Reviewerly/exHarmony huggingface dataset:
White Blood Cell Classification Based on Shape and Deep Features

Classification of the white blood cells (WBCs) in blood smear images is essential for providing important information to the physicians. In addition, manual analyzing of the blood smear images for determining the various WBCs is a time-consuming issue for the physicians. In this paper, a hybrid method is proposed for the classification of the WBCs. Image processing (IP) and machine learning (ML) are used to determine and classify the WBCs in blood smear images. In the IP perspective, various IP algorithms are used to segment the WBCs and in ML perspective, feature extraction and classification are employed. RGB to HSV transformation, color to gray tone conversion, filtering operations, thresholding, and morphological processes are used for determining the WBCs. Median filtering and adaptive histogram equalization are used for filtering and image enhancement and Otsu thresholding is considered for thresholding due to its simplicity. Shape based features and deep features are used for characterization of the WBCs and long-short term memory (LSTM) network is employed for classification. A dataset containing totally 349 blood smear images is considered in the evaluation of the proposed method. 10-fold cross validation is used in experiments and classification accuracy is calculated accordingly. While the shape based features produce 80.0% accuracy, deep features obtain 82.9% accuracy. When both shape and deep features are concatenated, 85.7% accuracy score is obtained.
Face Recognition Based on MTCNN and Convolutional Neural Network

MTCNN is a face detection method based on deep learning, which is more robust to light, angle and facial expression changes in natural environment, and has better face detection effect.At the same time, the memory consumption is small, and real-time face detection can be realized.Therefore, a method based on MTCNN and improved convolution neural network is proposed in this paper.Firstly, MTCNN is used to detect and align faces.Then, the output image is used as the input data of the improved convolution network, and multi-level convolution training is carried out.Finally, the accuracy of the model is tested.
Analysis and Selection Criteria of Lakes and Dams of Pakistan for Floating Photovoltaic Capabilities

Abstract The gross domestic product (GDP) of Pakistan is deteriorating by power shortage. The floating PV (FPV) on lakes and dams can address this problem by generating energy at cheaper rates and reducing evaporation of water at the same time. The best location is selected by comparing the available solar resources, nearby load, storage of land, and performance parameters such as energy yield (EY), capacity factor (CF), levelized cost of energy (LCOE), and net present cost (NPC). The analytic hierarchy process (AHP) shows that the best location for the FPV in Pakistan is Chinna Creek in the megacity of Karachi. The global horizontal irradiance (GHI) in Chinna Creek is 6.1 kWh/m2/day, where land is scarce. This site can generate electrical energy by the FPV system at an energy yield of 2345 kWh/kW, which is 8.6% higher than the land-based PV (LBPV) system. The total national installed capacity (IC) of Pakistan in 2017 is 28 GW, while the analysis shows that Pakistan has capabilities of 190 GW IC in the form of FPV systems. A detailed analysis of the FPV system and its comparison with the LBPV is provided as a guideline for policymakers.
An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.
A Hybrid Neural Network Model for Commonsense Reasoning

This paper proposes a hybrid neural network (HNN) model for commonsense reasoning.An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers.HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%.An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both.The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn.
Feasibility of twin-field quantum key distribution based on multi-mode coherent phase-coded states

In this paper we propose feasibility demonstration of twin-field quantum key ditribution system based on multi-mode weak coherent phase-coded states. Their utilization provides indisputable advantages described in the paper. We also provide the detailed description of nontrivial interference scheme for those states and derive detection and quantum bit error rates. Since we propose the feasibility scheme we present in this paper only asymptotic secure key estimation and show that in principle it can beat well-known fundamental limit of repeaterless quantum communications , i.e., the secret key capacity of the lossy communication channel. Also we present here the experimental setup and provide the experimental values of detection rates dependence on the modulation signals phase difference.
Truth or backpropaganda? An empirical investigation of deep learning theory

