Reviewerly/exHarmony huggingface dataset:
Use of a convolution neural network for the classification of E. Coli and V. Cholara bacteria in wastewater

Identifying the microbial population and type of them is a crucial measure in the water and wastewater treatment processes, reuse of wastewater, and sludge treatment system. Todays, manual methods are usually used to count and detect the type of bacteria in water and sewage laboratories which mostly suffer from human errors. This study aims at presenting an accurate method based on image analysis through the convolution neural network (CNN) to classify Escherichia coli (E. coli) and Vibrio cholera (V. cholera) bacteria, in wastewater. About 9,000 Red-Green-Blue (RGB) microscopic images of the sewage sample containing the stained bacteria were used as the input datasets. The results showed that the bacteria would be classified and counted with the accuracy of 93.01% and 97.0%, respectively. While CNN performed pretty well in counting the number of bacteria for both RGB and grayscale color models, its classification performance is only satisfactory in the RGB images. The sensitivity analysis of CNN illustrated that the Gaussian noise enhancement caused to the increment in the standard deviation () that proportionally decreased the CNN accuracy.
Modulation recognition of communication signals based on deep learning

Deep residual shrinkage network was used to realize modulation signals feature extraction and classification recognition according to the characteristics of signals to further improve the accuracy of communication signals modulation recognition under low signal-to-noise ratio. The deep residual shrinkage network can extract feature from noised signals more effectively with the help of soft thresholding under the attention mechanism, and its modulation recognition rate especially under low signal-to-noise ratio compared with convolutional neural network and deep residual network has improved significantly through experiments. The network pruning method was used to optimize the model to improve the training efficiency of the deep residual shrinkage network model. The method effectively compresses and accelerates the model while ensuring that the model still has good recognition accuracy.
Performance of Artificial Electric Field Algorithm on 100 Digit Challenge Benchmark Problems (CEC-2019)

The Artificial Electric Field Algorithm (AEFA) is a population-based stochastic optimization algorithm for solving continuous and discrete optimization problems, and it is based on Coulomb’s law of electrostatic force and Newton’s laws of motion. Over the years, AEFA has been used to solve many challenging optimization problems. In this article, AEFA is used to solve 100 digit challenge benchmark problems, and the experimental results of AEFA are compared with recently proposed algorithms such as dragonfly algorithm (DA), whale optimization algorithm (WOA), the salp swarm optimization (SSA), and fitness dependent optimizer (FDO). The performance of AEFA is found to be very competitive and satisfactory in comparison with other optimization algorithms chosen in the article.
An Enhanced RBMT: When RBMT Outperforms Modern Data-Driven Translators

Although prominent translators, such as Google, Yahoo Babel Fish, Bing, etc., perform better when translating most widely used languages, they tend to commit fundamental mistakes in working with low-resource languages such as Bengali, Romanian, Arabic, etc. Such translators (e.g. Google Translate) use different data-driven translation approaches, such as neural machine translation (NMT), statistical machine translation (SMT), etc., to develop their polyglot translation system. However, performances of these data-driven approaches entirely rely on the attainability of significantly large parallel corpora of the translating language pairs. As a consequence, numerous popular languages, such as Bengali, remain barely explored not only in machine translation but also in other fields of natural language processing. Therefore, the target of this study is to explore effective translation from Bengali to English by accomplishing several Bengali language processing tasks. To be precise, we adopt a basic rule-based machine translator for translating from Bengali to English. Next, we enhance its performance by considering the veracious interpretation of the Bengali names as subjects (and nouns) in a sentence. Besides, we propose a Bengali verb identification and optimization technique by root-word detection (stemming) of the Bengali verbs. Finally, we unfold the efficacy of our proposed techniques through a comparative analysis with popular data-driven translators using a novel customized dataset focusing on Bengali-to-English translation.
Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques

