Reviewerly/exHarmony huggingface dataset:
Class Attendance Checking System based on Deep Learning and Global Optimization

This paper proposes a class attendance checking system based on deep learning approach. Because classrooms are non-constrained environments and each student often has only one sample photo in the learning management system, traditional methods are difficult to achieve good results. Considering the deep learning methods' excellent performance in complex scenes, we build a class attendance checking system using deep learning methods. First, the Faster-RCNN model is used for face detection. Then, we track the detected faces and extract the features of the face images through a CNN model. After calculating the distance between the detected face features and the sample face features, a global optimization method is used to accomplish the recognition of all face tracks at the same time. This paper begins with an introduction to the application background, and reviews the related works in the field of face-recognition-based attendance checking in the classroom. Then, this paper introduces our system architecture and the methodology used in each section. Finally, we test the system on real class surveillance videos. The results demonstrate the validity of our system architecture and the methods we used.
Semirings of Evidence

In traditional justification logic, evidence terms have the syntactic form of polynomials, but they are not equipped with the corresponding algebraic structure. We present a novel semantic approach to justification logic that models evidence by a semiring. Hence justification terms can be interpreted as polynomial functions on that semiring. This provides an adequate semantics for evidence terms and clarifies the role of variables in justification logic. Moreover, the algebraic structure makes it possible to compute with evidence. Depending on the chosen semiring this can be used to model trust, probabilities, cost, etc. Last but not least the semiring approach seems promising for obtaining a realization procedure for modal fixed point logics.
Denoising framework based on external prior guided rotational clustering

The image denoising model based on non-local self-similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. Meanwhile, the modelling of natural images, such as Gaussian mixture models (GMMs), has been successful in all aspects of computer vision by reducing the patterns of image patches. However, the version of its geometric transformation (e.g. rotational transformation) cannot be matched directly by using distance. How to further reduce the patterns of the patches by geometric prior and accelerate rotational matching through parallel calculation is an issue that needs to be solved. In this study, an external guided rotational matching denoising framework is proposed. The proposed framework combines non-local, sparse and low-rank image priors and we design a parallel computing scheme. They demonstrate the performance improvement of the proposed algorithm on images with strong rotational properties and the comparison with traditional state-of-the-art denoising methods. The scalability and effectiveness of the new framework are verified by simulation experiments in public and real datasets.
Workload-Aware Scheduling for Data Analytics upon Heterogeneous Storage

A trend in nowadays data centers is that equipped with SSD, HDD, etc., heterogeneous storage devices are widely deployed to meet diverse demands of various big data workloads. Since the reading performance of various storage devices are quite different, traditional concurrent data fetching easily incurs unbalanced use among devices. As a result, the straggler in terms of the data fetching, derived from the unbalanced use, directly increases the overall latency of data analytics. To avoid such unbalanced use on fetching large volume of data concurrently from storage devices, we formulate Workload-Aware Scheduling problem for Heterogeneous storage devices (WASH), the goal of which is to minimize the maximum data fetching time for parallel data analytical tasks. We design a randomized algorithm (rWASH) to select a proper source device for each task based on delicate calculated probabilities, which can be proved concentrated on its optimum with high probability. Extensive experiments show that rWASH reduces the average data fetching time for tasks by up to 55% over the state-of-the-art algorithms.
Measuring the Coexistence Competitiveness of ECN- or RTT-Based ExpressPass and TCP in Data Centers

As the data center grows, the aggregate sending rates frequently exceed the capacity of the network. Therefore, high-speed data center networks (DCNs) require efficient congestion management. To address this challenge, credit-scheduled congestion control has been proposed. One promising protocol is ExpressPass. However, ExpressPass is not deployed in real network fabrics. One reason is that not all traffic in the DCNs is regulated by ExpressPass. Research on traffic composition demonstrates that ingress/egress traffic can be reduced but is inevitable. However, highly planned ExpressPass is unbearable for traffic that is not regulated by its host. Therefore, we should narrow the competitiveness difference between ExpressPass and datacenter TCPs. The simplest and most effective method is to make use of congestion signals (e.g., drop, explicit congestion notification (ECN), or round trip time (RTT)) and redefine the algorithm of ExpressPass. To determine the best congestion signal for mitigating the cross-protocols competitiveness difference, we designed ECN-based and RTT-based feedback control algorithms for ExpressPass and measured their performance. To the best of our knowledge, it is the first attempt to harmonize ExpressPass with traditional TCPs. Furthermore, we use the lesson learned from the measurement and analysis to address the more generalized question: should we prefer either ECN or RTT for integrating various protocols in future DCNs? After the detailed measurement, the results show that ECN is a better congestion signal for future DCNs because the commercial switches support ECN and the stability and response speed of ECN are better than RTT, due to the fundamental limitation of RTT.
Image understanding via learning weakly-supervised cross-modal semantic translation

