Reviewerly/exHarmony huggingface dataset:
Identifying elephant flows using dynamic thresholds in programmable IXP networks

Abstract Internet eXchange Points (IXPs) are Internet infrastructures composed of high-performance networks that allow multiple autonomous systems to exchange traffic. Given the challenges of managing the flows that cross an IXP, identifying elephant flows may help improve the quality of services provided to its participants. In this context, we leverage the new flexibility and resources of programmable data planes to identify elephant flows in IXP networks adaptively via the dynamic adjustment of thresholds. Our mechanism uses the information reported by the data plane to monitor network utilization in the control plane, calculating new thresholds based on previous flow sizes and durations percentiles and configuring them back into switches to support the local classification of flows. Thus, the thresholds are updated to make the identification process better aligned with the network behavior. The experimental results show that it is possible to identify and react to elephant flows quickly, less than 0.4ms, and efficiently, with only 98.4KB of data inserted into the network by the mechanism. In addition, the threshold updating mechanism achieved accuracy of up to 90% in our evaluation scenarios.
WITHDRAWN: Classification of leukemia patients with different clinical presentation of blood cells

Leukemia – a type of blood cancer which was caused by abnormal growth of White Blood Cells (WBC) in bone marrow. Accurate identification of effected patients and diagnosing within time is most important otherwise it causes to death. Machine learning algorithms are most popular for accurate identification of patient status. Presenting 152 patients data which was clinically collected. In this paper, proposing Bagging with Random split function for classification of leukemia patients. Also compared the classifier performance with various ensemble techniques – Multiclass classifier, Logit Boost, Stacking and Random Committee classifiers. Experiments are conducted and proven that bagging with Random split function shows 95% accuracy on classification of leukemia disease dataset.
Dark-Aware Network For Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) is an emerging topic in high-level computer vision. Among existing methods, edge-only information based ones are convenient to use but incompetent in distinguishing among ambiguous samples. On the other hand, even though the methods using additional color information can significantly improve the retrieval performance, they are not convenient for practical use as the increase of sketch complexity. This paper fills the gap between these two kinds of methods by proposing a novel method, namely dark-aware sketch-based image retrieval (DA-SBIR), which incorporates the dark region information of images into FG-SBIR without a sacrifice of convenience. Our model consists of two branches to process edge structure and dark region, respectively. Specifically, instead of using real color, DA-SBIR only requires some additional free-hand black sketch to represent the dark region. Besides, a Split Generative Adversarial Network is introduced to automatically split a sketch into edge-structure-only and dark-region-only sketches. Moreover, we build a new clothes dataset, SJTU-Cloth, with more ambiguous samples for FG-SBIR. Experimental results on the QMUL-Shoe dataset and our SJTU-Cloth dataset show that our approach achieves consistent improvements over state-of-the-arts. Code and dataset are available at https://github.com/y2242794082/SBIR.git.
Responsiveness of the Sensor Network to Alarm Events Based on the Potts Model

The paper aims to present modelling the sensor network operation based on the Potts model. The authors presented own approach based on three states in which each node can be. The change in the state of a given node depends on its current state, the impact of surrounding nodes on it, but also values of the parameters measured. Therefore, the Hamiltonian was introduced as a dependence of both exceeding the limit value of a measured parameter (corresponding to an alarm event), and the state of the battery powering a given node of a sensor. The simulations of the implemented algorithm based on the adopted model presented in the paper relate to the measurement of temperature by a network of sensors. However, this model is universal and can be applied to examine the behaviour of the sensor infrastructure performing various measurements. Moreover, it may simulate the functioning of the critical network infrastructure or sensor networks and industrial sensors supporting the functioning of Industry 4.0.
3D Brain MRI Reconstruction based on 2D Super-Resolution Technology

