Reviewerly/exHarmony huggingface dataset:
Privacy and Security Aspects of E-Government in Smart Cities

E-government is an indispensable part of a Smart City. Information and communication technologies transform the relationship between citizens, businesses, and government departments, which enables the implementation of e-government, making operational processes efficient and speedy. This chapter investigates the current deployment strategies and the technological solutions of e-government in terms of security and privacy in a Smart City environment; it also identifies the challenges of adoption. In addition, this chapter proposes a decentralized framework based upon blockchain and artificial intelligence to provide a secure and privacy-preserving infrastructure. The proposed framework integrates technologies to provide mutual trust between individuals, businesses, and governments, leading to a greater transparency of activity and less operational overhead. The reduction in process overhead results in lower running costs (therefore increasing revenue) and improves the speed of cross-boundary transactions.
Multiobjective e-commerce recommendations based on hypergraph ranking

Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Verifiable Sealed-Bid Auction on the Ethereum Blockchain

The success of the Ethereum blockchain as a decentralized application platform with a distributed consensus protocol has made many organizations start to invest into running their business on top of it. Technically, the most impressive feature behind the success of Ethereum is its support for a Turing complete language. On the other hand, the inherent transparency and, consequently, the lack of privacy poses a great challenge for many financial applications. In this paper, we tackle this challenge and present a smart contract for a verifiable sealed-bid auction on the Ethereum blockchain. In a nutshell, initially, the bidders submit homomorphic commitments to their sealed-bids on the contract. Subsequently, they reveal their commitments secretly to the auctioneer via a public key encryption scheme. Then, according to the auction rules, the auctioneer determines and claims the winner of the auction. Finally, we utilize interactive zero-knowledge proof protocols between the smart contract and the auctioneer to verify the correctness of such a claim. The underlying protocol of the proposed smart contract is partially privacy-preserving. To be precise, no information about the losing bids is leaked to the bidders. We provide an analysis of the proposed protocol and the smart contract design, in addition to the estimated gas costs associated with the different transactions.
A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis

This research proposes a novel genetic algorithm-based online gradient boosting (GAOGB) model for incremental breast cancer (BC) prognosis. The development of clinical information collection technologies has brought in increasingly large amounts of stream data for BC research. Traditional batch learning models have shown limitations in: (1) real-time prognosis accuracy from losing the information of incremental changes of a patient’s pathological condition by time; (2) high redundancy due to the time required to retrain models every time new data are received. Online boosting is an efficient technique for learning from data streams. However, difficulties in parameter assignment and the lack of adaptiveness for batch learning base learners can degrade the performances of typical online boosting algorithms. The main objective of this research is to propose an incremental learning model for BC survivability prediction. To render a boosting algorithm with superiority in global optimal parameters, the genetic algorithm (GA) is integrated to an online gradient boosting scenario at the parameter selection phase, enabling real-time optimization. To enhance adaptiveness, an adaptive linear regressor is adopted as the base learner with minimal computational efforts, and updated in symphony with the online boosting model. The proposed GAOGB model is comprehensively evaluated on the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program breast cancer dataset in terms of accuracy, area under the curve (AUC), sensitivity, specificity, retraining time, and variation at each iteration. Experimental results show that the proposed GAOGB model achieves statistically outstanding online learning effectiveness. With a highest 28% improvement on testing accuracy over its base learners, outperforming current state-of-art online learning methods, and approximating batch learning boosting algorithms, the GAOGB algorithm validates the impact of parameter, adaptiveness and convergence in devising practical online learning algorithms. The proposed GAOGB model demonstrates potential for practical incremental breast cancer prognosis, promising a combination of training effectiveness and efficiency.
Metrics for analyzing variability and its implementation in software product lines: A systematic literature review

Context: Software Product Line (SPL) development requires at least concepts for variability implementation and variability modeling for deriving products from a product line. These variability implementation concepts are not required for the development of single systems and, thus, are not considered in traditional software engineering. Metrics are well established in traditional software engineering, but existing metrics are typically not applicable to SPLs as they do not address variability management. Over time, various specialized product line metrics have been described in literature, but no systematic description of these metrics and their characteristics is currently available. Objective: This paper describes and analyzes variability-aware metrics, designed for the needs of software product lines. More precisely we restrict the scope of our study explicitly to metrics designed for variability models, code artifacts, and metrics taking both kinds of artifacts into account. Further, we categorize the purpose for which these metrics were developed. We also analyze to what extent these metrics were evaluated to provide a basis for researchers for selecting adequate metrics. Method: We conducted a systematic literature review to identify variability-aware implementation metrics. We discovered 42 relevant papers reporting metrics intended to measure aspects of variability models or code artifacts. Results: We identified 57 variability model metrics, 34 annotation-based code metrics, 46 code metrics specific to composition-based implementation techniques, and 10 metrics integrating information from variability model and code artifacts. For only 31 metrics, an evaluation was performed assessing their suitability to draw any qualitative conclusions. Conclusions: We observed several problematic issues regarding the definition and the use of the metrics. Researchers and practitioners benefit from the catalog of variability-aware metrics, which is the first of its kind. Also, the research community benefits from the identified observations in order to avoid those problems when defining new metrics.
UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size K-means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.
Fast short-term global solar irradiance forecasting with wrapper mutual information

