Reviewerly/exHarmony huggingface dataset:
CrawlBot: A Domain-Specific Pseudonymous Crawler

The Dark Net, a dark side of the internet, became a perfect hosting ground for criminal activities and services, including significant drug marketplaces, child and women sexual abuse, forged documents, homemade explosives, and likewise. The nature of the Dark Net makes it indifferent to searching through the indexed mechanism. Diverse approaches are suggested and attempted to gain useful insights from the Dark Net. In this paper, a focused crawling framework for uncovering material of child and women abuse that lies in the Surface and the Dark Net is proposed. A focused crawler is domain-specific and trained to selectively retrieve web pages that are related to a given domain from the Internet. One of the novel attributes of the crawler is traversing the Surface Net and the Dark Net in a pseudonymous way. During a single crawl session, it automatically selects hyperlinks, which are most likely to give related web pages and downloads pages that are relevant. The hyperlink selection method uses anchor text and local context of the hyperlink as a feature vector. Numerous web data mining and classification tasks utilize local contexts of hyperlinks. Focused or Topical crawlers have a major dependence on local contexts of hyperlinks. The relevancy of the page and hyperlinks is calculated using natural language processing and a sophisticated Artificial Neural Network (ANN) based classifier. The evaluation of the crawler indicates the high harvest rate and effectiveness of the crawler.
Domain Generalization Using Ensemble Learning

Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model’s weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.
On the asymptotic distribution of randomly weighted averages of random vectors

Let X1,X2,…,Xn be a sequence of mutually independent continuous random vectors with respective positive definite covariance matrices Σ1,Σ2,…,Σn. The main purpose of this article is to derive the asymptotic distribution of Sn:n, a randomly weighted average of the sequence X1,X2,…,Xn, as n→∞. The random weights are the cuts of (0, 1) by an increasing ordered array of the ordered statistics of n independent and identically uniformly distributed random variables. We prove that under certain assumptions on the covariance matrices, n(Sn:n−μ) converges in distribution to the multivariate normal distribution with zero mean and covariance matrix 2Σ, where Σ is the limit of the expression in terms of Σis. Finally, we give an application for the multivariate randomly weighted averages in modeling (height, general intelligence) parents’ gene compositions given to their offspring. The simulation results which are based on three distributions (Bivariate t, Dirichlet and Normal) which confirm the main theorem of the paper.
A Novel Sigma-Lognormal Parameter Extractor for Online Signatures

Online signature analysis can be widely applied in e-security and health. The latest method combines the Sigma-Lognormal model and visual feedback to extract the kinematic and spatial parameters of online signatures, but the model still does not perform well in complex handwriting signatures. Inaccurate parameters cannot reveal health information about users and cannot correctly reconstruct the online signature. This paper presents a novel Sigma-Lognormal parameter extractor for this drawback. On the one hand, this extractor estimates the parameters of pen-up and optimizes the parameters without the stroke midpoint. On the other hand, the extractor dynamically corrects the salient point position deviation by the velocity minimum point and velocity intersection point of adjacent strokes. The new extractor solves the parameter distortion caused by the ignored pen-ups and the hidden time deviation. The experiments demonstrate the accuracy and robustness of our method on multiple databases and verifiers, and the results show that the performance of the new extractor is better than the state-of-the-art method.
A Parametric Study of Arabic Text-Based CAPTCHA Difficulty for Humans

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed scheme, we ran four filter attacks, which led to a success rate of less than 5%. Thus, we developed a defensive method against adaptive attacks which is a standard for evaluating defenses to adversarial examples. This method is ensemble adversarial training, and it gave an accuracy result of 41.51%. For the usability aspect, we conducted an experimental study, and the results showed that it can be solved by humans in a few seconds with a success rate of 93.10%.
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images

We present a novel problem of text-based visual question generation or TextVQG in short. Given the recent growing interest of the document image analysis community in combining text understanding with conversational artificial intelligence, e.g., text-based visual question answering, TextVQG becomes an important task. TextVQG aims to generate a natural language question for a given input image and an automatically extracted text also known as OCR token from it such that the OCR token is an answer to the generated question. TextVQG is an essential ability for a conversational agent. However, it is challenging as it requires an in-depth understanding of the scene and the ability to semantically bridge the visual content with the text present in the image. To address TextVQG, we present an OCR-consistent visual question generation model that Looks into the visual content, Reads the scene text, and Asks a relevant and meaningful natural language question. We refer to our proposed model as OLRA. We perform an extensive evaluation of OLRA on two public benchmarks and compare them against baselines. Our model – OLRA automatically generates questions similar to the public text-based visual question answering datasets that were curated manually. Moreover, we 'significantly' outperform baseline approaches on the performance measures popularly used in text generation literature.
A Framework Integrating Internet of Things and Blockchain in Clinical Trials Reverse Supply Chain

