Reviewerly/exHarmony huggingface dataset:
Nuclear Density Distribution Feature for Improving Cervical Histopathological Images Recognition

Cervical carcinoma is a common type of cancer in the female reproductive system. Early detection and diagnosis can facilitate immediate treatment and prevent progression of the disease. However, in order to achieve better performance, DL-based algorithms just stack various layers with low interpretability. In this paper, a robust and reliable Nuclear Density Distribution Feature (NDDF) based on priors of the pathologists to promote the Cervical Histopathological Image Classification (CHIC) is proposed. Our proposed method combines the nucleus mask segmented by U-Net with the segmentation grid-lines generated from pathology images utilizing SLIC to obtain the NDDF map, which contains information about the morphology, size, number, and spatial distribution of nuclei. The result shows that the proposed model trained with NDDF maps has better performance and accuracy than that trained on RGB images (patch-level histopathological images). More significantly, the accuracy of the two-stream network trained with RGB images and NDDF maps is steadily improved over the corresponding baselines of different complexity.
Summary of Research on News Text Classification

With the rapid development of modern technology and the rapid spread of network information, the way people obtain news information has gradually shifted from traditional paper documents to electronic text. How to efficiently classify these electronic news texts has become a current research hotspot. In this paper, we introduce the concept of text classification and its main process, then summarize the existing classification methods and challenges now, as well as forecast the future trends.
Practical Approach for Apps Design in Compliance With Accessibility, Usability, and User Experience

There is a huge proliferation of digital products on the market today for both large enterprises and small businesses. Most of these companies have experienced the development of software products for the mobile market and have been faced with the major challenge of capturing the customer's attention. There is a great focus on making a great first impact and providing the audience with the best possible digital experience. Accordingly, issues related to usability, accessibility, and user experience are extremely relevant. This chapter addresses how these practices can be used in practice by building an app that offers car cleaning services. Several approaches based on building app interfaces that increase user engagement and retention levels are explored and discussed.
Entropy analysis of the discrete-time quantum walk under bit-flip noise channel

We study the behavior of tunable one-dimensional discrete-time quantum walk (DTQW) in the presence of decoherence modeled by the flip-bit noise channel. By varying the noise intensity, we obtain a wide range of probability distributions of noisy walks, which can be loosely characterized as pure quantum walk, quantum-like walk, semi-classical like walk, and classical-like walk. We show the maximum Shannon entropy of the walk is not obtained under maximum decoherence, but instead at a lower degree of decoherence. This result may be useful for the implementation of quantum error correction, quantum cryptography, and quantum communication protocol, where one might expect the qubit internal state to be flipped due to noise.
Open-Set Person Re-Identification Through Error Resilient Recurring Gallery Building

In person re-identification, individuals must be correctly identified in images that come from different cameras or are captured at different points in time. In the open-set case, the above needs be achieved for people who have not been previously recognised. In this paper, we propose a universal method for building a multi-shot gallery of observed reference identities recurrently online. We perform L2-norm descriptor matching for gallery retrieval using descriptors produced by a generic closed-set re-identification system. Multi-shot gallery is continuously updated by replacing outliers with newly matched descriptors. Outliers are detected using the Isolation Forest algorithm, thus ensuring that the gallery is resilient to erroneous assignments, leading to improved re-identification results in the open-set case.
Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study.
Feasibility study of launching mobile-based postgraduate research products based on artificial intelligence

As people's enthusiasm for postgraduate entrance examinations has increased, mobile postgraduate entrance examination products have become increasingly important in the product market. By collecting the prices of domestic postgraduate entrance examination products from 2016 to 2017, a polynomial fitting curve mathematical model is established to predict mobile postgraduate entrance examinations. The purpose of the product price change trend. Aiming at the potential market share and development trend of the mobile end-of-study product, a Markov chain prediction method model and multivariate fitting are combined with genetic algorithms to perform intelligent prediction, and the contribution of each indicator is allocated according to the weight to obtain The relative importance of the indicators completes the possibility of launching a mobile end-of-year research product. The study also found that in different cities, the mobile terminal's feasibility is the same in different cities, and the model can indeed solve relatively many different universities. The city fully considers the characteristics of economic, social conditions and postgraduate education, and evaluates the possibility of launching mobile terminal postgraduate research products through computer artificial intelligence algorithm models.
ASBNN: Acceleration of Bayesian Convolutional Neural Networks by Algorithm-hardware Co-design

