Reviewerly/exHarmony huggingface dataset:
A Taxonomy of Training Data

Abstract Many machine learning applications depend on unauthorized uses of copyrighted training data. Scholars and lawmakers often articulate this problem as a deficiency in copyright’s exceptions and limitations. In fact, today’s predicament results not from inadequate exceptions to copyright, but rather from two systemic features of the regime—the absence of formalities and the low threshold of copyrightable originality—combined with technology that turns routine activities into acts of authorship. This chapter taxonomizes AI applications by their training data. Four categories emerge: (1) public-domain data, (2) licensed data, (3) market-encroaching uses of copyrighted data, and (4) non-market-encroaching uses of copyrighted data. Copyright can only regulate market-encroaching uses, but these uses comprise only a narrow subset of AI, and copyright’s remedies are ill-suited to address them. The chapter concludes with a discussion of solutions to the problems it identifies. It observes that the exception for Text and Data Mining in the European Union’s Directive on Copyright in the Digital Single Market represents a positive development because the exception addresses structural causes of AI’s copyright problems. The TDM provision styles itself as an exception, but it may be better understood as a formality: rights holders must affirmatively reserve the right to exclude materials from training datasets. Thus, the TDM exception addresses a cause of AI’s copyright dilemma. The next step for an equitable AI framework will be to transition towards rules that not only clarify that non-market-encroaching uses do not infringe copyright, but also facilitate remunerated uses of copyrighted works for market-encroaching purposes.
Title

We consider the problem of counting the number of DAGs which are Markov-equivalent, i.e., which encode the same conditional independencies between random variables. The problem has been studied, among others, in the context of causal discovery, and it is known that it reduces to counting the number of so-called moral acyclic orientations of certain undirected graphs, notably chordal graphs.Our main empirical contribution is a new algorithm which outperforms previously known exact algorithms for the considered problem by a significant margin. On the theoretical side, we show that our algorithm is guaranteed to run in polynomial time on a broad class of chordal graphs, including interval graphs.
Voice Controlled Wheelchair Based on a Tmega

The dependency of people depends on others especially when they get to travel from one place to another. For those who are physically ill requires wheelchair for their daily needs, and who can help them to make the wheelchair move. By having a controlled wheel-chair system will empower weakened individuals to wind up independent. The structure is a remote wheelchair control system that uses a voice acknowledgment application for actuating and controlling all of its improvements. The wheelchair responds to the voice request from its customers to play out any advancement limits. It joins a microcontroller, Bluetooth module, control interface engine, transfer engine, which uses board through which wheel chair can move. By using the structure, the customers can work in the wheel-chair by basically address the mobile phone antenna. The fundamental improvement limits fuse advance and alter heading, left and right turns and stop It utilizes an Atmega328 to control the system assignments. It talks with the Bluetooth module to recognize word communicated and after that chooses the contrasting yield request with drive, the right and left engines. To accomplish this endeavour, in the controller’s memory a low-level registering program is set.
Updated determination of earthquake magnitudes at the Swiss Seismological Service

&amp;lt;p&amp;gt;The Swiss Seismological Service (SED; http://www.seismo.ethz.ch) at ETH Z&amp;amp;#252;rich is the federal agency in charge of monitoring earthquakes in Switzerland and neighboring areas, and for the assessment of seismic hazard and risk for the region. The SED seismic network largely relies on software and databases integrated in the SeisComP3 monitoring suite for waveform acquisition, automatic and manual event processing, event alerting, web infrastructure, data archiving and dissemination. Data from all digital seismic stations acquired by the SED over the last 30 years - broadband (presently ~230), strong-motion (~185), short-period (~65), permanent and temporary - are homogeneously integrated in the seismic network processing tools and products. Waveform data from the Swiss National Seismic Networks are openly available through the SED website and ORFEUS EIDA / Strong-Motion (http://orfeus-eu.org/data/) data gateways. The SED earthquake catalogue is publicly available through FDSN Event web services at&amp;amp;#160; the SED (http://arclink.ethz.ch/fdsnws/event/1/). The Swiss seismic hazard maps are integrated in the EFEHR portal (http://www.efehr.org). The SED is updating its strategy for magnitude determination to make it fully consistent with the state-of-the-art in engineering seismology and seismic hazard studies in Switzerland, and to optimise the use of its dense seismic monitoring infrastructure. Among the planned changes are the: (a) adoption of a new ML relationship applicable in the near-source region at epicentral distances smaller than 15-20 km; (b) inclusion of ML station corrections based on empirically observed (de)amplification with respect to the Swiss reference rock velocity model and associated predictions; (c)&amp;amp;#160; seamless computation of Mw based on spectral fitting of recorded FAS using a Swiss specific model. In this contribution we present and discuss the updated magnitude computations for a playback dataset of thousands of recorded earthquakes, and compare them with the current official estimates. We discuss the expected impacts of the new magnitude determination strategy on the SED event processing chain in SeisComP3, the SED catalogues and other seismological products. We welcome community feedback on our planned transition strategy.&amp;lt;/p&amp;gt;
Automated Ontology Building for News Text using Associative Word Properties

