Reviewerly/exHarmony huggingface dataset:
Saliency Guided Image Watermarking for Anti-forgery

When hiding information in a host image, different types of image features can be used as secret key. The cryptanalytic watermarking approach is recognized as robustness improvement solution for authentication stems and anti-forgery from attack. This work contributes a novel learning technique using saliency features of sub regions to establish a secret key and then apply the key like a parameter for both watermark embedding and watermark extracting. However, there is modification of the image features through insertion of information in watermark embedding. We propose to use learning methods together with saliency models, for assuring robustness of watermark extracting. Here, the image watermarking method is described with SVM learning and assistance of a number of saliency models. Our results show that the cryptanalytic watermarking method is sufficient to achieve invisibility and stability of watermark. Experimental results on a benchmark indicate the advantage of the saliency feature based method for anti-forgery applications.
A Fast Mode of Tweets Polarity Detection

Polarity detectionLijo,V. P. is an emerging area of research in text mining. PolaritySeetha,Hari detection is observing and identifying the sentiment inclination of text, whether it is positive or negative. In this paper, a fast mode of supervised learning for polarity detection on tweets is proposed, that is using datasets available in public. The feature selection strategy ensures reduced dimensionality. The low dimension data processing on Apache Spark supports scalability for large datasets. The experiment shows that the method is supporting high scalability and efficiency.
Protests, a Destabilizing Factor of Economic Growth in Arab Republic of Egypt

With the start of the revolution of January 2011 until 2013, many indicators and economic research confirms the entry of the Egyptian economy to a stage of deterioration. With the military council headed by Marshal Tantawi taking over the country, the growth of the economy fell remarkably to less than 2% Unemployment reached over 10%, and the fiscal deficit increased to 8.6% of GDP, which was accompanied by inflation of 11%. That one of the biggest economic mistakes committed by the government system is to keep the exchange rate of the pound fixed, which led to the depletion of about $ 20 billion of cash reserves between December 2010 to May 2012, which has negatively affected the rating of Egyptian Institutions International Finance. After that, Egypt’s economic situation during the rule of Mohammed Morsi and the Muslim Brotherhood, which can be described as lacking in experience in the management of the state. The political challenges and obstacles, as well as the absence of any economic plan, contributed to the increasing complexity of the economic situation of Egypt, Resulting in a rise in the unemployment rate to 12.5%. The Brotherhood’s government failed to provide resources to cope with the crisis, relying only on the collection of aid and subsidies from some regimes in the Middle East, resulting in worsening conditions until the army intervened and President Mohamed Morsi was removed on 3 July 2013. With the arrival of interim President Adli Mansour to power in August 2013, we note that the Egyptian economy witnessed a slight improvement, which was reflected according to the published report that Egypt received about 12 billion dollars in foreign aid from Kuwait, Saudi Arabia and the United Arab Emirates, However, the economic growth rate remained below 2% as unemployment and inflation continued to spread.
Anomaly Detection on Data Streams – A LSTM’s Diary

In the past years, the importance of processing data streams increased with the emergence of new technologies and application domains. The Internet of Things provides many examples, in which processing and analyzing data streams are critical success factors. An important use case is to identify anomalies, i.e. something that is different or unexpected. We often have to cope with anomaly detection use cases in sequences within data streams, for instance in network intrusion, in predictive analytics or in forecasting. Sequence analysis can be performed using recurrent neural nets and in particular, we use long short-term memory (LSTM) neural nets. An LSTM is not only capable of storing a sequence of data but also of deciding to forget certain parts of it. Unfortunately, the internal representation of learned data does not clearly illustrate what was learned. Moreover, like many neural net-based approaches, these nets tend to need a high volume of data in order to produce valuable insights. In this paper, we want to present an experimental setting, comprising an architecture, a structured way of producing sample data and end-to-end pipelines to store and evaluate the hidden state of a LSTM per training batch. Main purpose is to extract the hidden state as well as to analyze changes during training and thus to identify patterns in the hidden state as well as anomalies.
VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Anomaly detection in time series data is a known problem, but recent growth in the number of units that can produce data require models that work on unlabelled and diverse types of data. We propose to adapt the neural network introduced by Simonyan and Zisserman in 2015 called VGG16 and used to detect and classify objects in images. We show that the VGG16 architecture with 2-dimensional convolutions replaced with 1-dimensional version could be a building block of an autoencoder approach to detect anomalies. Additionally we show that the proposed model achieves results that are similar or better than of classical anomaly detection methods.
A Local Optimum Matrix Construction for Matrix Embedding Steganography

