Reviewerly/exHarmony huggingface dataset:
Construction of Attribute Dataset with SNS Mining for Generic Object Recognition

This paper proposes a framework to automatically construct an attribute dataset with images and tags on the Social Network Service (SNS). In the field of general object recognition, attributes that represent characteristics and categories of objects (e.g., ‘cat’ has ‘white’ and ‘cute’ as its attributes) showed high effectiveness. In the existing study, the attributes have been prepared by researchers, and the costs of the annotation and the subjectivity of the annotator should be the issues to construct attribute dataset. In this paper, we verified the feasibility that tags on the SNS could be assumed the attributes. Based on the study, the proposed method automatically collects images and their corresponding attributes from the SNS; the attributes satisfy a)understandable for humans, b) understandable for computer, and c) available on multiple categories. The experiments confirmed the effectiveness of the attribute datasets constructed by the proposed method in the generic object recognition; each dataset from Instagram and Flicker showed 71.6% and 79.3% accuracy, respectively. It was suggested that the attribute datasets that the proposed method automatically constructed showed almost the same effectiveness as the human-created dataset.
Research, Marketing, and Managing Resources

This chapter falls into these related parts. The first deals with research: why it is different today than before, why a library is no longer essential, as well as the training of faculty members to become good researchers. Strong, relevant research is a key driver for marketing, and the Dean/President typically would play a key role here, above all by communicating effectively key research findings. The Dean/President is typically also critical in finding a reasonable balance when it comes to how much resources that is spent on research verses on marketing.
RE2B: Enhancing Correctness of Both Requirements and Design Models

Software requirements are the criteria for the correctness of the software behaviors. How to verify the correctness of the requirements is a big challenge in requirements engineering. Among various requirements approaches, the environment modeling based requirements engineering(EBRE) gains great popularity due to its explicit environment model. However, it still lacks of a formal approach to verify the correctness of the requirements models proposed in EBRE. Event-B is a popular formal method employing step-wise refinement to construct system models and verify their correctness according to the system requirements. This paper combines EBRE with Event-B to merge the advantages of the two methods. On the one hand, we transform the EBRE models to the initial Event-B model to guide the further design and development of the system. On the other hand, the transformed Event-B model can provide formal proof on the consistency of the requirements model. We also implement a plug-in tool on the Rodin platform to realize the automatic transformation from EBRE models to Event-B model and to commit a formal verification on the correctness of the transformed requirements model.
A Modified Low Rank Learning Based on Iterative Nuclear Weighting in Ripplet Transform for Denoising MR Images

In recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated systematically based on the WNNM problem in input MR images, and some others are defined according to the problem situations. The proposed method is compared with recent state-of-the-art denoising methods by the synthetic and actual MR image datasets in the presence of the Rician and Gaussian noises. The three experimental outcomes investigate the ability of the proposed method in reducing the noise in PSNR-range [0.5-3] dB and enhance the similarity performance with range [1]–[10] percent in comparison to the other methods.
The properties of particleboards made from corn stalks and bagasse at various compositions

Abstract Corn stalks and bagasse are materials that contain lignocellulose and have the opportunity to be used as raw material for particleboard manufacturing. This study aims to analyze the effect of the composition of corn stems and bagasse on the physical and mechanical properties of particleboards. The composition of corn stalk and bagasse as raw material for making particleboard in this study consists of several ratios, namely 100/0, 80/20, 60/40, 40/60, 20/80, and 0/100. The adhesive used in this research was isocyanate. The thickness and target densities were 1 cm and 0.70 g/cm 3 , respectively. The results showed that the board density value did not reach the target, and the thickness swelling of the board was still quite high. Board with a composition of a mixture of corn stems and bagasse at an 80/20 ratio is a board that has better properties compared to other ratios.
Young Russians: are they prepared to cybernatization of their own body?

