Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Map Code Recognition Method

For the efficient digitalization of maps, it is neces-sary to register map code, which indicates scale and area range. The manual register of the map code is time-consuming and laborious. Moreover, the recognition accuracy of the traditional text detection method based on template matching cannot be guaranteed. In this study, we propose a deep learning method to recognize image code and solve the problem of inaccurate detection and inaccurate positioning of text areas in map code recognition. A map code target recognition neural network referred to as YOLO-MCRNN is trained first to identify, crop, and binarize the code area and obtain detailed images. Architec-turally, an attention mechanism is added to a bidirectional long short-term memory structure and integrated into the map code target recognition network. Single numbers were cut according to variations in pixel value to establish a template library used in the matching method to recognize the code. Then, we conducted a comparative experiment to demonstrate the efficacy of the proposed deep learning method, and the results show that the detection network model improved on the performance of the existing state-of-the-art methods. Recognition experiments were carried out on the image dataset of code images taken by a digital map instrument. The single character and code recognition accuracy of the proposed YOLO-MCRNN model were 0.956 and 0.985, respectively, exhibiting obvious advantages over conventional matching methods and the unimproved text detection model CRNN(Convolutional Neural Network). The YOLO-MCRNN map code recognition network can accurately locate text areas, extract effective deep features of an input image, and identify map code. Compared with CRNN neural networks, which lack regional positioning, YOLO-MCRNN can improve recognition accuracy and reduce time consumption. Thus, the proposed model can be highly effective as an automatic map code registration method.
Development of a Website for Malarial Detection using Deep Learning

Malaria is a bacterial disease which is commonly caused by mosquitoes. In India, approximately, 7500 people have died due to malaria. The screening methods for malaria include the analysis of blood samples. This research aims the development a website that is capable of predicting malaria by analyzing the images of blood cells. For this purpose, images of blood cells are collected as a dataset from Kaggle. Both healthy and blood cells infected with malaria are included in this dataset. Next, the dataset's photos are selected for training, testing, and validation. The picked pictures are then scaled down to a specific size. With the help of the Convolutional Neural Network (CNN), a Deep Learning (DL) model is created. The preprocessed photos are then used to train this model. Model validation follows the training. The training and validation results are tallied and examined. Next, the model's accuracy and loss are evaluated. The highest accuracy of the model developed is 9% which was attained during the training. The model also produced the lowest loss value of 13% during the final epoch of the validation process. The model is then tested if the findings are satisfactory. The tested model is then deployed on a website. This website can be used as a pre-screening test for malaria in times when a person cannot reach out to the nearest doctor. This website can also be updated and converted as a software application in the future.
Computer Virtual Technology for Smart Martial Arts Training based on Body Action Recognition Algorithms

Computer virtual technology for the smart martial arts training based on body action recognition algorithms is the focus of this study. The proposed algorithm clustered the training samples, obtained M cluster centers, and calculated the variance of each dimension of the corresponding clusters, and used them as the initialization parameters of the fuzzy classifier. All the operations in the virtual machine will not affect the host environment, especially the damage to the hardware equipment is completely avoided. Furthermore, the designed model is then applied to the smart martial arts training. The whole pipeline is designed and tested to validate the performance.
Images Segmentation using Deep Learning Algorithms and Metaheuristics

Deep learning is a subset of machine learning that encompasses a variety of neural network architectures used to perform diverse computer vision tasks such as medical image classification and segmentation, which are time-consuming, effortful, delicate, and extremely tedious for doctors. The high variability of shape, location, size and texture of the medical images as well as the noise and parasites that degrade the image quality present a big problem for the segmentation process, therefore, a various segmentation methods based on deep learning have been proposed in the literature to fully automated the segmentation process. At the same time, the large number of hyperparameters of a deep learning algorithm in general and of a convolutional neural network in particular presents a problem when developing an automatic segmentation system with an appropriate structure and hyperparameters. Metaheuristics are approximate optimization methods to solve this type of problems. In this study, we review the most used and efficient segmentation methods based on deep learning for medical images segmentation, their optimization with metaheuristics as well as we compared three deep CNN encoder-decoder architectures, namely FCN, SegNet and Unet. These architectures trained and tested on MRI (Magnetic resonance imaging) images in order to study each of those architectures, compare them and finally choose the most efficient model.
Analysis of Virtual Reality and Augmented Reality SDK’s and Game Engines: A Comparison

