Reviewerly/exHarmony huggingface dataset:
The Use of English Comic-Strip in Teaching Narrative Text Writing. (An Experimental Research at the first grade of SMA Negeri 8 Pandeglang)

Anjas Azizah. 152301841. 2019. The Use of English Comic-Strip in Teaching Narrative Text Writing. (An Experimental Research at the first grade of SMA Negeri 8 Pandeglang). This research investigated Teaching Writing (Narrative Text) Using ComicStrips.It was conducted based on two main problems. 1) How is the students writing on narrative before and after using comic-strips in SMAN 8 Pandeglang ? 2) How is the effectiveness of using comic-strips in narrative? The researcher collected the data from 50 students consisted experiment class and control class. The instruments were pre-test, and post-test. The result showed that the students Writing Narrative text abilities which used English comic- strip had better performance than those who had not used English Comic-strips. The result of the data analysis was using t-test, the value of t observation was 3,87 with degree of freedom 48 In the degree significance 5% = 2,01 in degree of significance 1% = 2,68. It was found that to : tt = 3,87 > 2,68 in degree there significance 5% and to : tt = 3,87 > 2,68 in degree significance 1%. It meant was significant effect of using Comic-strip on Narrative Text Writing. Keyword: Comic-Strips, Teaching Writing, Narrative Text
LibGuides: LinkedIn Learning New Platform: Release activation email

Steps to migrate Lynda account or create a new account to the new LinkedIn Learning Platform
Creating in an age of algorithms: won’t somebody think of the children?

The next generation of artists, those just starting to learn art, music and performance in schools right now, look set to confront a very different world to that of previous generations. They’ll be creating and sharing work in a world awash with algorithmic systems. In some cases, algorithms will be helpful tools, augmenting their skill sets and facilitating precise distribution and advertising. But, if current practices in algorithmic copyright enforcement are anything to go by, they may also pose a substantial barrier to creative practice.
Regulating Cryptocurrencies to Combat Terrorism-Financing and Money Laundering

The emergence of cryptocurrencies has opened a new avenue for terrorist outfits and crime sydnicates to carry out their illegitimate monetary transactions. The ever-increasing use of cyrptocurrencies for terror financing, drug dealings, human trafficking and corruption has forced lawmakers around the world to undertake action to regulate the sale, purchase and exchange mechanisms of cryptocurrencies. While some countries like China have imposed an outright ban on cryptocurrencies, several other countries have embraced the potentials of cryptocurrency through mainstreaming and applying reasonable regulations. Amidst international efforts to regulate and harness the potential of cryptocurrency, Pakistan has imposed a ban on virtual assets and tokens. The rationale behind banning cryptocurrency is the number of challenges that the country will have to encounter in the form of tax evasion, transnational crimes, terrorism financing, cybercrimes, corruption, and kidnapping for ransom. While the current approach of Pakistan is very much in line with China, Pakistani policymakers need to take a hybrid approach of regulation for its economic and regulatory environment. Caution is necessary instead of taking a hasty decision and pursuance of long term planning for facilitating the introduction of cryptocurrencies in the country.
Some subgroups of $\mathbb{F}_q^*$ and explicit factors of $x^{2^nd}-1\in\mathbb{F}_q[x]$

Let $mathcal{S}_q$ denote the group of all square elements in the multiplicative group $mathbb{F}_q^*$ of a finite field $mathbb{F}_q$ of odd characteristic containing $q$ elements‎. ‎Let $mathcal{O}_q$ be the set of all odd order elements of $mathbb{F}_q^*$‎. ‎Then $mathcal{O}_q$ turns up as a subgroup of $mathcal{S}_q$‎. ‎In this paper‎, ‎we show that $mathcal{O}_q=langle4rangle$ if $q=2t+1$ and‎, ‎$mathcal{O}_q=langle trangle $ if $q=4t+1$‎, ‎where $q$ and $t$ are odd primes‎. ‎Further‎, ‎we determine the coefficients of irreducible factors of $x^{2^nt}-1$ using generators of these special subgroups of $mathbb{F}_q^*$
Characterization and Generation of Absolutely Separable States

We characterize the boundary of the convex compact set of absolutely separable states that cannot be transformed to entangled states by global unitary operators in $2\otimes d$ Hilbert space. In particular, we show that the absolutely separable states of rank-$(2d-1)$ are extreme points of such sets. Moreover, we prove the existence of full-rank extreme and boundary points for those Hilbert spaces. Properties of certain interior points are also explored. We further show that by exploring the boundary of the above set, it is possible to develop an algorithm to generate the absolutely separable states which stay outside the maximal ball. By exploring paradigmatic noise models, we find the amount of local noise which the input entangled states can sustain, so that the output states do not become absolutely separable. Interestingly, we report that with the decrease of entanglement of the pure input state, critical depolarizing noise value, transferring an entangled state to an absolutely separable one, increases, thereby showing advantages of sharing nonmaximally entangled states.
Rekommendationssystem : En studie om upplevelsen från användarnas perspektiv

