Reviewerly/exHarmony huggingface dataset:
Malaria Detection on Giemsa-Stained Blood Smears Using Deep Learning and Feature Extraction

Malaria is a severe and fatal disease leading to exacerbation of a person's health and even death. Thus identifying whether a person is suffering from malaria is much preponderant. General and famous practice for the identification of malaria is usually the analysis done by microscope on the thin and thick stained blood smears. The identification of the parasitized blood cells is a laborious and challenging task as it involves the very convoluted methods such as spotting the parasite in the blood, counting the number of the parasites, pretending its type, etc. The fidelity for the stated task depends a lot on the experience of individual technician performance and relies a lot on him/her. The Convolution Neural Network models are famous for the automatic selection of these features. In this paper, 128 features from a custom-build Convolution Neural Model (CNN) were extracted and fed to the Support Vector Machine (SVM) classifier. The results were then compared with the custom-build, 17 layers deep CNN model and also with various Transfer Learning (TL) models, both as feature extractors and fine-tuned. After feeding the right features, the Support Vector Machine resulted in an accuracy of 98.8% on the dataset containing a total of 27,558 labeled red-blood-cell (RBC) images which were re-sampled to 40 * 40 * 3. Other performance matrices were also evaluated and are as sensitivity being 98.3%, specificity 97.6%, and F1 score resulting in 97.95%. Comparing with the state-of-literature, transfer learning models and custom-build model, CNN-based SVM classifier model performed the best.
A Comparative Study of Mobile Applications for Crime Scene Measurements- A Digital Approach

Crime Scene is a place where the crime has occurred. Crime scene recording is a crucial part of the investigation and reconstruction of an incident. An Investigation Officer usually records the crime scene with the aid of photography, videography, sketching, and preparing notes. During the sketching of the crime scene, measurements should be taken for the overall crime scene and the evidences for permanent records, court presentation, and crime reconstruction. Usually, measurement taking is a tedious task and time consuming as well. It requires much effort and several personals involvement. In this study, an attempt has been made to utilize the mobile applications available on iOS (iPhone Operating System) for crime scene measurement. Also, this study throws light on the efficacy of these tools during crime scene recording. As per the analysis, we found out that the mobile application methods are comparatively more efficient than manual tape measurement. These methods provide an easy tool for the crime scene measurements in a given time and keep a permanent record along with the pictures of the evidences and the measurements.
A Restricted Boltzmann Machine-based Recommender System For Alleviating Sparsity Issues

With the explosive growth of the Internet and the Web, assisting users and facilitate their access to resources that might be of their interest and that are adapted to their personal needs is a tedious task. Efficient management of large amounts of information becomes an increasingly significant challenge. Hence, recommender systems have proved, in recent years, to be a valuable asset to dealing with the problem of information overload by assisting the users and providing them with more effective access to information. To this end, these systems must be able to predict users' interests based on their prior feedback. However, sparsity issues arise when necessary transactional information is not available for inferring users and items similarities, which deteriorate the quality and accuracy of the recommender system. To fill these gaps, we propose in this paper a Restricted Boltzmann Machine-based model to learn hidden factors and reconstruct sparse input rating data. Experimental results show that our proposed approach can effectively deal with data sparsity in MovieLens dataset, containing a massive amount of scarce information.
An Unsupervised Anomaly Detection Approach Based on Industrial Big Data

The development of condition monitoring system provides large amounts of operational data. These data present typical characteristics of multiple sources, polymorphism, diversity and mass, and can reflect the service quality and operating state of the equipment. How to use these data becomes one of the main problems in data analysis. To address the issue, this paper presents a generalized model for complex mechanical system anomaly detection based on the data collected from distributed control system (DCS). The Stacked Auto-encoder network is used to achieve the automatic extraction of the hidden features in multidimensional polymorphic data. The isolation forest (IF) method is used to achieve the anomaly detection, which only needs to use the DCS monitoring data during normal operation of the unit for network training and model fitting without fault data. And the method can realize the abnormal detection during the operation of the unit without the traditional signal processing method for feature extraction. The proposed method has been used for real compressor. The results show that the proposed approach can detect the anomalies without the need for fault data. And the proposed method is more effective than traditional methods.
Fisher information of accelerated two-qubit system in the presence of the color and white noisy channels

In this paper, an accelerated two-qubit system initially prepared in maximum or partial entangled state, which interacts locally with white/color or white-color noises, is considered. Due to the acceleration process and the noise effect, the entanglement degraded. Therefore, the effect of noise strength, initial state settings and the acceleration on the survival entanglement are investigated by means of the concurrence. Moreover, the initial parameters that describe this system are estimated by using the quantum Fisher information, where two forms are considered, namely by using a single and two-qubit forms. It is shown that, by using the two-qubit form, the estimation degree of these parameters is larger than that displayed by using a single-qubit form.
Adaptive Workflow of Service Oriented IoT Architectures for Small and Distributed Automation Systems