At present, many immersive technologies such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR) and Extended Reality (XR) technologies are in demand because people tend to learn more through picturing the things rather than learning the things theoretically. Theoretical learning leads to boredom in Education. Therefore, with the aim of providing knowledge to the peers, this paper provides the discussion on the main foundation of the immersive technologies which are Game Engines and Software Development Kits (SDK’s). Many people assume Game engine and SDK to be same that is why this paper brings out more clarity about the difference between the two.
Information Extraction and Sentence Ordering in Multi-Document Summarization using Preference Learning

Multi-document summarizing is a process that automatically extracts information from many texts that are related to the same subject. For the purpose of information extraction, a technique that uses multi-document summarization which is based on phrase frequency is used. As a result of the phrases being picked from the documents depending on how important they are, the summary loses their coherence and the sequence in which the information is presented, which reduces the readability of the summary. A method of sentence ordering that is predicated on the chronological order of the phrases has been used in order to address this issue. According to the findings of this research, a multi-document summarizer that is based on a word frequency approach performs very well when it comes to the process of extracting relevant content units and increasing the readability of the summary via sentence sequencing.
A Multi-stage Hybrid Approach for Mapping Applications on Heterogeneous Multi-core Platforms

Due to the incorporation of heterogeneous cores in modern multi-core systems, the exploitation of their full potential strongly depends on the proper mapping of an application to the platform. This work presents an approach to map static applications on heterogeneous platforms minimizing their makespan based on the Benders decomposition principle combined with an Integer Linear Programming (ILP) model. The proposed approach adopts a three-stage decomposition scheme, finding permutations of infeasible solutions to generate multiple cuts in every iteration. The first stage deals with the assignment of the tasks to the cores and the last one with their scheduling, whereas the second stage propagates new bounds based on the current assignment and provides an explanation of the infeasibility in the form of subsets of assignment variables. Based on that, other infeasible combinations are computed by checking their permutations and more Benders cuts are produced. The proposed method is compared with a two-stage decomposition approach and an ILP model and exhibits better performance in terms of solution time and number solved instances to optimality.
Integrated Cloud Security for Data Storage and Access

During the COVID-19 pandemic, cloud computing & storage emerged as a viable alternative to traditional storage systems. Almost all firms are transitioning from traditional modes of operation to online modes of operation. The vast majority of businesses have the long-term goal of moving all or a significant part of their operations to an online (Work-from-Home) format. There are various advantages to cloud computing and storage, including cost savings, mobility, platform independence, accessibility, and elasticity. However, security is the primary impediment to adopting cloud storage services, particularly cloud retrieving. To address the issue of cloud storage security, this research study has proposed a method for cloud storage data security. The suggested strategy makes use of integrated cryptographic techniques to monitor, secure user as well as data that is kept in the cloud storage systems. The method that has been suggested constantly checks user data by performing the sequence of securities. The performance of proposed security is measured by downloading and uploading data iteratively and analyzed by computing time taken for cloud operations. Thus, the proposed strategy not only secures data but also increases user confidence in cloud-based storage services. The proposed approach can be further researched for dynamic data and variable data sizes.
A Power Reduction Technique Based on Linear Transformations for Block Ciphers

In this paper, we present a novel method for power reduction based on linear transforms for realization of cryptographic block ciphers on hardware platforms. The proposed technique is applicable to both substitution-permutation and Feistel networks, which are widely-used design choices for block ciphers. In our method, an isomorphic transform and its inverse is applied within every round of the target cipher, while the inputs and outputs are also transformed in order to have the same behavior as the original cipher. Hence, our method does not lead to any change of the cryptographic properties of the original cipher. We showcase our method on certain lightweight block ciphers, including the ISO-standard PRESENT, in order to achieve even lower power consumption figures compared to the original designs. Using our proposed technique, we reach power reductions of up to 20%. In some cases, the method can also lead to area reduction in addition to power reduction, which is surprising because the selected lightweight ciphers were already optimized for low area.
System Construction of Immersive Smart Teaching Environment based on Virtual Spatial Data Multi-Dimensional Imaging Technology