Steganography is the task of embedding a secret message in a cover media (e.g. image, video and voice) such that the stego media is not intuitively separable from the original ones. It is one of the interesting fields of security especially in transferring digital media as the major components of the web sites, emails and any web-based communications. Matrix embedding is a general approach used in many steganography schemes, especially where the cover size is small e.g. Voice Over IP (VOIP) packets. In this approach, the message is mapped to a series of bits (stego) to replace the same number of low significant bits in the cover. The embedding matrix is used for extracting the message by a linear combination of the stego bits. For a given matrix and secret message, there are specific series of stego-bits from which the message can be extracted. Embedding is done by an inverse problem to minimize a cost as the difference between the stego and the original bits. Hence, each message has a specific embedding cost based on the matrix. In this paper, a method is proposed to find an embedding matrix which minimizes the expectation of embedding cost for a uniform distribution of messages. To that end; a dynamic programming algorithm is proposed to efficiently find the expected cost for a given matrix. By use of this algorithm, any search method may be used to solve the problem. In this paper, a fast Hill-climbing search strategy is designed to find a local optimum matrix in an allowable time.
Traceability mining between unit test and source code based on textual analysis applied to software systems

Abstract Correctness of the designed system is one of the most important issues in the software development process. Therefore, various tests have been defined and designed to help software teams develop software with little or no problem. Finding a proper link between test class and the class under the test is an important but difficult task. Finding this relation helps the developers to conduct regression tests more efficiently. In this paper, we seek to propose a model for recovering traceable links between test classes and the classes under the test. The proposed method encompasses three parts: (1) method for extracting keywords and the measure of similarity of a specific part of code, (2) backward chain method based on a rule-based system, (3) using hybrid model to find traceable links between test classes and the code under test. This study uses three open-source and one industrial source projects to conduct experiments. The results are satisfactory compared to previous studies.
Neighbouring Relationship Exploration Based on Graph Convolutional Network for Object Detection

Current object detection methods are mostly based on the Faster R-CNN which is composed of two stages: 1) the region proposal network which roughly sifts anchors and produces object proposals and 2) the detection network which inputs the generated region proposals and makes predictions of category and bounding box for each region. However, the object proposals are always treated individually in the detection network without taking their relationship into consideration. Specifically, most researches utilize the non-maximum suppression algorithm to reduce the number of proposals by subtracting the neighbouring proposals for either training or testing process. Though a single proposal may be not accurate enough, a union of its neighbouring proposals and itself would represent this object region more comprehensively. Since the neighbouring relationship can be expressed by an edge between two proposals (nodes), the set of region proposals can be modelled in a graph structure. In this paper, we propose a novel neighbouring relationship exploration model (NREM) to improve the object detection by aggregating the neighbouring proposal graph (NPG) based on the graph convolutional network (GCN). Owing to the effective exploration of complementary relationship among neighbouring proposals, our method can improve the detection results significantly. Experiments on the PASCAL VOC 2007 dataset demonstrate the superiority of our proposed method.
Impact of Taqtaq oil field on the surrounded groundwater quality

Oil industry is usually accompanied by the release of many pollutants, especially the liquid ones, due to the use of formation water. The impact of Taqtaq oil fields on drinking water quality is studied in this research. GIS drawing is considered. Water samples were collected at various location outside the boarder of the oil fields. The heavy metals content of the groundwater around oil wells was measured to evaluate the extent of the element pollution. Laboratory analysis for more than twenty samples of water sources was done to determine the existence of heavy elements. It is shown that there is out of drinking water Iraqi standard specification regarding the TDS, and the heavy metals Nickel Ni, and one location with lead Pb. The pollutants concentrated on the east of the oil fields at a distance of 6-10 km.
Variation of a Theme of Landau--Shanks in Positive Characteristic

A := F q [t] be a polynomial ring over a finite field F q of odd characteristic and let D ∈ A be a square-free polynomial.Denote by N D (n, q) the number of polynomials f in A of degree n which may be represented in the form u•f = A 2 -DB 2 for some A, B ∈ A and u ∈ F × q , and by B D (n, q) the number of polynomials in A of degree n which can be represented by a primitive quadratic form of a given discriminant D ∈ A, not necessary square-free.If the class number of the maximal order of F q (t, √ D) is one, then we give very precise asymptotic formulas for N D (n, q).Moreover, we also give very precise asymptotic formulas for B D (n, q).
Training a Feed-Forward Neural Network Using Cuckoo Search