Fusing cross-modal features is significant for image understanding, which aims at describing objects inside an image by optimally combining multiple visual channels. In the literature, low-level based multimodal feature fusion have achieved impressive performance. However, the semantic gap is a big limitation, i.e., these methods cannot reflect the how humans perceive image semantic objects. Supervised learning-based methods require intolerably expensive manual labeling, which is not a good choice in practice. To alleviate these limitations, we present an image understanding method by learning weakly-supervised based cross-modal semantic translation. More specifically, we design a manifold embedding algorithm to automatically translate image-level text semantic labels into several pixel-level image regions. Subsequently, we leverage a three-level spatial pyramid model to extract both local and global features of objects from training images. Afterwards, these cross-modal features are seamlessly concatenated to form a multiple feature matrix. Afterwards, these cross-modal features are seamlessly concatenated to form a multiple feature matrix. The feature matrix can be employed to learn a kernel SVM and ranking SVM for image classification and retrieval respectively. Comprehensive experiments on image recognition, classification and retrieval have demonstrated the effectiveness of our method.
Reducing Style Overfitting for Character Recognition via Parallel Neural Networks with Style to Content Connection

There is a significant style overfitting problem in neural-based character recognition: insufficient generalization ability to recognize characters with unseen styles. To address this problem, we propose a novel framework named Style-Melt Nets (SMN), which disentangles the style and content factors to extract pure content feature. In this framework, a pair of parallel style net and content net is designed to respectively infer the style labels and content labels of input character images, and the style feature produced by the style net is fed to the content net for eliminating the style influence on content feature. In addition, the marginal distribution of character pixels is considered as an important structure indicator for enhancing the content representations. Furthermore, to increase the style diversity of training data, an efficient data augmentation approach for changing the thickness of the strokes and generating outline characters is presented. Extensive experimental results demonstrate the benefit of our methods, and the proposed SMN is able to achieve the state-ofthe-art performance on multiple real world character sets.
Denoising Documents Using Image Processing for Digital Restoration

This paper develops an algorithm that will help decipher the text in decrepit documents, which if put in simpler terms aims at converting stained, blotted, creased, and faded documents into a cleaner and legible format. Handwritten or printed records are carelessly stacked away without undertaking measures for preserving them. They are subjected to degradation because of mishandling and improper storage conditions. This ultimately results in the loss of important documentation owing to the inability of reproduction or recovery of original data. Digital image preprocessing techniques are used to convert a color (RGB) image into a grayscale image for further processing. Image denoising is one of the most sought areas after research in image processing, and in this paper, we use image segmentation and median filter to achieve this. In this paper, we attempted to come up with an approach to remove noise from the image by applying image segmentation and thresholding, histogram, and median filter.
Efficient Banding-Alleviating Inverse Tone Mapping for High Dynamic Range Video

In recent years, there has been an exponential growth in the number of devices that offer High Dynamic Range (HDR) viewing experience. An efficient and popular approach to constructing an HDR image from a standard dynamic range (SDR) image is to use "inverse tone mapping (iTM)." However, it can create or amplify visual artifacts such as banding/false contouring in the resulting HDR images. To address this, we propose a novel method to efficiently construct iTM to reduce banding in the highlight regions of HDR images. Our proposed approach uses a given iTM curve to estimate the banding-risk in each luminance range, based on the input SDR image properties. Then, the risk measure is used to adjust the local slope of the iTM to avoid banding in the resulting HDR images. Experimental results exhibit that our proposed method is highly effective in reducing banding in the HDR images.
Ink Analysis Using CNN-Based Transfer Learning to Detect Alteration in Handwritten Words