Accurate solar irradiance forecasts are now key to successfully integrate the (variable) production from large solar energy systems into the electricity grid. This paper describes a wrapper forecasting methodology for irradiance time series that combines mutual information and an Extreme Learning Machine (ELM), with application to short forecast horizons between 5-min and 3-h ahead. The method is referred to as Wrapper Mutual Information Methodology (WMIM). To evaluate the proposed approach, its performance is compared to that of three dimensionality reduction scenarios: full space (latest 50 variables), partial space (latest 5 variables), and the usual Principal Component Analysis (PCA). Based on measured irradiance data from two arid sites (Madina and Tamanrasset), the present results reveal that the reduction of the historical input space increases the forecasting performance of global solar radiation. In the case of Madina and forecast horizons from 5-min to 30-min ahead, the WMIM forecasts have a better coefficient of determination (R2 between 0.927 and 0.967) than those using the next best performing strategy, PCA (R2 between 0.921 and 0.959). The Mean Absolute Percentage Error (MAP) is also better for WMIM [7.4–10.77] than for PCA [8.4–11.55]. In the case of Tamanrasset and forecasting horizons from 1-h to 3-h ahead, the WMIM forecasts have an R2 between 0.883 and 0.957, slightly better than the next best performing strategy (PCA) (R2 between 0.873 and 0.910). The Normalized Mean Squared Error (NMSE) is similarly better for WMIM [0.048–0.128] than for PCA [0.105–0.130]. It is also found that the ELM technique is considerably more computationally efficient than the more conventional Multi Layer Perceptron (MLP). It is concluded that the proposed mutual information-based variable selection method has the potential to outperform various other proposed techniques in terms of prediction performance.
Establishing a software defect prediction model via effective dimension reduction

With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%.
Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images

Computer Aided Diagnosis (CAD) systems can support physicians in classifying different kinds of breast cancer, liver cancer and blood tumours also revealed by images acquired via Computer Tomography, Magnetic Resonance, and Blood Smear systems. In this regard, this survey focuses on papers dealing with the description of existing CAD frameworks for the classification of the three mentioned diseases, by detailing existing CAD workflows based on the same steps for supporting the diagnosis of these tumours. In detail, after an appropriate acquisition of the images, the fundamental steps carried out by a CAD framework can be identified as image segmentation, feature extraction and classification. In particular, in this work, specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods. The cited methodology is focused on sharing and reviewing an amount of specific works. Then, the performance of three selected case studies are carefully reported, designed with the aim of showing how final outcomes can vary according to different choices in each step of the adopted workflow. More in detail, these case studies concern with breast images acquired by Tomosynthesis and Magnetic Resonance, hepatocellular carcinoma images acquired by Computed Tomography and enhanced by a triphasic protocol with a contrast medium, peripheral blood smear images for cellular blood tumours and are used to compare their performance.
Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning

The exact measure of mitotic count is one of the crucial parameters in breast cancer grading and prognosis. Detection of mitosis in standard H & E stained histopathology images is challenging due to diffused intensities along object boundaries and shape variation in different stages of mitosis. This paper explores the feasibility of transfer learning for mitosis detection. A pre-trained Convolutional Neural Network is transformed by coupling random forest classifier with the initial fully connected layers to extract discriminant features from nuclei patches and to precisely prognosticate the class label of cell nuclei. The modified Convolutional Neural Network accurately classify the detected cell nuclei with limited training data. The designed framework accomplishes higher classification accuracy by carefully fine tuning the pre-trained model and pre-processing the extracted features. Moreover, proposed method is evaluated on MITOS dataset provided for the MITOS-ATYPIA contest 2014 and clinical data set from Regional Cancer Centre, Thiruvananthapuram, India. Significance of Convolutional Neural Network based method is justified by comparing with recently reported works including a Multi Classifier System based on Deep Belief Network. Experiments show that the pre-trained Convolutional Neural Network model outperforms conventionally used detection systems and provides at least 15% improvement in F-score on other state-of-the-art techniques.
High-Quality Image Captioning With Fine-Grained and Semantic-Guided Visual Attention

