Reviewerly/exHarmony huggingface dataset:
Extraction of Bilingual Dictionary from Comparable Corpora for Resource Scarce Languages

Visvesvaraya Technological University, Belagavi, Karnataka, India One of the promising resources to extract dictionaries are said to be parallel corpora. Majority of the substantial works are based on parallel corpora, whereas for the resource scarce language pairs building a parallel corpus is a challenging task. To prevail over this issue, researchers found comparable corpora could be an alternative to extract dictionary. Proposed approach is to extract dictionary for a low resource language pair English and Kannada using comparable corpora obtained from Wikipedia dumps and corpus received from Indian Language Corpus Initiative (ILCI). Dictionary constructed comprises of both translation and transliteration entities with term level associations from English to Kannada. Resultant dictionary is of size 77545 tokens with precision score of 0.79. Proposed work is independent of language and could be expanded to other language pairs.
Problems of identification of the legal regime for objects created using neural networks technology

The article deals with the mechanism of creating copyright objects (e.g. artwork, musical composition) using neural nerworks. Taking into account the peculiarities of the creation of such objects, the article gives a comparative legal description of the expediency of extending to the following legal regimes: objects of copyright, public domain, regime of objects which have no copyright protection or attribution to information object with open access mode.
Performance Analysis of SVM and KNN in Breast Cancer Classification: A Survey

Breast cancer is one among the foremost widely recognized kind of cancer among feminine population in the entire world. It is still challenging task to detect and classify the cancer tumor precisely. Mammography is considered as a standout amongst the most conclusive and dependable method for proper identification and classification of the breast cancer. Here, in this paper we are proposing a system based on machine learning for classification of breast cancer (BC) along with the comparative study of two machine learning (ML) classifier. The idea is to select the region of interest (ROI) at very first from the mammograms. At that point important features has been extracted using GLCM (grey level co-occurrence matrix). Thereafter, extracted features are then utilized to train our classifiers SVM and KNN individually. The mammogram are then characterized either into benign or malignant using the trained classifier. Proposed system is implemented on standard MIAS databases. Classification performance of both classifiers are contrasted in terms of accuracy, recall, precision, specificity and F1 score. We found that SVM achieved higher accuracy of 94% than KNN with better recall and F1 score.
Example of the Use of Artificial Neural Network in the Educational Process

An example of an artificial neural network intended for use in the educational process (in such disciplines as “The socio-political importance of artificial intelligence systems”, “History and philosophy of science”, etc.) is presented. The neural network provides automatic processing of critical reviews written by students for pseudoscientific works, presented in abundance in the current periodical press. This makes it possible to transfer such an innovative form of study as the writing of critical reviews by students to the distance learning mode. An additional function of this neural network is testing of students in order to identify individuals with a psychological type that is appropriate to the scientist in the true meaning of the word.
Reliable Domain Adaptation With Classifiers Competition for Image Classification

Unsupervised domain adaptation (UDA) tries to utilize the labeled source domain knowledge to help the learning of unlabeled target domain. Existing methods address this problem either by minimizing joint distribution divergence and generating the pseudo target labels by source classifier, or by aligning two domains in manifold subspace. However, they ignore two significant issues: 1) unreliable distribution alignment, which means the source classifier always misclassifies partial target data which may deteriorate adaptation performance when using pseudo labels, 2) insufficient divergence reduction, which means that distribution alignment often focuses on reducing domain shift in original space, where large discrepancy and feature distortion are hard to overcome. On the other hand, reducing distribution divergence only in manifold space is often not sufficient. To alleviate these issues, a Reliable Domain Adaptation (RDA) method is proposed in this brief. Specifically, double task-classifiers and dual domain-specific projections are introduced to align easily misclassified and unreliable target samples into reliable ones in an adversarial manner. Moreover, the domain differences in both manifold and category space are eliminated. Extensive experiments on diverse databases prove the effectiveness of RDA over state-of-the-art unsupervised domain adaptation methods.
Kitchen Utensils Recognition using Fine Tuning and Transfer Learning

To support blind persons at home especially in the kitchen, this work proposes the recognition of kitchen utensils using video sunglasses. The recognition system is based on transfer learning/fine tuning an existing deep learning algorithms, VGG16. Initially, our system can recognize 6 kitchen items using 1354 images in 6 classes. The training/validation and evaluation sets are set at 80% and 20% respectively. Most of the training data was downloaded from the Internet. In this challenging and noisy data, we achieved and accuracy of 95% using the fine tuning learning.
Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection Masks