The creation of appropriate adaptive control loop for interconnection of the different levels of municipality' distributed service supply systems, including the legacy systems, is a new challenge for designers of automated control tools due to incompatibility of systems that apply a number of appropriate technical solutions and protocols that assures collaboration between automation devices such as sensors, actuators, and controllers. This paper enhances the Arrowhead Framework with support of such workflows, which promote systems within a System-of-System (SoS) to work towards its predefined goal, taking into account the dynamically changing environment, system capabilities, and use case needs. This paper offers to enhance one of the Arrowhead core systems, namely Event Handler System (EHS), which provides among others orchestration services. We describe its main services and operations, implemented by data-driven implementation way, based on dynamic workflow execution, and by employing Node-RED programming tool. Furthermore, we demonstrated implementation of EHS workflow Internet of Things (IoT) architecture for small and distributed automation systems, namely, for municipality' systems and adaptive biological systems, like beekeeping processes automation.
Advanced Wheelchair Movement System Using Facial Gestures For Aiding Human With Tetraplegia Condition

The major problem faced by the paralyzed people is their mobility. The quadriplegia, one of the serious disabilities affects all the four limbs and such persons need an external help to perform their daily activities. The main objective of this project is to provide an automated system for such people. Abstract discusses the possibility of developing a wheelchair whose motion can be controlled using eye blinks. User's eye gestures are translated to screen position using optical sensing in MATLAB. A camera mounted on the wheel chair recognizes the eye gaze/blinks and if it matches with the gestures in memory, motion control commands are generated by the micro controller depending on the coding. These commands drive the motors of the wheelchair.
A Convolutional Neural Network for Arabic Document Analysis

In this study we apply a CNN (convolutional neural network) to segment a document image into its page components. Our approach consists of two main steps. Firstly, there is a new method of extracting layouts based on sharpness/smoothing filters, adaptive thresholding techniques, morphological operations, along with a CC (connected-component) technique and ARLSA (Adaptive run-length smoothing algorithm) for the patch extraction phase. Secondly, the extracted patches will be put into six classes (text, table, figure, title, legend, author) using a CNN, and four other classes (straight-line, text-line, block, article) using PP (projection profiles) analysis and geometric features. The method was tested on smartphone-captured newspaper images selected from the PATD [1] (Printed Arabic Text Database). The results indicate that the proposed method is suitable for detecting multiple labels in Arabic newspaper pages.
Reinforcement Learning Based Approach For Mobile Robot Navigation

Autonomous navigation of mobile robots has been a subject of research for many decades. Many algorithms, both classical and heuristic have been developed for the navigation of the wheeled mobile robots. Classical approaches can get tedious and can get stuck at local optima as the environment gets more complex. Heuristic approaches are gaining prominence nowadays because of its closeness to human way of behavioral learning. Reinforcement learning (RL) is the idea of having a robot learn how to accomplish a task. It is one of the heuristic approaches which gains knowledge by trial and error, so there is no need for any expert knowledge. In the current work, an attempt has been made to implement reinforcement learning for autonomous navigation of wheeled mobile robots. A RL agent is created for controlling the obstacle avoidance and goal-seeking behavior of a single robot. Then, a kinematic controller is developed to help the robot to move through the waypoints generated by the RL algorithm. Subsequently, the performance analysis of the controller is carried out and the benefits and limitations of the RL algorithm is studied.
UAV Route Planning Based on QPSO Algorithm under Rolling Time Domain Control

Aiming at the path planning problem of UAVs in complex environments, a RHC-QPSO path planning algorithm is proposed based on the quantum particle swarm optimization algorithm. The algorithm uses a quadtree to establish an environment model to reduce the storage of environmental information when there are many obstacles. Based on the QPSO algorithm, the UAV path optimization is performed. Combined with the RHC method, the active evasive strategy is adopted for the obstacles. The minimization of and are selected as the process performance index and used as the rolling optimization window. Optimize the indicator and change it to offline calculation for real-time online calculation. The simulation results show that the proposed algorithm can not only realize the dynamic path planning of UAVs with certain prior knowledge of the map in real time and effectively, but also prevent the UAV from making large maneuvers to avoid obstacles during the planning process. The smoothness of the path improves the safety of the drone.
A Video Salient Object Detection Model Guided by Spatio-Temporal Prior

