Reviewerly/exHarmony huggingface dataset:
A Model to Stabilize E-Loyalty to Healthcare Services in the Context of the Internet of Things by using Fuzzy AHP Method

The Internet of Things (IoT) is a concept that has received a lot of attention both at university and industry recently. Today, the necessity of the Internet of Things in the field of health is well felt. It can also be argued that when health information is placed in the context of information and communication technology, information exchange will be faster and less error-prone. This research studied the stabilizing e-loyalty to healthcare services in the context of IoT. The evaluation was carried out among 29 experts who have studied or researched in this field. The Fuzzy AHP method was used in this article. The findings indicate that the variables of electronic loyalty, satisfaction and information privacy are more important than perceived usefulness, self-efficacy and personal innovation.
Cloud Computing: A New Success Model

Cloud computing is a major trend in the past decade where individuals and organizations obtain computing resources over the Internet. This paper reviews recent literature about cloud computing as well as empirical studies conducted to highlight the benefits associated with this relatively new technology. The paper further introduces a new model for cloud computing success based on the well-established models of Information Systems (IS) Success of DeLone and McLean 2003 and the Technology Acceptance Model of Davis 1989. This new success model of cloud computing can guide individuals and organizations on how to get the best of cloud computing and recognize its benefits.
A blockchain-based protocol for message exchange in a ICS network

The Internet and the Internet of Things (IoT) are fostering the evolution of Industrial Control Systems (ICS). The devices in this new ICS are highly interconnected but also more exposed to security problems due the lack of security primitives in devices and communication protocols. This paper presents a communication protocol that combines the blockchain technology and the Secure Scuttlebutt protocol (SSB), which was developed for decentralized message exchange applications. The proposed approach provides (a) a straightforward identification mechanism based on invitation; (b) a flexible signature-based authentication method; (c) the use of a chained feed of encrypted private messages to support integrity and confidentiality of communication; (d) a pruning strategy to reduce device storage constraint problems.
Using Image Boards to Analyze A Series of Cyborg Design Sketches

We present the main conclusions from an analysis of a cyborg design session in which sketches were created to brainstorm novel cyborg applications. We take these sketches and analyze them using image boards, which are a collage of pictures used to communicate a description of design aesthetics and intent. We used this technique to derive patterns and conclusions from the large set of sketches, which can inform the design of future cyborg applications. The sketches reveal that wearable arms are useful for reducing physical strain and multi-tasking, but should incorporate a ''rest'' mode in full-time applications.
A Reflection on the Predictive Accuracy of Dynamic Impact Analysis

Impact analysis as a critical step in software evolution assists developers with decision making as regards whether and where to apply code changes in evolving software. Dynamic approaches to this analysis particularly focus on the effects of potential code changes to a program with respect to its concrete executions. Given the existence of a number of prior approaches to dynamic impact analysis as opposed to a lack of systematic understanding of their performance, the first comprehensive study of the predictive accuracy of dynamic impact analysis was conducted, comparing the performance of representative techniques in this area against various kinds of realized code changes. This paper reflects on the progress in dynamic impact analysis, concerning the impact of that earlier study on later research. We also situate dynamic impact analysis within the current research and practice on impact analysis in general, and envision relevant future research vectors in this area.
A recurrent reinforcement learning approach applicable to highly uncertain environments

Reinforcement learning has been a promising approach in control and robotics since data-driven learning leads to non-necessity of engineering knowledge. However, it usually requires many interactions with environments to train a controller. This is a practical limitation in some real environments, for example, robots where interactions with environments are restricted and time inefficient. Thus, learning is generally conducted with a simulation environment, and after the learning, migration is performed to apply the learned policy to the real environment, but the differences between the simulation environment and the real environment, for example, friction coefficients at joints, changing loads, may cause undesired results on the migration. To solve this problem, most learning approaches concentrate on retraining, system or parameter identification, as well as adaptive policy training. In this article, we propose an approach where an adaptive policy is learned by extracting more information from the data. An environmental encoder, which indirectly reflects the parameters of an environment, is trained by explicitly incorporating model uncertainties into long-term planning and policy learning. This approach can identify the environment differences when migrating the learned policy to a real environment, thus increase the adaptability of the policy. Moreover, its applicability to autonomous learning in control tasks is also verified.
An Analysis of Financial Fraud through PNB Bank Scam and its technical Implications