This article is based on the goal of "cultivating high-quality talents" based on the research of information flow data mining, and exerts common advantages; builds a communication platform of "three main bodies" for graduate students, tutors, and counselors to promote effective communication; The respective work characteristics of the instructors can complement the advantages of the two; and through the establishment of a collaborative education model, the relevant mechanism should be improved to strengthen the connection between the postgraduate instructors and the instructors, so that the postgraduate instructors and instructors can coordinate and cooperate in the postgraduate training process, walk in the same direction, focus on students, pay attention to students, and serve students to improve postgraduate training in colleges and universities.
Toward Large Scale All-Optical Spiking Neural Networks

Silicon Photonics is a promising technology to develop neuromorphic hardware accelerators. Most optical neural networks rely on wavelength division multiplexing (WDM), which calls for power hungry calibration to compensate for non-uniformity fabrication process and thermal variations of microring resonators (MRR). This imposes practical limits on neuromorphic photonic hardware since only a small number of synaptic connections per neuron can be implemented. As a result, the mapping of neural networks (NN) on a hardware platform require pruning of synaptic connections, which drastically affects the accuracy. In this work, we propose a method to efficiently map pre-trained NN on an all-optical spiking neural network (SNN), with the aim to optimize hardware utilization while minimizing accuracy loss. The method relies on weight partitioning and unrolling to reduce synaptic connections. The resulting neural networks are mapped on an architecture we propose, allowing to estimate accuracy and power consumption. Results show the capability of weight partitioning to implement a realistic NN while attaining 58% reduction in energy consumption compared with unrolling.
The influence of informal mechanisms of university knowledge transfer on firm level innovation performance: an empirical analysis in Ghana

This study seeks to investigate firm-level innovation, exploring whether social relationships located in knowledge networks influence the transfer of university knowledge to Ghanaian firms. Firms’ informal relationships with universities have been under-researched in developing economies. The extant literature suggests that informal relationships in the context of economies with advanced regional and national innovation systems have a positive association with firm level innovation performance. The research project employs a cross-sectional survey of 245 firms in Ghana. The aim of the project is to explore the influence of informal mechanisms of university knowledge transfer on firm level innovation performance in Ghana. The study adopts a structural model with partial least squares as an analytical technique. The findings reveal that to deliver positive results in a firm’s innovation performance by informal means, a well-coordinated social system to attract research knowledge from all aspects of the university system is required. The research project’s implications for Ghana’s innovation system include a need to be aware of the impact of corruption and lack of intellectual property rights on the efficacy informal knowledge transfer.
<i>Taejabo</i>: Remediations and Materiality of South Korean Wall Posters

Abstract An iconic medium of underground antiauthoritarian student activism in the 1980s, taejabo, or large “big-character” paper posters, has experienced a revival in postmillennial South Korea. Despite democratization and the availability of numerous online platforms, taejabo remains an important low-tech medium for students’ expression on matters of local, national, and international significance. This article explores taejabo’s transformations—from flourishing as an analogue counter-establishment medium in the 1980s, to experimental digital adaptations and the medium’s decline in the 1990s, to taejabo’s comeback in the 2010s as a postdigital medium. Drawing on scholarship on remediation, media materiality, and postdigitality, this article argues that taejabo’s abiding relevance and coherent identity have been anchored in its ontology as publicly displayed large sheets of inscribed paper, and in its material performativity. In particular, taejabo’s spatiality enables it to not simply represent different ideas but emplace them, literally confronting onlookers and materially transforming campuses into places of contestation. Contemporary taejabo’s intermediality between paper and digital illustrates how online media, rather than being disruptive, are incorporated into established media practices.
A graph anonymity-based privacy protection scheme for smart city scenarios

The development of science and technology has led to the construction of smart cities, and in this scenario, there are many applications that need to provide their real-time location information, which is very likely to cause the leakage of personal location privacy. To address this situation, this paper designs a location privacy protection scheme based on graph anonymity, which is based on the privacy protection idea of K-anonymity, and represents the spatial distribution among APs in the form of a graph model, using the method of finding clustered noisy fingerprint information in the graph model to ensure a similar performance to the real location fingerprint in the localization process, and thus will not be distinguished by the location providers. Experiments show that this scheme can improve the effectiveness of virtual locations and reduce the time cost using greedy strategy, which can effectively protect location privacy.
Conditional Random Fields Based on Neiderreit Sequence Initialized Eagle Perching Optimizer

