Reviewerly/exHarmony huggingface dataset:
Survey on Domain Name System Security Problems - DNS and Blockchain Solutions

Domain Name System constitutes the major part in the Internet services. The machine or computers identify a website through an IP address, but the users can remember the names. The translation from name to IP address can be achieved easily through DNS. DNS includes root severs, which are operated by govt organizations. Although DNS system is implemented, there are threats within them. Also, these root servers have risks of censorship, data tracking, privatization and commercial use etc. To overcome these threats and issues, various solutions have been provided by research community and various organizations. This paper surveys on the security problems of DNS and the solutions proposed by various authors including Blockchain based solutions. Blockchain based solution bring immutability of data, which avoid issue like DNS Cache poisoning or corrupting DNS data. Blockchain also helps in removing censorship of govt or any specific private agencies on DNS servers ownership.
A Survey on Automated Detection of Breast Cancer based Histopathology Images

The increasing mortality rate in the women population is mainly due to breast cancer. Diagnosing breast cancer in its early stages will always remain crucial. Hence identifying and treating the disease at the earliest will increase the possibilities of survival. Recently, by using ultrasound images a computer-aided diagnosis (CAD) system is being developed to help radiologists to attain higher accuracy for identification. Normally, a CAD system comprises of different phases such as pre-processing, segmentation for regions of interest, feature selection & extraction, and last phase is to do classification. This paper illustrates the various methods used to deploy an automated CAD system development for the early identification of cancer disease. In this paper, various approaches used are abridged and their pros and cons are compared. The performance evaluation of the CAD system is also depicted as well. The dataset of breast cancer histology images (BACH) is made available to participate in a grand challenge aimed at the classification of microscopy and whole slide images, whereas it is made publicly available for the challenge to promote further improvements for developing an intelligent classification system in digital pathology. According to the number of diagnostic classes and image types (Microscopy and whole slide images), an intelligent system is implemented for initial detection for deploying a proper treatment for breast cancer.
Sparse two level topic model for extraction of general summary words

Abstract Extractive multi-document summarization methods based on topic models find relevant general concepts or topics that are most representative of the documents. These topics are used for sentence ranking and selection. In this paper, a two level topic model using spike and slab prior is proposed that identify better general topics for summarization. Spike and slab prior is used earlier for finding aspect specific topics. Proposed two level model uses spike and slab prior to achieve better general topics at high level of topic hierarchy. Experiments conducted on DUC2007 dataset show that proposed model is able to identify more summary oriented general words and improve ROUGE score.
Comparative Analysis of Different Machine Learning Techniques

Artificial learning (AI) is one of the areas of computer science which develops the system in a way that our system starts learning and gives the reaction as a human brain does. This paper discussed one of the branches of AI i.e. Machine learning as the name suggests it focuses on the development of the computers especially their programs that access the data that can be used for self-development. The main objective of the paper is to compare the best machine learning model by using the performance parameter R squared and Mean square Error (MSE). The data set was taken from the promise repository for the analysis. Feature selection technique Boruta was applied to find the important/confirmed variables from the dataset which are having information regarding the JAVA projects. This paper finds out that among different algorithms which one outperforms when the comparative analysis was done to find the best model.
The Marlowe corpus revisited

Abstract Following Dr Barber’s unfortunate criticism (Barber, 2018, Marlowe and overreaching: a misuse of stylometry. Digital Scholarship in the Humanities, 34: 1–12), in which she, with an obvious lack of familiarity with them, subjected the Rolling Delta procedures used, to the caveats of Delta and traditional stylometry, this article makes use of an extended methodological framework and applies Rolling Delta to the target texts with a totality of reference texts. The outcome is different from the expected, since the author of Tamburlaine 1 and 2 emerges as stylistically also dominant in the anonymous play The Tragedy of Locrine, in Kyd’s closet play Cornelia, in Peele’s The Battle of Alcazar and David and Bethsabe. In contrast, the official Marlowe corpus relates stylistically to contemporary authors, but not to the two Tamburlaines. Traditional scholarship and learning do not refute conjectures of misattributed Peele plays and there are also strong indications that plays associated with Lord Strange’s Men nominally became Marlowe plays when Henslowe acquired them in 1594 for his Admiral’s Men and printers made use of the cult of personality, in which the author’s death became an important factor in the marketing of printed playbooks. Otherwise, there is no documentary and empirical evidence that Marlowe wrote the plays in question. The canonization of the plays occurred only in the nineteenth century, and the Marlowe we have inherited—the poet, spy, atheist, homosexual, and so on—is almost entirely an invention of the twentieth century (Hooks, 2018, Making Marlowe. In Melnikoff, K. and Knutson, R. (eds), Christopher Marlowe, Theatrical Commerce, and the Book Trade. Cambridge: University Press, p. 98).
Image Aesthetic Prediction Model Based on Deep Convolutional Neural Network