The government is looking forward for the betterment of banks and coming out with various new schemes, the PNB scam came as a huge blow to the entire banking sector and brought almost everything on a stand still. This 11,700-crore scam involved more than four banks which is a big question mark on the financial safety of banks. This scam was almost five years old and was not detected by anyone. Therefore, there are serious questions on the internal operations and auditing processes of the bank and also a the main regulatory body RBI is also under serious mode of self-check. This case study aims to identify and analyze the major factors that led to this huge scam and also tries to detect the involvement of various businessmen who has created an impact on the economy as a whole.
Evolution and set up the maps for solar radiation of Iraq using Data observation and Angstrom model during monthly July2017

Abstract The development that solar energy will have in the next years needs a reliable estimation of available solar energy resources. Several empirical models have been developed to calculate global solar radiation using various parameters such as extraterrestrial radiation, sunshine hours, albedo, maximum temperature, mean temperature, soil temperature, relative humidity, cloudiness, evaporation, total perceptible water, number of rainy days, and altitude and latitude. In present work i) First part has been calculated solar radiation from the daily values of the hours of sun duration using Angstrom model over the Iraq for at July 2017. The second part has been mapping the distribution of solar radiation energy to Iraq using interpolation technique in Arc _GIs software Version 10.3.
Bayesian networks for cell differentiation process assessment

The way cell differentiate from bone marrow to peripheral blood level plays a crucial role in understanding and treating rare diseases and more common tumours. The main goal of this paper is to introduce a flexible statistical framework able to describe the cell differentiation process and to reconstruct a dependence structure along different levels of differentiation. We use next generation sequencing data on haematological diseases (severe combined immunodeficiency) within a gene therapy framework. The proposed statistical approach is based on Bayesian networks (BNs) and aims at finding a probabilistic model to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions among genes or the confounding effects of experimental conditions. Bayesian networks enable analyses on gene therapy‐treated patients in a data‐driven fashion and allow for exploring all relationships among different blood cell types integrating biological information, subject‐matter knowledge, and probabilistic principles.
An Improved SSD and Its Application in Train Bolt Detection

A multi-scale fusion and multi-window target detection method is proposed. Firstly, the model and principle of the classical single shot multi box detector (SSD) method are expounded. From the principle analysis, explain the reason why the SSD method is not good in detecting small targets. On this basis, a multi-window and multi-scale fusion model is proposed and explains its model and working principle. This paper evaluates the detection accuracy of the original SSD model and the improved SSD model by using the same data set. The experimental results show that the accuracy of the original model is about 0.8, and the improvement is about 0.95. Therefore, the improved SSD model is more advantageous and accurate than the traditional SSD detection model in bolt detection.
Sentence-SFV Model: Make Full Use of Word Embedding Actively

Word Embedding is a method that can learn and capture the grammatical and semantic features contained in the corpus through unsupervised training. Because of this superior characteristic, it is very common to use word vectors as inputs in the downstream tasks. However, little attention has been paid to the active mining of semantic information carried by word vectors. Therefore, we propose a Sentence based Scenario Feature Vector (Sentence-SFV) Model to represent the scenario related grammatical features of sentences quantitively in vector form. With the help of Sentence-SFV model, we extract scenario related features of different corpora and project them into a 3D feature space for visual analysis, by which means we demonstrate the interesting ability of our model like distinguishing grammatical features and explaining grammatical differences between different corpora. We also discuss on the methods to further improve our model, and verify the improvement effect by apply optimized SFV in CNN based sentiment classification experiments.
An optimization of a Lexicon Based Sentiment Analysis Method on Indonesian App Review

Over the past years, the popularity of mobile applications (or known as mobile apps) is continuously growing from year to year in the last decade. The development of mobile apps now becomes a part of business activity. It is essential for each developers to understand the users' need to keep their users on using their app. Sentiment analysis or opinion mining is an approach that can be used to analyze and conclude the opinion of the users of mobile apps. Recent method using lexicon based approach which is proposed by the previous study still has poor performance and still can be improved. There are two optimization chances that can be explored: lexicon resource usage evaluation and the application of domain specific features. This study tries to explore these two optimization chances in order to improve and optimize the method proposed by the previous study. The result shows that SentiWordNet can outperform other lexicon resources and the additional of domain specific features has a good impact on the performance of the classifier both on overall accuracy and f-measure average evaluation parameters compared to not only the results of this study but also compared to the result of the previous study.
ESAE: Evolutionary Strategy-Based Architecture Evolution

