Reviewerly/exHarmony huggingface dataset:
Building and Protecting Human Capital: The Technology Landscape for Service Delivery in South Asia

No AccessOct 2021Building and Protecting Human Capital: The Technology Landscape for Service Delivery in South AsiaAuthors/Editors: Sajitha Bashir, Carl J. Dahlman, Naoto Kanehira, Klaus TilmesSajitha BashirSearch for more papers by this author, Carl J. DahlmanSearch for more papers by this author, Naoto KanehiraSearch for more papers by this author, Klaus TilmesSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1719-9_ch3AboutView ChaptersPDF (0.6 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Describes the landscape of technologies being deployed in South Asia in health, education, and social protection to help build and protect human capital by (1) discussing the opportunities and the characteristics of technology markets in human development; (2) reviewing of the inequalities in digital access that limit these opportunities; and (3) summarizing the technology landscape in health, education, and social protection in the region, including a brief look at two case studies. The opportunities from new digital, data-driven, and converging technologies arise from their ability to reach the unreached, improve quality and efficiency, and enable personalization and differentiation according to need. In the public sector, the potential also lies in reimagining citizen-centric service delivery models and fostering data-driven decision-making. Yet these opportunities remain limited by inequalities in digital access, not only in broadband connectivity and electricity availability, but also in devices, people’s digital skills, and local content, which risk deepening the human capital inequalities in the region. Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: October 2021ISBN: 978-1-4648-1719-9e-ISBN: 978-1-4648-1720-5 Copyright & Permissions Related RegionsSouth AsiaRelated TopicsEducationEnergyHealth Nutrition and PopulationInformation and Communication TechnologiesPoverty ReductionScience and Technology Development KeywordsCASE STUDYHUMAN CAPITALTECHNOLOGY DEVELOPMENTINNOVATIONDIGITAL DIVIDESCIENCE AND TECHNOLOGY EDUCATIONSOCIAL SERVICE DELIVERYPUBLIC SERVICE DELIVERYINEQUALITYBROADBAND SERVICEELECTRICITY PDF DownloadLoading ...
A Hybrid evolutionary algorithm based on Artificial Bee Colony algorithm and Differential Evolution

In order to make up for the lack of local development capabilities of the classic artificial bee colony algorithm (ABC), an improved artificial bee colony search strategy is proposed: a new solution search equation is introduced, and a combined search strategy is formed with the original solution search equation in the classic artificial bee colony algorithm. To further improve the convergence speed and accuracy of the algorithm, an improved differential evolution strategy is proposed: a probability parameter is introduced to adjust the selection of the crossover probability of the differential evolution algorithm(DE); finally, in order to alleviate the harm of premature convergence caused by increasing the convergence speed, introduce An improvement mechanism; based on these three major improvements, a hybrid evolutionary algorithm based on ABC and DE is proposed. Then, based on a simulation experiment composed of benchmark test functions, the entire algorithm was verified. The results compared with the classic ABC and DE show that the improvement has obvious effects.
IoRT Based Denoising Video Streams Through Employing Special Filtering Systems in Health Care Region

The IoT is an edition on the Web which doesn't need man interaction. The IoT is a worldwide community of vast amounts of cognitive as well as a wired “things” that link virtual and physical entities to grow the planet's borders. Each and every day, these common wise issues create huge quantities of information, necessitating immediate information processing on an assortment of sensible products. Interconnecting nodes when they're divided of big topographical places that occur in Internet of Remote Things has been already highlighted as a significant struggle for Internet of Remote Things (IoRT). It might be expected to predominate within remotely overseeing individuals inside cases in which connectivity infrastructure which allows videos transmissions from ICU is crucial. Sound, which is typical in low light factors or perhaps against restricted functionality digital cameras, is a tremendous reason for compression as well as quality wreckage. Denoising was utilized no matter the code for post-processing or pre-processing in previous investigations that attempted to incorporate a denoise algorithm along with a video recording encoder. Denoising is an action within the pre processing method which gets better the picture quality and also causes it to be much more uplifting. The SNR ratio is rather high, thus to eradicate it a non linear air filter strategy known as Median Filter is utilized. A lot of Median filter systems can be found but 3D air filtering allows for more effective steps within protecting the tips of video clip frames. For benchmarking collective functionality PSNR, SSI and UIQI are widely-used as quality evaluation metrics to make a high performance racket clear video clip. The simulation results reveal that the three dimensional median versions outshine additional picture interference air filtering approaches within a selection of metrics, leading to a powerful racket filtration system for IoT devices.
A Deep-Learning Approach to Single Sentence Compression

