Reviewerly/exHarmony huggingface dataset:
A Depiction and Classification of the Stolen Data Market Ecosystem and Comprising Darknet Markets: A Multidisciplinary Approach

Scant research has investigated the illicit online ecosystem that enables the sale of stolen data. Even fewer studies have examined the longitudinal trends of the markets on which these data are bought and sold. To fill this gap in the literature, our research team identified 30 darknet markets advertising stolen data products from September 1, 2020, through April 30, 2021. We then developed python web scrapers to systematically extract information pertaining to stolen data products on a weekly basis. Using these data, we calculated the number of vendors, listings, sales, and revenue across the markets and at the aggregate, ecosystem level. Moreover, we developed a data-driven market classification system drawing from ecological principles and dominant firm theory. Findings indicate that markets vary in size and success. Although some markets generated over $91 million in revenue from stolen data products, the median revenue across markets during the observational period was only $95,509. Variability also exists across markets in respect to the number of vendors, listings, and sales. Only three markets were classified as financially successful and stable (i.e., dominant firms). We argue resources should be allocated to target markets fitting these criteria.
Keeping Pace With the Evolution of Illicit Darknet Fentanyl Markets: Using a Mixed Methods Approach to Identify Trust Signals and Develop a Vendor Trustworthiness Index

Illicit darknet markets (DNMs) are highly uncertain and in a perpetual state of flux. These markets thrive in a zero-trust, high-risk environment. However, the trustworthiness of vendors plays a critical role in illicit transactions and the sustainability of the illegal trade of goods and services on DNMs. Focusing on the illicit fentanyl trade and applying signaling theory and embedded mixed methods design, we examined different ways that trustworthiness is signaled by vendors on darknet sites. Fentanyl, a synthetic opioid, in recent years, has been declared a public health emergency in the United States due to its high potency and unprecedented number of deaths associated with its use; however, the topic remains understudied and requires urgent attention. There are few studies that have focused on fentanyl trafficking on DNMs and no mixed method studies that have focused specifically on trust signals in DNM fentanyl networks. In our research, first, we conducted a focus group and in-depth interviews with criminal justice professionals to understand the inner workings of darknet sites, fentanyl networks, and how trust is assessed. Second, we scraped select darknet sites to collect and curate scraped data for later examination of vendor trustworthiness on DNMs. Third, using signaling theory to understand how vendors signal trustworthiness on select darknet sites selling drugs, including fentanyl, we applied both qualitative and quantitative content analysis of DNM features, and language used in vendor profiles, listings, and product/vendor reviews, to inform the development of a trustworthiness index. In this research, we used software, such as Atlas.ti and Python, to analyze our data. The main purpose of this article is to provide an in-depth description of the mixed methods approach we used to inform the development of a vendor trustworthiness index, which we used to examine trust between illicit fentanyl vendors and buyers. Our research can serve as a guide for the development of DNM vendor trustworthiness index for future research on other illegal markets.
A geometry-aware deep network for depth estimation in monocular endoscopy

Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the important geometric structural consistency, which inevitably leads to performance degradation and distortion of 3D reconstruction. To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures. In addition, we develop a synthetic RGB-Depth dataset that captures the anatomical structures under reflections and illumination variations. The proposed method is extensively validated across different datasets and clinical images and achieves mean RMSE values of 0.066 (stomach), 0.029 (small intestine), and 0.139 (colon) on the EndoSLAM dataset. The generalizability of the proposed method achieves mean RMSE values of 12.604 (T1-L1), 9.930 (T2-L2), and 13.893 (T3-L3) on the ColonDepth dataset. The experimental results show that our method exceeds previous state-of-the-art competitors and generates more consistent depth maps and reasonable anatomical structures. The quality of intraoperative 3D structure perception from endoscopic videos of the proposed method meets the accuracy requirements of video-CT registration algorithms for endoscopic navigation. The dataset and the source code will be available at https://github.com/YYM-SIA/LINGMI-MR.
LightCert4IoTs: Blockchain-Based Lightweight Certificates Authentication for IoT Applications