Efficiency and resilience of the clinical trials supply chain are of particular prevalence in the current global context. The unique characteristic of the reverse logistics flow in the supply chains for clinical trials is the foundation for the digital transformation framework presented in this paper. This paper proposes a novel framework that integrates internet of things (IoT) and blockchain technology for the reverse logistics supply chain for clinical trials. The framework is implemented in a Contract Research Organisation operating clinical trials in Europe and North Africa and results are discussed. The main contribution of the proposed novel framework is the integration and interaction of both IoT and blockchain in a reverse logistics process.
Explanation-Based Serendipitous Recommender System (EBSRS)

Recommender systems (RSs) have gained immense popularity and achieved great success as intelligent information system that helps to deal with information overload problem. Recommender systems (RSs) have been very long evaluated for accuracy. Nowadays, along with the accuracy of the presented recommendation, other factors like novelty, diversity and serendipity have become an important aspect of recommendation systems. In this paper, we propose Explanation-based Serendipitous Recommender System (EBSRS) to generate explanation for the serendipitous recommendations presented to the user. Hereby, the approach integrates the concept of serendipity in recommendations, ensuring the relevance of recommendations while generating serendipitous recommendation. The approach generates the explanation for the serendipitous recommendations to provide a justification for the recommended list. The proposed approach is evaluated using accuracy and relevancy measures. Precision, recall and f-measure are used as the accuracy measure, whereas explanation coverage and unexpectedness is used to get the relevancy measure.
Performance of Supervised Learning Algorithms on Imbalanced Class Datasets

Abstract In this paper, we measure the performance of supervised learning algorithms on imbalanced class datasets. Supervised learning is considered to be the most advanced and mature from other types of learning in machine learning. On the other hand, imbalanced class data sets refer to an unequal amount of data for each class in the data sets. Many real-world datasets exhibit an imbalanced class distribution. Hence, this paper aims to compare the supervised learning algorithms in classifying the output for imbalanced dataset. This paper also focused on finding the significance of balancing the dataset to the results. This study is conducted by using three different datasets and made a comparison with three supervised learning algorithms chosen which are Logistic Regression, Random Forest and k-Nearest Neighbours. In the experiments, all three datasets used are labelled data with imbalanced class. Due to the imbalanced class for all datasets chosen, a sampling technique with combination of under-sampling and oversampling is implemented to all datasets in data preparation steps. The performance of the algorithms is measured through Classification Accuracy (CA) value. From the experiments, it is proven that Random Forest shown the best results, for both imbalanced and balanced datasets.
EEM: An End-to-end Evaluation Metric for Scene Text Detection and Recognition

An objective and fair evaluation metric is fundamental to scene text detection and recognition research. Existing metrics cannot handle properly one-to-many and many-to-one matchings that arise naturally from the bounding box granularity inconsistency issue. They also use thresholds to match the ground truth and detection boxes, which leads to unstable matching result. In this paper, we propose a novel End-to-end Evaluation Metric (EEM) to tackle these problems. EEM handles one-to-many and many-to-one matching cases more reasonably and is threshold-free. We design a simple yet effective method to find matching groups from the ground truth and detection boxes in an image. We further employ a label merging method and use normalized scores to evaluate the performance of end-to-end text recognition methods more fairly. We conduct extensive experiments on the ICDAR2015, RCTW dataset, and a new general OCR dataset covering 17 categories of real-life scenes. Experimental results demonstrate the effectiveness and fairness of the proposed evaluation metric.
On Security of Key Derivation Functions in Password-based Cryptography

Most common user authentication methods use some form of password or a combination of passwords. However, encryption schemes are generally not directly compatible with user passwords and thus, Password-Based Key Derivation Functions (PBKDFs) are used to convert user passwords into cryptographic keys. In this paper, we analyze the theoretical security of PBKDF2 and present two vulnerabilities, γ-collision and δ-collision. Using AES-128 as our exemplar, we show that due to γ-collision, text encrypted with one user password can be decrypted with γ 1 different passwords. We also provide a proof that finding− a collision in the derived key for AES-128 requires δ lesser calls to PBKDF2 than the known Birthday attack. Due to this, it is possible to break password-based AES-128 in O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">64</sup> ) calls, which is equivalent to brute-forcing DES.
Cleaning the NVD: Comprehensive Quality Assessment, Improvements, and Analyses

