Reviewerly/exHarmony huggingface dataset:
Evaluation of password hashing schemes in open source web platforms

Nowadays, the majority of web platforms in the Internet originate either from CMS to easily deploy websites or by web applications frameworks that allow developers to design and implement web applications. Considering the fact that CMS are intended to be plug and play solutions and their main aim is to allow even non-developers to deploy websites, we argue that the default hashing schemes rarely are modified. Also, recent studies suggest that even developers do not use appropriate hash functions to protect passwords, since they may not have adequate security expertise. Therefore, the default settings of CMS and web applications frameworks play an important role in the security of password storage. This paper evaluates the default hashing schemes of popular CMS and web application frameworks. First, we formulate the cost time of password guessing attacks and next we investigate the default hashing schemes of popular CMS and web applications frameworks. We then apply our framework to perform a comparative analysis of the cost time of password guessing attacks between the various CMS and web application frameworks. Finally, considering that intensive hash functions consume computational resources, we analyze hashing schemes from a different perspective. That is, we investigate if it is feasible and under what conditions to perform slow rate denial of service attacks from concurrent login attempts. Through our study we have derived a set of critical observations. We have discovered that many CMS and web application frameworks use outdated hash functions, arbitrary number of hash iterations, while there is a lack of password policies and salt. Notably, the popular WordPress still uses MD5 with low number of hash iterations. Overall, we believe that the security status of the hashing schemes of CMS and web application frameworks calls for changes to the default settings from an opt-in to an opt-out security policy. More security audits and official library implementations are also required to accelerate the adoption of memory hard functions both by policy makers and the industry.
Detection of Waste Containers Using Computer Vision

This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.
Conceptual framework for blockchain-based metering systems

The smart grid environment requires the enhancement of various computational tools, especially for routine tasks of data acquisition and system monitoring. This paper presents the building blocks of a conceptual framework to be used as the basis for
Adaptive Threshold Based Clustering

Partitioning-based clustering methods have various challenges especially user-defined parameters and sensitivity to initial seed selections. K-means is most popular partitioning based method while it is sensitive to outlier, generate non-overlap cluster and non-deterministic in nature due to its sensitivity to initial seed selection. These limitations are regarded as promising research directions. In this study, a deterministic approach which do not requires user defined parameters during clustering; can generate overlapped and non-overlapped clusters and detect outliers has been proposed. Here, a minimum support value has been adopted from association rule mining to improve the clustering results. Further, the improved approach has been analysed on artificial and real datasets. The results demonstrated that datasets are well clustered with this approach too and it achieved success to generate almost same number of clusters as present in real datasets.
Data clustering using efficient similarity measures

AbstractAbstractThe need for appropriate applications of the various similarity measures for clustering has arisen over the years as data massively keep on increasing. The issue of deciding which similarity measure is the best and on what kind of dataset have been a very cumbersome task in the field of data mining, data science, other related fields, and organizations that highly depends on the knowledge outcome from a huge set of data to make some vital / crucial decisions. This is because various datasets portray some common features associated with them; the need for clearer understanding of the various similarity measures for clustering different datasets is needed.This paper presents a critical review of various similarity measures applied in text and data clustering. A theoretical comparison has been made to check the suitability of the measures on different kind of data sets.Keywords: Similarity measureDocument clusteringText documentEuclidean distance and Edit distance
A novel real-time DIC-FPGA-based measurement method for dynamic testing of light and flexible structures

A high-speed 3D digital image correlation (DIC) setup is developed and coupled with an FPGA-based real-time operating system (RT-FPGA) for dynamic testing of light and flexible structures. An in-house image correlation program is developed using MATLAB. This program is validated using a virtual stereovision system created in AUTODESK 3DS MAX to simulate a translating and rotating plate and generate synthetic images. An experimental setup is designed and fabricated to mount the high-speed cameras at precise relative locations and orientations. The developed 3D-DIC technique is validated by comparing it with a laser displacement sensor while measuring point displacement of a rotating plate mounted on a servo mechanism. The displacement measurements obtained from the DIC setup match those obtained from the laser sensor with less than 2% error. The high-speed cameras are coupled and synchronized with an accelerometer and displacement sensor using the RT-FPGA system. A LabVIEW-FPGA program is developed for the coupling, synchronization and data acquisition tasks. The DIC-FPGA coupling is validated by measuring the oscillatory motion of a rigid block and structural dynamic parameters of an ASTM-standard beam. This technique is successfully applied for structural dynamic testing of a hyperviscoelastic membrane, a polymeric sandwich beam and a biomimetic flapping wing. The present study successfully demonstrates the development, validation and application of a novel real-time high-speed non-contact DIC-FPGA technique. Using the reported validation and application exercises, similar measurement methodology can be developed at any lab and used for dynamic testing of light and flexible structures.
Lower- and higher-order nonclassical features in non-degenerate hyper-Raman processes

