Reviewerly/exHarmony huggingface dataset:
Identification of Fake Indian Currency using Convolutional Neural Network

The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges are turning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-houses, yet presently anybody can print a money paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning-based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake.
Cyborg Moth Flight Control Based on Fuzzy Deep Learning

Cyborg insect control methods can be divided into invasive methods and noninvasive methods. Compared to invasive methods, noninvasive methods are much easier to implement, but they are sensitive to complex and highly uncertain environments, for which classical control methods often have low control accuracy. In this paper, we present a noninvasive approach for cyborg moths stimulated by noninvasive ultraviolet (UV) rays. We propose a fuzzy deep learning method for cyborg moth flight control, which consists of a Behavior Learner and a Control Learner. The Behavior Learner is further divided into three hierarchies for learning the species' common behaviors, group-specific behaviors, and individual-specific behaviors step by step to produce the expected flight parameters. The Control Learner learns how to set UV ray stimulation to make a moth exhibit the expected flight behaviors. Both the Control Learner and Behavior Learner (including its sub-learners) are constructed using a Pythagorean fuzzy denoising autoencoder model. Experimental results demonstrate that the proposed approach achieves significant performance advantages over the state-of-the-art approaches and obtains a high control success rate of over 83% for flight parameter control.
«New Normal» of Students’ Educational Practices in the Coronavirus Pandemic Reality

The article presents the results of qualitative sociologic survey aimed at analyzing students’ subjective evaluations related to “new” educational practices from the perspective of the experience gained in the pandemic period, as well as the dynamics of changes in the content of these evaluations during the year due to the transition to new learning formats. The purpose of the study is to assess the impact of forced online learning on the transformation of traditional educational practices. Following the results of work with qualitative data, the main trends observed in the informants’ answers are shown – increase of academic workload and, as a consequence, the time spent for its performance; absence of former attitude and concentration of attention on the learning material caused by the impossibility to draw boundaries between private and public, that in its turn had an impact on the quality of education and gave rise to new practices of academic dishonesty and cheating; and as a whole, downturn of psychological wellbeing. Main positive moments named by the informants were increase of the capabilities of the educational field and a certain level of comfort in the sphere of everyday life, logistics and work. It is noted that even with the outright victory over the COVID-19 it will be impossible to return completely to pre-covid variant of organization of educational process; whether we like it or not, electronic educational environments have already become an integral part of our life and higher education. The main questions that must be asked now – which of pre-covid and gained practices should be taken into the future, how to find the balance between intramural and online learning formats. Search for solutions depends to a large extent on the success of interaction and cooperation between the direct participants of educational process. Main method of data collection is semi-structured interviews; field works have been carried out in spring of 2020 and 2021. The interview method made it possible to study the situation in detail as it has developed. Based on the generalization of qualitative data, the substantial characteristics of the informants’ value judgments are formed, strong and weak sides of the learning process related to introduction of new formats of interaction “university – teacher – student”. The ambivalent attitude of students to full-time and distance learning was revealed. The scientific novelty of the study lies in the data acquisition, systematization and analytical generalization of the original material on the transformation of students’ educational practices as a result of a forced mass transition to distance education. The obtained materials provide grounds for clarifying the process and results of these changes and can be used to justify management decisions.
Intelligent deep fusion network for urban traffic flow anomaly identification

This paper presents a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with an efficient decomposition strategy is explored to find the anomalous behavior of urban traffic flow data. The urban traffic flow data set is decomposed into similar clusters, each containing homogeneous data. The convolutional neural network is used for each data cluster. In this way, different models are trained, each learned from highly correlated data. A merging strategy is finally used to fuse the results of the obtained models. To validate the performance of the proposed framework, intensive experiments were conducted on urban traffic flow data. The results show that our system outperforms the competition on several accuracy criteria.
Computer assisted mitotic figure detection in histopathology images based on DenseNetPCA framework

Mitotic detection in histopathology images is considered a golden standard method adopted for the purpose to study tumour growth. The procedure results in clinical information about critical factors such as disease prognosis, details about the aggressiveness of the cancer stage. Due to structural similarities that exist between mitosis and non-mitosis, it is a challenging task for pathologists to classify these cells in whole slide images (WSI) manually and it is labor-intensive work. In this paper, to accurately detect mitosis, we constructed a supervised deep framework called DenseNet combined Principal Component Analysis (PCA) model for mitosis classification. This ensemble framework is accurately segmenting the cells and classifies the cells into mitotic or nonmitotic figures. The framework consists of three stages (1) Instance level deep features extraction by DenseNet 121 architecture (2) PCA based feature subset selection (3) mitosis classification. Experimental results were conducted on the available benchmark dataset, MITOS-ATYPIA 14 Hammamatsu Scanner images. The results obtained by our mitosis detection framework shows the effectiveness of the approach.
Private Lives Matter