The digital growth of the 21st century has led to the development and establishment of recommendation systems for digital services. This is to allow users to find relevant content in the vast amoun ...
Towards Interdisciplinary Research: A Bibliometric View of Information Communication Technology for Development in Different Disciplines

We present a novel bibliometric view to create a taxonomy of the interdisciplinary field of Information Communication Technology for Development (ICTD or ICT4D), using scientific documents published in the fields related to information communication technologies that were indexed in the Scopus database from 2001 to 2015. Our research approach utilizes a set of relevant terms and venues (journals and conferences) to procure publications on ICTD topics, and further to analyze them statistically to identify emerging or emerged sub-disciplines. The sub-disciplines prominently include Economic Growth/E-Government/Political Economy, Cloud Enterprises/Entrepreneurship/ICT Innovation, and Smart Energy/Smart Grid. We identify the relevance and uniqueness of all ICTD topics, generated by the algorithm, by employing well-known indices of the coherence coefficient and Kullback-Leibler divergence, indicating the qualifications of a given topic as an established sub-discipline of ICTD. This paper attempts to discuss the interdisciplinary nature of the research field ICTD and recommends prospective directions for new subdisciplines based on the text analysis.
Construction of Human Behavior Cognitive Map for Robots

With the advancement of robotics, the importance of service robots in society is increasing. It is crucial for service robots to understand their environment so that they can offer suitable responses to humans. To realize the use of space, robots primarily use an environment model. This paper is focused on the development of an environment model based on human behaviors. In this model, a new neural network structure called dynamic highway networks is applied to recognize humans’ behaviors. In addition, a two-dimensional pose estimator, Laban movement analysis, and the fuzzy integral are employed. With these methods, two new behavior-recognition algorithms are developed, and a method to record the relationship between behavior and environment is proposed. Based on the proposed environmental model, robots can identify abnormal behavior, provide an appropriate response and guide a person toward the desired normal behavior by identifying abnormal behavior. Simulations and experiments justify the proposed method with satisfactory results.
Learning to Transform, Combine, and Reason in Open-Domain Question Answering.

Users seek direct answers to complex questions from large open-domain knowledge sources like the Web. Open-domain question answering has become a critical task to be solved for building systems that help address users' complex information needs. Most open-domain question answering systems use a search engine to retrieve a set of candidate documents, select one or a few of them as context, and then apply reading comprehension models to extract answers. Some questions, however, require taking a broader context into account, e.g., by considering low-ranked documents that are not immediately relevant, combining information from multiple documents, and reasoning over multiple facts from these documents to infer the answer. In this paper, we propose a model based on the Transformer architecture that is able to efficiently operate over a larger set of candidate documents by effectively combining the evidence from these documents during multiple steps of reasoning, while it is robust against noise from low-ranked non-relevant documents included in the set. We use our proposed model, called TraCRNet, on two public open-domain question answering datasets, SearchQA and Quasar-T, and achieve results that meet or exceed the state-of-the-art.
Teleportation criteria for the shared $d\otimes d$ dimensional mixed state: Beyond Singlet Fraction

Partial transposition being a non-physical operation so it is not possible to implement it in a laboratory. Thus, we apply an alternative method known as structural physical approximation (SPA) of partial transposition to estimate the optimal value of the singlet fraction of an arbitrary two qubit mixed state. By applying this method, we have shown that optimal value of the singlet fraction can be estimated experimentally but this result can be applied only on $2\otimes 2$ dimensional system. Therefore, we derive an alternative criteria for the detection of $d\otimes d$ dimensional negative partial transpose (NPT) entangled state, which are useful in quantum teleportation. The newly derived criteria are based on the maximum eigenvalue of the NPT entangled state, which is easier to determine experimentally than to completely reconstruct the state via tomography. We then illustrate our criteria by considering a class of qubit-qubit system and qutrit-qutrit system.
Cross-Domain Neural-Kernel Networks.

