Reviewerly/exHarmony huggingface dataset:
QoE-Aware Energy Efficient Hierarchical Small Cell Deployment for Multimedia IoT Services

Traffic proliferation induced by mobile video streaming has been evoking tremendous challenges to resource management in 5G Internet-of-Things (IoT) networks. Although small cell based heterogeneous network is capable of traffic offloading, it still requires a systemic deployment with optimal allocation and resource management to deal with high data rates and network densification of mobile video streaming users. In this paper, we propose a two-tier hierarchical small cell based heterogeneous network infrastructure to tackle the aforementioned challenges. In particular, we develop an offline tier for the long-term small cell allocation, formulated with energy efficiency and predicted quality of experience (QoE) maximization. We also propose an online tier, characterizing the real-time on-demand network-slicing based small cell deployment, to optimize the dynamic QoE of users. Simulation results show that the proposed scheme achieves optimal energy efficiency and real-time improved QoE performance simultaneously, for different scale of heterogeneous IoT networks with dynamic mobile video streaming requirements by users, and clear advantages in highly dense networks with high percentage of video streaming services in IoT HetNets.
Multicriteria Evaluation of Deep Neural Networks for Semantic Segmentation of Mammographies

Breast segmentation plays a vital role in the automatic analysis of mammograms. Accurate segmentation of the breast region increments the probability of a correct diagnostic and minimizes computational cost. Traditionally, model-based approaches dominated the landscape for breast segmentation, but recent studies seem to benefit from using robust deep learning models for this task. In this work, we present an extensive evaluation of deep learning architectures for semantic segmentation of mammograms, including segmentation metrics, memory requirements, and average inference time. We used several combinations of two-stage segmentation architectures composed of a feature extraction net (VGG16 and ResNet50) and a segmentation net (FCN-8, U-Net, and PSPNet). The training examples were taken from the mini Mammographic Image Analysis Society (MIAS) database. Experimental results using the mini-MIAS database show that the best net scored a Dice similarity coefficient of 99.37% for breast boundary segmentation and 95.45% for pectoral muscle segmentation.
Real-time Projection of Lip Animation onto Face Masks using OmniProcam

This paper describes an OmniProcam system, which enables 360 degree horizontal projection by a fisheye lens with a coaxial procam in which the optical axes of the camera and projector are exactly matched. Combined with 2D marker recognition, the OmniProcam can display images onto screens at arbitrary positions in 3D space. As an example application, we developed a system which projects lip animation onto the user’s face masks for better communication at the physical meeting in current COVID-19 situation. The system recognizes the user’s speech, generates the lip animation using Lipsync, and projects the animation onto the user’s face masks.
Evaluation of Teaching Performance in the Virtual Teaching-Learning Environment, from the Perspective of the Students of the Professional School of Mechanical Engineering

The objective of this article is to evaluate the effect that virtual teaching-learning generates in the evaluation of teaching performance, from the perspective of the students, of the professional mechanical engineering school. During the devel-opment of the research, it has been determined that the planning, communication and overall performance factors of the teaching performance have experienced a positive variation of 63.64%, increasing by 3.8%, when moving from the face-to-face environment to the virtual teaching-learning environment. While the didactic strategies, organization and subject mastery factors have experienced a positive variation of 59.09%, this increase represents 1.92%. Although a positive effect has been generated on teacher performance, it has been determined that, of the 6 factors, it is the Communication factor which has presented the greatest negative variation, being of the total of 22 teachers, 11 who have decreased their qualification.
More results about a class of quadrinomials over finite fields of odd characteristic

Let Fq denote the finite field with q elements. In this paper, we study the permutation property of the class of quadrinomials x3+axq+2+bx2q+1+cx3q∈Fq2[x], where char(Fq)∈{3,5}. With some additional assumption on the coefficients, we propose several new necessary and sufficient conditions on the coefficients that yield permutation quadrinomials.
Hybrid Words Representation for the classification of low quality text