Image aesthetic quality assessment is a very challenging task because of its subjectivity. Previous research based on Deep Convolutional Neural Network (DCNN) usually regarded image aesthetic quality assessment as a classification problem, which is to divide the image aesthetic quality into "high quality" or "low quality". Still others have regarded this problem as a regression problem, ultimately giving the image an aesthetic quality score. Both methods ignore the problem that the original aesthetic annotations of the image are given in the form of a score histogram, which has more information than binary labels and single-valued scores. In order to solve this problem, this paper proposes a method for predicting the histogram of image aesthetic scores. This paper trains and tests our model on a large-scale dataset for aesthetic visual analysis (AVA) and reports the experimental results in detail.
Using Personas as a Tool for Teaching Civic Communication

Abstract Questions of how to support students in conversation across and about different perspectives regularly appear in discussions about civic engagement. Students with clear political positions and a desire to engage with political institutions, however, are themselves a rarified group on many campuses. The gulf between students who seek to engage their peers in civic action and those who see establishment politics as irrelevant, alienating, or hopeless may be more significant than any partisan divides. Thus, a liberal arts program committed to voter mobilization should help engaged, active students think carefully about the audience for their efforts and how best to communicate with those whose perspective of political participation varies significantly from their own. This article argues that personas, a tool common in design, can be incorporated into civic engagement programs to help students with a strong civic identity understand how to bridge this divide. This article discusses the importance of helping students communicate across the division of interest and apathy, the role of personas as a tool in design, and our experience using personas to help students develop and implement a nonpartisan voter mobilization drive. It closes with tips for those interested in using personas in their civic engagement projects.
Carcinoma Classification from Breast Histopathology Images Using a Multi Level Spatial Fusion Mechanism of Deep Convolutional Features from Differently Stain Normalized Patches

Breast cancer is one of the leading causes behind death of women worldwide. Core needle biopsy of breast tissue, followed by analysis of the breast histopathological image is one of the most widely used breast cancer diagnosis techniques today. Deep learning pipelines involving a single stain normalization of the raw histology images, followed by patch extraction and CNN based patch wise classification methods with integration of patch wise results by majority voting for image wise classification, have already been proposed in literature. This paper presents a novel architecture, based on spatial fusion of features of two different stain normalizations of the an input patch. The underlying mechanism of stain normalization algorithms being different, they bring forth different salient features of a histopathology image. This will aid a CNN network learn better representations, which will not be possible had only one stain normalization method been used like previous methods. The final feature representation of a patch is rich and multilevel, that is it takes both low level (finer features) and high level (coarser features) features into account. Finally, the feature vector extracted from every patch is fed to a Bidirectional Long Short Term Memory (BLSTM) network for image wise classification, to exploit long term and short term contextual information between neighbourhood patches. Experiments reveal that our model achieved a $$87.75 \%$$ and $$97.5\%$$ for patch and image wise classification respectively on the ICIAR 2018 breast histopathological images dataset, thereby outperforming other state of the art methods to the best of our knowledge.
Run Length Encoding Based Lossless MRI Image Compression Using LZW and Adaptive Variable Length Coding

Medical image compression is considered one of the most important research fields nowadays in biomedical applications. The majority of medical images must be compressed without loss because each pixel information is of great value. With the widespread use of applications concerning medical imaging in the health-care context and the increased significance in telemedicine technologies, it has become crucial to minimize both the storage and bandwidth requirements needed for archiving and transmission of medical imaging data, rather by employing means of lossless image compression algorithms. Furthermore, providing high resolution and image quality preservation of the processed image data has become of great benefit. The proposed system introduces a lossless image compression technique based on Run Length Encoding (RLE) that encodes the original magnetic resonance imaging (MRI) image into actual values and their numbers of occurrence. The actual image data values are separated from their runs and they are stored in a vector array. Lempel–Ziv–Welch (LZW) is used to provide further compression that is applied to values array only. Finally the Variable Length Coding (VLC) will be applied to code the values and runs arrays for the precise amount of bits adaptively into a binary file. These bit streams are reconstructed using inverse LZW of the values array and inverse RLE to reconstruct the input image. The obtained compression gain is enhanced by 25% after applying LZW to the values array.
An effective method to obtain contour of fisheye images based on explicit level set method