The use of data combined with tailored statistical analysis has presented a unique opportunity to organizations in diverse fields to observe users' behaviors and needs, and accordingly adapt and fine-tune their services. However, in order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. The use of statistical databases for releasing data analytics is growing exponentially, and while many cryptographic methods are utilized to protect the confidentiality of the data -- a task that has been ably carried out by many authors over the years -- only a few %rudimentary number of works focus on the problem of privatizing the actual databases. Believing that securing and privatizing databases are two equilateral problems, in this paper, we propose a hybrid approach by combining Functional Encryption with the principles of Differential Privacy. Our main goal is not only to design a scheme for processing statistical data and releasing statistics in a privacy-preserving way but also to provide a richer, more balanced, and comprehensive approach in which data analytics and cryptography go hand in hand with a shift towards increased privacy.
Condensation as a predictor of PV soiling

• The effect of condensation on PV soiling was studied quantitatively using 2.5 years of field data in Doha, Qatar. • It was necessary to measure condensation directly, as it was not reliably estimated from dew-point and surface temperatures. • There was low correlation between meteo variables (including dew) and the PV soiling rate, due to complexity of PV soiling. • Condensation was among the more beneficial input variables to PV soiling models. Condensation (dew) strongly affects PV soiling: it causes dust particles to stick to PV modules, yet conversely it can also clean modules as the dew droplets run off. These mechanisms have been well studied qualitatively, but because weather stations at PV sites rarely include condensation sensors, the quantitative link between dew and PV soiling rate is not well known. The goal of this study was to investigate this relationship numerically, and especially whether condensation data improves the accuracy of PV soiling models. We performed a field study in Doha, Qatar, in which condensation on a PV module, “standard” meteorological parameters, and the daily PV soiling rate were measured for 2.5 years. We found low correlation between all variables and the soiling rate, however condensation was among the more beneficial variables to include in soiling models (depending on the statistical analysis method used). Also, it is necessary to measure condensation directly, because it was not accurately predicted from dew-point and module surface temperatures.
High-Resolution Boundary-Constrained and Context-Enhanced Network for Remote Sensing Image Segmentation

The technology of remote sensing image segmentation has made great progress in recent years. However, there are still several challenges which need to be addressed (e.g., ground objects blocked by shadows, higher intra-class variance and lower inter-class variance). In this paper, we propose a novel high-resolution boundary-constrained and context-enhanced network (HBCNet), which combines boundary information to supervise network training and utilizes the semantic information of categories with the regional feature presentations to improve final segmentation accuracy. On the one hand, we design the boundary-constrained module (BCM) and form the parallel boundary segmentation branch, which outputs the boundary segmentation results and supervises the network training simultaneously. On the other hand, we also devise a context-enhanced module (CEM), which integrates the self-attention mechanism to advance the semantic correlation between pixels of the same category. The two modules are independent and can be directly embedded in the main segmentation network to promote performance. Extensive experiments were conducted using the ISPRS Vahingen and Potsdam benchmarks. The mean F1 score (m-F1) of our model reached 91.32% and 93.38%, respectively, which exceeds most existing CNN-based models and represents state-of-the-art results.
SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism

The Siamese-based object tracking algorithm regards tracking as a similarity matching problem. It determines the object location according to the response value of the object template to the search template. When there is similar object interference in complex scenes, it is easy to cause tracking drift. We propose a real-time Siamese network object tracking algorithm combined with a compensating attention mechanism to solve this problem. Firstly, the attention mechanism is introduced in the feature extraction module of the template branch and search branch of the Siamese network to improve the feature representation of the network to the object. The attention mechanism of the search branch enhances the feature representation of both the target and the similar backgrounds simultaneously. Therefore, based on the above two-branch attention, we propose a compensated attention model, which introduces the attention selected by the template branch into the search branch, and improves the discriminative ability of the search branch to the object by using the feature attention weighting of the template branch to the object. Experimental results on three popular benchmarks, including OTB2015, VOT2018, and LaSOT, show that the accuracy and robustness of the algorithm in this paper are adequate. It improved occlusion cases, similar object interference, and high-speed motion. The processing speed on GPU reaches 47 fps, which can achieve real-time object tracking.
Cascade light field disparity estimation network based on unsupervised deep learning

Light field disparity estimation is an important task in light field applications. However, how to efficiently utilize the high-dimensional light field data is a very worthy issue to investigate. Besides, existing supervised deep learning based algorithms are limited to scenes with ground truth disparity for training. In this paper, we propose a light field disparity estimation network which adopts a cascade cost volume architecture and can predict disparity maps in a coarse to fine manner by fully exploring the geometry characteristics of sub-aperture images. In addition, we design a combined unsupervised loss to train our network without a ground truth disparity map. Our combined loss consists of occlusion-aware photometric loss and edge-aware smoothness loss which can bring targeted performance improvements in occlusion and textureless regions, respectively. Extensive experiments demonstrate that our approach can achieve better results compared to existing unsupervised disparity estimation method and show better generalizability compared to supervised methods.
An Efficient and Secured Multiple Keyword Cloud Data Searching Scheme with Dynamic Encryption Procedure

