Reviewerly/exHarmony huggingface dataset:
Deep CNN-Supported Ensemble CADx Architecture to Diagnose Malaria by Medical Image

Identifying patients, infected with the virulent disease, malaria requires a reliable and quick diagnosis of blood cells. This paper presents a computer-aided diagnosing (CADx) method supported by a deep convolutional neural network (CNN) for assisting clinicians to detect malaria by medical image. We employed the VGG-19 and ResNet-50 architectures to create several models for two types of study (parasitized and uninfected erythrocytes). To enhance the model’s performance, an ensemble technique was applied, followed by which, the best model selected by performance measuring metrics. Our proposed model was qualified and examined upon a standard microscopic set of images collected from the National Institute of Health (NIH). The final result was analogized with other techniques, where the accuracy of this model was 96.7% for patient-level detection. To resolute the limitations and minimizing errors regarding automated malaria detection, the proposed model proved to be an appropriate strategy for distant regions and emergencies.
RC4 Algorithm and Steganography to Double Secure Messages in Digital Image

Human life today, we know that the security of information is an important thing. Cryptography and Steganography help us to keep our information secure. If someone finds out the unknown message we known as ciphertext, they will crack it, and it's the weakness of the cryptography itself. Combining the RC4 Algorithm and LSB Cryptography will solve that problem. So, we can keep the confidentiality in the messages, the messages that can maintain the security, and the secret of the messages simultaneously. In this research, we use the time process of encryption and decryption as a parameter, and we also compare the Stego image based on BITMAP and JPEG format. The result of the Stego image in the BITMAP format shows us that this method is better than the Stego image in JPEG format.
Expiry date recognition using deep neural networks

This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.
Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.
Blockchain-Based Transparent Disaster Relief Delivery Assurance

Blockchain technology presents benefits that change the way business partners interact. This new way of establishing democratic trust encourages business owners to think differently. Disaster relief and aid industries are built on the power of collaborating participants. A very high number of participants in different hierarchies, including donors, charities, disaster victims, insurance companies and government agencies interact under extraordinary circumstances of a disaster and hard times. Establishing a new way of trust brings forward a better disaster recovery. In this paper, we propose a blockchain-based ecosystem. The blockchain-based disaster recovery not only would enhance the basic processes around disaster relief, but also promote the willingness of help by transparency and potential fraud prevention. This new blockchain system introduces an opportunity to be more resilient, to react rapidly, to communicate transparently, and to include new contributors such as IoT.
Towards cardiac intervention assistance

Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the cine MRI frames need to be segmented on-the-fly to avoid noticeable visual lag. In addition, considering reliability and patient data privacy, the computation is preferably done on local hardware. State-of-the-art MRI segmentation methods mostly focus on accuracy only, and can hardly be adopted for real-time application or on local hardware. In this work, we present the first hardware-aware multi-scale neural architecture search (NAS) framework for real-time 3D cardiac cine MRI segmentation. The proposed framework incorporates a latency regularization term into the loss function to handle realtime constraints, with the consideration of underlying hardware. In addition, the formulation is fully differentiable with respect to the architecture parameters, so that stochastic gradient descent (SGD) can be used for optimization to reduce the computation cost while maintaining optimization quality. Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3.5× and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
Which Metrics Should Researchers Use to Collect Repositories: An Empirical Study

GitHub is a huge publicly available development platform for hosting a version control system based on Git; software developers prefer to host their various software projects in GitHub. Therefore researchers who are interested in mining software repository frequently use GitHub to collect software projects as datasets. GitHub provides us with repository metrics such as popularity, contribution, and interest. We believe that such metrics are related to the quality of software; we use them to opt for studied repositories according to our research purpose. However, to the best of our knowledge, nobody has any evidence to support this assumption.Our main purpose is to provide researchers who study software quality, especially issue management, with repository metrics to select appropriate repositories for their studies. In this paper, we study the relationship between the characteristics of the issue pages of repositories that are selected by repository metrics in order to figure out the best repository metric to select proper repositories. The following findings are the highlights of our study: (1) The number of contributors that indicates the number of developers who contribute to a GitHub repository can be used to select the repositories having issue pages that are well-maintained. More specifically, such issue pages include more issues and in which developers use the labels more frequently rather than those that are selected by other metrics. (2) The number of dependencies opts for the repositories that have fewer issues and in which developers use the labels less often rather than those that are selected by other metrics.
Mobile-based Network Monitoring System Using Zabbix and Telegram

Network monitoring system is a system that works to observe and monitor the situation on a running computer network. Network monitoring is very important to keep the network running well and to know the current condition of the network. Generally, network monitoring requires the administrator to stay and to keep an eye in front of the monitor screen to find out any problems that occur. The administrator or the operator also need network notification problem in real time. This makes the network supervision process very dependent on time and place. This research develops a system for helping administrator or network operator to monitor the network using mobile phone at any time and any place. This monitoring process in this paper using Zabbix. In monitoring the computer network is also needed a notification. The notification of the problem in the network will be sent using Telegram Messenger. This research was conducted in Indonesian Internet Service Provider Association (APJII). The method used in this research is Network Development Life Cycle (NDLC).
A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