The purpose of this paper is to acquaint readers with the research conducted by students and employees of the State Technical University N.E. Bauman, who reveals the current attitudes of young generation to the issue of cybernatization of their own body. Which part of the body they are ready to transform? How do they visualize this process? Will there be some age, gender, cultural, differences in relations to the parts of the body they want to change? Are there any specific features that distinguish Russians from others in the processes of body cybernatization? This is what this paper says.
Concepts as decision functions. The issue of epistemic opacity of conceptual representations in artificial computing systems

The treatment of concepts as decision functions characteristic for computer science and Artificial Intelligence has its roots in the logical tradition of Gottlob Frege. In its modern incarnation, decision functions, embedded in concrete knowledge representations, describe either crisp or vague assignments of categorization decisions to objects. In order to allow for effective communication between humans and computer systems, these decisions should be either effectively explained by the system or explainable with respect to the system, which in turn depends on the degree of epistemic transparency of the computer system and the conceptual representations it uses. In this, paper we distinguish between two basic types of representation: logic-based and nature-based. With respect to the latter, we identify five contributing factors to their relative epistemic opacity: A. structural complexity, B. learning procedures, C. operating on uncertain data, D. lack of translation procedures for low-level rules and representations, and E. lack of complete knowledge about natural computing systems. We consider the fourth factor (D.) to be the most important source of opacity on the level of computer systems and the fifth (E.) the main contributing factor on the level of human knowledge.
Research on the Function of Intelligent Service Platform of Library Based on Computer

Abstract The development of library is gradual, and every major change is accompanied by the innovation and progress of science and technology. With the formal proposal of the concept of Internet of Things and its rapid spread to the whole world, in the library field, following the composite library and digital library, the intelligent library based on information technology, which is marked by digitalization, networking and intelligence is coming into people's vision. So, this paper will lead you to explore the functions of intelligent library service platform based on computer.
Optimizing the Flight Route of UAV Using Biology Migration Algorithm

Abstract Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. Simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms.
Human Fall Detection Algorithm Based on Mixed Attention Mechanism

The health of older people is always a matter of concern, and falls can cause injuries and even death in severe cases. At present, the fall detection algorithm based on computer vision has a large amount of computation and is easy to be interfered by shielding objects. In addition, there are also problems such as a high rate of missed detection and poor real-time performance. This paper proposes a fusion hybrid attention mechanism of human body fall detection algorithm, a YOLOv3-tiny as a benchmark algorithm, add channel attention in the process of feature extraction and spatial attention mechanism, using the channel the different characteristics of attention to change the network the attention of the weight, the spatial attention change characteristics of pixels of attention weights in the figure, by increasing attention in the process of the fall detection selectivity of focusing not obscured human body parts, filter other essential features. Experimental results show that the optimized human fall detection algorithm improves the mAP of the test set by 8.93% compared with the benchmark YOLOv3-tiny algorithm and can also solve the problem of obstructing the human body with obstacles. In addition, this algorithm can detect falling and fall to the ground in real-time. On average, the falling action is detected 625.58ms in advance, and the FPS is 45.35. Therefore, it has practical feasibility and effectiveness.
Effect (love, respect, appreciation) to the student's educational achievement (An exploratory study in the schools of Baghdad governorate)

The research discusses the problem of raising the scientific level of students by creating a leadership relationship between the student and the teacher governed by (love, respect, appreciation), and from here came the objectives of the research to test the relationship of influence between love, respect, appreciation and the scientific level of the student, and for that purpose a scheme was designed Hypothesis explains the nature of the relationship between its variables. In order to test the research hypotheses, it was applied based on a questionnaire that was designed to collect the required data. The sample size was 190 students in the sixth preparatory grade distributed among six different schools affiliated with the education of Baghdad / Karkh II. The effect relationship was tested using a set of statistical methods based on the statistical program (SPSS.V. 26).The research reached a set of conclusions, the most important of which is the existence of a positive statistically significant  effect between (love, respect, appreciation) and the Student's academic achievement.
Supervised Non-negative Matrix Factorization Induced by Huber Loss

