Reviewerly/exHarmony huggingface dataset:
Automatic Cytoplasm and Nucleus detection in the white blood cells depending on hisogram analysis

Abstract Programmed identification of white platelets (WBCs) stays an uncertain issue in medicinal imaging. The first step in this algorithm is to detect a white blood cell (Lymphocyte) and then segments the Cytoplasm and Nucleus in this cell. This was done by using image processing Techniques. The suggested method depending on the binary conversion of red, blue and hue compounds depending on threshes values. These values were calculated from histogram analysis within specific ranges. The proposed algorithm was compared with several other algorithms for detection by using an accuracy scale in the detection. where the proposed algorithm obtained a high distinction accuracy reached 98% compared to other methods.
Enhance Categorisation Of Multilevel High-Sensitivity Cardiovascular Biomarkers From Lateral Flow Immunoassay Images Via Neural Networks And Dynamic Time Warping

Lateral Flow Immunoassays (LFA) are low cost, rapid and highly efficacious Point-of-Care devices. Traditional LFA testing faces challenges to detect high-sensitivity biomarkers due to low sensitivity. Unlike most approaches based on averaging image intensity from a region-of-interest (ROI), this paper presents a novel system that considers each row of an LFA image as a time series signal and, consequently, does not require the detection of ROI. Long Short-Term Memory (LSTM) networks are used to classify LFA data obtained from multilevel high-sensitivity cardiovascular biomarkers. Dynamic Time Warping (DTW) was incorporated with LSTM to align the LFA data from different concentration levels to a common reference before feeding the distance maps into an LSTM network. The LSTM network outperforms other classifiers with or without DTW. Furthermore, performance of all classifiers is improved after incorporating DTW. The positive outcomes suggest the potential of the proposed methods for early risk assessment of cardiovascular diseases.
Security Analysis of NIST CTR-DRBG

We study the security of $$\mathsf {CTR\text {-}DRBG}$$ , one of NIST’s recommended Pseudorandom Number Generator (PRNG) designs. Recently, Woodage and Shumow (Eurocrypt’ 19), and then Cohney et al. (S&P’ 20) point out some potential vulnerabilities in both NIST specification and common implementations of $$\mathsf {CTR\text {-}DRBG}$$ . While these researchers do suggest counter-measures, the security of the patched $$\mathsf {CTR\text {-}DRBG}$$ is still questionable. Our work fills this gap, proving that $$\mathsf {CTR\text {-}DRBG}$$ satisfies the robustness notion of Dodis et al. (CCS’13), the standard security goal for PRNGs.
Fast colonic polyp detection using a Hamilton–Jacobi approach to non-dominated sorting

This paper describes a novel method for fast colonic polyp detection in colonoscopy images. Firstly, polyp detection is formulated as a similarity-based anomaly detection method, which formally involves non-dominated sorting based on multiple objectives. The chosen objectives rely on the main physical and visible differences, observed in colonoscopy images, between regions containing colonic polyps and the surrounding normal mucosa. These differences are defined primarily according to the contrast in shape, texture, and color. Secondly, as non-dominated sorting is of combinatorial nature and is costly to compute, it is replaced by a fast algorithm that approximates the sorting in the continuum limit. The fast algorithm involves numerical solutions to a particular Hamilton–Jacobi equation. The proposed similarity-based anomaly detection is thus reformulated into a fast polyp detection method. Several experiments were conducted with a proprietary medical data set, containing 1640 instances of 41 different polyps. The results show that the proposed Hamilton–Jacobi approach to non-dominated sorting speeds up the non-dominated sorting procedure, by more than 500%, and, when compared with other existing methods, it is also faster without lost of accuracy. Moreover, the tests conducted for streaming data, reveal an outstanding performance, in terms of sensitivity and specificity, as well as, a fast auto-adaptability, which demonstrate the power of the proposed approach towards a real-time and automatic detection, undoubtedly beneficial for clinical practice.
Efficient Spatial Pyramid of Dilated Convolution and Bottleneck Network for Zero-Shot Super Resolution