Language enables humans to communicate with others. For instance, we talk, give our opinions and suggestions all using natural language; to be more precise, we use words while communicating with others. However, in today's world, we wish to communicate with computers, just like humans. It is not an easy task because human communicate in an unstructured and informal way, whereas computers need structured and clean data. So it is essential for computers to understand and classify text accurately for proper human-computer interactions. For classifying a text, the first question we must address is how to improve the low-quality text. The next immediate challenge is to have the best representation so that text can be classified accurately. The way text is organized reflects polysemy, semantic and syntactical coupling relationships which are embedded in its contents. The effective capturing of such content relationships is thereby crucial for a better understanding of text representations. This is especially challenging in the environments where the text messages are short, informal and noisy, and involves natural language ambiguities. The existing sentiment classification methods are mainly for document and clean textual data which can not capture relationship, different attributes and characteristics within tweet messages.Social media analysis, especially the analysis of tweet messages on Twitter has become increasingly relevant since the significant portion of data is ubiquitous in nature. The social media-based short text is valuable for many good reasons, explored increasingly in text analysis, social media analysis and recommendation. In the same time, there is a number of challenges that need to be addressed in this space. One of the main issues is that the traditional word embeddings are unable to capture polysemy (assigns the same representation of a word irrespective of its context and meaning) and out of vocabulary words (assigns a random representation). Furthermore, traditional word embeddings fail to capture sentiment information of words which results in similar word vector representations having the opposite polarities. Thus, ignoring polysemy within the context and sentiment polarity of words in a tweet reduces the performance for tweets classification.In order to address the above-mentioned research challenges and limitations associated with word-level representations, this thesis focuses on improving the representation of low-quality text by improving the unstructured and informal nature of tweets to utilize the information thoroughly and manages the natural language ambiguities to build a more robust sentiment classification model. As compared to previous studies, the proposed models can deal with the ubiquitous nature of the short text, polysemy, semantic and syntactical relationships within a content, thereby addressing the natural language ambiguity problems.Chapter 4 presents the effects of pre-processing techniques using two different word representation models with the machine and deep…
A fast physical layer security-based location privacy parameter recommendation algorithm in 5G IoT

The 5G IoT (Internet of Things, IoT) is easier to implement in location privacy-preserving research. The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most existing spatial cloaking algorithms cannot work well because of man-in-the-middle attacks, high communication overhead, time consumption, and the lower success rate. This paper proposes an algorithm that can recommend terminal's privacy requirements based on getting terminal distribution information in the neighborhood after cross-layer authentication and therefore help 5G IoT terminals find enough collaborative terminals safely and quickly. The approach shows it can avoid man-in-the-middle attacks and needs lower communication costs and less searching time than 520ms at the same time. It has a great anonymization success rate by 93% through extensive simulation experiments for a range of 5G IoT scenarios.
Research on Accounting Information Security Management Based on Blockchain

At present, accounting information presents various and complex characteristics, which leads to the decline in the comprehensive scheduling level of accounting information security management system. For this problem, a blockchain-based accounting information security management information model is designed. This paper constructs the blockchain accounting information security association blockchain Big Data analysis model and processes the sample data, uses the semantic rough feature matching method to decompose the characteristics of blockchain accounting information, realizes the feature information fusion and autocorrelation feature matching and finally reorganizes and manages the blockchain accounting information security. The simulation results show that this method has better comprehensive scheduling ability, information fusion scheduling ability is greater than 92%, convergence is greater than 91.8%, feature recognition rate is greater than 90.1%, and management accuracy is greater than 95.6%. The design method can effectively improve the security and stability of accounting information storage and management.
The DNA of VUCA

“The DNA of VUCA: A Framework for Building Learning Agility in an Accelerating World” presents a practical guide to building learning agility and resilience for leaders to thrive in the face of crisis, disruption, complexity, and exponential change. It decomposes the term <italic>VUCA</italic> into two elements<italic>—</italic>volatility and complexity (which are best understood and addressed rationally) and uncertainty and ambiguity (which typically generate strong emotional reactions such as fear, stress, and anxiety and are best addressed by communication, emotional support, and human connection). Preparing leaders to thrive in VUCA times requires that they increase their exposure to diverse, novel, and adverse experiences (DNA) so they build capabilities and comfort in making decisions and taking decisive action under conditions of uncertainty and risk and projecting confidence and clarity in leading people through times of doubt and distress. In addition, leaders need to build their capacity to reflect on their experiences to see meaningful patterns and make informed choices about the best course of action going forward.
Comparative Experimental Investigation and Application of Five Classic Pre-Trained Deep Convolutional Neural Networks via Transfer Learning for Diagnosis of Breast Cancer