Non-negative matrix factorization (NMF) has the ability for non-negative feature extraction and is successfully exploited for parts-based image representation. Most NMF-based algorithms utilize loss function with \(l_2\)-norm or Kullback-Leibler divergence to evaluate the quality of factorization. However, these measurements are sensitive to noise and outliers. Also, NMF is an unsupervised learning method and thus cannot acquire the desired performance in classification tasks. To address the problems of the NMF algorithm, this paper proposes a supervised non-negative matrix factorization (HSNMF) approach using Huber loss function, which is more robust to noise and outliers than \(l_2\)-norm. To enhance the discriminative power of NMF, we establish the objective function by incorporating two quantities including intra-class and inter-class information into the Huber loss function. The updating rules of HSNMF are derived using KKT conditions. The proposed HSNMF is shown to be convergent via the auxiliary function technique. Experimental results on face recognition demonstrate the robustness and superior performance of our algorithm when compared with the state-of-the-art algorithms.
Deep CNN with Residual learning and Dilated Convolution for Image Denoising

In the field of digital image processing, filtering noise from the image to extract a high-quality image is an important pre-processing step for object detection, segmentation, tracking, etc. Convolutional Neural Network (CNN) has gained great attention in the domain of Image Denoising, with its flexible architecture. There are some challenges in using the deep CNNs for image denoising tasks like training the model and performance saturation when the depth of the network is increased. In the proposed model, two networks are connected parallelly to increase the width of the network rather than depth, and hence obtain more context information and make it less prone to performance saturation. Batch Normalization improves the performance of the network and mitigates the internal covariate shift problem. Dilated convolutions are used in the proposed model to extract more features by enlarging the receptive field. Residual learning is adopted to overcome exploding gradient and vanishing gradient problems. Experimental results have shown that our proposed model is more efficient than many existing filters.
Dynamic Optimization Algorithm Based on Multi-modal Niche Lion Swarm by Sensitive Individuals

Abstract Aiming at the problem that the model of complex dynamic system which is difficult to track its global optimal solution, this paper proposes a multi-modal niche lion swarm optimization algorithm based on sensitive individuals to solve the optimization problem of complex dynamic system. Sensitive individuals are set in the lion swarm algorithm to perceive changes in the dynamic environment, and to track the global optimal value in time. It combines with niche technology to increase the diversity of the population and avoids falling into local extremes. The dynamic environment is simulated by the dynamic function generator and compared with other optimization algorithm to verify the superiority of the algorithm proposed in this paper.
Global Trends and Transitions in Think Tanks, Politics, and Policy Advice in the Age of Policy Dilemmas and Disruptions

Throughout the twentieth century think tanks stood at the forefront of academic work examining global developments, collecting and analyzing information to provide important insights that form and guide policy- and decision-making. However, the general trend of globalization, as well as digitalization, informatization, and changes in how information is obtained and consumed, has placed think tanks at a disadvantage. These trends are undermining the authoritative position of think tanks and requiring them to find new and innovative ways to present information so that they remain relevant in today’s world. Think tanks are uniquely equipped to provide policy suggestions that governments and business need to make correct and effective decisions. However, crises like COVID-19 also present new obstacles for think tanks in terms of how to communicate information and promote engagement to ensure that they remain relevant and valuable. Over the last decade, forces have redefined the strategy and structure of many think tanks. This momentum is primarily driven by changes in politics, how think tanks are funded, and advances in technology and communications. During this period, there have been five key trends originating in the fourth industrial revolution that will transform jobs and lives over the next 10 years.
Data Center Networks: Address Scheming, Traffic Distribution, and Load Balancing

Data center networks are a critical part in building a powerful data center, while tasks may start at any container and VMs may move around in physical machines as well. Not mentioning network traffic should be balanced while using network resources such as switches as efficient as possible. This paper summarizes two papers that talks about the data center networks. The first section summarizes a paper that describes efficient protocols in data centers, PortLand[1], where VMs could move to new physical machines without breaking TCP connection and achieve fast plug-and-play for new machine. The second section summarizes a survey paper which studies one of Facebook's data centers that provides web services. It mainly focuses on network traffic stability on server hosts and the traffic patterns on switches with different responsibilities.
Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Abstract Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.
Development Survey of a Monitoring and Early Warning System for Banks and Dams of Expansive Soil