These in recent days, the proliferation of the internet of things (IoT) within the emergence of five-generation (5G) networks has received a huge attention in both industrial and academic domains. A 5G network is a cornerstone of realizing the full potential of the IoT, which interconnects billions of devices wirelessly. However, wireless communication in IoT devices reveals tremendous security risks in different dimensions and precisely in the distribution of the user certificates. The existing X.509 PKI, or the proposed decentralized PKI based on blockchain solutions have lacked practicality, and continue to have security flaws, or have not yet gained widespread acceptance owing to complexity and deployment issues. We present a lightweight certificate in size (LightCert4IoTs) that is not issued by Certification Authorities (CAs) due to the cost and complexity of the assignment of a signed certificate. In LightCert4IoTs first, an end-user (i.e., mobile and IoT devices) issues a self-signed certificate and lets Local Registration Authorities (LRAs)/EDGE nodes to verify and validate the binding identity-self signed certificate of the users through the Ethereum blockchain where The Ethereum network is used as the global notary for the IoT light certificates by saving them in the blockchain immutable ledger. The LightCert4IoTs leverages the advantages of blockchain technology and smart contracts to address the existing challenges of PKI certificates in IoT devices, which neatly achieve certificate issuance, update, and revocation more securely and efficiently. Finally, the LightCert4IoTs experimental results show that LightCert, as compared to relevant solutions/baselines, achieves reasonable overheads and is suitable for use in low- constrained IoT devices where the memory and processor power are optimized.
Develop Model for Recognition of Handwritten Equation Using Machine Learning

The problems that stand with the existing solution of handwritten equation recognizer have numerous problems such as not being purely based on ML and thus unable to occupy dominance of the large processing capacity of the machines that stand in this scenario and also not having an adequate amount of data to precisely estimate the characters, symbols, and numbers that are drawn out from the image being satiated under the software. The current handwritten equation recognizer solution is applied, and imitation utilizing different competence and performance levels is evaluated on real photos. Neural networks which give an accuracy of 95% are used for recognition of the alphabets, all the numbers, mathematical symbols, and some special symbols like π and ψ (Hossain et al. in 2018 Joint 7th International Conference on Informatics, Electronics and Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision and Pattern Recognition (icIVPR). IEEE, New York, pp. 250–255, 2018). The major goal of this work is to improve a software programme that detects handwritten equations, phrases, and paragraphs generated by handwritten numbers, characters, words, and mathematical symbols utilizing CNN, and some image processing expertise to achieve appropriate accuracy. The primary goal of this research work is to execute fundamental operations on the identified equation, such as number addition and subtraction. We also do text recognition, such as reading a handwritten sentence or paragraph and translating it to computer-typed format. Finally, the observational results suggest that our current software application is a huge success.
ResNet: Solving Vanishing Gradient in Deep Networks

Training of a neural network is easier when layers are limited but situation changes rapidly when more layers are added and a deeper architecture network is built. Due to the vanishing gradient and complexity issues, it makes it more challenging to train neural networks, which makes training deeper neural networks more time consuming and resource intensive. When residual blocks are added to neural networks, training becomes more effective even with more complex architecture. Due to skip connections linked to the layers of artificial neural networks, which improves residual network (ResNet) efficiency, otherwise it was a time consuming procedure. The implantation of residual networks, their operation, formulae, and the solution to the vanishing gradient problem are the topics of this study. It is observed that because of ResNet, the model obtains good accuracy on image recognition task, and it is easier to optimize. In this study, ResNet is tested on the CIFAR-10 dataset, which has a depth of 34 layers and is both, more dense than VGG nets and less complicated. ResNet achieves error rates of up to 20% on the CIFAR-10 test dataset after constructing this architecture, which takes 80 epochs. More epochs can decrease the error further. The outcomes of ResNet and its corresponding convolutional network (ConvNet) without skip connection are compared. The findings indicate that ResNet offers more accuracy but is more prone to overfitting. To improve accuracy, overfitting prevention techniques including stochastic augmentation on training datasets and the addition of dropout layers in networks have been used.
A Survey of Cloud Architectures: Confidentiality, Contemporary State, and Future Challenges

Cloud computing is a technology that allows users to upload personal data across different computers that are provided by a third party and delivers good quality services with enhanced efficiency. Cloud Server and Computing Technology is a relatively new notion for providing alternative infrastructure and easy accessibility, but it still has security vulnerabilities. Our data analysis major focus is on understanding cloud infrastructure, cloud concepts, security difficulties, and obstacles. In this study, we examined cloud security problems in depth, as well as the three basic cloud information protection aspects: Confidentially, Integrity, and Resilience, as well as ways for enhancing and preserving security features.
Reversible Data Hiding in Shared Images based on Syndrome Decoding and Homomorphism

