Reviewerly/exHarmony huggingface dataset:
Line laser point cloud segmentation based on the combination of RANSAC and region growing

RANdom SAmpling Consensus (RANSAC) and region growing algorithms are widely used in image processing and point cloud segmentation, but the RANSAC algorithm used for point cloud segmentation will cause insufficient segmentation. The region growing algorithm can divide point cloud data into points based on the curvature and normal characteristics of the point cloud. Multiple clusters are easy to be over-segmented. To solve this problem, this paper proposes to use the RANSAC algorithm to perform coarse segmentation to segment the point cloud data into a foreground point cloud with more geometric features and a background point cloud that is only a plane. Then use the region growing algorithm. The foreground point cloud is finely segmented. Besides, the curvature characteristics of the region growing process are used to optimize the plane extraction of the RANSAC algorithm. The experimental results show that this method can reduce over-segmentation to a certain extent and significantly improve the speed of the algorithm.
Volume Sweeping: Learning Photoconsistency for Multi-View Shape Reconstruction

We propose a full study and methodology for multi-view stereo reconstruction with performance capture data. Multi-view 3D reconstruction has largely been studied with general, high resolution and high texture content inputs, where classic low-level feature extraction and matching are generally successful. However in performance capture scenarios, texture content is limited by wider angle shots resulting in smaller subject projection areas, and intrinsically low image content of casual clothing. We present a dedicated pipeline, based on a per-camera depth map sweeping strategy, analyzing in particular how recent deep network advances allow to replace classic multi-view photoconsistency functions with one that is learned. We show that learning based on a volumetric receptive field around a 3D depth candidate improves over using per-view 2D windows, giving the photoconsistency inference more visibility over local 3D correlations in viewpoint color aggregation. Despite being trained on a standard dataset of scanned static objects, the proposed method is shown to generalize and significantly outperform existing approaches on performance capture data, while achieving competitive results on recent benchmarks.
Towards Energy Efficiency and Power Trading Exploiting Renewable Energy in Cloud Data Centers

This study investigates the energy cost and carbon emission reduction problem in geographically distributed cloud data centers (DCs), where each DC is connected with its own renewable energy resources (RERs) for green energy generation. We consider four cloud DCs that are operated by a single cloud service provider. They consume energy from both RERs and from the commercial grid to meet the demand of cloud users. For energy pricing, we consider four different energy markets that offer varying energy prices per hour. Additionally, our proposed strategy enables DCs to sell excess electricity to the commercial grid in peak-price hours and purchase in low-cost hours according to power trading. This work also exploits energy storage devices (ESDs) to store energy for future use. We utilize real-time data requests, weather data, and pricing data for performing simulations and results affirm the effectiveness and productiveness of our proposed method to mitigate the energy cost and carbon emission of cloud DCs.
INTERNAL PROFESSIONAL MOBILITY OF LYCEUM TEACHERS BELONGING TO DIFFERENT PAST GENERATIONS

Purpose of the study: to identify the dynamics of internal professional mobility (level of motivation, pedagogical reflection, innovation orientation, style of educational communication) of teachers ‒ participants of the program to increase internal professional mobility. They belong to the “Transitional” generation, which appeared in the Soviet Union and the “Information” generation, which was established in the period of perestroika.&#x0D; Methodology: The subjects of the study are high school teachers, Russia. The comparative psychodiagnostic method was used before and after the training program. The purpose of data collection was to fill in the methodology forms. The data were analyzed by factor analysis, methods for determining the significance of differences in indicators.&#x0D; Main Findings: Before the program, teachers of the “Transition” generation expressed a desire to retrain and be active in innovation, and teachers of the “Information” generation expressed a desire to make suggestions. The motivation to avoid failure prevailed in the groups. Pedagogical reflection prevailed among teachers of the “Transitional” generation. After passing the program, teachers began to show motivation for achievement and teachers of the “Information” generation increased their pedagogical reflection.&#x0D; Applications of this study: The research can be useful in the field of methodological support for improving internal professional mobility of teachers. Teachers must constantly change to offer new and interesting projects for students that belong to another generation. Internal attitudes, values, motivation, innovative orientation, and communication style of the teacher affect the success of students and reduce the stress of teaching activities.&#x0D; Originality of this study: the study complements the idea of the influence of the factor ‒ generation ‒ on balancing the pedagogical activity of teachers, their readiness to change, and their high motivation to new projects. The professional mobility program increases their subjective well-being and harmonizes educational communication between teachers and students.
Deep or simple models for semantic tagging?