Continual learning is training of a single identical network with multiple tasks sequentially. In general, naive continual learning brings severe catastrophic forgetting. To prevent it, several methods of continual learning for Deep Convolutional Neural Networks (CNN) have been proposed so far, most of which aim at image classification tasks. In this paper, we explore continual learning for the task of image translation. We apply Piggyback [1], which is a method of continual learning using task-dependent masks to select model weights, to an Encoder-Decoder CNN so that it can perform different kinds of image translation tasks with only a single network. By the experiments on continual learning of semantic segmentation, image coloring, and neural style transfer, we show that the performance of the continuously trained network is comparable to the networks trained on each of the tasks individually.
Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living

Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities.
On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream

Data Stream represents a significant challenge for data analysis and data mining techniques because those techniques are developed based on training batch data. Classification technique that deals with data stream should have the ability for adapting its model for the new samples and forget the old ones. In this paper, we present an intensive comparison for the performance of six of popular classification techniques and focusing on the power of Adaptive Random Forest. The comparison was made based on four real medical datasets and for more reliable results, 40 other datasets were made by adding white noise to the original datasets. The experimental results showed the dominant of Adaptive Random Forest over five other techniques with high robustness against the change in data and noise.
To the question on identification and differentiation of a philosophical zombie

This article examines the task of identification and differentiation of a so-called &amp;ldquo;philosophical zombie&amp;rdquo; in order to form a set of operational criteria for determining the agency of artificial intelligence systems. This task can be viewed as one of the possible ways towards solution of a &amp;ldquo;hard problem of consciousness&amp;rdquo;. Despite the fact that the proposed approach alone does not solve the &amp;ldquo;hard problem&amp;rdquo;, it reveals certain aspects of neurophysiology, cybernetics and information theory towards its solution. The relevance of this task results from the more extensive implementation of artificial cognitive agents in human life &amp;ndash; the boundary that distinguishes an intelligent creature from an artificial cognitive agent, endows an object with agency. Therefore, the development of more complicated artificial cognitive agents (artificial intelligence systems) would ultimately lead to a contentious debate on the topic. The author attempts to introduce the procedure of identification of a philosophical zombie and its restrictions, as well as explores the idea whether or not the artificial cognitive agents would obtain qualia. The article is valuable of those interested in artificial intelligence in all of its aspects, as well as in the philosophy of consciousness.
Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology

Liquid Based Cytology (LBC) is an effective technique for cervical cancer screening through the Papanicolaou (Pap) test. Currently, most LBC screening is done by cytologists, which is very time consuming and expensive. Reliable automated methods are needed to assist cytologists to quickly locate abnormal cells. State of the art in cell classification assumes that cells have already been segmented. However, clustered cells are very challenging to segment. We noticed that in contrast to cells, nuclei are relatively easier to segment, and according to The Bethesda System (TBS), the gold standard for cervical cytology reporting, cervical cytology abnormalities are often closely correlated with nucleus abnormalities. We propose a two-step algorithm, which avoids cell segmentation. We train a Mask R-CNN model to segment nuclei, and then classify cell patches centered at the segmented nuclei in roughly the size of a healthy cell. Evaluation with a dataset of 25 high resolution NDPI whole slide images shows that nuclei segmentation followed by cell patch classification is a promising approach to build practically useful automated Pap test applications.
Compositional data analysis

Recently, Keskitalo et al presented a study focused on the association of bronchoalveolar lavage fluid (BALF) cell differential counts (CDCs) with the smoking habits and the number of asbestos bodies (ABs) in a population of patients with asbestosis.1 They also dealt with the dependence between the survival time of the patients and the BALF CDCs, as well as the number of ABs. However, we have serious concerns about the correctness of the statistical analysis used in the study.It is crucial to observe that in the article, the BALF CDCs are expressed as percentages, the data having a fixed constant sum constrained to 100. Such kinds of observations are defined …
State estimation in pairwise Markov models with improved robustness using unbiased FIR filtering