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures.
An Ontology for OASIS TOSCA

To improve cloud portability and interoperability many standards have been proposed in order to facilitate the design, the development and deployment of cloud applications. One of the most used is TOSCA (Topology and Orchestration Specification for Cloud Applications). However since TOSCA helps modeling the structure of an application there are some information regarding the domain of the application that are missing on the standard. We propose to use an ontology representation of that standard to fill the gap between the structural aspect and the domain of the applications.
Vector Quantization for Large Scale CBIR

In order to meet the requirements of image retrieval and real-time index storage of massive datasets, a Content-based Image Retrieval (CBIR) based on vector quantization is used in this paper. By the feature extraction of depth residual network, the improved Product Quantization (PQ) coding algorithm is used to generate index and dynamic database under FAISS Framework, which can realize nonlinear large-scale CBIR with high robustness. Empirical results show that the CBIR based on improved PQ has achieved good performance in index compression rate, recall rate and retrieval time compared with PQ and Locality-Sensitive Hashing (LSH) in SIFT1M dataset.
An Image Dehazing Algorithm Based on the Improved CGAN

Abstract In order to improve the haze removal effect of image, a modified Conditional Generative Adversarial Nets (CGAN) based algorithm is proposed. In the new algorithm, the pre-trained visual geometry group (VGG) model is adopted, the DenseNet instead of the traditional U-net as the network structure of the generator, the Patch-GAN as the network structure of the discriminator, and the loss function is modified by the total variation regularization gradient. The defogged image can be obtained without estimating the projection map and the related defogging feature. The experiments indicate that our new algorithm effectively reduces the halo phenomenon and haze residue problem caused by the traditional dehazing method, and can preserve more details of the image, the structural similarity is improved from 75.9% to 92.6%.
Proceedings 9th International Conference on Quantum Simulation and Quantum Walks

This volume contains a selection of papers presented at the 9th in a series of international conferences on Quantum Simulation and Quantum Walks (QSQW). During this event, we worked on the development of theories based upon quantum walks and quantum simulation models, in order to solve interrelated problems concerning the simulation of standard quantum field theory, quantum gravity and cosmological models, dissipative quantum computing, searching on complex quantum networks, and the topological classification of multi-particle quantum walks.
Energy Noise Detection FCM for Breast Tumor Image Segmentation

The traditional fuzzy C@hyphenmeans (FCM) algorithm ignores the spatial information of pixels and is sensitive to noise images. To overcome this disadvantage, this paper proposes a fuzzy C@hyphenmeans clustering algorithm based on energy noise detection (ENDFCM) for mammographic image segmentation. Based on the weighted mean filtering, the algorithm employs the information of the spatial pixel points to enhance its noise resistance. By introducing the energy curve and designing the spatial distance, it constrains the objective function to increase its parameter adaptability. Then, it derives the new subordinate function to increase the relevance of its subordinacy, which is updated continuously during the clustering process. The experimental results show that the algorithm has better segmentation accuracy and robustness compared with the traditional FCM and its improved methods, and can accurately segment the noisy breast tumor images.
A Multispectral Image Edge Detection Algorithm Based on Improved Canny Operator

The traditional canny operator performs edge detection, which needs to artificially intervene in the variance of Gaussian filtering, and the choice of variance will affect the edge retention and denoising effects. When filtering out the noise, many edge details are lost. Aiming at the shortcomings of the traditional canny algorithm, this paper proposes an improved canny algorithm for edge detection. After multispectral image Gaussian filtering, the mixed and enhanced operation is made on multispectral image. This operation filters out the noise and retains the important edge detail information. In addition, when the gradient amplitude image is obtained, more edge information are obtained by changing the size of the sobel operator. The edge details of the multispectral image processed by this operation are more abundant and the detection is more accurate. The multispectral image is then subjected to non-maximum suppression and double threshold processing. Experiments show that compared with the traditional canny edge detection effect, the algorithm proposed in this paper has greatly improved the effect of edge connection and pseudo edge removal, and the objective evaluation and visual effect have been greatly improved than before.
Attribute-Based Access Control Schemes in Cloud: Performance and Research Directions