Semantic tagging, which has extensive applications in text mining, predicts whether a given piece of text conveys the meaning of a given semantic tag. The problem of semantic tagging is largely solved with supervised learning and today, deep learning models are widely perceived to be better for semantic tagging. However, there is no comprehensive study supporting the popular belief. Practitioners often have to train different types of models for each semantic tagging task to identify the best model. This process is both expensive and inefficient. We embark on a systematic study to investigate the following question: Are deep models the best performing model for all semantic tagging tasks? To answer this question, we compare deep models against "simple models" over datasets with varying characteristics. Specifically, we select three prevalent deep models (i.e. CNN, LSTM, and BERT) and two simple models (i.e. LR and SVM), and compare their performance on the semantic tagging task over 21 datasets. Results show that the size, the label ratio, and the label cleanliness of a dataset significantly impact the quality of semantic tagging. Simple models achieve similar tagging quality to deep models on large datasets, but the runtime of simple models is much shorter. Moreover, simple models can achieve better tagging quality than deep models when targeting datasets show worse label cleanliness and/or more severe imbalance. Based on these findings, our study can systematically guide practitioners in selecting the right learning model for their semantic tagging task.
Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules

Semantic segmentation with convolutional neural networks under a complex background using the encoder-decoder network increases the overall performance of online machine vision detection and identification. To maximize the accuracy of semantic segmentation under a complex background, it is necessary to consider the semantic response values of objects and components and their mutually exclusive relationship. In this study, we attempt to improve the low accuracy of component segmentation. The basic network of the encoder is selected for the semantic segmentation, and the UPerNet is modified based on the component analysis module. The experimental results show that the accuracy of the proposed method improves from 48.89% to 55.62% and the segmentation time decreases from 721 to 496 ms. The method also shows good performance in vision-based detection of 2019 Chinese Yuan features.
Knowledge distillation for BERT unsupervised domain adaptation

A pre-trained language model, BERT, has brought significant performance improvements across a range of natural language processing tasks. Since the model is trained on a large corpus of diverse topics, it shows robust performance for domain shift problems in which data distributions at training (source data) and testing (target data) differ while sharing similarities. Despite its great improvements compared to previous models, it still suffers from performance degradation due to domain shifts. To mitigate such problems, we propose a simple but effective unsupervised domain adaptation method, adversarial adaptation with distillation (AAD), which combines the adversarial discriminative domain adaptation (ADDA) framework with knowledge distillation. We evaluate our approach in the task of cross-domain sentiment classification on 30 domain pairs, advancing the state-of-the-art performance for unsupervised domain adaptation in text sentiment classification.
Descriptor Revision for Conditionals: Literal Descriptors and Conditional Preservation

Descriptor revision by Hansson is a framework for addressing the problem of belief change. In descriptor revision, different kinds of change processes are dealt with in a joint framework. Individual change requirements are qualified by specific success conditions expressed by a belief descriptor, and belief descriptors can be combined by logical connectives. This is in contrast to the currently dominating AGM paradigm shaped by Alchourrón, Gärdenfors, and Makinson, where different kinds of changes, like a revision or a contraction, are dealt with separately. In this article, we investigate the realisation of descriptor revision for a conditional logic while restricting descriptors to the conjunction of literal descriptors. We apply the principle of conditional preservation developed by Kern-Isberner to descriptor revision for conditionals, show how descriptor revision for conditionals under these restrictions can be characterised by a constraint satisfaction problem, and implement it using constraint logic programming. Since our conditional logic subsumes propositional logic, our approach also realises descriptor revision for propositional logic.
Maintaining SmartX multi‐view visibility for OF@TEIN+ distributed cloud‐native edge boxes

