Reviewerly/exHarmony huggingface dataset:
Web service classification based on non-functional parameters using vote based classifier

Today the internet has become a very vital part of everyone’s life. Nowadays, Companies are offering their business in the form of web services to reach their customers. Web services help in business by providing a standard way to communicate with their customers. But the rapid introduction of modern web services in this dynamic business environment influences the service quality and customer satisfaction. So, there is a requirement to focus on the functional as well as the non-functional aspect of web services. In this paper, for accurately classifying the web services vote based classifier has been used. For classification J48, Naive Bayes, SVM and Random Forest are used as a base learner. The proposed vote based classifier model provides accurate classification and prediction by improving the recall and precision value as compared to individual classifiers. The experiment is conducted on a real dataset of web services to find out the accurate result. The results show that the vote based classifier performs better in comparison to others. Experimental outcomes show that the proposed approach determines the most promising results.
BlockMeds: A Blockchain-Based Online Prescription System with Privacy Protection

Since the authentication of digital prescription is a lengthy and error-prone process by pharmacy employees, nowadays in many countries around the world, the paper-based prescription is still the only valid document for patients to purchase their prescribed medication from a pharmacy. Moreover, as a prescription can contain a lot of private information about the patients and their illness, the security and privacy issues in using digital prescription also raise big concerns. Recently, Blockchain has been widely regarded as a promising technology to secure online business data and transactions. In this paper, we present BlockMeds, a Blockchain based online prescription system which enables the authentication of digital prescriptions. Meanwhile, to address the privacy issue during the authentication and transaction for buying the medication, a privacy protection strategy is also implemented in the system. BlockMeds provides the proof of concept for a Blockchain based online prescription system. It also demonstrates the need for privacy protection which is often overlooked in a Blockchain-based system. BlockMeds can be used as a prototype system by both researchers and industrial practitioners who are interested in Blockchain-based medical service systems.
Spatio-Temporal Correlation based Anomaly Detection and Identification Method for IoT Sensors

Status monitor and anomaly detection of sensor nodes is critical for reliability of internet of things (IoT) system. Due to the complex causes of sensor anomalies, traditional methods that estimate only sensor state or environment state when detect anomalies cannot satisfy practical requirements. And existing methods often neglect spatial and temporal dependencies among IoT sensor observations. This paper defines faulty nodes and event nodes and proposes a novel anomaly detection and identification method implemented in two stages: 1) Anomaly detection stage by a customized composite distance metric and sensor clustering. 2) Anomaly source identification stage by fuzzy logic system based on spatio-temporal correlation. Experiments demonstrated that the proposed method can effectively detect anomaly sensors and capture spatio-temporal correlation to identify the anomaly source.
A simple Galois Power-of-Two real time embedding scheme for performing Arabic morphology deep learning tasks

This paper describes how a simple novel Galois Power-of-Two (GPOW2) real-time embedding scheme is used to improve the performance and accuracy of downstream NLP tasks. GPOW2 computes embeddings live on the fly (real time) in the context of target NLP tasks without the need for tabulated pre-embeddings. One excellent feature of the method is the ability to capture multilevel embeddings in the same pass. It simultaneously computes character, word and sentence embeddings on the fly. GPOW2 has been derived in the context of attempts to improve the performance of the SWAM Arabic morphological engine, which is a multipurpose tool that supports segmentation, classification, POS tagging, spell checking, word embeddings, sematic search, among other tasks. SWAM is a pattern-oriented algorithm that relies on morphological patterns and POS tagging to perform NLP tasks. The paper demonstrates how GPOW2 led to improvements in the accuracy of POS tagging and pattern matching, and accordingly the performance of the whole engine. The accuracy for pattern prediction is 99.47% and is 98.80% for POS tagging.
Spam Email Image Classification Based on Text and Image Features