Conditional random field (CRF) is a spatial state model proposed by the probability graph school of thought, and it belongs to the undirected probability inference model in the area of probability graph model. CRF combines the advantages of several classical models in machine learning (ML) and plays an important role in solving the model parameters of ML algorithms which describe unconstrained optimization problems. However, CRF also have its own defects, for some functions, such as multi-modal non convex smooth objective functions, it is difficult for it to find the global optimal solution by using gradient dependent methods, and the solution process is easy to fall into local optimal solution. At present, many scholars have studied CRF related issues, but most of them have just tried to use CRF algorithm to solve specific application problems in various industries, there are few reports on the optimization of CRF algorithm itself, especially its solution process. Thus, in this paper, a derivative free optimization swarm intelligence method, namely Neiderreit sequence initialized eagle percolating optimizer (NSIEPO) has been proposed to replace the gradient dependent method in finding the global optimal solution. By numerical analysis, the effectiveness of the proposed algorithm has been verified.
Dictionary Attacks based on TDOA Using a Smartphone

Recent years, side channel attack has received more and more attention from security researchers because of its unexpected and unpredictable characteristics. Recovering the keystrokes using keyboard acoustic emanation is one of the most popular research. However, Prior attacks often rely on specialized equipments, complex deployment environments or extensive data, which limits their usage scenarios. To solve these problems, we propose a method to obtain the relative position of keys by Time Difference of Arrival (TDoA) method and then recover the words using a dictionary. This attack only uses one phone and does not require any training. Results show that our attack has a 45.2% success rate of finding the correct words in the TOP50 candidates over all the words we tested. For words with more than 10 letters, it has a 46.3% success rate of finding the correct words in the TOP 25 candidates.
Dynamic Smoothing of the Program Motions for Single-Channel Electromechanical Control Plant

For a single-channel electromechanical control plant, we have developed a dynamic generator to handle non-smooth program motions, simplifying the planning of realizable trajectories. The generator generates a smooth motion reference signal in real time while tracking an initial step signal, subject to the velocity and acceleration constraints of the control plant.
Estimation of Roof-mounted Solar Photovoltaic Potential Using a Multicriteria Methodology-A Case Study in an University Campus in Loja, Ecuador

Over the last decade, the electric grid decarbonization have been impulsed for the inclusion of renewable energy resources, one of the most promising and mature technologies is the solar photovoltaic, nevertheless, many challenges should to be addressed in order to increase the penetration level specially on distribution networks, a key challenge is to estimate the solar photovoltaic potential in heterogeneous surfaces as rooftops. In this study, we analyse a method that use not only the solar irradiation it also includes intrinsic characteristics for the effective area, converters, conductors and power losses to obtain a most precise estimation.
Predictive Dynamic Scheduling Approach for Heterogeneous Resource Pool

In recent years, heterogeneous resource pools play an increasingly important role in high-performance computing. There are some challenges for the existing scheduling algorithms due to the increasing complexity of jobs submitted by users on the heterogeneous resource pools. This paper proposed a heterogeneous GPU cluster scheduling approach based on predicting job resource usage to obtain the optimal scheduling strategy, which constructs a prediction model based on the operation of the classified jobs on each heterogeneous chip. The core of the approach is to predict the computing usage characteristics of each job over a period and deploy them to appropriate nodes, when new jobs are waiting in the resource pool. The scalability and utilization of the resource pool are significantly improved, especially in the complex and heterogeneous GPU cluster environment. In addition, the approach is capable of decoupling and separating the jobs and scheduling them in parallel. Experimental results show the execution efficiency of the system is dramatically increased.
Script event prediction method based on self-attention mechanism and graph representation learning

Script event prediction usually refers to giving a sequence of contextual events and then selecting the most likely subsequent event from multiple candidate events. Usually, script event prediction methods are based on event pairs or event chain methods to build prediction models, but these methods can not fully capture the complex relationship between context events and candidate events. Aiming at the two difficulties of how to fully mine the meaning of events in text data to accurately represent events and how to make full use of the potential information between event nodes in narrative event graph to improve the accuracy of prediction tasks, this paper proposes a script event prediction model Bert-SatGNN, which combines Bert pre-training model, structural self-attention mechanism and narrative event graph. Our model introduces Bert pre-training mechanism into script event prediction for the first time so that it can more accurately represent the event nodes input into the prediction model. And use the multi-head structure self-attention module to learn the structural information of the event nodes in the narrative event graph, so as to capture the potential information of evolutionary events with causal logic, and finally combine the structural information and time series information to predict the final events. In this paper, experiments are carried out on the widely used New York Times data set, and the experimental results show that our models are better than the most advanced methods.