Cuckoo Search (CS) is a nature-inspired and metaheuristic algorithm which is based on a brood reproductive strategy of cuckoo birds to increase their population. This algorithm mainly serves to determine the maximum or minimum value of a particular problem which is known as the objective function. CS has reportedly outperformed other nature-inspired algorithms in terms of computational efficiency and the speed of convergence to reach an optimal solution. This chapter aims at exploring the application of CS to determine the parameters of Artificial Neural Networks (ANN). The inherent problem with traditional training of ANNs using backpropagation is that the learning process cannot guarantee a global minimum solution and has a tendency of getting trapped in local minima. The working of such ANN models is restricted to a differentiable neuron transfer function. The CS algorithm has been observed to provide a solution without the use of derivates to optimize such convoluted problems. The usage of ANNs across a wide range of problems including classification tasks, image processing, signal processing, etc. justifies the application of CS to the backpropagation procedure of ANNs to achieve a faster rate of convergence and avoid the local minima problem. This chapter also presents discussions and results on how ANNs optimized with variants of CS perform when applied to the detection of chronic kidney disease, modelling of operating photovoltaic module temperature and forest type classification.
Enhancement in Algorithmic Design for Scalability Measurement of Web Application

Scaling the Web applications with more workload and the number of users becomes very crucial. Performance testing of a Web application specifies the applications’ functionality, stability, and robustness. As the scalability testing is the extension of performance testing, it identifies the major workload and alleviates bottlenecks that can hamper the scaling of the application and it specifies the degree to which the test is developed. This paper proposes an algorithmic design for measuring scalability of Web applications to get the increased performance with few steps to do a test. This design allows adding additional users and working load to the available environment and encountering the linear changes with software and hardware which affect the server performance. An application development process requires careful planning for making scalable application, and it is important to test scalability problems regularly and rigorously.
Automation of Synchronicity Identification in Hippocampal Neurons through Intelligent Data Clustering Approach

Neuronal synchronicity is central in controlling the cognitive functions and disruption in neuronal synchronicity may lead to diseased state. Since the neurons show significant heterogeneity in firing pattern in case of in vitro and in vivo cell recordings, automated identification of synchronous and asynchronous neurons in a large population remains challenging. In this context, an efficient data analytics approach is proposed where the time-lapse data is primarily obtained from imaging of intracellular Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> in primary cultures of hippocampal neurons. Here, Fluo-4 is used as the fluorescent indicator for measuring for cytosolic calcium through imaging using confocal microscope. To categorize synchronous response from a set of heterogeneous Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> spiking data, an efficient artificial neural networks based clustering algorithm has been proposed, which proceeds through a variable reduction approach. This algorithm further enables the usage of evolutionary optimizers to solve the optimization problem of Fuzzy C-means (FCM) clustering. Moreover, the novel algorithm estimates the optimal number of cluster and optimal artificial neural network topology, which remains to be a longstanding issue. In order to validate the obtained clustering solution, the cross-correlation coefficient and spiking pattern is measured for the clustered neuron cell data. The obtained solution is compared with that of conventional FCM algorithm such that the efficiency of proposed approach could be tested.
Attainable Knowledge and Omniscience

The paper investigates an evidence-based semantics for epistemic logics. It is shown that the properties of knowledge obtained from a potentially infinite body of evidence are described by modal logic S5. At the same time, the properties of knowledge obtained from only a finite subset of this body are described by modal logic S4. The main technical result is a sound and complete bi-modal logical system that describes properties of these two modalities and their interplay.
Multi-Objective Scheduling Based on Ant Lion Optimizer in Cloud

Nowadays, computational cloud is becoming popular for large-scale compute intensive business and scientific applications requiring immense processing capabilities. Such applications can typically be modeled as precedence constrained parallel applications or workflows. To utilize the cloud resources efficiently, workflow scheduling has gained attention. Workflow scheduling is the method of allocating task to the appropriate resources so that all tasks will finish in minimum time. Further, the ongoing growth of cloud computing leads to large amount of energy consumption and in turn releases carbon footprints that have a negative impact on the environment. Then the cloud service provider wants environment-friendly solutions to minimize the energy consumption. Thus, in the current study, we proposed a meta-heuristic workflow scheduling algorithm namely Multi-Objective Ant-Lion Optimisation with the aim to optimize makespan and energy consumption. Non-domination sorting is used to make sure that all the best solutions in the archive are saved and provides faster convergence. Further, crowding distance is used to sustain the diversity of non-dominated solution in the archive. The simulation result signifies that the proposed MALO algorithm provides faster convergence toward the Pareto front and generates good trade-off solutions.
Research of road scene object detection algorithm based on mobile platform