Bayesian Convolutional Neural Networks (BCNNs) have been proposed to address the problem of model uncertainty in conventional neural networks. By treating weights as distributions rather than deterministic values, BCNNs mitigate the problem of overfitting, training with a small amount of data, and uncertainty evaluations. However, computing the distributions of BCNN outputs is time- and energy-consuming because it requires computing multiple forward passes.To address this computational problem, we propose a novel algorithm-hardware co-design approach with an approximation algorithm and hardware support for the rapid computation of BCNN. Our observations of the absolute number of each layer’s input and the input difference among multiple forward passes show that most of these values are significantly small compared with other large values. Our algorithm treats these small values as zero and makes them sparser. The extracted sparsity allows us to skip most multiplications. As a result, it achieves a computation reduction of 81.1 % in classification tasks and 77.7 % in regression tasks. Additionally, to support the algorithm-level approximation on hardware, we propose a novel dataflow that is specialized for our algorithm, and develop a new accelerator architecture, accelerator for sparse Bayesian Neural Networks (ASBNN), that can handle sparsity extracted by the algorithm. Our evaluation demonstrates that the ASBNN successfully exploits the algorithmic computation reduction to improve the computation time by 3.3× and energy efficiency by 3.7× compared with the naive implementation of dense BCNN accelerators.
Multi-View Normalization For Face Recognition

Unlike the majority of face normalization that focuses on single-view frontalization, we propose the Multi-View Normalization (MVN) framework to normalize an arbitrary face to multiple desired poses with balanced illumination and neutral expression. Taking the advantages of generative and adversarial learning, the proposed MVN transforms a face into a set of multi-view faces with facial identity well preserved, offering a better representation to handle face recognition. The MVN is designed to learn the transformation from a input set to seven output sets. The input set contains faces collected in the wild with arbitrary poses, lighting conditions and expressions. The seven output sets include seven poses from $0^{\circ}\sim 90^{\circ}$ in yaw with $15^{\mathrm{\circ}}$ interval with balanced illumination and neutral expression. The MVN is composed of one face encoder, seven pose-specific generators and seven sets of discriminators. The encoder is made of a face recognition expert network, which is not updated during training and acts as a facial feature extractor. The generators are trained to transform the input set to the seven output sets. The discriminators are trained to not only ensure the photo-realistic quality of the generated faces, but also force the poses of the generated faces to the desired poses with corresponding facial appearances. Experiments on several benchmark datasets show that the proposed MVN demonstrates a competitive performance to state-of-the-art approaches.
Governing changes in government: the water story of Mahabubnagar

Theoretical basis Kotter’s framework of change management adapted to the situation of public policy implementation under the leadership of a civil service officer in the rural areas of a developing economy in South Asia. Research methodology This case has been written using the primary data collected from the protagonist through personal and computer-based interviews. Some of the documents associated with the event shared by the protagonist are also reproduced as case exhibits. Secondary data from government official websites were also used to enrich the case. Case overview/synopsis Mahbubnagar, an arid agricultural district in central India faced the threat of a water crisis owing to the unscientific water extraction by the resident farmers. The government appointed a task force to investigate the problem. The team executed the idea to harvest excess water from the fields through a cheap and efficient method. Though it showed spectacular results in the initial months, the farmers gave up the innovation soon. When the team met two years later, they were shocked by the unenthusiastic response of the farmers. This case pertains to the failure of policy innovations and change management in government. Complexity academic level This case is useful for undergraduate-level courses in public management, public policy and governance with modules in change management, innovation management, rural development and programme implementation. Training modules for novice public service professionals and programme management personnel in government organisations. Elective courses on public policy, government relations and public sector management for undergraduate students of business administration.
Identification of Congestive Heart Failure Patients Through Natural Language Processing