The nonclassical properties of the different modes in the stimulated, spontaneous, and partially spontaneous multi-photon pump non-degenerate hyper-Raman processes are investigated by obtaining a perturbative analytic operator solution of a completely quantum mechanical Hamiltonian of this process, which can be considered as the most general Raman process. The nonclassical nature of the multi-photon pump non-degenerate hyper-Raman processes is witnessed by means of intermodal entanglement of different orders as well as lower- and higher-order photon antibunching. Interestingly, manifesting the multi-photon nature of the pump modes, a bunch of nonclassicality involving them are observed due to self-interaction of various pump modes. Further, it is shown that the obtained solution is very general in nature as the solutions of various other Raman processes (e.g., degenerate hyper-Raman and stimulated Raman processes) can be obtained as special cases of the present solution, and thus the nonclassical properties of those processes can also be witnessed.
Motion-Guided Cascaded Refinement Network for Video Object Segmentation

In this work, we propose a motion-guided cascaded refinement network for video object segmentation. By assuming the foreground objects show different motion patterns from the background, for each video frame we apply an active contour model on optical flow to coarsely segment the foreground. The proposed Cascaded Refinement Network (CRN) then takes as guidance the coarse segmentation to generate an accurate segmentation in full resolution. In this way, the motion information and the deep CNNs can complement each other well to accurately segment the foreground objects from video frames. To deal with multi-instance cases, we extend our method with a spatial-temporal instance embedding model that further segments the foreground regions into instances and propagates instance labels. We further introduce a single-channel residual attention module in CRN to incorporate the coarse segmentation map as attention, which makes the network effective and efficient in both training and testing. We perform experiments on popular benchmarks and the results show that our method achieves state-of-the-art performance with high time efficiency.
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning

In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning agents from high dimensional image representations can be very expensive and time consuming. Autoencoders are deep neural network used to compress high dimensional data such as pixelated images into small latent representations. This compression model is vital to efficiently learn policies, especially when learning on embedded systems. We have implemented this model on the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption, throughput, and energy consumption of the autoencoders for various CPU/GPU core combinations, frequencies, and model parameters. Additionally, we have shown the reconstructions generated by the autoencoder to analyze the quality of the generated compressed representation and also the performance of the reinforcement learning agent. Finally, we have presented an assessment of the viability of training these models on embedded systems and their usefulness in developing autonomous policies. Using autoencoders, we were able to achieve 4-5X improved performance compared to a baseline RL agent with a convolutional feature extractor, while using less than 2W of power.
Short-Term Solar PV Forecasting Using Gated Recurrent Unit with a Cascade Model

The fluctuation in solar photovoltaic (PV) generation system causes inefficiency in PV power management. Thus, predicting solar PV power is essential to assist PV system in improving the overall performance of a solar plant operation. In this paper, solar PV forecasting model with multiple Gated Recurrent Unit (GRU) networks is proposed to effectively improve the prediction accuracy and the training time compared to the typical GRU network. In addition, other popular prediction machine learning algorithms, namely Feed-forward Artificial Neural Network (ANN), Support Vector Regression (SVR) and K Nearest Neighbors (KNN), were implemented for comparison with the proposed model. Each model was evaluated with Normalized Root Mean Squared Error (NRMSE). The proposed model, GRU, Feed-forward ANN, SVR, and KNN has NRMSE of 9.64%, 10.53%, 11.62%, 11.45%, and 11.89%, respectively. Hence, the proposed model provides enhanced prediction accuracy with improved speed compared with a GRU network.
Deep residual networks for pre-classification based Indian language identification

This paper proposes a pre-classification based language identification (LID) system for Indian languages. In this system, firstly, languages are pre-classified into tonal and non-tonal categories and then individual languages are identified from the languages of the respective category. In this wo rk, language discriminating ability of various acoustic features like, pitch Chroma, mel-frequency Cepstral coefficients (MFCCs) and their combination has been investigated. The system performance has been analyzed for features extracted using different analysis units, like, syllables and utterances. The effectiveness of deep residual networks (ResNets) model in identification of Indian languages has been studied. Also, the system performance has been compared with the performances of other deep neural network architectures like, Convolutional Neural network (CNN) model, cascade CNN-long short-term memory (LSTM) model and shallow architecture like, ANN. Experiments have been carried out on NIT Silchar language database (NITS-LD) and OGI-Multilingual database (OGI-MLTS). Experimental analysis suggests that proposed ResNets model, based on syllable-level features, outperforms the other models. The pre-classification module provides accuracies of 96.6%, 93.2% and 90.6% for NITS-LD, and 92.1%, 89.3% and 85.4% for OGI-MLTS database, with 30s, 10s and 3s test data respectively. The pre-classification module helps to improve the system performance by 3.8%, 4.1% and 4.3% for 30s, 10s and 3s test data respectively. For OGI-MLTS database, the respective improvements are 6.8%, 6.5% and 5.4%.
Real-time gesture recognition based on feature recalibration network with multi-scale information

