechanism, the neighborhood context between entity pairs is used to measure the importance scores of relational paths and then we integrate the path aggregation feature into the triple matrix. Experiments show that our model PathConvKB has achieved consistent improvements on typical evaluation task for knowledge representation against state-of-the-art methods.
Hybrid Extreme Learning Machine dan Firefly Algorithm untuk Meramalkan Nilai Tukar Rupiah terhadap Dolar

Every country has a currency as a medium of exchange and the movement of its exchange rate can affect the economy of the country. In Indonesia, since the freely floating exchange rates system has been applied in August 1997, the value of rupiah currency in the foreign exchange market can change at any time. Considering the massive impacts of exchange rate fluctuation on the economy, then forecasting the exchange rate of rupiah against the US dollar is important to help Indonesia’s economic growth. The aims of this thesis is to predict the estimated exchange rate of rupiah against the US dollar in the future by using hybrid artificial neural network extreme learning machine (ELM) method and firefly algorithm (FA). In the training process, ELM-FA hybrid has a role to obtain the best weight and bias. The weight and bias that obtained will be used for forecasting and to know the success rate of the training process, the validation test process is required. Based on the implementation of program and simulation for some parameter values on the exchange rate data from Jan 2015 until Jan 2018, with four input and hidden nodes, and one output node, obtained the smallest MSE of the training is 0.000480513 with MSE of the testing is 0.0000854107. The relatively small MSE value indicates that ELM-FA network is able to recognize the data pattern well and able to predict the test data well.
Sharpness Enhancement of Stereo Images Using a Depth-Based Per-Pixel Regularization

Blur is one of the causes of visual discomfort in stereopsis. The application of 2D image sharpening algorithms to the left and right view can produce an interdifference which causes eyestrain and visual fatigue for the viewer. Additionally, it has been shown, through subjective tests, that the perception of sharpness is affected by depth. A 3D sharpness enhancement method for stereo images that incorporates binocular vision cues as well as depth information is presented. The proposed algorithm decomposes each of the input stereo images into a base and a detail layer. The visibility thresholds maps given by the Binocular Just Noticeable Difference (BJND), which include binocular mechanisms such as luminance and contrast masking are used as guidance maps in the computation of the base layer; additionally, the depth of objects in the scene is used to provide a per-pixel depth-weighted regularization to the computation of the base layer. The detail layer, defined using the input images and the computed base layer is boosted and then added to the base layer to provide the final sharpness enhanced images. The proposed sharpness enhancement method results in a low interdifference error of corresponding positions of the stereo pair and in an enhanced subjective visual quality. Comparative quantitative results in terms of interdifference using a publicly available dataset show that the proposed algorithm outperforms state-of-the-art algorithms. Qualitative and subjective evaluation results are also included in order to show the perceived visual quality improvement provided by the proposed algorithm.
Emerging Trends in Information Technology and Its Impact on Business Organizations

Information technology (IT) is an industry on the rise with the latest and emerging trends. IT trends are changing the business structure, creating new job portfolios, and going to occupy a significant place in the business organization in the coming years. The present recent and emerging trends are solving business problems with innovative IT applications and also helping the business organizations, improving the quality of services and products, and presenting new functions in fields like medicine, entertainment, business, education, marketing, law enforcement, and more. The present research used descriptive cum exploratory methodology to list the recent and emerging trends in business organizations and also gave an attempt to identify the benefits and risks associated with IT trends in business organizations in addition to how IT is solving business problems.
Automating Web-based Infrastructure Management via Contextual Imitation Learning

Web-based management dashboards provide intuitive interfaces to support various infrastructure management tasks. However, the lack of programmability in these dashboards makes it difficult to automate management tasks. To address this limitation, we propose a system call MAIL (Management Automation through Imitation Learning) that learns from an operator's demonstrations using imitation learning to automate management tasks on web-based dashboards. A novel Contextual Imitation Learning algorithm is proposed in MAIL to overcome the learning challenges brought by the requirements of infrastructure management automation. The effectiveness of the MAIL system has been demonstrated using both synthetic environments and production environments. It outperforms existing methods and is able to achieve an 86%-100% success rate for most tasks.
Ensemble of Adapters for Transfer Learning Based on Evidence Theory