We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. We study the prevalence of local minima in loss landscapes, whether small-norm parameter vectors generalize better (and whether this explains the advantages of weight decay), whether wide-network theories (like the neural tangent kernel) describe the behaviors of classifiers, and whether the rank of weight matrices can be linked to generalization and robustness in real-world networks.
Intelligent Path Planning for AUVs in Dynamic Environments: An EDA-Based Learning Fixed Height Histogram Approach

Autonomous underwater vehicles (AUVs) are robots that require path planning to complete missions in different kinds of underwater environments. The goal of path planning is to find a feasible path from the start-point to the target-point in a given environment. In most practical applications, environments have dynamic factors, such as ocean flows and moving obstacles, which make the AUV path planning more challenging. This paper proposes an estimation of distribution algorithm (EDA) based approach, termed as learning fixed-height histogram (LFHH) to solve path planning problems for AUVs in dynamic environment. The LFHH uses a learning transformation strategy (LTS) to improve its accuracy and convergence speed. Besides, a smooth method is employed to accelerate the speed of finding feasible paths. Moreover, a planning window is adopted to help handle dynamic factors. LFHH is tested in both complex 2-D and 3-D environments with time-variant dynamic factors, and experimental results validate the effectiveness of LFHH.
Pedestrian Detection Under Partial Occlusion by using Logic Inference, HOG and SVM

This article presents an algorithm for the detection of pedestrians in urban driving environments during the day. The main contribution is in the design of a new classifier to discriminate between the person and the background, under partial occlusion. To construct the classifier, the HOG (Histogram of Oriented Gradients) descriptor was used together with the SVM (Support Vector Machine) and IL (Logic Inference) algorithms.The input image has been divided into twelve regions, and for each of them the feature vector has been extracted and a classifier based on SVM has been built. With this design it is possible to capture the specific detail of each part of the human body, such as head, legs, arms and body. Subsequently, they have been joined in a final classifier using IL, in order to obtain an efficient algorithm to discriminate between partially occluded pedestrians and the background, in urban environments during the day. The experiments related to the classifier were developed onseveral public databases, in various degrees of partial occlusion; and the experiments linked to the detection were generated on the visual information obtained by the experimental platform ViiA, to validate the proposal under real driving conditions.
A hierarchical loss and its problems when classifying non-hierarchically

Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called "loss" or "win") used in textual or visual classification/recognition via neural networks seldom leverage a-priori information, such as a sheepdog being more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier's classes. An ultrametric tree is a tree with a so-called ultrametric distance metric such that all leaves are at the same distance from the root. Unfortunately, extensive numerical experiments indicate that the standard practice of training neural networks via stochastic gradient descent with random starting points often drives down the hierarchical loss nearly as much when minimizing the standard cross-entropy loss as when trying to minimize the hierarchical loss directly. Thus, this hierarchical loss is unreliable as an objective for plain, randomly started stochastic gradient descent to minimize; the main value of the hierarchical loss may be merely as a meaningful metric of success of a classifier.
Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings.
Single Camera Training for Person Re-Identification

Person re-identification (ReID) aims at finding the same person in different cameras. Training such systems usually requires a large amount of cross-camera pedestrians to be annotated from surveillance videos, which is labor-consuming especially when the number of cameras is large. Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera. To the best of our knowledge, this setting was never studied before. SCT enjoys the advantage of low-cost data collection and annotation, and thus eases ReID systems to be trained in a brand new environment. However, it raises major challenges due to the lack of cross-camera person occurrences, which conventional approaches heavily rely on to extract discriminative features. The key to dealing with the challenges in the SCT setting lies in designing an effective mechanism to complement cross-camera annotation. We start with a regular deep network for feature extraction, upon which we propose a novel loss function named multi-camera negative loss (MCNL). This is a metric learning loss motivated by probability, suggesting that in a multi-camera system, one image is more likely to be closer to the most similar negative sample in other cameras than to the most similar negative sample in the same camera. In experiments, MCNL significantly boosts ReID accuracy in the SCT setting, which paves the way of fast deployment of ReID systems with good performance on new target scenes.
Scale-Wise Convolution for Image Restoration

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g., multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, “scale-wise convolution” is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_sr.
A chaos based image encryption algorithm using Rubik’s cube and prime factorization process (CIERPF)

