Reviewerly/exHarmony huggingface dataset:
Automated Smart Attendance System Using Face Recognition

In the human body, the face is the most crucial factor in identifying each person as it contains many vital details. There are different prevailing methods to capture person's presence like biometrics to take attendance which is a time-consuming process. This paper develops a model to classify each character's face from a captured image using a collection of rules i.e., LBP algorithm to record the student attendance. LBP (Local Binary Pattern) is one among the methods and is popular as well as effective technique used for the image representation and classification and it was chosen for its robustness to pose and illumination shifts. The proposed ASAS (Automated Smart Attendance System) will capture the image and will be compared to the image stored in the database. The database is updated upon the enrolment of the student using an automation process that also includes name and rolls number. ASAS marks individual attendance, if the captured image matches the image in the database i.e., if both images are identical. The proposed algorithm reduces effort and captures day-to-day actions of managing each student and also makes it simple to mark the presence.
t-vMF Similarity For Regularizing Intra-Class Feature Distribution

Deep convolutional neural networks (CNNs) leverage large-scale training dataset to produce remarkable performance on various image classification tasks. It, however, is difficult to effectively train the CNNs on some realistic learning situations such as regarding class imbalance, small-scale and label noises. Regularizing CNNs works well on learning with such deteriorated training datasets by mitigating overfitting issues. In this work, we propose a method to effectively impose regularization on feature representation learning. By focusing on the angle between a feature and a classifier which is embedded in cosine similarity at the classification layer, we formulate a novel similarity beyond the cosine based on von Mises-Fisher distribution of directional statistics. In contrast to the cosine similarity, our similarity is compact while having heavy tail, which contributes to regularizing intra-class feature distribution to improve generalization performance. Through the experiments on some realistic learning situations such as of imbalance, small-scale and noisy labels, we demonstrate the effectiveness of the proposed method for training CNNs, in comparison to the other regularization methods. Codes are available at https://github.com/tk1980/tvMF.
PRISM: Private Verifiable Set Computation over Multi-Owner Outsourced Databases

This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.
Perishable Food Products Contains Safe in Cold Supply Chain Management Using Blockchain Technology

Blockchain is booming technology in many industries. Especially, supply chain management. This paper talks about how the perishable food product to take safely in cold supply chain management with the help of blockchain technology. Here, we proposed the immutable ledger from farm to consumer. This ledger is distributed. So, whoever (farm to consumer) can take this data and check the details. With the help of an immutable ledger, no one can change the food item data. Example: what temperature we assigned to the perishable products and which company manufacture the food products and date, etc., all data stored forever. No one can be changed the data. So, with this technology, we can reduce the food losses, reduce the economic losses for a manufacturing company, and avoid the foodborne illness of our valuable consumer. Eventually, we increase consumer trust.
An Electronically Stabilized Multi-Color Multi-Ring Structured Light Sensor for Gas Pipelines Internal Surface Inspection

Natural gas pipeline network extends over 2.5 million miles in the united states of America. The aging of these infrastructures combined with damage from various external threats are creating safety concerns for both operators and residents in the vicinity of these pipelines. In this paper, we are presenting a miniaturized structured light based three-dimensional (3D) imaging sensor for the inspection of internal walls of plastic natural gas distribution pipelines. The sensor is an 18-mm diameter optical inspection device that can be inserted inside the pipe to provide the operator with a 3D map of the internal surface of the inspected pipe. The introduction of the 3D map simplifies the identification of damages like deformations and material loss and leads to a better evaluation of the damage severity when compared to LED assisted cameras. The sensor is also accompanied with an embedded electronic stabilization algorithm to reduce the effect of misalignment when the sensor is attached to a scanning platform, and to facilitate the registration of consecutive frames from the sensor. The algorithm exploits the geometry constraints provided by the cylindrical shape of the pipe to estimate the orientation and position of the sensor with each collected frame.
An improved artificial tree algorithm with two populations (IATTP)