Abstract This paper introduces a novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem. The proposed model consists of two stream neural-kernel networks corresponding to the source and target domains which are enriched with a coupling term. Each stream neural-kernel networks follows a combination of neural network layer and an explicit feature map constructed by means of random Fourier features. The introduced coupling term aims at enforcing correlations among the output of the intermediate layers of the two stream networks as well as encouraging the two networks to learn shared representation of the data from both source and target domains. Experimental results are given to illustrate the effectiveness of the proposed approaches on synthetic and real-life datasets.
The Influence of Clustering Technique in Descriptive Writing (A Quasi-Experimental Research at Tenth Grade of SMAN 1 Petir)

Roihatul Fauziyah. 152301889. 2019. “The Influence of Clustering Technique in Descriptive Writing (A Quasi-Experimental Research at Tenth Grade of SMAN 1 Petir),” (Undergraduated Research Paper, Department of EnglishEducation, Faculty of Education and Teachers Training, the State IslamicUniversity Sultan Maulana Hasanuddin Banten). Adviser: Dr. H. Abdul Mu’in, S.Ag., M.M and Hj. Eulis Rahmawati, M.Pd.Writing is one of ability that must mastered by students. Learning writinghave difficult in class whereas it is very important to learn. The objectives of this research are: 1) To know students’ descriptive writing ability at SMAN 1 Petir 2) To know whether there is any influence of clustering technique toward students’writing ability in descriptive text. The method used in this research is quasiexperimental research. The research conducted at tenth grade of SMAN 1 Petir asthe sample. The researcher took sample from class X MIPA 1 consists of 36students as control class and X MIPA 2 consists of 36 students as experimentalclass. The data was gathered through pre-test and post-test. Based on the result, theresearcher got mean pre-test score 52.99 and post-test 62,6 from control class.While from experimental class the researcher got mean pre-test 50.1 and meanpost-test 78.5. The researcher got to (t observation) 8 is higher than tt (t table) 1,66.It means that Ha (alternative hypothesis) of the research is accepted that there issignificant improvement of using clustering technique toward writing ability. Fromthe result of the test, it can be concluded that clustering technique gives thesignificant influence in teaching writing descriptive text.Keywords: clustering technique, descriptive text, and writing ability
Do they really value your privacy?: An exploratory analysis of what can be learned about companies from their privacy statements

This master thesis explores to what degree information about a company's commitment to privacy can be extracted from a privacy statement. To do this, firstly a new database of more than 1500 privacy statements is created using Amazon Mechanical Turk. Next, 72 different aspects related to privacy of the statements are enumerated by using natural language processing. Multiple analyses are run on this data to look for signals which can indicate commitment to privacy. Ultimately, the analyses result in a number of ways which the quality and completeness can be assessed, and a method to find out the degree to which a privacy statement has likely copied other statements. This can be useful for privacy regulators in identifying companies which are less likely to comply with existing privacy regulations.
A Novel Robust Blind Digital Image Watermarking Scheme Against JPEG2000 Compression

In this paper, a novel robust blind digital image water marking scheme is proposed by jointly using discrete wavelet transform (DWT), stationary wavelet transform (SWT), discrete cosine transform (DCT) and singular value decomposition (SVD). Firstly host image is decomposed by DWT and the obtained approximation coefficient is portioned into non-overlapping blocks. For each block, SWT is applied to affine redundant low frequency sub-bands which are subsequently processed by DCT and SVD. Watermark bit is embedded through quantifying the obtained greatest singular value. Extraction of proposed scheme is blind without any referring to the original image or watermark. Experimental result show that watermarked image is visually invisible of which peak signal to noise ratio (PSNR) is above 44 dB. Besides, by comparing with other DWT-SVD robust watermarking approaches, proposed scheme significantly outperforms in robustness against JPEG2000 compression. Performance of proposed scheme is also superior or competitive against other attacks such as rotation, filter or scaling.
Analysis of the influence of motivation and reward in software experiments with student subjects

Context: Empirical studies have become a key tool in software engineering. One of the most used strategies when carrying out empirical studies is the one called Controlled Experiment. Additionally, motivation can be determinant for altering human behavior when participating in a controlled experiment. Aim: This study aims to analyse the influence of motivation by rewards in student subjects when participating in an experiment. Method: An exploratory study based on a mixture of empirical methods was conducted. This mixture consists of a Survey for understanding the optimum magnitud for a reward in case of participating in an experiment. And Finally, an Experiment was designed and executed for studying the effects of motivation in students when participating in an experiment. Results: The Survey results show that depending on the Experiment duration, students may need a reward in return to participate. In addition to this, the analysis of the driven Experiment suggests that motivation did not positively influence the results of the Experiment in terms of quality, time spent and productivity. Conclusions: Further research is needed to provide better knowledge about how motivation by reward can alter results in Experiments.
32 Identifying feature importance in post-mortem outcome with gradient boosting machines