Most CNN-based super-resolution networks require a large number of samples for model training, which may cause overfitting when trained on a specific set, and the internal self-similarity of the test image in this way has also been discarded. To resolve this problem, this paper proposes a novel zero-shot learning (ZSL) network for super resolution. In this model, the training samples are cropped from the input image, which means no extra dataset is required for model training, the overfitting problem in this way can be well avoided. Besides, since all the training samples are cropped from the input itself, the nonlocal self-similarity attributes of the test image can be fully utilized. Moreover, the efficient spatial pyramid of dilated convolutions network with bottleneck (ESP-BNet) is applied in the model as an efficient computational structure to enhance the feature representation. Comparison experiments show that the proposed approach achieves PSNR/SSIM of 31.13dB/87.9% on Set5 dataset and PSNR/SSIM of 27.62dB/78.1% on Set14 dataset, which is around 3dB better than the traditional method and about 1dB higher than those of the state-of-art method.
Inferring 3D Shapes from Image Collections Using Adversarial Networks

We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called projective generative adversarial network (PrGAN) trains a deep generative model of 3D shapes whose projections (or renderings) matches the distribution of the provided 2D views. The addition of a differentiable projection module allows us to infer the underlying 3D shape distribution without access to any explicit 3D or viewpoint annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained directly on 3D data. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage of our model is that it estimates 3D shape, viewpoint, and generates novel views from an input image in a completely unsupervised manner. We further investigate how the generative models can be improved if additional information such as depth, viewpoint or part segmentations is available at training time. To this end, we present new differentiable projection operators that can be used to learn better 3D generative models. Our experiments show that PrGAN can successfully leverage extra visual cues to create more diverse and accurate shapes.
ForeSight - An AI-driven Smart Living Platform, Approach to Add Access Control to openHAB

We created an approach for a smart living platform called ForeSight which consists of different modules: a service engineering module, a Web of Things (WoT)-based Internet of Things (IoT) module and an artificial intelligence (AI) component. This paper describes how openHAB, a smart home middleware, is extended to fulfill platform requirements related to a successful interaction with the IoT module of ForeSight, more precisely, to add identity and access management (IAM) to openHAB and comply with European privacy laws.
Bidirectional LSTM Deep Model for Online Doctor Reviews Polarity Detection

Online medical reviews contain patients' subjective evaluations and reflect their satisfaction with the treatment process and doctors. Mining and analysis of sentiment expressed in these medical data may be vital for different applications including adverse drug effects detection, doctor recommendation, and healthcare quality assessment. Nevertheless, medical sentiment analysis is a challenging and complex task because patients who write the reviews are usually non-professional users and tend to use informal language. The problem is more challenging in the Persian language due to its resource scarcity and complex structure. In this study, we introduce PODOR, a Persian dataset of online doctor reviews extracted from social web. Also, we propose a deep model based on the bidirectional long short-term memory for polarity detection of PODOR reviews. To show the effectiveness and suitability of the proposed model, we compared the model with six traditional supervised machine learning methods and three deep models. Preliminary comparative results indicated that our model outperformed traditional methods by 8% and 7%, and deep models by 2% and 3% in terms of accuracy and f1-measure.
Efficient universal cycle constructions for weak orders

A weak order is a way n competitors can rank in an event, where ties are allowed. A weak order can also be thought of as a relation on {1,2,…,n} that is transitive and complete. We provide the first efficient algorithms to construct universal cycles for weak orders by considering both rank and height representations. Each algorithm constructs the universal cycles in O(n) time per symbol using O(n) space.
Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

Within the sentiment classification field, the convolutional neural network (CNN) and long short-term memory (LSTM) are praised for their classification and prediction performance, but their accuracy, loss rate, and time are not ideal. To this purpose, a deep learning structure combining the improved cross entropy and weight for word is proposed for solving cross-domain sentiment classification, which focuses on achieving better text sentiment classification by optimizing and improving recurrent neural network (RNN) and CNN. Firstly, we use the idea of hinge loss function (hinge loss) and the triplet loss function (triplet loss) to improve the cross entropy loss. The improved cross entropy loss function is combined with the CNN model and LSTM network which are tested in the two classification problems. Then, the LSTM binary-optimize (LSTM-BO) model and CNN binary-optimize (CNN-BO) model are proposed, which are more effective in fitting the predicted errors and preventing overfitting. Finally, considering the characteristics of the processing text of the recurrent neural network, the influence of input words for the final classification is analysed, which can obtain the importance of each word to the classification results. The experiment results show that within the same time, the proposed weight-recurrent neural network (W-RNN) model gives higher weight to words with stronger emotional tendency to reduce the loss of emotional information, which improves the accuracy of classification.
Nakdan: Professional Hebrew Diacritizer