In this work, we characterize sentence compression (SC) as a task where a sentence is represented with less number of words without negotiating its original meaning. We propose a deletion based approach to sentence compression using LSTM, where SC is modeled as a two-class classification problem and the task is to decide whether a word of a sentence to be retained or to be removed based on its context like previous and next words in the sequence. Dataset creation is one of the challenging tasks in natural language processing task, in this work; two different datasets are created for this compression task. One dataset is created based on the hand-crafted features. The other one is created using the pre-trained model of GloVe. Two different categories of models are created based on the input provided to the system for training. One category of models created using the hand-crafted feature dataset and other category using the GloVe pre-trained model dataset. The model with the highest accuracy is considered for evaluation. The system is evaluated manually as well as automatically. The two important quality metrics of sentence compression, rate of compression and informativeness are used to determine the quality of compression. We have obtained 74.5 and 42.64 percent accuracy in informativeness and compression rate respectively.
A Parsing Scheme of Mind-Map Images

At present, there are a large number of mind-map images generated by different software on the Internet. These images contain rich knowledge that has been summarized by people for learning or teaching. In order to exploit knowledge and reconstruct the mind-map, we propose a scheme to parse the image of mind-maps. Firstly, we apply text detection and deep learning-based OCR models to extract the text content and the corresponding position information, and then use the line segment detection model to detect the line segments which represent the structural relationships between text blocks. The detected line segments will be matched with the text blocks with the image features of the mind map. The results are utilized to analyze the parent-child relationship between the text blocks. Experiments have been conducted to illustrate the reconstruction of mind-map with the parsing of the corresponding images.
A Novel Fault Diagnosis Method Via Data Dynamic Property Inspired Density-Based-Clustering

State recognition is the crucial stage in the fault diagnosis, where clustering technique usually has abilities to provide effective support, especially under the condition of lacking prior knowledge. In this paper, a dynamic property of data points is proposed from the perspective of movement. Based on this favorable property we develop an effective density-based algorithm by defining a cluster with new connection rule. The property sustains our algorithm on extracting clusters with widely variation in density from datasets, and gain more procedural conciseness in our algorithm by eliding the need of density threshold. The capability of our method is validated by experimental evaluation on both synthetic benchmark datasets and real-world rolling bearing vibration data, which shows certain advantages of our method over conventional and recent algorithms.
Detecting owner-member relationship in fisheye camera system with graph convolution network

The owner-member relationship between wheels and vehicles contributes significantly to the 3D perception of vehicles, especially in embedded environments. However, two major challenges must be addressed to leverage the relationship: i) Standard IoU-based heuristics have difficulty dealing with instances of occluded traffic congestion. ii) It is challenging to demonstrate the solution’s efficacy and applicability in a vehicle-mounted system. To address these issues, we propose a novel technique for relationship prediction called DeepWORD which is based on the architecture of a graph convolutional network (GCN). Specifically, we employ feature maps with local correlation as input to the nodes to improve the information richness. Subsequently, we present a graph attention network (GAT) to rectify the priori estimate bias dynamically. Furthermore, we created a large-scale benchmark dataset called WORD that includes annotated owner-member relationships. Sufficient experiments demonstrate that the proposed method is capable of providing state-of-the-art performance and effects in real-time. The WORD dataset is publicly available at https://github.com/NamespaceMain/ownermember-relationship-dataset.
Research Area as a Cornerstone of Developing Engineering Education in Global Society

In the context of globalization processes, the concept of research area is found increasingly frequently and has several definitions. Main characteristics that can be differentiated among them are “interaction of research centers and schools,” “environment that ensures functions and development of science and technology,” and in a more general terms, it is defined as a “moving system.” Research collaboration suggests interaction at several levels: Interstate, regional and interregional, interdepartmental, as well as at the levels of relations among research institutions, research teams, academic institutions, and researchers. Such collaboration is characterized by intelligent manageability of its components. The main purpose is to exchange information on relevant trends and results of various studies.
Design and Application of Digital Resource Sharing Talent Training Program System for Big Data Technology