In this study, for the diagnosis and classification of breast cancer, the authors used and applied five classical pretrained deep convolutional neural network models (DCNN) which have proven successful many times in different fields AlexNet, GoogleNet and SuffleNet).In order to make pre-trained DCNN models suitable for the purpose of this study, some layers were updated according to the new situation by using the transfer learning technique.The weights of all layers used in these five pre-trained DCNN models were not changed.Instead, new weights were given to the new layers so that new layers adapt faster to the emerging new DCNN models.With these five pre-trained DCNN models, a quadruple classification as "cancer", "normal", "actionable" and "benign", and a binary classification as "actionable + cancer" and "normal + benign" was realized.With these two separate classification and diagnosis studies, comparative experimental examination and analysis of pre-trained DCNN models for breast cancer diagnosis were carried out.In the study, it was concluded that successful results can be achieved with pre-trained DCNN models without extra time-consuming procedures such as feature extraction, as well as DCNN can perform quite successfully in cancer diagnosis and image comment.
Detection and Localization of Mask Occluded Faces by transfer learning using Faster RCNN

Here in this work we utilized the novel transfer learning based technique, where a pretrained network model weights are used to train our Faster Region Convolutional Neural Network (FRCNN). With the help of captured mask-face dataset, we further propose a masked face detection and recognition work. It incorporate three major network training modules. Proposed network module cascade two pre-trained CNNs to extract target facial features and strong region of interest (ROI) from the input dataset image for transformed domain represention with higher spatial descriptors. By similar method, omitted facial feature cues are sufficiently reconstructed and the distortion introduced by noise pixs cues by masks are reduced. Lastly, the pre-trained CNN module is used to detect and identify masked face regions of the image.The network estimates mask detection accuracy by putting bounding boxs around all the occluded mask face for performing the classification and line regression work. Experiments conducted on proposed dataset shows good accuracy and reduced running time overfew state-of-the-arts by 11.46 %.
Toward Efficient Online Scheduling for Distributed Machine Learning Systems

Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. However, the proliferation of distributed ML frameworks also introduces many unique technical challenges in computing system design and optimization. In a networked computing cluster that supports a large number of training jobs, a key question is how to design efficient scheduling algorithms to allocate workers and parameter servers across different machines to minimize the overall training time. Toward this end, in this paper, we develop an online scheduling algorithm that jointly optimizes resource allocation and locality decisions. Our main contributions are three-fold: i) We develop a new analytical model that considers both resource allocation and locality; ii) Based on an equivalent reformulation and observations on the worker-parameter server locality configurations, we transform the problem into a mixed packing and covering integer program, which enables approximation algorithm design; iii) We propose a meticulously designed approximation algorithm based on randomized rounding and rigorously analyze its performance. Collectively, our results contribute to the state of the art of distributed ML system optimization and algorithm design.
Too many cooks: Coordinating multi-agent collaboration through inverse planning

Collaboration requires agents to coordinate their behavior on the fly,sometimes cooperating to solve a single task together and other times dividingit up into sub-tasks to work on in parallel. Underlying the human ability tocollaborate is theory-of-mind, the ability to infer the hidden mental statesthat drive others to act. Here, we develop Bayesian Delegation, a decentralizedmulti-agent learning mechanism with these abilities. Bayesian Delegationenables agents to rapidly infer the hidden intentions of others by inverseplanning. These inferences enable agents to flexibly decide in the absence ofcommunication when to cooperate on the same sub-task and when to work ondifferent sub-tasks in parallel. We test this model in a suite of multi-agentMarkov decision processes inspired by cooking problems. To succeed, agents mustcoordinate both their high-level plans (e.g., what sub-task they should workon) and their low-level actions (e.g., avoiding collisions). BayesianDelegation bridges these two levels and rapidly aligns agents' beliefs aboutwho should work on what without any communication. When agents cooperate on thesame sub-task, coordinated plans emerge that enable the group of agents toachieve tasks no agent can complete on their own. Our model outperformslesioned agents without Bayesian Delegation or without the ability to cooperateon the same sub-task.
Research on Audit Data Analysis under the Background of Big Data

With the arrival of the era of big data, the audit thinking mode has been promoted to change. Under the influence of big data, audit will become an activity of continuous behavior. Through cloud data, the staff can control the operation status and risk assessment of the whole enterprise, timely analyze, control and respond to risks, and protect the enterprise to reduce risks. With the advent of the era of big data, audit data analysis is becoming more and more important. At the same time, a large amount of data analysis also brings challenges to auditors. Methods to deal and solve the challenges has become an urgent problem to be solved at present. This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data, so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.
A Learning-Based Hybrid Framework for Dynamic Balancing of Exploration-Exploitation: Combining Regression Analysis and Metaheuristics