This study investigates the utility of using remote sensing and geographic information system techniques to accurately infer the presence of radioactive minerals in a typical high background radiation area (HBRA) by analyzing spectral signatures of associated soil, rocks and vegetation. To accomplish this, both unsupervised (K-Means Clustering) and supervised classification techniques based on a maximum likelihood classifier (MLC) were applied to Landsat-8 Imager data from Mrima Hill on Kenya's south coast. The hill is surrounded by dense tropical forest and deeply weathered soils which are rich in Nb, Th, and rare earth elements. Due to high activity concentrations of 232Th (>8 times higher than the world average value for soil), the hill has been designated as a geogenic HBRA. Based on the underlying geological formations, four classifications of vegetation and two classifications of soil/rocks were established and used to indicate the presence of radioactive minerals in the area. Measurements of air-absorbed gamma dose-rates in the area were successfully used to validate these findings. The application of the MLC method on Landsat satellite data shows that this method can be used as a powerful tool to explore and improve radioactive minerals mapping in HBRAs, the overall classification accuracy of Landsat8 OLI data using botanical technique is 80% and the Kappa Coefficient is 0.6. The overall classification accuracy using soil/rocks spectral signatures is 91% and the Kappa Coefficient is 0.7. Finally, the study demonstrated the general utility of remote sensing techniques in radioactive mineral surveys as well as environmental radiological assessments, particularly in resource-constrained settings.
Empowering Reality: The Development of an ICT4Injury Prevention System to Educate Parents While Staying at Home

Injury at living spaces accounts for over 70% of injuries among children under the age of 5 years and its prevention is essentially important in terms of children’s health. This paper proposes an effective method of integrating knowledge graphs along with object recognition to determine the dangerous situations for infants and provide preventive measures to avoid such situations. This also leads to the proposal of a new system called “Empowering Reality (ER)” that enables a lecturer to educate parents on preventing childhood unintentional injuries at home while communicating with the parents using Augmented Reality (AR) technology. The proposed ER system consists of knowledge graphs for explaining dangerous situations, an online video capturing part, and a situation recognition part. This paper describes major advantages of knowledge graphs that takes into consideration of not only relation between objects and injuries but also dangerous layouts with the help of “inclusion” and “collocation” features. The feasibility and effectiveness of the system was evaluated through tests among 11 parents. The system allows the lecturer to conduct in-situ suggestion on specific preventive measures adapted to the home environment via online learning.
Optimal Object Classification Model for Embedded Systems based on Pre-trained Models

Machine Learning technology grows in the field of automatic waste sorting machines equipped with an intelligent unit. This intelligent unit runs on an embedded system that mostly has lower computation power (both CPU and GPU) and lower RAM. However, to archive a higher accuracy rate on classification one has to use a sophisticated classification AI model, which needs less computational power. We considered our experiment using 3 pre-trained models based on COCO dataset, namely the ssd_mobilenet_v2_coco, the ssd_inception_v2_coco, and the ssd_resnet_50_fpn_coco due to their good quality. The results show that although the AI model based on the ssd_resnet_50_fpn_coco has the highest accuracy (99.75&#x0025;), it consumes the most computational power. In contrast, the one based on the ssd_mobilenet_v2_coco has acceptable accuracy (98.83&#x0025;) and it consumes the lowest computational power. We decided that the most suitable AI model for embedded systems is the one that is trained with the pre-trained ssd_mobilenet_v2_coco model.
Enhanced Cloud Security using Cryptography and Steganography Techniques

Data security is a key concern for organizations considering a transfer of their on-premises applications to the cloud. Organizations must shift their security controls from historical perimeter and detection-based technologies to a focus on establishing enhanced protection at the application and data levels to ensure the confidentiality, integrity, and availability of these various systems and datasets. Data integrity is a critical component of cloud data security, preventing unauthorized alteration or removal and guaranteeing that data stays as it was when it was initially uploaded. This article presents a compacting with steganography technique which is used to hide data with substantial security and also perfect invisibility while utilizing a combination of DES, AES, and RC4 encryption methods. The objective of this study is to provide data security using steganography with the Least Significant Bit (LSB) Algorithm and Hybrid Encryption that encrypts user input and conceals it in an image file to provide the highest level of security for messages sent and received.
Interpretable Machine Learning in Social Sciences: Use Cases and Limitations

The increasing use of intelligent technologies, the development and implementation of machine learning systems in various spheres of life require explaining machine learning-based decisions in such systems. This need for interpretation leads to the increasing development of new methods for interpreting machine learning models and their more intense use in real systems. The paper reviews existing studies with applications of the interpretable machine learning (IML) methods in social sciences and summarizes results using bibliometric analysis. In total, seven research topics were described based on 210 papers. Moreover, the paper discusses the opportunities, limitations, and challenges of the interpretable machine learning approach in social science research.
Learning State Assessment in Online Education Based on Multiple Facial Features Detection