Magnetic resonance imaging (MRI) is one of the most important diagnostic imaging methods, which is widely used in diagnosis and image-guided therapy, especially imaging diagnosis of the brain. However, MRI images have the characteristics of low resolution, and there are limitations such as long imaging time and noise. Super-resolution techniques have been studied on three-dimensional MRI images using three-dimensional convolutional neural network. Based on some related techniques of super-resolution reconstruction of two-dimensional MRI slices, we evaluated the capability of several super-resolution technologies. We utilize the super-resolution algorithm based on generative adversarial network ESRGAN to realize super-resolution reconstruction of two-dimensional MRI slices, and then we further demonstrate that frequent details can be obtained from ESRGAN. In the aspect of two-dimensional to three-dimensional reconstruction, we use the technique of two-dimensional super-resolution on slices from three different latitudes. We rebuild reconstructed two-dimensional images into a three-dimensional form. Then based on the principle of linear interpolation, we use the surrounding effective pixel values to interpolate the null value of each slice, and realize the reconstruction of three-dimensional brain MRI.
Main principles of designing the modern protection systems architecture

The problem of building a unified protection system based on qualitatively different sub-systems responsible for their classes of attacks is considered. The following five basic principles of designing architectures of such systems are proposed: artificial decomposition, necessity and sufficiency of subsystems, structural invariance, universalization of interaction, and a single in-formation space. Graphical diagrams of examples of application of the principles are given.
Hermitian Self-Dual GRS and Extended GRS Codes

Self-dual MDS codes over finite fields are linear codes with important cryptographic and combinatorial applications. In this correspondence, a different approach is proposed to obtain Hermitian self-dual MDS codes from (extended) generalized Reed-Solomon (abbreviation to GRS and EGRS) codes. The necessary and sufficient conditions of a Hermitian self-dual (extended) GRS code are presented. With the help of this new method, we revise and improve the results in Niu and Yue <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (IEEE Communications Letters, 23(5): 781-784, 2019). Furthermore, we suppose that there exist Hermitian self-dual (extend) GRS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q^{2}}$ </tex-math></inline-formula> if and only if their code length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\leq q+1$ </tex-math></inline-formula> .
Automatic analysis of passive constructions in Korean

Abstract The present study aims to explore the applicability of automatic analysis to L2-Korean learner corpora, with a special focus on learners’ use of a clause-level construction. For this purpose, we investigate L1-Mandarin L2-Korean learners’ written production of two passive construction types in Korean – suffixal and periphrastic – by devising a pattern-extraction process through NLP techniques. We focus on reporting how the passive constructions are identified and extracted from learner writing automatically, given language-specific features involving the passive. A total of 72 essays were analysed by adapting an existing pipeline (developed by Shin, forthcoming ), with enhanced tokenisation and annotation through manual revision of the data. Results showed that our automatic pattern-finder identified more instances than manual extraction for the suffixal passive and yielded a perfect match with manual extraction for the periphrastic passive. Implications of the findings are discussed in regard to strengths and drawbacks of the automatic analysis of learner writing, with suggestions for improving currently available tools for learner corpus research in Korean.
University faculty motivation to students’ distant evaluation

During the period of remote learning in Russia, caused by measures to combat coronavirus infection in 2020, many university professors realized that dissatisfaction with the quality of their teaching activities, the teaching methods and the conditions in education was carried out, significantly reduced their motivation to teaching activity. The analysis of scientific literature showed that there is a lack of research on the motivation of university faculty, which is explained by the public attitude towards the initially high motivation of professors and the lack of methodological tools for conducting research. We used the following research methods: competence-based and system-activity approaches, as well as several theories of motivation developed by Russian and foreign scientists. Realizing that faculty motivation is one of the main factors that can ensure high quality education through the introduction of advanced teaching technologies, the article presents the results of an empirical study that made it possible to determine four main groups of negative factors that affect faculty motivation to carry out distant evaluation of the students. The recommendations are formulated for the university faculty, educational and methodological departments and administration of universities, which can contribute to the growth or maintenance of the faculty motivation both in “face-to-face” and distant educational process.
Reconstructing Normal Section Profiles of 3-D Revolving Structures via Pose-Unconstrained Multiline Structured-Light Vision

