Reviewerly/exHarmony huggingface dataset:
Pricing of illicit drugs on darknet markets: a conceptual exploration

Purpose Trading illicit drugs on cryptomarkets differs in many ways from material retail markets. This paper aims to contribute to existing studies on pricing by studying the relationship between price changes in relation to changes in nominal value of the cryptocurrency. To this, the authors qualitatively study product descriptions and images to expand the knowledge on price formation. Design/methodology/approach The authors analysed 15 samples based on visual and textual scrapes from two major drug markets – for Dream Market between January 2014 and July 2015 and for Tochka between January 2015 and July 2015. This longitudinal study relates changes in process to variations in the Bitcoin exchange rate and selling strategies. The analysis of the marketing of drugs online also addressed the development of the vendor profile and product offers. Findings Product prices change in relation to variations in the Bitcoin exchange rate. This points to the application of mechanisms for automatic price adaptations on the market level. Real prices of the drug offers constantly increase. The authors assert that there is a bidirectional relationship. Vendors structure price and discounts to encourage feedback. And feedback in combination with signals of commitment and authenticity inform pricing. Product descriptions are an important feature in the successful marketization of goods, whereas product images are predominantly used as an aspect of recognisability and feature of the vendor’s identity. Research limitations/implications Findings suggest that there is great potential for further qualitative research into the relationship between the online and offline identity of drug vendors, as well as price setting when entering the market and subsequent changes for offered products. Practical implications Findings also suggest that further investigation into the constitution and management of vendor’s identity on the cryptomarkets would allow a better understanding of vendors and their interactions on cryptomarkets. Social implications A better understanding of drug trading on cryptomarkets helps to more effectively address potentials for harm in the online drug trade. Also targetting crime would benefit from a better understanding of vendor idenities and pricing. Originality/value The findings represent a valuable contribution to existing knowledge on drug trading on cryptomarkets, particularly in view of pricing and vending strategies.
Optimizing Approach of Recommendation System using Web Usage Mining and Social Media for E-commerce

The recommendation system is very popular and plays an important role in the information system or web pages these days.The recommendation system can personalize its website with persons who suggest things to the user's needs.In the field of recommendation systems, the performance of all the recommendation algorithms is limited and each has its strengths and weaknesses, so much attention is paid to hybrid recommendation algorithms.In the proposed work, prediction using collaborative filtering, socio-demographic methodology, and sentiment analysis are integrated into a weighted system that is consistent with producing a single recommendation.This optimization approach improves the effectiveness of the recommendation process.
Experimental Review of the IKK Query Recovery Attack: Assumptions, Recovery Rate and Improvements

In light of more data than ever being stored using cloud services and the request by the public for secure, privacy-enhanced, and easy-to-use systems, Searchable Encryption schemes were introduced. These schemes enable privacy-enhanced search among encrypted documents yet disclose (encrypted) queries and responses. The first query recovery attack, the IKK attack, uses the disclosed information to (partly) recover what plaintext words the client searched for. This can also leak information on the plaintext contents of the encrypted documents. Under specific assumptions, the IKK attack has been shown to potentially cause serious harm to the security of Searchable Encryption schemes. We empirically review the IKK query recovery attack to improve the understanding of its feasibility and potential security damage. In order to do so, we vary the assumed query distribution, which is shown to have a severe (negative) impact on the accuracy of the attack, and the input parameters of the IKK attack to find a correlation between these parameters and the accuracy of the IKK attack. Furthermore, we show that the recovery rate of the attack can be increased up to 10% points, while decreasing the variance of the recovery rate up to 78% points by combining the results of multiple attack runs. We also show that the including deterministic components in the probabilistic IKK attack can increase the recovery rate up to 21% points and decrease its variance up to 57% points.
Penetration Testing and Security Assessment of Healthcare Records on Hospital Websites

At present, computer security is the flourishing field in the IT industry. Nowadays, the usage of computers and the Internet grows drastically, and hence, computers become vehicles for the attackers to spread viruses and worms, to distribute spam and spyware, and to perform denial-of-service attacks, etc. The IT engineers (even users) should know about network security threats, and at the same time, to some extent, they should know techniques to overcome the issues. The reliability and privacy of healthcare records of the patients are the most critical issue in the healthcare business industry sector. The security safeguards, such as physical, technical, and administrative safeguards, are crucial in protecting the information in all aspects. This article deals with the forty popular hospital portals in India related to the professional and network security related issues such as operating system guesses, number of open/closed/filtered ports, the name of the Web server, etc. The Nmap (network mapper) tool is used to analyze the results belong to the security perspective.
Quantum-enhanced algorithms for classical target detection in complex environments