A prior knowledge of the background parameters such as clutter rate and detection profile is of critical importance in the tracking algorithms under the theory of random finite sets for extended objects which would lead to restrictions in the application. To accommodate this problem, a multiple extended target tracking algorithm based on the generalized labelled multi-Bernoulli (GLMB) filter under the circumstance of unknown clutter rate and detection profile is proposed in this article. After introducing a clutter generator, this new algorithm establishes augmented state space model for targets and clutter and propagates them in parallel by applying multi-class GLMB theory. We then employ Beta to describe detection probability. Target extension is modelled as an ellipse by using gamma Gaussian inverse Wishart distribution. Simulation results indicate that the proposed algorithm has better performance in estimating trajectories and extended shapes compared with the conventional filter having prior knowledge.
Action Recognition Based on Linear Dynamical Systems with Deep Features in Videos

We propose a new framework that boasts of low training costs and high generalization performance in capturing human action expressions simultaneously on spatial and temporal structures. First, a video slicing process is established. Then, in order to capture the divergence and likelihood expression of spatial structure in each video slice, a pipeline is introduced using a pre-trained CNN. In addition, any pre-trained network can be used to extract these features. Subsequently, Linear Dynamical Systems (LDS) is established to determine the timing relationship between two adjacent slices to obtain the temporal structure of divergence and likelihood features, which are expressed as LD-Divergence and LD-Likelihood. In UCF50 and UCF101 datasets, we analyzed the impact of different feature dimensions retained by PCA on recognition. Finally, we combined LD-Divergence and LD-Likelihood to improve accuracy to 0.961 and 0.949 on UCF50 and UCF101 datasets. Experimental results show that proposed framework simultaneously expresses spatial and temporal structures, which in turn produce state-of-the-art results.
Optimal Trajectory Planning for a Robotic Manipulator Palletizing Tasks

In recent years, the employment of robots has become a value-added entity in the industries in gaining their competitive advantages. Moreover, thanks to Industry 4.0 paradigm, many production tasks have grown in terms of dimensionality, complexity and higher precision and need to be performed by robots. Among them, the palletizing task is still highly dependent on the particular problem to solve, and its optimization needs to be performed basing on the ground condition. In this paper a palletizing task problem performed by a robotic manipulator is studied. More in detail, some objects have to be transported from a pre-determined storage area to a delivery area. In the storage area the objects are stacked one on the other in columns, while in the delivery area the robotic manipulator poses the objects in horizontal levels, one over another. The process is optimized by minimizing the total distance travelled by the robotic manipulator to transport all the objects from the storage area to the delivery area. An Integer Linear Programming (ILP) problem is formalized and tested by simulations and experimental results.
Revisiting State-of-the-Art Applications of the Blockchain Technology: Analysis of Unresolved Issues and Potential Development

This chapter focuses on state-of-the-art applications of the blockchain technology. The authors provide a comprehensive analysis of the blockchain technology and its applications that are deployed across various domains and research areas. Additionally, the authors identify outstanding issues and propose potential solutions of high relevance and value for academics and practitioners.
Ontology Learning Procedures Based on Web Mining Techniques

Ontology establishes an approach for knowledge representation, sharing and reusing across various applications. It is also the backbone of the semantic web. Therefore, there is a requirement of rapid and effective ontology construction methodology for developing sophisticated knowledge-based and semantic web-based system. However, setting up ontology manually is a tough, time consuming, error-prone, costly, and inflexible task which requires the significant participation of a knowledge engineer or domain expert. An automatic or semi-automatic ontology construction methodology will be a better solution to this problem. Over the last decade, some attempts have been made by the researchers that provide assistance to ontology engineer to build ontologies. Still, there are several open issues in this field. The paper presents a brief overview of different tasks in relation to ontology learning method and reviews some of the ontology learning frameworks or methodologies based on web mining techniques. The comparative analysis of them in the tabular form based on the distinguishing ontology learning tasks has also been presented. Furthermore, we have discussed some of the recent problems and major drift related to the construction of ontology from semi-structured web pages.
Coherence and path information