Many new bio-inspired algorithms are recently being proposed, artificial tree (AT) algorithm, inspired by the growth of trees and the update behavior of branches, is one of them. There are also some improved AT algorithms being proposed to improve their calculation accuracy. However, the main challenges of AT algorithms lie in the insufficiencies in the design of update operators as well as the position interaction between branches and the capture of key information and the performance of AT algorithms needs to be enhanced. This work proposes an improved AT algorithm with two populations (IATTP). In IATTP, the update strategies of branches are redesigned, and a variety of efficient update operators are designed and applied. The branch population is changed from one to two, and the competition mechanism between populations is proposed. Through the migration of branches between populations, the scale of population with better efficiency is expanded and the size of population with lower efficiency is reduced, thus a reasonable interaction between populations and branches is realized. With above strategies, the efficiency and accuracy of IATTP are significantly improved. The results of IATTP are proved to be advantageous when the performance of IATTP is compared with AT algorithm, improved artificial tree (IAT) algorithm and feedback artificial tree (FAT) algorithm through typical test problems. Meanwhile, the results of IATTP in current state are also preferable when IATTP is compared with other improved algorithms in high dimensional problems. The experimental results prove that IATTP is competitive in solving optimization problems.
A simple but effective method for Indonesian automatic text summarisation

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A recent survey on the applications of genetic programming in image processing

Abstract Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.
TransET: Knowledge Graph Embedding with Entity Types

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.
AIoTES: Setting the principles for semantic interoperable and modern IoT-enabled reference architecture for Active and Healthy Ageing ecosystems

The average life expectancy of the world's population is increasing and the healthcare systems sooner than later will be compromised by its reduced capacity and its highly economic cost; in addition, the age distribution of the population is leading towards the older spectrum.This trend will lead to immeasurable and unexpected economic problems and social changes.In order to face up this challenge and complex economic and social problem, it is necessary to rely on the appropriate digital tools and technological infrastructures for ensuring that the elderly are properly cared in their everyday living environments and they can live independently for longer.This article presents ACTIVAGE IoT Ecosystem Suite (AIoTES), a concrete reference architecture and its implementation process that addresses these issues and that was designed within the first European Large Scale Pilot, ACTIVAGE, a H2020 funded project by the European Commission with the objective of creating sustainable ecosystems for Active and Healthy Ageing (AHA) based on Internet of Things and big data technologies.AIoTES offers platform level semantic interoperability, with security and privacy, as well as Big Data and Ecosystem tools.AIoTES enables and promotes the creation, exchange and adoption of crossplatform services and applications for AHA.The number of existing AHA services and solutions are quite large, especially when state-of-the-art technology is introduced, however a concrete architecture such as AIoTES gains more importance and relevance by providing a vision for establishing a complete ecosystem, that looks for supporting a larger variety of AHA services, rather than claiming to be a unique solution for all the AHA domain problems.AIoTES has been successfully validated by testing all of its components, individually, integrated, and in real-world environments with 4345 direct users.Each validation is contextualized in 11 Deployment Sites (DS) with 13 Validation Scenarios covering the heterogeneity of the AHA-IoT needs.These results also show a clear path for improvement, as well as the importance for standardization efforts in the ever-evolving AHA-IoT domain.
MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning

User intent detection is vital for understanding their demands in dialogue systems. Although the User Intent Classification (UIC) task has been widely studied, for large-scale industrial applications, the task is still challenging. This is because user inputs in distinct domains may have different text distributions and target intent sets. When the underlying application evolves, new UIC tasks continuously emerge in a large quantity. Hence, it is crucial to develop a framework for large-scale extensible UIC that continuously fits new tasks and avoids catastrophic forgetting with an acceptable parameter growth rate. In this paper, we introduce the Meta Lifelong Learning (MeLL) framework to address this task. In MeLL, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning. We design global and local memory networks to capture the cross-task prototype representations of different classes, treated as the meta-learner quickly adapted to different tasks. Additionally, the Least Recently Used replacement policy is applied to manage the global memory such that the model size does not explode through time. Finally, each UIC task has its own task-specific output layer, with the attentive summarization of various features. We have conducted extensive experiments on both open-source and real industry datasets. Results show that MeLL improves the performance compared with strong baselines and also reduces the number of total parameters. We have also deployed MeLL on a real-world e-commerce dialogue system AliMe and observed significant improvements in terms of both F1 and the resources usage.
A Privacy-Preserving Identity Authentication Scheme Based on the Blockchain