Alteration of words in handwritten financial documents such as cheques, medical claims, and insurance claims may lead to monetary loss to the customers and financial institutions. Hence, automatic identification of such alteration in documents is a crucial task. Therefore, an ink color based analysis using Convolutional Neural Network (CNN) automation method has been introduced for alteration detection. Pre-trained AlexNet and VGG-16 architectures have been used to study the effect of transfer learning on the problem at hand. Further, two different shallow CNNs have been employed for recognition. A data set has been created using ten blue and ten black pens to simulate the word alteration problem. The dataset captures the word alteration by addition of the characters (or even pen strokes) in the existing word. Experiments have revealed that the transfer learning based deep CNN architectures have outperformed the shallow CNN architectures on both blue and black pens.
Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD

In decentralized distributed systems the data resides on the compute devices, which are connected through a high latency network that can adversely impact the communication cost. In such systems, it is desirable to employ a training regime that is inherently decentralized, where learning algorithms operate on local hosts using only the local data partitions. To ensure their convergence to a joint model, the parameters of the local models have to be regularly averaged. As each averaging operation incurs network communication costs, the right balance has to be found between either communication intensive dense averaging operations or sparse averaging operations which slows down the convergence. We propose a hierarchical two-layer sparse communication topology, a ring of fully-connected meshes of workers that communicate with each other (Ring-Star). Ring-Star allows a principled trade-off between the convergence speed and communication overhead and is well suited to loosely coupled distributed systems. We demonstrate on an image classification task and a batch stochastic gradient descent learning (SGD) algorithm that our proposed method shows similar convergence behavior as Allreduce while having lower communication cost of Ring.
Segmentation of Magnetic Resonance Brain Images Through the Self-Adaptive Differential Evolution Algorithm and the Minimum Cross-Entropy Criterion

The segmentation is regarded as a vital step in preprocessing techniques for image analysis. Automatic segmentation of brain magnetic resonance images has been extensively investigated since with a precise segmentation can be identified and diagnosed several brain diseases. Thresholding is an important simple but efficient technique of image segmentation. Various strategies have been submitted to find optimal thresholds. Amongst those methods, the minimum cross-entropy (MCE) has been broadly implemented due to its simpleness. Although MCE is quite effective in bilevel thresholding, the computational cost increases exponentially the higher the number of thresholds (th) to find. This article introduces a new approach called MCE-SADE for multilevel thresholding using the Self-Adaptive Differential Evolution (SADE) algorithm. SADE is a robust metaheuristic algorithm (MA) that resolve general problems efficiently since, through evolution, the parameters and the proper learning strategy are continuously adjusted pursuant to prior knowledge. The optimum th values are found minimizing cross-entropy through SADE algorithm. The proposed method is tested in two groups of reference images; the primary group is formed of standard test images, while the following group consists of brain magnetic resonance images. In turn, MCE-SADE is compared with two metaheuristic algorithms, Grey Wolf Optimizer (GWO) and Competitive Imperialist Algorithm (ICA). From the experimental results, it is observed that MCE-SADE results improve in terms of consistency and quality in contrast to GWO and ICA based methods.
Adaptive Filtering in In-Memory-Based Architectures

Deep in memory architecture (DIMA) has been proposed as a means to improve energy efficiency and latency over conventional digital architectures. DIMA reads multiple bits per bit-line (BL) in each cycle, and performs mixed-signal processing at the periphery of the bit cell array (BCA). While DIMA provides considerable performance benefits, the multi-row read is a non-linear operation. This work studies the impact of non-linearity and variations on stochastic gradient descent (SGD). The analysis is carried out in the context of LMS adaptive filters. The steady state MSE of the filter remains unaffected while convergence rate depends parameters associated with DIMA read operation. The insights are useful in context of learning systems employing gradient descent techniques.
Making Federated Networks More Distributed

Federated networks such as Mastodon or Matrix have seen rising usage thanks to their ability to provide users with good privacy and independence from large service providers, while retaining the familiar model of server-backed websites or mobile apps with its advantages of speed, availability and ease of use. However such systems are fragile since each individual server of the federation is a single point of failure for its users. We argue that new secure distributed algorithms could be conceived and applied without changing the server-backed nature of the system, and that such a configuration would provide systemic resilience and better independence of end users from their service providers, without sacrificing privacy, availability, efficiency or ease of use.
Efficient ECM factorization in parallel with the lyness map