Research in biomedical field requires technical infrastructures to deal with heterogeneous and multi-sourced big data. Biomedical informatics primarily use data in electronic health to better understand how diseases spread or to gather new insights from patient history. One of the prominent use cases of electronic health records is identification of patient cohort (group) with specific disease or some common characteristics, so that useful inferences may be drawn via these records. This paper proposes a methodology for identification and analysis of cohorts for patients having congestive heart failure problem among obesity patients. This may help doctors and medical researchers in predicting outcomes, survival analysis of patients, clinical trials, and other types of retroactive studies. cTAKES tool was used to apply natural language processing technique in order to identify patients belonging to a particular cohort. All clinical terms were identified and were mapped to its matching terms in the UMLS Metathesaurus. Also, negated statements were detected and removed from the final cohort. The method is reasonably automated and achieves accuracy, precision, recall, and F-score values of 0.970, 0.972, 0.958, and 0.965, respectively. Results were compared against the experts annotations. Additionally, manual review of clinical records was performed for further validation.
Color-indoor: Incorporating Depth into Room Decoration Visualization

Combined with computer vision technology, we propose an system to automatically visualize the decoration effect of 3D complex indoor scenes, named Color-indoor. Given a preferred color and RGB-D images, the Color-indoor system can be used for color replacement, editing texture, and synthesizing 3D result for specified semantic regions of the input image. The key idea of the proposed Color-indoor is leveraging depth information to guide the entire segmentation process and 3D data synthesis. We propose an depth-fusion criss-cross attention semantic segmentation framework (DFCCN) for parsing the indoor semantic scene, and introduce a depth branch to better extracted geometry information from different semantic areas. We utilize DFCCN to extract and fuse features from RGB branch and depth branch, so that the segmentation network can obtain more geometry information and enrich the structural details of features. Located the specified semantic regions, a simple yet effective editing algorithm is proposed for color and texture replacement. Combined the camera parameters, the 3D data synthesis algorithm are used to generate 3D results from edited images and depth images. For training and testing, we set up a new RGB-D dataset upon NYUv2 including 6 semantic labels. The experimental and visual results are demonstrated that our proposed Color-indoor can generate harmonious 3D results.
Construction Method of Network Teaching Resources Based on Deep Learning

In this paper, the construction method of network teaching resources based on deep learning is researched. In thispaper, a teaching resource integration method based on convolutional neural network is designed, including thedesign of Arduino device identification program based on convolutional neural network and the construction ofArduino device learning resource library. Based on this, this paper proposes a network multimedia teachingresource integration system based on constrained clustering algorithm. In order to realize the system, the usecases of the system are analyzed. On the basis of use case analysis, the function and overall architecture of thesystem are designed respectively, and the collaborative recommendation module is mainly designed. In order toimprove the accuracy of recommendation, this paper uses constrained clustering algorithm to complete theintegration of network teaching resources. The experimental results show that the system can improve theaccuracy of recommendation matching and the integration efficiency of network teaching resources.
Patient’s Medical Data Security via Bi Chaos Bi Order Fourier Transform

Telemedicine is used in wireless communication networks to detect and treat illnesses of patients in isolated areas. The transfer of electronic patient data is also considered one of the most critical and confidential data in information systems but because of lack of protection standards in online communication, hackers and others are often vulnerable to medical information. Therefore, sending medical information over the network needs a powerful encryption algorithm such that it is immune against various online cryptographic attacks. Among the three security destinations for the security of data frameworks to be specific privacy, trustworthiness, and accessibility, privacy is the most significant perspective that should be taken a lot of care. It is also very important to ensure the protection of patients’ privacy. In this paper, we have combined the two chaotic maps for increasing the complexity level and blend it with the fractional Fourier transform with order m. The simulation is done on MATLAB platform, and the analysis is done through the PSNR, MSE, SSIM, and Correlation Coefficient.
Offline Signature Verification Using Galois Field-Based Texture Representation

Signature verification has been one of the popular research areas with face recognition being one among the other physiological traits in biometric recognition. Offline signatures can be treated as texture images, and thus, texture representation methods can be applied to such images. One such texture representation is based on Galois fields. In this work, after application of Galois field operator, the cumulative histogram is built, and it is normalized. Thus, obtained bin values are considered as features of a signature image. k-NN classifier is used for offline signature verification. Experiments conducted on the benchmark dataset, namely GPDS synthetic signature database, have supported the application of the proposed method.
Geographic Information System Of Fishing Spot Tourism Search In Sidoarjo Website Based

