Reviewerly/exHarmony huggingface dataset:
Photovoltaic Potential Assessment and Dust Impacts on Photovoltaic Systems in Iran: Review Paper

Increasing energy demand, together with environmental concerns, results in a significant tendency toward the research and development of renewable systems and particularly solar energy. Locating in the sunbelt of earth, Iran has great solar potential. However, due to the noticeable amount of dust activities in the Middle East, there are several challenges in the way of development of the photovoltaic systems. The current article provides a comprehensive literature review on the photovoltaic status in Iran regarding the climate conditions, photovoltaic development potentials, energy policies within the country, and photovoltaic experiences. A regionally focused review of the conducted works is provided to investigate the effect of dust activities on the output performance of photovoltaic systems. A focus on dust origins, frequency, and specifications is a priority in order to analyze its impacts on the performance of photovoltaic systems. Furthermore, a review of cleaning methods to overcome the dust deposition problem is provided. The comprehensive literature review conducted in this article is represented to researchers, designers, and engineers dealing with photovoltaic systems in regions with dust problems such as the Middle East and particularly Iran.
Transformer-Based Online CTC/Attention End-To-End Speech Recognition Architecture

Recently, Transformer has gained success in automatic speech recognition (ASR) field. However, it is challenging to deploy a Transformer-based end-to-end (E2E) model for online speech recognition. In this paper, we propose the Transformer-based online CTC/attention E2E ASR architecture, which contains the chunk self-attention encoder (chunk-SAE) and the monotonic truncated attention (MTA) based self-attention decoder (SAD). Firstly, the chunk-SAE splits the speech into isolated chunks. To reduce the computational cost and improve the performance, we propose the state reuse chunk-SAE. Sencondly, the MTA based SAD truncates the speech features monotonically and performs attention on the truncated features. To support the online recognition, we integrate the state reuse chunk-SAE and the MTA based SAD into online CTC/attention architecture. We evaluate the proposed online models on the HKUST Mandarin ASR benchmark and achieve a 23.66% character error rate (CER) with a 320 ms latency. Our online model yields as little as 0.19% absolute CER degradation compared with the offline baseline, and achieves significant improvement over our prior work on Long Short-Term Memory (LSTM) based online E2E models.
Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.
An efficient image encryption scheme based on chaotic and Deoxyribonucleic acid sequencing

In this research article, we have designed a novel image encryption scheme based on Deoxyribonucleic acid (DNA) and chaotic sequencing. We have studied sequences of different genes consisting of four bases, Adenine (A), Cytosine (C), Thymine (T) and Guanine (G), also known as nucleotides which are the fundamental code of life. The purpose of DNA is to store, copy and transmit the genetic information of the living organism. The proposed image encryption scheme uses the chaotic system to generate random sequences and to choose between two or more options. The image is totally transformed by first encoding it into DNA nucleotides, shuffling to achieve diffusion and then substitution is performed to achieve confusion. Then later the DNA fusion operation is performed on the DNA image and the DNA complements are applied on the resulting DNA image, a random number of times to break the residual correlation between the pixels. The cipher image has been tested to fulfill all the standard benchmarks to be categorized as a good ciphered image which includes entropy, correlation coefficient, Pearson’s chi square, peak signal to noise ratio, mean square error, mean absolute error, structure similarity indexed measures, differential analysis and histogram uniformity test.
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection

A classical approach to abnormal activity detection is to learn a representation for normal activities from the training data and then use this learned representation to detect abnormal activities while testing. Typically, the methods based on this approach operate at a fixed timescale - either a single time-instant (e.g. frame-based) or a constant time duration (e.g. video-clip based). But human abnormal activities can take place at different timescales. For example, jumping is a short-term anomaly and loitering is a long-term anomaly in a surveillance scenario. A single and pre-defined timescale is not enough to capture the wide range of anomalies occurring with different time duration. In this paper, we propose a multi-timescale model to capture the temporal dynamics at different timescales. In particular, the proposed model makes future and past predictions at different timescales for a given input pose trajectory. The model is multi-layered where intermediate layers are responsible to generate predictions corresponding to different timescales. These predictions are combined to detect abnormal activities. In addition, we also introduce a single-camera abnormal activity dataset for research use that contains 483,566 annotated frames. Our experiments show that the proposed model can capture the anomalies of different time duration and outperforms existing methods.
PALK: Password-based anonymous lightweight key agreement framework for smart grid

Smart grid (SG) is a highly efficient and more reliable framework that controls the computer, automation, new technologies and equipment. In SG, electricity utilization and communication are exchange between consumers and the power grid. Security and privacy are essential components of SG communication. Various key agreement protocols have been presented for SG communication. Among these some protocols have security issues or high computational overhead. In the proposed paper, we design a password-based anonymous lightweight key agreement framework for smart grid (PALK) by using elliptic curve cryptography. We provide a formal security analysis of the PALK which is based on the random oracle model and informal security analysis. Moreover, we prove that PALK is security against man-in-the-middle attack and replay attack using the simulation tool “AVISPA”. Also, we demonstrate that PALK has better computation and communication costs than other related protocols in the same environment. The proposed framework is a real-life application in SG communication.
Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images