The idea of hybrid approaches have become a powerful strategy for tackling several complex optimisation problems. In this regard, the present work is concerned with contributing with a novel optimisation framework, named learning-based linear balancer (LB2). A regression model is designed, with the objective to predict better movements for the approach and improve the performance. The main idea is to balance the intensification and diversification performed by the hybrid model in an online-fashion. In this paper, we employ movement operators of a spotted hyena optimiser, a modern algorithm which has proved to yield good results in the literature. In order to test the performance of our hybrid approach, we solve 15 benchmark functions, composed of unimodal, multimodal, and mutimodal functions with fixed dimension. Additionally, regarding the competitiveness, we carry out a comparison against state-of-the-art algorithms, and the sequential parameter optimisation procedure, which is part of multiple successful tuning methods proposed in the literature. Finally, we compare against the traditional implementation of a spotted hyena optimiser and a neural network approach, the respective statistical analysis is carried out. We illustrate experimental results, where we obtain interesting performance and robustness, which allows us to conclude that our hybrid approach is a competitive alternative in the optimisation field.
Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning

While microscopy enables material scientists to view and analyze microstructures, the imaging results often include defects and anomalies with varied shapes and locations. The presence of such anomalies significantly degrades the quality of microscopy images and the subsequent analytical tasks. Comparing to classic feature-based methods, recent advancements in deep learning provide a more efficient, accurate, and scalable approach to detect and remove anomalies in microscopy images. However, most of the deep inpainting and anomaly detection schemes require a certain level of supervision, i.e., either annotation of the anomalies, or a corpus of purely normal data, which are limited in practice for supervision-starving microscopy applications. In this work, we propose a self-supervised deep learning scheme for joint anomaly detection and inpainting of microscopy images. The proposed anomaly detection model can be trained over a mixture of normal and abnormal microscopy images without any labeling. Instead of a two-stage scheme, our multi-task model can simultaneously detect abnormal regions and remove the defects via jointly training. To benchmark such microscopy application under the real-world setup, we propose a novel dataset of real microscopic images of integrated circuits, dubbed MIIC. The proposed dataset contains tens of thousands of normal microscopic images, while we labeled hundreds of them containing various imaging and manufacturing anomalies and defects for testing. Experiments show that the proposed model outperforms various popular or state-of-the-art competing methods for both microscopy image anomaly detection and inpainting.
Media as a Source of Popular Science Information during COVID-19 Pandemic

Popular science journalism is an effective method of communication between the scientific community and ordinary people. In the article it is considered popular science media as an important part of mediaspace. On the basis of overview of Ukrainian media, it is done the analysis of the current situation with popular science information in media. To understand the expectations of audience it was conducted the survey (N = 200). Thus it was determined the role of scientific information for the audience, as well as identified relevant methods of presenting scientific information. The survey showed that popular science journalism plays an important role in the lives of Ukrainians. Especially it is actual during world pandemic, when citizens want to know about the newest means of treating from COVID-19. Most respondents, as expected, actively use the Internet to learn about the latest advances in science and technology. As it turned out, Ukrainians read not only domestic publications, but also foreign ones, but there are still respondents who are poorly oriented in the topic, are not informed in the current situation with new in science and in particular with the COVID-19 researchings. Copyright © 2021 by Academic Publishing House Researcher s.r.o.
A Computable Gaussian Quantum Correlation for Continuous-Variable Systems

Generally speaking, it is difficult to compute the values of the Gaussian quantum discord and Gaussian geometric discord for Gaussian states, which limits their application. In the present paper, for any (n+m)-mode continuous-variable system, a computable Gaussian quantum correlation M is proposed. For any state ρAB of the system, M(ρAB) depends only on the covariant matrix of ρAB without any measurements performed on a subsystem or any optimization procedures, and thus is easily computed. Furthermore, M has the following attractive properties: (1) M is independent of the mean of states, is symmetric about the subsystems and has no ancilla problem; (2) M is locally Gaussian unitary invariant; (3) for a Gaussian state ρAB, M(ρAB)=0 if and only if ρAB is a product state; and (4) 0≤M((ΦA⊗ΦB)ρAB)≤M(ρAB) holds for any Gaussian state ρAB and any Gaussian channels ΦA and ΦB performed on the subsystem A and B, respectively. Therefore, M is a nice Gaussian correlation which describes the same Gaussian correlation as Gaussian quantum discord and Gaussian geometric discord when restricted on Gaussian states. As an application of M, a noninvasive quantum method for detecting intracellular temperature is proposed.