In 2020 fishing hobby is increasingly popular with young people, especially in the Sidoarjo area. In addition to wanting to feel the sensation of fishing, in some fishing tourism locations there are usually many additional facilities to attract the place such as coffee shop facilities, lesehan, toys for children and so on which are used as time to gather with families and organizations. The Fishing Tourism Location Information System that is currently running in the Sidoarjo area is still using the method of word of mouth, so it will take a lot of time so that the location can be known by various people, especially the newly established fishing tourism location. The results of this study are to help fishing tourism owners promote their tours so that they are easy to find, and to help anglers make it easier to find fishing spots that suit their desires.
Semasuperpixel: A Multi-Channel Probability-Driven Superpixel Segmentation Method

Superpixel, an efficient image segmentation approach, aggregates a group of similar pixels into the same cluster. Existing superpixel algorithms still mainly focus on the color information while ignoring the semantic distribution knowledge. In this paper, we propose a semantic information-driven method that adopts multi-channel semantic probabilities for superpixel segmentation. By conducting statistical analysis on the semantic output and then formulating the distance measure, the prior knowledge of the semantic with a dynamic confidence value could be utilized by our method during the global update effectively. Extensive experimental evaluations show that our method achieves a leading segmentation quality and convergence speed, compared to other five state-of-the-art algorithms, as measured by boundary recall, undersegmentation error, and explained variation.
Selective Deep Convolutional Framework for Vehicle Detection in Aerial Imagery

Advancements in computer vision techniques have played a significant role in object detection. Vehicle detection from a moving platform is an exigent problem. Researchers of automated vehicle detection systems need to focus on designing a system robust to the environmental variations and reducing the processing time. Due to this complexity, there is a necessity of employing different strategies for structured object detection with requisite precision. The work carried out in this paper compares handcrafted features such as SIFT and SURF with novel shape descriptors like convolutional neural network (CNN). CNNs are proven to be powerful in extracting sparse object information present in the images. Moreover, fine-tuning of CNNs using feature ontology is adopted in this work to increase the discriminative power of descriptors. A semantic classification model proposed in this paper intends to employ modern embedding and aggregating methods which considerably enhance feature discriminability and boost the performance of the CNN. The performance of this framework is exhaustively tested across a wide dataset. The intuitive and robust systems that use these techniques play a vital role in various sectors like security, military, automation, industries, medical, and robotics.
Ensuring Security of Digital Voting Through Blockchain Technology

Recently, blockchains have found a place in all sorts of industries, ranging from healthcare to the government sector, finance, real estate and utilities. Blockchain has proved to be advantageous to the applications which previously required a central authority and a trusted intermediary, as now blockchain allows them to operate in a decentralised manner and helps them to achieve the same functionality much more efficiently. Transactions in blockchains are trustless, that is, the involved parties without knowing each other and without establishing any trust relations, sit in any corner of the world and such transactions thus occur without any trusted middlemen. For authorisation of these transactions, strong cryptographic principles, which form an essential part of the blockchain infrastructure, are used. Smart contracts basically add a layer of logic and computation to the already existing trust infrastructure of the blockchain, and thus allows for appropriate, heavily automated and distributed workflows, to function efficiently. In this chapter, we outline the art of using smart contracts and blockchain technology to implement a decentralized, secure, transparent yet anonymous digital voting system.
Test for high-dimensional mean vector under missing observations

In this paper, we examine the problem of testing for high-dimensional mean vector under missing observations. By assuming missing at random, a test statistic is proposed and the asymptotic distribution is established pursuant to the framework that the sample size and the dimension of population both tend to infinity. Simulation studies show that the test procedure performs well in various situations.
CNN-Based Video Codec Classifier For Multimedia Forensics

In video forensics, identification of codec type is complicated by a lack of standards compliant compressed bitstreams. Previous work is unable to identify codec types without actually decoding the file successfully. This paper presents a CNN classifier derived from the AlexNet architecture that can detect codec types without decoding the bitstream. It is based on classification of the raw bitstream data itself without decoding. The algorithm is tested on real data in a video forensics setting as well as user generated content supplied in the YouTube test set. Our results show better than 96.73% accuracy with over 43 combinations of codec/containers and, at least, 88.59% accuracy at 20% data corruption across both test sets.