The cloud computing platform using Web applications in Google infrastructure can realize the integration and sharing of distributed resources. By using Google App Engine cloud platform of distributed database technology and BigTable’s Web-Base GAE database Datastore, a talent training program system for resource sharing of higher vocational enrollment expansion based on big data technology is developed. According to empirical research, there are more than 500 talent training programs applied to 11 kinds of student enrollment, especially in higher vocational enrollment expansion. Through systematic use, the work intensity of relevant stakeholders is greatly reduced, and the time spent on formulation and fine-tuning is shortened by 50%. In short, the efficiency of talent training management in vocational education is improved.
Extending dynamic logic with refinements of abstract actions

Abstract Humans can make a plan by refining abstract actions. Dynamic logic, which enables reasoning about the dynamics of actions, does not support this form of planning. This paper investigates the extension of dynamic logic with a refinement relation that specifies how an abstract action can be refined into a more specific (composite) action. The paper investigates the properties of the refinement relation, the derivation of the refinement relation and a proof system based on a prefixed-tableau.
Financial Market Infrastructure and Implementation of Blockchain Technology

Blockchain is very crucial for paving new ways for how economies will function in the future, as it helps in eliminating intermediaries and thus saving time and cost. Various important uses of this new blockchain technology have been highlighted, but research and practice are still in their formative phases. The core objective of this study is to describe the impact of blockchain on existing and novel business prototypes while managing operational risk. Consequently, the authors pinpoint five similarities that improve the understanding of how blockchain technology affects current financial structure and creates new business models. This research suggested using these findings to ascertain further designs initiated by blockchain and how firms can use it to revolutionize its business model. Though there are many favorable uses of this new technology, inquiry and established procedures are still in their embryonic stages, which means it will take some time before blockchain's impact is felt on a larger scale.
Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines

We propose three techniques for improving accuracy and speed of margin stochastic gradient descent support vector machines (MSGDSVM). The first technique is to use sampling with full replacement. The second technique is to use the new update rule derived from the squared hinge loss function. The third technique is to limit the number of values for tuning of the margin hyperparameter M. We also provide theoretical analysis of a novel optimization problem for the proposed update rule. The first two techniques improve accuracy of MSGDSVM and the last one speed of tuning. Experiments show that the proposed method achieves superior accuracy compared to MSGDSVM for binary and multiclass classification, with similar generalization performance to sequential minimal optimization (SMO) and is faster than MSGDSVM.
Intra and Inter Class Consistency Domain Adaptation for Semantic Segmentation

We consider the problem of unsupervised domain adaptation for the task of semantic segmentation. Existing methods mainly focus on matching the marginal distributions between two domains from the global level through adversarial learning. However, this global alignment method does not consider the class-level joint distribution, which will increase the domain invariance of class features but weaken the discriminability. In this paper, we propose a novel category-level domain adaptation network for semantic segmentation, which explicitly reduces the divergence of intra-class features and enhances the separability of inter-class features. Extensive experiments verify the effectiveness of our proposed IaICC model on challenging unsupervised domain adaptation tasks, i.e., GTA5-Cityscapes.
Car Detection and Recognition using Deep Learning Techniques

The paper focuses on the study and practices of Deep Learning techniques. We propose a novel architecture to detect and segregate different classes of cars using the You Only Look Once (YOLO) algorithm. For real-time object detection YOLO algorithm is used as it is quick in performing a single convolutional neural network. This paper also uses object localization techniques to locate and detect multiple cars in realtime systems. For categorization and filtering of cars, the MobileNet architecture was used. Datasets like COCO and Stanford are used to train the algorithms. We aim to detect all the cars in a single frame of the footage and categorize each car according to its feature into either big/heavy or small/light categories.
Optical Handwritten with Character Recognition

Optical Written with character recognition is a software where you can upload any of you written text and it will be converted into a digital form. This will further provide the user an opportunity to do whatever they want to do with the digital text. The focus of this project will be on 4 main types of recognition. The first one is simple character recognition which will recognize the alphabetic characters that are either handwritten or in digital format (in pdf etc.). The second one will be the cheque recognition type in which the software will recognize the content of a cheque (alphabets, cash, date, and bank account etc.). The third one will be the postal address recognition which will recognize the address written on a mail. The fourth and last one will be the menu card recognition which will recognize the dishes and the amounts that are written on the menu card. We used the CNN (Convolution Neural Network) & ResNet (Residual Network) to make our software.
The Field Intensity Study of a Three-Phase AC Power Line

The purpose of the report is to create an neuron network that can provide information on the levels of electromagnetic fields created around conductors of an alternating current power line. The simulated is made by Matlab/Simulink, the received results are shown graphically.
A Survey of Person Re-identification Based on Deep Learning

