Reviewerly/exHarmony huggingface dataset:
Improving the Privacy of Tor Onion Services

Onion services enable bidirectional anonymity for parties that communicate over the Tor network, thus providing improved privacy properties compared to standard TLS connections. Since these services are designed to support server-side anonymity, the entry points for these services shuffle across the Tor network periodically. In order to connect to an onion service at a given time, the client has to resolve the .onion address for the service, which requires querying volunteer Tor nodes called Hidden Service Directories (HSDirs). However, previous work has shown that these nodes may be untrustworthy, and can learn or leak the metadata about which onion services are being accessed. In this paper, we present a new class of attacks that can be performed by malicious HSDirs against the current generation (v3) of onion services. These attacks target the unlinkability of onion services, allowing some services to be tracked over time. To restore unlinkability, we propose a number of concrete designs that use Private Information Retrieval (PIR) to hide information about which service is being queried, even from the HSDirs themselves. We examine the three major classes of PIR schemes, and analyze their performance, security, and how they fit into Tor in this context. We provide and evaluate implementations and end-to-end integrations, and make concrete suggestions to show how these schemes could be used in Tor to minimize the negative impact on performance while providing the most security.
Internet-Based Healthcare Things Driven Deep Learning Algorithm for Detection and Classification of Cervical Cells

Cervical cancer has been one of the major health concerns as it has increased the death rates caused by cancer among women. However, its early detection can definitely have a huge impact by reducing the possible complications and other mishaps. Internet of Healthcare Things (IoHT) is termed for collectively addressing the unique set of healthcare devices which have been interconnected to the Internet so as to communicate and exchange data with each other. Deep Learning, one of the major subsets of Artificial Intelligence (AI), offers a plethora of algorithms which can be extensively utilized for cell detection and classification of the extracted images. Convolutional Neural Network (CNN) models can be used to analyse and survey the features and attributes which have been highlighted through the deep learning techniques. Despite the fact that cervical cancer is a highly preventable disease, the population ratio of women who have been affected and adversely exposed to its consequences are extremely high. This paper discusses about the techniques which can be used to detect the presence of cervical cancer by integrating IoHT and deep learning related algorithms.
A Process to Support the Remote Tree Testing Technique for Evaluating the Information Architecture of User Interfaces in Software Projects

Nowadays, due to technological advancement, people are bound to use multiple digital tools and interact with them through a User Interface. For this reason, User Experience (UX) is one of the most important keys to success. UX includes the design and evaluation of an adequate Information Architecture. To design Information Architecture, the best-known technique is Card Sorting. For evaluation, there is the Tree Testing or Reverse Card Sorting technique. This technique can be applied in remote or non-remote ways. We identified three main issues: 1. The remote processes to apply Tree Testing are not standardized; 2. the tools were modeled and built after a process defined by the supplier of those tools, most of which are now discontinued. 3. In many development projects these techniques are left aside, the Information Architecture is assessed using other methods and techniques (i.e. User Testing) in later phases of the project. These result in Information Architecture errors and defects found late or not found at all. Hence, this study is focused on developing and validating a standardized remote Tree Testing process with an emphasis on automation. To meet the objectives, we conducted interviews and literature reviews to find out how Tree Testing is currently carried out and what tools are used to support the technique (AS-IS workflow). Subsequently, we proposed a process that can support the technique, considering the pain points found on the current processes, resulting in a TO-BE workflow. As a result, a proposal of this standardized process was modeled using the notation BPMN, and validated by the expert judgment of specialists in Human-Computer Interaction (HCI). Finally, it is important to mention that Tree Testing is useful, practical in its remote application, and should be applied in the early stages of software projects; a tool to support the whole process should be implemented.
Explainable discrete Collaborative Filtering

Using hashing to learn the binary codes of users and items significantly improves the efficiency and reduces the space consumption of the recommender system. However, existing hashing-based recommender systems remain black boxes without any explainable outputs that illustrate why the system recommends the items. In this paper, we present a new end-to-end discrete recommendation framework based on the multi-task learning to simultaneously perform explainable and efficient recommendation. Toward this goal, an Explainable Discrete Collaborative Filtering (EDCF) method is proposed to preserve the user-item interaction features and semantic text features into binary hash codes by adaptively exploiting the correlations between the preference prediction task and the explanation generation task. At the online recommendation stage, EDCF makes efficient top-K recommendation by calculating the Hamming distances between the feature hash codes, and simultaneously generates natural language explanations for recommendation results through the explanation generation module. To obtain the hash codes directly from the end-to-end neural network, we introduce an attentive TextCNN and an Adaptive Tanh layer in the preference prediction task. For explanation generation, Long Short-Term Memory is employed to generate the explanations for recommendation results from the binary hash codes of user and item. Experiments demonstrate the superiority of the proposed method.
Deep Learning Based Framework for Breast Cancer Mammography Classification Using Resnet50