We propose a novel estimation procedure for linear time-varying pairwise Markov models (PMM), that is robust to system parameter uncertainties occurring in real-world applications. In order to cope with mismodeling errors and ignorance of noise/initial state statistics, we solve a finite-horizon state estimation problem. The resulting unbiased finite impulse response filter for PMMs (PMM-UFIR) is first derived in batch form and then converted to a recursive Kalman-like form for the sake of complexity reduction. Closed forms for the error covariance matrix of the state estimate are also provided for analytical performance assessment. Numerical results illustrate the effectiveness of the proposed estimation method over Gaussian processes, by showing that the PMM-UFIR is nearly as accurate as (resp. more robust than) optimal filtering under perfect (resp. uncertain) system parameters after tuning the horizon size.
Convolutional Neural Network Models for Content Based X-Ray Image Classification

In this paper, transfer learning using pre-trained Convolutional Neural Network models such as GoogleNet and AlexNet is employed for X-Ray image classification and retrieval. This is carried out on IRMA database comprising of 55 categories of 6406 X-Ray images. These images are preprocessed using adaptive histogram equalization. The performance of the networks have been evaluated over five trials considering an 80:20 training to validation set split resulting in an average accuracy of 0.87 and 0.93 for GoogleNet and AlexNet, respectively. Confusion matrices are generated which is used to estimate the precision and recall of the networks. The proposed work resulted in a robust X-Ray image classification mechanism.
A Metasynthesis of Solo Software Development Methodologies

Delivering quality software is every software developer's responsibility. The use of software development methodologies increases chances of delivering quality software, within stipulated timelines and budget. Limited research exists on solo software development methodologies, thus solo developers resort to adapting team software development methods for their development efforts. The dilemma is that adaptation of team software methodologies may compromise the quality of the resulting methodologies. This paper addresses this dilemma by presenting results of a qualitative meta-synthesis conducted to synthesize the existing few solo software development methodologies into a high-quality solo software development framework. Articles published between January 2000 and December 2017 were identified using a predefined search string. Six primary studies were included in the synthesis after meeting the criteria set for the study. Using meta-ethnography, the identified solo software development methodologies were interpreted and translated into each other resulting in a six staged solo software development framework. The six stages are: Management Buy-in and Standards adoption; Requirements Elicitation; Release and Sprint Planning; Development; Sprint Review and Close; and Evaluation. This study therefore contributes to knowledge through theory building in solo (freelance) software development. Freelance developers can adopt this framework to enhance the quality of their software. Further research on the derived framework should validate the framework empirically creating more knowledge in the field of solo software development.
Enabling Internet of Things (IoT) Security via Blockchain Framework

In today’s world of technological advancements, technologies such as data science, big data and IoT play a huge role in simplifying human life. Access control is a major concern for any data source. Legitimate access to confidential data is important to ensure privacy, integrity, authenticity and confidentiality. It is further very important to devise access control in situations where data is critically private, such as in the case of health care. Access control policies stored in a centralized storage are prone to security breaches due to single point of failure. Blockchain is a decentralized storage system which is based on peer-to-peer network architecture. It is immutable and helps in recording transactions between two parties in a permanent and verifiable manner. The project concentrates on storing the access control policies of a healthcare data source and the transactions that follows in a blockchain. The results of storage of policies and transactions are shown in Remix IDE using Ethereum blockchain framework.
Efficient FPGA Implementation of Unified Point Operation for Twisted Edward Curve Cryptography

Combination of efficient point operation architecture and high intractability feature of cryptographic curve optimizes the performance of the Edward curve cryptography (EdCC) processor ensuring desired security. This research employs the most recently invented twisted Edward curve for performing point operation. A novel architecture of unified point operation has been designed over the 256-bit prime field which can execute Edward curve point doubling and point addition in one module instead of two divide modules. The proposed architecture has been implemented on Virtex-5 field-programmable gate array technology and achieved 5.48 μs computational delay and only 10% of available slices for 256-bit point operation. Our design reduces almost 50% of the required clock cycles compared to other designs. Also, it assures a better security characteristic as to the conventional approach. To our best knowledge, the proposed architecture provides better performance in terms of throughput on FPGA than the other work which is effective for the design of the EdCC processor.
Hyperplane Arrangements of Trained ConvNets Are Biased

We investigate the geometric properties of the functions learned by trained ConvNets in the preactivation space of their convolutional layers, by performing an empirical study of hyperplane arrangements induced by a convolutional layer. We introduce statistics over the weights of a trained network to study local arrangements and relate them to the training dynamics. We observe that trained ConvNets show a significant statistical bias towards regular hyperplane configurations. Furthermore, we find that layers showing biased configurations are critical to validation performance for the architectures considered, trained on CIFAR10, CIFAR100 and ImageNet.
Axiom Pinpointing