Considering that most of online training is not effectively supervised, this article presents an online leaning state assessment approach which combines blink detection, yawn detection, and head pose estimation. Blink detection is realized by computing the eye aspect ratio and the ratio of closed eye frames to the total frames per unit time to evaluate the degree of eye fatigue. Yawn detection is implemented by computing the aspect ratio of the mouth by using the feature points of the inner lip and combining it with the time of opening mouth to distinguish the mouth state. Head pose estimation is first implemented by calculating the head rotation matrix by matching the feature points of 2D face with the 3D face model and then calculating the Euler angle of the head according to the rotation matrix to evaluate the change of the head pose. Especially in yawn detection, we employ the feature points of inner lips in the calculation of the mouth aspect ratio to avoid the impact of lip thickness of various participants. Furthermore, the blink detection, yawn detection, and head pose estimation are first calculated based on the two-dimensional grayscale image of human face, which could reduce the computational complexity and improve the real-time performance of detection. Finally, combining the values of blinking, yawning, and head pose, multiple groups of experiments are carried out to assess the state of different online learners; then, the learning state is evaluated by analyzing the numerical changes of the three characteristics. Experimental results show that our approach could effectively evaluate the state of online learning and provide support for the development of online education.
Optimizing Link-Level Entanglement Generation in Quantum Networks with Unequal Link Lengths

Quantum internet offers a variety of applications that either enhance or surpass its classical counterpart. Quantum repeaters are imperative to a quantum network as they connect between two quantum nodes, and create quantum entanglement between two targeted nodes with entanglement swapping protocol which require resource from both connections. As a quantum state has a finite decoherent time, it is crucial that a connection protocol is performed efficiently and within suitable time interval, so that two end-to-end matter qubits are entangled. We simulate a second generation network of quantum repeaters, one that is capable of quantum error correction, to estimate the waiting time needed to establish connection between distant nodes. Simulations are performed for a linear chain of nodes with unequal path lengths, and loss rates. We find that asymmetry of path lengths contributes to the waiting time of physical qubits. By simply adjusting the photon generation rate from one of its nodes, the waiting time is reduced while the time to complete quantum state tomography remains unaffected. This simple adjustment can play important roles in compensating for distance asymmetry when a quantum repeater is not stationary.
Quantitative Kinematic Assessment of the Sit-to-Stand Transition using an IMU Sensor

Objective: The sit-to-stand transition is an important task for the evaluation of physical independence, especially in fragile subjects like elderly patients, to monitor and reduce the risk of falls or damage. This study aims at assessing the kinematic characteristics of the sit-to-stand transition from a quantitative perspective, using a wireless IMU sensor, and describes a set of parameters suitable for evaluating the movement performance in everyday practice. Methods: An acquisition campaign was designed and carried out on 20 healthy subjects and 13 patients to evaluate acceleration and angular velocity generated during a sit-to-stand task. For each subject, at least four acquisitions were performed, to create a final dataset of 173 acquisitions. The values of the proposed parameters were computed, and results were analyzed with descriptive statistics. Results: According to the proposed parameters definitions and the analyzed dataset, acceleration-and angular velocity-related parameters present statistically significant differences between healthy and pathological data samples, unlike total time TT.
Gaussian Mixture Distribution Makes Data Uncertainty Learning Better

As a mainstream method in face recognition, extracting separable facial features by deep CNNs in the latent space has achieved remarkable success. In most existing works, people often view the embedding features as points. Dealing with entirely unconstrained face images, DUL and PFE demonstrated that point estimation shows weak robusticity on the inherent noise in the input images (data uncertainty) and introduced the distribution estimation by modeling each latent feature using a Gaussian distribution. However, these two methods only apply a unimodal Gaussian prior distribution, which is insufficient to represent wild faces with complex variations. In this paper, we propose a novel face recognition framework based on the multivariate Gaussian mixture distribution (DUL-GM). Through numerous experiments, we show that compared with the prior works, the features modeled by multivariate Gaussian mixture distribution have a better interference suppression ability and achieve state-of-the-art performance on extensive challenging benchmarks.
Sequential Attention-Based Distinct Part Modeling for Balanced Pedestrian Detection