Neurobiology researches suggest that the motion information attracts more attention of human visual system than other low-level features such as brightness, color and texture. Consequently, video saliency detection methods not only consider the spatial saliency caused by the underlying features of images, but also the motion information in temporal domain. In this study, we proposes a model of video salient object detection based on a two-pathway framework that the spatio-temporal contrast guides the search for salient targets. Firstly, along the non-selective pathway, which is computed with the intra-frame and inter-frame maps of the color contrast and motion contrast, combining with the previous saliency map, to represent the prior information of the possible target locations. In contrast, the low-level features such as brightness, color and motion features are extracted in the selective pathway to search target accurately. Finally, the Bayesian inference is used to further obtain the optimal results. Experimental results show that our algorithm improves the performance of salient object detection on video compared to the representative method of Contour Guided Visual Search.
Scalable Object Tracking in Smart Cities

In smart cities equipped with cameras, one desirable use-case is to detect and track objects. While object detection has been implemented using various methods, object tracking poses a different problem; to track an object requires object permanence to be established between each frame of video. While many technologies have been proposed as a solution for problem, an implementation with scalability in mind has not been developed and poses many new challenges. This paper proposes e-SORT, a solution for scalable object tracking using an enhanced version of the Simple Online and Realtime Tracking (SORT) algorithm. Beyond its scalability, e-SORT stores a mapping of each objects' locations such that the full path of each object is available and several metrics (such as velocity and acceleration) can be calculated. Both e-SORT's abilities and our proposed solution to scalable object tracking are tested and evaluated on Chattanooga Tennessee's live urban testbed.
A Holistic and Local Feature Learning Method for Machine Health Monitoring with Convolutional Bi-directional LSTM Networks

In the modern industry, machine health monitoring systems are critical to the modern manufacturing industry. In the machine health monitoring, due to the advanced sensor technologies, the data-driven approach has became the most popular way. However, how to deal with the noise data and realize the spatial and temporal correlations presented in the data is a challenge. The traditional models focus on using the feature extraction method to solve these problems, but it deeply relies on high-quality expertise for achieving it. With the rapid development of deep learning methods, the capture of data features has been achieved effectively. For example, the LSTM method can effectively capture the long-term dependence of data. The purpose of this paper is to predict tool wear by using the proposed Holistic-Local LSTM Model (HLLSTM). With the proposed method, learning local characteristics by cutting data into fragments. The training is divided into holistic training and local training to capture the characteristics implied by data, so as to realize the timing of changing the wear tool. Finally, the experimental results show the performance of our proposed approach is better than other deep learning approaches in the metric of root-mean-square error (RMSE).
Evaluating Methods of Different Paradigms for Subjectivity Classification in Portuguese

This paper presents the study and comparison of methods of different paradigms to perform subjectivity classification of sentences in Portuguese. This task is part of the sentiment analysis area and consists of automatically identifying the presence or not of opinative information in texts of interest. We have investigated methods based on sentiment lexicons, graphs, machine learning and word embeddings, using datasets of different domains. We achieve good results, outperforming the previous approaches for Portuguese.
Autocorrelation based Chordiogram Image Descriptor for Image Retrieval

This paper proposes an autocorrelation based chordiogram image descriptor (ACID) for image searching and retrieval. The standard 4D chordiogram image descriptor is a ordered collection of local edgel chordiogram (LEC) of all the patches of image and it encodes length of the line segment among the pair of edgels, orientation of each edgel and degree of angle among the line segment and horizontal plane for every pair of predominant edgels in a patch whereas proposed approach uses autocorrelation for computing the LEC and it exploits the spatial correlation and above mentioned features of standard LEC among the identical pair of edgels at distance d. The novelty of proposed CID is, it captures localized texture, spatial details besides to the geometric details offered by the standard CID except length of the line segment which is 1 in the proposed approach. The proposed ACID leads to better retrieval with resilient to illumination, scaling, translation, noise and small rotation. In our proposed approach, geometric details also more localized one. The experiments have been done for demonstrating the worth of proposed ACID on four different types datasets: (i). Gardens Point Walking, (ii) St. Lucia, (iii) UA Campus, (iv) Self photographed image. The results confirm the supremacy of proposed ACID approach by means of average retrieval precision and average retrieval rate over the state-of-the-art approaches.
A Semi-Supervised Approach for Identification of the Sections in Charge of RFQ Documents

Identification of sections in charge of a request for quotation (RFQ), a type of business-specific document that seeks an itemized list of prices for a product or service, is usually performed manually and is very time-consuming, especially in the manufacturing industry. This study presents a simple semi-supervised classification approach for automatic section identification of RFQ documents. We conceive the identification task as text classification task for different sections and introduce novel features derived from unlabeled data to enhance the performance. We evaluate the usefulness of our approach in a series of experiments on a collection of RFQ documents in the actual business operations and obtain satisfactory results for most test collections.
Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier

We propose a method that assists legislative drafters in locating inappropriate legal terms in Japanese statutory sentences and suggests corrections. We focus on sets of mistakable legal terms whose usages are defined in legislation drafting rules. Our method predicts suitable legal terms using a classifier based on a BERT (Bidirectional Encoder Representations from Transformers) model. We apply three techniques in training the BERT classifier, specifically, preliminary domain adaptation, repetitive soft undersampling, and classifier unification. These techniques cope with two levels of infrequency: legal term-level infrequency that causes class imbalance and legal term set-level infrequency that causes underfitting. Concretely, preliminary domain adaptation improves overall performance by providing prior knowledge of statutory sentences, repetitive soft undersampling improves performance on infrequent legal terms without sacrificing performance on frequent legal terms, and classifier unification improves performance on infrequent legal term sets by sharing common knowledge among legal term sets. Our experiments show that our classifier outperforms conventional classifiers using Random Forest or a language model, and that all three training techniques contribute to performance improvement.
Intuitionistic Fuzzy Neural Networks with Interval Valued Intuitionistic Fuzzy Conditions

This paper describes a new approach for Intuitionistic fuzzy neural networks with interval valued intuitionistic fuzzy conditions. The theoretical basis for the new approach is presented, and well as examples to illustrate the proposed concepts and ideas. This paper can be viewed as the initial foundation of the area of intuitionistic fuzzy neural networks, and in future papers more results will be presented.
Weighted Focus-Attention Deep Network for Fine-grained Image Classification

Fine-Grained Visual Classification (FGVC) is a challenging task, due to the small variation of visual representations from different categories. An effective solution is utilizing the bounding boxes centering the object parts to extract the discriminative representations. However, regular rectangles contains the background when the shape of the part is irregular, which may interfere with the classification. In this paper, we propose a weighted focus-attention deep network (FA-Net) to address the problem of background interference in fine-grained classification. In our FA-Net, a focus-attention module is proposed to identify the foreground region from the class activation map and remove the background. Two branches are employed to obtain the primary and secondary attention regions with focus-attention module, and a weighted layer is utilized to integrate the attention regions. Experiment results on three challenging fine-grained classification datasets (e.g., CUB-200-2011, Stanford Dogs and FGVC Aircraft) show that our FA-Net obtains state-of-the-art results and outperforms the other fine-grained algorithms.
CDISS-BEMOS: A New Color Document Image Steganography System Based on Beta Elliptic Modeling of the Online Signature

Based on the Beta elliptic Modeling, a new signature steganography color document image system is proposed in this paper. This system uses the Binary Robust Invariant Scalable Keypoint (BRISK) detector to obtain the potential feature points used for constructing the embedding regions. The Beta elliptic signature is transformed into a secret message bits representation using the Huffman Coding (HC) to increase the performance of our system. Then, the secret message bits are divided into three sub message bits $$\mathrm{m}_\mathrm{R}$$, $$\mathrm{m}_\mathrm{G}$$, and $$\mathrm{m}_\mathrm{B}$$, using the weights $$\upalpha _\mathrm{R}$$, $$\upalpha _\mathrm{G}$$, and $$\upalpha _\mathrm{B}$$, respectively. Finally, each sequence bits is embedded into the corresponding channel, red (R), green (G), and blue (B) by modifying the first Least Significant Bit (LSB) of the embedding regions pixels. The robustness evaluation, quantitative and qualitative experimental results on multiple datasets: L3iDocCopies, LRDE Document Binarization Dataset, and on standard test images, demonstrates that the proposed color document images steganography system in the spatial domain maintains a better visual quality measured by Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Matrix (SSIM), and Human Visual System (HSV) metrics, with relatively less computational complexity, which approves its effectiveness as compared to existing systems.
Using Motion Compensation and Matrix Completion Algorithm to Remove Rain Streaks and Snow for Video Sequences

The current outdoor surveillance equipment and cameras are vulnerable to be influenced by rain, snow, and other inclement weather, reducing the performance of the surveillance systems. In this paper, we propose a method to detect and remove rain streaks even snow artifacts from video sequences, using motion compensation and low-rank matrix completion method. First, we adopt the optical flow estimation method between consecutive frames to get a warped frame and obtain an initial binary rain map. We further use morphological component analysis method to dilate the tiny rain streaks. Then we employ the online dictionary learning for sparse representation technique and SVM classifier to refine the rain map by getting rid of parts which are not rain streaks. Finally, we reconstruct the video sequence by using low-rank matrix completion techniques. The experimental results demonstrate the proposed algorithm and perform qualitatively as well as quantitatively better in terms of PSNR/SSIM.