Disclosure of sensitive data leads to identity theft and violation of privacy. The untrusted cloud service provider (CSP) may try to disclose/misuse the data. It is necessary to provide access control and security over the outsourced and shared data to hide it from the CSP and unauthorized users. Traditional access control schemes are prone to security threats in the cloud environment. Attribute-based access control schemes (ABAC) are well suited for the cloud environment. Attribute-based encryption (ABE) is a promising cryptographic solution to provide fine-grained access control over the shared data. It selectively shares the data among the users and hides data from the CSP and unauthorized users. It preserves the privacy of users and the security of data being shared. Users can decrypt the data only if their attributes are satisfied with the access policy associated in the ciphertext. This paper presents a comprehensive survey of the ABE schemes. Taxonomy, performance comparison, and applications of ABE schemes are dealt with. The taxonomy and performance comparison help the selection of the most suitable ABE scheme based on specific usage scenarios. Thus, the survey opens up very interesting avenues for further research in this area, which are also discussed.
Rapid region analysis for classification

The authors describe and evaluate a method that detects ridges (symmetric axes) in an Euclidean distance map. The method detects ridge-pixels with a local-maxima search using only relational operations and has therefore minimal complexity. The resulting ridges exhibit a height profile that is suitable for region abstraction by means of simple parameterisation. The method is firstly evaluated on artificial stimuli with systematic shape variations using spatial jitter and contour fragmentation. Then it is mentioned, how the method can be used for developing region abstractions. Such abstractions have been already exploited in two tasks, hand-written digit identification (MNIST database) and image classification (satellite images and images of urban/natural landscapes); the classification results of those systems are competitive.
Evaluation of the Influences of Hyper-Parameters and L2-Norm Regularization on ANN Model for MNIST Recognition

Artificial neural networks (ANNs) have achieved the state-of-the-art performance in many tasks. Although ANNs do not require manually engineered features, there are lots of hyper-parameters to be optimized. The choices of hyper-parameters significantly impact the performance of the model. A number of methods like manual, grid, random search, and Bayesian optimization using Gaussian Processes (GPs) have been proposed to do the optimization. However, the work of exploring the individual effects of each parameter is rarely conducted. In this paper, we highlight on the effects of each individual hyper-parameter (involving the learning rate, batch size, the regularization item, and the number of iterations) on the model accuracy. We firstly construct a performance evaluation architecture that is based on method of normalized scoring to quantify the model performance. Then we establish an ANN for MNIST recognition and classification task to validate the proposed performance evaluation architecture. The experimental results show that a small learning rate and a small batch size not only affect the model's convergence speed but also make the model difficult to converge. The L2-norm regularization is critical for the overfitting elimination. Such an evaluation architecture can also be promoted to other similar related research areas.
SYOLO: An Efficient Pedestrian Detection

Pedestrian detection is an important branch of object detection. It plays a vital role in many fields such as intelligent monitoring systems. The premise of the pedestrian recognition algorithm in the application of the industrial scene is accurate pedestrian detection. Our paper proposes a model to solve real-time pedestrian detection with high accuracy base on YOLO v3. We provide a method to select the size and number of anchor boxes for predicting bounding boxes accurately. Then we use a modified shuffle unit to lightweight the backbone of YOLO v3, which reduces the 67.3% FLOPs and 65.1% parameters. We train and validate our model on CrowdHuman detection dataset, SYOLO gets 62.7 mAP for face and 62.0mAP person with 0.748 average IOU. Our network processes images in real-time at 185.8 FPS for network and 12.3 FPS for the entire model on CrowdHuman.
Using Sedentary Activity Classification Model to Illustrate an Adaptable Freelance Workspace

Freelancing and remote-work is fast gaining acceptance in the global community, and thus resulting into sedentary behaviours that poses threat to healthy living. Recent studies have shown that several health hazards are related to sedentary behaviors. Specifically, too much sitting is adversely associated with health outcomes, of which includes cardio-metabolic risk biomarkers, type 2 diabetes and premature mortality. In this paper a one dimensional(1D) convolutional neural network based sedentary activity classification model is proposed for classification of sit, stand and sit-stand transitions using sensor data fusion collected with a tri-axial Inertial Measurement Unit (IMU) sensor. Specifically, normalized data from the IMU axes within a specific time window were considered to classify sedentary behaviors (sit, stand and sit-stand transition). The precision of the classification of the three studied sedentary behaviors exceeded 96.8% using IMU data fusion. The applicability of the proposed classification model is extended to a simulated condition of a typical freelancer by modeling an adaptable automated workspace.