The early detection of breast cancer tumors by mammography is critical to minimizing the disease's complications and ensuring a successful cure. False-positive findings from a mammogram might lead to unnecessary biopsy procedures, which could harm a patient's chances of survival. Since the incidence of inaccurate diagnosis and misinterpretations of breast masses may be reduced by techniques that learn to distinguish breast masses, Conventional classification methods rely on domain-specific feature extraction approaches to solve a given issue. The various problems with feature-based methods are being addressed by deep learning algorithms, which are emerging as viable alternatives. Convolutional Neural Network (CNN)-based deep learning is used in this research to extract features at different densities and to distinguish between normal and suspicious areas in mammography. This paper includes a comparison of the VGG-16, MobileNetV2, and ResNet50 models. As can be shown, the testing accuracy of these models starts low in epoch 1 and rapidly improves until epoch 50. Finally, it achieves 97.22%, 98.61%, and 100.00% accuracy for VGG-16, MobileNetV2, and ResNet50, respectively. The suggested model's improvement and validation apply to traditional pathological techniques, which might potentially lessen pathologists' stress when predicting clinical outcomes by evaluating patients' mammography pictures.
Achieving Corruption-Transparency in Service Governance Processes with Blockchain-Technology Based e-Participation

Corruption takes place in public procurement by public servants through intermediaries due to the use of centralized systems and complicated processes. Blockchain and Web3 has the potential to remove these intermediaries, instead allowing institutions to build trust among public servants and citizens through a decentralized web. It is feasible to positively reinforce the transparency in tackling corruption in public procurement by establishing an e-participatory governance infrastructure using token economics from smart-contract blockchain technology. The overall success of public procurement in terms of service delivery to citizens is associated with citizen e-participation. Thus, increased e-participation through automated processes makes the government accountable and transparent in the provision of services that lead to the progress and economic growth of a country. In this paper, we investigate the potential of blockchain and smart-contracts to improve the efficiency, trust, and transparency of public procurement in the case of Afghanistan. Moreover, we identify the existing barriers namely lack of trust, transparency, the complexity of procurement documents, and inappropriate record-keeping system. To address these issues, we propose a blockchain-based e-participatory infrastructure to boost transparency by curbing public procurement corruption.
The Patentability of AI Invention

Artificial intelligence has gained momentum in the recent past due to technological advancements. As a result, it has elicited an endless debate particularly in light of intellectual property laws. Artificial Intelligence is currently considered as an emerging issue. This make it as a challenging issue for the existing legal frameworks which are unable to govern and regulate it in a proper and effective way. KSA has put more effort and resources in streamlining the legal aspects to govern AI and related innovations. The current research aims to explore some of the potential difficulties encountered in patentability of AI inventions. It also aims to determine the legal challenges the researcher expect to emerge with the development of AI technology. The findings of this study indicate that KSA still finds a huge gap in terms of laws that governs AI generated innovations. This study is significant in the sense that it has pointed out some of the challenges experienced in enacting effective intellectual property laws to govern AI innovations.
Learning Tone Curves for Local Image Enhancement

Image enhancement methods can be formulated as global transformations, local transformations, pixel-wise processing, or a mixture of these operations. Global transformations are limited in enhancing local image regions. Existing local and pixel-wise methods mitigate this issue, but give rise to the additional challenge of limited interpretability. Bridging the gap between global and local methods, we propose a local tone mapping network (LTMNet) that learns a grid of tone curves to locally enhance an image. Tone curves are commonly used by photo-editing software and offer an intuitive representation to photographers, facilitating subsequent customization of the image. Tone curves are also widely used in image signal processors (ISPs), making our method easy to deploy on cameras. Because existing datasets contain image enhancement and photofinishing beyond global and local tone mapping, we also propose a new dataset representative of local tone mapping&#x2014;the LTM dataset. We evaluate our method on this new dataset as well as MIT-Adobe and HDR+ datasets. We show that the proposed LTMNet outperforms existing methods in local tone mapping while achieving competitive performance modeling additional photofinishing. Furthermore, we show that our method can be assistive in user-interactive photo-editing tools. Our code, model, and data will be released publicly.
A Data Transfer and Relevant Metrics Matching Based Approach for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) is a promising research area in the software defect prediction domain to handle the unavailability of the past homogeneous data. In HDP, the prediction is performed using source dataset in which the independent features (metrics) are entirely different than the independent features of target dataset. One important assumption in machine learning is that independent features of the source and target datasets should be relevant to each other for better prediction accuracy. However, these assumptions do not generally hold in HDP. Further in HDP, the selected source dataset for a given target dataset may be of small size causing insufficient training. To resolve these issues, we have proposed a novel heterogeneous data preprocessing method, namely, Transfer of Data from Target dataset to Source dataset selected using Relevance score (TDTSR), for heterogeneous defect prediction. In the proposed approach, we have used chi-square test to select the relevant metrics between source and target datasets and have performed experiments using proposed approach with various machine learning algorithms. Our proposed method shows an improvement of at least 14% in terms of AUC score in the HDP scenario compared to the existing state of the art models.
A Method of UAV Formation Transformation Based on Reinforcement Learning Multi-agent