Vulnerability databases are vital sources of information on emergent software security concerns. How reliable and accurate are these databases though? This paper explores this question through the National Vulnerability Database (NVD), the U.S. government’s vulnerability repository that arguably serves as the industry standard. Our investigations uncover data inconsistency or incompleteness in the NVD that can impact its practical usage, by affecting information such as the vulnerability publication dates, the affected vendor and product names, severity scores, and vulnerability type fields. Preliminary results suggest shifting trends, unveiling common mistakes, suggestions, and strategies for their avoidance.
Efficient identity-based encryption with Hierarchical key-insulation from HIBE

Abstract Hierarchical key-insulated identity-based encryption (HKIBE) is identity-based encryption (IBE) that allows users to update their secret keys to achieve (hierarchical) key-exposure resilience, which is an important notion in practice. However, existing HKIBE constructions have limitations in efficiency: sizes of ciphertexts and secret keys depend on the hierarchical depth. In this paper, we first triumph over the barrier by proposing simple but effective design methodologies to construct efficient HKIBE schemes. First, we show a generic construction from any hierarchical IBE (HIBE) scheme that satisfies a special requirement, called MSK evaluatability introduced by Emura et al. (Des. Codes Cryptography 89(7):1535–1574, 2021). It provides several new and efficient instantiations since most pairing-based HIBE schemes satisfy the requirement. It is worth noting that it preserves all parameters’ sizes of the underlying HIBE scheme, and hence we obtain several efficient HKIBE schemes under the k -linear assumption in the standard model. Since MSK evaluatability is dedicated to pairing-based HIBE schemes, the first construction restricts pairing-based instantiations. To realize efficient instantiation from various assumptions, we next propose a generic construction of an HKIBE scheme from any plain HIBE scheme. It is based on Hanaoka et al.’s HKIBE scheme (Asiacrypt 2005), and does not need any special properties. Therefore, we obtain new efficient instantiations from various assumptions other than pairing-oriented ones. Though the sizes of secret keys and ciphertexts are larger than those of the first construction, it is more efficient than Hanaoka et al.’s scheme in the sense of the sizes of master public/secret keys.
An Accelerated Algorithm for 3D Reconstruction of Groove Structure Based on Parallel Light and White Light Interference

We propose a three-dimensional image reconstruction acceleration algorithm based on parallel light white light interference system. We move the sample or mirror to be tested longitudinally to adjust the optical path, and then, collect and number the interference images in the 2um range near the position of the sample surface where the interference fringes are obvious. The group’s images around the upper and lower surfaces which are the interference images with the largest contrast in each group of images are partially reconstructed in 3D. Use the contrast information to segment the interference images with the largest contrast in each group of images collected, ensure that each sub-region can contain the maximum and minimum values of interference fringes on the iso-optical path surface. The contrast is compared with the contrast of the entire image, and the position of the iso-optical path surface is extracted, and the local 3D reconstruction results are spliced using the obtained position of the iso-optical path surface to complete the overall 3D shape detection. The reconstruction result of the algorithm in this paper is significantly improved compared with the reconstruction result directly using the differential centroid method. In terms of speed, the reconstruction time of the algorithm in this paper is 0.0429s, and the reconstruction time directly using the differential centroid method. is 1.540s.
M-DFNet

Content based medical image retrieval (CBMIR) plays a great role in computer aided diagnosis for assisting radiologists to detect and characterize focal liver lesions (FLLs). Deep learning has gained exciting performance on CBMIR. While the features generated by deep learning models trained using softmax loss are always separable but not discriminative enough, which is insufficient for retrieval task. In this paper, we propose a multi-phase discriminative feature network (M-DFNet) with a DeepExtracter and a feature refine module (FRModule) to learn discriminative and separable features under a joint supervision of center loss and softmax loss. The hybrid loss enables to minimize intra-class variations and enlarge inter-class differences as much as possible. The FRModule is proposed to recalibrate the deep features based on the learned class centers to tackle the complex imaging manifestations of FLLs and further enhance both the feature discrimination and generalization. Multi-phase computed tomography (CT) images contain pivotal information for diagnosis of FLLs. Thus the M-DFNet is designed to cope with multi-phase information and we explore an appropriate and effective method for multi-phase feature integration on limited data. Experimental results clearly demonstrate strong performance superiority by our proposed method.
Usage of Multiple Independent Blockchains for Enhancing Privacy Using the Example of the Bill of Lading

