Reviewerly/exHarmony huggingface dataset:
Improving Salsa20 Stream Cipher Using Random Chaotic Maps

The efficiency of a stream cipher is an important property that measures its robustness against the attacks and analysis. The robustness of the stream cipher based on the complexity of the generated keystream that is used to secure text and images via digital transition through open networks and such as emails and use images that are used in computers and phones. The randomness and complexity of the keystream had great attention in many studies to improve the stream ciphers. A new generation of stream ciphers based on the chaotic map is presented. This paper proposed a new method to generate a keystream based on the Salsa20 algorithm with a set of chaotic functions that are chosen depending on our previous evaluations. The selected chaotic map is implemented in the inner processes of Salsa20 to obtain more complexity. The encrypting text, gray images, and the colored image has been tested by Histogram comparison that resulted no informative data about plain image, three correlation of image in the vertical, horizontal and diagonal way of the plain and ciphered image also have been tested, that results larger than 0.15, plain and ciphered images are compared by the concept of peak signal to noise ratio and mean square error with no significant differences. The gray and colored images are tested by Structural Similarity Index (SSIM), information entropy with significant differences. Finally, the NIST is applied to the outputs. The result of the tests showed high randomness with p value of 0.9. the encryption time is in about 38 sec.
New Technique to Rank Without Off Page Search Engine Optimization

Over the years search engines have become more efficient in delivering relevant pin pointed results. For digital marketers that means more hard work because the actual metrics used to measure the content relevancy and quality are not yet disclosed by the search engines. The paper discusses a new technique named All in Title Query Result Analysis for identifying keywords with low competition. The posts written for such keywords would rank without any off-page search engine optimization. The research spans to almost 36 months of data taken from Google Analytics. The results show the supremacy of the new technique that can help new digital marketers or startups to launch digital platforms with reduction in the off-Site search engine optimization cost.
On Numerical Implementation of the Laplace Equation-Based Image Inpainting

Abstract In the current study, we focus on the development of a numerical algorithm for the Laplace equation-based image inpainting problem. A software program to implement the proposed algorithm is also developed. In experiments, the proposed method recovered corrupted images effectively. It can also remove defects as well as unnecessary objects successfully. The program provides an effective, convenient, and easy solution to perform the image inpainting in practical tasks. It also provides a useful feature to assess image quality after processing with both full reference and blind image quality assessment.
Artificial Intelligence in Practice

In this chapter, the authors discuss machine learning techniques and artificial intelligence applications, their role in business, and present a practical application of it. They try to highlight how important machine learning can be in data-driven organisations, where the cost and/or the advantages to implement such tools are far greater than having a human—or a team of humans—doing it.
Evaluation of Digital Image Forensics

The development of modern picture handling and altering programming has made the control of advanced pictures simple and impalpable to the unaided eyes. This has expanded the interest to evaluate the dependability of advanced pictures when utilized in wrongdoing examination, as proof in official courtroom and for reconnaissance purposes. This paper presents a complete examination of the advancement and difficulties in the field of computerized picture crime scene investigation to help the apprentices in building up the comprehension, catching the necessities and recognizing the exploration holes in this area.
A Comprehensive Study Toward Women Safety Using Machine Learning Along with Android App Development

In this modern era, women have shown excellence in all walks of life and stand equally with men in all sectors. So has increased violence against women due to this exposure. Hence, women security during travel is one of the critical issues faced by society, from the past and even today. The paper aims at providing a technological solution to this problem. For this, a system for forewarning and safeguarding the female cab users using machine learning is developed. An Android application is developed to collect data, and a virtual panic environment was created to collect the anomalies. The accelerometer data along the three coordinates was captured corresponding to normal and anomalies. Three machine learning models viz support vector machines, logistic regression, and Naïve Bayes are trained on data collected, and their test performance is compared in terms of accuracy. This trained model is further integrated with an Android application for real-time data collection and feedback.
Research on intelligent recognition and encryption algorithm of network big data image

Abstract In view of the identification and encryption of massive network information in the era of big data, this paper proposes to use intelligent image recognition technology to analyze the required information, then classify it according to the big data clustering method, and use pseudo pixel encryption algorithm for multiple encryption. The simulation results show that the algorithm has high recognition rate, good encryption effect, strong anti-interference ability and good ability to protect network image information.
Research on Recognition of Working Area and Road Garbage for Road Sweeper Based on Mask R-CNN Neural Network