Axiom pinpointing refers to the task of finding the specific axioms in an ontology which are responsible for a consequence to follow. This task has been studied, under different names, in many research areas, leading to a reformulation and reinvention of techniques. In this chapter, we present a general overview to axiom pinpointing, providing the basic notions, different approaches for solving it, and some variations and applications which have been considered in the literature. This should serve as a starting point for researchers interested in related problems, with an ample bibliography for delving deeper into the details.
Image Semantic Representation for Event Understanding

Different events, such as terrorist acts and natural catastrophes, frequently occur across the world. The availability of images on the internet can help to understand events. However, manually selecting representative (helpful) images from a massive amount of data can be infeasible. Here, we propose an image semantic representation method that helps to understand the discrimination of Representative Images (RI) from Non-representative Images (NRI). Our method, called Event Semantic Space (ESS), generates a low-dimensional image representation by exploiting the semantics of some images with high representativeness and some representative components of the events (e.g., places, objects, and people). Results on three real-world events attest the capability of our method to represent events, outperforming three image descriptors individually in ranking tasks and presenting capability of learning patterns of Representative Images.
A Cloud computing-based Advanced Encryption Standard

Today cloud computing is emerging as an indispensable factor in our day-to-day lives. Its fame is rising at an exponential rate along with its growth. It serves its client with distinct computing capabilities like computation, storage, networking, applications, etc., over the internet upon a particular request. Among all the above capabilities, storage plays a crucial role in facilitating its client by giving its framework on rent basis. Even though cloud computing is having a lot of benefits, its acceptance is still lagging behind as a result of security concerns. The information that we keep in clouds is likely to be abused by an unapproved individual or the cloud specialists to cooperate itself. The paper gives a brief idea of cloud computing data security, and various methods are used in ensuring the security of data and we have compared various encryption algorithm used for encrypting data in the cloud.
PESC

Stack canary is the most widely deployed defense technique against stack buffer overflow attacks. However, since its proposition, the design of stack canary has very few improvements during the past 20 years, making it vulnerable to new and sophisticated attacks. For example, the ARM64 Linux kernel is still adopting the same design with StackGuard, using one global canary for the whole kernel. The x86_64 Linux kernel leverages a better design by using a per-task canary for different threads. Unfortunately, both of them are vulnerable to kernel memory leaks. Using the memory leak bugs or hardware side-channel attacks, e.g., Meltdown or Spectre, attackers can easily peek the kernel stack canary value, thus bypassing the protection. To address this issue, we proposed a fine-grained design of the kernel stack canary named PESC, standing for Per-System-Call Canary, which changes the kernel canary value on the system call basis. With PESC, attackers cannot accumulate any knowledge of prior canary across multiple system calls. In other words, PESC is resilient to memory leaks. Our key observation is that before serving a system call, the kernel stack is empty and there are no residual canary values on the stack. As a result, we can directly change the canary value on system call entry without the burden of tracking and updating old canary values on the kernel stack. Moreover, to balance the performance as well as the security, we proposed two PESC designs: one relies on the performance monitor counter register, termed as PESC-PMC, while the other one uses the kernel random number generator, denoted as PESC-RNG. We implemented both PESC-PMC and PESC-RNG on the real-world hardware, using HiKey960 board for ARM64 and Intel i7-7700 for x86_64. The synthetic benchmark and SPEC CPU2006 experimental results show that the performance overhead introduced by PESC-PMC and PESC-RNG on the whole system is less than 1%.
Utilization of Moodle in Learning

Abstract The purpose of this study was to determine the effect of Moodle on student learning motivation as well as to find out the result of learning improvement of students who were learned using Moodle. This research was conducting by experimental study which using the Descent Nonequivalent Control Group Design using two groups, the experimental group, and the control group. This research used test and questionnaire (non-test) to collect the data. Based on the result of the study it showed that by using Moodle learning significantly affect motivation of the students. Also, based on the test description it was found that the average score of learning outcomes was increased. Students’ average score on pretest was 67.6 while on posttest the average score was 92.3. it means that there was an average increase of 24.6.therefore it may be concluded that the use of Moodle in learning can improve students ‘motivation and learning outcomes.