Filtering of spam image-based email remains a major challenge for researchers. This paper presents a proposed work which is based on several facts such that spam images containing a large percentage of text which has characteristics or features different from other types of images. In addition to that, there is much similarity between the features of these images. These facts can be used to distinguish text regions spam images from others. A hybrid method based on combined features vector from text regions and features of the image is proposed. Two types of features are extracted. The first features extraction method is the local binary pattern (LBP) with extricating the image texture features directly, while the second is utilised to extricate features of image text regions only. The extracted features are used in individual and combination style in order to learn classifiers at the training stage. A one-class KNN classifier and two-class KNN classifier are applied separately. Each classifier was used in three fashion, with the text-regions features, with texture features in the image, and with merging both those features respectively. Experimental results showed that the appropriation of both image and text features together improves the effectiveness of the classification concerning the case in which only image or text features are used.
Efficient Training of Deep Classifiers for Wireless Source Identification Using Test SNR Estimates

We study efficient deep learning training algorithms that process received wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available. We focus on two tasks that facilitate source identification: 1- Identifying the modulation type, 2- Identifying the wireless technology and channel in the 2.4 GHz ISM band. For benchmarking, we rely on recent literature on testing deep learning algorithms against two well-known datasets. We first demonstrate that using training data corresponding only to the test SNR value leads to dramatic reductions in training time while incurring a small loss in average test accuracy, as it improves the accuracy for low SNR values. Further, we show that an erroneous test SNR estimate with a small positive offset is better for training than another having the same error magnitude with a negative offset. Secondly, we introduce a greedy training SNR Boosting algorithm that leads to uniform improvement in accuracy across all tested SNR values, while using a small subset of training SNR values at each test SNR. Finally, we demonstrate the potential of bootstrap aggregating (Bagging) based on training SNR values to improve generalization at low test SNR values with scarcity of training data.
Constructs for Artificial Intelligence Customer Service in E-commerce

The emergence of electronic commerce (Ecommerce) has changed the way of people buying as well as experience in online customer services. E-commerce is adopted as strategy to enhance business competitiveness but success in the online business world usually starts with providing the best customer service. Although organizations are struggling on improving their performance on customer service, some issues of traditional customer service continue to appear such as limited availability, inefficiency and costly. In order to overcome the limitation of traditional customer service, artificial intelligence (AI) is increasingly utilized in service nowadays to act as an assistant for human beings. Along with the integration of AI in customer service, human agents and AI can work together to provide perfect services to customers and increase productivity. Hence, this study aims to identify the constructs and characteristics of AI customer service in Ecommerce. This paper started with formulating research questions, setting up objectives and proposing an AI customer service model. The proposed model was derived based on the understanding of traditional and AI customer service in ecommerce as well as the related models and papers from prior research. Finally, overall conclusions, limitations and future works will be presented with regard to AI customer service in E-commerce.
IoT-Enabled Streaming Image Analytics With Privacy-Aware Self-Adaptive and Reflective Designs

With the development of the Internet of Things (IoT), a large number of cameras are able to generate large amounts of real-time data. This has attracted increasing attention to streaming-image analytics incorporating edge computing. Since streaming images do not have ground-truth labels, their outputs may become untrustworthy once any concept drift or uncertainty occurs. To keep analytics models updated in response to concept drift, much human intervention is required, particularly in system maintenance for IoT-enabled applications with a plethora of smart cameras. To address this issue and follow the trend of anthropomorphic machine learning, this article incorporates self-regulated learning from human pedagogy, which comprises forethought, performance, and reflection. Our article proposes counterparts for streaming-image analytics, including siaForethought, siaPerformance, and siaReflection, respectively. The experiment results show that siaForethought can effectively blur the detected pedestrians or the whole image to improve privacy awareness. Next, siaPerformance can help reduce the model adaptation time by 38%, even when using blurred pedestrians or images. Finally, siaReflection reduces about 50% of human intervention for labeling data, thus greatly reducing human interventions in case of concept drift.
DCM-CNN: Densely Connected Multiloss Convolutional Neural Networks for Light Field View Synthesis