It is known in the literature that ontology had been used extensively in machine learning for performance enhancement for text retrieval. It is also shown that robust ontology with detailed description of the domain knowledge will contribute to the accuracy in the retrieval. Nevertheless, we argue in some domain such as news text retrieval, building an ontology manually can be costly for a large-scale news repository and especially with the changes in content due to the dynamic events. In addition, maintenance can be a dauting task to keep up with new words that are associated with new events. This paper demonstrates the attempt to fully automate the development of an ontology for identifying the news domain and its subdomain. The ontology specification is defined based on the needs of the accuracy in retrieval. The mechanism of generating the ontology specification is defined and the results of the retrieval performance is discussed.
User Persona of Mother of Preterm Neonate

This research paper presents a user persona of Indian mothers of preterm neonates.Many of these preterm neonates require hospitalization in Neonatal Intensive Care Units (NICUs), leading to mental stress for mothers and their families.The mother's persona is proposed based on hypothesis, user interviews and data analysis.The participant mothers of preterm neonates are graduates and homemakers from semiurban areas around Pune, India.These mothers prefer non-vegetarian diet, they visit a pediatrician more frequently and presently, they do not use any mobile healthcare app or YouTube videos for information about neonatal care.A mobile app will be developed for these mothers in future with due consideration to their user persona.
A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features

There has been a revolution in multimedia with technological advancement. Hence, Video recording has increased in leaps and bounds. Video retrieval from a huge database is cumbersome by the existing text based search since a lot of human effort is involved and the retrieval efficiency is meager as well. In view of the present challenges, video retrieval based on video content prevails over the existing conventional methods. Content implies real video information such as video features. The performance of the Content Based Video Retrieval (CBVR) depends on Feature extraction and similar features matching. Since the selection of features in the existing algorithms is not effective, the retrieval processing time is more and the efficiency is less. Combined features of color and motion have been proposed for feature extraction and Spatio-Temporal Scale Invariant Feature Transform is used for Shot Boundary Detection. Since the characteristic of color feature is visual video content and that of motion feature is temporal content, these two features are significant in effective video retrieval. The performance of the CBVR system has been evaluated on the TRECVID dataset and the retrieved videos reveal the effectiveness of proposed algorithm.
Sign Language Recognition using Hybrid Neural Networks

Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.
SUPERSINGULAR EDWARDS CURVES AND EDWARDS CURVE POINTS COUNTING METHOD OVER FINITE FIELD

We consider problem of order counting of algebraic affine and projective curves of Edwards [2, 8] over the finite field $F_{p^n}$. The complexity of the discrete logarithm problem in the group of points of an elliptic curve depends on the order of this curve (ECDLP) [4, 20] depends on the order of this curve [10]. We research Edwards algebraic curves over a finite field, which are one of the most promising supports of sets of points which are used for fast group operations [1]. We construct a new method for counting the order of an Edwards curve over a finite field. It should be noted that this method can be applied to the order of elliptic curves due to the birational equivalence between elliptic curves and Edwards curves. We not only find a specific set of coefficients with corresponding field characteristics for which these curves are supersingular, but we additionally find a general formula by which one can determine whether a curve $E_d [F_p]$ is supersingular over this field or not. The embedding degree of the supersingular curve of Edwards over $F_{p^n}$ in a finite field is investigated and the field characteristic, where this degree is minimal, is found. A birational isomorphism between the Montgomery curve and the Edwards curve is also constructed. A one-to-one correspondence between the Edwards supersingular curves and Montgomery supersingular curves is established. The criterion of supersingularity for Edwards curves is found over $F_{p^n}$.
Challenges of Internet of Things: Development and Application of Conceptual Framework