Reversible Data Hiding in Encrypted Images (RDHEI) has drawn increasing concern in multimedia cloud computing scenarios. It embeds secret message into the encrypted carrier while preserving the confidentiality of the image. However, most RDHEI schemes have only one hider and one image carrier which are not efficient for the distributed system with multiple participants. Moreover, if the stego-image is lost, the cover image and the embedded data cannot be restored. To solve above issues, this paper proposes Reversible Data Hiding in Shared Images based on syndrome decoding and homomorphism (RDHSI). In RDHSI, the cover image and secret data are distributed using secret sharing. Multiple data hiders embed shared data into the shared images based on the Syndrome Decoding of Hamming codes and the additive homomorphism of the polynomial. On the receiver side, the lossless cover image and secret data are obtained. The reversibility and privacy preserving of the cover image, the complete extraction of secret data, and the fault-tolerance of the proposed schemes are achieved. The experimental results show that PSNR of the marked image is above 54 dB at the embedding rate of 0.43 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bpp</i> . In order to improve the payload of secret data, the Modified RDHSI is further proposed. Generally, the proposed schemes are secure, effective and fault-tolerant.
One More Time about the Age of Teachers in Russian Universities: Old Problems and New Trends

The paper presents results of analyzing age structure of Russian state universities teaching staff and its dynamics in the post-Soviet period. It is shown that due to the rapid decline after 2010 in the proportion of young people under 30 years of age and increasing share of population over 65 years of age, the imbalance is growing in favor of older people. Comparing average age of university professors and that of the employed population in dynamics shows some exceedance of the former. At the same time, for more than two decades their ratio stayed constant – the average age of teachers was 20 percent or about 8 years more than that of the employed population, which is caused, among other things, by a longer-term professional training. Analyzing age structure for various positions reveals its overall balance, with professors being the oldest in terms of average age, and assistants being the youngest. The study found significant changes of a position structure, affecting primarily professors, whose share has increased several times, as well as assistants and teachers, who occupy two lower positions in the teaching staff. Their share has decreased even more. Insight into teaching staff age structure and osition structure in Russian universities shows that changes in age characteristic, especially shrinking young people population, are associated with job positions, especially with almost complete disappearance of the teaching staff on assistant and lecturer positions. A teaching staff distribution by position also shows that more than three-quarters of the total staff have positions no higher than associate professor. It means this position is actually a “glass ceiling” for teachers in Russian higher education. This fact and almost complete lack of assistants and lecturers result in a two-stage career ladder (senior lecturer – associate professor) for the vast majority of teaching staff in Russian universities, which will inevitably have a negative impact on the level of his qualifications.
GraphRevisedIE: Multimodal information extraction with graph-revised network

Key information extraction (KIE) from visually rich documents (VRD) has been a challenging task in document intelligence because of not only the complicated and diverse layouts of VRD that make the model hard to generalize but also the lack of methods to exploit the multimodal features in VRD. In this paper, we propose a light-weight model named GraphRevisedIE that effectively embeds multimodal features such as textual, visual, and layout features from VRD and leverages graph revision and graph convolution to enrich the multimodal embedding with global context. Extensive experiments on multiple real-world datasets show that GraphRevisedIE generalizes to documents of varied layouts and achieves comparable or better performance compared to previous KIE methods. We also publish a business license dataset that contains both real-life and synthesized documents to facilitate research of document KIE.
Knowledge-extracted deep learning diagnosis and its cloud-based management for multiple faults of chiller