Complementarity, or more specifically, the wave-particle duality, and quantum coherence are two fundamental concepts in quantum mechanics. Recently, motivated by the progress of the quantification of quantum coherence, the complementary relation between coherence and path information is investigated by many authors, and various duality relations between them are established. Such relations not only provide insights into fundamental problems of quantum mechanics, such as the understanding of quantum coherence and wave-particle duality; but also are important in applications of quantum technologies. In this paper, based on the Bures distance and unambiguous quantum state discrimination, systematic analysis of the complementarity between the quantum coherence and path information in two path interferometers is carried out. Similarly as other related works, the wave aspect, or the visibility of the interferometer, is quantified by the &lt;i&gt;l&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-norm measure of quantum coherence, and the path information is considered via unambiguous quantum state discrimination. In this way, a novel duality relation in two path interferometers is obtained. Compared with known results, our work considers mixed states as well as pure states; considers the path predictability resulting from the intrinsic path asymmetry of the quantum state, as well as the path distinguishability resulting from the use of a which-path detector. Therefore, our work systematically generalizes known results in two path interferometers by removing all the unnecessary restrictions. Specifically, the most general form of quantum states in two path interferometers is considered and the duality relation between quantum coherence and path information is proved based on the positivity property of density matrices. The cases of path predictability and path distinguishability are considered separately. For path predictability, the proof is straightforward; whereas some advanced mathematical techniques, such as the Schur-Weyl inequality, properties of the fidelity and properties of positive matrices, are required in order to give a rigorous proof of the duality relation between coherence and path distinguishability. Concrete examples are provided to illustrate the abstract method and results. Our work concerns about two path interferometers exclusively and depends heavily that the dimensionality is two, therefore it would be an interesting task to generalize the results in this paper to &lt;i&gt;n&lt;/i&gt;-path interferometers.
Cloud Computing Security: Hardware-Based Attacks and Countermeasures

Despite its many technological and economic benefits, Cloud Computing poses complex security threats resulting from the use of virtualisation technology. Compromising the security of any component in the cloud virtual infrastructure will negatively affect the security of other elements and so impact the overall system security. By characterising the diversity of cyber-attacks carried out in the Cloud, this paper aims to provide an analysis of both common and underexplored security threats associated with the cloud from a technical viewpoint. Accordingly, the paper will suggest emerging solutions that can help to address such threats. The paper also offers future research directions for cloud security that we hope can inspire the research community to develop more effective security solutions for cloud systems.
RETRACTED: Dynamic 3D image simulation of basketball movement based on embedded system and computer vision

Traditional empirical basketball teaching methods can be repeated, affecting serious basketball training efficiency and the acquisition of technical essentials. Based on this problem, the basketball training reproduction framework is built utilizing augmented reality innovation. The framework sets up a virtual reenactment model of a ballplayer planning a player's track. Simultaneously, as a helping player, it captures the basketball player's actual situation, compares them with the simulated trajectories, and provides more targeted training. Based on virtual reality-based Virtual Data Augmentation Technology (VDRT), basketball technology's teaching mode allows players to acquire key points of sports skills and significantly improve basketball players' training efficiency as soon as possible. With the quick improvement of current science and innovation, for example, center, science and innovation, and electronic data innovation, more educational activities are being applied. However, modern educational methods' important content is to master and use modern educational equipment and processes. This article uses basic concepts, characteristics, and virtual reality techniques and literature and information methods to explain the types of role play in basketball lessons. Finally, it analyzes the application programs of basketball theory education, technical education, tactical instruction and educational competitions that provide scientific standards for future basketball education reform.
RRT Based Obstacle Avoidance Path Planning for 6-DOF Manipulator

Obstacle avoidance path planning is an important research topic in robot operation. As a complex system with multiple inputs and multiple outputs, highly nonlinear and strong coupling, the manipulator cannot be directly regarded as a particle in Cartesian space, so many path planning algorithms for mobile robots cannot be directly extended to manipulators. In this issue, based on the rapidly-exploring random tree algorithm, this paper proposed an improved path planning method. The path is searched in the joint space of the manipulator, collision detection is performed in Cartesian space by solving forward kinematics, and then the Bezier curve is used to smooth the path. The experimental results indicate that the proposed method can effectively plan a smooth, collision-free and less expensive path.
A Sentiment-based Hotel Review Summarization using LSTM Neural Networks

Hotel customers express reviews for every accommodation service provided and/or for the accommodation as a whole. On the other hand, reviews are particularly interested for the tourism industry in order to extract customers' opinions and aspects, which will assist them to improve their provided services. The reviews can be thoroughly found in the Web and their quantity can be considered as big data in Natural Language Processing. In this paper, we have designed an architecture and have implemented a system that initially utilizes some preprocessing techniques, as classic Natural Language Processing approaches, namely TF-IDF bag of words and word embeddings, are employed. These approaches can be further used as the input of various classifiers and Long Short Term Memory Neural Networks. The accuracy of these classifiers is evaluated on a dataset consisting of several reviews for the well known evaluation metrics, namely Precision, Recall and Fl-Measure.
A Web News Classification Method: Fusion Noise Filtering and Convolutional Neural Network

As the way of Internet information transfer, web news plays a significant role in information sharing. Considering that web news usually contains a lot of content, after in-depth analysis, we found that not all content is related to the news topic, and a lot of web news contains some noise content, and these noises content have serious interference to the text classification task. So, how to filter noise and purify web news content to improve the accuracy of web news classification has become a challenging problem. In this paper, we proposed a web news classification method via fusing noise detection, BERT-based semantic similarity noise filtering and convolutional neural network (NF-CNN) to solve the problem. In order to comprehensively evaluate the performance of the method, we use the Chinese public news classification dataset to evaluate it. The experimental results demonstrate that our method can effectively detect and filter a lot of noise text and the average F1 score can reach 95.61% on web news classification task.