Digitization of trade documents such as the Bill of Lading (B/L) is advancing. Several approaches have discussed blockchain as a suitable technology in general but state that they lack privacy aspects. Motivated by the antitrust-by-design approach from antitrust law, we propose a novel approach called one B/L, one blockchain in which we aim to create a dedicated blockchain for each B/L process. With this approach, we aim to enhance privacy and confidentiality. We gathered first insights by implementing the approach using Hyperledger Fabric. The first results show that the approach is technically feasible but requires manual steps and must be further refined.
Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments

Owing to the recent advancements in Internet of Things technology, social media, and mobile devices, real-time stream balancing processing systems are commonly used to process vast amounts of data generated in various media. In this paper, we propose a dynamic task scheduling scheme considering task deadlines and node resources. The proposed scheme performs dynamic scheduling using a heterogeneous cluster consisting of various nodes with different performances. Additionally, the loads of the nodes considering the task deadlines are balanced by different task scheduling based on three defined load types. Based on diverse performance evaluations it is shown that the proposed scheme outperforms the conventional schemes.
Enterprise Resource Planning (ERP) System Customization with the Inventory Management Module: Case Study of Rumah Yatim

Zakat plays a very important role in improving people's well-being. This is based on BAZNAS Survey 2016 data, the Zakat potential in Indonesia may reach Rs. 286 trillion. So, in fact, zakat collection is only $ 6 trillion, not even 5%. Therefore, relevant authorities must properly collect, manage and distribute zakat funds. In this case, Rumah Yateem, one of the zakat institutions operating in the country, needs to ensure that the process is properly implemented by supporting the ERP system in accounting. Job. Customization of an ERP system enables real-time business process automation and simultaneous integrations. In this study, we implemented it using the Odoo app stock management module. This was done using Odoo QuickStart method as best practice provided in the literature, which is expected to improve the effectiveness and efficiency of the business process for coordination in order achieve the goals of the institution.
Research on The Word Segmentation Model Construction Based on CNN+BiLSTM+HMM

In natural language processing, Chinese word segmentation is a common and key task. For a great quantity of chinese natural language processing tasks, word segmentation is the basis for completing the follow-up work. At present, the most commonly used word segmentation methods are machine learning and deep learning methods. However, many models have problems such as the inability to identify unregistered words, complex models, and greater dependence on manual processing features. Therefore, in this paper, we propose a word segmentation model based on CNN+BiLSTM+HMM. First, the semantic representation of the word is obtained by using CNN, and then the context information is obtained through BiLSTM. Finally, the HMM is used to classify predict and output the optimal label sequence. This approach can not only improve the accuracy of high score words but also effectively retain longdistance sentence information. The MSRA corpus provided by Bakeoff 2005 to conduct experiments in this paper. The experiment shows that the word segmentation model based on CNN+BiLSTM+HMM is 1.92% higher than the BiLSTM word segmentation model inaccuracy and 1.41% higher than the BiLSTM+HMM word segmentation model. Respectively, which proves that the segmentation model based on CNN+BiLSTM+HMM has certain advantages.
Research on Roundness Detection and Sorting of Oil Cooling Pipe Based on Machine Vision

In the production process of the oil cooling pipe, the roundness error of the pipe is mainly measured manually, which has the problem of low detection efficiency and inability to detect in real-time. Based on machine vision, this paper proposes a method for roundness detection and sorting of oil cooling pipes. By establishing the kinematics model of the robot SR7CL, then using Adams and Matlab to simulate kinematics and dynamics to verify the correctness of the model and ensure the accuracy of grasping. Taking the nozzle of the oil cooling pipe as the research object, the characteristics of the nozzle are highlighted through adaptive threshold segmentation, and the contour information are extracted by the Canny algorithm. Finally, used the least square method to detected the roundness error, and the cooling pipes are determined according to the roundness error. This system not only improves the quality and efficiency of detection, but also solves the real-time problems.
Bitcoin Prediction Using Multi-Layer Perceptron Regressor, PCA, and Support Vector Regression (SVR)

Bitcoin has gained a tremendous amount of attention lately because of the innate nature of entering cryptographic technologies and money-related units in the fields of banking, cybersecurity, and software engineering. This chapter investigates the effect of Bayesian neural structures or networks (BNNs) with the aid of manipulating the Bitcoin process's timetable. The authors also choose the maximum extensive highlights from Blockchain records that are carefully applied to Bitcoin's marketplace hobby and use it to create templates to enhance the influential display of the new Bitcoin evaluation process. They endorse actual inspection to check and expect the Bitcoin technique, which compares the Bayesian neural network and other clean and non-direct comparison models. The exact tests show that BNN works well for undertaking the Bitcoin price schedule and explain the intense unpredictability of Bitcoin's actual rate.