Different from traditional cameras, the light field cameras record spatial information with its angular information. Since the angular and spatial resolutions are limited due to the hardware shortage, view synthesis is required to provide arbitrary views. In this paper, we propose densely connected multiloss convolutional neural networks for light field view synthesis, called DCM-CNN. We build DCM-CNN for view synthesis based on the feature reuse, thereby leading to low computational complexity. Moreover, we present a multiloss function that consists of pixel loss, feature loss and edge loss to produce accurate edges and textures in the synthesized views. Experimental results show that the proposed method generates high quality views from light field data with low computational complexity and outperforms state-of-the-art ones in terms of structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), and runtime.
Security and Privacy in the Internet of Things (IoT): Survey

Internet of things becomes important and its applications have widely used in the last years. These applications need a protection system against different attacks. This paper study the recent IoT Security researches and classifies them into the following tends Cryptography, Authentication, Blockchain, and data privacy preserving. And at last of this paper gives the conclusions and future work.
Augmented Reality for Assistive Maintenance and Real-Time Failure Analysis in Industries

We present a methodology to solve the problem of maintenance for any machinery using augmented reality (AR) guided assistive systems. The same system can be used to implement real time fault analytics system. Given a visual feed of object, our methodology can accurately determines, tracks and maps the object in the world. The objects are categorized as stationary and nonstationary by the user. The first method presented will address the maintenance solution for object which are stationary using spatial mapping and localization technique. The second method presented will address the problem using a deep learning model that predicts accurate pose of the object in world space. The instruction sets and the necessary animations are overlaid on the objects. This methodology of ours to find the object pose is robust to occlusion, lighting conditions and works in real time on computationally inexpensive hardware.
An Improved Reversible Data Hiding on Encrypted Images by Selective Pixel Flipping Technique

Reversible data hiding on encrypted images is an active area of research in the field of information security. In this paper, we revisit the reversible data hiding scheme proposed by Xinpeng Zhang in 2011. To ensure the exact image recovery at the receiver side, the existing scheme uses a block size of 32 × 32 pixels, this leads to an embedding rate of 9.76 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> bite per pixels. We also observed that the existing scheme is prone to fail while recovering an image block which contains highly correlated pixel values (very smooth region). In this manuscript, we propose a new scheme which will help to reduce the block size without compromising the bit error rate. The experimental study of the new scheme is carried on USC-SIPI image dataset managed by the University of Southern California, and the results show that the proposed scheme outperforms the existing scheme.
Carpool for Big Data: Enabling Efficient Crowd Cooperation in Data Market for Pervasive AI

Big data is the fuel for powering pervasive artificial intelligence (AI) applications. Aiming at promoting efficient cooperation in data market for big data, in this paper we propose a novel cooperative data purchase framework, by leveraging the power of the data user crowd and their intrinsic trustworthy collaboration relationships. For achieving efficient cooperative data purchase, we develop a comprehensive approach consisting of both data purchase group formation and selection. For the data purchase group formation, we partition the users into multiple data purchase groups for the purpose of budget pooling, by taking into account their data interest and budget levels, meanwhile respecting their underlying collaboration relationships and the maximum allowable group size for data sharing enforced by the data market platform. For the data purchase group selection, we construct a data purchase flow network formulation and devise a minimum cut based solution for selecting the proper set of data purchase groups to fully support their demands subject to the budget constraints. We extensively evaluate the performance of cooperative data purchase framework using both Erdos-Renyi and scale-free collaboration graphs. The numerical results demonstrate that the proposed framework can achieve superior performance, with more than 40% and 100% performance gain over the case without cooperation in terms of the total received payment and the number of satisfied users, respectively.
Exploring the Phenomenon of Cyber Economic Crime