Machine learning has the inspiring potential for fault prediction in HVAC systems, which is essential for the system energy efficiency. For the scenario of multiple faults, however, machine learning model is not efficient because the training datasets of this scenario are difficult to collect in the real applications. This paper proposes the novel knowledge-embedded deep belief network (DBN) method to diagnose the electronic-thermal and thermal-thermal multiple faults for chillers in the buildings. Firstly, the characteristics of electronic-thermal and thermal-thermal faults are analyzed through the experiments. Through detecting the sensor biases, the sensor-thermal faults are decoupled successfully. Secondly, the representative features are extracted using the proposed DBN model. In the fine-tuning process, the original DBN network is optimized through embedding the extracted knowledge rules. We integrate the knowledge-embedded DBN, extreme learning machine (ELM) and k-nearest neighbor (KNN) as the diagnosis model. The experimental datasets are used to validate the proposed multi-fault diagnosis method. The results show that its diagnosis performance is satisfactory when the training dataset of some fault is absent. Finally, we develop the cloud-based diagnosis and online management platform. The proposed method is deployed on the cloud to realize online diagnosis and smart management.
An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Although the particular technique has performed better on the smaller data set, accuracy still needs to catch up in most of the data, which needs to be fairer to apply in the real-time medical environment. In the proposed research, state-of-the-art deep learning techniques, such as transfer learning, based cross model classification (TLBCM), convolution neural network (CNN) and transfer learning, residual network (ResNet), and Densenet proposed for efficient prediction of breast cancer with the minimized error rating. The convolution neural network and transfer learning are the most prominent techniques for predicting the main features in the data set. The sensitive data is protected using a cyber-physical system (CPS) while using the images virtually over the network. CPS act as a virtual connection between human and networks. While the data is transferred in the network, it must monitor using CPS. The ResNet changes the data on many layers without compromising the minimum error rate. The DenseNet conciliates the problem of vanishing gradient issues. The experiment is carried out on the data sets Breast Cancer Wisconsin (Diagnostic) and Breast Cancer Histopathological Dataset (BreakHis). The convolution neural network and the transfer learning have achieved a validation accuracy of 98.3%. The results of these proposed methods show the highest classification rate between the benign and the malignant data. The proposed method improves the efficiency and speed of classification, which is more convenient for discovering breast cancer in earlier stages than the previously proposed methodologies.
Separation, reserve estimation and radioactivity responsibility of the economic heavy minerals of East El- Arish black sand, North Sinai, Egypt

Multi-heavy mineral separation techniques like density, magnetic, and electromagnetic techniques are followed by picking, grain counting, and mineralogical examinations conducted with an environmental scanning electron microscope (ESEM). These techniques have been applied to several drill hole (well) composite samples representing beach sand and dunes of East El-Arish area, North Sinai, Egypt. The results explain the remarkable enrichment in total heavy minerals (THM) of beach sand (4.4%) compared to sand dunes (2.6%): this is due to the natural concentration of the currents of the Mediterranean Sea. After characterizing the heavy minerals in terms of the abundance of each mineral species, the mean values of content relative to total economic heavy minerals (TEHM) were determined as 70.03% ilmenite, 3.81% leucoxene, 3.03% magnetite, 8.7% garnet, 10.6% zircon, 3.13% rutile, 0.37% monazite, and 0.31% titanite in the sand dune samples. The total economic heavy minerals in the beach samples were 64.08% ilmenite, 1.6% leucoxene, 2.84% magnetite, 18.16% garnet, 10.4% zircon, 2.18% rutile, 0.61% monazite, and 0.13% titanite. Monazite, thorite, zircon, and apatite are the main radioactive minerals in the study area. The combination of two specific characteristics of the studied monazite (relatively high Th-U content and high frequency) makes monazite the main contributor to radioactivity of the study area.
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models

Active inference is a first principles approach for understanding the brain in particular, and sentient agents in general, with the single imperative of minimizing free energy. As such, it provides a computational account for modelling artificial intelligent agents, by defining the agent’s generative model and inferring the model parameters, actions and hidden state beliefs. However, the exact specification of the generative model and the hidden state space structure is left to the experimenter, whose design choices influence the resulting behaviour of the agent. Recently, deep learning methods have been proposed to learn a hidden state space structure purely from data, alleviating the experimenter from this tedious design task, but resulting in an entangled, non-interpretable state space. In this paper, we hypothesize that such a learnt, entangled state space does not necessarily yield the best model in terms of free energy, and that enforcing different factors in the state space can yield a lower model complexity. In particular, we consider the problem of 3D object representation, and focus on different instances of the ShapeNet dataset. We propose a model that factorizes object shape, pose and category, while still learning a representation for each factor using a deep neural network. We show that models, with best disentanglement properties, perform best when adopted by an active agent in reaching preferred observations.
Blockchain in the shipping industry: A proposal for the use of blockchain for SMEs in the maritime industry

The main objective of this study is to address one of the significant challenges maritime SMEs, as well as the whole industry, are facing concerning the general necessity to digitize the global supply chain: the lack of transparent information flows between participants and the non-existing documentation standards. This objective includes a proposal of using blockchain technology for data sharing as a solution to counter this challenge. To understand how a blockchain-based data-sharing platform would succeed and tackle this challenge, TradeLens was selected as a case study. The literature review has addressed the shipping industry as a whole, blockchain technology, and its features and current initiatives that facilitate this technology. The case study analysis and discussion focused on the architecture, data sharing model, and standards for documentation as well as challenges associated with the solution TradeLens is offering for maritime enterprises. From this analysis, implications for SMEs have been elaborated. Concluding these implications have been validated through interviews with industry experts and strategic decision-makers in maritime SMEs.