Reconstructing normal section profiles is an effective approach at monitoring the shape of a 3-D revolving-symmetry structure. The existing reconstruction methods typically require a sensor to work in a constrained pose and thus suffer from poor flexibility and limited view-angle. This article proposes a pose-unconstrained normal section profile reconstruction framework for 3-D revolving-symmetry structures, which estimates the normal section profile using multiple 3-D general section profiles acquired by a multiline structured light vision sensor. First, we establish a model to estimate the revolving axis and calculate the normal section profile using corresponding points. Then, the model is embedded into an iterative algorithm to optimize the corresponding points and calculate the accurate normal section profile. Simulations show that our algorithm is applicable to 3-D revolving structures of various shapes and is robust against noise and local surface defects. Real experiments were conducted on reconstructing the normal section profile of a 3-D wheel. The results demonstrate that our algorithm reaches the mean precision of 0.065 mm and repeatability with the STD of 0.007 mm. It is also robust to pose and position of the sensor.
Class label autoencoder with structure refinement for zero-shot learning

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) for recognizing unseen classes. However, the projection function is difficult to generalize the relationship description between the feature space and multi-semantic embedding spaces(all kinds of class names are embedded as different vectors in terms of the various semantic interpretation, for example attributes, word vectors, global vectors and hierarchical embedding), which have the diversity characteristic for detailing the different semantic information of the same class. To deal with this issue, we present a novel method to ZSL based on learning class label autoencoder with structure refinement(CLASR). CLASR can not only build a scalable framework for adapting to multi-semantic embedding spaces, but also utilize the encoder-decoder paradigm for constraining the bidirectional projection between the feature space and the class label space. Moreover, CLASR can focus on the fusion model between the relationship of feature classes (visual feature classes structure) and the relevance of the semantic classes (semantic classes structure) for improving zero-shot classification by structure refinement. The CLASR solution can provide both unseen class labels and the relation of the different classes structure(visual feature and semantic classes structure) that can encode the intrinsic structure of classes. Extensive experiments demonstrate the CLASR outperforms the state-of-art methods on four benchmark datasets, which are AwA, CUB, Dogs and ImNet-2.
A Graph Based Approach to Automate Essay Evaluation

Despite studies of over six decades, research on automated essay scoring continues to grab ample attention in the Natural Language Processing (NLP) community in part because of its commercial and educational value. However, evaluating such writing compositions or essays in terms of reliability and time is a very challenging process. The need for reliable and rapid scores has elevated the need for a computer system that can answer essay questions that fit precise prompts automatically. NLP and machine learning strategies use Automated Essay Scoring (AES) systems to solve the difficulty of scoring writing tasks. In this paper, we suggest an AES approach that involves not only rule-based grammar and consistency tests, but also the semantic similarity of sentences, thus giving priority to question prompts. Similarity vectors are used obtained after applying semantic algorithms and calculated statistical features. Our system uses 22 features with high predicting power, which is less than current systems, while considering every aspect a human grader may focus on.Predicting scores is achieved using the data provided by Kaggle's ASAP competition using Random Forest. The resulting agreement between the score of the human grader and the prediction of the system is compared with promising results through experimental evaluation.
Securing Healthcare Data by Using Blockchain

Healthcare is an important aspect of the development of the nation, and healthcare data are a key element in curing patient health. Healthcare information exchange is a very important aspect that benefits patients as well as health service providers. Due to a large amount of patient data, service providers use many cloud-based solutions, but insecurity arises in the case of the third-party service provider. This issue has been addressed by the authors in this chapter. However, existing techniques mainly focus on collecting patients' records from medical examination. In the present, IoT is widely used in mobile applications for monitoring patient's health and maintaining records. Then, collecting records could be sent to laboratories for further analysis and diagnosis. But, present techniques are too rigid in exchange of metadata. Authors tried to develop a method using smart contracts in blockchain which helps in securing the data over transfer. Each and every aspect of healthcare has been taken into consideration including the lab results, clinical trials, reimbursements, medical charts, etc. In this chapter, the role of blockchain in making digital records and transfer of the same in the healthcare system safe and encrypted is discussed. Different technology used for managing healthcare data is also discussed in this chapter. Later on, the use of smart contracts in blockchain and migration of the existing model of healthcare system into blockchain is discussed. Based on the proposed model, result evaluation is discussed for securing healthcare data using blockchain.
Improvement of Particle Swarm Optimization Focusing on Diversity of the Particle Swarm