We present a system for automatic diacritization of Hebrew text.The system combines modern neural models with carefully curated declarative linguistic knowledge and comprehensive manually constructed tables and dictionaries.Besides providing state of the art diacritization accuracy, the system also supports an interface for manual editing and correction of the automatic output, and has several features which make it particularly useful for preparation of scientific editions of Hebrew texts.The system supports Modern Hebrew, Rabbinic Hebrew and Poetic Hebrew.
The Attendance Marking System based on Eigenface Recognition using OpenCV and Python

Abstract Attendance of students in a large classroom is hard to be handled by the traditional system, as it is time-consuming and has a high probability of error during the process of inputting data into the computer. This paper proposed automated attendance marking system using face recognition technique. The system deployed Haar cascade to find the positive and negative of the face and eigenface algorithm for face recognition by using python programming and OpenCV library. The proposed method using PCA to resolved the problems such as lightning of the images, noise from the camera, and the direction of the student faces. The attendance of the student was updated to the Excel sheet after student’s face has been recognized.
Gaussian Privacy Protector for Online Data Communication in a Public World

Capturing privacy requirements for the Internet of Things (IoT) devices is essential to create sufficient public confidence to adopt and promote such devices. However, data privacy-preserving has a particular issue in the era of IoT because of a wide variety of facets and aspects that makes it difficult to formulate. The objective of the proposed research is to design a novel continuous high-dimensional data release mechanism called Gaussian Privacy Protector (GPP). In such cases, GPP can prevent adversaries from mining private information from the released data while maximizing the amount of information revealed about the utility data. We utilize variational lower bounds of mutual information approximation that is implemented as supervised learning using an adversarial training algorithm. We use two datasets: MNIST (hand-written digits) and HAPT-Recognition (Human Activities and Postural Transitions’ Recognition using smartphone data) to evaluate our algorithm. Our experimental results on these datasets show that our approach is highly effective in removing private information from the datasets while allowing non-private data to be mined effectively.
A Comprehensive Review on Content-Based Image Retrieval System: Features and Challenges

Over a couple of years, huge attention is being paid by the researchers on the content-based image retrieval (CBIR) in order to successfully retrieve the contents from large-scale multimedia databases. Typically, each day gigabytes of multimedia contents are being generated by the digital camera, cell phone, and PC and they are available in the form of multimedia database. It is critical to find out the desired data from this vast collection of database. CBIR is not only efficient in performing the image retrieval, but also organizes the common contents of a digital library in the indented database. In this work, totally 25 research works are reviewed under CBIR techniques with respect to certain analytical views. On the basis of different algorithmic models, they are categorized into transform-based CBIR technique, metaheuristic-based CBIR technique, learning-based CBIR technique, fuzzy-learning-based CBIR technique, and other CBIR techniques. The analytical representations are defined by means of graphs and tabular columns. Finally, a detailed description of research gaps and challenges is also presented under this scenario.
A Hybrid Symmetric Key Cryptography Method to Provide Secure Data Transmission

Confidentiality of data is required when it travels over the insecure network such as e-mail transfer using Pretty Good Privacy (PGP), image transfer, etc. The proposed symmetric cryptographic algorithm is suitable for such data transfer. A number of other algorithms can be formulated by combining the existing algorithms to incorporate the algorithms in the relevant applications. The modified algorithm for session key generation is a combination of Affine cipher and hill cipher. A systematic study of the symmetric-key algorithm consisting of multiple keys with regular dependency has been carried out in order to show the efficacy. Security analysis of the proposed algorithm reveals the weakness of the algorithm.
The Impact of the Public Disclosure of Curved Inspection Scores Using Emojis on Hygiene Violations in Food Establishments

Policymakers increasingly develop initiatives to influence business and consumer behavior. Among the initiatives to increase the compliance of food establishments to hygiene standards is the public disclosure of hygiene inspection scores. In this study, we analyze the impact of the 2017 law change in King County (Washington state, USA) mandating the presentation of hygiene inspection scores at the front door using an emoji-based display with information about the food establishment’s relative performance to other food establishments in the zip code area. Based on information from 82,545 food inspections in 8,010 food establishments in the period August 2014 to May 2019, we find that the rolling implementation of these displays had a small but meaningful impact on food inspection scores and hygiene violations. As a result of the new display, hygiene scores improved and the odds of failing inspection declined.
Evaluation and Optimization of Dual-Arm Robot Path Planning for Human–Robot Collaborative Tasks in Smart Manufacturing Contexts