In order to reduce the energy waste of the road sweeper and simplify the operation process of the driver, a set of intelligent cleaning device for road sweeper is designed, which can automatically identify road garbage and adjust the cleaning mechanism to the required power. This device is composed by a monocular camera, an industrial computer, a vehicle DC power, and a cleaning mechanism. In terms of algorithms, two Mask R-CNN neural network models are used to detect road garbage. First, the road surface information is obtained by the first model to obtain the workable area of the road sweeper, which can reduce the influence of factors such as vehicles and pedestrians. Secondly, the road surface information in the workable area is divided into two types, road-specific information and garbage, the garbage detection and marking are completed after the second model is tested. Finally, the garbage coverage rate is used as a feature to adjust the power of the cleaning device. The result of testing and analysis of this algorithms shows that the real-time performance and recognition accuracy can achieve the expected results.
Improve K-Mean Clustering Algorithm in Large-Scale Data for Accuracy Improvement

If you want to classify object and you do not have any specific labels, then how can you classify that objects? Clustering is the best way to classify this kind of object. Whenever we are talking about large-scale data relating to variety of fields, beginning of new gen techniques for concentrate collection of data was resulted. Typical database query processing is not able to find exact information from large number of data, and hence, clustering is important analysis method for large-scale data. Among these many clustering algorithms, k-means and k-medoids are superiorly utilized for large-scale database. Initially, select centroids in k-mean algorithm and medoids for k-medoids algorithm for batter quality of the resulting clusters. Key highlight of this algorithm is that, whenever number of iterations increase, it will also increase the computational time. Proposed k-means algorithm initially finds the starting level centroids “k” as per requirements and returns with better compare to previous, effective and very stabilized cluster. Here proposed algorithm consumes very less time for execution as it segregates unnecessary computational distance because it uses the previous iteration of cycle. On the basis on initial centroids, here proposed algorithm systematically selects initial k-medoids. And thus, it will produce stable clusters for efficiency improvement.
DANet: Deformable Alignment Network for Video Inpainting

The goal of video inpainting is to fill the missing holes in a given video sequence. Due to the additional dimension, the video inpainting task is considerably more challenging to generate a plausible result than the image inpainting task. In this paper, we propose a novel video inpainting network based on deformable alignment, named Deformable Alignment Network (DANet). Given several consecutive images, DANet can align the image features from the global-level to pixel-level in a coarse-to-fine fashion. After alignment, DANet applies a fusion block to fuse the aligned features with neighboring frames and generates an inpainted frame. The coarse-to-fine alignment architecture ensures a better fusion result, which leads to temporal and spatial consistency combined with the fusion block. Experiment results demonstrate that DANet is more semantically correct and temporally coherent, and is comparable with state-of-the-art video inpainting methods.
Transfer Learning for Constrained Stochastic Control Using Adjustable Benders Cuts

Stochastic optimal control problems are generally difficult to solve to optimality, and it is desirable to be able to transfer a high-quality solution learned for one such problem to a new, related problem at low computational cost. This is useful in cases such as a known change in dynamics, e.g., when a vehicle's payload increases. We describe a way of doing this for discrete time, infinite horizon problems on continuous spaces. The solutions take the form of Q-function approximations, consisting of “Benders cuts” that lower-bound the optimal Q-function. This form induces an easy to evaluate control policy. We describe how to adjust a set of existing cuts at low computational cost while preserving the lower bounding property for the new problem. This transfers prior training effort to the new problem far more cheaply than starting from scratch. We use random problem instances to demonstrate that our transfer procedure yields near-optimal results on the new model, outperforming a clipped linear-quadratic regulator (LQR) on average. The Benders approach, while model-based, is strongly related to model-free reinforcement learning, in that it uses sampled experience evaluating the so-called Bellman error to learn the value function.
An adaptive fusion panoramic image mosaic algorithm based on circular LBP feature and HSV color system

In view of the sharp edge of stitching line, object splitting, uneven color fusion and obvious stitching traces in the process of panoramic image stitching, a new panoramic image stitching algorithm is proposed. Before stitching, the adaptive image color fusion based on HSV color system is carried out, the local texture features of the image are described by circular LBP Operator, and the energy accumulation distribution table is obtained by using the optimized pruning dynamic planning algorithm. Finally, the image fusion is carried out in the overlapping area by interlacing backtracking and optimizing Gaussian filtering wave. Experimental results show that compared with the traditional stitching algorithm, the image quality level is improved, the color is too uniform, and there is no obvious hum in stitching.
A fast learning approach for autonomous navigation using a deep reinforcement learning method