The purpose of this paper is to discuss the potential of internet of things and primary challenges like threat to personal information, Government surveillance and cybercrime faced by internet of things. This is conceptual paper that advances testable preposition based on the the technological overview and use of existing literature review. This paper concludes the challenges of internet of things that can be resolved by following the conceptual framework discussed in this paper. This framework suggests users to read privacy policy, make strong password, and manufacturers to provide proper security and government to make strict laws against it. This paper has conceptualised the challenges of internet of things and the steps that can be established to resolve the issues. If users, manufacturers and government follow these steps then the challenges faced by internet of things can be solved with some extent
Multi-document-based text summarisation through deep learning algorithm

The proposed approach is provided an effort in terms of deep leaning algorithm to retrieve an effective text summary for a set of documents. Basically, the proposed system consists of two phases such as training phase and the testing phases. The training phase is used for exploiting the three different algorithms to make the text summarisation process an effective one. Similar to every training phase, the proposed training phases is also possessed of known data and attributes. After that, the testing phase is implemented to test the efficiency of the proposed approach. For experimentation, we used four documents sets which are selected from the DUC (2002). The experimental evaluation showed expected results as, the average precision of 78%, the average recall of 1 and the average f-measure of 84%.
A machine-learning framework to predict TMO preference based on image and visual attention features

Tone-mapping operator (TMO) plays a crucial role in the task towards displaying high dynamic range (HDR) contents on standard displays. Similarly, visual attention (VA) is a vital feature of the human visual system (HVS), while it has not been sufficiently investigated in preference of tone-mapped images. The potential benefits of visual attention-based features for quality assessment of tone-mapped images are studied in this paper. A novel framework is proposed for tone-mapped image quality assessment to predict image preference. The framework is evaluated on two different datasets. Experimental results illustrate the importance of visual attention for improving the performance of objective metrics. The proposed framework outperforms the existing methods and presents as a competitive alternative for tone-mapped images evaluation.
Retraction Note: Preparation and UV-protective property of PVAc/ZnO and PVAc/TiO2 microcapsules/poly(lactic acid) nanocomposites

The above article from the journal of Fibers and Polymers has been retracted by agreement by all the authors, the journal editor-in-chief Won Ho Jo, and Springer Nature. The authors have retracted this article [1] because they were unable to reproduce the experimental results in a follow-up study. Therefore, the authors have concluded that there is a fundamental error that has made this study irreproducible. All authors agree to this retraction.
Cache-collision side-channel analysis and attacks against AES-GCM

Data security is an important issue in big data applications. The sheer data volume provides way more opportunities for a potential attacker to observe and identify patterns in computation and data. In this paper, we reveal that the data/computation patterns derived from the observation of large volume of data can be associated with the key used in the AES-GCM algorithm, one of the foundation algorithms in data security. The paper presents a software-based cache-collision timing attack against the well known authenticated encryption scheme AES-GCM. The attack can be successful if enough data (plaintext-ciphertext pairs) are processed and the hash key H used for generating look-up tables in software implementation. We present an attack model and an implementation of the attack based on OpenSSL, a widely used library that provides security-related functions for many applications. In most cases, our attack methodology is able to converge and extract the hidden key.
Symmetric key generation algorithm using image-based chaos logistic maps

One of the latest hotspot in the current security trends is crypto image security i.e., security with images. In this paper, an algorithm for random bit sequence generator, based on images and two chaos logistic maps, is proposed. A symmetric key of length as per user's choice can be generated by choosing a number of points on a selected image. The chosen point pixel values are applied onto the chaos logistic map to obtain corresponding symmetric key. The selected pixel coordinates are transmitted to the receiver in a secure manner to generate the same key at the receiver. Our proposed methodology concentrates on symmetric key generation at the sender and the receiver rather than transmitting the key itself. The strength of the generated key is tested using NIST test suite and found that time required per bit is less and randomness is more when compared with Li et al. (2011), Zhao et al. (2009) and Hu et al. (2009).
Evaluating the Impact of Fault-Tolerance Capability of Deep Neural Networks Caused by Faults