Abstract In human–robot collaborative tasks, the performance of robot path planning has a direct impact on the robot-to-human hand-over process, or even the collaboration quality. In this work, we propose an evaluation study on multiple robot path planners with different metrics and reveal their pros and cons in representative human–robot collaborative manufacturing contexts. Afterward, based on the proposed metrics, we define a cost function for the dual-arm robot to choose optimized path planning solutions with maximum efficiency for its human partner in human–robot collaboration. We implement the proposed evaluation and optimization approaches to multiple realistic human–robot collaborative manufacturing contexts. Experimental results and evaluations suggest that our approaches are able to provide positive solutions for the robot path planner selection and also open a window for exploring more complicated and general robot path planning applications to human–robot collaborative tasks in smart manufacturing contexts.
Efficient attribute-based encryption with repeated attributes optimization

Internet of Things (IoT) is an integration of various technologies to provide technological enhancements. To enforce access control on low power operated battery constrained devices is a challenging issue in IoT scenarios. Attribute-based encryption (ABE) has emerged as an access control mechanism to allow users to encrypt and decrypt data based on an attributes policy. However, to accommodate the expressiveness of policy for practical application scenarios, attributes may be repeated in a policy. For certain policies, the attributes repetition cannot be avoided even after applying the boolean optimization techniques to attain an equivalent smaller length boolean formula. For such policies, the evaluated secret shares are also multiple for repeated attributes; hence, the ciphertext computed for those irreducible policies is long and computational effort is more. To address this issue, a new CP-ABE scheme is proposed which employs our Repeated Attributes Optimization algorithm by which the Linear Secret Sharing Scheme matrix sent along with ciphertext will contain the access structure of policy including attributes appearing multiple times, but the ciphertext will only be evaluated for unique non-repeated attributes. Security and performance analysis show that the proposed construction fulfils its goals of achieving desired security with low communication overhead and computational cost for resource-constrained devices.
Digital Footprint Analysis to Develop a Personal Digital Competency-Based Profile

Social networks have become an integral and important part of younger generation’s life today. The information that users place on their web pages on the social network is the product of their activities. It reflects the users’ needs and values. The analysis of the digital footprint left on social media has undeniable potential to improve all processes in society in general, and the educational process in particular. The purpose of this study is to build a model for developing a personal digital competency-based profile through the analysis of web users’ digital footprints left on the social network. According to the analysis report on digital footprints, the author of the study developed a model of the user’s digital competency-based profile. Intellectual efforts of universities, scientists and trainers in order to create digital competency-based profiles and improve the personal educational trajectory can lead to educational system improvement and increase the chances for personal career growth under the conditions of the digital economy.
PassGAN Based Honeywords System for Machine-Generated Passwords Database

A game between the attacker and the defender is simulated in this work to assess their possible strategies in a honeyword system where all of the original passwords are machine-generated is simulated. Five PassGAN-based honeywords generation strategies are used by the defender while three guessing strategies are employed by the attacker including TopPW attack and two PassGAN-based methods. The experiment results show that both the PassGAN-based attack obtained a higher success rate than the Top-PW attack that consider the most popular password as the correct password. Meanwhile, from the defender’s point of view, the hybrid PassGAN-based techniques can be considered as the best honeywords generations strategy for the defender due to its ability to combine the benefit provided by the PassGAN trained on leaked password dataset and the PassGAN trained on the random password dataset.
A Blockchain Approach for the Future Renewable Energy Transaction

Blockchain technology holds great promise to rewire the current financial system which relies on the third party. Every transaction is recorded in a secure and transparent for the parties in the blockchain network. Blockchain technology in the energy sector becomes an interesting topic among researchers at this moment. The use of blockchain allows producers and consumers to trade energy transactions through smart grids because of a decentralized energy trading system. The trading activities without a third party involved would reduce the cost of a transaction thus it brings to a new level of quality of service in the trading system. In this paper, we propose an architectural model for decentralized energy trading system among the neighbors that allows the producer who has the surplus energy to conduct a trading activity with his/her neighbors. The transactions manage by the miners in the same blockchain network. Moreover, we analyze the security issues from various attacks and presenting the performance of the selected attack that might occur in the model.