A novel Rubik’s cube based pixel level scrambling and simple XOR based diffusion is proposed in this paper to safely transmit multimedia information (images) through an untrusted channel, such as adaptive image content (i.e., plain image related) based initial random value generation is introduced to achieve high plain image sensitivity in order to overcome plain image related attacks. From this random value, the initial vectors of Henon map is obtained and this is iterated to obtain the key sequences to be applied over the Rubik’s cube row and column confusion processes. Also, the same random seed is involved in the key generation process based on prime factorization to be applied in diffusion. For each and every encryption of different plain image, the random list is changed dynamically and it is shown that the CIERPF method is secure against differential attacks. The CIERPF method is checked against various simulations to illustrate the security level of the technique. From the simulations, it is shown that the proposed methodology has good key space, high key sensitivity, and uniform distribution of cipher image pixels. Also differential cryptanalysis is performed for the proposed cryptosystem to prove its effectiveness towards differential attacks.
Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection

This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method generates various lesion images by the combination of three kinds of generation methods: (1) synthesis using Poisson Blending, (2) generation based on CT value distributions, and (3) generation using deep convolutional generative adversarial networks (DCGANs). The proposed method constructs two kinds of detectors by using synthetic (fake) lesion images generated by the methods as well as real ones. One of the detectors is a 2D CNN for detecting candidate regions in a CT image, and the other is a 3D CNN for validating the candidate regions. Experimental results showed that the proposed method gave 0.30 improvement from 0.65 to 0.95 in terms of the detection rate, and 0.70 improvement from 0.90 to 0.20 in terms of the number of false detections per case. From the results, we confirmed the effectiveness of the proposed method.
Symbolic Execution for Importance Analysis and Adversarial Generation in Neural Networks

Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with serious safety and security concerns. This paper describes DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. DeepCheck implements novel techniques for lightweight symbolic analysis of DNNs and applies them to address two challenging problems in DNN analysis: 1) identification of important input features and 2) leveraging those features to create adversarial inputs. Experimental results with an MNIST image classification network and a sentiment network for textual data show that DeepCheck promises to be a valuable tool for DNN analysis.
Hand-guided qualitative deflectometry with a mobile device

We introduce a system that exploits the screen and front-facing camera of a mobile device to perform three-dimensional deflectometry-based surface measurements. In contrast to current mobile deflectometry systems, our method can capture surfaces with large normal variation and wide field of view (FoV). We achieve this by applying automated multi-view panoramic stitching algorithms to produce a large FoV normal map from a hand-guided capture process without the need for external tracking systems, like robot arms or fiducials. The presented work enables 3D surface measurements of specular objects ’in the wild’ with a system accessible to users with little to no technical imaging experience. We demonstrate high-quality 3D surface measurements without the need for a calibration procedure. We provide experimental results with our prototype Deflectometry system and discuss applications for computer vision tasks such as object detection and recognition.
An intelligent use of stemmer and morphology analysis for Arabic information retrieval

Arabic Information Retrieval has gained significant attention due to an increasing usage of Arabic text on the web and social media networks. This paper discusses a new approach for Arabic stem, called Arabic Morphology Information Retrieval (AMIR), to generate/extract stems by applying a set of rules regarding the relationship among Arabic letters to find the root/stem of the respective words used as indexing terms for the text search in Arabic retrieval systems. To demonstrate the usefulness of the proposed algorithm, we highlight the benefits of the proposed rules for different Arabic information retrieval systems. Finally, we have evaluated AMIR system by comparing its performance with LUCENE, FARASA, and no-stemmer counterpart system in terms of mean average precisions. The results obtained demonstrate that AMIR has achieved a mean average precision of 0.34% while LUCENE, FARASA and no stemmer giving 0.27%, 0.28% and 0.21, respectively. This demonstrates that AMIR is able to improve Arabic stemmer and increases retrieval as well as being strong against any type of stem.
Reconstructing the patient’s natural history from electronic health records

The automatic extraction of a patient’s natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient’s medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a precision of 0.852.