There are many object detection methods in terms of object recognition based on traditional methods, but they are not sufficient to meet the demand for accuracy and speed in real-life scenarios. And compared with mobile platform, cloud service is also not conducive to the use in practical scenarios. Therefor we optimize the YOLO (You Only Look Once, a method for real-time detection of objects) algorithm through renormalization processing, build the Chinese road sign dataset and perform random affine transformation, random blur, and brightness transformation processing on the dataset to enhance the generalization ability of the final model. The parameters of the model are fine-tuned to reduce the period required to train the model and improve the performance of deep learning. Finally, the deep learning model of object detection will be transplanted to iOS mobile terminal to meet the requirements of real-time and accuracy in automatic driving scenarios. We identifie three types of road objects. The detection accuracy of pedestrians on road scenes reaches 75.9%, and the average detection accuracy of buses, cars, bicycles, and motorcycles is 72%. The detection accuracy of road signs is 69%. Total accuracy is 74.31%. The average detection rate of running tests on mobile phones is 12.5 frames per second.
On Convergence of Pigeon Inspired Optimization Algorithm

The pigeon inspired optimization (PIO) is a metaheuristic algorithm which finds an optimal solution in the complex search spaces using homing behavior of pigeons. PIO algorithm has been applied to solve various optimization problems in different domains and is empirically shown to perform well. However, the convergence of this algorithm has not been established analytically in the literature. In this paper, the update equations of PIO algorithm are regarded as a discrete time-varying system and its convergence is analysed. This paper attempts to establish the convergence of PIO algorithm using two methods. The first method uses the state transition matrix approach and the second method is based on showing the convergence using the solution of linear discrete time-varying systems. Further, the appropriate choice of the parameter in PIO algorithm and its influence on the convergence of the algorithm is also discussed.
Ternary codes, biplanes, and the nonexistence of some quasisymmetric and quasi‐3 designs

Abstract The dual codes of the ternary linear codes of the residual designs of biplanes on 56 points are used to prove the nonexistence of quasisymmetric 2‐ and 2‐ designs with intersection numbers 0 and 3, and the nonexistence of a 2‐ quasi‐3 design. The nonexistence of a 2‐ quasi‐3 design is also proved.
Real-Time 3D Visualization of Queues with Embedded ML-Based Prediction of Item Processing for a Product Information Management System

This paper showcases a prototype system for predicting and visualizing distributed product information queues at one of the largest global appliance companies, BSH Hausgeräte GmbH (BSH). A design science methodology is employed to develop a viable artifact (a prediction and visualization system) as a technology-based solution to an important and relevant business problem (product information updates in a distributed environment) using novel solutions (machine learning and visualization), to demonstrate the value of the design (through user interviews), and to disseminate the results to both technical and managerial audiences. The results contribute to the ongoing academic discourse regarding the applications of artificial intelligence to business and the practical development of prediction and visualization solutions for similar environments in terms of distributed environment complexity and company size.
Vocabulary Improvement by Using Smart Mobile Application—A Pilot Study

The aim of this study is to explore the usefulness of smart mobile application in the process of English language learning with special focus on vocabulary acquisition. As vocabulary learning is considered to be a boring, difficult and long process, it is beneficial to transform it into an easy, practical, and pleasurable experience, which can be achieved by using technology. Recently, mobile devices have become an essential part of our everyday life. Regarding this, it is reasonable to incorporate them into the learning process, including language learning. The current pilot study describes an experiment dealing with using a smart vocabulary learning mobile application in the process of vocabulary improvement. The results show the main differences between the experimental group (using mobile devices) and the control group (exposed to the traditional methods of vocabulary learning and given the vocabulary summary through using an electronic notice board).
Payment card fraud: global trends and empirical evidence on Internet card fraud in Italy

The expansion of retail trade at global level, spurred by online transactions (e-commerce), has led the European legislator to strengthen safety//security measures. This paper aims to assess the trend of card fraud in recent years, taking into account the latest regulations. After an extended period of growth, since 2016 the online fraud rate has recorded a decline in Italy and Europe. Empirical analyses of banks’ data carried out in Italy show that the decrease is correlated with successive improvements in anti-fraud measures over recent years. The paper also shows the main anti-fraud measures adopted by intermediaries (e.g. strong authentication and risk mitigation models) as well as protection mechanisms for customers in the event of fraud (e.g. exercise of the right to a refund within one day after filing the request), making it safer to buy online.