Quantum computational approaches to some classic target identification and localization algorithms, especially for radar images, are investigated, and are found to raise a number of quantum statistics and quantum measurement issues with much broader applicability. Such algorithms are computationally intensive, involving coherent processing of large sensor data sets in order to extract a small number of low profile targets from a cluttered background. Target enhancement is accomplished through accurate statistical characterization of the environment, followed by optimal identification of statistical outliers. The key result of the work is that the environmental covariance matrix estimation and manipulation at the heart of the statistical analysis actually enables a highly efficient quantum implementation. The algorithm is inspired by recent approaches to quantum machine learning, but requires significant extensions, including previously overlooked `quantum analog--digital' conversion steps (which are found to substantially increase the required number of qubits), `quantum statistical' generalization of the classic phase estimation and Grover search algorithms, and careful consideration of projected measurement operations. Application regimes where quantum efficiencies could enable significant overall algorithm speedup are identified. Key possible bottlenecks, such as data loading and conversion, are identified as well.
Potential of International Project Activity for University Development

In the context of global competition, Russian universities search for the most efficient solutions to achieve competitiveness in the global market. The particular challenges that Russian universities face today include enhancing the quality of education and research, development of export potential, integration into international academic environment, diversification and search for new financial sources. The article considers international project activity as the most efficient tool of university internationalization process and solving the university tasks. Fundraising and international projects provide substantial support for achieving the set objectives in terms of both allocating additional financial resources and attracting intellectual capacity of international partners. Due to such features as practice orientation, time limits, and the ability to integrate different types and forms of international cooperation, the projects help university to address the challenges in a complex way and achieve multiple effects and, therefore, contribute significantly to developing world-demanded research, creating scholars networks and publishing, strengthening international partnerships, capacity building, enhancing academic mobility, developing the university campus and its infrastructure. The authors present the management model of international fundraising and projects implementation at Northern (Arctic) Federal University named after M. Lomonosov, describe specific features of carrying out research and educational projects in cross-cultural context (also within the framework of cross-border cooperation in Barents Euro-Arctic Region). The article outlines the competences required for successful project implementation, gives examples of NArFU international projects aimed at solving challenges in the Arctic Region. The efficiency of the given management model is analyzed, also by presenting the results of sociological survey made with involvement of the projects participants.
A study of edge preserving filters in image matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.
Visual exploration of Internet news via sentiment score and topic models

Abstract Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We first collect Internet news from different sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and effectiveness of our proposed system using case studies involving blockchain news.
Exploring the Characteristics of Identifiers: A Large-Scale Empirical Study on 5,000 Open Source Projects

Informative identifiers are crucial for the comprehensibility and maintainability of programs. Exploring properties of identifiers and investigating their impact on software artifacts have been an important research focus. However, to enable such capabilities, fundamentally we need to have comprehensive understanding on the main characteristics of identifiers at the first place, which is unfortunately not sufficiently studied. For example, it remained unclear what Part of Speech (POS) tags that developers commonly use to define identifiers. To answer such open issues, we conducted a large-scale empirical study on the naturalness of identifiers, based on 5,000 open source Java and Android projects, concerning five dimensions of identifiers: distributions, compositions, POS tags, lengths, and initializations of identifiers. Results of the empirical study contain five key findings for identifiers in programs, including, e.g., the observation that the three POS tags (i.e., nouns, verbs, and adjectives) are the most commonly used ones when developers define identifiers. Furthermore, based on our findings, we provide implications and insights for developers, researchers, and Integrated Development Environments (IDEs) in the context that identifier-related activities are performed or functionalities are enabled.
Associative Partial Domain Adaptation

Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both class-level and sample-level to mitigate negative transfer, current approaches mostly rely on only one of them. In this paper, we propose a novel approach to fully exploit multi-level associations that can arise in PDA. Our Associative Partial Domain Adaptation (APDA) utilizes intra-domain association to actively select out non-trivial anomaly samples in each source-private class that sample-level weighting cannot handle. Additionally, our method considers inter-domain association to encourage positive transfer by mapping between nearby target samples and source samples with high label-commonness. For this, we exploit feature propagation in a proposed label space consisting of source ground-truth labels and target probabilistic labels. We further propose a geometric guidance loss based on the label commonness of each source class to encourage positive transfer. Our APDA consistently achieves state-of-the-art performance across public datasets.
Deep Adversarial Residual Convolutional Neural Network for Image Generation and Classification

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.
Optimizing People Sourcing Through Semantic Matching of Job Description Documents and Candidate Profile Using Improved Topic Modelling Techniques