PSO (Particle Swarm Optimization) is attracting attention in recent years to solve the multivariate optimization problems. In PSO, multiple individuals (particles) which records its own position and velocity information are placed in the corresponding search space, and the particle swarm move to discover the optimal solution by sharing information with other particles. The search process of PSO has problem such that it is difficult to deviate from the local solution because of convergence speed of the swarms is too fast. In TCPSO (Two-Swarm Cooperative PSO), particle swarm consists of two different types of particles (a master particle swarm and a slave particle swarm) with different characteristics of search process. Experimental results of using several benchmark problems indicate that TCPSO has high performance of finding optimal solutions for multidimensional and nonlinear problems. This study introduces the concept of specificity of each master particle which indicates the diversity of master particle swarm, and proposes an algorithm that improves the efficiency of the solution search process in TCPSO by periodically analyzing the behavior of master particle swarm. This study conducts several numerical experiments for verifying the effectiveness of the proposed method.
Distributed multi-target tracking over an asynchronous multi-sensor network

This paper addresses distributed multi-target tracking (DMTT) over an asynchronous multi-sensor network (AMSN). Within the AMSN, the sensor nodes are usually misaligned in time due to different sampling instants and/or rates. At the same time, time-offsets among nodes are always imprecise or even unknown. In such cases, time alignment (TA) needs to be carried out before fusion of information between different nodes. In the considered AMSN for DMTT, Probability Hypothesis Density (PHD) filters are run in each node for propagating in time a local first-order statistic, called intensity, of the target set, while arithmetic average (AA) fusion is used to combine intensities from different nodes. Recalling that AA intensity fusion admits an information-theoretic interpretation in terms of minimizer of the weighted average of Cauchy-Schwartz divergences (CSDs) with respect to the local intensities, the corresponding minimum weighted average CSD (MWCSD) is adopted as cost to be minimized for TA purposes. To ensure good convergence of the TA parameters, a convex combination of the instantaneous cost and the squared difference between current and previous estimates, is proposed. Furthermore, a sampling technique is adopted to solve the optimization problem. Finally, simulation experiments are provided to demonstrate the effectiveness of the proposed approach.
Cross-Modal Learning Based on Semantic Correlation and Multi-Task Learning for Text-Video Retrieval

Text-video retrieval tasks face a great challenge in the semantic gap between cross modal information. Some existing methods transform the text or video into the same subspace to measure their similarity. However, this kind of method does not consider adding a semantic consistency constraint when associating the two modalities of semantic encoding, and the associated result is poor. In this paper, we propose a multi-modal retrieval algorithm based on semantic association and multi-task learning. Firstly, the multi-level features of video or text are extracted based on multiple deep learning networks, so that the information of the two modalities can be fully encoded. Then, in the public feature space where the two modalities information are mapped together, we propose a semantic similarity measurement and semantic consistency classification based on text-video features for a multi-task learning framework. With the semantic consistency classification task, the learning of semantic association task is restrained. So multi-task learning guides the better feature mapping of two modalities and optimizes the construction of unified feature subspace. Finally, the experimental results of our proposed algorithm on the Microsoft Video Description dataset (MSVD) and MSR-Video to Text (MSR-VTT) are better than the existing research, which prove that our algorithm can improve the performance of cross-modal retrieval.
Implementation of Compressed YOLOv3-tiny on FPGA-SoC

This paper presents the implementation of YOLOv3-tiny, a lightweight object detection algorithm on an FPGA-SoC embedded platform for real-time detection. First, YOLOv3-tiny is compressed by a model quantization method to reduce the memory size for speed improvement. Then, the backbone architecture, which is a heavy load process, is accelerated on the FPGA by applying the parallel pipeline method. The detection algorithm on the FPGA is evaluated with the performance metrics of the memory size, inference speed and accuracy. Inference on the self-collected pedestrian signals dataset showed that the proposed system could compress the memory size by 75% for real-time detection of 104.17 FPS.
Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm

In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.