The Lyness map is a birational map in the plane which provides one of the simplest discrete analogues of a Hamiltonian system with one degree of freedom, having a conserved quantity and an invariant symplectic form. As an example of a symmetric Quispel-Roberts-Thompson (QRT) map, each generic orbit of the Lyness map lies on a curve of genus one, and corresponds to a sequence of points on an elliptic curve which is one of the fibres in a pencil of biquadratic curves in the plane.
A Multi-model Object Tracking Algorithm based on Texture and Color Features

Mean Shift is widely concerned because of its advantages like fast convergence, real-time performance and simple procedure. However, the tracking performance of the traditional Mean Shift algorithm is obviously interfered when the background has the similar color as the target or the illumination changes. Besides, the tracking is easy to defeat in the case of target occlusion and loss of important frames. Therefore, this paper proposes two improvements based on the traditional Mean Shift tracking algorithm. First, the HLBP texture feature and color feature are employed to describe the target feature in order to improve the robustness of the tracking algorithm. Second, multiple models are taken into account to provide more abundant choices for the tracking process which can improve the tacking performance. The result of experiments show that our algorithm is more robust under the case of object occlusion and posture change, and gets better performance in accuracy when the background color and target color are similar or the illumination changes.
Assistive mobile robot for industrial and academic applications

With the rapid development of robotic technology, a number of robotic units have been deploying in the developed societies to assists humans in routine jobs. Specifically, domain of assistive robotics has been expanding and providing benefits to educational and commercial entities. In developing societies, no significant usage of robots has been present and clearly, they are far behind to grab advantages of technologies. This paper is presenting development and application of custom made assistive robotic unit for educational and commercial use. The complete mechanical and electronic hardware of the robot has been designed and manufactured locally by keeping local needs and financial constraint in mind. Main features of the robot are to assists humans by receiving user command electronically. In order to localize in crowded vicinity, 2D laser scanner and up view camera have been used to locate the robot using Kalman Filtering (KF) technique. An open source implementation of KF has been adopted using Robot Operating System (ROS) platform. In addition to this, it can transfer surveillance image to server, interact through gestures at 12″ inch LCD screen which is acting like face of robot. It has been tested inside university in working time. Satisfactory results have been found by viewing ground truth. In addition, it has been tested as demonstrator in front of primary school students and found positive results by observing responses of students. Complete system has been prepared for deployment in educational and commercial institutes. This work is partially supported with HEC-NRPU-6061 research grant.
A High-Performance Self-Learning Antelopes Migration Algorithm (SAMA) for Global Optimization

In global optimization, Nature-Inspired Computing (NIC) has attracted increasing attention in recent years. However, a common predicament for most optimization algorithms is the poor tradeoff between global exploration and local exploitation, which results in premature convergence. Moreover, the complex mechanism of algorithms makes them inconvenient to use for actual users, who are not specialists in computational intelligence (CI). To handle these defects, this paper proposes a succinct high-performance nature-inspired algorithm for global optimization, namely, Self-learning Antelopes Migration Algorithm (SAMA), which imitates the migration behavior of Tibetan antelopes on the HohXil grassland of China. The antelope herd always migrates to the most fertile place they have found, where ordinary members graze about here and some vigorous members selected as scouts to explore farther to seek a better place. The interesting behaviors of different antelopes guarantee the development and best state of the herd. They also have learning capability which adaptively corrects their searching strategy through the past migration experience. To verify the high performance of the proposed SAMA, we compare our proposed algorithm with five advanced nature-inspired algorithms. A series of benchmark tests demonstrate that our SAMA has remarkable superiority on both optimization accuracy and speed.
Research on Emergency Repair Scheme of Grid Power System Using Gis

To provide better services for customers, achieve the effective integration of professional data, and improve the efficiency of the grass-roots emergency repair work, this paper proposes the design and application of multi-dimensional grid distribution emergency repair intelligent management platform. GIS based multi-dimensional grid distribution emergency repair improves the efficiency and quality of grid distribution emergency repair by multi-dimensional support of a single grid. In the aspect of structural scheme design, the multi-dimensional grid distribution emergency repair management system architecture of power grid equipment operation and maintenance service is adopted, and the platform architecture principle, operation environment, hardware and software, technology and equipment are also determined. Through the test and analysis of the application cases in the actual operation, it is proved that the application of the platform greatly improves the efficiency of the grass-roots emergency repair work, and the service quality based on management innovation and technical innovation.