Expansive soil is a kind of soil with special properties, widely distributed, difficult treatment technology, and affecting the engineering safety of banks and dams. It is particularly necessary to summarize the development status and trend of the monitoring and warning system of banks and dams for expansive soil based on the characteristics of expansion soil. Based on extensive literatures, the progress of the hardware and software were systematically analyzed for the monitoring and warning system of banks and dams for expansive soil. Specially, the hardware mainly includes: sensors, data acquisition, data transmission, and postprocessing platform and so on; The software mainly includes: multi-sensor integration, data sharing, post-processing, human-computer interface, and route planning of unmanned aerial vehicle and so on. Accordingly, the research directions of the hardware and software of monitoring and early warning system are put forward, and the results will be used for references in the development of monitoring and warning system, engineering construction, engineering management, and emergency management and so on for banks and dams of expansive soil.
A Bayesian approach to distributed optimal filtering over a ring network

This paper is concerned with the state estimation over a sensor network. Distributed estimation algorithms enable us to estimate the system state using the information from other sensors, even when the state is not completely observable from some sensors. The extension of the Kalman filter to the distributed case has been actively studied for the last decade. Many of the previous distributed Kalman filtering algorithms were proposed by incorporating a consensus control in the observer structure. However, these methods do not guarantee the optimality of the state estimates, and it turns out that their estimation accuracy will be significantly deteriorated even for a very simple network topology. To overcome this difficulty, we formulate the distributed state estimation problem based on the Bayesian inference. Then, we derive the optimal distributed estimation algorithm for a sensor network with the ring topology. The effectiveness of the proposed algorithm is also investigated by numerical experiments.
Preparing and simplifying method for pedestrian object training set based on surveillance video

Object detection has a wide range of applications in daily life and industrial fields. However, the success of object detection depends on a huge amount of manually labeled data. In this paper, based on the YOLO object detection model, two types of pedestrians are identified. After data enhancement and training, the performance of the model is analyzed. This paper also studies the simplification method for the training set, through the down-sampling method, continuously reduces the number of the training set, and finally obtains the simplification strategy when preparing the training set. This paper aims to provide a training set preparation and simplification method for object detection in a specific scene, so as to save computational cost and improve the efficiency of resource use.
Study on least squares identification algorithm with slope parameter search optimization for fuzzy cognitive map

To solve the identification of a fuzzy cognitive map (FCM) with the unknown slope parameter, we propose a least-squares algorithm with slope parameter optimization for FCM in this paper. Firstly, all possible structures of FCM are decomposed according to the column vectors of the adjacency matrix. Subsequently, a functional index function is constructed to evaluate the optimal structures and weights of FCM. Then, multiple rounds of optimization search for slope parameter are performed. The weights of all structures are estimated from the observed sample data, and the optimal solution of this round is selected by the minimum index value. The range of the slope parameter is compressed according to the optimal solution until the performance is satisfied. Finally, the experiment results show the effectiveness of the proposed algorithm.
Light Syntax Parsing and Fuzzy Systems for Rhetorical Structure Theory Text Segmentation

We present fuzzy boundaries, a method of segmenting text whereby we consider a population of boundaries to be in a fuzzy-state. Furthermore, we aim to use this method to present a multifaceted segmentation approach that is applicable across various domains that require the use of text segmentation: Text summerisation, Rhetorical Stricture Theory, sentence-based segmentation, paragraph-based segmentation and search-algorithms that require topic-based querying. The work first outlines the subject domain together with previous work surrounding the topic of text segmentation. We move to discussing the rationale, exploring our motivations for such a method. The model and its composition is then described next along side the direction taken in the implementation of the model. We move to the results and performance of our method concluding finally with a discussion on the benefits and justifications for our propositions.