The soft-attention mechanism is regarded as one of the representative methods for image captioning. Based on the end-to-end convolutional neural network (CNN)-long short term memory (LSTM) framework, the soft-attention mechanism attempts to link the semantic representation in text (i.e., captioning) with relevant visual information in the image for the first time. Motivated by this approach, several state-of-the-art attention methods are proposed. However, due to the constraints of CNN architecture, the given image is only segmented to the fixed-resolution grid at a coarse level. The visual feature extracted from each grid indiscriminately fuses all inside objects and/or their portions. There is no semantic link between grid cells. In addition, the large area “stuff” (e.g., the sky or a beach) cannot be represented using the current methods. To address these problems, this paper proposes a new model based on the fully convolutional network (FCN)-LSTM framework, which can generate an attention map at a fine-grained grid-wise resolution. Moreover, the visual feature of each grid cell is contributed only by the principal object. By adopting the grid-wise labels (i.e., semantic segmentation), the visual representations of different grid cells are correlated to each other. With the ability to attend to large area “stuff,” our method can further summarize an additional semantic context from semantic labels. This method can provide comprehensive context information to the language LSTM decoder. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can accurately link the relevant visual information with each semantic meaning inside the text. Demonstrated by three experiments including both qualitative and quantitative analyses, our model can generate captions of high quality, specifically high levels of accuracy, completeness, and diversity. Moreover, our model significantly outperforms all other methods that use VGG-based CNN encoders without fine-tuning.
Phrase-based image caption generator with hierarchical LSTM network

Automatic generation of caption to describe the content of an image has been gaining a lot of research interests recently, where most of the existing works treat the image caption as pure sequential data. Natural language, however possess a temporal hierarchy structure, with complex dependencies between each subsequence. In this paper, we propose a phrase-based image captioning model using a hierarchical Long Short-Term Memory (phi-LSTM) architecture to generate image description. In contrast to the conventional solutions that generate caption in a pure sequential manner, phi-LSTM decodes image caption from phrase to sentence. It consists of a phrase decoder to decode the noun phrases of variable length, and an abbreviated sentence decoder to decode the abbreviated form of the image description. A complete image caption is formed by combining the generated phrases with sentence during the inference stage. Empirically, our proposed model shows a better or competitive result on the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the art models. We also show that our proposed model is able to generate more novel captions (not seen in the training data) which are richer in word contents in all these three datasets.
Active Object Detection With Multistep Action Prediction Using Deep Q-Network

In recent years, great success has been achieved in visual object detection, which is one of the fundamental tasks in the field of industrial intelligence. Most of existing methods have been proposed to deal with single well-captured still images, while in practical robotic applications, due to nuisances, such as tiny scale, partial view, or occlusion, one still image may not contain enough information for object detection. However, an intelligent robot has the capability to adjust its viewpoint to get better images for detection. Therefore, active object detection becomes a very important perception strategy for intelligent robots. In this paper, by formulating active object detection as a sequential action decision process, a deep reinforcement learning framework is established to resolve it. Furthermore, a novel deep Q-learning network (DQN) with a dueling architecture is proposed, the network has two separate output channels, one predicts action type and the other predicts action range. By combining the two output channels, the action space is explored more efficiently. Several methods are extensively validated and the results show that the proposed one obtains the best results and predicts action in real time.
Controlling Polarization in Personalization

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can learn and propagate systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms that are constrained to combat bias and the resulting echo-chamber effect. We propose a versatile framework that allows for the possibility to reduce polarization in personalized systems by allowing the user to constrain the distribution from which content is selected. We then present a scalable algorithm with provable guarantees that satisfies the given constraints on the types of the content that can be displayed to a user, but -- subject to these constraints -- will continue to learn and personalize the content in order to maximize utility. We illustrate this framework on a curated dataset of online news articles that are conservative or liberal, show that it can control polarization, and examine the trade-off between decreasing polarization and the resulting loss to revenue. We further exhibit the flexibility and scalability of our approach by framing the problem in terms of the more general diverse content selection problem and test it empirically on both a News dataset and the MovieLens dataset.
Facial expression recognition with local prominent directional pattern

Local edge-based descriptors have gained much attention as feature extraction methods for facial expression recognition. However, such descriptors are found to suffer from unstable shape representations for different local structures for their sensitivity to local distortions such as noise and positional variations. We propose a novel edge-based descriptor, named Local Prominent Directional Pattern (LPDP), which considers statistical information of a pixel neighborhood to encode more meaningful and reliable information than the existing descriptors for feature extraction. More specifically, LPDP examines a local neighborhood of a pixel to retrieve significant edges corresponding to the local shape and thereby ensures encoding edge information in spite of some positional variations and avoiding noisy edges. Thus LPDP can represent important textured regions much effectively to be used in facial expression recognition. Extensive experiments on facial expression recognition on well-known datasets also demonstrate the better capability of LPDP than other existing descriptors in terms of robustness in extracting various local structures originated by facial expression changes.