Abstract Maintaining knowledge of infrastructure has always been a key element to ensure the performance of complex distributed systems, being the first step to control quality of service, identify key performance bottlenecks, or make decisions about resource allocation and job scheduling, to name a few. In cloud‐native edge boxes, edge computing leverages cloud‐native applications to process/analyze some of their computing/storage own close to the location where the data is generated by a large number of heterogeneous devices. Some of the differences between this approach and more conventional architecture, like cloud are that in a distributed environment, these resources/applications have varying running conditions depending on resource availability, quality of network connection, and being geo‐distributed. These aforementioned challenges in operations, however, contribute to a requirement toward identifying efficient methods for monitoring operational environment with continuity in order to maintain and sustain services. In this article, we present setup and configuration of distributed resources with cloud‐native edge capabilities and having centralized connectivity in a specialized multi‐site cloud testbed. Second, we propose a solution to persistently maintain multi‐view (ie, resource‐layer and flow layer) visibility data (monitoring) collection under varying situations and environments. Third, we present verification results on a prototype implementation and interactive visualization support. This solution allows maintaining monitoring visibility data that conform to the specific demands of cloud‐native edge computing resources. We evaluate the research by maintaining visibility data of multi‐site cloud at 14 research institutes for several months and we demonstrate that it fulfills the requirements we previously enumerated.
Progressive Bayesian Filtering with Coupled Gaussian and Dirac Mixtures

Nonlinear filtering is the most important aspect in state estimation with real-world systems. While the Kalman filter provides a simple though optimal estimate for linear systems, feasible filters for general systems are still subject of intensive research. The previously proposed Progressive Gaussian Filter PGF42 marked a new milestone, as it was able to efficiently compute an optimal Gaussian approximation of the posterior density in nonlinear systems [1]. However, for highly nonlinear systems where true posteriors are “banana-shaped” (e.g., cubic sensor problem) or multimodal (e.g., extended object tracking), even an optimal Gaussian approximation is an inadequate representation. Therefore, we generalize the established framework around the PGF42 from Gaussian to Gaussian mixture densities that are better able to approximate arbitrary density functions. Our filter simultaneously holds approximate Gaussian mixture and Dirac mixture representations of the same density, what we call coupled discrete and continuous densities (CoDiCo). For conversion between discrete and continuous representation, we employ deterministic sampling and the expectation-maximization (EM) algorithm, which we extend to deal with weighted particles.
PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds

The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling; and (iv) other essential models to abstract a real datacenter. This paper describes modifications needed in the classical CloudSim to support realistic experimentations that closely match experimental outcomes in a real system. We extend, and partially re-factor CloudSim to “PerficientCloudSim” in order to provide support for large-scale computation over heterogeneous resources. In the classical CloudSim, we add several classes for workload performance variations due to: (a) CPU heterogeneities; (b) resource contention; and (c) service migration. Through plausible assumptions, our empirical evaluation, using real workload traces from Google and Microsoft Azure clusters, demonstrates that “PerficientCloudSim” can reasonably simulate large-scale heterogeneous datacenters in respect of resource allocation and migration policies, resource contention, and platform heterogeneities. We discuss statistical methods to measure the accuracy of the simulated outcomes.
A Family of Single-Channel Spectrally-Null-Constrained Sequences With Low Correlation

Spectrally-constrained sequences (SCSs) with good correlation properties, and low spectral power under the spectral constraint have found many practical applications in communication, and radar systems operating over non-contiguous carriers or frequency slots. In this letter, a construction of single-channel spectrally-null-constrained sequence (SNCS) families with good correlation is proposed. Following the proposed construction, a single-channel polyphase SNCS family with period N(N + 1), and family size (p - 1) can be obtained, which has zero power leakage underthe spectral constraint{1 + a(N + 1) : a ∈ ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> }, where N is an odd integer, and p is the minimum prime factor of N. Note that the maximum periodic correlation magnitude of the derived single-channel SNCS family is (N + 1), which asymptotically achieves the correlation lower bound as p increasing.
AwezaMed: A Multilingual, Multimodal Speech-To-Speech Translation Application for Maternal Health Care

The language contexts of multilingual developing countries such as South Africa are often characterised by communication challenges resulting from language differences. AwezaMed is a multilingual, multimodal speech-to-speech translation application for the health care domain, which was designed to assist in bridging communication barriers and mitigate the risks of miscommunication. The application focuses on the domain of maternal health care. It uses English as source language and Afrikaans, isiXhosa and isiZulu as target languages to enable health care providers to communicate with patients in their own language. It incorporates automatic speech recognition, machine translation and text-to-speech to deliver speech-to-speech translation functionality in a scalable way via a REST API to an Android mobile application. It is being piloted at various health care facilities across South Africa.
Reliability analysis and optimization of computer communication network based on genetic algorithm