<h3></h3> Post-mortems are complex procedures that utilise a significant amount of hospital resources, yet despite this, cause of death is only determined in 45% of cases. The event itself can be very traumatic for the parents of the child yet is essential for providing further clinical understanding of the patient’s cause of death. Given this, there is an imperative to extract the greatest possible value from the data. Here, we investigated whether machine learning could be used to derive novel insights from the prediction of post-mortem outcomes. A post-mortem database containing 7000 events across 300 attributes was analysed and categorised into stage of examination (external and internal). The outcome of the examination was summarised as either ‘cause of death determined’ or ‘not determined’. From these summarised data, cases were filtered by children aged ≤ 2 years, resulting in a dataset of 3,100 events. Following this, within a GOSH Digital Research Environment (DRE) workspace, decision tree, random forest, and gradient boosting machine models were iteratively built for each stage of the post-mortem examination and compared using their accuracy metrics. The naïve decision tree model using external examination data had a predictive performance of 67%. Model performance notably increased when trained on internal examination data. At each stage of the examination, a core set of data items, of which the final set included age, BMI, and heart weight were highlighted using model feature importance as key variables for determining post-mortem outcome. The use of increasingly complex modelling techniques was able to boost the predictive performance of the model by as much as 10%. This project clearly shows the value of collecting clinical procedural data which can then be modelled using machine learning techniques to inform clinical practice. With more time, further modelling, including unsupervised clustering could be undertaken to derive further insights.
A Voting Application Which Provides Data Security via Blockchain Technology

The main concerns which are coming to mind in the digital voting and election applications are data manipulation or deletion. When these concerns are considered, Blokchain is a foremost technology which proved itself on disposal of distrust and providing security of the data. Therefore, a voting or election system which is implemented with using Blockchain technology enable us to minimize the concerns which are stated above. In this study, a blockchain based voting application which aims to prohibit data manipulation is developed.
Self-repairing object tracking method by adopting multi-level features

Visual object tracking is a fundamental problem in computer vision community and has been studied for decades. Trackers are prone to drift over time without other information. In this paper, we propose a self-repairing online object tracking algorithm based on different level of features. The fine-grained low-level features are used to locate the specific object in each frame and the coarse-grained high-level features are used to describe the category-level representation. We design a tracking kernel updating mechanism based on category-level description to revise the online tracking drift. We tested our proposed algorithm on OTB-50 dataset and compared the proposed method with some popular real-time online tracking algorithms. Experimental results demonstrated the effectiveness of our proposed method.
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior.

We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities between an image pair. The minimization is usually regularized with manually engineered priors, which limits the potential of the registration. By learning transformation priors from a large dataset, CNNs have achieved great success in deformable registration. However, learned methods are restricted to domain-specific data and the required amounts of medical data are difficult to obtain. Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors. The proposed method is applied to brain MRI scans. We show that our approach registers image pairs with state-of-the-art accuracy by providing dense, pixel-wise correspondence maps. It does not rely on prior training and is therefore not limited to a specific image domain.
A comparison of random forests and cluster analysis to identify ore deposits type using LA-ICPMS analysis of pyrite

As exploration for new resources increasingly relies upon deeper and deeper drilling to investigate through overburden, exploration projects will encounter significantly higher drilling costs to sample target areas and to open new areas to exploration. Therefore, as much information as possible must be extracted from every drill hole. One tool that can be used is the in situ trace element analysis of individual mineral phases using LA-ICPMS. In this study, we investigate the use of pyrite trace element chemistry to fingerprint different ore deposit types so that appropriate geologic models can be employed at an early stage of exploration in new greenfields areas. While this data is effective at identifying ore deposit type, variability within the raw data leads to inherent complications for manual analysis. One way to deconvolute this data is to employ machine learning algorithms to aid in the classification. Here we develop a classifier using supervised classification (Random Forests) and further test non-supervised classification (cluster analysis) algorithms. The results of using Random Forests and cluster analysis to identify ore deposit type are then compared.
Detecting Periodicity in Digital Images by the LLL Algorithm

In this paper we provide an algorithm to decide (or, to help the decision about) whether some repeatedly occurring pattern in a digital image can be considered to have periodical nature or not. Our approach extracts specific image components and represent them by single pixels. To decide upon the gridness nature of the resulting point set we use lattice theory and the LLL algorithm to fit lattices to the point set, and an efficient lattice point counting method of Barvinok. With this work we complete some of our corresponding former results, where the fitting of the lattice ignored possible holes inside the point set. Namely, now after some appropriate transformations we consider the convex hull of the point set which way we can detect and punish such fitted lattice points that fall in holes of the original point set, or equivalently image pattern. As a practical demonstration of our method we present how it can be applied to recognize segmentation errors of atypical/typical pigmented networks in skin lesion images.