Abstract In the face of increasingly complex combat tasks and unpredictable combat environment, a single UAV can not meet the operational requirements, and UAVs perform tasks in a cooperative way. In this paper, an improved heuristic reinforcement learning algorithm is proposed to solve the formation transformation problem of multiple UAVs by using multi-agent reinforcement learning algorithm and heuristic function. With the help of heuristic back-propagation algorithm for formation transformation, the convergence efficiency of reinforcement learning is improved. Through the above reinforcement learning algorithm, the problem of low efficiency of formation transformation of multiple UAVs in confrontation environment is solved.
Complex Layers of Formal Neurons

Data sets consisting of a relatively small number of high-dimensional feature vectors often appear, e.g. in bioinformatics preoblems. This data structure complicates the design of classification or regression models. Complex layers of formal neurons (linear classifiers) can be designed on the basis of data sets composed of high-dimensional feature vectors. Linear classifiers of a given complex layer are designed on disjoint subsets of features obtained as a result of well-conditioned clustering. This feature clustering technique is related to matrix regularization.
A Preliminary Investigation of Authentication of Choice in Health-Related Mobile Applications

User authentication is a security measure that involves authenticating the identity claimed by a user before granting access to a system or an application. Even though there are numerous studies that examined users' experiences with a variety of authentication methods, very few have focused on the approach of granting users the freedom to select one or more authentication methods of their own choice. Initial work in this area suggested that the 'Authentication of Choice" (AoC) approach has the potential to serve as a usable and secure authentication solution on mobile devices. However, the studies only evaluated the AoC approach on the Android platform when users executed tasks with low security and privacy concerns. To address this gap, we investigated the 'Authentication of Choice' approach in mobile health apps that potentially involve more sensitive information. An online longitudinal study was conducted with 30 participants to assess three authentication processes. The result of the study provides insight into how users interact with and perceive the AoC approach in the context of mobile health apps.
TrueType Transformer: Character and Font Style Recognition in Outline Format

We propose TrueType Transformer (T $$^3$$ ), which can perform character and font style recognition in an outline format. The outline format, such as TrueType, represents each character as a sequence of control points of stroke contours and is frequently used in born-digital documents. T $$^3$$ is organized by a deep neural network, so-called Transformer. Transformer is originally proposed for sequential data, such as text, and therefore appropriate for handling the outline data. In other words, T $$^3$$ directly accepts the outline data without converting it into a bitmap image. Consequently, T $$^3$$ realizes a resolution-independent classification. Moreover, since the locations of the control points represent the fine and local structures of the font style, T $$^3$$ is suitable for font style classification, where such structures are very important. In this paper, we experimentally show the applicability of T $$^3$$ in character and font style recognition tasks, while observing how the individual control points contribute to classification results.
Intuitionistic Fuzzy Estimations of Uncertainty of a Parallel Composition of Services

The problem for representation of uncertainty in service systems is studied in [6, 7] where the ITU traffic characterization is extended with the proposed three Intuitionistic Fuzzy (IF) characterizations: IF flow characterization, IF traffic characterization, IF time characterization. In this paper, a conceptual model of a parallel composition of services is proposed which includes the intuitionistic fuzzy characterizations. On the basis of this model, three estimations of the uncertainty of the parallel composition are obtained. They are Quality of Service (QoS) indicators of a parallel composition of services.
Data Mining Methods for Modeling in Water Science