Obtaining the effective contour from an image taken by fisheye lens is important for the following transactions. Many studies try to develop suitable methods to get accurate contours of fisheye images. Using the traditional level set method (CV model) is hard to meet the desire task that the final segmentation region is a circle. Therefore, the preprocessing of fisheye images and the improvement of traditional level set method are redesigned to get a final circular segmentation which may be suitable to other applications. In this paper, we use the local entropy method to make the value of pixels be even inside the effective circular region, further threshold method to remove the hole(s), and at last the explicit circular level set method to get final segmentation. The final experimental results show that the segmentation is effective.
Lightness enhancement for video images based on nonlinear filter

It is difficult to enhance low-light video images, because of the irregularity of lightness for each frame and due to the length of execution time required for enhancement. In this research, the videos were optimised using a method based on the development of the technique of the histogram equalisation by using the sigmoid function as a non-linear conversion of the light function in the HSV colour space, this algorithm called Adapted Histogram-Equalization based on Sigmoid Function (AHSF). In order to know the efficiency of the enhancement of the videos, the proposed method was compared with several other methods including traditional as Histogram Equalization (HE) and Multi-Scale Retenix with Color Restoration (MSRCR) non-traditional as Fusion at Weakly Illuminated Images (FWII), This comparison was made by calculating the entropy rate for each video as well as calculating the execution time. By analysing the results, it was found that the proposed algorithm AHSF has good results in improvement and less implementation time.
Deep Learning-Based Character Gesture Recognition

The article describes a new text input method based on gesture recognition, which enables direct physical-to-digital text input. This enables hand-free and in-air writing without any need for keyboards, mice, etc. This is done with the help of state-of-the-art deep learning methods and a single Kinect sensor. The authors were able to achieve a high-accuracy recognition rate by using any wearable device, in contrast to the existing methods, and utilizing a single sensor. Furthermore, among several existing deep learning structures, the authors determined that the best deep learning structure suitable for the character-based gesture data is the DenseNet Convolutional neural network. For instance, the training loss curve shows that DenseNet has the fastest converging curve compared to the others despite maintaining the highest accuracy rate. Our proposed method allows for the improvement of the recognition rate from 96.6% (in the existing algorithms) to 98.01% when the DenseNet structure is used despite using only a single sensor instead of multiple cameras. The use of the Kinect sensor not only reduces the number of cameras to one but also overrides the necessity for any additional hand detection algorithms. These results aid in improving the speed and the efficiency of the character-based gesture recognition. The proposed system can be used in applications that require accurate decision making, such as in operation rooms.
Dominoes: An Interactive Exploratory Data Analysis Tool for Software Relationships

Project comprehension questions, such as “which modified artifacts can affect my work?” and “how can I identify the developers who should be assigned to a given task?” are difficult to answer, require an analysis of the project and its data, are context specific, and cannot always be pre-defined. Current research approaches are restricted to post hoc analyses over software repositories. Very few interactive exploratory tools exist since the large amount of data that need to be analyzed prohibits its exploration at interactive rates. Moreover, such analyses typically require the user to create complex scripts or queries to extract the desired information from data. Here we present Dominoes, a tool for interactive data exploration aimed at end users (i.e., project managers or developers). Dominoes allows users to interact with different types and units of data to investigate project relationships and view intermediate results as charts, tables, and graphs. Additionally, it allows users to save the derived data as well as their exploration paths for later use. In a scenario-based evaluation study, participants achieved a success rate of 86 percent in their explorations, with a mean time of 7.25 minutes for answering a set of (project) exploration questions.
Background Subtraction (BS) Using Instant Pixel Histogram

Video computer vision applications require moving objects detection as a first phase of their operation. Therefore, background subtraction (BS), an investigate branch in computer vision with intensive published research, is applied to obtain the “background” and the “foreground.” Our study proposes a new BS model that utilizes instant pixel histogram, which is implemented to extract foreground objects from two datasets, the first Visor (different human actions) and the second Anomaly Detection Dataset UCSD (Peds2). The model when using the Visor dataset gives 100% detection rate with 8% false alarm rate, whereas, when using UCSD (Peds2), it achieves a detection rate and false alarm rate of 77% and 34% respectively.
Implementation of Elliptic Curve Cryptography over a Server-Clie|nt network