Traditional identity authentication solutions mostly rely on a trusted central entity, so they cannot handle single points of failure well. In addition, most of these traditional schemes need to store a large amount of identity authentication or public key information, which makes the schemes difficult to expand and use in distributed situations. In addition, the user prefers to protect the privacy of their information during the identity verification process. Due to the open and decentralized nature of the blockchain, the existing identity verification schemes are difficult to apply well in the blockchain. To solve this problem, in this article, we propose a privacy protection identity authentication scheme based on the blockchain. The user independently generates multiple-identity information, and these identities can be used to apply for an identity certificate. Authorities use the ECDSA signature algorithm and the RSA encryption algorithm to complete the distribution of the identity certificate based on the identity information and complete the registration of identity authentication through the smart contract on the blockchain. On the one hand, it can realize the protection of real identity information; on the other hand, it can avoid the storage overhead caused by the need to store a large number of certificates or key pairs. Due to the use of the blockchain, there is no single point of failure in the authentication process, and it can be applied to distributed scenarios. The security and performance analysis show that the proposed scheme can meet security requirements and is feasible.
Diving Into The Relations: Leveraging Semantic and Visual Structures For Video Moment Retrieval

Existing dominant approaches for video moment retrieval task are to learn semantic correlation between a given query and the video. However, these methods rarely explore the fine-grained semantic structure and comprehensive visual structure, leading to insufficient utilization of textual and visual relations. In this paper, we propose a unified framework for video moment retrieval, which considers to simultaneously encode semantic and visual structures. Specifically, a semantic role tree is built to reveal the fine-grained semantic information by generating hierarchical textual embeddings. Then the semantic structure is adopted to facilitate the visual structure learning with a contextual attention-based proposal interaction module. Finally, we adaptively aggregate and obtain the visual-semantic matching information through a multi-level fusion strategy to select the best matching moment proposal. Extensive experiments on two popular benchmarks (Charades-STA and ActivityNet Captions) show that our proposed method achieves state-of-the-art performance. Codes are available in the Supplementary Material.
Multiframe Video Satellite Image Super-Resolution via Attention-Based Residual Learning

Video satellite can generate video image sequences with rich dynamic information, thus providing a new way for monitoring moving objects. However, to maintain high temporal resolution, video satellite images usually sacrifice their spatial resolution. Therefore, super-resolution (SR) plays a vital role in improving the quality of video satellite images. In this article, we propose a multiframe video SR neural network (MVSRnet) for video satellite image SR reconstruction. The proposed MVSRnet consists of three main subnetworks: an optical flow estimation subnetwork (OFEnet), an upscaling subnetwork (Upnet) and an attention-based residual learning subnetwork (ARLnet). The OFEnet aims to estimate low-resolution (LR) optical flow of multiple image frames. Upnet is then constructed to enhance the resolution of both input frames and the estimated LR optical flows. Motion compensation is subsequently performed according to the high-resolution (HR) optical flows. Finally, the compensated HR cube is fed to the ARLnet to generate SR results. Different from existing video satellite image SR methods, the proposed MVSRnet is a multiframe-based method with an attention mechanism, which can merge the motion information among adjacent frames and highlight the importance of extracted features. Experiments conducted on Jilin-1 and OVS-1 video satellite images demonstrate that the proposed MVSRnet significantly outperforms some state-of-the-art SR methods.
A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Abstract Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.
Three-dimensional tuning of idiophone bar modes via finite element analysis

The timbre of marimba and other idiophone bars can sometimes be polluted by untuned torsional modes, leading to substandard instruments or rejected materials. Makers have complained of problems with these untuned modes over a specific range of notes. Marimba, vibraphone, and similar idiophone bars are tuned by carving one side of the bar to bring up to three flexural modes into harmonic relationships. Torsional and other mode types are commonly left untuned. The relative frequency of these untuned modes with respect to the fundamental mode will vary along the keyboard. This paper investigates tuning both torsional and flexural modes simultaneously. This tuning is achieved using sophisticated carved geometries, and without employing concentrated masses or additional materials. Bars are modeled using three-dimensional finite elements. Geometry is defined by a large number of input parameters. Algorithms are implemented to identify bar modes automatically, eliminating the need for human intervention. Tuning is performed via a Newton-Raphson approach using the Moore-Penrose generalized matrix inverse to solve systems of tuning equations. This method is found to be effective at finding satisfactory bar geometries in proximity to initial conditions. Numerous example marimba and vibraphone bar models are provided, representing both typical and atypical modal tuning ratios.
Control Synthesis as Machine Learning Control by Symbolic Regression Methods

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.