This chapter provides an understating of the phenomenon of cybercrimeCybercrime , its evolution, and typology. Further it also sheds light on the economic lens of cybercrimeCybercrime . It explores the phenomenon of cyber economic crimeCyber economic crime in the Indian context and elaborates on the concept of cyber economic crimeCyber economic crime , its nature, extent, victims, accused characteristics, and typology.
Coverless Image Steganography using Haar Integer Wavelet Transform

Coverless image steganography is a recently discovered steganography method Which does not modify the image carrying the secret information. However, the techniques proposed until now have low efficiency capable of transferring few bits per image. This paper focuses on increasing the amount of data transmitted in an image, when using coverless image steganography through the use of the Haar lnteger Wavelet Transform. In our method, we take a single image of resolution 256×256 and split it into 1024 sub matrices. After that, we use integer wavelet transform to generate the coefficients in each sub-matrix. These sub-matrices are reshaped into arrays. Then the coefficients of each sub-matrix are changed into binary bits, by checking whether the succeeding value in the array is larger or smaller than the current value which leads to an array of length 1064*63. finally, we fragment the secret message into 8 bits and match it with the block and starting location in the array. A location map is generated which can be used by the receiver to obtain the secret message.
A linear-space data structure for range-LCP queries in poly-logarithmic time

Let T[1,n] be a text of length n and T[i,n] be the suffix starting at position i. Also, for any two strings X and Y, let LCP(X,Y) denote their longest common prefix. The range-LCP of T w.r.t. a range [α,β], where 1≤α<β≤n isrlcp(α,β)=max⁡{|LCP(T[i,n],T[j,n])||i≠jandi,j∈[α,β]} Amir et al. [2] introduced the indexing version of this problem, where the task is to build a data structure over T, so that rlcp(α,β) for any query range [α,β] can be reported efficiently. They proposed an O(nlog1+ϵ⁡n) space structure with query time O(log⁡log⁡n), and a linear space (i.e., O(n) words) structure with query time O(δlog⁡log⁡n), where δ=β−α+1 is the length of the input range and ϵ>0 is an arbitrarily small constant. Later, Patil et al. [5] proposed another linear space structure with an improved query time of O(δlogϵ⁡δ). This poses an interesting question, whether it is possible to answer rlcp(⋅,⋅) queries in poly-logarithmic time using a linear space data structure. In this paper, we settle this question by presenting an O(n) space data structure with query time O(log1+ϵ⁡n) and construction time O(nlog⁡n).
Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation

Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.
A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.
Adaptive and Dynamic Characteristics in Hybrid Agile Management Model for Software Development Project Success

The management of traditional software development methodologies and agile software development methodologies over the decades have not worked to its expectation in increasing the success rates of software projects. The Agile Manifesto in 2001 had promised to better manage IT projects with its 4 values and 12 principles but project success still appears to be elusive. The study and application of critical success and failure factors have been done by researchers but these factors are quick remedy solutions and not long-term robust improvements in the management of development methodologies. More research is required into the management of traditional and agile methodologies characteristics instead of success and failure factors. The coexistence of these characteristics as hybrid methods has also provided evidence of an increase in productivity and further research is required to strengthen and close current gaps in the management of hybrid methodologies. An empirical method with a quantitative approach is adopted to collect data with questionnaires from software development focus groups involved in industry projects. The data collected is analyzed using the Statistical Package for the Social Sciences (SPSS) technique and the Partial Least Squares (PLS) tool. The findings provide a set of significant characteristics of adaptive and dynamic features, which are combined with organizational objectives to prove there is a strong relationship between project success with hybrid methods and hybrid combination patterns.
Orchestrator Controlled Navigation of Mobile Robots in a Static Environment

The paper presents an orchestrated controlled model for automated guided vehicle (AGV) like mobile robots (RBs) to perform a transportation task in environment such as a warehouse or a factory. The AGV uses graph-based approach for path planning. It uses a proposed navigation algorithm for following the path trajectory. It also avoids a collision during transportation between start and end points. The proposed model also prevents the AGV from a deadlock situation. The performance of model has been validated experimentally.