One of the most useful research fields with many real-life applications, such as in water science, is the subject of data mining. Data mining (DM) is considered a process to extract valuable data from a wide range of information stored in various databases. The data is categorized into the form of patterns, associations, changes, anomalies and significant structures. In water recourses management and environmental engineering, predicting and modelling parameters play an integral role in decision making. The most critical freshwater water resource for millions of people worldwide are rivers with a dynamic nature (floods/droughts), in terms of available freshwater quantity and quality. With various basin characteristics, river flow and sediment regime may be influenced by natural processes such as erosion and sediment transport as well as anthropogenic factors such as urban stormwater runoff and semi-treated sanitary/industrial sewage discharge. Therefore, artificial intelligence (AI) techniques are used to decrease model development costs and improve prediction errors, achieving more efficient models. In this chapter, some well-known techniques and AI-based methods are introduced, and their applications are elaborated. The models are comprised of extreme learning machine (ELM), least square support vector machine (LSSVM), genetic programming (GP), adaptive neural-fuzzy inference system (ANFIS), and multivariate adaptive regression spline (MARS). Each technique, then, is illustrated with a brief literature review. Having being evaluated in terms of the basic concept, the methods are addressed based on a mathematical statement. In the last part, the pseudocode of the ways, an acceptable guideline for coding the methods, is pointed out. This chapter is collected for graduate students, researchers, educators, and practitioners interested in engineering optimization.
Few-Shot Object Detection of Remote Sensing Image via Calibration

Few-shot object detection (FSOD), which aims at detecting rare objects based on few training samples has attracted significant research interest. Previous approaches find that the performance degradation of FSOD is mainly caused by category confusion (high false positives). To solve this issue, we propose a two-stage fine-tuning approach via classification score calibration for remote sensing images, named TFACSC, which follows the flowchart of base training and few-shot fine-tuning to train the detector. First, the backbone with strong representation ability is employed to extract the multi-scale features of the query image. Then, these features are aggregated by a novel multi-head scaled cosine non-local module (MSCN). Next, the aggregated features are used to generate the objectiveness proposals by a region proposal network. Eventually, the generated proposals are refined by a bounding box prediction head for the final category and position prediction, and the category score is calibrated by a novel classification score calibration module (CSCM). Extensive experiments conducted on NWPU VHR 10 and DIOR benchmarks demonstrate the effectiveness of our model. Particularly, for any shot cases, our method greatly outperforms the baseline and achieves state-of-the-art performance.
Text sentiment classification method based on DPCNN and BiLSTM

In recent years, deep learning network models have been widely used in the aspect of text emotion classification and have achieved remarkable achievements. The traditional TextCNN network can only extract local spatial features of sentences, while the improved DPCNN model has the ability to capture long-distance dependence of the text by deepening the network depth. At the same time, bi-LSTM model is characterized by learning temporal information of text. Therefore, this paper combines the two models, which can not only obtain the spatial local information of the text, but also further strengthen the ability to understand and learn the semantic association information of the text. Experimental results show that the classification effect of the model used in this paper is better than the single model.
A High-dimensional Outlier Detection Approach Based on Local Coulomb Force

Traditional outlier detections are inadequate for high-dimensional data analysis due to the interference of distance tending to be concentrated (“curse of dimensionality”). Inspired by the Coulomb’s law, we propose a new high-dimensional data similarity measure vector, which consists of outlier Coulomb force and outlier Coulomb resultant force. Outlier Coulomb force not only effectively gauges similarity measures among data objects, but also fully reflects differences among dimensions of data objects by vector projection in each dimension. More importantly, Coulomb resultant force can effectively measure deviations of data objects from a data center, making detection results interpretable. We introduce a new neighborhood outlier factor, which drives the development of a high-dimensional outlier detection algorithm. In our approach, attribute values with a high deviation degree is treated as interpretable information of outlier data. Finally, we implement and evaluate our algorithm using the UCI and synthetic datasets. Our experimental results show that the algorithm effectively alleviates the interference of “Curse of Dimensionality”. The findings confirm that high-dimensional outlier data originated by the algorithm are interpretable.
Facial Mask Region Completion Using StyleGAN2 with a Substitute Face of the Same Person

In recent years, there has been a worldwide outbreak of coronaviruses, and people are wearing facial masks more and more often. In many cases, people wear masks even when taking photos of themselves, and when photos with the lower half of the face hidden are uploaded to web pages or social networking sites, it is difficult to convey the attractiveness of the photographed persons. In this study, we propose a method to complete the masked region in a face using StyleGAN2, a kind of Generative Adversarial Networks (GAN). In the proposed method, we prepare an image of the same person who is not wearing a mask, and change the orientation and contour of the face of the person in the image to match those of the target image using StyleGAN2. Then, the image with the changed orientation is combined with the target image in which the person is wearing the mask to produce an image in which the mask region is completed.