Despite pedestrian detectors having made significant progress by introducing convolutional neural networks, their performance still suffers degradation, especially in occlusion scenes with more false positives (FPs) and false negatives (FNs). To alleviate the problem, we propose a novel Sequential Attention-based Distinct Part Modeling (SA-DPM) for balanced pedestrian detection. It takes one step further in constructing more robust representation that supports detection with fewer FNs and FPs. Specifically, the Sequential Attention serves as one internal perception process that captures several distinct part areas step by step from each pedestrian proposal (full-body). Different from the previous either-or feature selection, the following Joint Learning attempts to seek a reasonable trade-off between part and full-body features, and combines both features for more accurate classification and regression. Evaluation on the widely used pedestrian datasets including Caltech and Citypersons shows that the proposed SA-DPM achieves promising performance for both non-occluded and occluded pedestrian detection tasks, especially on Caltech Heavy Occlusion set, which yields a new state-of-the-art miss rate by 30.18% and outperforms the second best detector by 6.32%.
Comparison Study of the PSO and SBPSO on Universal Robot Trajectory Planning

Industrial robots were modified over the years. The benefit of robots is making production systems more efficient. Most methods of controlling robots have some limitations, such as stopping the robots. The robot stops by various reasons, such as collisions. The goal of this study is to study the comparison of improving the Artificial Potential Field (APF) by the traditional Particle Swarm Optimization (PSO) algorithm and the Serendipity-Based PSO (SBPSO) algorithm to control the path of a universal robot UR5 with collision avoidance. Already, the metaheuristic algorithm kinds deal with a premature convergence. This paper presents a new approach, which depends on the concept of serendipity and premature convergence applied to the path of the universal manipulator UR5 and also compares it with traditional the PSO. The features of the SBPSO algorithm prototype are formalized in this paper using the concept of serendipity in two dimensions: intelligence and chance. The results showed that the SBPSO is more efficient and has better convergence behavior than the traditional PSO for controlling the trajectory planning of the UR5 manipulator with obstacle avoidance.
Erythrocyte (red blood cell) dataset in thalassemia case

Red blood cell (RBC) dataset was obtained from four thalassemia peripheral blood smears and a healthy peripheral blood smear. The dataset contains 7108 images of individual red blood cells for nine cell types. The first process is image acquisition, which is the process of retrieving microscopic image data from peripheral blood smears through a Olympus CX21 microscope using an Optilab advance plus camera. Laboratory assistants helped obtain ideal erythrocyte images. We provide peripheral blood smear from four thalassemia patients in the ThalassemiaPBS dataset. After image acquisition, the image is resized from 4100 × 3075 pixels to 800 × 600 pixels to reduce the computing load in the next stage. We extracted the green color component (green channel) of the RGB image and used it in the next process. We chose the green channel because it is not affected by variations in color and brightness. Furthermore, the segmentation stage is carried out to obtain an object in the form of a single red blood cell. After that, the object can be classified according to the type of red blood cell. This dataset can become an opportunity for international researchers to develop the classification method for red blood cells.
Getting acquainted with social networks and apps: understanding, preparing for and responding to online harassment

Purpose To help users understand, prepare for and respond to the threat of online harassment. Design/methodology/approach This column provides an introduction to tools and resources on the topic of online harassment. A discussion on the definitions, understandings and library applications to teaching about online harassment are presented from available literature and news articles. Findings Various methods of protecting privacy and responding to online harassment are revealed. Practical implications An understanding of the importance of the threat of harassment in a variety of online spaces and how to prepare for and respond.
A Snapshot of Antisemitism on Social Media in 2021

This chapter provides an introduction to the research field of antisemitism on social media. It first outlines the role of social media’s technology and business model in amplifying antisemitic hate speech and then assesses existing counter-strategies such as hate detection through artificial intelligence, reporting and removal of antisemitic content, and counter-speech. After summarizing the small body of existing research that is focused on antisemitism on social media, the chapter identifies numerous research gaps, and highlights challenges researchers face. Overall, the chapter argues for a much-needed shift in antisemitism research toward social media content, and it concludes with concrete recommendations how to achieve it.
Customer satisfaction model in cloud computing environment

Service level agreements are widely accepted mechanism for gaining enterprise-level information technology (IT) resources. The IT infrastructure provided by the Cloud provider must meet SLA established with the customer. SLA is generally used mechanism to determine contracts accessing requirements in business content. It defines a set of non-operating requirements of the service and also, incorporates penalties for unsatisfied requirements. The numbers ofcloud customers are increasing day-by-day resulting in the increased loads on cloud service providers, thus, making Service Level Agreement (SLA) a prime need of time for ensuring the service quality. This helps in meeting the challenges presented by enterprise-oriented application software's towards traditional sales models. This paper is an attempt to devise a mathematical model for better handling of cloud's resources and increasing the satisfaction level of cloud customers by meeting SLA constraints.