Transfer learning hopes to borrow transferable knowledge from source domain (related domain) to build up an adapter for target domain. Since the adapter is built on the source domain, the robustness and generalization of a single adapter are more likely to be limited. To further improve the performance of the adapter, in this paper, we propose a parallel ensemble strategy based on evidence theory. Specifically, firstly, we quantify an adaptation degree for instances of source domain based on evidence theory. Secondly, we redefine Determinantal Point Processes (DPP) sampling with adaptation degree, and use the improved DPP sampling to generate k different subsets. Finally, we select and combine the base adapters that are trained by the subsets. In the proposed ensemble strategy, the adaptation degree can ensure the higher transferability of the base adapters, DPP sampling can increase the diversity among the base adapters. Thus, the ensemble strategy can reduce the conflict between accuracy and diversity, and improve the robustness and generalization of the adapters. Numerical experiments on real-world applications are given to comprehensively demonstrate the effectiveness and efficiency of our proposed ensemble strategy. The results show that the ensemble strategy can improve transfer performance.
Improved Bootstrapping by FFT on Encrypted Multi Operands Homomorphic Addition

Bootstrapping is a technique that was introduced by Gentry in 2009. It is based on reencryption which allows an encryption scheme to perform an unlimited number of processing on encrypted data. It is a bottleneck in the practicability of these schemes because of multiplication operations which are costly in complexity. This complexity was reduced in TFHE by processing bootstrapping on the result of a two-bit logic gate in thirteen milliseconds using the Fast Fourier Transform. Building on this advance, an implementation of the addition of ten (10) numbers of 32-bits was performed based on the 32-bit Carry Look ahead Adder and was executed in less than 35 seconds using the configured SPQLIOS Fast Fourier transform to manipulate AVX and FMA instructions. This connector improves performance to a higher level than FFTW3 and NAYUKI.
Context-Aware Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

Multi-turn response selection is a major challenge for chatbot dialogue systems. The existing methods either ignore the interactions among previous utterances for context modeling, or regard all the previous utterances of the same importance. In this paper, we propose a context-aware network (CAN) for the multi-turn response selection task. The main idea of CAN is to learn the unified representation vector of the utterance and response for response matching. First, CAN uses a hierarchical attention mechanism to extract important features of utterances for modeling the utterance representation . Then CAN separates the current message from the historical dialogues, and calculates the matching degree between the current message and each historical dialogue to get the utterances aggregation representation. Finally, CAN uses multi-layer perceptron (MLP) to predicate the score of each response. Experiments on three public datasets show that CAN outperforms the baseline models on most metrics and demonstrates compatibility across domains for multi-turn response selection.
A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation

Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is often the case that user interests may change from their past routines. However, multi-perspective attention has broad horizon, which covers different characteristics of human reasoning, emotions, perception, attention, and memory. In this paper, we attempt to introduce the multi-perspective attention and sequence behaviour into multitask learning. Our proposed method offers better understanding of user interest and decision. To achieve more flexible parameter sharing and maintaining the special feature advantage of each task, we improve the attention mechanism at the view of expert interactive. To the best of our knowledge, we firstly propose the implicit interaction mode, the explicit hard interaction mode, the explicit soft interaction mode, and the data fusion mode in multitask learning. We do experiments on public data and lab medical data. The results show that our model consistently achieves remarkable improvements to the state-of-the-art method.
Formalizing Galois Theory

We describe a project to formalize Galois theory using the Lean theorem prover, which is part of a larger effort to formalize all of the standard undergraduate mathematics curriculum in Lean. We discuss some of the challenges we faced and the decisions we made in the course of this project. The main theorems we formalized are the primitive element theorem, the fundamental theorem of Galois theory, and the equivalence of several characterizations of finite degree Galois extensions.