Deep reinforcement learning-based methods employ an ample amount of computational power that affects the learning process. This paper proposes a novel approach to speed up the training process and improve the performance of autonomous navigation for a tracked robot. The proposed model named “layer normalization dueling double deep Q-network” has been trained in a virtual environment and then implemented it to a tracked robot for testing in a real-world scenario. Depth images have been used instead of RGB images to preserve the temporal information. Features are extracted using convolutional neural networks, and actions are derived using the dueling double deep Q-network. The input data has been normalized before each convolutional layer, which reduces the covariate shift by 69%. This end-to-end network architecture of the proposed model provides stability to the network, relieves the burden of computational cost, and converges in much less number of episodes. Compared with three Q-variant models, the proposed model demonstrates outstanding performance in terms of episodic reward and convergence rate. The proposed model took 12.8% fewer episodes for training compared to other models.
Cluster-based certificate revocation in industrial loT networks using Signaling game

Industrial IoT network (IIoT) is the result of integrating Internet of Things into the industrial processes to make manufacturers more efficient and reactive. This integration brings with it new security and privacy challenges because of the sensitive information exchanged between devices and the infrastructure. In order to prevent malicious nodes from abusing the IIoT system, this paper investigates a new distributed certificate revocation protocol. Hence, we propose cluster-based certificate revocation mechanism for IIoT network by using game theory. Indeed, our proposed signaling game allows to make a decision on a targeted node after evaluating its behaving. In this approach, we assume an IIoT network organized as a set of clusters called communities. Each community consists of a set of member nodes and a community leader who uses a multi-stage game to renew the certificate of well behaving member nodes and revoke the certificate of malicious member nodes. Based on the Perfect Bayesian Equilibrium, we describe a reactive algorithm implementing the certificates update mechanism. By simulations, we evaluate the convergence speed, the dynamic and the accuracy of the leader's posterior belief on nodes with different behaviors.
Early Detection of Flawed Structural Dependencies During Software Evolution

During software evolution, complex structural dependencies between source files pose a great challenge on maintenance activities. Some of these dependencies propagate defects among files, incurring frequent bugs or changes, and consuming significant maintenance costs. They can be referred to as flawed structural dependencies. In this paper, we proposed a method to identify these potential problematic dependencies at an early stage during software evolution, by combing structural and semantic dependencies, so that developers can save maintenance costs by fixing these issues in time. Our method works as follows: First, we extract structural dependencies from the source code syntax and semantic dependencies from the source code lexicon. Second, we collect suspect file pairs by calculating the difference between structural and semantic dependencies. Next, we exhaustively examine each source file in the system and locate the interaction of its impacted subordinated files and suspect file pairs (SFP) as suspect dependencies. Finally, we gather all the suspect dependencies as flawed structural dependencies candidates. We evaluate our method using 838 releases of 15 open source projects, including 33353 bug reports and 86690 revision commits. The detection result shows that our identified dependencies use 14% of all the files to capture almost 70% of top 10% bug-prone files or change-prone files with enough high precision: 92%. Moreover, our identified dependencies also incur 957% of bug frequencies and 1050% of change frequencies than average in future versions. In summary, our method can effectively and efficiently detect flawed structural dependencies in time during software evolution.
Analyzing Comments in Ticket Resolution to Capture Underlying Process Interactions

Activities in the ticket resolution process have comments and emails associated with them. Process mining uses structured logs and does not analyze the unstructured data such as comments for process discovery. However, comments can provide additional information for discovering models of process reality and identifying improvement opportunities efficiently. To address the problem, we propose to extract topical phrases (keyphrases) from the unstructured data using an unsupervised graph-based approach. These keyphrases are then integrated into the event log to derive enriched event logs. A process model is discovered using the enriched event logs wherein keyphrases are represented as activities, thereby capturing the flow relationship with other activities and the frequency of occurrence. This provides insights that can not be obtained solely from the structured data. To evaluate the approach, we conduct a case study on the ticket data of a large global IT company. Our approach extracts keyphrases with an average accuracy of around $$80\%$$ . Henceforth, discovered process model succinctly captures underlying process interactions which allows to understand in detail the process realities and identify opportunities for improvement. In this case, for example, manager identified that having a bot to capture specific information can reduce the delays incurred while waiting for the information.
Corpus-Based Sociolinguistic Study of Corporate Emails and the Implication for Business English Teaching

Corporate emails, as a register, have specific linguistic features, which are important for business English learners and can be comprehensively studied under sociolinguistic framework. In order to better teach business English, especially business email writing, a corpus of corporate email corpus was compiled and linguistic features of intra-company email messages extracted and investigated. Statistical data of lexical, grammatical and conventional textual features show that corporate email messages have characteristics of both personal email communication and professional information exchange. The research result also shows that business English includes various sub-registers, which should be widely studied for teaching advanced business English.
Radiogenomics and DCIS: Can the Mammographic Appearance of Microcalcifications Tell us More Than Just the Likelihood of Malignancy?