People sourcing for a particular job role in any corporate venture is a painstaking, progress-impeding task, especially given the changing job market trends, quality of candidate profile and the sheer number of applicants. This paper elucidates an improvised topic modelling approach, where the semantic analysis stage is prepended with text summarization, for the procedure of people sourcing for job roles using topic modelling and machine learning techniques. The distinction between choosing the perfect candidate for the job and choosing a good candidate who is adept in certain domains but not relevant for the job and getting the candidate up to speed by providing on job training, and the downtime involved in the process is certainly a deciding factor in hiring a potential employee. The following paper aims to alleviate this issue by describing the algorithm which identifies the most suitable candidate in the applicants’ pool through a novel, robust approach which uses topic modelling techniques like Latent Semantic Indexing (LSA) and Latent Dirichlet Allocation (LDA). It is empirically found that the precision of profile matching task was enhanced by using text summarization (through TextRank model) for both LSA and LDA by 21.4% and 50% respectively, and LSA outperforming LDA with regard to precision, in both with and without text summarization cases by 23.8% and 51.57%, respectively.
Real-Time Detection of Distracted Drivers Using a Deep Neural Network and Multi-threading

Convolutional neural network (CNN) is a very popular deep learning architecture used for the problem of object detection and classification of images and videos. CNN is appropriate for image detection as it prunes the computational overheads as well as provides better performances. Visual Geometry Group (VGG) model is an instance of CNN architecture to solve the problem of object detection from images. VGG-16 model is one of the most potent classifiers that had won the second place in the ImageNet ILSVRC-2014 challenge. VGG also helps in reducing latency of detection of distracted drivers in real time (RT). The technique of transfer learning was applied using VGG-16, pre-trained on the ImageNet dataset intended to extract bottleneck feature that is further used to train a classifier. This technique thrives on the combined power of VGG convolutions and multi-layer perceptron. A key highlight of this paper is the coupling of transfer learning using VGG-16 with multi-threading to achieve a real-time prediction of distraction, from a video input. The proposed model performed notably well on real-time video, achieving an accuracy of 96%, and a video output of 26 frames per second which is comparable to the state-of-the-art algorithms, for real-time classification of objects.
Anonymous and Efficient Certificateless Multirecipient Signcryption Scheme for Ecological Data Sharing

Air pollution, water pollution, soil erosion, land desertification, and other environmental issues are becoming more and more serious. And ecological security has become a key issue for the sustainable development of the world, so research on ecology has received more and more attention. At present, ecological data is collected and stored separately by various departments or agencies. In order to conduct better research, various institutions or individuals begin to share their own data. However, data sharing between different organizations is affected by many factors, especially data security issues. At the moment, there is no scheme that has been commonly recognized to solve the problem of ecological data sharing. To provide a secure data sharing way for ecological research, a certificateless multireceiver signcryption scheme is proposed. In this paper, the proposed scheme can solve the key escrow problem, and it can improve efficiency on the basis of ensuring security by adopting elliptic curve cryptography (ECC). A rigorous security analysis proves that the scheme can resist various security attacks and ensure privacy protection based on a random oracle model. Performance analysis also shows that this scheme has the advantage of computational overhead compared to the same type of scheme. Therefore, the scheme is very suitable for the safe sharing of ecological data.
Cross-Model Deep Feature Fusion for Face Detection

Face detectors using the single shot multibox detector have a small number of discontinuous fixed anchor scales that cannot fully match continuous faces, and these may degrade the accuracy when detecting tiny faces that are not fully matched. To address this issue, we utilize the capabilities and complementary features of the heterogeneous submodel to help the main model extract more abundant features and scale-invariant features from the heterogeneous submodel. First, we propose a deep feature fusion model of the heterogeneous convolutional neural networks to refine and integrate different levels of semantics and obtain a semantically enhanced feature pyramid. We then present a composite anchor loss function that trains the generated network end-to-end, which solves the accuracy-diversity tradeoff of these hybrid submodels. Finally, we apply the proposed approach to the a benchmark for face detection in unconstrained settings (FDDB) and WIDER FACE benchmarks and present the results using their respective standard evaluation protocols. Experiments result in our method surpassing the existing single-shot face detector and further improves the average accuracy of the original model.
Artificial bee colony–based Kalman filter hybridization for three–dimensional position estimation of a quadrotor

Purpose The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics. Design/methodology/approach Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter. Findings Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m 2 ]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x -axis, 3.4 to 0.14 [m] for y -axis and 3.7 to 0.2 [m] for z -axis, respectively. Originality/value The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.
Application of Naive Bayes Method for IUGR (Intra Uterine Growth Restriction) Diagnosis on The Pregnancy

Intra Uterine Growth Restriction is a condition when the infant fails to grow as expected (smaller) than normal size and can be affected to the safety of pregnant women and the fetus. Data mining can be used as a tool to make prediction and diagnosis of IUGR. This study deals with IUGR dataset from the hospital and applied Naïve Bayes method as one of the potential method in the Data Mining. The diagnosis can be classified based on existing aspects. In the experiment result, the average accuracy value obtained from testing of Naive Bayes is 84%, with precision 52.5%, recall 86.7% and specificity 83.8%, which results show a pretty good performance.