Increasingly, data owners are choosing to shift their information to the cloud servers in order to benefit from more ease and lower costs in managing their information. Furthermore, in order to meet privacy regulations, confidential information should be protected by using cipher techniques before being outsourced, rendering outdated methods of data retrieval such as keyword assisted indexing. Using encrypted data in the cloud, a safe multiple keyword based ranked search method is described, that allows dynamic update activities such as content removal and accession. A novel Multi-Data Handling Cipher Policy (MDHCP) is introduced in this paper, in which it is used to build indexes and generate queries. The Greedy assisted Depth First Search (DFS) technique is designed based on a tree based data structure indexing and it allows for fast multiple keyword based ranked search mechanism. On top of that, it ensures correct relevance score computation between the encrypted index and query vectors by using the secured ensemble approach. Search results can be blinded by synthetic phrases inserted to the indexing vector in necessary to defend against statistical assaults. With our tree oriented indexing structure and the suggested technique is able to achieve sub linear search times and handle content deletions and insertions more easily. For the suggested plan to be proven effective, extensive testing is carried out. The resulting section shows the efficiency of the proposed approach and the graphical illustrations prove the ability of the proposed technique to maintain the data in safe manner over the cloud environment.
Design of an FPGA-Based High-Quality Real-Time Autonomous Dehazing System

Image dehazing, as a common solution to weather-related degradation, holds great promise for photography, computer vision, and remote sensing applications. Diverse approaches have been proposed throughout decades of development, and deep-learning-based methods are currently predominant. Despite excellent performance, such computationally intensive methods as these recent advances amount to overkill, because image dehazing is solely a preprocessing step. In this paper, we utilize an autonomous image dehazing algorithm to analyze a non-deep dehazing approach. After that, we present a corresponding FPGA design for high-quality real-time vision systems. We also conduct extensive experiments to verify the efficacy of the proposed design across different facets. Finally, we introduce a method for synthesizing cloudy images (loosely referred to as hazy images) to facilitate future aerial surveillance research.
Shared Multi-Keyboard and Bilingual Datasets to Support Keystroke Dynamics Research

Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.
Decentralized management of online user reviews with immutability using IPFS and Ethereum blockchain

As industry 4.0 is more focused on the digital transformation of industries, various industrialists (including both the private and public sectors) want to increase their brand values, in order to reach the maximum number of peoples. To achieve this, most of them using online platforms including social media. Presently, various companies such as Flipkart, Amazon, eBay are selling their goods through the online platform. On the other side, customers are purchasing goods based on the review and rating of the concerned goods. In the current/traditional system, centralized architecture is utilized to store the review and rating given by customers. But, this architecture is subjected to the review tampering and manipulation. Thus, there is an urgent need to fix the above-mentioned problem. The blockchain is one of the revolutionizing technologies in the era of industry 4.0 that provides a trustworthy, transparent, and decentralized system, and capable to fix the issue associated with the current online review system. In this proposal, we provide a solution for it based on the ethereum blockchain, smart contracts, and Interplanetary File System (IPFS). Reviewer’s reviews are stored in the ethereum blockchain which is based on the logic (rules) written in the smart contracts. IPFS is a distributed storage system for storing the reviews, across the nodes of the connected computers based on the IPFS hash addresses. The Overall system functionalities are implemented in the solidity and smart contracts security tool is used to test and vulnerability analysis of the code.
FISEVAL-A novel project evaluation approach using fuzzy logic: The paradigm of the i-Treasures project

Efficient evaluation of a project is a vital factor for its successful realization during its life-time, as it can offer insight to the factors that could influence its quality. In this vein, a new approach in project evaluation, namely Fuzzy Inference System Evaluator (FISEVAL), is proposed here, which builds upon the fuzzy logic-based inference processes to provide tangible evaluation metrics at various levels of evaluation analysis. A generalized structure of the FISEVAL is proposed and the way it is adapted to the specific characteristics of a project is presented in details for the case of the European i-Treasures project. The latter is handled by a multi-partner consortium and it is realized via a series of technical modules that are used in (sub)use cases, which also involve the users’ interaction. The specific parts of the adapted FISEVAL, combine both technical assessment and users’ evaluation, for all levels of development are thoroughly presented and discussed. Experimental evaluation results from two developmental cycles of the i-Treasures project are presented, illustrating the efficiency of the FISEVAL approach to shed light upon micro, meso and macro factors that influence the successful realization of the project. The promising performance of the proposed FISEVAL approach, along with its adaptivity and user friendliness, act as a framework with transferability potential to variant project developmental settings.