With the rapid development and advancement in the field of cryptographic technology, people find various methods to hack information. For secured data communication, cryptography is one of the techniques. It fundamentally deals with encryption and decryption of a given data. ECC and RSA are two such methods of public key cryptography. The main advantage of ECC when compared with RSA is that it gives fairly equal amount of protection and safety against hackers even for lesser bit size of keys, hence reducing the computation power and bandwidth. This paper discusses the implementation of ECC using ElGamal algorithm over a server-client framework. Implementation is performed on Python IDE JetBrains PyCharm.
An iterative method for classification of binary data

Abstract In today’s data-driven world, storing, processing and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis.
Map and Trie based Compression Algorithm for Data Transmission

In-text compression algorithms, a set of symbols as alphabets are defined. These alphabets are the basic building blocks of the language. Traditionally 8-bit ASCII characters are chosen as alphabets. In this paper, an approach that chooses larger symbols as alphabets is proposed. For example, in the case of the English language, a set of words and special characters are chosen as alphabets. In the case of HTML documents, HTML tags are included along with alphabets from the English language described before. Better compression results can be achieved since words have larger byte size than individual ASCII characters but still get mapped to similar-sized encodings. Compressing the data to be transmitted across networks implies less usage of network bandwidth, faster transmission of data, sending several data files over a single transfer and faster reading and writing of data. Thus, the transmission of a compressed file is cost-effective and also saves time in communication. The results prove that the proposed approach gives better file size compression concerning the compression ratio when compared with state-of-the-art algorithms.
Dismath: Discrete Math Gamified Educational Tool in Logic with Minimax Algorithm and Pruning

This paper presents the design and development of Dismath, a gamified educational tool for propositional logic introduction to undergraduate computer engineering students. Additionally, an artificial intelligence (AI) agent is developed for the proposed Dismath game based on minimax tree search with alpha-beta pruning (MMAB). Dismath is an international checker variant inspired by Damath game in which operations are defined in every checkerboard positions. Thus, the final score is not only dependent on the pieces left but also in the operation results. In a capture move, the jumping chip is the first operand, the captured piece acts as the second operand, and the operation defined in the destination block where the chip landed after the jump is utilized for the binary operation. Instead of arithmetic operators, Dismath designates logical connectives on the checkerboard. All white and black pieces were assigned true and false truth values. For scoring, a true (T) and a false (F) truth values are calculated as +1 and -1, while a dama or king chip is valued +2 and -2 for White and Black pieces, respectively. If the game ends with a positive value, then white wins; otherwise black wins, unless the score is zero in which the outcome is draw. Board balancing experiments were conducted using the win-win ratio between the two random players as evaluation metric. Furthermore, the MMAB agent was characterized against a random player baseline. The results showed dominant performance of MMAB AI agent in proposed Dismath game losing only at depth equal to 1. Overall, this result is promising on its own demonstrating the automated gameplay of the proposed Dismath educational tool for logic introduction.
Proposed Hybrid Algorithm for Image Steganography in Text Security Using Fish Algorithm

The rapid growth of various technologies makes secure data transmission a very important issue. The cryptography technique is the most popular method in several security cases. The major issue is putting the security in place without disturbing the personal data. In such a case, steganography can be used as the most suitable alternative technique. Essentially, steganography is the art of hiding important data that we want to send over a channel or transmission medium. The hiding of information is made with the carrier, making the hacking of data difficult. Hiding the important data provides security and privacy at a high level. In general, there are five types of steganograph: text, audio, video, image and protocol. The carrier plays an important role in steganography. The selection of the carrier depends on the level of security. We proposed an algorithm which is useful for multi-level steganography, and is more advantageous than the standard, less significant, bit algorithm. In this paper, we used multi-level steganography. We focused on image steganography along with a fish algorithm in order to secure the text. We aimed to compare the performance of a basic algorithm to the proposed algorithm. The parameters which have been taken into consideration to compare these two algorithms are execution time and peak signal to noise ratio. We achieved the expected result which shows the applied security for the important data. We used MATLAB 10 software to get these results.