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning.
Novel Data Mining Approach Predicting Alerts in The Telecom Industry

Predictive maintenance is one of the top trending new concepts in event prediction especially in light of the recent interests surrounding Artificial Intelligence and Machine Learning. It comes with a slew of useful tools for forecasting events. For example, one of the key difficulties facing the mobile telecom business is service unavailability. At the moment, we only have reactive procedures in place to solve service unavailability. But the time it takes to fix it is costing money. If we found the behavior that leads to service disruptions in our network, we can predict them using AI algorithms, which would drastically improve customer experience and reduce revenue loss. Data mining techniques are one of the main AI algorithms for event prediction. The purpose of this paper is to demonstrate how we can tackle this legacy challenge using intense data mining methods. The study proposes a new approach for event prediction that employs concurrency for Rare Association Rule Mining (RAR). The proposed method is a prediction with a suggested course of action to avoid consequences.
Combined use of band shape algorithm, linear spectral un-mixing on Clementine &amp; Moon Mineralogy Mapper data for identifying the imprints of magmatic differentiation – A study around Aristarchus Plateau

In this study, we have implemented a band shape algorithm (BSA) to identify the surface distribution of different minerals on parts of the Aristarchus Plateau using selected bands of Clementine and hyperspectral Moon Mineralogy Mapper (M3) data. BSA mineral maps derived using spectral bands of Clementine and M3 sensors could determine broadly similar surface distribution of minerals in the study area. Here we show that M3 based BSA mineral map could detect additional mineralogical details in selected places, especially around Vallis Schroteri, Aristarchus etc. areas. Therefore, we implemented linear spectral un-mixing (LSU) method on hyperspectral bands of M3 data using the reference spectra of Reflectance Experiment Laboratory (RELAB) to estimate the relative abundance of different minerals in above-mentioned areas. Based on the relative abundance of minerals in the LSU map, we could infer the presence of different rocks such as dunite/troctolite, norite, harzburgite, olivine-pyroxenite in those sites of the study area. The relative abundance of each mineral in a particular place was used as the basis for delineating specific rock in that place of the study area. These mineralogically diverse rocks with distinct proportions of mafic mineral and plagioclase content are exposed along a lunar structure that has been delineated using Gravity Recovery and Interior Laboratory (GRAIL) data. We hypothesize that some of these rocks might have formed by a magmatic differentiation process triggered by the structure by separating magma from the crystallized rocks and allowing the magma to compositionally evolve to produce different rocks under the influence of syn-magmatic structural activity.
Bayesian Information Fusion for Imprecise Probabilistic Models with Different Types of Information

A novel approximate Bayesian information fusion method is proposed based on Wasserstein distance and it is applied it to imprecise probabilistic models with different types of information. The proposed method combines the principle of maximum relative entropy with approximate Bayesian computation and uses the Wasserstein distance to perform the approximate Bayesian computations. The key benefit of this approach is the capability to handle different types of information, such as point observed data and moment information. To verify the effectiveness of the proposed method, we apply it to a simple supported beam problem. The results are analyzed towards accuracy and the proposed method is compared to the classical Bayesian approach combined with maximum relative entropy.
A New Image Scrambling Technique Only With A Specific Permutation

Chaos theory consistently plays an activerole in modern cryptography. The appeal of using chaos as the basis for the development of a cryptosystem lies primarily in its random behavior and its sensitivity to initial conditions and parameter settings that meet traditional Shannon requirements of confusion and diffusion. In this paper, just with a specific permutation we have created an effective image transformation we consider this last process as a phase of confusion. A shuffled image based on dynamic P-Table is presented, in which the P-Table are constructed by chaotic systems. The plain image is divided into edges in which the pixels are permuted by P-Table and in order to crush the correlation of adjacent pixels. After shuffling previous edges, we use a new chaotic sequence for scrambling the next edges. This technique named the inverse blending of pixels from the edges to the inwards. The results of this technique were studied using a one-dimensional function PWLCM map and then through another two-dimensional function HENON- LOZI map and we compared our technique with some recent scrambling techniques. In the end, experimental images and graphical curves show that the proposed method has excellent scrambling performance and achieves a high degree of harmonic security and that has a secret key of 177 bits in length. The results also show greater efficacy using on the twodimensional chaotic function than the one-dimensional chaotic map.