Gesture recognition is important in human–machine interaction. Current methods for solving gesture recognition have several disadvantages such as low recognition rate, slow speed and poor performance on recognizing multiple targets or long-distance targets in complex environments. In view of the above problems, we propose a gesture recognition approach that can recognize gestures quickly and accurately from complex background. This approach works on a deep convolutional network, which consists of a basic network module for extracting feature information, a squeeze-and-excitation networks for increasing feature channel affinity and a feature pyramid attention module for fusing context information with different scales. To test the proposed approach, we make a data set that contains 3289 images from difference complex scenes. Generally gestures in those images can be generally classified into 16 types. We have uploaded this data set for researchers use. Experimental results demonstrate that the recognition accuracy and speed of the proposed method can achieve 83.45% and 32.2 frames per second respectively, which has better comprehensive performance compared with other state-of-the-art recognition algorithms.
A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.
Secure telemedicine using RONI halftoned visual cryptography without pixel expansion

To provide quality healthcare services worldwide telemedicine is a well-known technique. It delivers healthcare services remotely. For the diagnosis of disease and prescription by the doctor, lots of information is needed to be shared over public and private channels. Medical information like MRI, X-Ray, CT-scan etc. contains very personal information and needs to be secured. Security like confidentiality, privacy, and integrity of medical data is still a challenge. It is observed that the existing security techniques like digital watermarking, encryption are not efficient for real-time use. This paper investigates the problem and provides the solution of security considering major aspects, using Visual Cryptography (VC). The proposed algorithm creates shares for parts of the image which does not have relevant information. All the information which contains data related to the disease is supposed to be relevant and is marked as the region of interest (ROI). The integrity of the image is maintained by inserting some information in the region of non-interest (RONI). All the shares generated are transmitted over different channels and embedded information is decrypted by overlapping (in XOR fashion) shares in θ(1) time. Visual perception of all the results discussed in this article is very clear. The proposed algorithm has performance metrics as PSNR (peak signal-to-noise ratio), SSIM (structure similarity matrix), and Accuracy having values 22.9452, 0.9701, and 99.8740 respectively.
Adaptive Cyclopean Image-Based Stereoscopic Image-Quality Assessment Using Ensemble Learning

In this paper, we proposed an effective 3-D image-quality assessment method based on an adaptive cyclopean image by using ensemble learning. Our cyclopean image is not only suitable for a symmetrical distortion image, but also especially suitable for an asymmetrical distortion image. This adaptivity of our cyclopean image can be attributed to the consideration of gain control and gain enhancement in a binocular rivalry visual mechanism. In addition, we use a salient map to modify our cyclopean image to let the salient area of our cyclopean become more attractive. As a result, we can get better results. To remove redundant information out from our cyclopean, the sparse representation is applied to extract essential features. Finally, to get better regression accuracy on extracted feature, we use ensemble learning to get the final quality score of a stereoscopic image. The ensemble learner can improve the regression accuracy by 2% than a single learner. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods on two publicly available stereoscopic image-quality assessment databases LIVE I and LIVE II.
Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Abstract Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study.
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data

Spatio-temporal data are ubiquitous in the agricultural, ecological, and environmental sciences, and their study is important for understanding and predicting a wide variety of processes. One of the difficulties with modeling spatial processes that change in time is the complexity of the dependence structures that must describe how such a process varies, and the presence of high-dimensional complex datasets and large prediction domains. It is particularly challenging to specify parameterizations for nonlinear dynamic spatio-temporal models (DSTMs) that are simultaneously useful scientifically and efficient computationally. Statisticians have developed multi-level (deep) hierarchical models that can accommodate process complexity as well as the uncertainties in the predictions and inference. However, these models can be expensive and are typically application specific. On the other hand, the machine learning community has developed alternative "deep learning" approaches for nonlinear spatio-temporal modeling. These models are flexible yet are typically not implemented in a probabilistic framework. The two paradigms have many things in common and suggest hybrid approaches that can benefit from elements of each framework. This overview paper presents a brief introduction to the multi-level (deep) hierarchical DSTM (H-DSTM) framework, and deep models in machine learning, culminating with the deep neural DSTM (DN-DSTM). Recent approaches that combine elements from H-DSTMs and echo state network DN-DSTMs are presented as illustrations. Supplementary materials accompanying this paper appear online.
Predicting emotional reactions to news articles in social networks

After reading a news article, some readers post their opinion to social networks, particularly as tweets. These opinions (responses) have an important emotional content. By analyzing users’ responses in context, it is possible to find a set of emotions expressed in these tweets. In this work we propose a method to predict the emotional reactions that Twitter users would have after reading a news article. We consider the prediction of emotions as a classification problem and we follow a supervised approach. For this purpose, we collected a corpus of Spanish news articles and their associated tweet responses. Then, a group of annotators tagged the emotions expressed in them. Twitter users can express more than one emotion in their responses, so that in this work we deal with this characteristic by using a multi-target classification strategy. The use of this strategy allows an instance (a news article) to have more than one associated class (emotions expressed by users). In addition to that, the multi-target strategy permits to predict not only the emotional reactions, but also the intensity of these emotions, considering how often each specific emotion was triggered by users. By measuring the deviation of the predicted emotional reactions with regard to the annotated ones, we obtain an emotional reactions similarity of 89%.