Cervical cancer causes the fourth most cancer-related deaths of women worldwide. Early detection of cervical intraepithelial neoplasia (CIN) can significantly increase the survival rate of patients. In this paper, we propose a deep learning framework for the accurate identification of LSIL+ (including CIN and cervical cancer) using time-lapsed colposcopic images. The proposed framework involves two main components, i.e., key-frame feature encoding networks and feature fusion network. The features of the original (pre-acetic-acid) image and the colposcopic images captured at around 60s, 90s, 120s and 150s during the acetic acid test are encoded by the feature encoding networks. Several fusion approaches are compared, all of which outperform the existing automated cervical cancer diagnosis systems using a single time slot. A graph convolutional network with edge features (E-GCN) is found to be the most suitable fusion approach in our study, due to its excellent explainability consistent with the clinical practice. A large-scale dataset, containing time-lapsed colposcopic images from 7,668 patients, is collected from the collaborative hospital to train and validate our deep learning framework. Colposcopists are invited to compete with our computer-aided diagnosis system. The proposed deep learning framework achieves a classification accuracy of 78.33%—comparable to that of an in-service colposcopist—which demonstrates its potential to provide assistance in the realistic clinical scenario.
An ameliorated teaching–learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur’s entropy and Otsu’s between class variance

In this paper, multi-threshold image segmentation approaches using an improved teaching–learning-based optimization algorithm (DI-TLBO) are presented and the proposed DI-TLBO-based methods obtain satisfactory segmentation results. This work is presented as follows. Firstly, two random numbers are introduced to determine the learning methods of the learner in the teacher phases and the learner phases of DI-TLBO. Randomness of the learning methods further improves global optimization ability of DI-TLBO. Self-feedback learning phase and mutation-crossover phase are also introduced into DI-TLBO algorithm, which makes DI-TLBO achieve better exploration ability. The comparative results of DI-TLBO with other evolutionary algorithms (EAs) on a set of benchmarks functions demonstrate that DI-TLBO acquires better solution accuracy than other EAs. Then the proposed DI-TLBO algorithm is applied to solve multi-level threshold image segmentation problems modeled by Otsu’s between class variance function and Kapur’s entropy function. Experiments comparing DI-TLBO-based methods with other EAs based approaches on standard test images show that DI-TLBO-based methods possess superior performance in terms of both solution accuracy and stability of segmentation results. Finally, the proposed DI-TLBO-based methods are successfully applied in casting X-ray image segmentation for multi-level threshold. Although the defects in high resolution X-ray image (3072×2400) are easy to be ignored and omitted when being detected artificially, all the defects are segmented perfectly using the proposed DI-TLBO-based methods.
Compression-Then-Encryption-Based Secure Watermarking Technique for Smart Healthcare System

The smart healthcare system is an electronic patient records (EPR) sharing system, which significantly helps sharing of EPR data and provides appropriate medical assistance for the patients and a more suitable platform for the potential researchers. However, the security of EPR data is still a major issue in such systems. In this paper, we develop a compression-then-encryption-based dual watermarking to protect the EPR data for the healthcare system, which produces several significant features. Experiments conducted on a large set of medical data indicate the capability of our proposed method for smart healthcare. Finally, when compared with the existing technique, the proposed work offers better performance in terms of robustness and security.
A systematic review of the literature regarding socially assistive robots in pre-tertiary education

With rapid advances in Artificial Intelligence (AI) over the last decade, schools have increasingly employed innovative tools, intelligent applications and methods that are changing the education system with the aim of improving both user experience and learning gain in the classrooms. Even though the use of AI to education is not new, it has not unleashed its full potential yet. Much of the available research looks at educational robotics and at non-intelligent robots in education. Only recently, research has sought to assess the potential of Socially Assistive Robots (SARs), including humanoids, within the domain of classroom learning, particularly in relation to learning languages. Yet, the use of this form of AI in the field of mathematics and science constitutes a notable gap in this field. This study aims to critically review the research on the use of SARs in the pre-tertiary classroom teaching of mathematics and science. Further aim is to identify the benefits and disadvantages of such technology. Databases' search conducted between January and April 2018 yielded twenty-one studies meeting the set inclusion criteria for our systematic review. Findings were grouped into four major categories synthesising current evidence of the contribution of SARs in pre-tertiary education: learning gain, user experience, attitude, and usability of SARs within classroom settings. Overall, the use of SARs in pre-tertiary education is promising, but studies focussing on mathematics and science are significantly under-represented. Further evidence is also required around SARs' specific contributions to learning more broadly, as well as enabling/impeding factors, such as SAR's personalisation and appearance, or the role of families and ethical considerations. Finally, SARs potential to enhance accessibility and inclusivity of multi-cultural pre-tertiary classroom is almost unexplored.
Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3