Calcifications in the breast can represent benign, malignant, and high-risk lesions and have a wide range of morphologies and distributions. Microcalcifications in the breast are significant, as they are the most common mammographic presentation of ductal carcinoma in situ (DCIS) ( 1 Kim JH Ko ES Kim DY et al. Noncalcified ductal carcinoma in situ imaging and histologic findings in 36 tumors. J Ultrasound Med. 2009; 28: 903-910 Crossref PubMed Scopus (24) Google Scholar ). Microcalcifications that are associated with DCIS usually either represent the calcification of necrotic tumor debris within the ducts or calcification of secretory or mucinous material trapped within ducts by surrounding tumor. Frequently, on mammography, the secretory calcifications are round and punctate or amorphous, while those of necrotic etiology tend to be more pleomorphic or coarse heterogeneous ( 2 Gwak YJ Kim HJ Kwak JY et al. Ultrasonographic detection and characterization of asymptomatic ductal carcinoma in situ with histopathologic correlation. Acta Radiol. 2011; 52: 364-371 Crossref PubMed Scopus (19) Google Scholar , 3 Tse GM Tan PH Pang ALM Tang APY Cheung HS. Calcification in breast lesions: pathologists' perspective. J Clin Pathol. 2008; 61: 145-151 Crossref PubMed Scopus (60) Google Scholar , 4 Tse GM Tan PH Cheung HS Chu WC Lam WW. Intermediate to highly suspicious calcification in breast lesions: a radio-pathologic correlation. Breast Cancer Res Treat. 2008; 110: 1-7 Crossref PubMed Scopus (57) Google Scholar ). Radiologists can reliably detect most microcalcifications on mammograms and within the United States, the incidence of DCIS increased from 1% to 20%–30% of all asymptomatic breast cancers with the implementation of screening mammography ( 5 Kumar AS Bhatia V Henderson IC. Overdiagnosis and overtreatment of breast cancer. Rates of ductal carcinoma in situ: a US perspective. Breast Cancer Res. 2005; 7: 271-275 Crossref PubMed Scopus (25) Google Scholar , 6 Karamouzis MV Karatza EL Ravazoula P et al. Non-palpable breast carcinomas: correlation of mammographically detected malignant-appearing microcalcifications and molecular prognostic factors. Int J Cancer. 2002; 102 (96-90) Crossref PubMed Scopus (42) Google Scholar ).
Multiple Imputation of Missing Data in Marketing

Observations containing missing values are handled during data preprocessing phase. Marketing researchers have been handling the missing values in data mainly using statistical methods. Machine learning methods are infrequently used to handle missing data in the marketing domain. A systematic evaluation of treating missing data in marketing is required to verify if the current practices are indeed the best practices. We evaluate mean imputation, multiple imputation, sequential regression tree imputation and sequential random forest imputation on twenty real-world marketing datasets. Our results establish that multiple imputation and sequential random forest imputation perform better than the other methods under consideration.
Bionic Tracking: Using Eye Tracking to Track Biological Cells in Virtual Reality

We present Bionic Tracking, a novel method for solving biological cell tracking problems with eye tracking in virtual reality using commodity hardware. Using gaze data, and especially smooth pursuit eye movements, we are able to track cells in time series of 3D volumetric datasets. The problem of tracking cells is ubiquitous in developmental biology, where large volumetric microscopy datasets are acquired on a daily basis, often comprising hundreds or thousands of time points that span hours or days. The image data, however, is only a means to an end, and scientists are often interested in the reconstruction of cell trajectories and cell lineage trees. Reliably tracking cells in crowded three-dimensional space over many time points remains an open problem, and many current approaches rely on tedious manual annotation or curation. In the Bionic Tracking approach, we substitute the usual 2D point-and-click interface for annotation or curation with eye tracking in a virtual reality headset, where users follow cells with their eyes in 3D space in order to track them. We detail the interaction design of our approach and explain the graph-based algorithm used to connect different time points, also taking occlusion and user distraction into account. We demonstrate Bionic Tracking using examples from two different biological datasets. Finally, we report on a user study with seven cell tracking experts, highlighting the benefits and limitations of Bionic Tracking compared to point-and-click interfaces.