Summary In this paper, the design of reliable communication network is solved by genetic algorithm. In order to solve this problem, we must first provide the lowest acceptable network reliability. There are many methods to satisfy the network reliability constraints. The penalty function may produce an invalid solution, so it is not suitable to solve the problem. In order to overcome these difficulties, this paper proposes a heuristic repair method, which is based on the number of spanning trees in the graph. Compared with the heuristic maintenance method with lower cost, this method can generate a better solution with less computation. When the population size is 25, the hybridization rate is 0.96, and the mutation rate is 0.07, the best results can be obtained. This paper analyzes the node fault, grasps the influence rule of the fault on the network, and improves the network reliability. This paper summarizes and classifies the failure modes of communication network nodes. Combined with the classic BA network model in the complex network theory, through the simulation design of MATLAB and OPNET, the influence of node function failure and performance failure on network reliability is studied.
A deep multimodal system for provenance filtering with universal forgery detection and localization

Traditional multimedia forensics techniques inspect images to identify, localize forged regions and estimate forgery methods that have been applied. Provenance filtering is the research area that has been evolved recently to retrieve all the images that are involved in constructing a morphed image in order to analyze an image, completely forensically. This task can be performed in two stages: one is to detect and localize forgery in the query image, and the second integral part is to search potentially similar images from a large pool of images. We propose a multimodal system which covers both steps, forgery detection through deep neural networks(CNN) followed by part based image retrieval. Classification and localization of manipulated region are performed using a deep neural network. InceptionV3 is employed to extract key features of the entire image as well as for the manipulated region. Potential donors and nearly duplicates are retrieved by using the Nearest Neighbour Algorithm. We take the CASIA-v2, CoMoFoD and NIST 2018 datasets to evaluate the proposed system. Experimental results show that deep features outperform low-level features previously used to perform provenance filtering with achieved Recall@50 of 92.8%.
HealFavor: Dataset and A Prototype System for Healthcare ChatBot

A chatbot is a software application aimed at simulating real-time conversations. This system has been designed to address a plethora of domains where they have proved themselves worthy to complement or in some areas replace human-based information acquisition. Though some domains like travel and food have advanced with the growing consumer demand, the healthcare-based system does require significant advancement to address the issue of medical accessibility. The work aims at providing a suitable dataset as well as proposes a prototype system architecture. The prototype system with the self-created dataset is then analyzed on different parameters by numerous experts.
Chatbot, the Future of Learning?

Our position is that, in order to improve the quality of Romanian education, an intelligent learning system could become a substantial and versatile tool for aiding the achievement of every student’s potential, always aiding and encouraging him/her. The limits of the conventional learning process and mental health are major issues in many education systems. Chatbots are centered on assisting humans in performing their tasks efficiently and require a low amount of digital literacy to interact with. Chatbots have been shown to be effective in the fields of education and well-being. Due to the substantial difference between artificial intelligence technology waves, the interaction process can occur in several ways depending on the communication interface. How can we combine both traditional and automated educational approaches in the digital age? On one hand, an artificial intelligence tutor does not get angry or annoyed explaining the same problem at the student’s discretion and can also function as a personal therapist, while a human professor could be empathic, trying to keep a balanced teaching method adapted to all students.
Hoare-Style Logic for Unstructured Programs

Enabling Hoare-style reasoning for low-level code is attractive since it opens the way to regain structure and modularity in a domain where structure is essentially absent. The field, however, has not yet arrived at a fully satisfactory solution, in the sense of avoiding restrictions on control flow (important for compiler optimization), controlling access to intermediate program points (important for modularity), and supporting total correctness. Proposals in the literature support some of these properties, but a solution that meets them all is yet to be found. We introduce the novel Hoare-style program logic $$\mathcal {L}_A$$ , which interprets postconditions relative to program points when these are first encountered. The logic can support both partial and total correctness, derive contracts for arbitrary control flow, and allows one to freely choose decomposition strategy during verification while avoiding step-indexed approximations and global invariants. The logic can be instantiated for a variety of concrete instruction set architectures and intermediate languages. The rules of $$\mathcal {L}_A$$ have been verified in the interactive theorem prover HOL4 and integrated with the toolbox HolBA for semi-automated program verification, making it applicable to the ARMv6 and ARMv8 instruction sets.