We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels and an inefficient distribution of anchors. Poly-YOLO reduces the issues by aggregating features from a light SE-Darknet-53 backbone with a hypercolumn technique, using stairstep upsampling, and produces a single scale output with high resolution. In comparison with YOLOv3, Poly-YOLO has only 60% of its trainable parameters but improves the mean average precision by a relative 40%. We also present Poly-YOLO lite with fewer parameters and a lower output resolution. It has the same precision as YOLOv3, but it is three times smaller and twice as fast, thus suitable for embedded devices. Finally, Poly-YOLO performs instance segmentation by bounding polygons. The network is trained to detect size-independent polygons defined on a polar grid. Vertices of each polygon are being predicted with their confidence, and therefore, Poly-YOLO produces polygons with a varying number of vertices. Source code is available at https://gitlab.com/irafm-ai/poly-yolo .
Enhanced Crow Search Algorithm for Feature Selection

The crow search algorithm (CSA) is a recent metaheuristic inspired by the intelligent group behavior of crows. It has attracted the attention of many researchers because of its simplicity and easy implementation. However, it suffers from premature convergence because of its ability to balance between exploration and exploitation is weak. Therefore, we investigate in this paper, an enhanced version of CSA called by us ECSA as a wrapper feature selection method to extract the best feature subsets. This enhancement achieved by introducing three modifications to the original CSA to improve its performance. Firstly, we propose an adaptive awareness probability to enhance the balance between exploration and exploitation. Secondly, we replace the random choice of the crow to follow by the dynamic local neighborhood to guide the local search. Thirdly, we introduce a novel global search strategy to increase the global exploration capability of the crow. The performance of ECSA is measured using three performance metrics and statistical significance over 16 datasets from the UCI repository. The obtained results are compared with those of the original CSA and some state-of-the-art techniques in the literature. Experimental results showed that ECSA presents a better convergence speed and a better-quality solution.
Cloud based security on outsourcing using blockchain in E-health systems

Recently, Electronic Health Records (EHR) plays a significant role in E-health care system that allows data exchange of patient's health records via a portal designated by healthcare professionals. In this, the lack of confidentiality and integrity factors leads to different security issues on sensitive health information and causes a serious impact on a patient's life. To accomplish the security of medical data, we propose a new scheme named Elliptical Curve Certificateless Aggregate Cryptography Signature scheme (EC-ACS) for the public verification and auditing in the Medical Cloud Server (MCS) to secure EHR using authorized blockchain technology. In this, we use Elliptic Curve Cryptography (ECC) to encrypt medical data and the Certificateless Aggregate Signature scheme (CAS) to generate the digital signature for sharing and storing data in the cloud storage. This proposed scheme ensures security, privacy and safeguards the confidential information from unauthorized access in the cloud health system. Furthermore, the blockchain technique guarantees the integrity, traceability and secure storage of medical records in the cloud environment.
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution

In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single image super-resolution (SISR) methods. In contrast to conventional algorithms which utilize brute-force searches or optical flow estimations, the proposed algorithm is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation. Moreover, the proposed module is aware of not only the best matching position but also the relevancy of the best match. This makes our algorithm substantially robust when irrelevant reference images are given, overcoming the major cause of the performance degradation when using existing RefSR methods. Furthermore, our module can be utilized for self-similarity SR if no reference image is available. Experimental results demonstrate the superior performance of the proposed algorithm compared to previous works both quantitatively and qualitatively.
Multi-Path Learning for Object Pose Estimation Across Domains

We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-multi-decoder network is trained using a technique we denote ”multi-path learning”: While the encoder is shared by all objects, each decoder only reconstructs views of a single object. Consequently, views of different instances do not have to be separated in the latent space and can share common features. The resulting encoder generalizes well from synthetic to real data and across various instances, categories, model types and datasets. We systematically investigate the learned encodings, their generalization, and iterative refinement strategies on the ModelNet40 and T-LESS dataset. Despite training jointly on multiple objects, our 6D Object Detection pipeline achieves state-of-the-art results on T-LESS at much lower runtimes than competing approaches.
Rendering Natural Camera Bokeh Effect with Deep Learning

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website: https://people.ee.ethz.ch/ ihnatova/pynet-bokeh.html.
Estimating GPU memory consumption of deep learning models

Deep learning (DL) has been increasingly adopted by a variety of software-intensive systems. Developers mainly use GPUs to accelerate the training, testing, and deployment of DL models. However, the GPU memory consumed by a DL model is often unknown to them before the DL job executes. Therefore, an improper choice of neural architecture or hyperparameters can cause such a job to run out of the limited GPU memory and fail. Our recent empirical study has found that many DL job failures are due to the exhaustion of GPU memory. This leads to a horrendous waste of computing resources and a significant reduction in development productivity. In this paper, we propose DNNMem, an accurate estimation tool for GPU memory consumption of DL models. DNNMem employs an analytic estimation approach to systematically calculate the memory consumption of both the computation graph and the DL framework runtime. We have evaluated DNNMem on 5 real-world representative models with different hyperparameters under 3 mainstream frameworks (TensorFlow, PyTorch, and MXNet). Our extensive experiments show that DNNMem is effective in estimating GPU memory consumption.
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene. Code and models are publicly available.