Deep neural network (DNN) is one effective technique used for the artificial intelligence applications. A DNN consists of a large amount of neurons arranged in a form of multilayers. Typically, a DNN has over-provisioning neurons such that it has inherent fault-tolerance capability. However, how to evaluate the fault-tolerance capability of DNNs is a question. In this paper, we propose a simulator to estimate the loss of inference accuracy due to the faults in a DNN model or the memory faults in hardware accelerator. The simulator is implemented based on the platforms of Keras and Tensorflow. It can evaluate the fault-tolerance capability of a DNN at model and hardware levels.
Raspberry Pi GPIO

Previous chapters introduced the Raspberry Pi hardware, and you learned how to use Python to program it. You installed the operating system, configured it for your use, and set up remote access so that you can program the Pi without connecting a keyboard, mouse, and monitor directly to it. You learned the basic structure of a Python program, syntax, and enough about the language to start writing programs.
Content Based Image Retrieval using Statistical Parameters of a Medical Image

We present a framework that permits in classifying medical images so as to recognize conceivable diseases that affected. This is done by Image retrieval from the collection of dataset by inputting the query image. Content based Image retrieval (CBIR) is the way toward seeking comparable pictures from a picture database dependent on the visual substance of the given query image. Even though some studies present general method in image extraction, there are no efficient methods in medical image retrieval with accuracy. To overcome and to eliminate these flaws our proposed CBIR method examined with the accurate and efficient way for feature extraction from medical images. The images used are grey scale image. The dataset holds the n number of images related to medical particularly brain tumor images. To retrieve the related images from the dataset and get the corresponding details, image is given as an input i.e., query image. Initially, the query image is analyzed by shape, texture and histogram and the result obtained from this is compared with the similar images in dataset. The similarities between the images are found by implementing the Matching Score algorithm. This algorithm provides accuracy in matching the image that helps greatly at the time of classification. The results of computation is said to be the features for the given image. Also the cost for processing the image is comparatively low. The technique has been examined on standard image dataset and satisfactory results have been achieved.
Formation of competencies in the field of sustainable development among students of the direction of training “Geography”

The article shows that education for sustainable development is of paramount importance for the implementation of the concept of sustainable development and the achievement of sustainable development goals. The use of sustainable development ideas in higher education is becoming a key factor in training specialists, which necessitates the development of effective forms and technologies for their implementation. The article considers the experience of forming the education system for sustainable development in the North Caucasus Federal University on the example of training students in the direction of “geography”, analyzes the ideology and methodology of forming competencies in the field of sustainable development in higher education. The process of practical transition of higher geographical education to a new level is shown. The main vector of such education is the task of creating an integrated interdisciplinary educational system based on inter-subject connections. Three main directions of formation of education for sustainable development are proposed — expansion of the content of basic courses due to the problems of sustainable development, development of new independent author’s courses devoted to certain issues of sustainable development, and inclusion of an independent discipline “Sustainable development” in the curriculum. The problem of sustainable development with reference to specific academic disciplines is presented in sufficient detail. It is emphasized that the success of developing competencies in the field of sustainable development depends directly on the use of new educational technologies, in particular, interactive and project-based learning methods.
Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

The typical Internet of Things (IoT) device gathers a huge amount of data specifically termed as big data framework, which transfers the collected data from the sensing layer to the information processing layer. Various big data classification methods are adopted in the industrial applications, and smart cities, but accurately classifying the data in the IoT network poses a challenging task in the research community. Therefore, an effective big data classification model using spark-based architecture is proposed in this research. The big data classification is performed at the master node using the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm (FABC-CFFRideNN). The concept of fictional computing is adopted by the rider optimization algorithm (ROA) to update the position of rider groups based on success rate and the foraging behavior of fruit flies along with the rider parameters is used to enhance to performance of data classification using the proposed CFFRideNN classifier. Moreover, the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm attained better performance using the metrics, namely accuracy, specificity, and sensitivity with the values of 95.382%, 95.81%, and 98.824% for training percentage without node velocity.
X2Go Client Set-Up v1

X2Go Client set-up to run Linux from Windows computer (on and off campus). Use the following link for more information or help on setting up X2Go Client: https://helpdesk.engr.arizona.edu/support/solutions/articles/33000204314-how-can-i-access-engineering-linux-systems-from-home-